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Maximizing Reliability in Genomic Selection through Marker Combinations

This study explores the gains in reliability from combining subsets of genetic markers at different densities, evaluating the benefits of marker combinations in genomic selection. By strategically mixing markers, researchers achieve significant reliability improvements at lower costs, optimizing genetic evaluation processes. Through imputation techniques, missing genotypes are accurately filled using data from relatives or the general population, enhancing the quality of genomic evaluations. The findings demonstrate that mixing different chip densities can lead to substantial cost savings while maintaining reliability, emphasizing the importance of chip selection in genomic selection strategies.

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Maximizing Reliability in Genomic Selection through Marker Combinations

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  1. Gains in reliability from combining subsets of 500, 5,000, 50,000 or 500,000 genetic markers

  2. Introduction • Having more genetic markers can increase both reliability and cost of genomic selection. • Fewer markers can be used to trace chromosome segments within a population once identified by high-density haplotyping. • Combinations of marker densities can improve reliability at lower cost.

  3. Introduction cont. • Accurate genomic evaluations will be less costly if many animals are genotyped at less than the highest density • Missing genotypes are imputed (filled) using genotypes or haplotypes of relatives or from matching allele patterns in the general population

  4. How does imputation work? • Identify haplotypes in population using many markers • Track haplotypes with fewer markers • e.g., use 5 SNP to track 25 SNP • 5 SNP: 22020 • 25 SNP: 2022020002002002000202200

  5. Imputed Dams • If progeny and sire both genotyped • First progeny inherits 1 of dam’s 2 haplotypes • Second progeny has 50:50 chance to get same or other haplotype • Haplotypes known with 1, 2, 3, etc. progeny are ~50%, 75%, 87%, etc. • Dam genotype used if >90% known

  6. Using Imputed Genotypes July 2010 • 47,645 Holsteins • 2,035 cows imputed • 4,575 Jerseys • 153 cows imputed • 1,604 Brown Swiss • 66 cows imputed

  7. Marker Combinations Tested • Actual genotypes for 43,382 SNP combined with 3,209 SNP subset • All 40,351 Holsteins with 43,382 or 3,209 • Or half of young animals with low density • Or half of all animals with low density • Simulated genotypes for 500,000 SNP combined with 50,000 SNP subset • All 33,414 Holsteins with 500,000 or 50,000 • Or 1,500 or 3,726 or 7,398 bulls with 500,000, remaining animals with 50,000

  8. Real Data Tests • Half of young animals assigned 3K • Proven bulls, cows all had 43K • Dams imputed using 43K and 3K • Half of ALL animals assigned 3K • Could 3K reference animals help? • 10,000 proven bulls yet to genotype • Should cows with 3K be predictors?

  9. Results from 3K, 43K Actual Missing

  10. Correlations2 of 3K and PA with 43KHalf ofYOUNG animals had 3K PTA, half 43K PTA • Consistent gains across traits • Corr(3K,43K)2 ranged from .90-.94 • Corr(PA,43K)2 ranged from .42-.56 • Reliability gain from progeny with 3K was 79-87% of gain from 43K • Gain % = [Corr(3K,43K)2 - Corr(PA,43K)2] / [1 - Corr(PA,43K)2] • Large benefits for smaller cost

  11. Using 3K as Reference Genotypes Half ofALL animal NM$ were from 3K, half 43K • Gains in reliability as compared to genotyping all animals at 43K • 90% for young animals with 43K • 73% for young animals with 3K • 36% for dams imputed with 3K and 43K progeny instead of all 43K • Can use 3K reference genotypes

  12. Simulated 500K Tests • How many 500K genotypes needed? • Three subsets of mixed 500K and 50K: • Of 33,414 HO, only 1,586 (young) had 500K • Also bulls > 99% REL, total 3,726 • Also bulls > 90% REL, total 7,398 • Linkage generated in base population • Hopefully similar to actual linkage

  13. Results from 500K Simulation Missing

  14. REL Using Only 3K, 50K, or 500Kwith increasing numbers of bulls

  15. Conclusions • Genomic evaluations can mix different chip densities to save $ • Only a few thousand of highest density genotypes needed, and other animals imputed • More animals can be genotyped to increase selection differential and size of reference population • Breeders must optimize chip choice

  16. Better Communication is Needed • “Progeny genotypes should affect dam, but programs are not yet available” Jan 2009 USDA Changes Memo • “Programs are available to impute 1300 dams” Oct 2009 USDA report to Council • “Encourage USDA to use genotypes, derived by imputation, in genetic evaluation” Oct 2009Holstein USA Board of Directors (in Holstein Pulse)

  17. Better Communication is Needed • “…new genetic calculations should not be published when using female DNA (which is the intellectual property of each respective breeder) unless approved by the Holstein Association and its board of directors.” Resolution #2 – Adopted by Holstein Association USA Delegates, 125th annual meeting, July 2010

  18. Mixing Different Chips

  19. Acknowledgments • Curt Van Tassell (BFGL) selected the 3,209 low density SNP • Bob Schnabel (U. Missouri), Jeff O’Connell (U. Maryland), and George Wiggans fixed map locations for several SNP

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