Mrode, Raphael A.

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Better lives through livestockApplication of nuclear and genomic technologies for improving livestock productivity in developing world: Challenges and opportunitiesProf. Raphael MrodePrincipal ScientistLivestock  Genetics  ProgramIAEA International Symposium on Sustainable Animal Production and Health – Current Status and Way ForwardVienna.  28 June – 2nd July, 20212Outline 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• Conclusions3• 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 importanceBenefits of genomic technologies in developed countries4Proportion of inseminations to various categories of bulls: 2011-2018 in USA (Wiggans, 2019)4851 54586367 69 67000000 0 03939 37333128 29 3111 89 9 64 2 20%10%20%30%40%50%60%70%80%90%100%2011 2012 2013 2014 2015 2016 2017 2018YearYoung genotypedYoung non-genotyped1st crop genotyped1st crop non-genotypedOld genotypedOld non-genotyped5Enabling factors for huge success in genomic 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 companies6Enabling factors for huge success in genomic 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; mating7• 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’ roleOpportunities and challenges of genomic technologies in developing countries 8• 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)Some limiting data infrastructure  in developing countries & quick wins of genomics 9• 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 Quick wins of genomics: Illustration with  using Tanzania data10Genetic parameters from a  fixed (FRM)and  random regression (RRM) model with G matrix for Tanzania dataTraitModelHeritability Variance due to PeVariance due to herdPhenotypic VarianceMilk Yield FRM GBLUP 0.12±0.03 0.10±0.03 0.23±0.02 9.73±0.19ssGBLUP 0.12±0.03 0.12±0.03 0.22±0.02 9.68±0.16RRM GBLUP 0.22 0.14 0.21 9.76ssGBLUP 0.24 0.15 0.21 9.72Body WeightFRM GBLUP 0.24±04 0.20±0.04 0.22±0.03 1287.6±33.2ssGBLUP 0.22±04 0.22±04 0.26±03 1338.4±29.911Forward validation results for daily milk yield(kg) and body weight(kg)Trait Method Correlation RegressionMilk yield FRM-GBLUP 0.57 1.1FRM-ssGBLUP 0.59 1.0RRM-GBLUP 0.55 1.0RRM-ssGBLUP 0.53 0.92Body weight FRM-GBLUP 0.83 1.0FRM-ssGBLUP 0.77 1.112• 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 BLUPSimilar genomic  predictions in Beef cattle 13• Inadequate data structure ( small data sets and small sire progeny size) • The application appropriate genomic   methodologies• Incorporation of  external genomic information Some limiting data infrastructure  in developing countries & quick wins of genomics 14• 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 criticalSome limiting data infrastructure : The application appropriate genomic   methodology15• 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 consideredSome limiting data infrastructure : The application appropriate genomic   methodology16Some 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 sires17Across  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 countries18• 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)Incorporating genotypes from foreign sires19• Low density  SNP assay (200) developed for breed composition  determination (Strucken et al , 2017)• If parentage verification is included, assay expands to 400 SNPsGenomic  tools: Breed composition and  parentage verification20• 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. Understanding  and utilization of genetic basis of adaptation in indigenous livestock21• 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  siresUnderstanding and utilization of genomic basis of adaptation in indigenous livestock22Validated selection for stover quality without cost to grain 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. 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/s41598-020-80118-2Marker Density (SNPs)TraitsIVOMD% ME (MJ/kg)200 0.36 0.42500 0.42 0.431000 0.43 0.453000 0.44 0.46100000 0.45 0.4623Genomic selection in tropical forage grassese.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 qualitiesPCA biplot of 84 accession showing yield traits24• “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)The challenge of  reliable systems for data collection25Genetic parameters and  accuracy of prediction using part-lactation data100 DIM 200 DIM 300 DIM 400 DIM 500 DIMN 4400 8886 13177 17005 19599Heritability 0.19±0.05 0.17±0.04 0.16±0.04 0.14±0.03 0.11±0.03Rank correlations  of  gEBVs with those  from 500 DIMAll Bulls  (702) 0.87 0.93 0.97 0.99Top 20% 0.30 0.61 0.75 0.79Genetic prediction of 276 young animals born after 2014            with records excludedAccuracy 0.44 0.52 0.54 0.57 0.58Regression 0.83 0.95 0.97 1.04 1.0626Challenge of adequacy commercial SNP array: Examined  in three African cattle• Uniqueness  genotypes of indigenous  breeds leads to another challenge;  adequacy of commercial  SNPs panels27Assessment of the 23 commercial Bovine SNP arrays in 3 cattle breeds051015202530 Proportions of WGS in high correlation with array SNPsBoran Ndama HolsteinProportion of WGS SNPS28• 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   Cost efficiency  of genomics29• 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 servicesCost efficiency  of genomics30• 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 )Cost efficiency  of genomics31• 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 thatDeliverance of Improved genetics from genomics32• 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 traitsConclusions33• 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 geneticsConclusions34Dr Okeyo  Mwai                                  Dr  Chris  Jones (ILRI)Prof. John Gibson                               Dr Abdulfatai Tijjani (ILRI)Dr Julie Ojango                                   Dr Joram MwacharoDr Chinyere Ekine-Dzivenu Prof. Olivier HanotteAcknowledgementsTHANK YOUThank you for your attention 

Application of nuclear and genomic technologies for improving livestock productivity in developing world: Challenges and opportunities

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Application of nuclear and genomic technologies for improving livestock productivity in developing world: Challenges and opportunities

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