Activity Number:
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248
- Recent Advances in Genetic Association and Gene-Environment Interaction Studies
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Type:
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Contributed
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Date/Time:
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Tuesday, August 9, 2022 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistics in Genomics and Genetics
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Abstract #323159
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Title:
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Aggregated Cauchy Composite Kernel Association Test (ACCKAT) for SNP-Set Joint Assessment of Genotype and Genotype-By-Treatment Interaction in Large-Scale Data
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Author(s):
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Judong Shen* and Hong Zhang and Lan Luo
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Companies:
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Merck & Co., Inc. and Merck & Co., Inc. and Merck & Co., Inc.
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Keywords:
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ACCKAT;
genotype by environment interaction;
G+GxE joint analysis;
Kernel regression;
Cauchy combination;
whole exome sequencing
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Abstract:
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Complex traits and diseases are influenced by the interplay of genetic and environmental variables and large cohorts like UK Biobank provide a great opportunity for SNP-set based genotype by environment interaction (GxE or GEI) and G+GxE joint analyses. However, it is challenging for the existing kernel regression-based SNP-set tests to handle large memory requirement and time-consuming eigenvalue decomposition. To overcome the challenges, we propose Aggregated Cauchy Composite Kernel Association Test (ACCKAT) for SNP-set G+GxE joint analysis and GEI analysis in large-scale data. By constructing a composite genotype between G and GEI using a grid of weights, ACCKAT adopts an analytic procedure based on Cauchy combination of individual p-values to accurately calculate final p-value under the SKAT kernel regression framework. Simulation shows ACCKAT controls type I error well, provides robust power performance compared to the existing methods and runs efficiently for UK Biobank level data analysis. We applied ACCKAT to the UKBB whole exome sequencing data (N = 46,319) and identified significant gene-environment pairs, including both previously reported and novel associations.
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Authors who are presenting talks have a * after their name.
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