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John B. Cole Animal Improvement Programs Laboratory Agricultural Research Service, USDA Beltsville, MD 20705-2350 What can we do.

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Presentation on theme: "John B. Cole Animal Improvement Programs Laboratory Agricultural Research Service, USDA Beltsville, MD 20705-2350 What can we do."— Presentation transcript:

1 John 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?

2 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

3 NCSU, November 23, 2010 (3) Cole Data and evaluation flow Animal Improvement Programs Laboratory, USDA AI organizations, breed associations Dairy producers DNA laboratories samples genotypes nominations evaluations

4 NCSU, November 23, 2010 (4) Cole Reliabilities for young bulls GPTATraditional PA

5 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

6 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

7 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?

8 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)

9 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

10 NCSU, November 23, 2010 (10) Cole Predicted Mendelian sampling variance TraitBreedLowerExpectedUpper DPRBS0.091.451.57 HO0.571.454.02 JE0.090.981.27 MilkBS35,335215,168507,076 HO228,011261,3641,069,741 JE150,076205,440601,979 NM$BS2,53919,60240,458 HO16,60119,60287,449 JE3,97819,60244,552

11 NCSU, November 23, 2010 (11) Cole Predicted selection limits TraitBreedLowerUpperLargest DGV DPRBS20538 HO401398 JE19535 MilkBS14,19334,0234,544 HO24,88377,9237,996 JE16,13340,2495,620 NM$BS3,8579,1401,102 HO7,51523,5882,528 JE4,67811,5171,556

12 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

13 NCSU, November 23, 2010 (13) Cole Genotype Parents and Grandparents Manfred O-Man Jezebel O-Style Teamster Deva Dima

14 NCSU, November 23, 2010 (14) Cole Expected Relationship Matrix 1 PGSPGDMGSMGDSireDamBull Manfred 1.0.0.5.0.25 Jezebel.01.0.0.5.0.25 Teamster.0 1.0. 0.5.25 Dima.0 1.0.0.5.25 O-Man.5.0 1.0.0.5 Deva.0.5.01.0.5 O-Style.25.5 1.0 1 Calculated assuming that all grandparents are unrelated 1HO9167 O-Style

15 NCSU, November 23, 2010 (15) Cole Pedigree Relationship Matrix PGSPGDMGSMGDSireDamBull Manfred 1.053.090.105.571.098.334 Jezebel.0901.037.051.099.563.075.319 Teamster.090.0511.035.120.071.578.324 Dima.105.099.1201.042.102.581.342 O-Man.571.563.071.1021.045.086.566 Deva.098.075.578.581.0861.060.573 O-Style.334.319.324.342.566.5731.043 1HO9167 O-Style

16 NCSU, November 23, 2010 (16) Cole Genomic Relationship Matrix PGSPGDMGSMGDSireDamBull Manfred 1.201.058.050.093.609.054.344 Jezebel.0581.131.008.135.618.079.357 Teamster.050.0081.110.100.014.613.292 Dima.093.135.1001.139.131.610.401 O-Man.609.618.014.1311.166.080.626 Deva.054.079.613.610.0801.148.613 O-Style.344.357.292.401.626.6131.157 1HO9167 O-Style

17 NCSU, November 23, 2010 (17) Cole Difference (Genomic – Pedigree) PGSPGDMGSMGDSireDamBull 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-.021.097.029.059 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-Style

18 NCSU, November 23, 2010 (18) Cole Bull – MGS Relationships

19 NCSU, November 23, 2010 (19) Cole O-Style Haplotypes (chromosome 15)

20 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

21 NCSU, November 23, 2010 (21) Cole Sliding-window analysis

22 NCSU, November 23, 2010 (22) Cole Now what? We can’t find tissue from affected animals… We could make embryos… 25% ww Ww X 50% Ww 25% WW Genotype

23 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$

24 NCSU, November 23, 2010 (24) Cole Marker Effects for Dystocia Complex ARS-BFGL-NGS-109285

25 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

26 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

27 NCSU, November 23, 2010 (27) Cole Correlations among GEBV for NM, PL, SCE, DCE, STAT, STR, BDep, RWid

28 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

29 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

30 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

31 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

32 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

33 NCSU, November 23, 2010 (33) Cole 33 iBMAC ConsortiumFunding 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

34 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

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