Presentation is loading. Please wait.

Presentation is loading. Please wait.

Jeff O’ConnellInterbull annual meeting, Orlando, FL, July 2015 (1) J. R. O’Connell 1 and P. M. VanRaden 2 1 University of Maryland School of Medicine,

Published byErica Lewis Modified over 8 years ago

Similar presentations

Presentation on theme: "Jeff O’ConnellInterbull annual meeting, Orlando, FL, July 2015 (1) J. R. O’Connell 1 and P. M. VanRaden 2 1 University of Maryland School of Medicine,"— Presentation transcript:

1 Jeff O’ConnellInterbull annual meeting, Orlando, FL, July 2015 (1) J. R. O’Connell 1 and P. M. VanRaden 2 1 University of Maryland School of Medicine, Baltimore, MD, USA 2 Animal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, Beltsville, MD, USA joconnel@medicine.umaryland.edu Strategies to choose from millions of imputed sequence variants

2 Jeff O’ConnellInterbull annual meeting, Orlando, FL, July 2015 (2) Introduction l Whole-genome sequencing costs are rapidly declining l Large numbers of animals will be sequenced in the next few years l How will WGS be used in genomic evaluations?

3 Jeff O’ConnellInterbull annual meeting, Orlando, FL, July 2015 (3) Applications of WGS data l Develop reference panels for imputation using SNP chip l Discover QTLs l Discover rare and de novo mutations with deleterious effects for surveillance l Build better SNP chips

4 Jeff O’ConnellInterbull annual meeting, Orlando, FL, July 2015 (4) WGS implementation considerations l Cost of higher density SNP chips to industry l Imputation cost for weekly/monthly genomic evaluations l Redesigning chip incurs cost of generating new data for imputation w Build off the 60K chip

5 Jeff O’ConnellInterbull annual meeting, Orlando, FL, July 2015 (5) Project goal: Prepare for WGS l Develop programs to simulate genotypes and QTLs l Develop programs to process sequence data l Compare strategies for variant selection l Predict gains in reliability l Determine computational footprint for large populations

6 Jeff O’ConnellInterbull annual meeting, Orlando, FL, July 2015 (6) Data l 26,984 HOL bulls in U.S. reference population l 112,905 animals in the pedigree l 30 million simulated variants, 10,000 QTLs l 30 equal-length chromosomes (100 Mbases) l 3 different chip densities (HD, MD, LD) l 5 independent traits (same QTL locations)

7 Jeff O’ConnellInterbull annual meeting, Orlando, FL, July 2015 (7) Genotype simulation and pruning l Simulate 30 million genotypes plus QTLs in 1000 bulls using genosim.f90 l Remove loci with MAF 0.95 l Keep 500K loci near QTLs (genes) + 600K chip + 60K chip l 8,403,858 loci remain

8 Jeff O’ConnellInterbull annual meeting, Orlando, FL, July 2015 (8) QTL effect distribution (% variance) Top QTLs Trait Avg 12345 13.7133.93.55.65.9 1020.03421.023.029.025.4 10057.06359.057.062.059.6 1,00090.09292.0 93.091.8 10,000100.0100100.0

9 Jeff O’ConnellInterbull annual meeting, Orlando, FL, July 2015 (9) QTL correlation to 8M SNP set

10 Jeff O’ConnellInterbull annual meeting, Orlando, FL, July 2015 (10) Bull genotypes: Imputation findhap.f90 BullsGenotype densityVariants 1,000Sequenced pruned to8,403,858 773High density600,000 24,863Medium density60,000 348Low density12,000 26,984Total bulls, all imputed to8,403,858 17,896Phenotyped (old)DYD 9,088Validation (young)True BV

11 Jeff O’ConnellInterbull annual meeting, Orlando, FL, July 2015 (11) Computation required Step Proc- essors Time (hours) Memory (Gbyte) Disk (Gbyte) Simulate 30M15621032 Prune linkage1012710 Impute 8M203813220

12 Jeff O’ConnellInterbull annual meeting, Orlando, FL, July 2015 (12) Study1: Select 25K from 600K chip l Compare 60K and 600K REL using same data l Choose best markers from 600K, add to 60K w Select largest 5,000 for each of 5 traits w Multiple regression: By effect size, effect variance w GWA: By p-value significance w Merge and remove duplicates, adding  23K

13 Jeff O’ConnellInterbull annual meeting, Orlando, FL, July 2015 (13) Results: REL from PA, 60K, 85K, 600K 60K + 25K TraitPA60KGWASizeVar600K 124.477.979.281.681.380.3 231.277.979.381.481.280.1 332.778.379.581.381.580.4 423.376.677.780.279.878.6 530.478.380.082.582.281.2 Avg28.477.879.181.481.280.1

14 Jeff O’ConnellInterbull annual meeting, Orlando, FL, July 2015 (14) Genome-wide association l Single SNP regression within mixed model that accounts for the polygenic effect using pedigree kinship w Y= Xb + SNP + a + e l Multiple testing adjustment leads to stringent cutoffs for significance l Focus on estimation rather than prediction

15 Jeff O’ConnellInterbull annual meeting, Orlando, FL, July 2015 (15) GWA trait 2 imputed QTLs <5% QTL < 10e-5

16 Jeff O’ConnellInterbull annual meeting, Orlando, FL, July 2015 (16) Study 2: Select sequence variants l 8M – search everywhere l 1M – search by bioinformatics using 2.5kb window on QTL l QTLs – perfect functional knowledge l Include 60K chip for imputation, but assign all or most prior variance to the selected variants w Selection of variants may affect optimal shape/ choice of prior distribution

17 Jeff O’ConnellInterbull annual meeting, Orlando, FL, July 2015 (17) REL from 1M, 60K+1M subset, QTLs 1 million near QTLsInclude QTLs Trait600K60K+25KAll 1M60K+10K10K 180.385.486.784.687.2 280.185.387.784.987.7 380.484.986.185.087.8 478.683.584.882.985.9 581.286.087.685.287.5 Avg80.185.086.484.587.2

18 Jeff O’ConnellInterbull annual meeting, Orlando, FL, July 2015 (18) Computation required Step Proc- essors Time (hours) Memory (Gbyte) Disk (Gbyte) Simulate 30M15621032 Prune linkage1012710 Impute 8M203813220 Predict 1M52220<1 Select 25K from 8M 300.5<1

19 Jeff O’ConnellInterbull annual meeting, Orlando, FL, July 2015 (19) Conclusions l Selection of variants requires several steps l Potential gains from sequence are large l Still requires very large reference population l Methods scalable to more sequences l Bioinformatics can narrow the search l GWA is scalable to larger data but multiple regression give higher reliability

20 Jeff O’ConnellInterbull annual meeting, Orlando, FL, July 2015 (20) Current directions l Evaluate several additional SNP selection criteria including priors for GWA l Apply methods to real data from 1000 Bulls Project l Evaluate bovine bioinformatics resources for functional annotation

21 Jeff O’ConnellInterbull annual meeting, Orlando, FL, July 2015 (21) Acknowledgments l JRO supported by USDA SCA 58-45-14-070-1

Download ppt "Jeff O’ConnellInterbull annual meeting, Orlando, FL, July 2015 (1) J. R. O’Connell 1 and P. M. VanRaden 2 1 University of Maryland School of Medicine,"

Similar presentations

Analysis of imputed rare variants

Analysis of imputed rare variants
2007 Paul VanRaden, Mel Tooker, and Nicolas Gengler Animal Improvement Programs Lab, Beltsville, MD, USA, and Gembloux Agricultural U., Belgium

2007 Paul VanRaden, Mel Tooker, and Nicolas Gengler Animal Improvement Programs Lab, Beltsville, MD, USA, and Gembloux Agricultural U., Belgium
2007 Paul VanRaden 1, Jeff O’Connell 2, George Wiggans 1, Kent Weigel 3 1 Animal Improvement Programs Lab, USDA, Beltsville, MD, USA 2 University of Maryland.

2007 Paul VanRaden 1, Jeff O’Connell 2, George Wiggans 1, Kent Weigel 3 1 Animal Improvement Programs Lab, USDA, Beltsville, MD, USA 2 University of Maryland.
Meta-analysis for GWAS BST775 Fall 2013. DEMO Replication Criteria for a successful GWAS P

Meta-analysis for GWAS BST775 Fall DEMO Replication Criteria for a successful GWAS P
Added value of whole-genome sequence data to genomic predictions in dairy cattle Rianne van Binsbergen 1,2, Mario Calus 1, Chris Schrooten 3, Fred van.

Added value of whole-genome sequence data to genomic predictions in dairy cattle Rianne van Binsbergen 1,2, Mario Calus 1, Chris Schrooten 3, Fred van.
Perspectives from Human Studies and Low Density Chip Jeffrey R. O’Connell University of Maryland School of Medicine October 28, 2008.

Perspectives from Human Studies and Low Density Chip Jeffrey R. O’Connell University of Maryland School of Medicine October 28, 2008.
Genomic imputation and evaluation using 1074 high density Holstein genotypes P. M. VanRaden 1, D. J. Null 1 *, G.R. Wiggans 1, T.S. Sonstegard 2, E.E.

Genomic imputation and evaluation using 1074 high density Holstein genotypes P. M. VanRaden 1, D. J. Null 1 *, G.R. Wiggans 1, T.S. Sonstegard 2, E.E.
Ingredients for a successful genome-wide association studies: A statistical view Scott Weiss and Christoph Lange Channing Laboratory Pulmonary and Critical.

Ingredients for a successful genome-wide association studies: A statistical view Scott Weiss and Christoph Lange Channing Laboratory Pulmonary and Critical.
2007 Paul VanRaden and Jeff O’Connell Animal Improvement Programs Lab, Beltsville, MD U MD College of Medicine, Baltimore, MD

2007 Paul VanRaden and Jeff O’Connell Animal Improvement Programs Lab, Beltsville, MD U MD College of Medicine, Baltimore, MD
Use of Quantitative Trait Loci (QTL) in Dairy Sire Selection Fabio Monteiro de Rezende Universidade Federal Rural de Pernambuco (UFRPE) - Brazil.

Use of Quantitative Trait Loci (QTL) in Dairy Sire Selection Fabio Monteiro de Rezende Universidade Federal Rural de Pernambuco (UFRPE) - Brazil.
MSc GBE Course: Genes: from sequence to function Genome-wide Association Studies Sven Bergmann Department of Medical Genetics University of Lausanne Rue.

MSc GBE Course: Genes: from sequence to function Genome-wide Association Studies Sven Bergmann Department of Medical Genetics University of Lausanne Rue.
Computational Complexity The complexity of the MG model for a single SNP is determined by the complexity of the matrix operations in formulas used to iteratively.

Computational Complexity The complexity of the MG model for a single SNP is determined by the complexity of the matrix operations in formulas used to iteratively.
Give me your DNA and I tell you where you come from - and maybe more! Lausanne, Genopode 21 April 2010 Sven Bergmann University of Lausanne & Swiss Institute.

Give me your DNA and I tell you where you come from - and maybe more! Lausanne, Genopode 21 April 2010 Sven Bergmann University of Lausanne & Swiss Institute.
2007 Paul VanRaden 1, Curt Van Tassell 2, George Wiggans 1, Tad Sonstegard 2, Jeff O’Connell 1, Bob Schnabel 3, Jerry Taylor 3, and Flavio Schenkel 4,

2007 Paul VanRaden 1, Curt Van Tassell 2, George Wiggans 1, Tad Sonstegard 2, Jeff O’Connell 1, Bob Schnabel 3, Jerry Taylor 3, and Flavio Schenkel 4,
Mating Programs Including Genomic Relationships and Dominance Effects

Mating Programs Including Genomic Relationships and Dominance Effects
Mating Programs Including Genomic Relationships and Dominance Effects Chuanyu Sun 1, Paul M. VanRaden 2, Jeff R. O'Connell 3 1 National Association of.

Mating Programs Including Genomic Relationships and Dominance Effects Chuanyu Sun 1, Paul M. VanRaden 2, Jeff R. O'Connell 3 1 National Association of.
Chuanyu Sun Paul VanRaden National Association of Animal breeders, USA Animal Improvement Programs Laboratory, USA Increasing long term response by selecting.

Chuanyu Sun Paul VanRaden National Association of Animal breeders, USA Animal Improvement Programs Laboratory, USA Increasing long term response by selecting.
WiggansARS Big Data Workshop – July 16, 2015 (1) George R. Wiggans Animal Genomics and Improvement Laboratory Agricultural Research Service, USDA Beltsville,

WiggansARS Big Data Workshop – July 16, 2015 (1) George R. Wiggans Animal Genomics and Improvement Laboratory Agricultural Research Service, USDA Beltsville,
K.L. Parker Gaddis 1 and J.B. Cole 2,* 1 Department of Animal Sciences 2 Animal Genomics and Improvement University of Florida Laboratory Gainesville,

K.L. Parker Gaddis 1 and J.B. Cole 2,* 1 Department of Animal Sciences 2 Animal Genomics and Improvement University of Florida Laboratory Gainesville,
2007 J. B. Cole 1,*, P. M. VanRaden 1, J. R. O'Connell 3, C. P. Van Tassell 1,2, T. S. Sonstegard 2, R. D. Schnabel 4, J. F. Taylor 4, and G. R. Wiggans.

2007 J. B. Cole 1,*, P. M. VanRaden 1, J. R. O'Connell 3, C. P. Van Tassell 1,2, T. S. Sonstegard 2, R. D. Schnabel 4, J. F. Taylor 4, and G. R. Wiggans.

Similar presentations

Ads by Google