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Improving the accuracy of genomic predictions in small holder crossed-bred dairy cattle

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Presented by Raphael Mrode, Julie Ojango, John Gibson and Okeyo Mwai at the 7 All Africa Conference on Animal Agriculture (AACAA), Accra , Ghana 29 July– 2 August 2019

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Improving the accuracy of genomic predictions in small holder crossed-bred dairy cattle

  1. 1. SRUCLogo Partner Logo Improving the accuracy of genomic predictions in small holder crossed-bred dairy cattle Raphael Mrode, Julie Ojango, John Gibson and Okeyo Mwai 7 All Africa Conference on Animal Agriculture (AACAA), Accra , Ghana 29 July– 2 August 2019
  2. 2. Genomic system in developed countries • Rapid rate of genetic progress due to genomic selection with higher proportion of active AI bulls being gnomically evaluated • Genomic systems in developed countries are characterised • With large reference populations • Well defined phenotypes • But mostly within pure breeds • High accuracies – over 70 % for milk traits
  3. 3. Characteristics of genomic data in small holder systems • Challenges: • Small data sets • Difficult to define good reference and validation populations • Little data on pure breeds • Mostly on cross breeds animals • Most genotyped animals are females • Low accuracies – about 0.1 to 0.40
  4. 4. Characteristics of genomic data in small holder systems • Lack of pedigree, predictions uses the G matrix • 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. • 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
  5. 5. Characteristics of genomic data in small holder systems • Marker frequencies computed for breeds of origin in the crosses (breed-wise frequencies) • Small data sets, no A matrix and estimation of breed-wise alleles not feasible. • 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
  6. 6. Objectives of the study • Examine genomic models attempting to optimize the use of across breed frequencies with aim of improving accuracies. • 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 • GwtA-ind -- weighted with effects from only animals with < 0.65 exotic genes • Correspondingly : GwtB , GwtB-exotic, GwtB-ind from BayesB
  7. 7. Genotypic data • Genotypic data consisted of 1038 • Data consisted of 1038 cows genotyped with the 777K Illumina High density chip • Cows from 5 random sites in dairy production areas in Kenya • Crossbred cows between indigenous African breeds which (N’dama and Nellore) and 5 exotic dairy breeds (Ayrshire, Friesian, Holstein, Guernsey and Jersey). • Breed composition determined using admixture analysis • Cows classified into 4 classes based on percentage exotic genes: > 87.5% (C1), 61−87.5% (C2), 36−60% (C3), and < 36% (C4) exotic gene.
  8. 8. The DGEA Phenotypic data • Test day milk records were initially analysed with a fixed regression model obtaining a heritability of 0.19±0.05. • Yield deviations for milk yield generated from above models were used for all genomic predictions • Various G used were computed as follows: G = • G0.05 = same with frequencies set 0.5 • Gstd = Z*Z*'/m, Z* is Z*j = Zj / .1
  9. 9. The DGEA Phenotypic data • GwtA or GwtB = • D = SNP effects from either BayesA or B and was estimated from 3 different analyses • All cows, 669 cows >= 0.65 (exotic) and 335 cows <0.65 (Ind) • Accuracies of GEBV = correlation GEBV & YD for groups of animals with YD excluded (cross-validation)
  10. 10. Mean Allele Frequencies Variable Percentage exotic genes All cows (1038) >87.5 (304) 61 – 87.5 (457) 36-60 (212) <36 (61) Means 0.527 0.519 0.526 0.536 0.544 STD 0.263 0.283 0.265 0.260 0.279 0 10 20 30 40 50 60 70 80 90 100 InThousands Allele frequencies C1 C2 C3 C4 All
  11. 11. 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
  12. 12. Accuracy of Genomic Predictions –GBLUP and standardizing allele frequencies frequen
  13. 13. Accuracy of Genomic Predictions-BayesA and weighted Analyses
  14. 14. Accuracy of genomic prediction – BayesB and weighted analyses
  15. 15. • Ranking of indigenous cows in the top 40% increased by 14% with using SNP effects from indigenous or combined Weighted analysis using BayesA
  16. 16. Other options to improve genomic accuracies in small holder systems
  17. 17. Larger reference data: African Dairy Genetics Gain – genomic accuracies from forward validation Commenced analysis of ADGG data and some summary of some results using G. About 2000 genotyped cows with 9000 test days records: h2=0.22 FRM = Fixed regression model; RRM –random regression model
  18. 18. Pooling data and genotype exchange • Pool data across countries (increases up to 70% in accuracy) • Exchange (trading) of genotypes • This is the trend world-wide : Euro-Genetics and North America Consortium Plus UK and Italy: • Close to 40,000 bulls in reference populations • Need good protocol for data exchange ensuring confidentiality • Incorporation foreign sires with genotypes abroad but with only daughters in small holder systems: • About 2 to 45% improvement in genomic accuracy in Brazil
  19. 19. Conclusions • SNP allele frequencies for markers differ between animals of high and low exotic genes. • Frequencies were more towards fixation in cows with low exotic genes. Need to investigate in larger data set and different types of chips • While GBLUP seems very robust in genomic prediction across the range of crossbred animals. Methods that account for variation of allele frequencies and effects are • slightly better in prediction of cows with more indigenous proportions. • Increases their frequency in the top list • Larger data sets through cooperation and data exchange is critical
  20. 20. Dairy Farmers & Farmer organizations National/regional Institutions/govts. Acknowledgements
  21. 21. CRP and CG logos
  22. 22. better lives through livestock ilri.org This presentation is licensed for use under the Creative Commons Attribution 4.0 International Licence.

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