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Activity Number:
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47
- Challenges in the Integrative Analysis of Multiple Genomics Studies
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Type:
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Invited
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Date/Time:
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Sunday, July 30, 2017 : 4:00 PM to 5:50 PM
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Sponsor:
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Biometrics Section
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Abstract #322145
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Title:
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Estimating Effect-Size Distribution from Summary-Level Statistics for Large Genome-Wide Association Studies
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Author(s):
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Yan Zhang* and Nilanjan Chatterjee
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Companies:
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Johns Hopkins Bloomberg School of Public Health and Johns Hopkins University
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Keywords:
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GWAS ;
summary-level statistics ;
LD score ;
heritability ;
effect size distribution ;
EM algorithm
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Abstract:
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It has been shown recently that summary-level statistics from genome-wide association studies (GWAS) can be used to estimate heritability and co-heritability of traits that can be explained by common variants. In this talk, we will describe a novel likelihood based approach for analyzing summary-level statistics to estimate the effect-size distribution of underlying causal SNPs utilizing the linkage disequilibrium (LD) score for the SNPs which is available in public databases. Extensions of the methods to incorporate SNP-level characteristics and multiple traits will be discussed. Applications of the methods will be illustrated using realistic simulation studies and summary-level results from very large GWAS.
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Authors who are presenting talks have a * after their name.
