Limits...
Leveraging prior information to detect causal variants via multi-variant regression.

Long N, Dickson SP, Maia JM, Kim HS, Zhu Q, Allen AS - PLoS Comput. Biol. (2013)

Bottom Line: Here we develop and evaluate a Bayesian hierarchical regression method that incorporates prior information on the likelihood of variant causality through weighting of variant effects.By simulation studies using both simulated and real sequence variants, we compared a standard single variant test for analyzing variant-disease association with the proposed method using different weighting schemes.We found that by leveraging linkage disequilibrium of variants with known GWAS signals and sequence conservation (phastCons), the proposed method provides a powerful approach for detecting causal variants while controlling false positives.

View Article: PubMed Central - PubMed

Affiliation: Center for Human Genome Variation, Duke University School of Medicine, Durham, North Carolina, United States of America. n.long@duke.edu

ABSTRACT
Although many methods are available to test sequence variants for association with complex diseases and traits, methods that specifically seek to identify causal variants are less developed. Here we develop and evaluate a Bayesian hierarchical regression method that incorporates prior information on the likelihood of variant causality through weighting of variant effects. By simulation studies using both simulated and real sequence variants, we compared a standard single variant test for analyzing variant-disease association with the proposed method using different weighting schemes. We found that by leveraging linkage disequilibrium of variants with known GWAS signals and sequence conservation (phastCons), the proposed method provides a powerful approach for detecting causal variants while controlling false positives.

Show MeSH

Related in: MedlinePlus

Workflow of the simulation study.Before carrying out these steps, a large pool of haplotypes (n = 15,000) was simulated. Given GRR and MAF of causal variants, cases and controls were simulated by randomly choosing pairs of haplotypes and calculating the risk of each individual to probabilistically assign phenotype.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3675126&req=5

pcbi-1003093-g001: Workflow of the simulation study.Before carrying out these steps, a large pool of haplotypes (n = 15,000) was simulated. Given GRR and MAF of causal variants, cases and controls were simulated by randomly choosing pairs of haplotypes and calculating the risk of each individual to probabilistically assign phenotype.

Mentions: A diagram of the procedure of analysis is shown in Figure 1. Briefly, a large GWAS cohort (3000 cases and 3000 controls) was simulated to identify significant association signals (p<10−8) and a smaller sequencing sample (500 cases and 500 controls) was used to detect causal variants among candidate variants.


Leveraging prior information to detect causal variants via multi-variant regression.

Long N, Dickson SP, Maia JM, Kim HS, Zhu Q, Allen AS - PLoS Comput. Biol. (2013)

Workflow of the simulation study.Before carrying out these steps, a large pool of haplotypes (n = 15,000) was simulated. Given GRR and MAF of causal variants, cases and controls were simulated by randomly choosing pairs of haplotypes and calculating the risk of each individual to probabilistically assign phenotype.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3675126&req=5

pcbi-1003093-g001: Workflow of the simulation study.Before carrying out these steps, a large pool of haplotypes (n = 15,000) was simulated. Given GRR and MAF of causal variants, cases and controls were simulated by randomly choosing pairs of haplotypes and calculating the risk of each individual to probabilistically assign phenotype.
Mentions: A diagram of the procedure of analysis is shown in Figure 1. Briefly, a large GWAS cohort (3000 cases and 3000 controls) was simulated to identify significant association signals (p<10−8) and a smaller sequencing sample (500 cases and 500 controls) was used to detect causal variants among candidate variants.

Bottom Line: Here we develop and evaluate a Bayesian hierarchical regression method that incorporates prior information on the likelihood of variant causality through weighting of variant effects.By simulation studies using both simulated and real sequence variants, we compared a standard single variant test for analyzing variant-disease association with the proposed method using different weighting schemes.We found that by leveraging linkage disequilibrium of variants with known GWAS signals and sequence conservation (phastCons), the proposed method provides a powerful approach for detecting causal variants while controlling false positives.

View Article: PubMed Central - PubMed

Affiliation: Center for Human Genome Variation, Duke University School of Medicine, Durham, North Carolina, United States of America. n.long@duke.edu

ABSTRACT
Although many methods are available to test sequence variants for association with complex diseases and traits, methods that specifically seek to identify causal variants are less developed. Here we develop and evaluate a Bayesian hierarchical regression method that incorporates prior information on the likelihood of variant causality through weighting of variant effects. By simulation studies using both simulated and real sequence variants, we compared a standard single variant test for analyzing variant-disease association with the proposed method using different weighting schemes. We found that by leveraging linkage disequilibrium of variants with known GWAS signals and sequence conservation (phastCons), the proposed method provides a powerful approach for detecting causal variants while controlling false positives.

Show MeSH
Related in: MedlinePlus