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Low-Cost, Low-Density Genotyping and its Potential Applications

Low-Cost, Low-Density Genotyping and its Potential Applications. K.A. Weigel, O. González-Recio, G. de los Campos, H. Naya, N. Long, D. Gianola, and G.J.M. Rosa University of Wisconsin. Illumina BovineSNP50 Genotyping BeadChip. < $250 per animal today. Low-Cost Genotyping Assays.

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Low-Cost, Low-Density Genotyping and its Potential Applications

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  1. Low-Cost, Low-Density Genotyping and its Potential Applications K.A. Weigel, O. González-Recio, G. de los Campos, H. Naya, N. Long, D. Gianola, and G.J.M. Rosa University of Wisconsin

  2. Illumina BovineSNP50 Genotyping BeadChip < $250 per animal today

  3. Low-Cost Genotyping Assays •  At the current price, the BovineSNP50 BeadChip is limited to applications involving males and elite females • A low-cost assay with 300-1000 SNPs might deliver a substantial portion of the gain for a small fraction of the price  Applications may include: preliminary screening of young bulls, selection of replacement heifers, genomic mating programs, and parentage discovery

  4. Which SNPs to Select? Pick the SNPs with largest estimated effects? How many do we need? VanRaden, 2008

  5. Which SNPs to Select? Pick evenly spaced SNPs? How many do we need? VanRaden, 2008

  6. Entropy • A measure of the impurity of an arbitrary collection of examples (S) • Entropy (S) = - p+ log2p+ - p- log2p- • where: • p+ = proportion of positive examples in S • p- = proportion of negative examples in S

  7. Information Gain • A measure of the effectiveness of an attribute in classifying the data • Reduction in entropy caused by partitioning the examples into subsets (S1,...,Sn) based on values of a given attribute (A) • Information Gain (S,A) = • Entropy(S) – i=1,n (|Si|/|S|) Entropy(Si)

  8. Top 10% of SNPs for Net Merit (Info Gain for 20% highest bulls vs. 20% lowest bulls) 3252 SNPs 1181 SNPs (36.2%) in common (though many more in linkage disequilibrium) Number of SNPs Chromosome

  9. Effects of Top Net Merit SNPs 3252 SNPs Estimate Chromosome 3252 SNPs Estimate Chromosome

  10. Top 10% of SNPs for Specific Traits (Info Gain for 20% highest bulls vs. 20% lowest bulls; traditional coding) 3252 SNPs Number of SNPs 3252 SNPs Chromosome

  11. Top 2.5% of SNPs for Specific Traits (Info Gain for 20% highest bulls vs. 20% lowest bulls; traditional coding) 813 SNPs Number of SNPs 813 SNPs Chromosome

  12. Top Info Gain SNPs in Common by Trait (20% highest vs. 20% lowest bulls; traditional coding) top 10% of SNPs (3252) above the diagonal top 2.5% of SNPs (813) below the diagonal

  13. Bayesian LASSO • Bayesian least absolute selection and shrinkage operator • One-step method for estimating effects of important SNPs while shrinking estimates for unimportant SNPs towards zero • Assumes SNP effects follow a double exponential distribution (a few with large effects, many with negligible effects)

  14. Distribution of SNP Effects (analysis of Net Merit in training set with 32,518 SNPs) Number of SNPs Estimated SNP Effect (genetic SD)

  15. Distribution of SNP Effects (analysis of Net Merit in training set with 32,518 SNPs) Estimated Effect (genetic SD)

  16. Distribution of SNP Effects

  17. Validation of Genomic PTAs • Compute parent averages and genomic PTAs using 2003 data from 3,305 Holstein bulls born in 1952-1998 •  “Training Set” • Compare ability to predict daughter deviations in 2008 data for 1,398 bulls born from 1999-2002 •  “Testing Set”

  18. Predictive Ability for Net Merit(Genomic PTA vs. Progeny Test PTA in Testing Set) Corr. = 0.61 PTA from Progeny Testing (SD) 32,518 SNPs Predicted Genomic PTA from All SNPs (gen. SD)

  19. Corr. = 0.43 Corr. = 0.52 Predictive Ability for Net Merit(Genomic PTA from SNPs vs. Progeny Test PTA in Testing Set) 300 SNPs 750 SNPs PTA from Progeny Testing Corr. = 0.55 Corr. = 0.57 1250 SNPs 2000 SNPs Predicted Genomic PTA from Top ___ SNPs (gen. SD)

  20. Predictive Ability for Net Merit(Genomic PTA vs. Progeny Test PTA in Testing Set) Predictive Ability in Testing Set . . . Number of SNPs used for Prediction

  21. No. Bulls Chosen Correctly (of 1399) Note that we are predicting 2008 PTAs that have REL much less than 99% (not the true genetic merit of the bulls)

  22. Animal ID Applications(96+ SNPs in the parentage panel) •  Verify reported parents  Discover parents if unknown or incorrect •  Trace animals or animal products

  23. Effects on Inbreeding •  Traditional animal model evaluations favor co-selection of families or relatives  Genomic selection allows within-family selection, which leads to less inbreeding •  Low-cost, low-density genotyping assays will allow widespread screening of families that might provide unique genetic contributions to the population •  Identification and control of inherited defects will be greatly enhanced as well

  24. Potential for Mate Selection  Millions of cows are mated using computerized programs each year, based on faults in conformation or avoidance of inbreeding  SNP genotypes of AI sires and potential mates could be used to minimize inbreeding or to identify parents with “complementary” DNA profiles

  25. Possibilities for Novel Traits  Opportunities to collect DNA and phenotypes for traits not routinely assessed in national recording schemes  Examples include: feed intake, hormone level, immune function, hoof care, etc.  Potential resource populations include: experimental herds, calf ranches, heifer growers, commercial herds with specific milking/feeding/management equipment, veterinary databases (without sire ID)

  26. Novel Traits and Genomics Recorded Population (10,000-25,000 animals per trait or trait group) additive or non-additive inheritance no selection bias refine estimates of location or effect, add SNPs update estimates of SNP effects full genotyping selective genotyping Whole Genome Selection QTL Detection and MAS $200/genotype $100/trait 5 traits/group select high/low 10% cost ~ $7.0-17.5 mln per trait group cost ~ $1.2-3.0 mln per trait

  27. Synergy with Herd Management •  “Personalized medicine” is the Holy Grail of biomedical research • Examples include genotype-guided Warfarin dosing using two major genes • Cost-effective applications in livestock will involve a series of small returns from enhanced vaccination programs, ration formulation, mate selection, veterinary care, and animal grouping decisions •  Integration with herd management software will be the key to success

  28. UW-Madison Dairy Science…Committed to Excellence in Research, Extension and Instruction http://www.wisc.edu/dysci Any Questions?

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