THESIS
2022
1 online resource (viii, 61 pages) : color illustrations
Abstract
The large-scale multiple testing problem is one of the fundamental challenges
in genome-wide association studies (GWAS). The traditional approach,
Bonferroni correction, is overly conservative. The two-groups model offers a
Bayesian perspective in controlling false discovery rate, which is a less conservative
approach. The major assumption for the two-groups model is exchangeability,
essentially an equal prior for all tests. However, in genetic studies, risk
variants are not equally important, but are usually enriched in gene regulatory
regions or active in cell/tissue types relevant to diseases. This auxiliary information
embedded in biologically functional annotation data can be leveraged
in risk variants identification. A few FDR-based methods have been developed
to integrate GWAS da...[
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The large-scale multiple testing problem is one of the fundamental challenges
in genome-wide association studies (GWAS). The traditional approach,
Bonferroni correction, is overly conservative. The two-groups model offers a
Bayesian perspective in controlling false discovery rate, which is a less conservative
approach. The major assumption for the two-groups model is exchangeability,
essentially an equal prior for all tests. However, in genetic studies, risk
variants are not equally important, but are usually enriched in gene regulatory
regions or active in cell/tissue types relevant to diseases. This auxiliary information
embedded in biologically functional annotation data can be leveraged
in risk variants identification. A few FDR-based methods have been developed
to integrate GWAS data with functional annotations, but they still have some
limitations. First, most of the existing methods assume a linear model for risk
variant identification. Second, few of existing methods are scalable to handle a
large number of annotations while maintaining good interpretability. To address
these issues, we propose a powerful and adaptive latent model (PALM) to integrate
cell/tissue-specific functional annotations with summary statistics from
GWAS. Extensive simulations as well as real application in 30 GWASs with 127
cell-type/tissue specific functional annotations demonstrate the effectiveness of
PALM in risk variant prioritization and its advantages over existing methods.
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