plants Article Genome-Wide Association Studies for Sex Determination and Cross-Compatibility in Water Yam (Dioscorea alata L.) Jean M. Mondo 1,2,3 , Paterne A. Agre 1,* , Robert Asiedu 1 , Malachy O. Akoroda 4 and Asrat Asfaw 1 1 International Institute of Tropical Agriculture (IITA), Ibadan 5320, Nigeria; m.mubalama@cgiar.org (J.M.M.); r.asiedu@cgiar.org (R.A.); a.amele@cgiar.org (A.A.) 2 Institute of Life and Earth Sciences, Pan African University, University of Ibadan, Ibadan 200284, Nigeria 3 Department of Crop Production, Université Evangélique en Afrique (UEA), Bukavu 3323, Democratic Republic of the Congo 4 Department of Agronomy, University of Ibadan, Ibadan 200284, Nigeria; malachyoakoroda@gmail.com * Correspondence: p.agre@cgiar.org Abstract: Yam (Dioscorea spp.) species are predominantly dioecious, with male and female flowers borne on separate individuals. Cross-pollination is, therefore, essential for gene flow among and within yam species to achieve breeding objectives. Understanding genetic mechanisms underly- ing sex determination and cross-compatibility is crucial for planning a successful hybridization program. This study used the genome-wide association study (GWAS) approach for identifying genomic regions linked to sex and cross-compatibility in water yam (Dioscorea alata L.). We identified 54 markers linked to flower sex determination, among which 53 markers were on chromosome 6 and one on chromosome 11. Our result ascertained that D. alata is characterized by the male heterogametic   sex determination system (XX/XY). The cross-compatibility indices, average crossability rate (ACR) and percentage high crossability (PHC), were controlled by loci on chromosomes 1, 6 and 17. Of Citation: Mondo, J.M.; Agre, P.A.; the significant loci, SNPs located on chromosomes 1 and 17 were the most promising for ACR and Asiedu, R.; Akoroda, M.O.; Asfaw, A. Genome-Wide Association Studies for PHC, respectively, and should be validated for use in D. alata hybridization activities to predict Sex Determination and Cross- cross-compatibility success. A total of 61 putative gene/protein families with direct or indirect Compatibility in Water Yam influence on plant reproduction were annotated in chromosomic regions controlling the target traits. (Dioscorea alata L.). Plants 2021, 10, This study provides valuable insights into the genetic control of D. alata sexual reproduction. It 1412. https://doi.org/10.3390/ opens an avenue for developing genomic tools for predicting hybridization success in water yam plants10071412 breeding programs. Academic Editor: Agnes Farkas Keywords: dioecy; sex determination; cross-pollination success; marker development; Dioscorea alata Received: 17 June 2021 Accepted: 8 July 2021 Published: 10 July 2021 1. Introduction Yam (Dioscorea spp.) is an important food and cash crop in tropical and subtropical Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in areas [1]. It is extensively produced (~93% of world production) in the African yam belt, published maps and institutional affil- a six-country region from Cameroon to Côte d’Ivoire, where it plays significant economic, iations. sociocultural, and religious roles among ethnic groups [2]. Dioscorea alata, commonly referred to as water, winged or greater yam, is the most widely distributed and the second- most-produced yam species after D. rotundata worldwide [3]. The popularity of D. alata stems from its high yield potential (even under low soil fertility), ease of propagation, competition with weeds (early vigor) and tuber storability [4,5]. Yam yield has, however, Copyright: © 2021 by the authors. remained low over time because of several biotic (diseases and pests), abiotic (drought, low Licensee MDPI, Basel, Switzerland. soil fertility, etc.), and agronomic constraints [6,7]. Developing resistant/tolerant varieties This article is an open access article distributed under the terms and coupled with a robust seed delivery system could be an effective means of raising yields of conditions of the Creative Commons predominantly resource-poor farmers characterized by low use of external farm inputs. The Attribution (CC BY) license (https:// variety development process requires a thorough understanding of the crop’s reproductive creativecommons.org/licenses/by/ mechanisms. 4.0/). Plants 2021, 10, 1412. https://doi.org/10.3390/plants10071412 https://www.mdpi.com/journal/plants Plants 2021, 10, 1412 2 of 18 Yam is a monocotyledonous herbaceous vine plant that reproduces vegetatively through tubers or vines or sexually through botanical seeds [8]. Yam is mainly dioecious with male and female flowers on separate plants, although monoecious plants with both male and female flowers on the same individuals exist [3,9–11]. Flowering and flower sex in plants are most strongly determined by genotype, although environmental, hormonal and epigenetic cues, to some extent, bear influence. The genetic mechanisms range from a single locus to sex chromosomes bearing several linked loci required for sex determination [12,13]. Dioecy in plants is inherited via three sex chromosome systems: XX/XY, XX/X0 and WZ/ZZ, such that XX or WZ determines female sex phenotype and XY, X0 or ZZ the male sex phenotype [12–15]. Most of the studied yam species, such as D. alata [3,5], D. floribunda [16], and D. tokoro [17] are characterized by the male heterogametic (XX/XY) sex-determination system. However, D. rotundata [9] and D. deltoidea [18] possess a female heterogametic (ZZ/ZW) sex-determination system. It is noteworthy that the D. alata species used for this study is strictly dioecious, with no monoecy-reported cases [3]. Given the dioecious nature of D. alata plants, sex identification at the seedling stage is crucial for genetic improvement through breeding. As in most plant species, sex- ual/gender dimorphism (apparent morphological, physiological and life-history trait differences among females and males) in D. alata is negligible at the vegetative stage. Hence, male and female individuals may not be reliably identified before flowering by visual observations [19]. The use of molecular markers is the most reliable strategy in discriminating yam clones for flower sex at early growth stages [10,19]. Some markers linked to sex chromosomal regions have been identified for both popu- lar yam species (D. alata and D. rotundata) [3,5,9]. Tamiru et al. [9] identified a female-specific chromosomic region on the pseudo-chromosome 11 of D. rotundata. They developed a single nucleotide polymorphism, (SNP) marker sp16, for yam plant sex identification at the early seedling stage. However, the sp16 marker only predicts the likelihood of femininity and may not be transferable to other species. Tamiru et al. [9] also identified a DNA marker, sp1, linked to the putative Z-linked region predicting maleness. Using these markers to predict sex at earlier growth phases among D. rotundata accessions has been reported [10,20]. However, these markers’ prediction accuracy is not always perfect in yam sex identification at the seedling stage. A D. alata sex determination region was mapped on chromosome 6 and a kompetitive allele-specific PCR (KASP) marker for accurate cultivar sex estimation was developed [3,5]. However, no report exists on its practical application for identifying flower sex at the seedling stage in D. alata. Sex determination in yam plants could be controlled by more than one locus [20], and thus, identifying more sex markers is encouraged. Besides, the instability of the sexual phenotype across generations and environments is another indication that sex expression in yam is a polygenic trait [21]. Another major issue during yam hybridization activities is the low cross-compatibility rates among cultivars (~23 and 31% for D. rotundata and D. alata, respectively) [22]. How- ever, efforts to establish an efficient method to unravel the genetic basis of the cross- compatibility in yam are very limited. An accurate method for early detection of seedling sex and compatible fertile parents prior to designing cross-combinations would be helpful to improve cross-pollination success in yam breeding. Most of the previous studies on yam flowering and sex determination used bi-parental populations, with the probability that findings could have been related to parental speci- ficity. The use of the genome-wide association (GWAS) approach could be helpful to ascertain results from previous studies and to identify more genomic regions controlling target traits. Guo et al. [23] showed the potential of association mapping (GWAS) for con- necting genomics and phenomics for natural outcrossing in rice. Several other studies have successfully applied GWAS for flowering time and sex determination studies [21,24,25]. In this study, we used the Diversity Array Technology (DArT) for sequencing, which is a robust and low-cost high-throughput open platform method for DNA polymorphism analysis [26]. It provides high call rates and scoring reproducibility compared to other sequencing techniques. Besides, DArT has been successfully used in water yam research to Plants 2021, 10, 1412 3 of 18 explore genetic diversity, evolution, population structure and identification of loci linked to disease resistance and tuber quality traits [27–29]. The objective of this study was to investigate, using the GWAS approach, the genomic regions linked to sex determination and cross-compatibility for improving the pollination efficiency in water yam hybridization activities. 2. Results 2.1. Sex and Cross-Compatibility Indices of D. alata Clones Used for GWAS Analyses This study used 2010–2020 historical pollination data of 74 D. alata genotypes to investigate genomic regions controlling plant sex and cross-pollination success rate at the International Institute of Tropical Agriculture (IITA) breeding sites in Nigeria. Phenotypic data on flower sex, average crossability rate (ACR) and percentage high crossability (PHC) are presented in Table 1. Of the 74 genotypes, 33 were female and 41 were male flowering phenotypes. The ACR of the studied genotypes ranged from 1.59% for TDa9801176 to 91.04% for TDa1401253, with a mean of 49.4%. The PHC ranged from 0 to 100%. Among parental clones, TDa9900240 and TDa0200012 were the most used female and male parents, respectively, involved in over 40 cross-combinations. Table 1. Type, sex and cross-compatibility indices (ACR and PHC) of D. alata clones used for GWAS analyses. Presented information is the summary of 2010–2020 historical data at IITA breeding sites, Nigeria. Clone Name Type Sex ACR (%) PHC (%) Cross-Combinations TDa0000005 Breeding line Female 28.20 38.7 38 TDa0000194 Breeding line Female 16.64 15.0 20 TDa0100004 Breeding line Male 25.05 25.0 11 TDa0100029 Breeding line Female 7.74 0.0 10 TDa0100039 Breeding line Male 37.67 63.2 19 TDa0100041 Breeding line Female 31.19 35.3 17 TDa0100081 Breeding line Female 41.72 57.1 21 TDa0100299 Breeding line Female 18.41 21.4 14 TDa0200012 Breeding line Male 27.74 35.3 40 TDa0200061 Breeding line Female 46.80 60.0 5 TDa0500015 Breeding line Female 42.00 66.7 21 TDa0500056 Breeding line Male 69.12 100.0 3 TDa0700015 Breeding line Male 73.71 100.0 3 TDa0700154 Breeding line Female 25.98 40.0 5 TDa0800007 Breeding line Female 36.30 42.9 7 TDa0900026 Breeding line Male 28.51 35.7 14 TDa0900128 Breeding line Male 87.63 100.0 3 TDa0900146 Breeding line Male 73.21 100.0 3 TDa0900217 Breeding line Female 32.17 47.8 23 TDa0900376 Breeding line Female 36.76 56.5 23 TDa0900554 Breeding line Female 42.54 80.0 5 TDa0900602 Breeding line Female 57.14 100.0 3 TDa1000169 Breeding line Female 69.65 100.0 3 TDa1000365 Breeding line Male 69.29 100.0 4 TDa1000512 Breeding line Female 66.29 71.4 7 TDa1000592 Breeding line Female 39.75 60.0 5 TDa1000918 Breeding line Female 49.54 57.1 7 TDa1000994 Breeding line Female 56.21 87.5 8 TDa1100010 Breeding line Male 23.27 27.8 18 TDa1100014 Breeding line Female 10.56 0.0 3 Plants 2021, 10, 1412 4 of 18 Table 1. Cont. Clone Name Type Sex ACR (%) PHC (%) Cross-Combinations TDa1100175 Breeding line Male 84.31 100.0 3 TDa1100201 Breeding line Male 59.82 71.4 7 TDa1100202 Breeding line Male 76.32 100.0 3 TDa1100203 Breeding line Female 51.71 50.0 3 TDa1100242 Breeding line Male 69.25 100.0 3 TDa1100295 Breeding line Male 14.77 11.1 9 TDa1100299 Breeding line Female 63.48 80.0 5 TDa1100300 Breeding line Female 40.04 57.1 7 TDa1100302 Breeding line Male 77.10 100.0 3 TDa1100316 Breeding line Male 45.25 66.7 9 TDa1100432 Breeding line Male 59.73 100.0 6 TDa1100507 Breeding line Female 46.12 66.7 3 TDa1400051 Breeding line Male 64.60 100.0 3 TDa1400062 Breeding line Male 55.51 66.7 3 TDa1400064 Breeding line Male 59.50 85.7 7 TDa1400367 Breeding line Male 73.04 100.0 3 TDa1400380 Breeding line Male 30.05 0.0 3 TDa1400432 Breeding line Male 57.37 100.0 5 TDa1400483 Breeding line Female 82.99 100.0 4 TDa1400651 Breeding line Male 82.91 100.0 3 TDa1400911 Breeding line Male 58.11 100.0 3 TDa1401065 Breeding line Male 65.52 75.0 4 TDa1401132 Breeding line Male 70.92 100.0 7 TDa1401162 Breeding line Male 69.12 100.0 7 TDa1401166 Breeding line Female 68.00 100.0 3 TDa1401249 Breeding line Female 55.24 50.0 3 TDa1401253 Breeding line Male 91.04 100.0 3 TDa1401270 Breeding line Male 65.00 100.0 4 TDa1401359 Breeding line Male 90.27 100.0 3 TDa1401384 Breeding line Female 57.50 100.0 3 TDa1401400 Breeding line Male 56.89 80.0 5 TDa1401409 Breeding line Female 71.83 100.0 3 TDa1401619 Breeding line Female 20.93 20.0 5 TDa1401684 Breeding line Male 71.74 100.0 3 TDa1402043 Breeding line Male 75.45 100.0 3 TDa1403882 Breeding line Male 64.33 100.0 3 TDa291 Landrace Male 3.60 0.0 3 TDa8500250 Breeding line Male 17.40 13.8 29 TDa8701091 Breeding line Male 24.63 30.4 26 TDa922 Landrace Female 12.59 0.0 7 TDa9801174 Breeding line Male 28.24 33.3 30 TDa9801176 Breeding line Female 1.59 0.0 13 TDa98150 Landrace Male 21.57 11.1 18 TDa9900240 Breeding line Female 32.43 40.0 45 ACR: average crossability rate, PHC: percentage high crossability. 2.2. Chromosomic Regions Linked to D. alata Sex Determination and Cross-Compatibility The GWAS scan identified 54 SNP markers associated with variation for flower sex; 53 of these markers were located on chromosome 6 while one was on chromosome 11 (Table 2, Figure 1a). Of the total SNP markers associated with plant sex, the minor allele frequencies (MAF) ranged from 0.13 (Chr6_837364 and Chr6_843525) to 0.43 (Chr6_3465 and Chr6_53812). The total phenotypic variance explained (PVE) by inventoried SNP markers was high (49–86%). The marker effects ranged from −1.92 to 1.77. The logarithm of odd (LOD)-scores varied from 4.47 to 9.69 for sex markers (Table 2). Plants 2021, 10, 1412 5 of 18 Table 2. Loci associated with sex identity, average crossability rate (ACR) and percentage high crossability (PHC) in D. alata. Markers are arranged in declining LOD values for plant sex and by chromosomes for ACR and PHC. Traits SNP Markers Chr Position (bp) MAF PVE (%) Effect LOD Chr6_1920 6 1920 0.27 86 −1.92 9.69 Chr6_20526 6 20,526 0.27 86 −1.92 9.69 Chr6_21076 6 21,076 0.27 86 −1.92 9.69 Chr6_3968 6 3968 0.27 86 −1.92 9.69 Chr6_41989 6 41,989 0.27 86 −1.92 9.69 Chr6_44316 6 44,316 0.27 86 −1.92 9.69 Chr6_44382 6 44,382 0.27 86 −1.92 9.69 Chr6_4576 6 4576 0.27 86 −1.92 9.69 Chr6_4766 6 4766 0.27 86 −1.92 9.69 Chr6_4822 6 4822 0.27 86 −1.92 9.69 Chr6_48851 6 48,851 0.27 86 −1.92 9.69 Chr6_48895 6 48,895 0.27 86 −1.92 9.69 Chr6_5823 6 5823 0.27 86 −1.92 9.69 Chr6_58872 6 58,872 0.27 86 −1.92 9.69 Chr6_60741 6 60,741 0.27 86 −1.92 9.69 Chr6_60807 6 60,807 0.27 86 −1.92 9.69 Chr6_70719 6 70,719 0.27 86 −1.92 9.69 Chr6_74310 6 74,310 0.27 86 −1.92 9.69 Chr6_745 6 745 0.27 86 −1.92 9.69 Chr6_83712 6 83,712 0.27 86 −1.92 9.69 Chr6_88389 6 88,389 0.27 86 −1.92 9.69 Chr6_94183 6 94,183 0.27 86 −1.92 9.69 Chr6_140396 6 140,396 0.28 83 1.74 9.26 Chr6_141421 6 141,421 0.28 82 1.77 9.23 Chr6_2040 6 2040 0.28 82 1.77 9.23 Chr6_15081 6 15,081 0.28 82 −1.82 9.23 Chr6_4027 6 4027 0.28 82 −1.82 9.17 Plant sex Chr6_29692 6 29,692 0.28 82 −1.80 9.16 Chr6_659402 6 659,402 0.26 81 −1.83 9.06 Chr6_135364 6 135,364 0.26 77 1.63 8.53 Chr6_140205 6 140,205 0.24 76 −1.75 8.40 Chr6_135482 6 135,482 0.28 75 −1.69 8.34 Chr6_1507 6 1507 0.28 75 −1.56 8.31 Chr6_85928 6 85,928 0.28 74 1.56 8.15 Chr11_27942 11 27,942 0.26 72 −1.56 7.99 Chr6_66206 6 66,206 0.29 72 −1.59 7.89 Chr6_136378 6 136,378 0.34 72 1.22 7.87 Chr6_20788 6 20,788 0.34 72 1.22 7.87 Chr6_3465 6 3465 0.43 71 1.04 7.84 Chr6_53556 6 53,556 0.35 71 1.13 7.80 Chr6_53555 6 53,555 0.33 71 1.24 7.79 Chr6_1690 6 1690 0.27 70 −1.46 7.73 Chr6_14489 6 14,489 0.29 70 1.35 7.73 Chr6_53812 6 53,812 0.43 69 1.00 7.54 Chr6_20722 6 20,722 0.39 68 −1.02 7.46 Chr6_19703 6 19,703 0.41 68 −1.03 7.43 Chr6_9161 6 9161 0.24 68 −1.48 7.42 Chr6_120114 6 120,114 0.28 63 1.10 6.71 Chr6_80861 6 80,861 0.40 63 −1.06 6.67 Chr6_112146 6 112,146 0.26 62 1.18 6.55 Chr6_837364 6 837,364 0.13 52 −1.71 5.02 Chr6_843525 6 843,525 0.13 52 −1.53 5.02 Chr6_20935 6 20,935 0.15 49 −1.69 4.52 Chr6_25664 6 25,664 0.14 49 −1.55 4.47 Plants 2021, 10, x FOR PEER REVIEW 6 of 19 Plant Psla2n0t2s1 2, 01201, ,1 1401,2 x FOR PEER REVIEW 6 of 61 8of 19 Chr6_20935 6 20935 0.15 49 −1.69 4.52 Table 2. Cont. CChhr6r6__2205963654 66 2205963654 00.1.154 4499 −−11.6.595 44.5.427 CChhr6r6__235166614 66 253616641 00.1.245 4395 −114.5.052 44.4.778 TraAitCs R SNPCMhra1r1kersChr6_3_116214789 Chr P 61 1 o sition (b1p)32146718 9 MAF 0.3P0 VE (%) 33 Effect LOD0.25 35 1−41.70.24 6 44.7.685 ACR ChrC6Ch_hr31r11167_1_192449728 9 6 1117 31611294479829 0.25 00.3.209 35 3332 14.02−−1270.4.467 4 .7844.6.350 ACR ChrC1Ch1hr_1r1172__4297148599025PHC 6 11 171 124,789291459025 6 0.30 00.2.093 33 3229 −17.4−6−2403.4.171 4 .6544.3.001 ChrC1Ch7hr_1r96_4_29312252075 6 17 16 94922135202576 0.29 00.0.236 32 2299 −20.4−7−4237.1.316 4 .3044.0.014 PCHhCr: chroCmhorCs1oh_mr261e_5;3 0L25O267D : loga1rithm6 o f t2h1e5 o,0d5d6s3,2 M27A F: m0.0in3or0 a.2ll6e le 2fr9equ2e9n cy−, 4P3V.1E−12: 7p.h36e n4.o0t1ypPHC 4. i0c4 Cvharr:i achnrcoem eCxohpsrol6am_in3ee2; d2L7,O ADC:R lo: gavare6irtahgme corfo tshsea3 bo2id2li7dtys, rMatAe,F P: Hm0Ci.n2: o6pre arclleenleta fgr2ee9 qhuigenhc cyr,−o Ps2sV7a.Eb3:i6 lpithye. n4o.t0y4pic Chr:vcahrrioamncoeso emxep;lLaOinDe:dlo, gAaCritRh:m avofetrhaegoed cdrso,sMsaAbFi:limtyin roartael,l ePleHfrCe:q pueenrccye,nPtVaEg:ep hhiegnho tcyrpoicssvaabriialintcye. explained, ACR: average crossability rate, PHC: percentage high crossability. Figure 1. Genome-wide association analysis for plant sex determination in D . alata: (a) Manhattan plot, (b) quantile–qu an- FiguFrtiiegleu1 .r(QeG 1–e.Qn Go) mepnleoo-twm. Vied-eewratiidscseao lac bsiastorisco inraetalianotanel yatosni asthlfyeos ri2sp0 f lyoanra mtpsl eacxnhtrd oseemtxeor dmseoitmnearemtsio,i nngarientieoDnn .a iannlad Dt ar.:e a(dala )dtMao: ta(san i)hn Madtiatcananhteap tcltohatrn,o( pbml)ooqts,uo (ambn)et iqsl euw–aqintuhtia linen–tfiqlueueannc-e (Q–Qtiol)enp (tlQohte–. QtVa)er rpgteliocta tt.lr Vabiaetrr. sticrealla bteartso rtehleat2e0 tyoa tmhec 2h0ro ymamos ocmhreosm, gorseoemn easn,d grreedend aontsdi nreddic datoetsc hinrodmicaotseo mchersowmiothsoimnfleus ewnicteho innftlhueence targoent ttrhaeit .target trait. Three SNP markers distributed on three chromosomes (Figure 2a, Table 2) were iden- tiTfiherdTe hearsSe eNre SPsNpmoPn amsrikabrelker sefrodsr id stithsretibr giubetunetdoetdyo ponne’t sthh rareveeee rccahhgrreoo mmcrooosssoosammbeeilssi t(y(FF iriggautuerr ee( A22aCa, ,RTaT).ba Clbelh e2r)62 w_)3ew1r6ee1 ri deise nlo-- idetnictfaiifietededd a asat s r3er skepislopono-bsniasbsilbee l pefoafiror srth (tekh begpeg)ne oonntoy tcpyhepr’ose m’asvoaesvroeamrgaeeg 6cer wocrshosiaslseba itlbhitielyi tSryNatrPea tC(eAh(CrA1RC_)2R. 1C)5.h0C5r6h6_ ro36n1_ 63c11h 6ri1os mloo-- is locsacotaemtdeed a 1ta i3ts k3loilkcoai-ltboea-dbs eaa tsp e2a1ip rkasb i(rpks ba(pnk)db op Cn)h corhn1r7oc_mh9r4oo9sm2o omonseo c m6h rweohm6ilowes htohimlee eS tN1h7eP a SCt N9h krP1b_Cp2h.1 Pr51V0_5E26 1r oa5nn05 gc6ihnrogon fmroom- chrso3om2m oteos o 13m 5is%e lo 1wcaiastse ldoob casate t2er1vd ekadbt,p w2 a1intkhdb mCphinarn1o7dr _a9Cll4he9lr2e1 o7fr_ne9 qc4hu9re2onmocinoessc ohomfr o0em. 2157o– sa0ot. m395 ke, ba1pn7.d aP ttVh9Ee kmrabanprg.kienPrgV e fEfrfoemcts ran3gw2in egref rmarketroe 3f5 for%omm w3 2a-2t0o.4375 fects wse orebsfre t%rov w1e4da.,0s w2o (bTsaebrvlee d2),.w ith minorom −20.i4th7 mtoi1n4o.r0 2al(lTelaeb flree2q)u aelnle. ci leesf roefq 0u.2e5n–c0ie.3s5o, fa0n.d2 5t–h0e. 3m5a, raknedr tehffeects were from -20.47 to 14.02 (Table 2). FigurFeig2u. rGee 2n.o Gmeen-owmidee-wasidsoec aiastsioocniaatnioanly sainsafloyrsaisv feorra gaevecrroasgsea bcriloistysarbaitleit(yA rC aRte) (iAn CDR. a) lianta D: (.a a)laMtaa: n(ha)a tMtaannphlaottt,a(nb )pqlouta, n(bti)le q–uan- quanFtitigilleu–r(qeQu 2–a.Q nGt)ielpenl oo(Qmt.–eVQ-ew)r tpiidcloea tla. bsVaseorrsctiiracetalialo tbnea atrons atrhelyleas2tise0 ftyooar t mhaevc e2hr0ra oygmaem ocrs coohsmsraoebmsi,logitsryoe mernaetdes, o (gAtrseCienRnd) diicnoa ttDse i.c nahdlraiotcama:t (oea sc)oh Mmroaemns howasiottthmanien spfl wluoiett,nh (c bien) ofqlnuueannc-e the ttaiolreng– etqhtuetar tanaitrti.glee t( Qtr–aQit.) plot. Vertical bars relate to the 20 yam chromosomes, green dots indicate chromosomes with influence on the target trait. TwoTmwaor kmerasrwkeerrse wfoeurned fofourntdh efopre rtcheen tpaegrechenigtahgcer ohsisgahb ilcirtoys(sPaHbiCli)tyo n(PcHhrCo)m oonso cmherosmo- 1 ansdom6 (eFsi 1g uarned3 6a ()F. iTghuerem 3aa)r.k TehreC mhr1_215056 was from chromosome 1, at the physicalTwo markers were found foarr ktehre Cpherr1c_e2n1t5a0g5e6 h wigahs fcrroomss achbriloitmy o(sPoHmCe) 1 o, ant cthher opmhyos--posiitcion ofsomale ps o1s 2it1io5nk bopf ,21it5e kxbppla, iint eedxp2l9a%ineodf t2h9e%p ohfe tnhoet ypphiecnvoatyripaincc vea, rhiaadncae,m haardk ae rmeaffrekcetro efffect −43o.1f1 -4a3n.d aandL O6 D(F-isgcuorree 3a). The marker Chr1_215056 was from chromosome 1, at the phys- ical pos1it1i oann do fa 2 1L5O kDb-s ocfo4re.0 o1.f 4T.h01is. mThairsk mera’srkMerA’sF MwAaFs 0.03. On the other hand, the marker Chr6_3227 was retpr,i eivt eedxpaltatihneedp o2s9i%tio onf 3thkeb pphoenncohty pwicas 0.03. On the other hand, the marker Chr6_3227 was retrieved at the position 3 kbpro omno vsaormiaence, had a marker effect of -43.11 and a LOD-score of 4.01. This marker’s MAF was c0h.0r3o.m 6. Its MAF was 0.26, and it explained 29% of the phenotypic variance. The marker effect a O os ndn othmee o 6t.h Its MAF was LOD-sceorr ehwanedre, the −27m.3a6rkaenrd C4h.0r46,_r3e2s2p7e cwtiavse lryet(rTiaebvleed2 )a.t the position 3 kbp on chromosome 6. Its MAF was Plants 2021, 10, x FOR PEER REVIEW 7 of 19 0.26, and it explained 29% of the phenotypic variance. The marker effect and LOD-score were -27.36 and 4.04, respectively (Table 2). Plants 2021, 10, x FOR PEER REVIEW 7 of 19 Plants 2021, 10, 1412 0.26, and it explained 29% of the phenotypic variance. The marker effect and LOD-s7coofre18 were -27.36 and 4.04, respectively (Table 2). Figure 3. Genome-wide association analysis for percentage high crossability (PHC) in D. alata: (a) Manhattan plot, (b) quantile–quantile (Q–Q) plot. Vertical bars relate to the 20 yam chromosomes, green dots indicate chromosomes with influence on the target trait. FFigiguurere 33. .GGeennoomme-ew-widide eaasssosocicaitaitoinon ananalaylysissi sfofor rpperecrecnentatgage ehhigighh crcorosssasbabiliiltiyty (P(PHHCC) )inin DD. .aalalatata: :(a()a )MMaannhhatattatnan pplolot,t ,(b(b) ) qquuaanntitliele––qquuaanntitliel e(Q(Q––QQ) )pplolot.t T.VhVereetr itcqiacula lbabanratsri slreerel–alqatetu etaoton tthtihele e2 20(0 Qyya–ammQ c)ch hrproolmomotossso omgmeeenss, e,grgrareteeennd d dobotstys inipnddliocicatattetien ccghh rrotomhmeoo ssonomemgeesas twwivitiheth logarithms ininflfluueennccee oonn ththee tatarrggeet tttr(ra−aitilt.o . g10) of the p-values against their expected p-values showed appropriateness of the GWAST mThheoe dqqueual anfntoitlrie le–a–qllqu utahanenti tltielh e(rQ(eQe– –QtQr) a)piptlsol.ot stT sghgeeenrneeer arwatetaedsd babyny pipnlolfotltteitncintgig otnhthe eb nenetewggaaetitevinvee ollobogsgaearritvihthemdms s and ex- pec(−(t−elodlgo 1vg01a)0 lou) foe tfsh tfeho epr-p vt-aavlraugleueset satgaraagiiantissn,ts ttththeuiesri resexuxpppepcetocetrdetd ipnp-gv-v aaalusluseose scsishahotowiowened d baeaptppwproreopeprnir aitathetenene pesssh soeofn ftohthteye pe and maGrGkWWeArAsS S (mFmioogddueerlle ff oo1rrb aa,l llFlt ihtgheuet rhtehre r2eebetr aatrinatsdi.ts TF. hiTgehrueerwe a3wsbaa)sn. ainnfl iencftlieocntiboent wbeetewneoebns eorbvseedravnedd eaxnpde cetxe-d pveacltuedes vfaolruteasr gfoetr ttraarigtse,t thtruasitss,u tphpuosr tsiunpgpaosrstoinciga taiossnobcieattwioene nbethtwe epehne nthoety ppheeannodtympea raknedrs m(Faigu2.3. Arnkae rress (F1big,u2rblysis of thee a1nd Sbe, F 3b x iDg )u.ertee r2mb iannadti oFnig Suryes t3ebm). 2.T3.hAen halaypsilsootfytphee SveixeDwe toerfm minaartkioen Sy2.3. Analysis of the Sex Determination Srsy sa s tes tesmmo ciated with plant sex in female and male plants of D. alaTtah eshhoapwloetdy ptheavti etwheo sfemxa irsk ceorsnatsrsoolclieadte bdyw tihthe pmlaanltes pexarinenfetmThe haplotype (X alYe)a snidncmea tlhe ep lfaenmtsaolfes were 95.9D%. ahlaotamsohzoywgeodutsh a vtitehwe osef xmarkers associated with plan(XX) for ims caornkterorsll eldinbkyedth etom saelex pdaer te sex tenrtm(X in female and ma inYa) tsiionnce (tFhiegfuermea lel epslawnetrse o9f 5D.9. %alahtoa mshoozwygeodu tsha(Xt Xth)ef oserxm isa rckoenrtsrolilnlekded by the male parent (XY) since the fema l4e,s Swueprep lemen- tar9y5 T.9able S1). In contrast, markers linked two istehx pdleatenrtm sienxa tdioisnp(lFaigyuerde 844, S.9u6p%pl ehmeteenrtoarzyygosity Tab%le hSo1m). oInzycgoonutrsa (sXt,Xm) aforkr emrsalriknekresd liwnkitehd ptloa nsetxs edxedteirsmplianyaetidon84 (.F9i6g%urhee 4te, rSouzpypgloesmiteyni-n in ttahthreye mTmaaabllleee ggSe1en)n.o oItnyty pcpoenept orpaposupt,l aumtliaortnkioe(Fnrsi g (lFuinrikgeeu5d,r Sewu 5ipt,h pS lpuelmapnpetnl seteamxry ednTisatpablrlaeyS eT2d)a .8b4l.e9 6S%2 )h. eterozygosity in the male genotype population (Figure 5, Supplementary Table S2). Figure 4. Female haplotype view for plant sex markers in D. alata. Yellow color refers to homozygosity while the dark Figureg r4e.e nF ecmoloarle i nhdaicpaltoetsy hpeete vrozygosFigure 4. Female haplotypeievwie wfo irty p olfa nthte s celxo nme faor the particular marker haplotype. The white color is associated with missing SNP markers informationfo. rTpDlaa nsttasenxdsm faorrk rke eTrrs risn iDn. aDla.t aa.laYteall.o Yweclloolowr rceofelrosrt orehfoemrso ztoyg hoosimtyowzhyigleotshietyd awrkhgilree etnhe dark green bccrooelleoodrri niinngd dcicoicadatet seoshr eahtceecrteoeszrsyoiogznoy sgnituoymsoitbfyet hro eifn c tlthohneee ycflaoomrnt ebh refeoperda ritnth ope icpaal rDti.c uallaatar amnadr tkheer nhuampbloer following TDa is a mention of the igcu plarorgmramrk eorr hthape lIoItTyAp eG. eTnheetiwcty hRpietees.o cuTorlhcoeer Ciwseahnsitsteoerc . ciTaothleeod rvw aisriti ahabsmlseoi srcsoiiwantg ed with missinrgeSp NSrPNesmePna tmrsk teahrrsek i5en4rfs os reimxn afmotiaromrnk.eaTrtsDio iadnse.t naTtniDfdieasd fso btrayTn GrdoWpsi AcfaoSlr.D T. raolaptaicaanld Dth. eanlautma baenrdfo tllhoew innugmTDbeari sfaolmloewntiinong oTfDthae bisr eae dminegnctoidoen of the breedinogr accocdeses ioorn ancucmesbseior nin nthuemybamer binre ethdein ygapmro gbrraemedoirntgh epIrIoTgAraGmen oetri cthRee sIoITurAce GCeennetetri.c TRheesvoaurriacbel eCreonwterre.p Trehsee nvtasrtihaeble row represe5n4tsse txhme a5r4k esresxi dmenatrikfieerdsb iydeGnWtiAfiSe.d by GWAS. Plants 20P21la,n 1ts0,2 0x2 F1,O1R0, P14E1E2R REVIEW 8 of 18 8 of 19 Figure 5. Male haplotype view for plant sex markers in D. alata. Yellow color refers to homozygosity while the dark green Figure 5. Male haplotype view for plant sex markers in D. alata. Yellow color refers to homozygosity while the dark green color indicates heterozygosity of the clone for the particular marker haplotype. The white color is associated with missing color indicates heterozygosity of the clone for the particular marker haplotype. The white color is associated with missing SNP markers information. TDa stands for TropicalD D. .a laaltaata and the number following TDa is a mention of the breeding SNP markers information. TDa stands for Tropical and the number following TDa is a mention of the breeding code code oro arcaccecsessisoionn nnuummbbeerri innt htheey aymambr eberdeiendginprgo gprraomgroarmth eorII tThAeG IIeTnAeti Gc Reenseotuicrc Re eCseonuterrc.eT Cheenvaterira.b Tlehreo wvarreiparbelsee nrotswth ree5p4resents the 54 sseexx mmaarrkkeerrssi didenetnifitiefdiebdy bGyW GAWS.AS. 2.4. 2H.4a.pHloatpylpotey SpeegSreeggreagtaiotinon foforr AACCRR aannddP PHHCC TheT hheaphlaoptlyoptyep seesgergereggaattiioonn sshhoowweeddt hthaat ta mamonogntgh ethther tehermeea rmkearrskideresn tiidfieendtaifsieasds oa-s asso- ciatecdia tweditwh itAh ACR, Chr17_9492 was the most promising in discriminating genotyppollination CsuRcc, eCssh(rp1<7_09.0459)2. Owfaths etthher eme voasrti apnrtosm(TiTs,inCgT ainn ddCisCc)r,imtheinvaatriinagn tgCeCn esofowtay r spes for pollainssaotciioante dsuwcictehslso w(pA aos vdeersaclrlisbpeedc iens ′thmee yaanm× crop ontology [66]. AlPthHoCug(%h D) =. alata is strictlyN duimoebceirouofs c(rnoos smconmobecinioautiso ncasses reported), i1t 0e0xperien(3c)es irregular/erratic flowering like other yam species, such that a genotype may flower or not in aB pasaerdticounlaar pyreeavri o[u8,s1r1e]p. oFrotr, tchoenovveenriaelnltm aenaanlycsreoss,s wabei loitnylrya tfeocfuorseDd. oalna tgaeisno31ty.7p%es[ 2w2i]t.h Thsteapbolel/lirneagtuiolanr inflfoowrmeraitniogn o(vAeCr Rthaen dcoPnHsiCd)eroefdg epneortiyopde. sTuhsee dseixn tihnifsorsmtuadtyioins porfe sgeenntoedtyipnes Taubsleed1 i.nT thhiissi nstfuodrmy aist iponrewseanstesdu minm Taarbizlee d1.u sing a cross-tabulation function implemented in Microsoft Excel. Figure 8. Morphological dFififgeurerenc8e.s Mofo Drp. haloaltoag filcoawl edrisf fbearesendc eosno sfeDx:. (aal)a itnafflloorwesecresnbcae sweditho nmsaelex :fl(oaw) ienrfls,o (rbe)s icnefnlocerewsciethncme ale with female flowers. flowers, (b) inflorescence with female flowers. 4.43..2D.2N. GAeEnxottryapcetiso’n A, LCiRbr aarnydC PoHnsCtr uAcstsioenssamndenStN P Calling FoCraelcauclhatgioeno ptyrpoece, dwuerecos llfeocrt eAdCaRbo, uctrosnseabgirlaitmy oraftfer easnhd, h PeaHltCh ywaenrde yaoduonpgteldea vfreosm frMomonadfioe ledt -galr.o [w22n].p Tlahnet anvderiamgem cerdoisastaelbyilpitlya creadtet h(Ae sCaRm) pwleaos ncadlrcyuliactee.dT huesilnegaf 2s0a1m0p–2le0s20 wheirsetothriecnall ydoaptah iflriozmed IaITnAd kyeapmt actroasmsibnigen btlorocokms atte Imbapdearant uanred( A~2b5u◦jaC s)t.aDtieoonxsy, rNibiogneruiacl.e Tiche acAidC(RD cNonAs)iswteads oefx dtriavcidteidngf rtohme sluymop ohfi mlizeeadnsle oaff as agmenpolteyspues’sin cgrotshseabceiltiytylt raimteest bhy ltahme nmuom- - nibuemr obfr cormosids-ec(oCmTbAinBa) tpiorontso cino lw[6h7i]chw tihthe sgliegnhottychpaen wgeass. iTnhveolDvNedA frqoumal i2t0y1w0–a2s0a2s0s:e ssed on 0.8% agarose gel and concentration was estimated using nanodrop (Amersham Bioscience, Piscataway, NJ, USA) following the manufacturer’s directives. Subsequently, 50 µL of 50 ng/µL diluted DNA of each genotype was prepared and sent to Diversity Arrays Tech- nology (DArT) Pty Ltd., Australia, for sequencing-based DArT genotyping using the DArT Plants 2021, 10, 1412 14 of 18 marker procedure described by Agre et al. [28]. Complexity reduction methods optimized for yam at DArT were used: PstI_ad/TaqI/HpaII_ad with TaqI restriction enzyme used to eliminate a subset of PstI–HpaII. PstI-site specific adapter was tagged with 102 different barcodes enabling encoding a plate of DNA samples to run within a single lane on an Illumina GAIIx. After the sequencing, FASTQ files generated by DArT were aligned against the newly released D. alata genome reference [29]. Variants (SNP markers) were called using the DArT’s proprietary software, DArTSoft, as previously described [26] and a single row format was generated. Finally, hapmap and VCF files were developed from the single row format and used for the final analysis. 4.3.1. Genotypic Data Analysis Multiple sequences were generated by the DArTSeq platform using proprietary an- alytical pipelines (Diversity Array Technology, Canberra, Australia) and mapped to the D. alata v2 reference genome [29]. This produced a raw dataset (single row format) of 22,140 SNPs that were subjected to SNP markers filtering with the following criteria: mark- ers with low sequence depth < 5; SNP markers with missing values > 20%; minor allele frequency (MAF) < 0.05; genotype quality < 20; and heterozygosity > 50. This quality control filtering resulted in 9687 good-quality SNPs distributed across the 20 chromosomes [27]. 4.3.2. GWAS Analysis and Identification of Putative Genes A compressed mixed linear model (CMLM) implemented in the GAPIT R package was used to compute associations using the mixed model y = Xb + Zu + e [68], where y is the vector of the phenotypic observations estimated for the ACR and the PHC, X represents the SNP markers (fixed effect), Z is the random kinship (co-ancestry) matrix, b is a vector representing the estimated SNP effects, u is a vector representing random additive genetic effects, and e is the vector for random residual errors. A co-ancestry matrix from principal component representing the possible diversity subgroup and kinship was included as covariates in the GWAS model to account for population structure and familial relatedness, respectively, to reduce spurious associations. The Manhattan plot was also generated in R/CMplot to visualize GWAS results over the entire genome [69] using the GWAS output from GAPIT. The phenotypic variation explained by the model for a trait and a particular SNP was determined using stepwise regression implemented in lme4 R package. The SNP loci with significant association with the traits were determined by adjusted p-value using Bonferroni correction [70]. Quantile–quantile (QQ) plots were generated by plotting the negative logarithms (−log10) of the p-values against their expected p-values to fit the appropriateness of the GWAS model with the null hypothesis of no association and to determine how well the models accounted for the population structure. To inventory potential putative genes in the vicinity of associated SNP markers for target traits, we defined a window range of 1 Mb (upstream and downstream) and genes were searched from D. alata generic feature format (GFF3) of the reference genome. Public database Interpro, European Molecular Biology Laboratory—European Bioinformatics Institute (EMBL-EBI) allowed us to determine the functions of the genes associated with the different SNPs identified. A Google Scholar search allowed us to obtain more information on already known identified gene or protein families. For the sex determination, two sets of genotypes were developed, male and female, and the hapmap file of associated SNP markers was developed and viewed in Tassel 5. Proportion of heterozygosity and homozygosity level was estimated across the male and female genotypes. 5. Conclusions This study showed the potential of the GWAS in identifying chromosomal regions associated with sexual reproduction in D. alata. There is a probable association between the sex determination, ACR and PHC, since they are all controlled by the same chromo- somes. Haplotype analysis confirmed the male heterogametic sex determination system for Plants 2021, 10, 1412 15 of 18 D. alata. This species’ reproduction traits could be controlled by multiple genes. We identi- fied promising SNP markers for sex determination, ACR and PHC, which could be used in marker-assisted selection in yam breeding. Supplementary Materials: The following are available online at https://www.mdpi.com/article/ 10.3390/plants10071412/s1, Table S1: Haplotype view of markers associated with plant sex in female plant of D. alata, Table S2: Haplotype view of markers associated with plant sex in male plant of D. alata, Table S3: Candidate gene/protein families annotated in regions controlling target traits, Table S4: Weather and soil variables of the IITA yam breeding sites. Author Contributions: A.A. and P.A.A. designed the experiment. J.M.M. performed data com- pilation. P.A.A. performed data analysis. J.M.M. performed gene annotation. J.M.M. and P.A.A. drafted the manuscript with inputs from A.A. R.A. and M.O.A. contributed in writing up and revision. M.O.A., P.A.A. and A.A. performed supervision. All the authors have read the last ver- sion and approved its submission. All authors have read and agreed to the published version of the manuscript. Funding: The funding support from the Bill and Melinda Gates Foundation (BMGF) through the AfricaYam project of the International Institute of Tropical Agriculture (IITA) (OPP1052998) is ac- knowledged. The first author is grateful for the scholarship granted by the African Union Commission for his Ph.D. studies at the Pan African University—Institute of Life and Earth Sciences (PAULESI). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Most of the data are contained within the article and Supplementary Files. Additional data are available on request from the corresponding author. Acknowledgments: We are grateful for the technical support and practical experiences shared by the IITA yam breeding staff. Conflicts of Interest: The authors declare no conflict of interest. References 1. Asiedu, R.; Sartie, A. Crops that feed the World 1. Yams. Food Secur. 2010, 2, 305–315. [CrossRef] 2. 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, 139, 474–497. [CrossRef] 3. Cormier, F.; Martin, G.; Vignes, H.; Lachman, L.; Cornet, D.; Faure, Y.; Maledon, E.; Mournet, P.; Arnau, G.; Chaïr, H. Genetic control of flowering in greater yam (Dioscorea alata L.). BMC Plant Biol. 2021, 21, 1–12. [CrossRef] 4. Sartie, A.; Asiedu, R. Segregation of vegetative and reproductive traits associated with tuber yield and quality in water yam (Dioscorea alata L.). Afr. J. Biotechnol. 2014, 13, 2807–2818. [CrossRef] 5. Cormier, F.; Lawac, F.; Maledon, E.; Gravillon, M.C.; Nudol, E.; Mournet, P.; Vignes, H.; Arnau, G. A reference high-density genetic map of greater yam (Dioscorea alata L.). Theor. Appl. Genet. 2019, 132, 1733–1744. [CrossRef] [PubMed] 6. Frossard, E.; Aighewi, B.A.; Aké, S.; Barjolle, D.; Baumann, P.; Bernet, T.; Dao, D.; Diby, L.N.; Floquet, A.; Hgaza, V.K.; et al. The Challenge of Improving Soil Fertility in Yam Cropping Systems of West Africa. Front. Plant Sci. 2017, 8, 1953. [CrossRef] 7. Matsumoto, R.; Ishikawa, H.; Asfaw, A.; Asiedu, R. Low soil nutrient tolerance and mineral fertilizer response in White Guinea Yam (Dioscorea rotundata) genotypes. Front. Plant Sci. 2021, 12, 223. [CrossRef] [PubMed] 8. Mondo, J.; Agre, P.; Edemodu, A.; Adebola, P.; Asiedu, R.; Akoroda, M.; Asfaw, A. Floral Biology and Pollination Efficiency in Yam (Dioscorea spp.). Agriculture 2020, 10, 560. [CrossRef] 9. Tamiru, M.; Natsume, S.; Takagi, H.; White, B.; Yaegashi, H.; Shimizu, M.; Yoshida, K.; Uemura, A.; Oikawa, K.; Abe, A.; et al. Genome sequencing of the staple food crop white Guinea yam enables the development of a molecular marker for sex determination. BMC Biol. 2017, 15, 86. [CrossRef] 10. Agre, P.; Nwachukwu, C.; Olasanmi, B.; Obidiegwu, J.; Nwachukwu, E.; Adebola, P.; De Koeyer, D.; Asrat, A. Sample Preservation and Plant Sex Prediction in White Guinea yam (Dioscorea rotundata Poir.). J. Appl. Biotechnol. Rep. 2020, 7, 145–151. [CrossRef] 11. Mondo, J.; Agre, P.; Asiedu, R.; Akoroda, M.; Asfaw, A. Optimized Protocol for In Vitro Pollen Germination in Yam (Dioscorea spp.). Plants 2021, 10, 795. [CrossRef] [PubMed] 12. Grant, S.; Houben, A.; Vyskot, B.; Siroky, J.; Pan, W.-H.; Macas, J.; Saedler, H. Genetics of sex determination in flowering plants. Dev. Genet. 1994, 15, 214–230. [CrossRef] 13. Vyskot, B.; Hobza, R. The genomics of plant sex chromosomes. Plant Sci. 2015, 236, 126–135. [CrossRef] 14. Kumar, S.; Kumari, R.; Sharma, V. Genetics of dioecy and causal sex chromosomes in plants. J. Genet. 2014, 93, 241–277. [CrossRef] [PubMed] Plants 2021, 10, 1412 16 of 18 15. Montalvão, A.P.L.; Kersten, B.; Fladung, M.; Müller, N.A. The Diversity and Dynamics of Sex Determination in Dioecious Plants. Front. Plant Sci. 2021, 11, 2280. [CrossRef] 16. Martin, F.W. Sex Ratio and Sex Determination in Dioscorea. J. Hered. 1966, 57, 95–99. [CrossRef] 17. Terauchi, R.; Kahl, G. Mapping of the Dioscorea tokoro genome: AFLP markers linked to sex. Genome 1999, 42, 757–762. [CrossRef] 18. Bhat, B.K.; Bindroo, B.B. Sex chromosomes in Dioscorea deltoidea wall. Cytologia 1980, 45, 739–742. [CrossRef] 19. Barrett, S.C.; Hough, J. Sexual dimorphism in flowering plants. J. Exp. Bot. 2013, 64, 67–82. [CrossRef] 20. Denadi, N.; Gandonou, C.; Missihoun, A.A.; Zoundjihékpon, J.; Quinet, M. Plant Sex Prediction Using Genetic Markers in Cultivated Yams (Dioscorea rotundata Poir.) in Benin. Agronomy 2020, 10, 1521. [CrossRef] 21. Petit, J.; Salentijn, E.M.J.; Paulo, M.-J.; Denneboom, C.; Trindade, L.M. Genetic Architecture of Flowering Time and Sex Determi- nation in Hemp (Cannabis sativa L.): A Genome-Wide Association Study. Front. Plant Sci. 2020, 11, 1704. [CrossRef] 22. Mondo, J.M.; Agre, P.A.; Edemodu, A.; Asiedu, R.; Akoroda, M.O.; Asfaw, A. Cross-compatibility Analysis in Intra- and Interspecific Breeding Relationships in Yam (Dioscorea spp.). Front. Plant Sci. 2021. submitted. 23. Guo, L.; Qiu, F.; Gandhi, H.; Kadaru, S.; De Asis, E.J.; Zhuang, J.-Y.; Xie, F. Genome-wide association study of outcrossing in cytoplasmic male sterile lines of rice. Sci. Rep. 2017, 7, 3223. [CrossRef] [PubMed] 24. Xu, L.; Hu, K.; Zhang, Z.; Guan, C.; Chen, S.; Hua, W.; Li, J.; Wen, J.; Yi, B.; Shen, J.; et al. Genome-wide association study reveals the genetic architecture of flowering time in rapeseed (Brassica napus L.). DNA Res. 2015, 23, 43–52. [CrossRef] [PubMed] 25. Raman, H.; Raman, R.; Qiu, Y.; Yadav, A.S.; Sureshkumar, S.; Borg, L.; Rohan, M.; Wheeler, D.; Owen, O.; Menz, I.; et al. GWAS hints at pleiotropic roles for FLOWERING LOCUS T in flowering time and yield-related traits in canola. BMC Genom. 2019, 20, 636. [CrossRef] [PubMed] 26. Jaccoud, D.; Peng, K.; Feinstein, D.; Kilian, A. Diversity arrays: A solid state technology for sequence information independent genotyping. Nucleic Acids Res. 2001, 29, 25. [CrossRef] 27. Gatarira, C.; Agre, P.; Matsumoto, R.; Edemodu, A.; Adetimirin, V.; Bhattacharjee, R.; Asiedu, R.; Asfaw, A. Genome- Wide Association Analysis for Tuber Dry Matter and Oxidative Browning in Water Yam (Dioscorea alata L.). Plants 2020, 9, 969. [CrossRef] 28. Agre, P.; Asibe, F.; Darkwa, K.; Edemodu, A.; Bauchet, G.; Asiedu, R.; Adebola, P.; Asfaw, A. Phenotypic and molecular assessment of genetic structure and diversity in a panel of winged yam (Dioscorea alata) clones and cultivars. Sci. Rep. 2019, 9, 18221. [CrossRef] 29. Bredeson, J.V.; Lyons, J.B.; Oniyinde, I.O.; Okereke, N.R.; Kolade, O.; Nnabue, I.; Nwadili, C.O.; Hřibová, E.; Parker, M.; Nwogha, J.; et al. Chromosome evolution and the genetic basis of agronomically important traits in greater yam. BioRxiv 2021. [CrossRef] 30. Chen, Z.J. Genetic and epigenetic mechanisms for gene expression and phenotypic variation in plant polyploids. Annu. Rev. Plant Biol. 2007, 58, 377–406. [CrossRef] 31. Sardos, J.; Rouard, M.; Hueber, Y.; Cenci, A.; Hyma, K.E.; Van Den Houwe, I.; Hribova, E.; Courtois, B.; Roux, N. A Genome-Wide Association Study on the Seedless Phenotype in Banana (Musa spp.) Reveals the Potential of a Selected Panel to Detect Candidate Genes in a Vegetatively Propagated Crop. PLoS ONE 2016, 11, e0154448. [CrossRef] 32. García, A.; Aguado, E.; Garrido, D.; Martínez, C.; Jamilena, M. Two androecious mutations reveal the crucial role of ethylene receptors in the initiation of female flower development in Cucurbita pepo. Plant J. 2020, 103, 1548–1560. [CrossRef] 33. García, A.; Aguado, E.; Martínez, C.; Loska, D.; Beltran, S.; Valenzuela, J.L.; Garrido, D.; Jamilena, M. The ethylene receptors CpETR1A and CpETR2B cooperate in the control of sex determination in Cucurbita pepo. J. Exp. Bot. 2020, 71, 154–167. [CrossRef] 34. Girma, G.; Natsume, S.; Carluccio, A.V.; Takagi, H.; Matsumura, H.; Uemura, A.; Muranaka, S.; Takagi, H.; Stavolone, L.; Gedil, M.; et al. Identification of candidate flowering and sex genes in white Guinea yam (D. rotundata Poir.) by SuperSAGE transcriptome profiling. PLoS ONE 2019, 14, e0216912. [CrossRef] 35. Munné-Bosch, S. Sex ratios in dioecious plants in the framework of global change. Environ. Exp. Bot. 2015, 109, 99–102. [CrossRef] 36. Shchennikova, A.V.; Shulga, O.A.; Kochieva, E.; Beletsky, A.V.; Filyushin, M.; Ravin, N.V.; Skryabin, K.G. Homeobox genes encoding WOX transcription factors in the flowering parasitic plant Monotropa hypopitys. Russ. J. Genet. Appl. Res. 2017, 7, 781–788. [CrossRef] 37. Hamès, C.; Ptchelkine, D.; Grimm, C.; Thevenon, E.; Moyroud, E.; Gérard, F.; Martiel, J.L.; Benlloch, R.; Parcy, F.; Müller, C.W. Structural basis for LEAFY floral switch function and similarity with helix-turn-helix proteins. EMBO J. 2008, 27, 2628–2637. [CrossRef] [PubMed] 38. Lv, X.; Lan, S.; Guy, K.M.; Yang, J.; Zhang, M.; Hu, Z. Global Expressions Landscape of NAC Transcription Factor Family and Their Responses to Abiotic Stresses in Citrullus lanatus. Sci. Rep. 2016, 6, 30574. [CrossRef] [PubMed] 39. Cheng, X.; Peng, J.; Ma, J.; Tang, Y.; Chen, R.; Mysore, K.; Wen, J. NO APICAL MERISTEM (MtNAM) regulates floral organ identity and lateral organ separation in Medicago truncatula. New Phytol. 2012, 195, 71–84. [CrossRef] 40. Radkova, M.; Revalska, M.; Kertikova, D.; Iantcheva, A. Zinc finger CCHC-type protein related with seed size in model legume species Medicago truncatula. Biotechnol. Biotechnol. Equip. 2019, 33, 278–285. [CrossRef] 41. Gachomo, E.W.; Jimenez-Lopez, J.C.; Baptiste, L.J.; Kotchoni, O.S. GIGANTUS1 (GTS1), a member of Transducin/WD40 protein superfamily, controls seed germination, growth and biomass accumulation through ribosome-biogenesis protein interactions in Arabidopsis thaliana. BMC Plant Biol. 2014, 14, 37. [CrossRef] 42. Park, B.S.; Eo, H.J.; Jang, I.-C.; Kang, H.-G.; Song, J.T.; Seo, H.S. Ubiquitination of LHY by SINAT5 regulates flowering time and is inhibited by DET1. Biochem. Biophys. Res. Commun. 2010, 398, 242–246. [CrossRef] Plants 2021, 10, 1412 17 of 18 43. Singh, S.K.; Kumar, V.; Srinivasan, R.; Ahuja, P.S.; Bhat, S.R.; Sreenivasulu, Y. The TRAF Mediated Gametogenesis Progression (TRAMGaP) Gene Is Required for Megaspore Mother Cell Specification and Gametophyte Development. Plant Physiol. 2017, 175, 1220–1237. [CrossRef] 44. Cucinotta, M.; DI Marzo, M.; Guazzotti, A.; De Folter, S.; Kater, M.M.; Colombo, L. Gynoecium size and ovule number are interconnected traits that impact seed yield. J. Exp. Bot. 2020, 71, 2479–2489. [CrossRef] 45. Book, A.J.; Smalle, J.; Lee, K.-H.; Yang, P.; Walker, J.M.; Casper, S.; Holmes, J.H.; Russo, L.A.; Buzzinotti, Z.W.; Jenik, P.D.; et al. The RPN5 Subunit of the 26s Proteasome Is Essential for Gametogenesis, Sporophyte Development, and Complex Assembly in Arabidopsis. Plant Cell 2009, 21, 460–478. [CrossRef] 46. Zarkower, D.; Hodgkin, J. Zinc fingers in sex determination: Only one of the two C. elegans Tra-1 proteins binds DNA in vitro. Nucleic Acids Res. 1993, 21, 3691–3698. [CrossRef] [PubMed] 47. Massonnet, M.; Cochetel, N.; Minio, A.; VonDras, A.M.; Lin, J.; Muyle, A.; Garcia, J.F.; Zhou, Y.; Delledonne, M.; Riaz, S.; et al. The genetic basis of sex determination in grapes. Nat. Commun. 2020, 11, 2902. [CrossRef] [PubMed] 48. Yuan, Z.; Zhang, D. Roles of jasmonate signalling in plant inflorescence and flower development. Curr. Opin. Plant Biol. 2015, 27, 44–51. [CrossRef] [PubMed] 49. Luo, Y.; Pan, B.-Z.; Li, L.; Yang, C.-X.; Xu, Z.-F. Developmental basis for flower sex determination and effects of cytokinin on sex determination in Plukenetia volubilis (Euphorbiaceae). Plant Reprod. 2020, 33, 21–34. [CrossRef] 50. Li, S.-F.; Zhang, G.-J.; Zhang, X.-J.; Yuan, J.-H.; Deng, C.-L.; Gao, W.-J. Comparative transcriptome analysis reveals differentially expressed genes associated with sex expression in garden asparagus (Asparagus officinalis). BMC Plant Biol. 2017, 17, 143. [CrossRef] 51. Wang, W.; Zhang, X. Identification of the Sex-Biased Gene Expression and Putative Sex-Associated Genes in Eucommia ulmoides Oliver Using Comparative Transcriptome Analyses. Molecules 2017, 22, 2255. [CrossRef] 52. Hardenack, S.; Ye, D.; Saedler, H.; Grant, S. Comparison of MADS box gene expression in developing male and female flowers of the dioecious plant white campion. Plant Cell 1994, 6, 1775–1787. [CrossRef] 53. Li, H.-Y.; E Gray, J. Pollination-enhanced expression of a receptor-like protein kinase related gene in tobacco styles. Plant Mol. Biol. 1997, 33, 653–665. [CrossRef] 54. Murase, K.; Shigenobu, S.; Fujii, S.; Ueda, K.; Murata, T.; Sakamoto, A.; Wada, Y.; Yamaguchi, K.; Osakabe, Y.; Osakabe, K.; et al. MYB transcription factor gene involved in sex determination in Asparagus officinalis. Genes Cells 2017, 22, 115–123. [CrossRef] [PubMed] 55. Devani, R.S.; Chirmade, T.; Sinha, S.; Bendahmane, A.; Dholakia, B.B.; Banerjee, A.K.; Banerjee, J. Flower bud proteome reveals modulation of sex-biased proteins potentially associated with sex expression and modification in dioecious Coccinia grandis. BMC Plant Biol. 2019, 19, 330. [CrossRef] [PubMed] 56. Ventura, J. Characterization of Maize Sex-Determination Gene Orthologs in Rice (Oryza sativa L. japonica cv. Nipponbare). Master’s Thesis, University of Rhode Island, Kingston, RI, USA, 2012; p. 94. Available online: https://digitalcommons.uri.edu/theses/718 (accessed on 1 June 2021). 57. Wang, L.; Yin, H.; Qian, Q.; Yang, J.; Huang, C.-F.; Hu, X.; Luo, D. NECK LEAF 1, a GATA type transcription factor, modulates organogenesis by regulating the expression of multiple regulatory genes during reproductive development in rice. Cell Res. 2009, 19, 598–611. [CrossRef] [PubMed] 58. Guan, Y.; Ding, L.; Jiang, J.; Shentu, Y.; Zhao, W.; Zhao, K.; Zhang, X.; Song, A.; Chen, S.; Chen, F. Overexpression of the CmJAZ1-like gene delays flowering in Chrysanthemum morifolium. Hortic. Res. 2021, 8, 87. [CrossRef] 59. Pawełkowicz, M.; Pryszcz, L.; Skarzyńska, A.; Wóycicki, R.; Posyniak, K.; Rymuszka, J.; Przybecki, Z.; Plader, W. Comparative transcriptome analysis reveals new molecular pathways for cucumber genes related to sex determination. Plant Reprod. 2019, 32, 193–216. [CrossRef] 60. Footitt, S.; Dietrich, D.; Fait, A.; Fernie, A.R.; Holdsworth, M.; Baker, A.; Theodoulou, F.L. The COMATOSE ATP-Binding Cassette Transporter Is Required for Full Fertility in Arabidopsis. Plant Physiol. 2007, 144, 1467–1480. [CrossRef] 61. Arnaud, N.; Pautot, V. Ring the BELL and tie the KNOX: Roles for TALEs in gynoecium development. Front. Plant Sci. 2014, 5, 93. [CrossRef] 62. Yang, H.W.; Akagi, T.; Kawakatsu, T.; Tao, R. Gene networks orchestrated by MeGI: A single-factor mechanism underlying sex determination in persimmon. Plant J. 2019, 98, 97–111. [CrossRef] 63. Carmichael, S.N.; Bekaert, M.; Taggart, J.; Christie, H.R.L.; Bassett, D.I.; Bron, J.; Skuce, P.J.; Gharbi, K.; Skern-Mauritzen, R.; Sturm, A. Identification of a Sex-Linked SNP Marker in the Salmon Louse (Lepeophtheirus salmonis) Using RAD Sequencing. PLoS ONE 2013, 8, e77832. [CrossRef] [PubMed] 64. Graham, P.L.; Yanowitz, J.; Penn, J.K.M.; Deshpande, G.; Schedl, P. The Translation Initiation Factor eIF4E Regulates the Sex- Specific Expression of the Master Switch Gene Sxl in Drosophila melanogaster. PLoS Genet. 2011, 7, e1002185. [CrossRef] [PubMed] 65. Aso, K.; Kato, M.; Banks, J.A.; Hasebe, M.; Aso, K.; Kato, M.; Banks, J.A.; Hasebe, M. Characterization of homeodomain-leucine zipper genes in the fern Ceratopteris richardii and the evolution of the homeodomain-leucine zipper gene family in vascular plants. Mol. Biol. Evol. 1999, 16, 544–552. [CrossRef] [PubMed] 66. Asfaw, A. Standard Operating Protocol for Yam Variety Performance Evaluation Trial; IITA: Ibadan, Nigeria, 2016; 27p. Plants 2021, 10, 1412 18 of 18 67. Porebski, S.; Bailey, L.G.; Baum, B.R. Modification of a CTAB DNA extraction protocol for plants containing high polysaccharide and polyphenol components. Plant Mol. Biol. Rep. 1997, 15, 8–15. [CrossRef] 68. Yu, J.; Pressoir, G.; Briggs, W.H.; Bi, I.V.; Yamasaki, M.; Doebley, J.F.; McMullen, M.D.; Gaut, B.S.; Nielsen, D.M.; Holland, J.; et al. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat. Genet. 2005, 38, 203–208. [CrossRef] 69. Turner, S.D. Qqman: An R package for visualizing GWAS results using QQ and manhattan plots. Biorxiv 2014, 005165. [CrossRef] 70. Benjamini, Y.; Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B Stat. Methodol. 1995, 57, 289–300. [CrossRef]