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

This article explores the various applications of dairy cattle genomics, including using markers to track inheritance, estimating the impact of chromosomal segments on traits, combining genomic evaluations with traditional evaluations, and more.

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

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

  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. Data and evaluation flow AI organizations, breed associations nominations samples evaluations Animal Improvement Programs Laboratory, USDA Dairy producers samples samples genotypes DNA laboratories

  4. Reliabilities for young bulls Traditional PA GPTA

  5. 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. 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

  7. What about whole-genome sequencing? • 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

  8. 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. 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. Predicted Mendelian sampling variance

  11. Predicted selection limits

  12. 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. 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

  18. O-Style Haplotypes (chromosome 15)

  19. 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

  20. Sliding-window analysis

  21. Ww Ww X Now what? • We can’t find tissue from affected animals… • We could make embryos… 25% WW 25% ww 50% Ww Genotype

  22. 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$

  23. Marker Effects for Dystocia Complex ARS-BFGL-NGS-109285

  24. Refined Location Using HD Data ARS-BFGL-NGS-109285 141 HO and 69 BS with 17,702 SNP on BTA18

  25. 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

  26. Correlations among GEBV for NM, PL, SCE, DCE, STAT, STR, BDep, RWid

  27. 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

  28. 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

  29. 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

  30. Partners Deep SNP Discovery N’Dama Sahiwal Simmental Hanwoo Blonde d’Aquitaine Montbeliard BFGL Genome Assemblies Nelore Water Buffalo BFGL-Illumina Deep SNP Discovery Angus Holstein Limousin Jersey Nelore Brahman Romagnola Gir Pfizer Light SNP Discovery Angus Holstein Jersey Hereford Charolais Simmental Brahman Waygu

  31. Unresolved genotyping issues • 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

  32. Implementation Team iBMAC Consortium 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

  33. 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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