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Genomics Beyond EBVs

Genomics Beyond EBVs. Whole-genome selection (2008). Use many markers to track inheritance of chromosomal segments Estimate the impact of each segment on each trait Combine estimates with traditional evaluations to produce genomic evaluations ( GPTA )

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Genomics Beyond EBVs

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  1. Genomics Beyond EBVs

  2. Whole-genome selection (2008) • Use many markers to track inheritance of chromosomal segments • Estimate the impact of each segment on each trait • Combine estimates with traditional evaluations to produce genomic evaluations (GPTA) • Select animals shortly after birth using GPTA • Very successful worldwide

  3. Traditional data flow milk samples Milk testing laboratory DHI herd registered pedigree data test-day data component percentage somatic cell score management reports registered pedigree data Breed association Dairy records processing center lactation records genetic evaluations registered pedigree data grade pedigree data, genetic evaluations test-day data, pedigree data, breeding data bull status AI organization genetic evaluations AIPL health and fitness data On-farm computers

  4. Genomic data flow DHI herd DNA samples DNA samples genomic evaluations DNA samples DNA laboratory AI organization, breed association genotypes nominations, pedigree data genotype quality reports genomic evaluations genotypes AIPL

  5. Illumina genotyping arrays BovineSNP50 54,001 SNPs (version 1) 54,609 SNPs (version 2) 45,187 SNPs used in evaluation BovineHD 777,962 SNPs Only BovineSNP50 SNPs used >1,700 SNPs in database BovineLD 6,909 SNPs Allows for additional SNPs BovineSNP50 v2 BovineHD BovineLD

  6. Reliabilities for young Holsteins* 9000 50K genotypes 8000 3K genotypes 7000 6000 5000 Number of animals 4000 3000 2000 1000 0 40 45 50 55 60 65 70 75 80 Reliability for PTA protein (%) *Animals with no traditional PTA in April 2011

  7. Genotyped Holsteins *Traditional evaluation **No traditional evaluation

  8. Imputation • 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

  9. Phenotypes Animal model (linear) Yield (milk, fat, protein) Type (Ayrshire, Brown Swiss, Guernsey, Jersey) Productive life Somatic cell score Daughter pregnancy rate Heritability 25– 40% 7– 54% 8.5% 12% 4% • Sire – maternal grandsire model (threshold) • Service sire calving ease • Daughter calving ease • Service sire stillbirth rate • Daughter stillbirth rate 8.6% 3.6% 3.0% 6.5%

  10. What can we do beyond EBVs? • Quantitative Genetics • Validate theoretical predictions • Understand genetic variation • Functional Biology • Fine-map recessives • Relate phenotypes to genotypes • Identify important genes in complex systems

  11. Predicted selection limits

  12. What’s the best cow we can make? Cole and VanRaden, 2011 (J. Anim. Breed. Genet. 128:448-455) A “supercow” constructed from the best haplotypes in the Holstein population would have an EBV(NM$) of $7,515

  13. Genotype parents and grandparents

  14. Pedigree relationship matrix 1HO9167 O-Style

  15. Genomic relationship matrix 1HO9167 O-Style

  16. Difference (Genomic – Pedigree) 1HO9167 O-Style

  17. Bull–MGS relationships Van Tassell (personal communication)

  18. Should we really care about inbreeding? Cole and VanRaden, 2011 (J. Anim. Breed. Genet. 128:448-455) Bank semen and embryos to preserve genetic diversity and select the best haplotypes. Chromosomal EBV will reflect the value of marker diversity.

  19. O-Style haplotypes (chromosome 15)

  20. Recessive defect discovery

  21. Dystocia complex • Markers on chromosome 18 have large effects on several traits: • Dystocia and stillbirth: Sire and daughter calving ease and sire stillbirth • Conformation: rump width, stature, strength, and body depth • Efficiency: longevity and net merit • Large calves contribute to reduced lifetimes and decreased profitability

  22. Marker effects for dystocia complex ARS-BFGL-NGS-109285 Cole et al., 2009 (J. Dairy Sci. 92:2931–2946)

  23. Correlations in dystocia complex

  24. Biology of the dystocia complex • The key marker is ARS-BFGL-NGS-109285 at 57,125,868 Mb on BTA18 • Located in a cluster of CD33-related Siglec genes • Many Siglecs involved in leptin signaling • Recent results indicate effects on gestation lengthand calf birth weight

  25. One SNP isn’t the whole story! AIPL (http://aipl.arsusda.gov/Report_Data/Marker_Effects/marker_effects.cfm?Breed=HO&Trait=Sire_Calv_Ease)

  26. What do we do next? • Markers with large effects don’t explain that much variation • What about groups of SNP? • Individual markers may not have significant effects • Groups of markers may collectively have significant effects

  27. We have divergent populations Cole et al., 2005 (J. Dairy Sci. 88(4):1529–1539)

  28. Gene set enrichment analysis-SNP GWAS results Gene pathways (G) SNP in pathway genes (S) SNP ranked by significance (L) Includes all SNP, S, that are included in L Score increases for each Li in S The more SNP in S that appear near the top of L, the higher the Enrichment Score Score increase is proportional to SNP test statistic Permutation test and FDR Nominal p-value corrected for multiple testing Pathways with moderate effects Holden et al., 2008 (Bioinformatics 89:1669-1683. doi:10.2527/jas.2010-3681)

  29. We hope to identify regulatory networks Candidate genes and pathways that affect age at puberty common to both breeds Fortes et al., 2011 (J. Animal Sci. 89:1669-1683. doi:10.2527/jas.2010-3681)

  30. Challenges in pathway analysis • This is a new procedure for our lab • There are many steps involving lots of data sources • Positive results can be challenging to explain • Negative results are not necessarily definitive

  31. Unresolved issues in genomic research • Genotypes from universities and research organizations • More widespread sharing of genotypes across countries • Genotypes needed to predict SNP effects for future chips • Annotation of the bovine genome • http://www.innatedb.com/ • Intellectual property concerns

  32. Conclusions • We need more data • Genotypes AND phenotypes • Big p, small n • More complex methodology • We are all systems biologists now • Can genomics be used on the farm? • Mate selection • Identify animals susceptible to disease • Pedigree discovery

  33. iBMAC Consortium Implementation Team Funding • Illumina (industry) • Marylinn Munson • Cindy Lawley • Diane Lince • LuAnn Glaser • Christian Haudenschild • Beltsville (USDA-ARS) • Curt Van Tassell • Lakshmi Matukumalli • Steve Schroeder • Tad Sonstegard • Univ Missouri (Land-Grant) • Jerry Taylor • Bob Schnabel • Stephanie McKay • Univ Alberta (University) • Steve Moore • Clay Center, NE (USDA-ARS) • Tim Smith • Mark Allan • AIPL • Paul VanRaden • George Wiggans • John Cole • Leigh Walton • Duane Norman • BFGL • Marcos de Silva • Tad Sonstegard • Curt Van Tassell • University of Wisconsin • Kent Weigel • University of Maryland School of Medicine • Jeff O’Connell • Partners • GeneSeek • DNA Landmarks • Expression Analysis • Genetic Visions • USDA/NRI/CSREES • 2006-35616-16697 • 2006-35205-16888 • 2006-35205-16701 • 2008-35205-04687 • 2009-65205-05635 • USDA/ARS • 1265-31000-081D • 1265-31000-090D • 5438-31000-073D • Merial • Stewart Bauck • NAAB • Gordon Doak • Accelerated Genetics • ABS Global • Alta Genetics • CRI/Genex • Select Sires • Semex Alliance • Taurus Service 33

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