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Department seminar by Ruzong Fan, National Institute of Health Export this event to calendar

Thursday, June 23, 2016 — 4:00 PM EDT

Functional Regression Models for Gene-based Association Studies of Complex Traits

Ruzong Fan, Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institutes of Health (NIH)

By using functional data analysis techniques, fixed effect functional regression models are developed to test associations between complex traits and genetic variants adjusting for covariates. The genetic variants can be rare or common variants or a combination of the two. By treating multiple genetic variants of an individual in a human population as a realization of an underlying stochastic process, the genome of an individual in a chromosome region is a continuum of sequence data rather than discrete observations. The genome of an individual is viewed as a stochastic function which contains both physical position and linkage disequilibrium (LD) information of the genetic markers. The genetic effect of genetic variants is assumed to be a smooth function, i.e., a continuous genetic effect function, which can be estimated by B-spline or Fourier basis functions. The association between phenotypic traits and genetic variants is tested by testing a null that the genetic effect function is equal to 0. Test statistics of the proposed functional regression models are built to test association between phenotypic traits (which can be quantitative or dichotomous or survival) and genetic variants. Extensive simulation analysis and real data analysis are performed to demonstrate that the proposed models and tests perform better in most cases than existing popular procedure of sequence kernel association test (SKAT), its optimal unified test (SKAT-O), and a combined sum test of rare and common variant effect (SKAT-C). The functional regression models are useful in whole genome and whole exome association studies of complex traits.

Location 
M3 - Mathematics 3
3127

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