Application of nuclear and genomic technologies for improving liBevtteer livsest thoroucghk live stock productivity in developing world: Challenges and opportunities Prof. Raphael Mrode Principal Scientist Livestock Genetics Program IAEA International Symposium on Sustainable Animal Production and Health – Current Status and Way Forward Vienna. 28 June – 2nd July, 2021 2 Outline of the talk • Major drivers for the success of genomic technologies in developed countries • Opportunities in developing countries • Limited data infrastructure, genomic tools • Understanding and utilization of genetic basis of adaptation in indigenous livestock • Indirect opportunities for improving animal feeds • Challenges • Data capture, Cost efficiency • Adequacy of commercial genomic tools and delivery of superior genetics • Conclusions 3 Benefits of genomic technologies in developed countries • Benefits of genomic selection have well been demonstrated in developed countries • Higher rates of genetic gain • Reduced generation interval especially in dairy cattle • Accuracies of above 70% for production traits reported for young genomic proven bulls • High accuracies for cows for low heritability traits • Enabled genetic improvement in difficult to measure traits/predictive traits which are of global importance 4 Proportion of inseminations to various categories of bulls: 2011-2018 in USA (Wiggans, 2019) 100% 2 2 9 9 6 4 11 8 90% 28 29 3180% 31 33 37 70% 39 39 0 0 0 0 60% 0 0 50% 0 0 Young genotyped 40% Young non-genotyped 67 69 67 30% 6358 1st crop genotyped 51 54 48 1st crop non-genotyped 20% Old genotyped 10% Old non-genotyped 0% 2011 2012 2013 2014 2015 2016 2017 2018 Year Enabling factors for huge success in genomic 5 selection in developed countries • Existence of well-established infrastructure • Routine data capture systems Genetic evaluation Delivery system for superior genetics • Major drivers : Dairy Cattle - multi-national AI companies; Beef cattle &Sheep - driven by breed societies; Pigs and Poultry -- driven private breeding companies Enabling factors for huge success in genomic 6 selection in developed countries • Organization and design • Across country collaboration: Euro-Genetics , North America Consortium • Inter-genomics : Brown Swiss • Strategic genotyping of connected herds to handle difficult to measure traits – Australia AGIN • Huge role of farmer: genomics designed to address farmer’s needs • USA genotyped cows: 2015 =350K & 2020 = 900K • Selection - which calves to keep, cows to flush, breed with sexed semen ; Pedigree validation/determination; mating 7 Opportunities and challenges of genomic technologies in developing countries • Should be examined: • Not only in terms of the direct application of the principles of genomic technologies • But also, the important associated factors: infrastructure, design, organization, and farmers’ role 8 Some limiting data infrastructure in developing countries & quick wins of genomics • Lack of routine pedigree recording system • Genomic prediction using the G matrix—less reliance on pedigree • Limited herd sizes - animal effect confounded with herd effects • Use of haploblocks from G matrix from common sires used across herds (Powell et al, 2018) 9 Quick wins of genomics: Illustration with using Tanzania data • The African Dairy Genetics Gain (ADGG) Project at ILRI funded by BMGF and working in several African countries • Tanzania data to illustrate of quick wins of genomics • Genomic prediction on about 2000 animals genotyped with HD SNP chip • Limited pedigree: 88%, 11.4%and 0.6% with no, one and both parents identified • More than 50% cows in one herd 10 Genetic parameters from a fixed (FRM)and random regression (RRM) model with G matrix for Tanzania data Trait Heritability Variance due Variance due Phenotypic Model to Pe to herd Variance Milk Yield FRM GBLUP 0.12±0.03 0.10±0.03 0.23±0.02 9.73±0.19 ssGBLUP 0.12±0.03 0.12±0.03 0.22±0.02 9.68±0.16 RRM GBLUP 0.22 0.14 0.21 9.76 ssGBLUP 0.24 0.15 0.21 9.72 Body FRM GBLUP 0.24±04 0.20±0.04 0.22±0.03 1287.6±33.2 Weight ssGBLUP 0.22±04 0.22±04 0.26±03 1338.4±29.9 11 Forward validation results for daily milk yield(kg) and body weight(kg) Trait Method Correlation Regression Milk yield FRM-GBLUP 0.57 1.1 FRM-ssGBLUP 0.59 1.0 RRM-GBLUP 0.55 1.0 RRM-ssGBLUP 0.53 0.92 Body weight FRM-GBLUP 0.83 1.0 FRM-ssGBLUP 0.77 1.1 12 Similar genomic predictions in Beef cattle • Fernandes Júnior et al. (2016) examined genomic prediction for carcass traits (rib eye area (REA) ,back fat thickness (BF), and hot carcass weight (HCW) in Brazilian Nellore cattle • Total of 1756 steers genotyped with 777K HD Chip. • Accuracies estimates were of low-medium 0.21 (BT), 0.37 (HCW) and 0.46 (REA) when using YD • Silva et al 2016--- GS in experimental farm with 788 Nellore animals genotyped with HD but with 9551 with pedigree • Accuracies for FCR and RFI : 0.30 to 0.45 from ssGBLUP compared to 0.29 to 0.23 from BLUP 13 Some limiting data infrastructure in developing countries & quick wins of genomics • Inadequate data structure ( small data sets and small sire progeny size) • The application appropriate genomic methodologies • Incorporation of external genomic information 14 Some limiting data infrastructure : The application appropriate genomic methodology • Inadequate data structure • For example, the Simmental-Simbrah beef cattle population is one of the largest under genetic evaluation in Mexico • In the 2010 run, 5,159 bulls were evaluated but only 703 Simmental and 387 Simbrah with more than 10 daughters • The application of Single step methodology implies bulls with reliable evaluations are not critical 15 Some limiting data infrastructure : The application appropriate genomic methodology • Cows can now be genotyped to • Increase reference populations • Strategically handle difficult to measure traits • Single Step genomic methods can be used to compute evaluations using both pedigree and genomic information. • Bayesian methods could also be considered 16 Some limiting data infrastructure :Incorporation external information • Across country or regional opportunities : Brown swiss model or SNP-BLUP model or consortium model • Incorporating genotypes from foreign sires 17 Across country or regional opportunities • Pool genotypes to form a single reference population • InterGenomics : Brown Swiss populations from 7 main countries • InterGenomics-Holstein: Countries with small Holstein populations (Israel, Ireland, Slovenia, South Korea) • Share genotypes: Eurogenetics & North America Consortium with UK & Italy • Pooling genomic data across countries may be critical for GS in developing countries 18 Incorporating genotypes from foreign sires • Li et al 2016 examined the improvement in prediction reliabilities for 3 production traits in Brazilian Holsteins that had no genotypes • adding information from Nordic and French Holstein bulls that had genotypes. • Increases in reliabilities in some traits varied from 4 to 64% • Similar studies in China Holstein with increase in reliabilities from 0.266 to 0.330 from incorporating Nordic bulls (Ma et al 2014) 19 Genomic tools: Breed composition and parentage verification • Low density SNP assay (200) developed for breed composition determination (Strucken et al , 2017) • If parentage verification is included, assay expands to 400 SNPs 20 Understanding and utilization of genetic basis of adaptation in indigenous livestock • Indigenous breeds represent a unique set genotypes adapted to surviving under harsh conditions and are disease/parasite resistance. • Genomics provide the means for understanding the genetic basis of this adaptation • Kwondo et al 2020 - Several loci in African cattle related immunity, heat-tolerance trypanotolerance and reproduction-related genes. 21 Understanding and utilization of genomic basis of adaptation in indigenous livestock • Small ruminants -- adaptation to arid environments and resistance to endoparasites in sheep from Tunisia (Ahbara et al, 2021) • Paths for utilization • Incorporation of functional regions/genes in genomic prediction --- BayesR • Gene editing & surrogate sires Validated selection for stover quality without cost to grain 22 yield • Using genomic prediction as a tool to improve stover traits- (in-vitro organic matter digestibility (IVOMD) and metabolizable energy (ME)) • Supports the development of new dual-purpose maize varieties. Marker Density Traits (SNPs) IVOMD% ME (MJ/kg) 200 0.36 0.42 500 0.42 0.43 1000 0.43 0.45 3000 0.44 0.46 100000 0.45 0.46 Vinayan, M.T., Seetharam, K., Babu, R. et al. Genome wide association study and genomic prediction for stover quality traits in tropical maize (Zea mays L.). Sci Rep 11, 686 (2021). https://doi.org/10.1038/s41598-020-80118-2 23 Genomic selection in tropical forage grasses e.g. Napier grass • Five times more biomass than natural pastures • Increased yield when intercropped with legumes and irrigated • GWAS/Marker Assisted Selection under development • Agronomic performance and nutritional qualities PCA biplot of 84 accession showing yield traits 24 The challenge of reliable systems for data collection • “In the age of the genotype [genomics], phenotype is king”- Mike Coffey • Several digital tool being pioneered • ODK on Tablet and smart phone : ADGG & ICow • Farmer-based systems – suitable for USSD phones • Multi-component software, on dedicated “data loggers” and mobile phones - BAIF-India • AniCloud and AniCapture – CBBP (offline data capture) • Sensors to capture novel phenotypes on fertility (Muasa et al, 2019) 25 Genetic parameters and accuracy of prediction using part-lactation data 100 DIM 200 DIM 300 DIM 400 DIM 500 DIM N 4400 8886 13177 17005 19599 Heritability 0.19±0.05 0.17±0.04 0.16±0.04 0.14±0.03 0.11±0.03 Rank correlations of gEBVs with those from 500 DIM All Bulls (702) 0.87 0.93 0.97 0.99 Top 20% 0.30 0.61 0.75 0.79 Genetic prediction of 276 young animals born after 2014 with records excluded Accuracy 0.44 0.52 0.54 0.57 0.58 Regression 0.83 0.95 0.97 1.04 1.06 26 Challenge of adequacy commercial SNP array: Examined in three African cattle • Uniqueness genotypes of indigenous breeds leads to another challenge; adequacy of commercial SNPs panels 27 Assessment of the 23 commercial Bovine SNP arrays in 3 cattle breeds 30 Proportions of WGS in high correlation with array SNPs 25 20 15 10 5 0 Boran Ndama Holstein Proportion of WGS SNPS 28 Cost efficiency of genomics • Currently, most genotyped animals are females • an outcome of development projects • Breed societies in some cases in Brazil • Lack of major drivers AI and breeding companies and breed societies • Most cases, samples sent abroad for genotyping • Approaches needed to increase cost efficiency for wide application of genomics 29 Cost efficiency of genomics • One stop shop with modern breeding technologies and marker service laboratory , data management and analyses. • Bundled genomic services to individual farmers and farmer organizations : determination of parentage, breed composition, genomic selection and mating services 30 Cost efficiency of genomics • Cost efficiency increases when genomics is combined with reproductive technologies. • Use of sexed semen of genomically proven young bulls • Beef cattle : use of IVF, with embryos from genotyped donors gave 79% higher genetic gain (Carvalheiro, 2014 ) 31 Deliverance of Improved genetics from genomics • AI uptake still low and widespread use of local bulls • Therefore, genomic prediction must be extended to local bulls - ADGG • Improvement in AI services • Work with the countries NAIC, breed societies and farmer organization. • Understanding the breeding structure—CBBP for small ruminants and exploiting that 32 Conclusions • Genomics offers quick wins for developing countries: genomic prediction, parentage discovery reducing need for accurate pedigree. • Offers opportunity for across country or regional collaboration; this will be needed to ensure adequate data and best sires can be used across regions • In general, genotypic data offers opportunities to model underling genetics for resilience traits 33 Conclusions • Bundled genomic services in combination with reproductive technologies will needed to improve cost-efficiency and widespread application of GS • Of great importance is an efficient delivery mechanism needs to be in place for the superior genetics 34 Acknowledgements Dr Okeyo Mwai Dr Chris Jones (ILRI) Prof. John Gibson Dr Abdulfatai Tijjani (ILRI) Dr Julie Ojango Dr Joram Mwacharo Dr Chinyere Ekine-Dzivenu Prof. Olivier Hanotte THANK YOU Thank you for your attention