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What can we do with dairy cattle genomics other than predict more accurate breeding values?
 

What can we do with dairy cattle genomics other than predict more accurate breeding values?

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Presentation on applications of genomic information in additional to estimation of breeding values made to the Department of Animal Science at North Carolina State University at 2010.

Presentation on applications of genomic information in additional to estimation of breeding values made to the Department of Animal Science at North Carolina State University at 2010.

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What can we do with dairy cattle genomics other than predict more accurate breeding values? What can we do with dairy cattle genomics other than predict more accurate breeding values? Presentation Transcript

  • John B. ColeJohn B. Cole Animal Improvement Programs Laboratory Agricultural Research Service, USDA Beltsville, MD 20705-2350 john.cole@ars.usda.gov What can we do with dairy cattle genomics other than predict more accurate breeding values?
  • NCSU, November 23, 2010 (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
  • NCSU, November 23, 2010 (3) Cole Data and evaluation flow Animal Improvement Programs Laboratory, USDA AI organizations, breed associations Dairy producers DNA laboratories samples samples samples genotypes nominations evaluations
  • NCSU, November 23, 2010 (4) Cole Reliabilities for young bulls 0 250 500 750 1000 1250 1500 0 10 20 30 40 50 60 70 80 90 100 Bulls(no.) Protein reliability (%) GPTATraditional PA
  • NCSU, November 23, 2010 (5) Cole Genotyping options • Illumina • Infinium: 3K, 50K, 770K SNP • GoldenGate: 384 to 1,536 SNP • Affymetrix • High-density product (650K) expected in late 2010/early 2011 • We can impute from lower to higher densities with high accuracy
  • NCSU, November 23, 2010 (6) 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
  • NCSU, November 23, 2010 (7) Cole • Whole-genome sequences on individuals will be available in the next few years •How will we store and use those data? • Not feasible to calculate effects for 3,000,000,000 nucleotides • Best application may be SNP discovery What about whole-genome sequencing?
  • NCSU, November 23, 2010 (8) Cole Materials • 43,382 SNP from the Illumina BovineSNP50 • Genotypes from three breeds • 1,455 Brown Swiss males and females • 40,351 Holstein males and females • 4,064 Jersey males and females • Many phenotypes • Yield (5) • Health and fitness (7) • Conformation (3 composites, 14-18 individual)
  • NCSU, November 23, 2010 (9) Cole What else can we do with these data? • Quantitative Genetics • Validate theoretical predictions • Understand genetic variation • Functional Biology • Fine-map recessives • Relate phenotypes to genotypes • Identify important genes in complex systems • Phylogeny
  • NCSU, November 23, 2010 (10) Cole Predicted Mendelian sampling variance Trait Breed Lower Expected Upper DPR BS 0.09 1.45 1.57 HO 0.57 1.45 4.02 JE 0.09 0.98 1.27 Milk BS 35,335 215,168 507,076 HO 228,011 261,364 1,069,741 JE 150,076 205,440 601,979 NM$ BS 2,539 19,602 40,458 HO 16,601 19,602 87,449 JE 3,978 19,602 44,552
  • NCSU, November 23, 2010 (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
  • NCSU, November 23, 2010 (12) Cole How good a cow can we make in theory? A “supercow” constructed from the best haplotypes in the Holstein population would have an EBV(NM$) of $7,515
  • NCSU, November 23, 2010 (13) Cole Genotype Parents and Grandparents Manfred O-Man Jezebel O-Style Teamster Deva Dima
  • NCSU, November 23, 2010 (15) 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
  • NCSU, November 23, 2010 (16) 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
  • NCSU, November 23, 2010 (17) 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
  • NCSU, November 23, 2010 (18) Cole Bull – MGS Relationships
  • NCSU, November 23, 2010 (19) Cole O-Style Haplotypes (chromosome 15)
  • NCSU, November 23, 2010 (20) Cole Fine-mapping Weavers • 35,353 SNP on BTA4 • 69 Brown Swiss bulls with HD genotypes • 20 cases and 49 controls • No affected animals! • Microsatellite-mapped to the interval 43.2–51.2 cM
  • NCSU, November 23, 2010 (21) Cole Sliding-window analysis BTA4_43-60Mb 0 50 100 150 200 250 300 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Mb -log10p
  • NCSU, November 23, 2010 (22) Cole Now what? • We can’t find tissue from affected animals… • We could make embryos… 25% ww Ww WwX 50% Ww 25% WW Genotype
  • NCSU, November 23, 2010 (23) Cole Dystocia Complex • Markers on BTA 18 had the largest effects for 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 shorter PL and decreased NM$
  • NCSU, November 23, 2010 (24) Cole Marker Effects for Dystocia Complex ARS-BFGL-NGS-109285
  • NCSU, November 23, 2010 (25) Cole Refined Location Using HD Data ARS-BFGL-NGS-109285 141 HO and 69 BS with 17,702 SNP on BTA18
  • NCSU, November 23, 2010 (26) 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 • Preliminary results also indicate an effect on gestation length • Confirmed by Christian Maltecca
  • NCSU, November 23, 2010 (27) Cole Correlations among GEBV for NM, PL, SCE, DCE, STAT, STR, BDep, RWid
  • NCSU, November 23, 2010 (28) Cole Discovery of Fertility Genes Candidates for a fertility SNP chip Potentially important in physiological causes of infertility The Illumina GoldenGate Genotyping Assay uses a discriminatory DNA polymerase and ligase to interrogate 96, or from 384 to 1,536, SNP loci simultaneously. Blastoff: +3.4 DPR (=~13.6 days open) Milk +793
  • NCSU, November 23, 2010 (29) Cole Experimental Approach Identify 384 proven bulls with accurate estimates of DPR Based on two runs of the Illumina Golden Gate genotyping system (96 samples per run x 4 = 384) CDDR: Historical bulls (all available bulls in top and bottom 10%) and current bulls (randomly selected from > 3 and <-3) 192 High (> 2.7 DPR 192 Low (<-1.8 DPR) Find 384 SNPs in genes controlling reproduction Genotype each bull for all 384 SNPs Analyze the data to find relationships
  • NCSU, November 23, 2010 (30) Cole How Were Fertility Markers Selected? Candidates for a fertility SNP chip Potentially important in physiological causes of infertility Genes that are well known to be involved in reproduction (LH, FSH, genes involves in prostaglandin synthesis, etc) Genes that are higher in embryos that are more likely to establish pregnancy (i.e. genes found that are differentially regulated by CSF2 and IGF1) Genes in the literature that are expressed in the uterus and have been related to embryo survival (Schellander, Germany
  • NCSU, November 23, 2010 (31) Cole BFGL-Illumina Deep SNP Discovery Angus Holstein Limousin Jersey Nelore Brahman Romagnola Gir BFGL Genome Assemblies Nelore Water Buffalo Pfizer Light SNP Discovery Angus Holstein Jersey Hereford Charolais Simmental Brahman Waygu Partners Deep SNP Discovery N’Dama Sahiwal Simmental Hanwoo Blonde d’Aquitaine Montbeliard
  • NCSU, November 23, 2010 (32) Cole • Collection of genotypes from universities and public research organizations • 3K genotypes from cooperator herds need to enter the national dataset for reliable imputation • Encourage even more widespread sharing of genotypes across countries • Funding of genotyping necessary to predict SNP effects for future chips • Intellectual property issues Unresolved genotyping issues
  • NCSU, November 23, 2010 (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
  • NCSU, November 23, 2010 (34) Cole Conclusions • To answer interesting questions we need more data • Genotypes AND phenotypes • Big p, small n • More complex methodology • Can genomics be used to make better on-farm decisions? • Mate selection • Identify animals susceptible to disease • Pedigree discovery