Statistical methods for analyzing high-throughput genotype data Rapid technological progress makes high-throughput genotyping of thousands of SNPs feasible in large epidemiologic studies. Technologies to sequence an entire human genome for affordable cost is expected in near future. New statistical methods that are able to manage and analyze this sort of large scale data have not progressed as rapidly, however. This renewal application requests support to continue statistical methodological developments in analyzing large scale genetic data sets. We propose to use a variety of large genetic data sets to test the newly developed statistical methods.
Specific aims i nclude 1) Develop statistical methods to detect rare genetic variants using whole genome scan or sequence data. We will develop a variety of designs to cluster rare risk haplotypes and then perform association analysis with these risk haplotypes as a group in candidate gene association studies. 2) Develop statistical association methods that control for population stratification using whole genome data. 3) Develop statistical methods to jointly model admixture mapping and association in order to search for potential causal variants contributing to the admixture mapping signals. 4) Develop corresponding software that will be made available in the S.A.G.E. (Statistical Analysis for Genetic Epidemiology) program package which will be widely distributed. We will collaborate with laboratory-based investigators to obtain appropriate data sets, including our hypertension and obesity related data, and apply new analytic methods to this crucial practical problem in genetic epidemiology.

Public Health Relevance

This is a continuation of research with the aims of developing novel statistical methods for detecting genetic variants underlying common diseases. We will develop the statistical methods of detecting rare variants, controlling population stratification, performing the joint analysis of admixture mapping and association and developing new software.

National Institute of Health (NIH)
National Human Genome Research Institute (NHGRI)
Research Project (R01)
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Cardiovascular and Sleep Epidemiology (CASE)
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Brooks, Lisa
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Case Western Reserve University
Schools of Medicine
United States
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Wang, Ya-Juan; Tayo, Bamidele O; Bandyopadhyay, Anupam et al. (2014) The association of the vanin-1 N131S variant with blood pressure is mediated by endoplasmic reticulum-associated degradation and loss of function. PLoS Genet 10:e1004641
Chen, Guo-Bo; Liu, Nianjun; Klimentidis, Yann C et al. (2014) A unified GMDR method for detecting gene-gene interactions in family and unrelated samples with application to nicotine dependence. Hum Genet 133:139-50
Monda, Keri L; Chen, Gary K; Taylor, Kira C et al. (2013) A meta-analysis identifies new loci associated with body mass index in individuals of African ancestry. Nat Genet 45:690-6
Wu, Yubao; Zhu, Xiaofeng; Chen, Jian et al. (2013) EINVis: a visualization tool for analyzing and exploring genetic interactions in large-scale association studies. Genet Epidemiol 37:675-85
Wang, Xuefeng; Lee, Seunggeun; Zhu, Xiaofeng et al. (2013) GEE-based SNP set association test for continuous and discrete traits in family-based association studies. Genet Epidemiol 37:778-86
Sun, Xiangqing; Elston, Robert; Morris, Nathan et al. (2013) What is the significance of difference in phenotypic variability across SNP genotypes? Am J Hum Genet 93:390-7
Wang, Xuefeng; Morris, Nathan J; Zhu, Xiaofeng et al. (2013) A variance component based multi-marker association test using family and unrelated data. BMC Genet 14:17
Qin, Huaizhen; Zhu, Xiaofeng (2012) Allowing for population stratification in association analysis. Methods Mol Biol 850:399-409
Zhu, Xiaofeng (2012) The analysis of ethnic mixtures. Methods Mol Biol 850:465-81
Qin, Huaizhen; Zhu, Xiaofeng (2012) Power comparison of admixture mapping and direct association analysis in genome-wide association studies. Genet Epidemiol 36:235-43

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