Plant Breeding. 2019;00:1–24.    |  1wileyonlinelibrary.com/journal/pbr
1  | INTRODUC TION
Yam is a multi-species monocotyledonous crop extensively dis-
seminated in Africa, Asia, Oceania and South America. The genus 
Dioscorea to which yam belongs encompasses about 600 species 
(Burkill, 1960); however, a few are cultivated for food and income. 
Dioscorea alata, D. cayenensis and D. rotundata are by far the major 
cultivated species worldwide, while D. bulbifera, D. esculenta, D. op-
posita, D. japonica, D. nummularia, D. pentaphylla, D. transversa, D. tri-
fida and D. dumetorum are also economically important (Lebot, 2009; 
Sonibare, Asiedu, & Albach, 2010). Many wild yam species serve as 
sources of food in West Africa especially in times of food scarcity 
(Bahuchet, McKey, & Garine, 1991; Sato, 2001). Dioscorea rotundata 
is indigenous to West Africa and represents the most important 
species in terms of volume of production while D. alata, which was 
introduced to Africa from Asia, is the most widely cultivated species 
globally (Abang, Winter, Mignouna, Green, & Asiedu, 2003).
Yam is an important staple source of carbohydrates (starch, 
sugars and fibres), proteins, minerals, vitamins and small amounts 
of lipids in the diets of millions of people in the tropics and subtrop-
ics. Yam is not only cultivated for consumption and as a source of 
income, but it is a highly esteemed food crop integrated into the so-
cial, cultural, economic and religious aspects of life of West Africans 
(Zannou et al., 2004). The rituals, ceremonies and superstitions 
 
Received: 27 December 2018  |  Revised: 10 October 2019  |  Accepted: 26 October 2019
DOI: 10.1111/pbr.12783  
R E V I E W  A R T I C L E
Review of empirical and emerging breeding methods and tools 
for yam (Dioscorea spp.) improvement: Status and prospects
Kwabena Darkwa1,2,3  |   Bunmi Olasanmi2  |   Robert Asiedu3  |   Asrat Asfaw3
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, 
provided the original work is properly cited.
© 2019 The Authors. Plant Breeding published by Blackwell Verlag GmbH
1Institute of Life and Earth Sciences, Pan 
African University, University of Ibadan, 
Ibadan, Nigeria
2Department of Agronomy, University of 
Ibadan, Ibadan, Nigeria
3International Institute of Tropical 
Agriculture (IITA), Ibadan, Nigeria
Correspondence
Asrat Asfaw, IITA, PMB 5320, Oyo Road, 
Idi-Oshe, Ibadan, Nigeria.
Email: a.amele@cgiar.org
Funding information
Pan African University; Bill and Melinda 
Gates foundation, Grant/Award Number: 
OPP1052998
Communicated by: Jens Léon
Abstract
Yam (Dioscorea spp.) is an important tuber crop with tremendous potential as a func-
tional food in the tropics and subtropics. However, the crop has not shown progres-
sive productivity gain over decades due to various production constraints. This paper 
reviews the progress achieved in empirical breeding endeavours and the develop-
ment, status and application of emerging breeding tools and technologies to translate 
genetic gains in yam improvement. Significant progress has been made in yam genetic 
improvement over the years which has led to the identification and development of 
several improved clones and sources of variability for various economically important 
traits. Substantial efforts have also been made to develop diverse molecular markers, 
transcriptome and metabolome profiles of crucial traits, trait mapping and generate 
reference genome sequences of the key species. However, there seems to be a slow 
translation of research improvements into widespread applications. These advances 
and the integration of empirical and emerging methods and technologies into the 
breeding process will enhance yam breeding efforts and ensure the quick delivery of 
improved varieties that possess superior agronomic and quality traits.
K E Y W O R D S
Conventional breeding, Dioscorea, molecular breeding, molecular markers, QTL mapping, yam 
breeding
2  |     DARKWA et Al.
often associated with yam cultivation and utilization in West Africa 
is a strong indication of the antiquity of use of this crop (Norman, 
Pearson, & Searle, 1995). The new yam festival, which symbolizes 
the beginning of yam harvesting, is an outstanding social event al-
most everywhere in the yam-growing belt of West Africa (Coursey 
& Coursey, 1971). Dioscorea species are also recognized for their 
secondary metabolites aside their food value. They contain steroi-
dal saponins, diterpenoids and alkaloids, which have been exploited 
for making poisons (Neuwinger, 1996) and pharmaceutical products 
(Chu & Figueiredo-Ribeiro, 1991).
Although Africa accounted for over 97% of the total yam produc-
tion worldwide in 2017, only two countries in Africa are among the 
top five countries recording the highest yields per hectare (FAOSTAT, 
2017). The top five countries producing high yield per unit area are 
Ethiopia, Mali, Japan, Papua New Guinea, and Portugal, while Nigeria, 
Cote d’Ivoire, Ghana, Benin and Togo are the top five countries in the 
world in terms of total area under yam cultivation (FAOSTAT, 2017). 
A number of constraints including high cost of planting materials, 
high labour costs, poor soil fertility, low yield potential of local va-
rieties, pests and diseases (yam anthracnose, virus and nematodes), 
and shortage of quality seed yam of popular landraces and released 
varieties have been identified as the major constraints of yam pro-
duction in Africa (Aidoo et al., 2011; Baimey, Coyne, & Labuschagne, 
2006; Lopez-Montes, Bhattacharjee, & Tessema, 2012). The pres-
ence of virus in yam tubers not only causes yield loss but also hinders 
the international exchange of yam planting materials and germplasm 
(Asala et al., 2012; Kumar, 2015). The increase in yam production 
in Africa over the years is mainly due to the rapid increase in the 
area of yam fields planted into marginal lands and into non-tradi-
tional yam-growing areas (Mignouna, Abang, & Asiedu, 2008) rather 
than resulting from increased yield per unit land area. The current 
trend of area expansion with reduced fallow period will soon reach 
the limit. The need to provide farmers with improved varieties that 
combine high and stable yields with pest and disease resistance and 
acceptable tuber quality for processed and fresh tuber consumption 
markets can therefore not be over-emphasized.
A number of improved yam varieties having multiple pest and 
disease resistance, wide adaptability and good organoleptic at-
tributes have been developed with empirical breeding methods in 
Africa and Asia by collaborative efforts of the international institutes 
and national yam breeding programmes. This progress with selection 
based on phenotypic expression of the crop for the traits of interest 
has however been challenging and slow due to inherent biological 
constraints that impede the elucidation of the genetics of important 
traits in yam (Mignouna et al., 2008). Although empirical breeding 
approaches have led to crucial achievements, contemporary tools 
and resources must be developed and applied to create the para-
digm shift necessary to contribute significantly to feeding the rap-
idly increasing global population amidst climate change, dwindling 
resources and dynamics of consumer preferences.
The decreasing cost and rapid advancement in next-generation 
sequencing procedures, together with high-performance computa-
tional methodologies, have led to extensive discovery of advanced 
genomic resources in numerous model and non-model plants. 
Significant genetic gains have been recorded in several crops such as 
rice, soya bean, maize and wheat, and more recently cassava through 
the application of DNA-driven breeding techniques such as genomic 
selection and marker-assisted breeding. The wealth of knowledge 
emanating from genomes, metabolomes, transcriptomes, phenomes 
and gene expression profiles will immensely contribute to our un-
derstanding of underlying gene regulatory networks to enable sys-
tematic improvement of crop breeding. The deployment of these 
contemporary methods will therefore expedite yam breeding ef-
forts. However, much information is needed before full deployment 
of molecular breeding in yam as most of the information currently 
available for the crop is on diversity studies and few QTL detections 
with less emphasis on the validation and development of informative 
markers for direct use in breeding.
Earlier efforts in genomics research in yam were mainly focused 
on the development of polymorphic molecular markers and evalua-
tion of their potential applications for investigating genetic diversity, 
demarcation and identification of relationships among the various 
species. Quantitative trait loci (QTLs) controlling the resistance of 
yam to anthracnose and yam mosaic virus disease have also been 
identified (Bhattacharjee et al., 2018; Mignouna et al., 2002b; Petro, 
Onyeka, Etienne, & Rubens, 2011; Saski, Bhattacharjee, Scheffler, 
& Asiedu, 2015). Significant efforts have been made recently to de-
velop more genetic tools and genomic resources to transform yam 
breeding. This paper reviews the advancements achieved in empiri-
cal yam breeding endeavours and the development, status and appli-
cation of emerging breeding tools and methodologies for the genetic 
improvement of yam.
2  | GERMPL A SM RESOURCES FOR YAM 
IMPROVEMENT
Availability and access to smart germplasm resource is vital for the 
development of improved crop varieties that possess superior agro-
nomic and food quality traits. Yam germplasm collections conserved 
in the field and in vitro genebanks constitute a huge gene pool for 
yam improvement activities in order to achieve its optimum poten-
tial for food and income for farmers. By 2010, a total of 15,903 yam 
accessions were conserved in genebanks across the globe with IITA 
accounting for 21% (3,319) (FAO, 2010). Other institutions includ-
ing National University of Ivory Coast, University of Abomey Calavi 
(Benin), Plant Genetic Resources Research Institute (Ghana), Dodo 
Creek Research Station, Ministry of Home Affairs and Natural 
Development (Solomon islands) and Peradeniya University (Sri 
Lanka) also hold 10, 7, 5, 3 and 3% of the global yam collections, 
respectively, while 93 other institutions accounted for the remaining 
51% (FAO, 2010). Institutions such as CTCRI (Central Tuber Crops 
Research Institute) in India, PRC (Plant Resource Center) in Vietnam, 
PhilRootCrops in the Philippines, CRB-PT (Centre de Ressources 
Biologiques Plantes Tropicales) INRA-CIRAD (Institut National de 
la Recherche Agronomique—Centre de Coopération Internationale 
     |  3DARKWA et Al.
en Recherche Agronomique pour le Développement), Guadeloupe, 
France, VARTC (Vanuatu Agricultural Research and Training Centre) 
in Vanuatu, South Pacific islands (Papua New Guinea, Fiji, and New 
Caledonia islands), and Ethiopian Biodiversity Institute and National 
Research System in Ethiopia maintain a substantial number of 
Dioscorea species under ex situ collections (Arnau et al., 2017; Chi, 
Hue, & Trinh, 2007; Lebot, 2009; Lebot, Malapa, & Abraham, 2017; 
Mekbib & Deressa, 2015). IITA has increased tremendously its yam 
germplasm collections from the 3,319 in 2010 to 5,788 in 2018 
(IITA, 2018). The IITA collections encompass ten species: D. rotun-
data (68%), D. alata (21.8%), D. burkiliana (6.2%), D. abyssinica (1.6%), 
D. cayenensis (1.5%), D. dumetorum (1.3%), D. bulbifera (1.2%), D. escu-
lenta (0.4%), D. preusii (0.17%) and D. mangenotiana (0.14%).
The yam germplasm collections are rich in rare alleles for tar-
get traits from which breeders source genetic materials to expedite 
genetic gain in a sustainable way. However, the systematic use of 
genebank accessions has not progressed very far in yam breeding 
programmes. The development of core collections representing the 
diversity of the entire germplasm (Frankel & Brown, 1984) facilitates 
easy access and enhances the utilization of the main collection for 
fast evaluation in crop improvement programmes. The first core 
collection (391 accessions) from the IITA yam germplasm repre-
sented 75% of the morphological diversity of the entire collection 
using data on 77 morphological descriptors and country of origin 
(Mahalakshmi et al., 2007). This core collection represented 13% of 
the entire collection. With the inclusion of new germplasm and avail-
ability of information, the yam core collection at IITA was revisited 
and updated using 56 morphological traits (Girma et al., 2018). The 
updated core collection comprised 843 accessions and represented 
about 20% of the whole collection. Delineation of core collections 
and diversity studies in general has often been based on morpholog-
ical traits rather than molecular markers. The high phenotypic plas-
ticity of yam is a limitation to morphological characterization, hence, 
the need to enhance “omic” resources.
Phenotypic characterization and assessment of germplasm for 
morphological and agronomic traits, tuber quality, ploidy status, and 
flowering ability have resulted in the identification of accessions as 
parents with attributes pertinent to the objectives of the breeding 
programme (Lopez-Montes et al., 2012). To explore the use of aerial 
tubers of D. alata as alternative planting materials owing to the high 
cost and supply shortage of seed yam propagules, over 800 acces-
sions from the IITA germplasm collection were evaluated for aerial 
tuber production and a set of accessions bearing aerial tubers were 
identified (Girma, Gedil, & Spillane, 2015). A number of studies have 
successfully assessed the IITA’s and other institution's yam diversity 
represented by different genebank accessions, landraces and breed-
ing lines for host–plant resistance and quality traits and have shown a 
rich base of germplasm resource that can inform breeding strategies 
for resistance to major yam pest such as nematodes, anthracnose 
and virus diseases, and genetic enhancement for quality traits includ-
ing various secondary metabolites, tuber carotenoids and other food 
quality traits (Mohandas, Ramakrishnan, & Sheela, 1996; Plowright & 
Kwoseh, 2000; Mignouna, Abang, Green, & Asiedu, 2001; Mignouna, 
Njukeng, Abang, & Asiedu, 2001; Abang et al., 2003; Onyeka, Petro, 
Ano, Etienne, & Rubens, 2006; Arnau, Maledon, & Nemorin, 2007; 
Egesi, Odu, Ogunyemi, Asiedu, & Hughes, 2007; Kwosch, Plowright, 
Bridge, & Asiedu, 2007; Asiedu & Sartie, 2010; Kwon et al., 2015; 
Price, Wilkin, Sarasan, & Fraser, 2016; Lebot et al., 2018; Price, 
Bhattacharjee, Lopez-Montes, & Fraser, 2018). There is also an on-
going initiative at IITA to sequence all the genebank yam collections 
to expedite its use for breeding. This effort shall be intensified by 
systematically arranging the trait-specific core sets to identify and 
utilize useful genetic variability that exists in wild and cultivated ge-
nepools for long-term use in yam breeding. Extensive phenotyping 
of large set of genetically diverse yam germplasm for different traits 
and developing trait-specific mini-core sets such as biotic stress re-
sistance, quality and abiotic stress tolerance could provide an oppor-
tunity to mine novel alleles from underutilized germplasm resources 
for breeding next generation of yam varieties.
3  | GENETIC IMPROVEMENT OF YAM 
WITH EMPIRIC AL BREEDING
3.1 | Yam breeding targets and challenges
The breeding programmes in many countries focus on a 
few Dioscorea species, most specifically the dominantly cultivated 
yams such as D. rotundata and D. alata. The primary focus of yam 
breeding is the development and deployment of robust varieties 
with unique combinations of preferred traits required for produc-
tion and consumption. The programmes generally target traits re-
lated to increasing intrinsic tuber yield potential as well as increasing 
tolerance for, and resistance to, yield-limiting and quality-reducing 
factors. Still, the specific breeding targets varied according to the 
region and species involved (Mignouna, Abang, & Asiedu, 2007). The 
breeding targets have, however, evolved over the years to meet the 
changing needs and preferences of farmers and other end-users. 
From the inception of formal yam breeding since the 1970s, the 
breeding targets have been gradually adding new traits along with 
the primary focus on high and stable tuber yield, higher dry mat-
ter, resistance to economically significant diseases (e.g. anthracnose, 
viruses, tuber rots) and pests (e.g. nematodes), tuber characteris-
tics cherished by consumers (e.g. size, shape, and culinary quality), 
and plant architecture (e.g. dwarf genes) that reduces the need for 
staking (Asiedu, Ng, Bai, Ekanayake, & Wanyera, 1998; Mignouna 
et al., 2007). Additionally, traits such as colour of tuber flesh, tuber 
oxidation, starch properties and sugar contents are now routinely 
measured in breeding programmes as they influence the accept-
ability of newly developed yam varieties (Arnau, Maledon, Nudol, 
& Marie-Claire, 2016; De Koeyer et al., 2017). However, high-level 
resistance genes to viruses, anthracnose and nematodes; shrub-like 
or dwarf plant architecture with stiff or stout vine base and early 
branching; early maturity or tuber bulking; tubers less susceptible to 
deformation in the soil; tolerance for low soil fertility, drought and 
heat; long shelf life of fresh tubers or processed food products; and 
4  |     DARKWA et Al.
high levels of culinary attributes suited to consumer needs for fresh 
and processed yam are among the missing traits in current advanced 
breeding lines and released cultivars.
The breeding efforts over the last five decades have resulted in 
the identification of trait progenitors and commercial release of im-
proved cultivars. However, the local farmers’ varieties are still the 
leading and dominant cultivars in the yam cultivation and consump-
tion systems (Alene, Abdoulaye, Rusike, Manyong, & Walker, 2015). 
While this low market penetration of improved varieties could be 
attributed in part to the limited dissemination efforts, limited expo-
sure of the customers to the released varieties or lack of preferred 
attributes in the varieties meeting the needs and demands of farm-
ers and market, the setting of breeding goals or specifications for 
new varieties was refocused and restructured to raise the market 
penetration potential of future releases with correct product profile. 
The breeding targets have been transformed from undifferentiated 
product portfolio to a differentiated product concept where cus-
tomer needs are profiled and translated to product specifications. 
Clients/customer needs are now the main focus of the variety de-
velopment plan of yam breeding programmes in Africa. Accordingly, 
three key breeding product profiles have been carefully framed 
for targeting (Table S1) by IITA and its national partner breeding 
programmes in West Africa. The product concept where the right 
features or traits for the varieties that the clients (growers, proces-
sors, retailers and consumers) require or demand is identified and 
prioritized with a clear roadmap to achieve the target product in a 
specified timeframe: five years to replace the current market-leading 
varieties with improved features to drive adoption in short term (cur-
rent product profile) and ten years to develop parental lines feeding 
the variety development pipeline in medium term (future product 
profile). The yam breeding programme, therefore, envisions imple-
menting a product concept that can drive rapid and successful up-
take of new varieties in the production and consumption system.
Making breeding progress in yam has been challenging. Many 
factors such as unpredictable or no flowering, non-synchronous 
flowering of elite genotypes, lengthy growth cycle, low multiplica-
tion ratio, polyploidy and high heterozygosity negatively influence 
the genetic improvement of yam (Asiedu et al., 1998). Additionally, 
the dioecious nature of yam, cross-incompatibility of inter- and in-
tra-species hybridization, and the enormous phenotypic plasticity 
make the evaluation and stacking of several interdependent char-
acters a problematic process (Price, Bhattacharjee, Lopez-Montes, 
& Fraser, 2017). Limited understanding of the genetics of main and 
component traits contributing to enhanced productivity under cur-
rent production challenges and limited breeding enabling technol-
ogies that can contribute to its accelerated improvement have also 
thwarted yam breeding efforts. The influence of biochemical com-
position on organoleptic properties of yam is not well understood 
and, therefore a significant hindrance to detecting genetic/biochem-
ical trait markers effectively translating the genetic diversity to end-
user preferred genetic gain (Price et al., 2017).
Furthermore, the existing yam germplasm has not been well 
characterized, limiting the efficient utilization of available diversity 
in genetic improvement for the agronomic and food quality traits. 
Transfer of genes of interest from the secondary gene pool of wild 
relatives to the cultivated primary gene pool is still challenging in 
several crops, including yam (Lebot, Abraham, Kaoh, Rogers, & 
Molisalé, 2019; Spillane & Gepts, 2001). Nevertheless, the wild rel-
atives of yam could harbour essential genes and genetic variability 
useful in breeding endeavours to enhance the performance of yam 
for various economically important traits. Yam germplasm conser-
vation has predominantly focused on the main cultivated species 
with little emphasis on wild relatives; hence, future studies can be 
limited if conservation efforts are not increased. Basal species with 
diverse traits are also lacking (Price et al., 2016), although they could 
be especially useful for genome editing. Several wild relatives of 
yam recently appeared on the International Union for Conservation 
of Nature (IUCN) red data list and have been declared threatened 
and may further compound the problem of the availability of genetic 
diversity for utilization in the breeding of this valuable staple crop.
3.2 | Conventional hybridization and selection
Traditionally, yam breeding is a two-step process that utilizes both 
sexual and asexual reproduction. Different breeding schemes are 
followed by the breeding programmes depending on the type of 
trait/s targeted for improvement. The general breeding outline for 
developing superior yam genotypes involves artificial hand or natu-
ral open pollination for the generation of full-sib or half-sib prog-
enies segregating for desired traits and subsequent selection of their 
clonal derivatives for performance reproducibility and stability over 
seasons and locations. Yam species are mostly dioecious, and they 
produce male and female flowers on separate plants. Hence, sepa-
rate crossing blocks of male and female parents are established with 
multiple planting dates to enhance synchronization of the flowering 
in crossing blocks. The female flowers are bagged for two to seven 
days before the flower opens, depending on the spike length. The 
pollinated flowers are then kept bagged for two weeks to ensure the 
purity of offspring from crosses. It usually takes one to two days for 
a flower bud to show a sign of crack to open fully, and the stigma of 
the open flower remains receptive for six to seven days. Fruit setting 
usually takes place in about ten days after pollination, and in about 
12 weeks, the fruits start showing signs of maturity (yellowing). The 
botanical seeds from the controlled bi-parental crosses or the natu-
ral open pollination are collected from dried fruits before they dis-
perse from the capsules. The seeds are then processed and stored 
under room temperature until the end of dormancy which is three to 
four months in the case of D. rotundata, or until the desired time of 
planting as in D. alata, which has no seed dormancy (Abraham, 1992).
The botanical seeds are sown in nursery beds or seedling trays 
filled with suitable growing media such as carbonized rice husk and 
coco peat. Seed germination starts in ten days after sowing and 
continues for one month. The seedlings are then transplanted to 
the pots under screenhouse or nursery beds in the field for single 
plant selections at seedling and tuber progeny nurseries and the 
     |  5DARKWA et Al.
subsequent annual advancement of the clonal derivatives through 
early clonal generation evaluation nursery, preliminary perfor-
mance trial, advanced multi-location and multi-season perfor-
mance trial to on-farm variety validation trial for an official release 
and commercial deployment (Asiedu et al., 1998; Table 1). Crossing 
and selection have been used as a prime tool to develop improved 
varieties in yams. Table 2 summarizes the artificial cross-pollination 
and seedling production at IITA for the last five years (from 2014 
to 2018). At IITA, an average of 29,191 flowers of D. rotundata in-
volving 30 female and 25 male parents in 97 cross-combinations 
and 27,031 flowers of D. alata involving 22 female and 16 male par-
ents in 85 cross-combinations were hand cross-pollinated per year 
over the previous five years. On average, the percentage fruit and 
seed sets were 20.3% and 10.5% in D. rotundata and 28% and 9.3% 
in D. alata crossing blocks, respectively. The artificial cross-polli-
nation efforts over the five years have resulted in the production 
of 10,181 seedlings and 2,763 tuber progenies in D. rotundata and 
6,461 seedlings and 3,434 tuber progenies in D. alata breeding pipe-
lines per year for subsequent phenotypic selection. The success of 
cross-pollination and hybrid progeny generation for phenotypic 
selection was generally low compared to the pollination efficiency 
and seed set reported in Lebot, Abraham, et al. (2019) and has 
shown a lot of inter-year variances (Table 2). Lebot, Abraham, et 
al. (2019) reported variation in pollination efficiency, seed set and 
seedling survival in crosses involving diploid × diploid, diploid × tet-
raploid, and the same pollen parent in different cross-combinations. 
Sparse to non-flowering, flower asynchrony, pollen viability, the 
receptiveness of stigma, pollen–stigma compatibility, pollinators’ 
efficiency in loading sufficient pollen on the stigma, embryo abor-
tion, low seed germination, low seedling survival rate, and differ-
ential cross-fertility of parents are severely impacting the success 
rates of yam cross-pollination efforts. Improved techniques with 
flowering induction and pollen storage would increase the success 
rates in crosses. Moreover, ploidy determination, screening acces-
sions for specific or general cross-compatibility for fruit and seed 
set, seed germination and seedling survival rate and identifica-
tion of compatibility groups, development of functional molecular 
markers for flower sex, flowering intensity and cross-compatibility 
prediction, and confirmation of hybridity in progeny will contribute 
to overcome the low success rate and improve the efficiency of 
hybridization in yam breeding.
The choice of the right parents for use in the crossing scheme 
is a stepping stone towards success in yam breeding effort and 
entails the use of per se phenotypic performance for the trait of 
interest, estimation of breeding values, and dissection of genetic 
relatedness using pedigree and molecular marker information. 
Per se performance has been widely utilized but not always ef-
fective in producing heterotic progenies. Joining per se perfor-
mance along with their combining ability is an important tool for 
the selection of parents for hybridization. The use of such clas-
sical breeding technique alone is slow to achieve the anticipated 
breeding gain. Interspecific hybridization has been less exploited 
due to cross-incompatibility and non-synchronization of flower-
ing. Successful interspecific hybridization was reported between 
D. japonica and D. opposita under natural open pollination (Araki, 
Harada, & Yakuwa, 1983) and between D. alata and D. nummula-
ria under artificial hand pollination (Lebot, Abraham, et al., 2019). 
Dioscorea rotundata has been successfully crossed with D. cay-
enensis at IITA but hybridizing either of the two to D. alata has 
not been successful (Lopez-Montes et al., 2012). The unsuccessful 
interspecific hybridization between D. alata and other yam spe-
cies was also reported by Rao, Bammi, and Randhawa (1973). The 
Year Stage Site No of clones
No of plots and 
sites
1 Crossing block Research field 35–50 2 × 10 plants plot 
at 2 locations
2 Seedling progeny 
nursery
Greenhouse or 
direct transplant 
at research field
15,000–
20,000
Single stand plot 
at 2 locations
3 Tuber progeny nursery/
Tuber family evalua-
tion trial
Research field 5,000–
10,000
Single plant plot 
or replicated 
family plot at 2 
locations
4 Clonal generation 
nursery
Research field 400–600 3 plants plot at 2 
locations
5 Preliminary 
Performance trial
Research field 49–64 3 × 5 plants plot at 
2 locations
6 Advanced Performance 
trial
Research field 20–30 3 × 10 plants plot 
at 3 locations
7 Multi-location trial Research field 16–20 3 × 10 plants plot 
at > 5 locations
8 On-farm variety valida-
tion/verification trial
On-farm 2–3 10–16 plants plot 
at > 15 on-farms
TA B L E  1   Outline of breeding stages, 
number of clones/progenies selected 
and evaluated in IITA yam breeding 
programme from 2015 to 2018
6  |     DARKWA et Al.
T
A
B
L
E
 2
 
Su
m
m
ar
y 
st
at
is
tic
s 
on
 n
um
be
r o
f p
ar
en
ts
, c
ro
ss
-c
om
bi
na
tio
ns
, f
lo
w
er
s 
po
lli
na
te
d,
 fr
ui
t s
et
, h
yb
rid
 b
ot
an
ic
al
 s
ee
ds
 a
nd
 s
ee
dl
in
g 
tu
be
rs
 g
en
er
at
ed
 w
ith
 c
on
tr
ol
le
d 
ha
nd
 p
ol
lin
at
io
n 
pr
ac
tic
e 
at
 II
TA
 fr
om
 2
01
4 
to
 2
01
8
Ye
ar
N
o.
 o
f p
ar
en
ts
N
o.
 o
f 
cr
os
s-
co
m
bi
na
ti
on
s
Fl
ow
er
s 
po
lli
na
te
d
Fr
ui
t s
et
Se
ed
 s
et
N
um
be
r o
f s
ee
ds
 
ge
rm
in
at
ed
N
um
be
r o
f s
ee
d 
tu
be
rs
 p
ro
du
ce
d
Fe
m
al
e
M
al
e
C
ou
nt
%
C
ou
nt
%
D
. r
ot
un
da
ta
20
14
22
16
97
26
,1
90
7,
70
9
29
.4
15
,4
05
9.
8
5,
69
4
2,
38
4
20
15
17
13
54
22
,4
92
5,
09
1
22
.6
13
,6
84
10
.1
8,
52
7
16
74
20
16
49
36
11
7
53
,5
67
9,
64
3
18
.0
30
,4
69
9.
5
18
,7
68
4,
57
1
20
17
32
35
12
4
22
,7
53
4,
95
7
21
.8
21
,1
81
15
.5
8,
21
2
2,
42
3
20
18
29
26
91
20
,9
53
20
15
9.
6
9,
70
3
7.
7
9,
70
3
–a
Av
er
ag
e
30
25
97
29
,1
91
4,
94
3
20
.3
18
,0
80
10
.5
10
,1
81
2,
76
3
D
. a
la
ta
b
20
14
21
10
76
22
,8
89
9,
20
9
40
.2
15
,1
92
11
.1
8,
20
6
9,
38
5
20
15
11
10
48
23
,7
10
6,
59
1
27
.8
11
,8
20
8.
3
8,
96
8
15
36
20
16
28
24
11
3
35
,2
51
5,
64
1
16
.0
15
,3
44
7.
3
5,
85
1
19
49
20
17
26
21
10
4
26
,2
73
7,
36
4
28
.0
16
,4
41
10
.4
2,
81
7
86
7
Av
er
ag
e
22
16
85
27
,0
31
7,
72
1
28
.0
14
,6
99
9.
3
6,
46
1
3,
43
4
a P
la
nt
ed
 in
 th
e 
sc
re
en
ho
us
e 
in
 2
01
9 
an
d 
da
ta
 y
et
 n
ot
 c
ol
le
ct
ed
. 
b C
ro
ss
in
g 
bl
oc
k 
w
as
 n
ot
 e
st
ab
lis
he
d 
fo
r D
.a
la
ta
 in
 2
01
8.
 
So
ur
ce
: C
om
pi
le
d 
by
 a
ut
ho
rs
 fr
om
 II
TA
 p
ol
lin
at
io
n 
an
d 
se
ed
lin
g 
ge
ne
ra
tio
n 
da
ta
 re
co
rd
 fo
r t
he
 y
ea
rs
.
     |  7DARKWA et Al.
interspecific hybridization attempt at IITA has specifically targeted 
introgression of supposed unique traits such as high carotenoid 
content from D. cayenensis accessions, and aerial bulbil production 
from the D. alata accessions into the agronomically preferred back-
ground of the D. rotundata cultivars (Lopez-Montes et al., 2012). 
However, this effort has not progressed well due to the natural 
cross barrier among the different species. The failure/discontinua-
tion of interspecific hybridization of D. rotundata and D. cayenensis 
was mainly because D. cayenensis was wrongly believed to have a 
higher level of carotenoids (Price et al., 2018).
Phenotypic selection of the variants within natural barriers is 
a core yam breeding effort by different programmes. Three to four 
clonal selection and testing cycles (from tuber family evaluation to 
advanced performance trial) are often attempted to identify superior 
recombinants for further assessment under a wider range of environ-
ments and user preferences (Table 1). At seedling and first clonal gen-
eration, selections are based mainly on reaction to a natural infestation 
of anthracnose in D. alata and viruses in D. rotundata, along with tuber 
appearance. At subsequent clonal derivate stages, from the second 
clonal generation onwards, selection is made, among other things, for 
tuber shape and appearance (uniform, globular to cylindrical or round 
shape with smooth skin), tuber dry matter (≥25%), tuber enzymatic ox-
idation (non-oxidizing white or cream flesh after peeling or cutting the 
tubers for at least 180 minutes), tuber yield and stability (≥5% yield 
advantage over best check), yam mosaic virus and yam anthracnose 
tolerance (≤2.5 severity score under field natural infestation) and plant 
architecture or type for adaptation to no or minimal staking. At ad-
vanced clonal stages (advanced performance or multi-environment 
trials), the genotypes are subjected to series of food quality analysis, 
most specifically boiled and pounded yam quality test in participatory 
trial setup engaging end-users. The most promising two to three clones 
from multi-environment testing then enter the final wide-scale on-
farm validation or verification assessment for commercial deployment.
The current breeding scheme for yam is lengthy and slow. 
Setting testing and selection trials with the needed replications at 
scale is a challenge due to the difficulty to obtain sufficient quantity 
of uniform clonal planting materials. Propagules for planting from 
different position of the same mother tuber (proximal, central and 
distal parts) often produce heterogeneous sprouting and cause a 
high interplant variability within a clone in field trials (Cornet, Sierra, 
Tournebize, & Ney, 2014). With technological advancements, pres-
ent-day yam breeders should increasingly be able to select supe-
rior recombinants accurately in breeding populations with reduced 
breeding cycles, manipulate game-changing plant attributes beyond 
the natural barriers and also produce sufficient quality planting ma-
terials to expedite the genetic gain.
3.3 | Pattern of inheritance for key traits
Nature of trait heritability is essential for the design of an optimal 
breeding strategy. Traits can be classified as either qualitative or quan-
titative based on the pattern of their variation in a population. Variation 
is discrete for qualitative traits which are often controlled by one or a 
few genes whereas it is continuous for the quantitative traits which 
are mostly controlled by several genes with each having a minor ef-
fect. Few studies have dissected the nature of trait heritability in yam 
crop. Single dominant gene in a simple or major recessive gene in du-
plex condition controls the inheritance for yam mosaic virus (YMV) 
resistance in D. rotundata (Mignouna, Njukeng, et al., 2001). Genetic 
inheritance for reaction to anthracnose disease in D. alata, caused by 
Colletotrichum gloeosporioides, was reported as dominant gene action 
with quantitative inheritance (Mignouna, Abang, et al., 2001). Sartie 
and Asiedu (2014) assessed segregation for vegetative and reproduc-
tive traits related with tuber yield and quality in D. alata genetic popu-
lation and reported transgressive segregation for traits like flowering 
intensity, tuber shape, bulbil formation, tuber flesh oxidation and re-
action to anthracnose disease. Assessment of morphological and bio-
chemical traits in D. alata cultivars showed high heritability coupled 
with high genetic advance for internode length, petiole length, days to 
sprout emergence, total phenol and total sugar suggesting a predomi-
nance of additive gene control on the expression of these traits (Alam, 
Shylla, Bora, & Saud, 2014). Broad-sense heritability for several traits in 
D. rotundata and D. alata breeding trials at IITA has shown a lot of inter-
trial variabilities (Figure 1). For majority of tuber yield-related traits 
assessed in trials such as fresh tuber yield per unit area (t/ha), tuber 
yield per plant (kg/plant), tuber number per plant, average tuber weight 
(g) and tuber dry matter content, heritability estimate on average was 
high (>50%). Phenotypic coefficient of variation reported was higher 
than the genotypic coefficient of variation for tuber yield-related traits 
such as tuber width, tuber weight, tuber yield per plant, number of tu-
bers per plant and tuber dry matter in D. alata and D. rotundata geno-
types indicating the influence of environment on the heritability of 
the traits (Alam et al., 2014; Nwankwo & Bassey, 2013). The estimate 
for genetic parameters reported was much lower in seedling-derived 
plants compared to the clonal-derived plants (Akoroda, 1983) making 
reproducibility for the phenotypic selection at early generation stage 
a challenging task in yam breeding. Genes controlling essential traits 
such as tuber yield, tuber quality, resistance to pests, and diseases are 
quantitatively inherited and less likely linked, making their improve-
ment through empirical breeding a challenging task. The efficiency and 
effectiveness of breeding for essential traits in yam would significantly 
improve with the use of genomic and genetic assisted breeding.
3.4 | Envirotyping
Accurate examination of complex environmental factors for both 
target environments and specific genotypes provides a unique av-
enue for management and optimization of environmental variables 
for enhanced genetic improvement and efficient crop production in 
an era of global climate change. All environmental factors that influ-
ence plant growth and yield are defined as envirotypes, and the pro-
cess for determination and measurement of all the environmental 
factors is envirotyping (Xu, 2015, 2016). The concept “envirotyping” 
as suggested by Xu (2015) is another “typing” technique supporting 
8  |     DARKWA et Al.
phenotyping and genotyping to elucidate environmental effects on 
crops. Through its efficient mechanisms such as integrative pheno-
typing, genotype-by-environment interaction, genes responsive to 
environmental signals and biotic and abiotic stresses, envirotyping 
facilitates crop modelling and phenotype prediction (Xu, 2016).
The partitioning of yam production environments into similar 
mega-environments will have functional implications for variety test-
ing and performance prediction for final product recommendation 
thereby enabling a broader impact of breeding products. Alabi et al. 
(2019) mapped the yam production environment in four West African 
countries (Nigeria, Benin, Ghana and Cote d’Ivoire) that account for 
92% of global yam production, using 23 bioclimatic variables, 21 soil 
properties layers, 18 remote sensing vegetation layers and five topo-
graphic variables derived from digital elevation model data. Seven dif-
ferent yam mega-environments were identified, four of which were 
country-specific while three (representing 70% of the current yam 
production in the four countries) were featured across the four coun-
tries, indicating their usefulness in the distribution and testing of elite 
yam varieties in West Africa. Characterization of the yam production 
environments further revealed the existence of other potential areas 
that have received less attention, and therefore, yam cultivar testing 
and development could assign varieties onto target environments 
through upscaling of research outputs for extensive impact.
The practice of ennoblement by farmers contributes to the 
broad phenotypic plasticity and ecological adaptations retained in 
yam, as a result of which envirotyping is comparably more crucial in 
yam breeding than other domesticated crops. The partitioning of the 
yam production environments comes with additional cost implica-
tions for enhanced multi-environment/localized breeding.
3.5 | Variety release
A number of organizations such as IITA and its national part-
ner breeding programmes in Sub-Saharan African (SSA) coun-
tries, INRA and CIRAD in Guadeloupe and Vanuatu, CTCRI in 
India (Lebot, 2009), and EIAR (Ethiopian Institute of Agricultural 
Research) in Ethiopia are involved in yam breeding. These breed-
ing programmes have developed and released more resilient, pro-
ductive and end-user preferred varieties of the dominant yam 
species that are used for commercial cultivation. The IITA and its 
national partner breeding programmes in SSA countries have de-
veloped and released improved varieties of the two dominant yam 
species: white yam (D. rotundata) and water/winged yam (D. alata). 
So far, 89 yam varieties have been identified and/or listed for com-
mercial production in Africa (Alene et al., 2015; http://afric ayam.
org/relea sed-varie ties-yam-2/) of which 25 were IITA bred lines 
(14 varieties of D. rotundata and 11 varieties of D. alata) officially 
released in Ghana and Nigeria. The varieties mentioned for Benin, 
Togo and Côte d'Ivoire by Alene et al. (2015) are recommended 
F I G U R E  1   Pattern of trait heritability in D. rotundata (a) and D. alata (b) field trials at IITA. Dioscorea rotundata included 36 trials 
conducted across 18 locations over four years from 2015 to 2018 cropping season using different set of 265 advanced clones whereas 
the D. alata included 43 trials conducted across 15 locations over 4 years (2015–2018) using different set of 380 advanced clones. ATW, 
average tuber weight (g); AUDPCYAD, area under disease progression curve for yam anthracnose disease; AUDPCYMV, area under disease 
progression curve for yam mosaic virus; DM, tuber dry matter content (%); rAUDPCYAD, relative area under disease progression curve for 
yam anthracnose disease; rAUDPCYMV, relative area under disease progression curve for yam mosaic virus; TTNPL, total tuber number per 
plant (count); TTWPL, total tuber weight per plant (kg/plant); TYLD, fresh tuber yield (t/ha)
     |  9DARKWA et Al.
varieties by breeding programmes after testing for adaptation and 
suitability for consumption needs. Additionally, five farmer vari-
eties with preferred culinary and agronomic features have been 
recognized and registered for commercial distribution in Nigeria 
in 2016 (http://afric ayam.org/relea sed-varie ties-yam-2/). There 
are also more candidate improved varieties in the pipeline to be 
released in Nigeria, Ghana, Benin and Côte d'Ivoire. The cultivar 
testing and release mechanisms are not uniform across countries 
in West Africa. Ghana and Nigeria have testing and release mech-
anisms for official release and registration of new varieties, but 
the other countries in West Africa do not have this for yam. In 
addition, national programmes in Ethiopia and India also released 
a number of improved varieties. In India, CTCRI has released 11 
improved yam varieties (http://www.aicrp tc.in/intro ducti on.php). 
In Ethiopia, EIAR and its local research institutions have released 
three yam varieties (http://www.eiar.gov.et/index.php/crop-re-
search). The French institutes, INRA and CIRAD have also released 
improved varieties of D. alata and D. rotundata in Guadeloupe and 
Vanuatu (https ://ziyan mannou.cirad.fr/app_index ).
3.6 | Enabling seed technologies to expedite 
genetic gain
The low multiplication ratio of yam has significant effects on yam 
production, breeding and dissemination of released varieties to 
farmers. The regular replacement of stocks of seed yams infested 
by pests and diseases is usually not possible due to short supply of 
quality seed tubers at affordable prices thereby forcing farmers to 
recycle poor quality seed yams with the risk of low yields (Aighewi, 
Asiedu, Maroya, & Balogun, 2015). Farmers produce quality seed 
yams through traditional approaches such as selection of small 
whole tubers from crop harvest; stimulating the development of 
seed tubers by “milking” ware tubers while the leaves of the plant 
are still green (double harvest system); cutting ware tubers into setts 
about the same sizes as normal seed tubers; and the “Anambra” sys-
tem where seed tubers are produced from smaller setts (Aighewi et 
al., 2015). However, low multiplication ratio, high cost and high risk 
of exposure of seed tubers to contamination with pests and patho-
gens often characterize the traditional systems of seed yam produc-
tion (Nweke, Ugwu, Asadu, & Ay, 1991).
New methods such as the minisett, microsetts/microtubers, 
tissue culture, vine cuttings, aeroponics and bioreactor systems, 
and the recently adopted semi-autotrophic hydroponics (SAH) 
technology have been developed to address the challenges of 
quantity and quality of seed tubers. A detailed review of these 
new methods (excluding the SAH technology), as well as the tra-
ditional techniques, has been provided by Aighewi et al. (2015). 
Semi-autotrophic hydroponics is a novel, low-cost, licensed tech-
nology for high-ratio propagation of true-to-type (genetic) vi-
rus-free clonal plants. Semi-autotrophic hydroponics facility for 
yam was initiated at IITA-Ibadan in 2017 with the capacity to 
produce 1,000,000 plants cumulatively per year within an area 
of 39.5 m2 (Pelemo et al., 2018). Virus-free tissue culture plant-
lets, as well as botanical seeds from crossing blocks, are utilized as 
the starter materials for SAH. Semi-autotrophic hydroponics has 
the potential to facilitate large-scale early generation testing (up 
to 30 locations), in that, multi-location selection can be initiated 
within 2 years, instead of at least two locations within 4 years with 
the conventional breeding scheme (Pelemo et al., 2019). The SAH 
technique could help with extensive on-farm testing to create de-
mand for new varieties which is a key step missing with current 
public yam breeding efforts. The SAH technique can therefore re-
duce the breeding cycle and scale testing schemes to accelerate 
the yam breeding gain.
4  | GENOMIC TOOL S AND RESOURCES 
FOR YAM IMPROVEMENT
4.1 | 4.1 Reference genome sequence
The genomes of many important crops have been sequenced with 
the advent of next-generation DNA sequencing technologies. It 
is particularly valuable for understanding the underlying genet-
ics of complex traits in plants for their subsequent manipulation 
in crop improvement programmes. DNA sequence information 
is extremely relevant in identifying key genes regulating impor-
tant agronomic characters and for detecting genetic variability 
among cultivars (Edwards & Batley, 2010; Thottathil, Jayasekaran, 
& Othman, 2016). Advances in genome sequencing through next-
generation sequencing (NGS) technologies have made it possible 
to generate millions of novel markers and high-density genetic 
maps. The availability of the reference genome sequence of a 
species also helps to replace the conventional quantitative trait 
loci (QTL) mapping with association mapping. Quantitative trait 
loci mapping offers a wide genome range within which the gene 
is located while association maps mark traits with high resolution 
(Thottathil et al., 2016).
Tamiru et al. (2017) have developed and released the refer-
ence genome sequence of D. rotundata accession TDr96_F1 with a 
genome size of 594 Mb, out of which 76.4% is distributed among 
21 linkage groups. The D. rotundata reference genome is available 
at http://genome-e.ibrc.or.jp/home/bioin forma tics-team/yam. 
Similarly, Bredeson et al. (2018) have also developed and released an 
early draft assembly of the second most important species, D. alata 
reference genome (https ://www.ncbi.nlm.nih.gov/nucco re/CZHE0 
00000 00.2). The breeding line TDa95/00328 was used as the refer-
ence accession for D. alata. This sequence accounts for nearly 50% 
of the total genome but an estimated 80%–90% of protein-coding 
loci while long-read PacBio sequencing of the reference accession 
is currently ongoing along with the development of a dense genetic 
map from eight map-crosses (Bredeson et al., 2018). The develop-
ment and release of the reference genome sequences of the two 
most economically important species of yam have opened a new 
avenue for exploitation and in-depth understanding of the genetics, 
10  |     DARKWA et Al.
genomics and domestication of this crop (Scarcelli et al., 2019). 
These data also facilitate the detection of allelic variations and can-
didate genes modulating key agronomic and quality traits, which is 
essential for the success of yam breeding.
4.2 | Molecular markers and genotyping systems
Molecular markers are relevant tools for applications such as as-
sessing genetic diversity and phylogenetic relationships, cultivar 
identification, mapping of major effect genes and QTLs, estimating 
population structure, identification of elite genotypes in crop im-
provement programmes, and for validation of progenies emanating 
from genetic hybridizations (Tamiru et al., 2015). Many marker sys-
tems have been developed and routinely applied to yam improve-
ment activities (Table 3). Some of these marker systems applied in 
yam are listed below.
4.2.1 | Isozyme markers
Isozymes were the first molecular markers to be established which 
showed Mendelian inheritance, co-dominant expression, complete 
penetrance and non-existence of pleiotropic and epistatic interac-
tions (Weeden & Wendel, 1989). The potential of isozyme markers 
for molecular characterization within and between various Dioscorea 
species has been well established (Table 3).
4.2.2 | Random Amplified Polymorphic DNA 
markers (RAPD)
Many RAPD markers were developed and predominantly applied for 
genetic diversity studies of the several Dioscorea species (Table 3). 
Asemota, Ramser, Lopez-Peralta, Weising, and Kahl (1995) charac-
terized cultivars of D. alata, D. cayenensis, D. rotundata and D. trifida 
from Jamaica using RAPD markers. Substantial genetic variation 
was also detected among and between accessions of D. alata using 
RAPD markers (Cheng & Liu, 1996). Random amplified polymorphic 
DNA primers were employed to assess cultivars of D. cayenensis and 
D. rotundata originating from Benin Republic that could not be distin-
guished using isozyme markers (Dansi, Mignouna, Zoundjihékpon, et 
al., 2000; Zannou et al., 2009). The intraspecific diversity of D. bul-
bifera collections that originated from Africa, Asia and Polynesia was 
examined with RAPD primers (Ramser, Weising, Kahl, López-Peralta, 
& Wetzel, 1996).
4.2.3 | Restriction Fragment Length Polymorphism 
(RFLP)
Terauchi, Chikaleke, Thottappilly, and Hahn (1992) exploited heter-
ologous DNA sequences as a source of RFLP markers and developed 
the first RFLP markers for yam. Restriction fragment length poly-
morphism markers were successfully used to study the phylogeny 
and origin of Guinea yam (Terauchi et al., 1992) and genetic diversity 
of D. bulbifera accessions originating from Asia and Africa (Terauchi, 
Terauchi, & Tsunewaki, 1991).
4.2.4 | Amplified Fragment Length Polymorphism 
(AFLP)
Amplified fragment length polymorphism markers were success-
fully used to explore the genetic variation of D. alata, D. rotundata, 
D. bulbifera, D. dumetorum, D. pentaphylla, D. cayenensis, D. abyssinica, 
D. esculenta, D. nummularia, D. persimilis, D. trifida and D. transversa 
(Table 3). The comparative evaluation of three molecular marker 
systems (RAPD, AFLP and SSR) for characterization of D. rotun-
data revealed that AFLPs had the highest efficiency in discovering 
polymorphism and detected genetic relationships very similar to 
morphological classification (Mignouna, Abang, & Fagbemi, 2003). 
Scarcelli et al. (2006) studied the practice of ennoblement of D. cay-
enensis, D. rotundata, D. abyssinica and D. praehensilis by farmers in 
Benin Republic using 91 AFLP markers. Their study established that 
farmers generated new cultivars with novel genetic combinations 
through sexual hybridization of wild and cultivated yam.
4.2.5 | Simple Sequence Repeats markers (SSRs)
Microsatellites or SSR, due to their co-dominant nature, high level 
of polymorphism, high abundance and even distribution across the 
genome were necessary as yam genomics progressed. Terauchi and 
Konuma (1994) detected microsatellite polymorphisms in a natural 
population of D. tokoro. Even though the D. tokoro microsatellite 
primers failed to amplify any DNA when applied to other Dioscorea 
species, the study revealed the potential usefulness of these mark-
ers in yam. Further advancements in yam genomics resulted in the 
development of 10 SSR markers in D. japonica (Mizuki, Tani, Ishida, & 
Tsumura, 2005). Tostain et al. (2006) generated and characterized 16 
microsatellite markers in different Dioscorea species (D. alata, D. ab-
yssinica and D. praehensilis) and discovered that many of the markers 
were transferable to other Dioscorea species in the Enantiophyllum 
section. Siqueira, Marconi, Bonatelli, Zucchi, and Veasey (2011) de-
veloped 14 polymorphic SSR markers for D. alata by means of a mi-
crosatellite-enriched genomic library methodology. Additionally, six 
microsatellite markers with high cross-species amplification involving 
five Dioscorea species (D. alata, D. cayenensis, D. rotundata, D. prae-
hensilis and D. abyssinica) were developed in American yam, D. trifida 
(Hochu, Santoni, & Bousalem, 2006). Silva et al. (2014) isolated and 
characterized nine SSR markers from an enriched genomic library of 
D. cayenensis. Tamiru et al. (2015) developed 90 SSR markers from an 
enriched genomic library of D. cayenensis and found 94.4% and 56.7% 
of these SSRs transferable to D. rotundata and D. alata, respectively. 
Saski et al. (2015) developed 1,152 EST-SSRs from EST sequences 
     |  11DARKWA et Al.
T
A
B
L
E
 3
 
M
ol
ec
ul
ar
 b
re
ed
in
g 
to
ol
s 
an
d 
th
ei
r a
pp
lic
at
io
n 
in
 y
am
 im
pr
ov
em
en
t
M
ar
ke
r t
yp
e
N
o 
of
 a
cc
es
si
on
s 
st
ud
ie
d
N
um
be
r o
f 
m
ar
ke
rs
A
pp
lic
at
io
n
Sp
ec
ie
s
So
ur
ce
Is
oz
ym
e
-
-
G
en
et
ic
 d
iv
er
si
ty
 s
tu
di
es
D
. a
la
ta
, D
. c
ay
en
en
sis
/D
. r
ot
un
da
ta
, D
. e
sc
ul
en
ta
 a
nd
 
D
. b
ul
bi
fe
ra
Tw
yf
or
d,
 V
ia
na
, J
am
es
, a
nd
 M
an
te
ll 
(1
99
0)
26
9
4
G
en
et
ic
 d
iv
er
si
ty
 s
tu
di
es
D
. a
la
ta
Le
bo
t, 
Tr
ill
es
, N
oy
er
, a
nd
 M
od
es
to
 (1
99
8)
16
6
G
en
et
ic
 d
iv
er
si
ty
 s
tu
di
es
D
. a
la
ta
Br
es
sa
n,
 B
rin
er
 N
et
o,
 Z
uc
ch
i, 
Ra
be
llo
, a
nd
 
Ve
as
ey
 (2
01
1)
45
3
5
G
en
et
ic
 d
iv
er
si
ty
 s
tu
di
es
D
. c
ay
en
en
sis
/D
. r
ot
un
da
ta
H
am
on
 &
 T
ou
re
, 1
99
0)
15
6
6
Pa
re
nt
ag
e 
an
al
ys
is
D
. c
ay
en
en
sis
/D
. r
ot
un
da
ta
Zo
un
dj
ih
ek
po
n 
et
 a
l. 
(1
99
4)
46
7
7
G
en
et
ic
 d
iv
er
si
ty
 s
tu
di
es
D
. c
ay
en
en
sis
/D
. r
ot
un
da
ta
D
an
si
, M
ig
no
un
a,
 Z
ou
nd
jih
ek
po
n,
 e
t a
l. 
(2
00
0)
45
6
G
en
et
ic
 d
iv
er
si
ty
 s
tu
di
es
D
. c
ay
en
en
sis
/D
. r
ot
un
da
ta
H
am
on
 &
 T
ou
re
, 1
99
0)
21
7
G
en
et
ic
 d
iv
er
si
ty
 s
tu
di
es
D
. c
ay
en
en
sis
/D
. r
ot
un
da
ta
Br
es
sa
n,
 B
rin
er
 N
et
o,
 Z
uc
ch
i, 
Ra
be
llo
, a
nd
 
Ve
as
ey
 (2
01
4)
25
7
G
en
et
ic
 d
iv
er
si
ty
 s
tu
di
es
D
. t
rif
id
a
Ve
as
ey
 e
t a
l. 
(2
01
2)
R
A
PD
11
9
G
en
et
ic
 d
iv
er
si
ty
 s
tu
di
es
D
. a
la
ta
, D
. c
ay
en
en
sis
, D
. r
ot
un
da
ta
 a
nd
 D
. t
rif
id
a
A
se
m
ot
a 
et
 a
l. 
(1
99
5)
12
14
G
en
et
ic
 d
iv
er
si
ty
 s
tu
di
es
D
. a
la
ta
C
he
ng
 a
nd
 L
iu
 (1
99
6)
20
6
G
en
et
ic
 d
iv
er
si
ty
 s
tu
di
es
D
. a
la
ta
Za
nn
ou
 e
t a
l. 
(2
00
9)
70
6
G
en
et
ic
 d
iv
er
si
ty
 s
tu
di
es
D
. c
ay
en
en
sis
/D
. r
ot
un
da
ta
Za
nn
ou
 e
t a
l. 
(2
00
9)
23
12
G
en
et
ic
 d
iv
er
si
ty
 s
tu
di
es
D
. c
ay
en
en
sis
/D
. r
ot
un
da
ta
D
an
si
, M
ig
no
un
a,
 Z
ou
nd
jih
ék
po
n,
 e
t a
l. 
(2
00
0)
23
2
10
G
en
et
ic
 d
iv
er
si
ty
 s
tu
di
es
D
. b
ul
bi
fe
ra
Ra
m
se
r e
t a
l. 
(1
99
6)
RF
LP
15
9
G
en
et
ic
 d
iv
er
si
ty
 s
tu
di
es
D
. b
ul
bi
fe
ra
Te
ra
uc
hi
 e
t a
l. 
(1
99
1)
23
7
O
rig
in
 a
nd
 p
hy
lo
ge
ne
tic
 s
tu
di
es
D
. r
ot
un
da
ta
, D
. c
ay
en
en
sis
Te
ra
uc
hi
 e
t a
l. 
(1
99
2)
A
FL
P
53
3
G
en
et
ic
 d
iv
er
si
ty
 s
tu
di
es
D
. a
la
ta
Eg
es
i, 
A
si
ed
u,
 U
de
, O
gu
ny
em
i, 
an
d 
Eg
un
jo
bi
 
(2
00
6)
17
9
11
G
en
et
ic
 d
iv
er
si
ty
 s
tu
di
es
D
. a
la
ta
, D
. a
by
ss
in
ic
a,
 D
. b
ul
bi
fe
ra
, 
D
. c
ay
en
en
sis
/D
. r
ot
un
da
ta
,
D
. e
sc
ul
en
ta
, D
. n
um
m
ul
ar
ia
, D
. p
en
ta
ph
yl
la
, D
. p
er
sim
i-
lis
, D
. t
ra
ns
ve
rs
a 
an
d 
D
. t
rif
id
a
M
al
ap
a,
 A
rn
au
, N
oy
er
, a
nd
 L
eb
ot
 (2
00
5)
20
64
G
en
et
ic
 d
iv
er
si
ty
 s
tu
di
es
D
. r
ot
un
da
ta
M
ig
no
un
a,
 E
lli
s,
 K
no
x,
 A
si
ed
u,
 a
nd
 N
g 
(1
99
8)
21
3
91
G
en
et
ic
 d
iv
er
si
ty
 s
tu
di
es
D
. c
ay
en
en
sis
/D
. r
ot
un
da
ta
, D
. a
by
ss
in
ic
a 
an
d 
D
. p
ra
eh
en
sil
is
Sc
ar
ce
lli
 e
t a
l. 
(2
00
6)
53
10
G
en
et
ic
 d
iv
er
si
ty
 s
tu
di
es
D
. b
ul
bi
fe
ra
, D
. a
la
ta
, D
. c
ay
en
en
sis
 a
nd
 D
. r
ot
un
da
ta
Ta
m
iru
, B
ec
ke
r, 
an
d 
M
aa
ss
 (2
00
7)
53
12
G
en
et
ic
 d
iv
er
si
ty
 s
tu
di
es
D
. d
um
et
or
um
So
ni
ba
re
 e
t a
l. 
(2
01
0)
(C
on
tin
ue
s)
12  |     DARKWA et Al.
M
ar
ke
r t
yp
e
N
o 
of
 a
cc
es
si
on
s 
st
ud
ie
d
N
um
be
r o
f 
m
ar
ke
rs
A
pp
lic
at
io
n
Sp
ec
ie
s
So
ur
ce
SS
R
95
8
Cy
to
ge
ne
tic
 s
tu
di
es
D
. t
rif
id
a
Bo
us
al
em
 e
t a
l. 
(2
00
6)
42
10
G
en
et
ic
 d
iv
er
si
ty
 s
tu
di
es
D
. b
ul
bi
fe
ra
Si
lv
a 
et
 a
l. 
(2
01
6)
12
5
6
Cy
to
ge
ne
tic
 s
tu
di
es
D
. a
la
ta
A
rn
au
, N
ém
or
in
, M
al
ed
on
, a
nd
 A
br
ah
am
 
(2
00
9)
89
13
G
en
et
ic
 d
iv
er
si
ty
 s
tu
di
es
D
. a
la
ta
O
bi
di
eg
w
u,
 A
si
ed
u,
 E
ne
-O
bo
ng
, M
uo
ne
ke
, 
an
d 
Ko
le
sn
ik
ov
a-
A
lle
n 
(2
00
9)
21
9
15
G
en
et
ic
 d
iv
er
si
ty
 s
tu
di
es
D
. r
ot
un
da
ta
O
bi
di
eg
w
u,
 K
ol
es
ni
ko
va
-A
lle
n,
 E
ne
-O
bo
ng
, 
M
uo
ne
ke
, a
nd
 A
si
ed
u 
(2
00
9)
56
9
Pa
re
nt
ag
e 
an
al
ys
is
D
. r
ot
un
da
ta
; D
. a
la
ta
Sa
rt
ie
 &
 A
si
ed
u,
 2
01
1)
36
9
G
en
et
ic
 d
iv
er
si
ty
 s
tu
di
es
D
. a
la
ta
Si
qu
ei
ra
, D
eq
ui
gi
ov
an
ni
, C
or
az
on
-G
ui
vi
n,
 
Fe
ltr
an
, a
nd
 V
ea
se
y 
(2
01
2)
38
4
24
ge
ne
tic
 d
iv
er
si
ty
 s
tu
di
es
D
. a
la
ta
A
rn
au
 e
t a
l. 
(2
01
7)
14
6
10
G
en
et
ic
 d
iv
er
si
ty
 s
tu
di
es
D
. r
ot
un
da
ta
To
st
ai
n 
et
 a
l. 
(2
00
7)
2
57
3
–
D
. a
la
ta
Sa
sk
i e
t a
l. 
(2
01
5)
ES
T-
SS
R
10
8
90
G
en
et
ic
 d
iv
er
si
ty
 s
tu
di
es
D
. c
ay
en
en
sis
, D
. a
la
ta
, D
. r
ot
un
da
ta
Ta
m
iru
 e
t a
l. 
(2
01
5)
2
38
0
–
D
. a
la
ta
Sa
sk
i e
t a
l. 
(2
01
5)
 
94
38
0
G
en
et
ic
 m
ap
 a
nd
 Q
TL
 
id
en
tif
ic
at
io
n
D
. a
la
ta
Bh
at
ta
ch
ar
je
e 
et
 a
l. 
(2
01
8)
SN
P
95
2,
21
5
G
en
et
ic
 d
iv
er
si
ty
 a
nd
 p
lo
id
y 
an
al
ys
is
D
. r
ot
un
da
ta
, D
. c
ay
en
en
sis
, D
. m
an
ge
no
tia
na
, D
. p
ra
e-
he
ns
ili
s, 
D
. t
og
oe
ns
is,
 D
. b
ur
ki
lli
an
a 
an
d 
D
. a
by
ss
in
ic
a
G
irm
a 
et
 a
l. 
(2
01
4)
2
28
8,
50
5
–
D
. a
la
ta
Sa
sk
i e
t a
l. 
(2
01
5)
10
0
–
Q
TL
 a
na
ly
si
s
D
. r
ot
un
da
ta
Ta
m
iru
 e
t a
l. 
(2
01
7)
44
6,
37
1
G
en
et
ic
 d
iv
er
si
ty
 a
nd
 p
lo
id
y 
an
al
ys
is
D
. d
um
et
or
um
Si
ad
je
u 
et
 a
l. 
(2
01
8)
16
7
3,
57
0,
94
0
D
om
es
tic
at
io
n 
an
d 
ge
ne
tic
 
di
ve
rs
ity
D
. r
ot
un
da
ta
, D
. a
by
ss
in
ic
a,
 a
nd
 D
. p
ra
eh
en
sil
is
Sc
ar
ce
lli
 e
t a
l. 
(2
01
9)
32
0
1,
54
8
Li
nk
ag
e 
m
ap
 a
nd
 Q
TL
 
id
en
tif
ic
at
io
n
D
. a
la
ta
C
or
m
ie
r e
t a
l. 
(2
01
9)
T
A
B
L
E
 3
 
(C
on
tin
ue
d)
     |  13DARKWA et Al.
generated from two D. alata genotypes (susceptible and resistant) 
out of which 388 were validated as polymorphic showing a polymor-
phism rate of 34% when tested on two diverse parents targeted for 
anthracnose disease. Microsatellite markers have been utilized to in-
vestigate the genetic diversity, ploidy level, inheritance patterns and 
parentage analysis of various Dioscorea species (Table 3).
4.3 | Next-generation-based genotyping procedures
The field of genomics has been markedly transformed in recent 
years due to the advances in high-throughput sequencing technolo-
gies, making it feasible to generate large volumes of sequence data 
quickly and at a considerably lower cost (Elshire et al., 2011). The 
high-marker density methodologies presented by next-generation-
based genotyping procedures such as genotyping by sequencing 
(GBS) can help to unravel the magnitude of genetic similarity and 
diversity within and between cultivated species and their wild rel-
atives (Spindel et al., 2013). The GBS procedure is based on mini-
mizing genome complexity with restriction enzymes, coupled with 
multiplex next-generation sequencing for high-density single nucle-
otide polymorphism (SNP) discovery (Elshire et al., 2011).
Girma et al. (2014) used the GBS approach to expound the origin 
and domestication of D. rotundata as well as the contribution of wild 
relatives to the cultivated guinea yam. The 2,215 SNP markers gen-
erated from the GBS data enabled the elucidation of the nature of 
genetic diversity within and between D. rotundata and D. cayenensis 
and five wild relatives (D. mangenotiana, D. praehensilis, D. togoensis, 
D. burkilliana and D. abyssinica) (Girma et al., 2014).
Genotyping by sequencing of single nucleotide polymorphism 
(GBS-SNP) was successfully applied to determine the genetic diversity, 
population structure and ploidy level of 44 accessions of D. dumeto-
rum from Cameroon (Siadjeu, Mayland-Quellhorst, & Albach, 2018). 
Saski et al. (2015) utilized NGS techniques such as expressed sequence 
tags (EST) sequencing, de novo sequencing and GBS profiles on two 
D. alata genotypes viz. TDa95/00328 (resistant to anthracnose) and 
TDa95/310 (susceptible to anthracnose) to produce genomic re-
sources for use in yam improvement programmes. They developed a 
comprehensive set of EST-SSRs, genomic SSRs, whole-genome SNPs 
and reduced representation SNPs for resistance to yam anthracnose 
disease and other characters in D. alata. The limitations of this study as 
with all infection studies are that many of the SNPs identified are likely 
to be associated with broad stress or infection response.
4.4 | Transcriptome sequencing
Analysis of genome-wide differential RNA expression gives research-
ers a better understanding of biological pathways and molecular 
mechanisms that control important but complex traits in plants. Narina 
et al. (2011) investigated gene expression by large-scale genera-
tion of ESTs from a susceptible (TDa95/0310) and two resistant yam 
genotypes (TDa87/01091 and TDa95/0328) infected with the fungus 
C. gloeosporioides. They annotated and analysed nearly 56% of total 
ESTs for functional categorization and differential expression of genes 
for tolerance to anthracnose disease. The assembly process gener-
ated 15,196 ESTs in TDa95/0328; 15,984 ESTs in TDa95/0310; and 
13,577 ESTs in TDa87/01091 with average sequence lengths of 426, 
411 and 524 bases, respectively. TDa95/0328 and TDa87/01091 had 
115 and 180 unique ESTs, respectively, which could be responsible for 
or explain their tolerance to C. gloeosporioides. The unique ESTs of the 
resistant accessions were found to be related to carbohydrate metabo-
lism, cell wall biogenesis, lipid and amino acid metabolism, secondary 
and hormone metabolism, transcription factors, protein synthesis, and 
signalling proteins as well as multiple pathogenesis-related and host 
defence-related genes (Narina et al., 2011). A total of 104 candidate 
SNPs were also discovered between TDa95/0310 and TDa95/0328 
libraries that were homologous within each genotype and useful for 
genotyping and developing genetic maps in yam (Narina et al., 2011). 
A setback of this study is that some of the SNP markers identified are 
likely to be associated with broad stress or infection response and not 
necessarily anthracnose infection alone.
Wu et al. (2015) studied the gene expression of flavonoid (purple 
flesh colour) of D. alata tubers using Illumina sequencing to charac-
terize the transcriptomes of tubers from a purple-flesh and a white-
flesh variety. They found 125,123 unigenes from the purple-flesh 
and white-flesh cDNA libraries, out of which 49.5% were elucidated 
in publicly accessible protein databases. Biochemical pathway anal-
ysis and functional annotation showed that 511 genes were more 
than two-fold differentially expressed between the purple and white 
fleshed yam varieties of which 223 genes were down-regulated and 
288 genes up-regulated in the purple-flesh tubers. Sixty-one uni-
genes encoding various well-known enzymes in the flavonoid bio-
synthesis pathway were detected by transcriptome analysis, and 
their expression was further confirmed by quantitative real-time 
PCR (qRT-PCR) (Wu et al., 2015). With these unigenes, 11,793 SSRs 
were detected and 6,082 SSR markers were developed.
4.5 | Metabolomics
The efficacy of trait characterization is enormously enhanced when 
genomics is combined with other “–omics” approaches such as tran-
scriptomics, proteomics, metabolomics and phenomics (Fukushima, 
Kusano, Redestig, Arita, & Saito, 2009). This is very true of metabo-
lomics where, in many instances, desirable characters can be directly 
linked with metabolite composition (Bino et al., 2004). Metabolomic 
techniques produce extensive biochemical phenotypes that can be 
indicative of quality traits.
Detecting biochemical signals of phenotypic characters would 
permit metabolite-marker-based breeding (Fernie & Schauer, 2009). 
Association of specific plant traits to metabolite compositions pro-
vides quantifiable markers similar to genetic quantitative trait loci, 
for example metabolite quantitative trait loci (mQTL) which can be 
recorded through breeding programmes both independent of under-
lying genetic mechanisms and coupled with typical genomic analysis 
14  |     DARKWA et Al.
to improve association of genotype with phenotype, such as metab-
olite-based genome-wide association analysis (mGWAS) for func-
tional genomics (Adamski & Suhre, 2013; Luo, 2015).
Price et al. (2016) performed a detailed examination of polar ex-
tracts from leaf and petiole tissues of 28 Dioscorea accessions from 
19 species through gas chromatography-mass spectrometry. A total 
of 151 metabolites were detected and measured from the polar ex-
tracts of the leaves which allowed the demarcation of the differ-
ent species. The metabolite profiles observed in their study gave 
enormous insight into biochemically related species and revealed 
the Dioscorea species as potential sources of essential compounds 
such as shikimic acid (precursor utilized in the manufacture of the 
anti-viral oseltamivir). Their study further revealed a large number of 
unknown metabolites many of which are abundant and emphasizes 
the understudied nature of genus Dioscorea. Even though this study 
of Price et al. (2016) encompassed the major clades of the genus, it 
lacked intra-species diversity for non-cultivated crop wild relatives. 
Access to materials is a limitation for all studies as germplasm re-
sources are not diverse especially for the wild relatives.
Price et al. (2017) in their study of metabolite profiling of 49 yam 
accessions belonging to five species via gas chromatography-mass 
spectrometry produced a comprehensive visual pathway illustration 
of measured yam tuber metabolome as a resource for biochemical 
assessment of yam genotypes. The over 200 compounds detected 
and routinely measured in tubers offer a major development for the 
chemo-typing of yam. Leaf and tuber analysis identified a subgroup 
of metabolites useful for accurate species classification and em-
phasized the possibility of predicting tuber composition from leaf 
profiles. They also found that metabolic differences were accession 
specific and usually confined to compound classes and will there-
fore support trait-targeting for metabolite markers. Though much 
progress has been made for chemo-typing yam, there is the need for 
integration with other data (e.g. traits) to fully interpret biochemical 
compositions and identify biomarkers.
High-performance thin-layer chromatography (HP-TLC) is a 
novel technology for rapid nutritional analysis of multiple samples 
at a lower cost compared to column chromatography. Lebot, Malapa, 
and Molisalé (2019) have recently developed and optimized an HP-
TLC protocol for the rapid quantification of individual sugars, al-
lantoin, phenolic acids, catechins and saponins in yam tuber flours. 
This technique was successfully used for the rapid quantification of 
compounds related to tuber flour quality of 522 accessions of eight 
Dioscorea species.
Price et al. (2018) investigated the cross-species carotenoid 
profiling of 46 yam accessions belonging to five species (D. alata, 
D. bulbifera, D. cayenensis, D. dumetorum and D. rotundata) regularly 
utilized in yam improvement programmes and provided clarity about 
the carotenoid composition of various Dioscorea species includ-
ing the non-significant differences between the D. rotundata and 
D. alata accessions on β-carotene content and provitamin A activity. 
Their study further elucidated lack of a clear link between yellow 
tuber flesh colour and provitamin A content in yam as opposed to 
other crops like cassava. The methodology employed also allowed 
concurrent fingerprint profiling of other isoprenoids, such as tocoph-
erols and quinones. Their study revealed varied species profiles and 
analysis of elite genotypes identified those with high provitamin A 
content, putative blocks in the carotenoid biosynthetic pathway and 
tentative identification of the C25-epoxy-apocarotenoid persicax-
anthin, which may influence tuber dormancy. The possibility to as-
sociate biochemical signatures with several agronomic and sensory 
characters and study inheritance independent of the causal genetic 
mechanisms offers potential to expedite breeding by increasing the 
speed of selection.
4.6 | Identification of molecular markers linked 
to traits
Molecular markers are used to locate genes and QTLs responsible 
for the inheritance of traits. An important step in the process of trait 
mapping is the development of mapping populations. Several map-
ping populations have been developed to determine chromosomal 
regions having genes or QTLs for traits of interest in yam (Cormier 
et al., 2019; Mignouna et al., 2002a, 2002b; Petro et al., 2011; Sartie 
& Asiedu, 2011). Earlier efforts in trait mapping in yam predomi-
nantly focused on disease resistance. Mignouna, Abang, Onasanya, 
Agindotan, and Asiedu (2002) applied a bulked segregant analysis 
on an F1 progeny emanating from a cross between TDr89/0144 (re-
sistant male parent) and TDr87/00571 (susceptible female parent) 
with the aim of detecting RAPD markers linked to yam mosaic virus 
disease resistance. They identified a single locus conditioning yam 
mosaic virus resistance in TDr89/01444 and named it Ymv-1. They 
again found two RAPD markers tightly linked in coupling phase with 
Ymv-1 on the same linkage group. The two markers were successfully 
utilized to identify Ymv-1 resistant genotypes among D. rotundata 
varieties and in resistant F1 genotypes from crosses, demonstrating 
their potential usefulness in marker-assisted selection.
Mignouna et al. (2002b) applied 469 co-dominantly scored AFLP 
markers segregating in an intraspecific F1 cross to generate a genetic 
linkage map of D. alata. Quantitative trait loci mapping discovered 
one AFLP marker E-14/M52-307 positioned on linkage group 2 that 
was associated with anthracnose disease resistance, explaining 10% 
of the total phenotypic variance. A genetic linkage map of the D. ro-
tundata was developed based on 341 co-dominantly scored AFLP 
markers segregating in an intraspecific F1 cross (Mignouna et al., 
2002a). One QTL for yam mosaic virus resistance was associated 
with the marker P16/M16-126 on linkage group 1, which explained 
24% of the total phenotypic variance and two QTLs linked with P14/
M22-418 and P17/M22-238 on linkage group 8 which explained 22 
and 35% of the phenotypic variance on the maternal linkage group, 
respectively. Two QTLs for yam mosaic virus were also identified on 
the paternal linkage group 4 and were associated with the markers 
P12/M19-241 and P16/M15-81 that explained 13 and 16% of the 
phenotypic variation, respectively.
An intraspecific genetic linkage map of D. alata was constructed 
using 523 polymorphic AFLP markers and nine putative QTL(s) 
     |  15DARKWA et Al.
identified for anthracnose disease resistance on five different link-
age groups (Petro et al., 2011). The phenotypic variance explained 
by each QTL ranged from 7.0% to 32.9% while all significant QTLs 
accounted for 26.4 to 73.7% of total phenotypic variance depending 
on the isolate and the variable considered (Petro et al., 2011). A ge-
netic linkage map of D. alata was developed from 380 EST-SSRs on 
20 linkage groups for the identification of QTLs controlling anthrac-
nose disease resistance (Bhattacharjee et al., 2018). Linkage analysis 
conducted independently on data collected for three years and av-
erage scored data consistently found one QTL on linkage group 14. 
This QTL observed at a position interval of 71.1–84.8 cM explained 
68.5% of the total phenotypic variation in the average score data.
The genetics of sex determination was also elucidated by per-
forming whole-genome resequencing of bulked segregants in an 
F1 progeny segregating for male and female plants. This analysis 
revealed a genomic region on pseudo-chromosome 11 tightly asso-
ciated with femaleness within a female heterogametic sex-determi-
nation system and SNP markers developed for sex identification in 
D. rotundata plants at the seedling stage (Tamiru et al., 2017). These 
sex determination SNPs may not be transferable to the other species. 
Another limitation is that the ZW genotype shows unstable gender 
which is problematic; hence, the SNPs detected can only predict the 
likelihood of femaleness. Cormier et al. (2019) constructed the first 
high-density reference genetic map of D. alata from 1,579 SNPs on 
20 linkage groups using GBS data of two F1 outcrossed populations. 
One QTL was identified per population on linkage group 6 homologs 
and only in the male maps, which allowed the prediction of plant sex 
in 77% to 85% of the cases.
In a gene expression study of D. rotundata using SuperSAGE 
transcriptome profiling to identify flowering and sex-related genes, 
Girma et al. (2019) found 88 tags significantly differentially ex-
pressed in male, female and monoecious plants. Eighteen of these 
88 differentially expressed SuperSAGE tags corresponded to previ-
ously implicated genes for flower development and sex determina-
tion in many plant species.
Genetic mapping (GWAS and linkage mapping) studies are cur-
rently ongoing at IITA, Nigeria to identify QTLs for various traits 
in D. rotundata and D. alata under the ongoing AfricaYam and NSF-
BREAD projects. These studies will generate additional genomic 
information and resources to facilitate marker-assisted breeding in 
yam. Identification of the key genes underlying relevant traits will 
expedite their speedy incorporation into elite cultivars or farm-
er-preferred varieties via marker-assisted selection.
5  | HIGH-THROUGHPUT PHENOT YPING 
IN YAM
Visual selection has been the main breeding approach used in yam 
and other crops to identify individuals with striking plant type dif-
ferences. Making steady breeding progress in yield and quality traits 
with visual selection is challenging. The development and applica-
tion of new methods allowing for high-throughput screening or 
phenotyping of numerous accessions for nutritional and culinary 
quality, disease resistance, tuber characteristics and yield might op-
timize the evaluation accuracy and contribute to more precise selec-
tion processes.
Substantial progress has been made with the development of 
high-throughput phenotyping tools for yam. Near-infrared spec-
troscopy (NIRS) calibration for profiling quality traits, methods for 
mechanical yam virus inoculation and yam anthracnose assay, and 
tools and applications for rapid virus detection, and yam anthrac-
nose severity estimation are among the tools that are available for 
accurate phenotyping of the yam crop. Near-infrared spectroscopy 
calibration models have been developed for rapid and reliable as-
sessment of food quality attributes in various Dioscorea spp. (Alamu, 
Adesokan, & Maziya-Dixon, 2019; Ehounou et al., 2019; Lebot & 
Malapa, 2013; Zhuang, Ni, & Kokot, 2015). Equations of NIRS cali-
bration with high r2 values (>.84) for prediction of starch, sugars and 
proteins (equivalent N) in various Dioscorea spp. have been devel-
oped (Lebot & Malapa, 2013). Alamu et al. (2019) developed NIRS 
calibration model for prediction of moisture, ash, protein, crude fibre 
and tannin content in dry tuber samples (flour) of diverse D. rotun-
data breeding lines with high coefficient of determination values 
(r2 > .80) for the calibration curve and medium to high r2 values in 
cross-validation (r2 = .50 for tannin to .80 for moisture content). 
Ehounou et al. (2019) also demonstrated the feasibility of NIRS to 
predict physicochemical (dry matter content, starches, sugars and 
proteins) and textural characteristics (hardness, springiness, adhe-
siveness) on a panel of contrasting D. alata accessions. Development 
of reliable NIRS prediction models that adequately describe quality 
attributes in fresh tubers of yam would facilitate rapid selection of 
superior clones in breeding trials.
Furthermore, a method for successful transmission (with 
95% success rate) of YMV from source yam plants to and from 
Nicotiana benthamiana was developed at IITA. Silva et al. (2018) 
reported the development of a reliable, rapid and cost-effective 
detection method for the two most important potyviruses infect-
ing yam based on reverse transcription-recombinase polymerase 
amplification (RT-RPA). The method named “Direct RT-RPA” is an 
isothermal assay that offers robust, accurate, sensitive and quick re-
sults (under 15 minutes). It is a low-cost assay with minimal sample 
preparation requirements and applicable in laboratories with limited 
settings and also in the fields. The method for mechanical inocu-
lation and new assay for virus detection opens a new opportunity 
for accurate phenotyping of D. rotundata breeding population for 
virus resistance. Detached leaf assay (DLA), the “Leaf Doctor” and 
“ESTIMATE” applications have been standardized and optimized for 
high-throughput phenotyping of D. alata for anthracnose disease 
resistance screening under the ongoing AfricaYam project (https ://
afric ayam.org/the-afric ayam-new-app-for-yam-anthr acnose-disea 
se/; Kolade, Oguntade, Ajamu, Bhattacharjee, & Kumar, 2018). The 
“ESTIMATE” application uses yam anthracnose disease standard 
area diagrams for image-based phenotyping in the field and DLA. 
The “Leaf Doctor” and “ESTIMATE” applications are quantitative 
tools for precise evaluation of percentage symptomatic tissue area 
16  |     DARKWA et Al.
of a diseased leaf based on image analysis (Pethybridge & Nelson, 
2015). The applications help to differentiate healthy from diseased 
leaf tissues based on artificial intelligence and machine learning. A 
phenotyping system based on DLA, “Leaf Doctor” and “ESTIMATE” 
software enhances the throughput and precision and expedites the 
selection of promising lines for further evaluation (Kolade et al., 
2018). An essential feature of the tools is the elimination of bias 
that arises during conventional visual scoring. The optimization of 
the detached leaf assay and the software applications for yam an-
thracnose disease evaluation has resulted in the harmonization of 
the phenotyping process across different countries in addition to 
the comparison of results and performance of genotypes. The use of 
these high-throughput phenotyping methods optimized for yam and 
others such as NIRS and ground-penetrating radar (for non-destruc-
tive estimation of root bulking rate) is expected to provide more 
data that could contribute to efficient product profiling and value 
addition.
6  | INNOVATIVE METHODS OF GENETIC 
IMPROVEMENT IN YAM
6.1 | Genetic engineering and gene editing for yam 
improvement
Genetic engineering has emerged as an important alternative and 
complementary methodology to improve crops including yam. The 
application of transgenic methods to yam improvement is particu-
larly compelling due to the difficulties associated with conventional 
yam breeding. However, an efficient plant regeneration system is 
the main prerequisite for the achievement of successful transfor-
mation (Nyaboga, Tripathi, Manoharan, & Tripathi, 2014). Due to its 
ease of accessibility, ability to transfer low copies of DNA fragments 
carrying the desirable genes at higher efficiencies with minimal cost 
as well as the transfer of very large DNA fragments with low re-
arrangement, Agrobacterium-mediated gene delivery system is the 
most preferred (Gelvin, 2003; Shibata & Liu, 2000). Quain, Egnin, 
Bey, Thompson, and Bonsi (2011) developed a transient transforma-
tion of D. rotundata using Agrobacterium but generated no transgenic 
plants. Nyaboga et al. (2014) developed the first fast, efficient and 
reproducible protocol for Agrobacterium-mediated transformation 
of D. rotundata. This protocol resulted in the generation of stable 
transformations and the regeneration of complete transgenic plants. 
This advancement in Agrobacterium-mediated transformation has 
laid the foundation for the full implementation of genetic engineer-
ing and gene editing in yam.
Targeted genome alteration approach has become a promising 
tool for crop breeding. The recently established gene-editing tech-
nique, the clustered regularly interspaced short palindromic repeats 
(CRISPR/Cas9) system, resulting from the adaptive immune sys-
tem of Streptococcus pyogenes, has been demonstrated to be a po-
tent tool for targeted genome editing in many species (Feng et al., 
2018). To facilitate the use of gene editing and genetic engineering 
technologies in improving the productivity and nutritional quality of 
yam, the Genome-Enabled Platforms for Yam Project was launched 
in 2016 in collaboration between scientists at IITA and Iowa State 
University (https ://www.nsf.gov/award searc h/showA ward?AWD_
ID=1543888). Additionally, a genome-editing tool for yam using 
phytoene desaturase (a key enzyme in the β-carotene biosynthesis 
pathway, which converts the colourless phytoene to coloured ca-
rotenoids) as a marker is being developed (Tripathi, 2018). Targeted 
traits for yam gene editing and genetic engineering include resis-
tance to yam mosaic virus and anthracnose diseases, herbicide tol-
erance and nematode resistance.
Feng et al. (2018) successfully applied the CRISPR/Cas9-
mediated targeted mutagenesis in D. zingiberensis using an A. tu-
mefaciens-mediated transformation method aimed at the farnesyl 
pyrophosphate synthase gene (Dzfps) (an essential gene involved 
in the synthesis of secondary metabolites). They detected five 
types of mutations among the transformed plants at the predicted 
double-stranded break site. Feng et al. (2018) further discovered 
that the transcript levels of Dzfps and the content of squalene in 
isolated mutants were drastically decreased compared with those 
in wild-type plants and concluded that CRISPR/Cas9 provided a 
rapid and efficient approach for targeted genome modification in 
D. zingiberensis.
The successful application of the CRISPR/Cas9 technology 
to inactivate the endogenous banana streak virus by editing the 
virus sequences to develop resistant plantain (Tripathi et al., 2019) 
clearly indicates that such an approach can be implemented to de-
velop yam varieties resistant to yam mosaic virus, in that, as in 
the case of banana streak virus, viruses affecting yam have also 
been found to be integrated into the genome of yam (Seal et al., 
2014; Umber et al., 2014). Genome editing should therefore be 
incorporated into the yam improvement programme and traits 
to be targeted should be decided in consultation with breeders. 
The ethics and regulation of genetically modified and gene-edited 
crops should be taken into serious consideration in the applica-
tion of these technologies. The major challenge of the CRISPR/
Cas9 technology is that it may recognize sequences with up to 
five mismatched bases suggesting high rates of off-target effects 
(Roy et al., 2018). Some approaches such as DNA-RNA chimeric 
guides, Cpf1, a single RNA endonuclease that employs a T-rich 
PAM on the 5′ side of the guide, and specific point mutations have 
been developed to mitigate this limitation (Kleinstiver et al., 2016; 
Zetsche et al., 2016).
6.2 | Marker-assisted selection (MAS)
To our knowledge, there is no released yam variety whose devel-
opment utilized MAS technique till date, even though the potential 
usefulness of MAS in yam improvement has been demonstrated 
for important traits such as yam mosaic virus (Mignouna, Abang, 
Onasanya, et al., 2002) and yam anthracnose (Bhattacharjee et al., 
2018; Mignouna et al., 2002b) diseases and plant sex (Tamiru et 
     |  17DARKWA et Al.
al., 2017). The massive advancements in yam genomics such as 
the development of the reference genome sequence for the two 
most economically important species, generation of a wide array 
of molecular markers and progress in yam metabolomics and tran-
scriptomics open a new avenue for the successful deployment 
of DNA-informed breeding techniques such as marker-assisted 
breeding and genomic selection. QTLs controlling important traits 
in yam such as plant sex (Cormier et al., 2019; Tamiru et al., 2017), 
resistance to yam anthracnose disease (Bhattacharjee et al., 2018; 
Mignouna et al., 2002b; Petro et al., 2011) and yam mosaic virus 
disease (Mignouna, Abang, Onasanya, et al., 2002; Mignouna et 
al., 2002a) have been identified. Marker discovery for other es-
sential traits such as tuber flesh oxidation, starch property and dry 
matter content is ongoing at IITA and other research institutions 
in collaboration with several international partners and national 
agricultural research programmes across SSA and beyond (https 
://afric ayam.org). The next step is the conversion of these QTLs 
to diagnostic SNP markers. These markers will then go through 
verification and subsequent deployment in breeding programmes. 
The QTLs identified for plant sex determination in D. rotundata by 
Tamiru et al. (2017) have successfully gone through this process, 
and SNP markers are available for use. These SNP markers have 
been successfully deployed in ongoing work at IITA to predict or 
determine the sex of early generation genotypes in the yam breed-
ing programme. The application of these novel methods will en-
hance yam breeding efforts and ensure the quick delivery of high 
yielding, nutrient-dense and climate-resilient varieties to farmers.
6.3 | Parentage reconstruction
For many root and tuber crops such as yam, planned crosses are 
quite challenging due to inherent biological bottlenecks such as 
cross-incompatibility, non-synchronous and erratic flowering and 
polyploidy. A viable alternative for genetic improvement in outcross-
ing species that are constrained in executing planned and controlled 
crosses is half-sib breeding which entails the random pollination 
among desirable parents (Norman et al., 2018). The progeny ema-
nating from half-sib breeding must therefore be authenticated in 
order to ascertain the selection gain in the breeding programme. 
Parentage analysis anchored by DNA markers is a reliable method 
for breeders to improve genetic gain in a breeding programme. This 
entails DNA profiling of progeny and possible parents and com-
paring their alleles for determination and validation of existing re-
lationships (Jones, Small, Paczolt, & Ratterman, 2010). It can help 
to elucidate the identity of half-sib progenies and reconstruct the 
pedigree in the outcrossing crops (Telfer et al., 2015). Potential ap-
plication and usefulness of parentage analysis in crop improvement 
programmes include reliable estimation of paternal breeding values 
in the half-sib family, reduces pollination and labelling errors, assess-
ment of the level of inbreeding and incompatibility, and estimation 
of genetic effects including general and specific combining abilities 
in the breeding programme.
Zoundjihekpon, Hamon, Tio-Touré, and Hamon (1994) per-
formed the first parentage analysis in cultivated yam applying six 
isozyme markers. They validated the progenies of crosses involving 
well-known genitors (one male and three females). Parentage anal-
ysis facilitated by isozyme markers enabled the identification of 
half-sib progenies and reconstruction of the pedigree of progenies 
emanating from both controlled and open pollination. Sartie and 
Asiedu, (2011) employed nine SSR markers to determine the success 
of hybridization of seven D. rotundata and D. alata mapping popula-
tions. The genotypes of each of the mapping populations showed 
combinations of their parental alleles indicating the success of hy-
bridization. Information on segregation discrepancies in the ped-
igree information of yam germplasm and breeding lines is lacking. 
However, an ongoing study at IITA on paternity assignment using 
half-sib progenies derived from an open pollinated polycross nurs-
ery utilizing SNP markers from DArT genotyping platform effectively 
assigned respective male parents of the progenies. This paternity 
assignment was carried out on eight half-sib families derived from 
an open pollinated polycross mating design involving eight female 
and three male parents and using 6,602 informative SNP markers. 
Paternity identity of 352 half-sib progenies was reconstructed from 
the sample of 394 polycross-derived progenies. Among the three pa-
ternal genotypes, clone TDr9501932 made the largest contribution 
of 231 hybrid progenies (65.63%), compared to paternal genotypes 
TDr89027898 and TDr9902607, which contributed 35 (9.94%) and 
86 (24.43%) to total hybrid progenies developed. This finding opens 
up the whole potential of half-sib breeding for yam improvement 
in replacing the expensive and time-consuming controlled hand 
pollination.
6.4 | Polyploidy breeding
Yam is represented by multiple species with varying intraspecific 
and interspecific ploidy levels ranging from diploid to octoploid. 
The different ploidy levels in yam constitute a challenge but also 
an opportunity when it comes to its genetic improvement through 
breeding. It is a challenge as it hinders the effective transfer of 
relevant genes among individuals with different ploidy levels due 
to cross-incompatibility and erratic flowering. Ploidy manipula-
tion through induced polyploid or conventional hybridization, 
however, in many crops has been reported as a potent breeding 
tool for improving yield potential and also resistance to biotic and 
tolerance for abiotic stresses. Association of ploidy variation with 
yield potential and other plant traits has also been reported in 
yams. Higher vigour and tuber yield advantage were reported with 
tetraploid (2n = 80) and triploid (2n = 60) water yam compared to 
its diploid (2n = 40) counterpart (Arnau et al., 2007; Lebot, 2009). 
The positive effect of artificially induced polyploid on chlorophyll 
content, leaf shape, and stomata density, plant width, and fruit 
set has been reported in water yam (Abraham, Nemorin, & Lebot, 
2013; Ajayi, Adesoye, Asiedu, & Sartie, 2010; Babil et al., 2010). 
Kenji, Ohara, and Iwasa (2005) reported a shorter and thicker vine 
18  |     DARKWA et Al.
in D. japonica with artificially induced tetraploid. Likewise, Huang, 
Gao, Chen, and Jiao (2008) reported an increase in the diosgenin 
content in D. zingiberesis with induced polyploid. However, a pre-
liminary observation on artificially induced auto-tetraploid in 
D. rotundata accessions at IITA has not shown a clear advantage 
over their diploid counterparts for tuber yield traits.
The discovery of tetraploid (2n = 80) with a high degree of sex-
ual fertility in D. alata has opened a new perspective in polyploidy 
breeding using the conventional hybridization (Arnau et al., 2010). 
The tetraploid clones were found to be crossable among them-
selves, and the inter-ploidy crosses between diploid females and 
tetraploid males were reported successful. By this new avenue of 
breeding in D. alata, superior polyploid hybrid selections combin-
ing several desirable traits, including anthracnose resistance, were 
identified in Guadeloupe, French West Indies (Arnau et al., 2007). 
In Vanuatu (South Pacific), anthracnose-resistant D. alata hybrids 
were produced for the first time utilizing the tetraploid fertility in 
addition to combining other desirable traits (Lebot, Abraham, et 
al., 2019).
Ploidy breeding might hold potential for increasing yield, 
improving product quality and increasing resistance to biotic 
and tolerance for abiotic stresses in yam but its application re-
quires accurate knowledge of the level of ploidy of the genotype 
(Gamiette, Bakry, & Ano, 1999). Heritability and segregation stud-
ies have been instrumental in ploidy determination of yam. The 
analysis of the segregation of two isozyme loci and six microsatel-
lite markers revealed that only the diploid and tetraploid genetic 
models elucidated the segregation patterns shown by the eight 
markers (Scarcelli, Daïnou, Agbangla, Tostain, & Pham, 2005). 
However, due to the nature of the markers studied, Scarcelli et 
al. (2005) concluded based on the most parsimonious hypothesis 
that D. rotundata is diploid. Similarly, Bousalem et al. (2006) pro-
vided genetic evidence to buttress the tetrasomic behaviour of the 
D. trifida genome based on chromosomal segregation pattern anal-
ysis of eight SSR markers in three different crosses. All the afore-
mentioned reports show the potential of polyploidy breeding in 
genetic enhancement of yam. More specifically, the success with 
conventional hybridization in D. alata could be applied in other 
Dioscorea species as well as to develop agronomically successful 
cultivars.
6.5 | Breeding data management
The rapid advancement and application of high-throughput geno-
typing and phenotyping technologies, coupled with the expansion 
of breeding activities, has resulted in the generation of large vol-
umes of data. To make these data easily available and accessible 
in real time to the yam breeding research community scattered 
across Africa and beyond, a database system called “yambase” 
has been developed to store and retrieve phenotypic and geno-
typic data (https ://yamba se.org/) under the AfricaYam project. 
Several relevant statistical methods and bioinformatics tools for 
data quality control, pedigree information visualization, experi-
mental design, breeding value estimation and training population 
design have been developed and integrated into yambase (https 
://yamba se.org/). Yambase as of April 2019 holds about 414,000 
phenotype scores of over 55,434 accessions from over 325 trials 
conducted by seven breeding programmes as well as a detailed 
ontology of 182 traits.
7  | CONCLUSION AND PROSPEC TS
Though several inherent biological constraints make yam breeding 
a very arduous and lengthy endeavour, significant milestones have 
been achieved in empirical breeding leading to the development of 
many improved varieties. The advancement of yam improvement 
programmes to contemporary levels for developing new high yield-
ing varieties with resistance to and tolerance for biotic and abiotic 
stresses and other end-user-preferred attributes requires system-
atic knowledge and smart way of understanding of the genetics and 
genomics of the crop along with mechanisms of trait expression. A 
wide array of genomic resources, including markers for genetic stud-
ies, techniques for ploidy determination, and QTLs for crucial traits 
such as yam anthracnose and yam mosaic virus diseases and plant 
sex are now available. Prospect for discovering additional molecu-
lar markers linked to genes and QTLs of agronomic relevance, and 
applying marker-assisted selection in yam breeding is high primarily 
due to the drastic reduction in sequencing cost and availability of 
the reference genome in two widely cultivated species, D. rotundata 
and D. alata. The current progress with high-throughput “omics” 
tools will substantially increase the identification of functionally 
characterized regions of the genome causally affecting phenotypic 
trait variation for the development of informative markers in yam 
breeding. Moreover, some of the molecular markers have been op-
timized and now available, for instance, markers for plant sex assay 
in D. rotundata.
The practical translation of existing and emerging breeding 
technologies into improved cultivars in yam is expected to en-
hance the precision and efficiency of breeding. Use of functional 
markers would facilitate screening for alleles of interest in germ-
plasm collection and breeding populations, accurate description 
of the genetic potential of parents, crosses and new varieties, 
and fast track plants with desired alleles of the target traits to 
the advanced performance trials. Markers application would also 
facilitate the assessment of plant sex for efficient crossing plan, 
hybridity in progenies from artificial hand pollination, paternity in 
natural open pollination polycross blocks, variety description for 
registration, and tracking genetic purity of varieties in seed multi-
plication and breeding trials. The advances in genomics also pave 
the way for the implementation of genomic selection and predic-
tion in yam breeding programmes. The use of genomic tools will 
save time, resources and cycles required in the breeding pipelines 
and contribute to overcome the low success rates in crosses. The 
value of genomic tools in yam breeding would further increase 
     |  19DARKWA et Al.
with the integration of clonal propagation innovations such as the 
SAH system which is extremely valuable to rapidly supply the suf-
ficient quantity of quality planting materials required for accurate 
phenotyping in genetic study and breeding trials. The benefits of 
these new breeding tools cannot be over-emphasized; however, 
much preparation and the initial investments are required for full 
deployment. Molecular laboratories at the national breeding in-
stitutions will need upgrading, and the capacity of staff strength-
ened to handle the molecular breeding activities better. The IITA 
and other international partner institutes continuously play cru-
cial roles in re-tooling and capacity development of the national 
agricultural research systems; more investments are, however, 
required in this area. Recent advancements and application of 
other “omics” technologies such as metabolomics, transcriptomics 
and phenomics will provide a new understanding of trait expres-
sion in yam. The synergistic effect of integrating these “omics” 
approaches into current genetics-led yam breeding programmes 
would undoubtedly remove some of the constraints encountered 
for typical breeding approaches and expedite the delivery of im-
proved varieties to farmers.
ACKNOWLEDG EMENTS
This work was supported by the AfricaYam project, funded by 
the International Institute of Tropical Agriculture (IITA), Nigeria, 
through a research grant (OPP1052998) received from the Bill 
and Melinda Gates foundation. We are thankful to the African 
Union for the scholarship offered to the first author at the Pan 
African University, Institute of Life and Earth Sciences, University 
of Ibadan, Nigeria. We also thank anonymous reviewers for their 
insightful comments and suggestions that substantially improved 
the manuscript.
CONFLIC T OF INTERE S T
The authors declare no conflict of interest.
AUTHOR’ S CONTRIBUTION
AA and KD planned the content, wrote the draft and revision. BO 
and RA contributed in write up and revision. AA, KD and RA did 
formatting.
ORCID
Kwabena Darkwa  https://orcid.org/0000-0002-6877-8527 
Bunmi Olasanmi  https://orcid.org/0000-0002-8427-7992 
Robert Asiedu  https://orcid.org/0000-0001-8943-2376 
Asrat Asfaw  https://orcid.org/0000-0002-4859-0631 
R E FE R E N C E S
Abang, M. M., Winter, S., Mignouna, H. D., Green, K. R., & Asiedu, R. 
(2003). Molecular taxonomic, epidemiological and population ge-
netic approaches to understanding yam anthracnose disease. African 
Journal of Biotechnology, 2(12), 486–496. https ://doi.org/10.5897/
AJB20 03.000-1098
Abraham, K. (1992). Dormancy and viability of Dioscorea alata seeds. 
Journal of Root Crops, 18, 142–143.
Abraham, K., Nemorin, A., & Lebot, V. (2013). Meiosis and sexual fertility 
of autotetraploidy cones of- greater yam Dioscorea alata L. Genetic 
Resources and Crop Evolution, 60, 819–823. https ://doi.org/10.1007/
s10722-013-9973-4
Adamski, J., & Suhre, K. (2013). Metabolomics platforms for genome wide 
association studies—linking the genome to the metabolome. Current 
Opinion in Biotechnology, 24(1), 39–47. https ://doi.org/10.1016/j.
copbio.2012.10.003
Aidoo, R., Nimoh, F., Bakang, J. E. A., Ohene-Yankyera, K., Fialor, S. C., 
& Abaidoo, R. C. (2011). Economics of small-scale seed yam produc-
tion in Ghana: Implications for commercialization. African Journal of 
Sustainable Development, 13, 65–78.
Aighewi, B. A., Asiedu, R., Maroya, N., & Balogun, M. (2015). Improved 
propagation methods to raise the productivity of yam (Dioscorea ro-
tundata Poir.). Food Security, 7, 823–834. https ://doi.org/10.1007/
s12571-015-0481-6
Ajayi, T. A., Adesoye, A. I., Asiedu, R., & Sartie, A. (2010). Colchicine-
induced variations in survival rate and morphological characteris-
tics of water yam (Dioscorea alata) ratar. Povrt. / Field Vegetable Crop 
Research, 47, 485–492.
Akoroda, M. O. (1983). Variation, heritability and genetic advance of 
eight characters in white yam. Theoretical and Applied Genetics, 66(1), 
51–54. https ://doi.org/10.1007/BF002 81848 
Alabi, T. R., Adebola, P. O., Asfaw, A., De Koeyer, D., Lopez-Montes, A., 
& Asiedu, R. (2019). Spatial multivariate cluster analysis for defining 
target population of environments in West Africa for yam breed-
ing. International Journal of Applied Geospatial Research, 10(3), 1–30. 
https ://doi.org/10.4018/IJAGR.20190 70104 
Alam, S., Shylla, E., Bora, B., & Saud, B. K. (2014). Genetic variation in differ-
ent cultivars of greater yam (D. alata). Journal of Root Crops, 40(1), 1–5.
Alamu, O. E., Adesokan, M., & Maziya-Dixon, B. (2019). Calibration 
development for nutritional evaluation of yam (Dioscorea sp.) 
using Near-Infrared Reflectance Spectrophotometry (NIRS). 
Cogent Chemistry, 5(1), 1–13. https ://doi.org/10.1080/23312 
009.2019.1565623
Alene, A. D., Abdoulaye, T., Rusike, J., Manyong, V., & Walker, T. S. (2015). 
The effectiveness of crop improvement programmes from the per-
spectives of varietal output and adoption: Cassava, cowpea, soybean 
and yam in Sub-Saharan Africa and maize in West and central Africa. 
In T. S. Walker, & J. Alwang (Eds.), Crop improvement, adoption and 
impact of improved varieties in food crops in Sub-Saharan Africa (pp. 
74–122). Wallingford, UK: CGIAR and CABI.
Araki, H., Harada, T., & Yakuwa, T. (1983). Some characteristics of in-
terspecific hybrids between Dioscorea japonica Thunb. and Dioscorea 
opposite Thunb. Journal of Japanese Society of Horticultural Science, 
52(2), 153–158.
Arnau, G., Abraham, K., Sheela, M. N., Chair, H., Sartie, A., & Asiedu, R. 
(2010). Yams. In J. E. Bradshaw (Ed.), Root and tuber crops: Handbook 
of plant breeding (7th ed., pp. 127–148). New York: Springer.
Arnau, G., Bhattacharjee, R., Sheela, M. N., Malapa, R., Lebot, V., 
Abraham, K., … Pavis, C. (2017). Understanding the genetic diversity 
and population structure of yam (Dioscorea alata L.) using microsatel-
lite markers. PLoS ONE, 12(3), e0174150.
Arnau, G., Maledon, E., & Nemorin, A. (2007). Genetic improvement of 
the greater yam D. alata through polyploidy breeding. In W. Colon, 
& L. Wanda (Eds.), Marketing opportunities for agriculture and forestry 
products in the greater Caribbean. 43rd Annual meeting of Caribbean 
food crops society (p. 112). Costa Rica.
Arnau, G., Maledon, E., Nudol, E., & Marie-Claire, G. (2016). Progress 
and challenges in genetic improvement of yam (Dioscorea alata L.). In 
Electronic proceedings of world congress on root and tuber crops. 
Nanning: WCRTC, Résumé, 1 p. Retrieved from http://www.gcp21.
org/wcrtc/ S13.html
Arnau, G., Némorin, A., Maledon, E., & Abraham, K. (2009). Revision of 
ploidy status of Dioscorea alata L. (Dioscoreaceae) by cytogenetic 
20  |     DARKWA et Al.
and microsatellite segregation analysis. Theoretical and Applied 
Genetics, 118(7), 1239–1249.
Asala, S., Alegbejo, M. D., Kashina, B., Banwo, O. O., Asiedu, R., & 
Kumar, L. (2012). Distribution and incidence of viruses infecting 
yam (Dioscorea spp.) in Nigeria. Global Journal of Biotechnology and 
Bioscience, 1(2), 163–167.
Asemota, H. N., Ramser, J., Lopez-Peralta, C., Weising, K., & Kahl, G. 
(1995). Genetic variation and cultivar identification of Jamaican 
yam germplasm by random amplified polymorphic DNA analysis. 
Euphytica, 92(3), 341–351. https ://doi.org/10.1007/BF000 37118 
Asiedu, R., Ng, S. Y. C., Bai, K. V., Ekanayake, I. J., & Wanyera, N. M. 
W. (1998). Genetic improvement. In G. C. Orkwor, R. Asiedu, & I. 
J. Ekanayake (Eds.), Food yams: Advances in research (pp. 63–104). 
Nigeria: IITA and NRCRI.
Asiedu, R., & Sartie, A. (2010). Crops that feed the world 1: Yams for 
income and food security. Food Security, 2, 305–315. https ://doi.
org/10.1007/s12571-010-0085-0
Babil, P. K., Irie, K., Shiwachi, H., Tun, Y. T., Toyohara, H., & Fujimaki, H. 
(2010). Ploidy variations and their effects on leaf and stoma traits 
of water yam (Dioscorea alata L.) collected in Myanmar. Tropical 
Agriculture and Development, 54, 132–139.
Bahuchet, S., McKey, D., & De Garine, I. (1991). Wild yams revisited: Is in-
dependence from agriculture possible for rain forest hunter-gatherers? 
Human Ecology, 19(2), 213–243. https ://doi.org/10.1007/BF008 88746 
Baimey, H., Coyne, D., & Labuschagne, N. (2006). Effect of fertilizer ap-
plication on yam nematode (Scutellonema bradys) multiplication and 
consequent damage to yam (Dioscorea spp.) under field and storage 
conditions in Benin. International Journal of Pest Management, 52(1), 
63–70.
Bhattacharjee, R., Nwadili, C. O., Saski, C. A., Paterne, A., Scheffler, B. E., 
Augusto, J., … Bandyopadhyay, R. (2018). An EST-SSR based genetic 
linkage map and identification of QTLs for anthracnose disease resis-
tance in water yam (Dioscorea alata L.). PLoS ONE, 13(10), e0197717. 
https ://doi.org/10.1371/journ al.pone.0197717
Bino, R. J., Hall, R. D., Fiehn, O., Kopka, J., Saito, K., Draper, J., … Sumner, 
L. W. (2004). Potential of metabolomics as a functional genomics 
tool. Trends in Plant Science, 9(9), 418–425. https ://doi.org/10.1016/j.
tplan ts.2004.07.004
Bousalem, M., Arnau, G., Hochu, I., Arnolin, R., Viader, V., Santoni, S., & 
David, J. (2006). Microsatellite segregation analysis and cytogenetic 
evidence for tetrasomic inheritance in the American yam Dioscorea 
trifida and a new basic chromosome number in the Dioscorea. 
Theoretical and Applied Genetics, 113(3), 439–451.
Bredeson, J. V., Lyons, J. B., Shu, S., Ogunleye, I., Bhattacharjee, R., 
Obidiegwu, J., & Rokhsar, D. S. (2018). Release notes for Dioscorea 
alata genome assembly Version 1. Retrieved from ftp://ftp.yamba 
se.org/genom es/Diosc orea_alata/ D_alata_v1_relea se_notes.pdf
Bressan, E. D. A., Briner Neto, T., Zucchi, M. I., Rabello, R. J., & Veasey, E. 
A. (2011). Morphological variation and isozyme diversity in Dioscorea 
alata L. landraces from Vale do Ribeira, Brazil. Scientia Agrícola, 68(4), 
494–502.
Bressan, E. A., Briner Neto, T., Zucchi, M. I., Rabello, R. J., & Veasey, E. 
A. (2014). Genetic structure and diversity in the Dioscorea cayenen-
sis/Dioscorea rotundata complex revealed by morphological and iso-
zyme markers. Genetics and Molecular Research, 13(1), 425–437.
Burkill, I. H. (1960). The organography and the evolution of Dioscoreaceae, 
the family of the yams. Journal of the Linnean Society of London, Botany, 
56(367), 319–412. https ://doi.org/10.1111/j.1095-8339.1960.tb025 
08.x
Cheng, K. T., & Liu, S. Y. (1996). Genetic relationships in Dioscorea alata 
L. revealed by RAPD analysis. Chinese Pharmaceutical Journal, 48(3), 
197–205.
Chi, V. L., Hue, N. T. H., & Trinh, L. N. (2007). Root and tuber crops in 
Vietnam: Focus on yam germplasm. Ethnobotany Research and 
Applications, 5, 259–272. https ://doi.org/10.17348/ era.5.0.259-272
Chu, E. P., & Figueiredo-Ribeiro, R. C. L. (1991). Native and exotic species 
of Dioscorea used as food in Brazil. Economic Botany, 45(4), 467–479. 
https ://doi.org/10.1007/BF029 30709 
Cormier, F., Lawac, F., Maledon, E., Gravillon, M.-C., Nudol, E., Mournet, 
P., … Arnau, G. (2019). A reference high-density genetic map of 
greater yam (Dioscorea alata L.). Theoretical and Applied Genetics, 132, 
1733–1744. https ://doi.org/10.1007/s00122-019-03311-6
Cornet, D., Sierra, J., Tournebize, R., & Ney, B. (2014). Yams (Dioscorea 
spp.) plant size hierarchy and yield variability: Emergence time is 
critical. European Journal of Agronomy, 55, 100–107. https ://doi.
org/10.1016/j.eja.2014.02.002
Coursey, D. G., & Coursey, C. K. (1971). The new yam festivals of West 
Africa. Anthropos, 66, 444–484.
Dansi, A., Mignouna, H. D., Zoundjihékpon, J., Sangare, A., Ahoussou, N., 
& Asiedu, R. (2000). Identification of some Benin Republic's Guinea 
yam (Dioscorea cayenensis/Dioscorea rotundata complex) cultivars 
using randomly amplified polymorphic DNA. Genetic Resources and 
Crop Evolution, 47(6), 619–625.
Dansi, A., Mignouna, H. D., Zoundjihekpon, J., Sangare, A., Asiedu, R., & 
Ahoussou, N. (2000). Using isozyme polymorphism to assess genetic 
variation within cultivated yams (Dioscorea cayenensis/Dioscorea 
rotundata complex) of the Republic of Benin. Genetic Resources and 
Crop Evolution, 47(4), 371–383.
De Koeyer, D.Lopez-Montes, A., Amele, A., Bhattacharjee, R., Abberton, 
M., Maziya-Dixon, B., … Asiedu, R. (2017). AfricaYam – A project to 
strengthen yam breeding in West Africa. Retrieved from http://www.
gcp21.org/wcrtc/ abstr acts/S13/S1304.DeKoe yerDa vid.4214.3280.
doc
Edwards, D., & Batley, J. (2010). Plant genome sequencing: Applications 
for crop improvement. Plant Biotechnology Journal, 8(1), 2–9. https ://
doi.org/10.1111/j.1467-7652.2009.00459.x
Egesi, C. N., Asiedu, R., Ude, G., Ogunyemi, S., & Egunjobi, J. K. (2006). 
AFLP marker diversity in water yam (Dioscorea alata L.). Plant 
Genetic. Resources, 4(3), 181–187.
Egesi, C. N., Odu, B. O., Ogunyemi, S., Asiedu, R., & Hughes, J. (2007). 
Evaluation of water yam (Dioscorea alata L.) germplasm for re-
action to yam anthracnose and virus diseases and their effect 
on yield. Journal of Phytopathology, 155(9), 536–543. https ://doi.
org/10.1111/j.1439-0434.2007.01273.x
Ehounou, A. E., Cormier, F., Cornet, D., Maledon, E., Desfontaines, L., 
Marie-Magdeleine, C., Arnau, G. (2019). Development of NIRS and mo-
lecular marker to improve breeding efficiency in Greater Yam (Dioscorea 
alata L.) for key quality traits. International Conference on Applied 
Biochemistry and Biotechnology (ABB 2019). 2, Macau, Chine, 21–24 
July 2019. https://agritrop.cirad.fr/593382/
Elshire, R. J., Glaubitz, J. C., Sun, Q., Poland, J. A., Kawamoto, K., Buckler, 
E. S., & Mitchell, S. E. (2011). A robust, simple genotyping-by-se-
quencing (GBS) approach for high diversity species. PLoS ONE, 6(5), 
e19379. https ://doi.org/10.1371/journ al.pone.0019379
FAO (2010). The second report on the state of the world’s plant genetic re-
sources for food and agriculture (pp. 1–370). Rome, Italy: FAO.
FAOSTAT (2017). Food and Agriculture Organization of the United Nations 
Statistics database, FAOSTAT. Retrieved from http://www.fao.org/
faost at/en/#data/QC
Feng, S., Song, W., Fu, R., Zhang, H., Xu, A., & Li, J. (2018). Application 
of the CRISPR/Cas9 system in Dioscorea zingiberensis. Plant Cell, 
Tissue and Organ Culture (PCTOC), 135(1), 133–141. https ://doi.
org/10.1007/s11240-018-1450-5
Fernie, A. R., & Schauer, N. (2009). Metabolomics-assisted breeding: A 
viable option for crop improvement? Trends in Genetics, 25(1), 39–48. 
https ://doi.org/10.1016/j.tig.2008.10.010
Frankel, O. H., & Brown, A. H. D. (1984). Plant genetic resources today: 
A critical appraisal. In J. H. W. Holden, & J. T. Williams (Eds.), Crop 
genetic resources: Conservation and evaluation (pp. 249–257). London, 
UK: Allen and Unwin.
     |  21DARKWA et Al.
Fukushima, A., Kusano, M., Redestig, H., Arita, M., & Saito, K. (2009). 
Integrated omics approaches in plant systems biology. Current Opinion 
in Chemical Biology, 13(5–6), 532–538. https ://doi.org/10.1016/j.
cbpa.2009.09.022
Gamiette, F., Bakry, F., & Ano, G. (1999). Ploidy determination of some 
yam species by flow cytometry and conventional chromosomes 
counting. Genetic Resources and Crop Evolution, 46, 19–27.
Gelvin, S. B. (2003). Agrobacterium-mediated plant transformation: 
The biology behind the “Gene-Jockeying” tool. Microbiology and 
Molecular Biology Review, 67, 16–37. https ://doi.org/10.1128/
MMBR.67.1.16-37.2003
Girma, G., Bhattacharjee, R., Lopez-Montes, A., Gueye, B., Ofodile, S., 
Franco, J., & Abberton, M. (2018). Re-defining the yam (Dioscorea 
spp.) core collection using morphological traits. Plant Genetic 
Resources, 16(3), 193–200.
Girma, G., Gedil, M., & Spillane, C. (2015). Morphological, SSR and ploidy 
analysis of water yam (Dioscorea alata L.) accessions for utilization 
of aerial tubers as planting materials. Genetic Resources and Crop 
Evolution, 64(2), 291–305.
Girma, G., Hyma, K. E., Asiedu, R., Mitchell, S. E., Gedil, M., & Spillane, 
C. (2014). Next-generation sequencing based genotyping, cytometry 
and phenotyping for understanding diversity and evolution of guinea 
yams. Theoretical and Applied Genetics, 127(8), 1783–1794. https ://
doi.org/10.1007/s00122-014-2339-2
Girma, G., Natsume, S., Carluccio, A. V., Takagi, H., Matsumura, H., 
Uemura, A., … Spillane, C. (2019). Identification of candidate flow-
ering and sex genes in white Guinea yam (D. Rotundata Poir.) by 
SuperSAGE transcriptome profiling. PLoS ONE, 14(9), e0216912.
Hamon, P., & Toure, B. (1990). Characterization of traditional yam vari-
eties belonging to the Dioscorea cayenensis-rotundata complex by 
their isozymic patterns. Euphytica, 46(2), 101–107. https ://doi.org/ 
10.1007/BF000 22303 
Hochu, I., Santoni, S., & Bousalem, M. (2006). Isolation, characteri-
zation and cross-species amplification of microsatellite DNA loci 
in the tropical American yam Dioscorea trifida. Molecular Ecology 
Notes, 6(1), 137–140. https ://doi.org/10.1111/j.1471-8286.2005. 
01166.x
Huang, H., Gao, S., Chen, L., & Jiao, X. (2008). In vitro induction and 
identification of autotetraploids of Dioscorea zingiberensis. In Vitro 
Cellular and Developmental Biology- Plant, 44, 448–455.
IITA (2018). Our genetic resources. Retrieved from http://www.iita.org/
resea rch/genet ic-resou rces/
Jones, A. G., Small, C. M., Paczolt, K. A., & Ratterman, N. L. (2010). A practical 
guide to methods of parentage analysis. Molecular Ecology Resources, 
10(1), 6–30. https ://doi.org/10.1111/j.1755-0998.2009.02778.x
Kenji, S., Ohara, M., & Iwasa, H. (2005). Production of Japanese yam 
(Dioscorea japonica Thunb.) polyploids and characteristics of their 
polyploids. Bulletin of Chiba Agricultural Research Center, 4, 61–68.
Kleinstiver, B. P., Pattanayak, V., Prew, M. S., Tsai, S. Q., Nguyen, N. T., 
Zheng, Z., & Joung, J. K. (2016). High-fidelity CRISPR–Cas9 nucle-
ases with no detectable genome-wide off-target effects. Nature, 529, 
490–495. https ://doi.org/10.1038/natur e16526
Kolade, O., Oguntade, O., Ajamu, D., Bhattacharjee, R., & Kumar, L. 
(2018). High-throughput yam anthracnose phenotyping using detached 
leaf assay and digital imaging. In proceedings of the 18th Triennial 
symposium of the International Society for Tropical Root Crops, Cali 
- Columbia 22–25th October 2018.
Kumar, L. P. (2015) Viral disease threats to yam in West Africa. Retrieved 
from http://r4dre view.iita.org/index.php/2015/03/20/conti nuing- 
viral-disea se-threa ts-to-yam-in-west-afric a/
Kwon, Y. K., Jie, E. Y., Sartie, A., Kim, D. J., Liu, J. R., Min, B. W., & Kim, S. 
W. (2015). Rapid metabolic discrimination and prediction of dioscin 
content from African yam tubers using Fourier transform-infrared 
spectroscopy combined with multivariate analysis. Food Chemistry, 
166, 389–396. https ://doi.org/10.1016/j.foodc hem.2014.06.035
Kwosch, C. K., Plowright, R. A., Bridge, J., & Asiedu, R. (2007). Search 
for Sculellonema bradys resistance in yams (Dioscorea spp.). Journal. 
Science and Technology, 27(2), 33–42.
Lebot, V. (2009). Tropical root and tuber crops: Cassava, sweet potato, yams 
and aroids (p. 17). Wallingford, CT: CABI.
Lebot, V., Abraham, K., Kaoh, J., Rogers, C., & Molisalé, T. (2019). 
Development of anthracnose resistant hybrids of the Greater Yam 
(Dioscorea alata L.) and interspecific hybrids with D. nummularia 
Lam. Genetic Resources and Crop Evolution, 66(4), 871–883. https ://
doi.org/10.1007/s10722-019-00756-y
Lebot, V., & Malapa, R. (2013). Application of near infrared reflectance 
spectroscopy for the evaluation of yam (Dioscorea alata) germplasm 
and breeding lines. Journal of the Science of Food and Agriculture, 
93(7), 1788–1797. https ://doi.org/10.1002/jsfa.2013.93.issue-7
Lebot, V., Malapa, R., & Abraham, K. (2017). The Pacific yam (Dioscorea 
nummularia Lam.), An underexploited tuber crop from Melanesia. 
Genetic Resources and Crop Evolution, 64, 217–235. https ://doi.
org/10.1007/s10722-016-0475-z
Lebot, V., Malapa, R., & Molisalé, T. (2019). Development of HP-TLC 
method for rapid quantification of sugars, catechins, phenolic acids 
and saponins to assess Yam (Dioscorea spp.) tuber flour quality. Plant 
Genetic Resources: Characterization and Utilization, 17(1), 62–72. 
https ://doi.org/10.1017/S1479 26211 8000333
Lebot, V., Trilles, B., Noyer, J. L., & Modesto, J. (1998). Genetic relation-
ships between Dioscorea alata L. cultivars. Genetic Resources and Crop 
Evolution, 45(6), 499–509.
Lopez-Montes, A., Bhattacharjee, R., & Tessema, G. (2012). Yam breed-
ing at IITA: achievements, challenges, and prospects. Research for de-
velopment review. Retrieved from http://r4dre view.iita.org/index.
php/2012/05/14/yam-breed ing-at-iita-achie vemen ts-chall enges- 
and-prosp ects/
Luo, J. (2015). Metabolite-based genome-wide association studies 
in plants. Current Opinion in Plant Biology, 24, 31–38. https ://doi.
org/10.1016/j.pbi.2015.01.006
Mahalakshmi, V., Ng, Q., Atalobhor, J., Ogunsola, D., Lawson, M., & Ortiz, 
R. (2007). Development of a West African yam Dioscorea spp. core 
collection. Genetic Resources and Crop Evolution, 54(8), 1817–1825.
Malapa, R., Arnau, G., Noyer, J. L., & Lebot, V. (2005). Genetic diversity of 
the greater yam (Dioscorea alata L.) and relatedness to D. nummula-
ria Lam. and D. transversa Br. as revealed with AFLP markers. Genetic 
Resources and Crop Evolution, 52(7), 919–929.
Mekbib, Y., & Deressa, T. (2015). Exploration and collection of root and 
tuber crops in East Wollega and Illu Ababora zones: Rescuing declin-
ing genetic resources. Indian Journal of Traditional Knowledge, 15(1), 
86–92.
Mignouna, H. D., Abang, M. M., & Asiedu, R. (2007). Advances in yam 
(Dioscorea spp.) genetics and genomics proceedings of the 13th ISTRC 
symposium (pp. 72–81). Arusha, Tanzania.
Mignouna, H. D., Abang, M. M., & Asiedu, R. (2008). Genomics of yams, 
a common source of food and medicine in the tropics. In H. Moore, 
& R. Ming (Eds.), Genomics of tropical crop plants (pp. 549–570). New 
York: Springer.
Mignouna, H. D., Abang, M. M., & Fagbemi, S. A. (2003). A compara-
tive assessment of molecular marker assays (AFLP, RAPD and 
SSR) for white yam (Dioscorea rotundata) germplasm character-
ization. Annals of Applied Biology, 142(3), 269–276. https ://doi.
org/10.1111/j.1744-7348.2003.tb002 50.x
Mignouna, H. D., Abang, M. M., Green, K. R., & Asiedu, R. (2001). 
Inheritance of resistance in water yam (Dioscorea alata) to anthrac-
nose (Colletotrichum gloeosporioides). Theoretical and Applied Genetics, 
103, 52–55. https ://doi.org/10.1007/s0012 20000531
Mignouna, H. D., Abang, M. M., Onasanya, A., Agindotan, B., & Asiedu, 
R. (2002). Identification and potential use of RAPD markers linked 
to Yam mosaic virus resistance in white yam (Dioscorea rotundata). 
Annals of Applied Biology, 140(2), 163–169.
22  |     DARKWA et Al.
Mignouna, H. D., Ellis, N. T. H., Knox, M. R., Asiedu, R., & Ng, Q. N. (1998). 
Analysis of genetic diversity in Guinea yams (Dioscorea spp.) using 
AFLP fingerprinting. Tropical Agriculture (Trinidad), 75, 224–229.
Mignouna, H., Mank, R., Ellis, T., Van Den Bosch, N., Asiedu, R., Abang, M., 
& Peleman, J. (2002b). A genetic linkage map of water yam (Dioscorea 
alata L.) based on AFLP markers and QTL analysis for anthracnose 
resistance. Theoretical and Applied Genetics, 105(5), 726–735. https ://
doi.org/10.1007/s00122-002-0912-6
Mignouna, H., Mank, R., Ellis, T., Van Den Bosch, N., Asiedu, R., Ng, 
S., & Peleman, J. (2002a). A genetic linkage map of Guinea yam 
(Dioscorea rotundata Poir.) based on AFLP markers. Theoretical 
and Applied Genetics, 105(5), 716–725. https ://doi.org/10.1007/
s00122-002-0911-7
Mignouna, H. D., Njukeng, P., Abang, M. M., & Asiedu, R. (2001). 
Inheritance of resistance to Yam mosaic virus, genus Potyvirus, in 
white yam (Dioscorea rotundata). Theoretical and Applied Genetics, 
103, 1196–1200. https ://doi.org/10.1007/s0012 20100728
Mizuki, I., Tani, N., Ishida, K., & Tsumura, Y. (2005). Development and 
characterization of microsatellite markers in a clonal plant, Dioscorea 
japonica Thunb. Molecular Ecology Notes, 5(4), 721–723. https ://doi.
org/10.1111/j.1471-8286.2005.01020.x
Mohandas, C., Ramakrishnan, S., & Sheela, M. N. (1996). Evaluation of 
certain Dioscorea alata germplasm against Meloidogyne incognita. 
Root Crops, 22(1), 62–64.
Narina, S. S., Buyyarapu, R., Kottapalli, K. R., Sartie, A. M., Ali, M. 
I., Robert, A., … Scheffler, B. E. (2011). Generation and analy-
sis of expressed sequence tags (ESTs) for marker development in 
yam (Dioscorea alata L.). BMC Genomics, 12(1), 100. https ://doi.
org/10.1186/1471-2164-12-100
Neuwinger, H. D. (1996). African ethnobotany: Poisons and drugs: 
Chemistry, pharmacology, toxicology (pp. 422–432). Boca Raton, FL: 
CRC Press.
Norman, M. J. T., Pearson, C. J., & Searle, P. G. E. (1995). Tropical food 
crops in their environment (pp. 305–316). Cambridge UK: Cambridge 
University Press.
Norman, P., Asfaw, A., Tongoona, P., Danquah, A., Danquah, E., Koeyer, 
D., & Asiedu, R. (2018). Can parentage analysis facilitate breeding 
activities in root and tuber crops? Agriculture, 8(7), 95. https ://doi.
org/10.3390/agric ultur e8070095
Nwankwo, I. I. M., & Bassey, E. E. (2013). A study of variability and herita-
bility for yield and yield related traits in guinea white yam (Dioscorea 
rotundata Poir.) genotypes in Umudike, Southeastern Nigeria. 
Wudpecker Journal of Agricultural Research, 2(4), 122–127.
Nweke, F. I., Ugwu, B. O., Asadu, C. L. A., & Ay, P. (1991). Production 
cost in the yam-based cropping systems of southeastern Nigeria, RCPM 
Research Monograph No. 6 (p. 29). Ibadan, Nigeria: International 
Institute of Tropical Agriculture.
Nyaboga, E., Tripathi, J. N., Manoharan, R., & Tripathi, L. (2014). 
Agrobacterium-mediated genetic transformation of yam (Dioscorea 
rotundata): An important tool for functional study of genes and 
crop improvement. Frontiers in Plant Science, 5, 1–14. https ://doi.
org/10.3389/fpls.2014.00463 
Obidiegwu, J. E., Asiedu, R., Ene-Obong, E. E., Muoneke, C. O., & 
Kolesnikova-Allen, M. (2009). Genetic characterization of some water 
yam (Dioscorea alata L.) accessions in West Africa with simple sequence 
repeats. Journal of Food, Agriculture and Environment, 7(3&4), 634–638.
Obidiegwu, J. E., Kolesnikova-Allen, M., Ene-Obong, E. E., Muoneke, C. 
O., & Asiedu, R. (2009). SSR markers reveal diversity in Guinea yam 
(Dioscorea cayenensis/Dioscorea rotundata) core set. African Journal of 
Biotechnology, 8(12), 2730–2739.
Onyeka, T. J., Petro, D., Ano, G., Etienne, S., & Rubens, S. (2006). 
Resistance in water yam (Dioscorea alata) cultivars in the French 
West Indies to anthracnose disease based on tissue culture-de-
rived whole-plant assay. Plant Pathology, 55(5), 671–678. https ://doi.
org/10.1111/j.1365-3059.2006.01436.x
Pelemo, O., Benjamin, G., Adejumobi, I., Olusola, T., Odom-Kolombia, O., 
Adeosun, T., … Asfaw, A. (2019). Semi-Autotrophic Hydroponics: A po-
tential seed system technology for reducing the breeding cycle and rapid 
quality seed delivery (p. 1). Ibadan, Nigeria: International Institute of 
Tropical Agriculture.
Pelemo, O., de Koeyer, D., Matsumoto, R., Agre, P., Asiedu, R., & Asfaw, 
A. (2018). Semi-Autotrophic Hydroponics: A robust technique for accel-
erated basic seed yam production (p. 1). Ibadan, Nigeria: International 
Institute of Tropical Agriculture.
Pethybridge, S. J., & Nelson, S. C. (2015). Leaf Doctor: A new portable 
application for quantifying plant disease severity. Plant Diseases, 
99(10), 1310–1316. https ://doi.org/10.1094/PDIS-03-15-0319-RE
Petro, D., Onyeka, T. J., Etienne, S., & Rubens, S. (2011). An intraspecific 
genetic map of water yam (Dioscorea alata L.) based on AFLP mark-
ers and QTL analysis for anthracnose resistance. Euphytica, 179(3), 
405–416.
Plowright, R., & Kwoseh, C. (2000) Identification of resistance to major 
nematode pests of yams. (Dioscorea) in West Africa, Final technical re-
port (p. 30). Surrey, UK: CABI Bioscience.
Price, E. J., Bhattacharjee, R., Lopez-Montes, A., & Fraser, P. D. (2017). 
Metabolite profiling of yam (Dioscorea spp.) accessions for use in 
crop improvement programs. Metabolomics, 13(11), 144.
Price, E. J., Bhattacharjee, R., Lopez-Montes, A., & Fraser, P. D. (2018). 
Carotenoid profiling of yams: Clarity, comparisons and diversity. 
Food Chemistry, 259, 130–138. https ://doi.org/10.1016/j.foodc 
hem.2018.03.066
Price, E. J., Wilkin, P., Sarasan, V., & Fraser, P. D. (2016). Metabolite pro-
filing of Dioscorea (yam) species reveals under-utilized biodiversity 
and renewable sources for high-value compounds. Scientific Reports, 
6, 29136. https ://doi.org/10.1038/srep2 9136
Quain, M. D., Egnin, M., Bey, B., Thompson, R., & Bonsi, C. (2011). 
Transgenic potential of Dioscorea rotundata, using Agrobacterium-
mediated genetic transformation. Aspects of Applied Biology, 110, 
71–79.
Ramser, J., Weising, K., Kahl, G., López-Peralta, C., & Wetzel, R. (1996). 
Genomic variation and relationships in aerial yam (Dioscorea bulbifera L.) 
detected by random amplified polymorphic DNA. Genome, 39(1), 
17–25.
Rao, V., Bammi, R., & Randhawa, G. (1973). Interspecific hybridization in 
the Genus Dioscorea. Annals of Botany, 37(150), new series, 395–401.
Roy, B., Zhao, J., Yang, C., Luo, W., Xiong, T., Li, Y., … Kristiansen, K. 
(2018). CRISPR/Cascade 9-mediated genome editing-challenges 
and opportunities. Frontiers in Genetics, 9(240), 1–12. https ://doi.
org/10.3389/fgene.2018.00240 
Sartie, A., & Asiedu, R. (2011). Development of mapping populations for 
genetic analysis in yams (Dioscorea rotundata Poir. and Dioscorea alata 
L.). African Journal of Biotechnology, 10(16), 3049–3050.
Sartie, A., & Asiedu, R. (2014). Segregation of vegetative and repro-
ductive traits associated with tuber yield and quality in water 
yam (Dioscorea alata L.). African Journal of Biotechnology, 13(28), 
2807–2818.
Saski, C. A., Bhattacharjee, R., Scheffler, B. E., & Asiedu, R. (2015). 
Genomic resources for water yam (Dioscorea alata L.): Analyses of 
EST-sequences, de novo sequencing and GBS libraries. PLoS ONE, 
10(7), e0134031.
Sato, H. (2001). The potential of edible wild yams and yam-like plants as a 
staple food resource in the African tropical rain forest. African Study 
Monograph, 26, 123–134.
Scarcelli, N., Cubry, P., Akakpo, R., Thuillet, A.-C., Obidiegwu, J., Baco, 
M. N., … Vigouroux, Y. (2019). Yam genomics supports West Africa 
as a major cradle of crop domestication. Science Advances, 5(5), 
eaaw1947. https ://doi.org/10.1126/sciadv.aaw1947
Scarcelli, N., Daïnou, O., Agbangla, C., Tostain, S., & Pham, J. L. (2005). 
Segregation patterns of isozyme loci and microsatellite markers show 
the diploidy of African yam Dioscorea rotundata (2n= 40). Theoretical 
     |  23DARKWA et Al.
and Applied Genetics, 111(2), 226–232. https ://doi.org/10.1007/
s00122-005-2003-y
Scarcelli, N., Tostain, S., Mariac, C., Agbangla, C., Da, O., Berthaud, J., & 
Pham, J. L. (2006). Genetic nature of yams (Dioscorea sp.) domesti-
cated by farmers in Benin (West Africa). Genetic Resources and Crop 
Evolution, 53(1), 121–130.
Seal, S., Turaki, A., Muller, E., Kumar, P. L., Kenyon, L., Filloux, D., … 
Iskra-Caruana, M. L. (2014). The prevalence of badnaviruses in 
West African yams (Dioscorea cayenensis-rotundata) and evi-
dence of endogenous pararetrovirus sequences in their genomes. 
Virus Research, 186, 144–154. https ://doi.org/10.1016/j.virus 
res.2014.01.007
Shibata, D., & Liu, Y. G. (2000). Agrobacterium-mediated plant transfor-
mation with large DNA fragments. Trends in Plant Science, 5, 354–357.
Siadjeu, C., Mayland-Quellhorst, E., & Albach, D. C. (2018). Genetic 
diversity and population structure of trifoliate yam (Dioscorea du-
metorum Kunth) in Cameroon revealed by genotyping-by-sequenc-
ing (GBS). BMC Plant Biology, 18(1), 359. https ://doi.org/10.1186/
s12870-018-1593-x
Silva, D. M., Siqueira, M. V. B. M., Carrasco, N. F., Mantello, C. C., 
Nascimento, W. F., & Veasey, E. A. (2016). Genetic diversity among 
air yam (Dioscorea bulbifera) varieties based on single sequence re-
peat markers. Genetics and Molecular Research, 15(2), 1–12. https ://
doi.org/10.4238/gmr.15027929
Silva, L., Bajay, M. M., Monteiro, M., Mezette, T. F., Nascimento, W. F., 
Zucchi, M. I., … Veasey, E. A. (2014). Isolation and characterization 
of microsatellites for the yam Dioscorea cayenensis (Dioscoreaceae) 
and cross-amplification in D. rotundata. Genetics and Molecular 
Research, 13, 2766–2771. https ://doi.org/10.4238/2014.April.14.5
Silva, M., Oyekanmi, J., Nkere, C. K., Bömer, M., Kumar, P. L., & Seal, S. 
E. (2018). Rapid detection of potyviruses from crude plant extracts. 
Analytical Biochemistry, 546, 17–22. org/10.1016/j.ab.2018.01.019
Siqueira, M. V., Dequigiovanni, G., Corazon-Guivin, M. A., Feltran, J. C., 
& Veasey, E. A. (2012). DNA fingerprinting of water yam (Dioscorea 
alata) cultivars in Brazil based on microsatellite markers. Horticultura 
Brasileira, 30(4), 653–659. https ://doi.org/10.1590/S0102-05362 
01200 0400015
Siqueira, M. V. B. M., Marconi, T. G., Bonatelli, M. L., Zucchi, M. I., & 
Veasey, E. A. (2011). Development and characterization of micro-
satellite markers in water yam (Dioscorea alata). American Journal of 
Botany, 98, 142–146.
Sonibare, M. A., Asiedu, R., & Albach, D. C. (2010). Genetic diver-
sity of Dioscorea dumetorum (Kunth) Pax using amplified frag-
ment length polymorphisms (AFLP) and cpDNA. Biochemical 
Systematics and Ecology, 38(3), 320–334. https ://doi.org/10.1016/j.
bse.2010.01.012
Spillane, C., & Gepts, P. (2001). Evolutionary and genetic perspectives 
on the dynamics of crop gene pools. In D. Cooper, C. Spillane, & T. 
Hodgkin (Eds.), Broadening the genetic base of crop production (pp. 
25–70). Wallingford, CT: CABI.
Spindel, J., Wright, M., Chen, C., Cobb, J., Gage, J., Harrington, S., … 
McCouch, S. (2013). Bridging the genotyping gap: Using genotyp-
ing by sequencing (GBS) to add high-density SNP markers and new 
value to traditional bi-parental mapping and breeding populations. 
Theoretical and Applied Genetics, 126(11), 2699–2716. https ://doi.
org/10.1007/s00122-013-2166-x
Tamiru, M., Becker, H. C., & Maass, B. L. (2007). Genetic diversity in yam 
germplasm from Ethiopia and their relatedness to the main cultivated 
Dioscorea species assessed by AFLP markers. Crop Science, 47(4), 
1744–1753.
Tamiru, M., Natsume, S., Takagi, H., White, B., Yaegashi, H., Shimizu, 
M., … Terauchi, R. (2017). Genome sequencing of the staple food 
crop white Guinea yam enables the development of a molecular 
marker for sex determination. BMC Biology, 15(1), 86. https ://doi.
org/10.1186/s12915-017-0419-x
Tamiru, M., Yamanaka, S., Mitsuoka, C., Babil, P., Takagi, H., Lopez-
Montes, A., … Terauchi, R. (2015). Development of genomic simple 
sequence repeat markers for Yam. Crop Science, 55(5), 2191–2200. 
https ://doi.org/10.2135/crops ci2014.10.0725
Telfer, E. J., Stovold, G. T., Li, Y., Silva-Junior, O. B., Grattapaglia, D. G., 
& Dungey, H. S. (2015). Parentage reconstruction in Eucalyptus ni-
tens using SNPs and microsatellite markers: A comparative analysis 
of marker data power and robustness. PLoS ONE, 10(7), e0130601. 
https ://doi.org/10.1371/journ al.pone.0130601
Terauchi, R., Chikaleke, V. A., Thottappilly, G., & Hahn, S. K. (1992). 
Origin and phylogeny of Guinea yams as revealed by RFLP anal-
ysis of chloroplast DNA and nuclear ribosomal DNA. Theoretical 
and Applied Genetics, 83(6–7), 743–751. https ://doi.org/10.1007/ 
BF002 26693 
Terauchi, R., & Konuma, A. (1994). Microsatellite polymorphism in 
Dioscorea tokoro, a wild yam species. Genome, 37(5), 794–801.
Terauchi, R., Terauchi, T., & Tsunewaki, K. (1991). Intraspecific vari-
ation of chloroplast DNA in Dioscorea bulbifera L. Theoretical 
and Applied Genetics, 81(4), 461–470. https ://doi.org/10.1007/ 
BF002 19435 
Thottathil, G. P., Jayasekaran, K., & Othman, A. S. (2016). Sequencing 
crop genomes: A gateway to improve tropical agriculture. Tropical 
Life Sciences Research, 27(1), 93.
Tostain, S., Agbangla, C., Scarcelli, N., Mariac, C., Daïnou, O., Berthaud, 
J., & Pham, J. L. (2007). Genetic diversity analysis of yam cultivars 
(Dioscorea rotundata Poir.) in Benin using simple sequence repeat 
(SSR) markers. Plant Genetic Resources, 5(2), 71–81.
Tostain, S., Scarcelli, N., Brottier, P., Marchand, J. L., Pham, J. L., & Noyer, 
J. L. (2006). Development of DNA microsatellite markers in tropical 
yam (Dioscorea sp.). Molecular Ecology Notes, 6(1), 173–175. https ://
doi.org/10.1111/j.1471-8286.2005.01182.x
Tripathi, J. N., Ntui, V. O., Ron, M., Muiruri, S. K., Britt, A., & Tripathi, L. 
(2019). CRISPR/Cas9 editing of endogenous banana streak virus in 
the B genome of Musa spp. overcomes a major challenge in banana 
breeding. Communications Biology, 2(46), 1–11.
Tripathi, L. (2018). Prospects of genetic engineering and gene editing in yam 
improvement. Paper presented at the 17th Plant and Animal genome 
conference, San Diego, USA, 15th January 2018.
Twyford, C. T., Viana, A. M., James, A. C., & Mantell, S. H. (1990). 
Characterization of species and vegetative clones of Dioscorea food 
yams using isoelectric focusing of peroxidase and acid phosphatase 
isoenzymes. Tropical Agriculture, 67(4), 337–341.
Umber, M., Filloux, D., Muller, E., Laboureau, N., Galzi, S., Roumagnac, 
P., … Seal, S. E. (2014). The genome of African yam (Dioscorea cay-
enensis-rotundata complex) hosts endogenous sequences from 
four distinct badnavirus species. Molecular Plant Pathology, 15(8), 
90–801.
Veasey, E. A., Bressan, E. A., Siqueira, M. V. B. M., Borges, A., Queiroz-
Silva, J. R., Pereira, K. J. C., … Ming, L. C. (2012). Genetic charac-
terization of cassava (Manihot esculenta Crantz) and yam (Dioscorea 
trifida L.) local varieties in Swidden agriculture systems in Brazil. In 
P. Gepts, T. Famula, R. Bettinger, C. Qualset, P. McGuire, S. Brush, 
& A. Damania (Eds.), Biodiversity in agriculture: Domestication, evo-
lution and sustainability (pp. 344–360). Cambridge, UK: Cambridge 
University Press.
Weeden, N. F., & Wendel, J. F. (1989). Genetics of plant isozymes. In D. 
E. Soltis (Ed.), Isozymes in plant biology (pp. 46–72). Dordrecht, the 
Netherlands: Springer.
Wu, Z. G., Jiang, W., Mantri, N., Bao, X. Q., Chen, S. L., & Tao, Z. M. 
(2015). Transciptome analysis reveals flavonoid biosynthesis reg-
ulation and simple sequence repeats in yam (Dioscorea alata L.) 
tubers. BMC Genomics, 16(1), 346. https ://doi.org/10.1186/s12864- 
015-1547-8
Xu, Y. (2015). Envirotyping and its applications in crop science. Scientia 
Agricultura Sinica, 48, 3354–3371.
24  |     DARKWA et Al.
Xu, Y. (2016). Envirotyping for deciphering environmental impacts 
on crop plants. Theoretical and Applied Genetics, 129(4), 653–673. 
https ://doi.org/10.1007/s00122-016-2691-5
Zannou, A., Agbicodo, E., Zoundjihékpon, J., Struik, P. C., Ahanchédé, A., 
Kossou, D. K., & Sanni, A. (2009). Genetic variability in yam cultivars 
from the Guinea-Sudan zone of Benin assessed by random amplified 
polymorphic DNA. African Journal of Biotechnology, 8(1), 26–36.
Zannou, A., Ahanchédé, A., Struik, P. C., Richards, P., Zoundjihékpon, J., 
Tossou, R., & Vodouhè, S. (2004). Yam and cowpea diversity man-
agement by farmers in the Guinea-Sudan transition zone of Benin. 
NJAS-Wageningen Journal of Life Sciences, 52(3–4), 393–420. https ://
doi.org/10.1016/S1573-5214(04)80023-X
Zetsche, B., Gootenberg, J. S., Abudayyeh, O. O., Slaymaker, I. M., 
Makarova, K. S., Essletzbichler, P., … Koonin, E. V. (2016). Cpf1 is a 
single-RNA-guided endonuclease of a Class 2 CRISPR-Cas system. 
Transgenic Research, 25, 207.
Zhuang, H., Ni, Y., & Kokot, S. (2015). A comparison of Near- and Mid-
Infrared Spectroscopic methods for the analysis of several nutrition-
ally important chemical substances in the Chinese yam (Dioscorea 
opposita): Total sugar, polysaccharides, and flavonoids. Applied 
Spectroscopy, 69(4), 488–495.
Zoundjihekpon, J., Hamon, S., Tio-Touré, B., & Hamon, P. (1994). First 
controlled progenies checked by isozymic markers in cultivated yams 
Dioscorea cayenensis-rotundata. Theoretical and Applied Genetics, 
88(8), 1011–1016. https ://doi.org/10.1007/BF002 20809 
SUPPORTING INFORMATION
Additional supporting information may be found online in the 
Supporting Information section. 
How to cite this article: Darkwa K, Olasanmi B, Asiedu R, 
Asfaw A. Review of empirical and emerging breeding 
methods and tools for yam (Dioscorea spp.) improvement: 
Status and prospects. Plant Breed. 2019;00:1–24. https ://doi.
org/10.1111/pbr.12783