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Activity Number:
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530
- Integrative Genomics: EQTL and GWAS
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Type:
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Contributed
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Date/Time:
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Wednesday, August 1, 2018 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistics in Genomics and Genetics
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Abstract #329106
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Presentation
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Title:
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Integrating Data from GWAS and EQTL by Estimating Genetic Relatedness
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Author(s):
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Jianqiao Wang* and Hongzhe Li
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Companies:
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University of Pennsylvania and University of Pennsylvania
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Keywords:
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GWAS;
Genetic Relatedness;
linkage disequilibrium
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Abstract:
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Genome-wide association studies (GWAS) of human complex disease have identified a large number of disease associated genetic loci. However, most of these risk loci do not provide direct information on the biological basis of a disease or on the underlying mechanisms. Linkage disequilibrium among the SNPs further complicates the identification of causal variants. On the other hand, genome-wide expression quantitative trait loci (eQTLs) association studies have provided information on SNPs that are associated with gene expression variation. These eQTLs likely contribute to phenotype diversity and disease susceptibility. We propose a simple and consistent estimator of genetic correlation between disease and gene expression that take full account of the linkage disequilibrium between the SNPs, estimable from HapMap and 1000 Genome Project. The estimated genetic correlations can be use to rank the genes that are most likely to be causal for diseases. Simulations and real data analysis of human heart failure data sets are used to illustrate the proposed methods.
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Authors who are presenting talks have a * after their name.
