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Activity Number:
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355
- Analysis of Complex Genetic Data
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Type:
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Contributed
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Date/Time:
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Tuesday, July 31, 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 #327176
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Presentation
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Title:
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Efficient Statistical Methods for Genome-Wide Association Studies with Disease Family History Data
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Author(s):
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Annie Lee* and Baosheng Liang and Donglin Zeng and Karen Marder and Yuanjia Wang
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Companies:
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Columbia University and Beijing Normal University, Beijing, P. R. China. and UNC Chapel Hill and Columbia University and Columbia University
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Keywords:
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Mixed Effect Model;
Missing genotypes;
Alzheimer's disease
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Abstract:
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Multilevel models are powerful tools to test for association between genetic markers and correlated phenotypes, which offers an opportunity to estimate the personalized risk of disease to each individual's unique biomarkers. In many genetic studies, independent subjects are recruited with genome-wide association study (GWAS) data and phenotypes in their relatives are collected through a family history interview with missing genotype information on their relatives. We propose a fast mixed effects multilevel model to increase our ability to map genetic variants in the combined data of proband GWAS and family history in relatives in the present of missing relative genotypes and correlation among phenotypes. We show that the proposed method achieve the improved power of association testing through simulation studies. We apply our methods to analyze Washington Heights-Inwood Columbia Aging Project combined probands and relative data.
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Authors who are presenting talks have a * after their name.
