Genomics Beyond EBVs
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Genomics Beyond EBVs

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Presentation on alternative uses of genomic information made at the 2nd International Workshop on Genomics Applied to Livestock in Aracatuba, Brazil.

Presentation on alternative uses of genomic information made at the 2nd International Workshop on Genomics Applied to Livestock in Aracatuba, Brazil.

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  • We are all familiar with a traditional pedigree chart. Animal is expected to be an average of his parents. <br />
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Genomics Beyond EBVs Genomics Beyond EBVs Presentation Transcript

  • John B. ColeJohn B. Cole Animal Improvement Programs Laboratory Agricultural Research Service, USDA Beltsville, MD 20705-2350 john.cole@ars.usda.gov Genomics Beyond EBVs
  • 2nd International Workshop on Genomics Applied to Livestock, Araçatuba, Brasil, February 27, 2012 (2) Cole 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
  • 2nd International Workshop on Genomics Applied to Livestock, Araçatuba, Brasil, February 27, 2012 (3) Cole Traditional data flow AIPL AI organization Milk testing laboratory DHI herd Dairy records processing center Breed association registered pedigree data lactation records registered pedigree data registered pedigree data milk samples bull status genetic evaluations genetic evaluations grade pedigree data, genetic evaluations test-day data m anagem ent reports test-day data, pedigree data, breeding data component percentage somatic cellscore On-farm computers healthand fitnessdata
  • 2nd International Workshop on Genomics Applied to Livestock, Araçatuba, Brasil, February 27, 2012 (4) Cole Genomic data flow DHI herd DNA laboratory AI organization, breed association DNA samples genotypes genom ic evaluations nom inations, pedigree data genotype qualityreports genom ic evaluations DNA sam ples genotypes DNA sam ples AIPL
  • 2nd International Workshop on Genomics Applied to Livestock, Araçatuba, Brasil, February 27, 2012 (5) Cole 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 BovineLD BovineHD
  • 2nd International Workshop on Genomics Applied to Livestock, Araçatuba, Brasil, February 27, 2012 (6) Cole Reliabilities for young Holsteins* *Animals with no traditional PTA in April 2011 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 40 45 50 55 60 65 70 75 80 Reliability for PTA protein (%) Numberofanimals 3K genotypes 50K genotypes
  • 2nd International Workshop on Genomics Applied to Livestock, Araçatuba, Brasil, February 27, 2012 (7) Cole Genotyped Holsteins Date SNP Estimation* Young animals** All animalsBulls Cows  Bulls Heifers  04-10 9,770 7,415 16,007   8,630 41,822 08-10 10,430 9,372 18,652 11,021 49,475 12-10 11,293 12,825 21,161 18,336 63,615 04-11 12,152 11,224 25,202 36,545 85,123 08-11 16,519 14,380 29,090 52,053 112,042 09-11 16,812 14,415 30,185 56,559 117,971 10-11 16,832 14,573 31,865 61,045 124,315 11-11 16,834 14,716 32,975 65,330 129,855 12-11 17,288 17,236 33,861 68,051 136,436 01-12 17,681 17,418 35,404 74,072 144,575 02-12 17,710 17,679 36,597 80,845 152,831 *Traditional evaluation **No traditional evaluation
  • 2nd International Workshop on Genomics Applied to Livestock, Araçatuba, Brasil, February 27, 2012 (8) Cole • 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 Imputation
  • 2nd International Workshop on Genomics Applied to Livestock, Araçatuba, Brasil, February 27, 2012 (9) Cole Phenotypes • Animal model (linear) • Yield (milk, fat, protein) • Type (Ayrshire, Brown Swiss, Guernsey, Jersey) • Productive life • Somatic cell score • Daughter pregnancy rate Heritability 8.6% 3.6% 3.0% 6.5% Sire – maternal grandsire model (threshold) Service sire calving ease Daughter calving ease Service sire stillbirth rate Daughter stillbirth rate 25 – 40% 7 – 54% 8.5% 12% 4%
  • 2nd International Workshop on Genomics Applied to Livestock, Araçatuba, Brasil, February 27, 2012 (10) Cole 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
  • 2nd International Workshop on Genomics Applied to Livestock, Araçatuba, Brasil, February 27, 2012 (11) Cole Predicted selection limits Trait Breed Lower Upper Largest DGV DPR BS 20 53 8 HO 40 139 8 JE 19 53 5 Milk BS 14,193 34,023 4,544 HO 24,883 77,923 7,996 JE 16,133 40,249 5,620 NM$ BS 3,857 9,140 1,102 HO 7,515 23,588 2,528 JE 4,678 11,517 1,556
  • 2nd International Workshop on Genomics Applied to Livestock, Araçatuba, Brasil, February 27, 2012 (12) Cole What’s the best cow we can make? A “supercow” constructed from the best haplotypes in the Holstein population would have an EBV(NM$) of $7,515 Cole and VanRaden, 2011 (J. Anim. Breed. Genet. 128:448-455)
  • 2nd International Workshop on Genomics Applied to Livestock, Araçatuba, Brasil, February 27, 2012 (13) Cole Genotype parents and grandparents Manfred O-Man Jezebel O-Style Teamster Deva Dima
  • 2nd International Workshop on Genomics Applied to Livestock, Araçatuba, Brasil, February 27, 2012 (14) Cole Pedigree relationship matrix PGS PGD MGS MGD Sire Dam Bull Manfred 1.053 .090 .090 .105 .571 .098 .334 Jezebel .090 1.037 .051 .099 .563 .075 .319 Teamster .090 .051 1.035 .120 .071 .578 .324 Dima .105 .099 .120 1.042 .102 .581 .342 O-Man .571 .563 .071 .102 1.045 .086 .566 Deva .098 .075 .578 .581 .086 1.060 .573 O-Style .334 .319 .324 .342 .566 .573 1.043 1HO9167 O-Style1HO9167 O-Style
  • 2nd International Workshop on Genomics Applied to Livestock, Araçatuba, Brasil, February 27, 2012 (15) Cole Genomic relationship matrix PGS PGD MGS MGD Sire Dam Bull Manfred 1.201 .058 .050 .093 .609 .054 .344 Jezebel .058 1.131 .008 .135 .618 .079 .357 Teamster .050 .008 1.110 .100 .014 .613 .292 Dima .093 .135 .100 1.139 .131 .610 .401 O-Man .609 .618 .014 .131 1.166 .080 .626 Deva .054 .079 .613 .610 .080 1.148 .613 O-Style .344 .357 .292 .401 .626 .613 1.157 1HO9167 O-Style1HO9167 O-Style
  • 2nd International Workshop on Genomics Applied to Livestock, Araçatuba, Brasil, February 27, 2012 (16) Cole Difference (Genomic – Pedigree) PGS PGD MGS MGD Sire Dam Bull Manfred .149 -.032 -.040 -.012 .038 -.043 .010 Jezebel -.032 .095 -.043 .036 .055 .004 .038 Teamster -.040 -.043 .075 -.021 -.057 .035 -.032 Dima -.012 .036 -.021 .097 .029 .029 .059 O-Man .038 .055 -.057 .029 .121 -.006 .060 Deva -.043 .004 .035 .029 -.006 .087 .040 O-Style .010 .038 -.032 .059 .060 .040 .114 1HO9167 O-Style1HO9167 O-Style
  • 2nd International Workshop on Genomics Applied to Livestock, Araçatuba, Brasil, February 27, 2012 (17) Cole Bull–MGS relationships Van Tassell (personal communication)
  • 2nd International Workshop on Genomics Applied to Livestock, Araçatuba, Brasil, February 27, 2012 (18) Cole 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.
  • 2nd International Workshop on Genomics Applied to Livestock, Araçatuba, Brasil, February 27, 2012 (19) Cole O-Style haplotypes (chromosome 15)
  • 2nd International Workshop on Genomics Applied to Livestock, Araçatuba, Brasil, February 27, 2012 (20) Cole Recessive defect discovery
  • 2nd International Workshop on Genomics Applied to Livestock, Araçatuba, Brasil, February 27, 2012 (21) Cole 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
  • 2nd International Workshop on Genomics Applied to Livestock, Araçatuba, Brasil, February 27, 2012 (22) Cole Marker effects for dystocia complex ARS-BFGL-NGS-109285 Cole et al., 2009 (J. Dairy Sci. 92:2931–2946)
  • 2nd International Workshop on Genomics Applied to Livestock, Araçatuba, Brasil, February 27, 2012 (23) Cole Correlations in dystocia complex
  • 2nd International Workshop on Genomics Applied to Livestock, Araçatuba, Brasil, February 27, 2012 (24) Cole 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 length and calf birth weight
  • 2nd International Workshop on Genomics Applied to Livestock, Araçatuba, Brasil, February 27, 2012 (25) Cole 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)
  • 2nd International Workshop on Genomics Applied to Livestock, Araçatuba, Brasil, February 27, 2012 (26) Cole 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
  • 2nd International Workshop on Genomics Applied to Livestock, Araçatuba, Brasil, February 27, 2012 (27) Cole We have divergent populations 0 10 20 30 40 50 60 70 80 1 2 3 4 5 6 7 8 9 10 11 12 >12 %DBH PercentofScores Cole et al., 2005 (J. Dairy Sci. 88(4):1529–1539)
  • 2nd International Workshop on Genomics Applied to Livestock, Araçatuba, Brasil, February 27, 2012 (28) Cole Gene set enrichment analysis-SNP Gene pathways (G) GWAS results Score increase is proportional to SNP test statistic 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) SNP ranked by significance (L) SNP in pathway genes (S) Score increases for each Li in S Permutation test and FDR Includes all SNP, S, that are included in L The more SNP in S that appear near the top of L, the higher the Enrichment Score
  • 2nd International Workshop on Genomics Applied to Livestock, Araçatuba, Brasil, February 27, 2012 (29) Cole We hope to identify regulatory networks Fortes et al., 2011 (J. Animal Sci. 89:1669-1683. doi:10.2527/jas.2010-3681) Candidate genes and pathways that affect age at puberty common to both breeds
  • 2nd International Workshop on Genomics Applied to Livestock, Araçatuba, Brasil, February 27, 2012 (30) Cole 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
  • 2nd International Workshop on Genomics Applied to Livestock, Araçatuba, Brasil, February 27, 2012 (31) Cole • 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 Unresolved issues in genomic research
  • 2nd International Workshop on Genomics Applied to Livestock, Araçatuba, Brasil, February 27, 2012 (32) Cole 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
  • 2nd International Workshop on Genomics Applied to Livestock, Araçatuba, Brasil, February 27, 2012 (33) Cole 33 iBMAC Consortium Funding • 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 • 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 Implementation Team