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Innovative digital technology and genomic approaches to dairy cattle genetic improvement in developing countries

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Presented by R. Mrode, J. Ojango, Ekine Chinyere, John Gibson and Okeyo Mwai at the Strategic Interest Research Group Meeting on Genetic Improvement of Livestock II, IITA, Ibadan, 2-3 September 2019



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Innovative digital technology and genomic approaches to dairy cattle genetic improvement in developing countries

  1. 1. Innovative digital technology and genomic approaches to dairy cattle genetic improvement in developing countries R. Mrode, J. Ojango, Ekine Chinyere, John Gibson and Okeyo Mwai Strategic Interest Research Group Meeting on Genetic Improvement of Livestock II, IITA, Ibadan, 2-3 September 2019
  2. 2. Outline • Innovative and digital tools for data capture • National level • Herd level • Genomic tools and approaches • Dairy systems based primarily on importation of pure breeds • Dairy systems based primarily on cross breeding • Conclusions
  3. 3. Structure of most genetic Improvement Program in developed countries Data Collection and storage (PI) Analytical tools (PPP) Breeding Programs or Delivery of appropriate genetics to farmers (PI) Herd management information Genetic & Genomic evaluations Farmers Foreign Genetics Good Foundation for sustainability or continuous genetic progress
  4. 4. Structure of most genetic Improvement Program in developing countries Breeding Programs or Delivery of appropriate genetics to farmers (PI) Farmers Foreign Genetics Indigenous breeds or crosses No Foundation for sustainability or continuous genetic progress No Training No feedback
  5. 5. What is ADGG Platform for African Dairy Genetic Gains (ADGG) is a multi-country, multi-institution ILRI-led pioneering proof of concept which is: developing and testing a multi-country genetic gains platform that uses on-farm performance information and basic genomic data for identifying and proving superior crossbred bulls for AI delivery and planned natural mating for the benefit of smallholder farmers in Africa
  6. 6. ADGG: Approach and Objectives Innovative application of existing & emerging technologies 1. To establish National Dairy Performance Recording Centers (DPRCs) for herd and cow data collection, synthesis, genetic evaluation and timely farmer-feedbacks 2. To develop & pilot an ICT platform (FFIP) to capture herd, cow level & other related data & link it to DPRCs (feeds back key related herd/cow summaries, dairy extension & market info. etc.). 3. To develop low density genomic chip for breed composition determination & related bull certification systems for crossbred bulls
  7. 7. Cow Calendar AI/Animal Health Technician Genomic chip Increased efficiency and profitability Platform Platform
  8. 8. 8 Test day records Tanzania: 47,807 records from 11,438 cows Over 6.2 Million text messages on farmer feedback & training
  9. 9. Data collection - Sustainability • Several options are being explored • Training individuals to undertake a bundle of services: • AI services, supply inputs to farmers and data collection or verification • Easy to use phone based ICT tools • farmers can input data. Data is only verified by personnel providing bundle services occasionally during farm visits. • well supported with very good feedback that farmers can see benefits of data collection
  10. 10. Innovation and data to optimize productivity: herd level • Factors optimizing productivity improve efficiency achieve genetic potential • What data and innovation for each stage? • Calf – Heifer management • Adequate size & age at first calving between 22 and 24 months. Growth data & growth profile charts alerts • Reproductive efficiency • Cow calendar - Mobile App/digital tool (Icow) • Heat detection innovations: • pedometers • Cow-side, progesterone based lateral flow test for pregnancy diagnosis
  11. 11. Innovation and data to optimize productivity: herd level • Managing the lactation curve • Daily milk yield --- ODK, Mobile Apps • Drop in yield a good indicator of animal facing a disease challenge - Lactation curve profile - alerts • Managing Cow replacement • Milk yield, disease incidence & treatment cost – ODK/ Mobile Apps • Herd reports with simple ranking simple database • Heifers brought in are above herd average & analytic tool
  12. 12. Quick wins from genomics in dairy systems in developing countries systems • With lack of systematic data and pedigree, quick wins from genotypic data includes • Reduces the need for accurate pedigree recording as genomic relationship can easily be computed • Parentage discovery using SNP data • Determining breed composition • The use of few herds with detailed recording for difficult to measure traits to undertake genomic prediction applicable to the whole population • Example :Genomic Information Nucleus (Ginfo) in Australia :103 herds – fertility traits, diseases (mastitis) traits • Requirements – herds connected to the whole population
  13. 13. Developing a low density SNP Chip for breed composition • Low density snp assay can be developed that gives accurate estimates of dairy proportion
  14. 14. Low density assay for breed composition • Based on DGEA data • A low density assay of about 200 SNPs have been identified for determination of breed composition • If parentage verification is included, assay expands to 400 SNPs • Piloting its utilization among farmers for certification of bulls, heifer and cows.
  15. 15. Genomics tools in Dairy Systems in developing countries • Systems based on importation – purebred • Genomic predictions to screen heifers for replacement and male calves as possible breeding bulls on GEBVs • Quick win: simple tool for converting foreign evaluations to importing country scale ( which country ? which breed?) • Steps • Identification of animals and data collection in local country • Genotyping of cows using of low density (3K -10k) • Most sires in exporting countries are genotyped – need for collaboration to get genotypes for imputation or public databases • Imputation and genomic prediction (>= 1000 cows) • Genomic predictions to screen heifers and bulls
  16. 16. Genomics tools in Dairy Systems in developing countries • Requirements: • Maintain International identities of imported animals • Obtain figures of their genetic merit & pedigree in exporting countries • Data pooled from several farms operating this system • Initially simple tool for conversion can be based on prediction equations involving yield deviations of bull daughters in local country and foreign proofs of bulls
  17. 17. Genomics tools in Dairy Systems in developing countries • Systems based on cross breeding • Optimizing productivity and adaptation • Interaction between various agro-ecologies and genotype • Genomic tools • Determine breed composition - Low density SNP assay • Genomic prediction • Genomic algorithms to optimizing productivity and adaptation • SNP effects • Selection of crossbred bulls on GEBVs • For screening foreign bulls in terms of their performance in crossing in developing countries
  18. 18. ADDG example :Genotyping strategy • GeneSeek Genomic Profiler (GGP) Bovine 50K used for genotyping: • 47843 SNPs returned • About 1500 considered to have private content and not distributed • About 5600 animals genotyped from Tanzania using hair samples • After QA, 40581 SNPs remained and these were imputed to HD
  19. 19. PCA First 3 PCs explain 69.96, 5.7 and 2.71 % of the genetic variance First PC separates exotic dairy from Nellore, with N’Dama about half way between the two major Taurus groups
  20. 20. Average breed proportions of 4614 Tanzanian crossbred samples Dairy Ayrshire Friesian Guernsey Holstein Jersey N’Dama Nelore Average 0.785 0.176 0.296 0.089 0.133 0.091 0.04 0.176 SD 0.171 0.173 0.196 0.111 0.177 0.117 0.032 0.143 MAX 0.99998 1 0.99994 0.99994 0.985 0.991 0.99994 0.295 0.902 MIN 0.00005 0.00001 0.00001 0.00001 0.00001 0.00001 0.00001 0.00001
  21. 21. Genetic Parameter estimation • Subset of 1930 cows with 9193 test days records with genotypes were used • Cows classified to 4 breed classes based on proportion of exotic genes : >0.875,0.61-0.875,60-36 , and < 0.36 • G matrix used for all analysis • Models Examined • Fixed Regression model : Fixed ward, age nested parity, test- year-season, fixed curves with Legendre polynomials nested within breed classes by parity interaction plus random herd animal and pe
  22. 22. Genetic Parameter estimation • Fixed Regression model + plus dominance as random effect and D matrix computed from genotypes • Random regression model with Legendre polynomials of order 2 for animal and Pe effects • Bayesians models (Bayes A, B, and C) using YD deviations from fixed regression model with weights - function on the number of records each cow has and the variance of YD
  23. 23. Genetic parameters Parameters FRM FRM + Dominance RRM Heritability 0.14 ± 0.04 0.14 ± 0.04 0.26 Variance due to Pe 0.10 ± 0.04 0.08 ± 0.08 0.16 Variance due to herd 0.26 ± 0.02 0.26 ± 0.02 0.24 Variance due to dominance 0.03 ± 0.08
  24. 24. Validation • Cross-classified validation excluding records of a particular breed class (FRM) • Forward validation: 254 cows born after 2014
  25. 25. Cross –Validation results Class of cows N GBLUP GBLUP + Dominance Corr Reg Corr Reg >0.875 705 0.28 1.7 0.27 1.7 0.61- 0.875 942 0.28 1.6 0.27 1.6 0.36 – 0.60 239 0.38 1.7 0.37 1.8 <0.36 43 0.44 2.6 0.42 2.6
  26. 26. Forward validation results Model Corr Reg FRM 0.31 1.2 FRM + Dominance 0.30 1.2 RRM 0.43 1.1 BayesA 0.11 0.08 BayesB 0.21 0.21 BayesC 0.10 1.4
  27. 27. Genomic prediction in Tanzania Two approaches : GBLUP • About 2000 cows with genotypes and data • 530 Males with only genotypes • 1537 cows with only genotypes • FRM used for parameter estimation • Single step • Data as in GBLUP • Plus cow with sire or dam or both identified • 638 cows with 2787 test day records were included
  28. 28. Example of some Cow GEBVs AnimalTag HairsampleID noTDs MeanMilk MeanYD GEBV StdGEBV TZN000000000001 00001 10 20.400 1.319 3.560 129 TZN000000000002 00002 4 18.375 2.490 2.983 124 TZN000000000003 00003 3 20.833 3.029 2.799 123 TZN000000000004 00004 10 16.400 0.927 2.697 122 TZN000000000005 00005 1 30.000 9.100 2.651 121 TZN000000000006 00006 4 17.500 1.796 2.639 121 TZN000000000007 00007 6 18.333 1.189 2.556 121 TZN000000000008 00008 6 17.667 0.926 2.524 120
  29. 29. Optimizing Adaptation and productivity • Use of frequencies computed across all cross-bred data in GBLUP tends to produce top cow lists to be dominated with cows of high degree of exotic genes • However in G markers are weighted by their expected variances - solely function of their allele frequencies • Crossbred data - some animals with >87% exotic genes and others less than 36%. • Allele frequencies differ among these categories of animals • Usually more cows with >50 exotic genes in the data and dominate allele frequencies estimates.
  30. 30. Optimizing Adaptation and productivity . • Can we optimize the use of across-breed frequencies in terms of accuracy? We can examine approaches that • Standardizes allele frequencies of markers and therefore equalizes their relative contribution • Weights markers on the basis of their effects on traits of interest
  31. 31. Optimizing Adaptation and productivity • Examine genomic models attempting to optimize the use of across breed frequencies with aim of improving accuracies and ranking. • GBLUP • Regular G matrix • Gstd from standardized allele frequencies • G0.5 with allele frequencies set to intermediate (0.5) • Weighted G matrices • GwtA -- G weighted by SNP effects from BayesA from all data • GwtA-exo -- weighted with effects from only animals with > 0.65 exotic genes (669 cows) • GwtA-ind -- weighted with effects from only animals with < 0.65 exotic genes (335)
  32. 32. Correlation among allele frequencies for different cows of different breed proportion. Categor y of cows No. All > 0.875 0.61-0.875 0.36-0.60 <0.36 All 1034 1.00 > 0.875 304 0.97 1.00 0.61 - 0.875 457 1.00 0.98 1.00 0.36 – 0.60 212 0.96 0.86 0.94 1.00 < 0.36 61 0.85 0.67 0.82 0.95 1.00
  33. 33. Accuracy of Genomic Predictions –GBLUP and standardizing allele frequencies frequen
  34. 34. Accuracy of Genomic Predictions-BayesA and weighted Analyses
  35. 35. • Ranking of indigenous cows in the top 40% increased by 14% with using SNP effects from indigenous or combined • Looking at methods that determine breed origin of alleles or haplotypes for individuals • Need for broad based index of selection considering production and fitness traits Weighted analysis using BayesA
  36. 36. • Existing and emerging digital tools offers us the opportunity to explore innovative data capture cheaply in dairy systems in developing countries. • Genomics offers diary systems in developing countries some quick wins and initial prediction results are encouraging • Given data structure, fine tuned genomic methodologies may be needed to optimize productivity and adaptability • In order to benefit from large numbers and diverse genetic backgrounds, regional programs pooling data together may be useful Conclusions
  37. 37. Dairy Farmers & Farmer organizations National/regional Institutions/govts. Acknowledgements
  38. 38. This presentation is licensed for use under the Creative Commons Attribution 4.0 International Licence. better lives through livestock ilri.org ILRI thanks all donors and organizations who globally supported its work through their contributions to the CGIAR system

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