Xiaoquan Wen

  1. Bayesian Methods for Genetic Association Analysis with Heterogeneous Subgroups: from Meta-Analyses to Gene-Environment Interactions.

    Authors: Xiaoquan Wen, Matthew Stephens
    Subjects: Methodology
    Abstract

    In genetic association analyses, it is often desired to analyze data from
    multiple potentially-heterogeneous subgroups. The amount of expected
    heterogeneity can vary from modest (as might typically be expected in a
    meta-analysis of multiple studies of the same phenotype, for example), to large
    (e.g. a strong gene-environment interaction, where the environmental exposure
    defines discrete subgroups). Here, we consider a flexible set of Bayesian
    models and priors that can capture these different levels of heterogeneity.

  2. Using linear predictors to impute allele frequencies from summary or pooled genotype data.

    Authors: Xiaoquan Wen, Matthew Stephens
    Subjects: Applications
    Abstract

    Recently-developed genotype imputation methods are a powerful tool for
    detecting untyped genetic variants that affect disease susceptibility in
    genetic association studies. However, existing imputation methods require
    individual-level genotype data, whereas, in practice, it is often the case that
    only summary data are available. For example, this may occur because, for
    reasons of privacy or politics, only summary data are made available to the
    research community at large; or because only summary data are collected, as in
    DNA pooling experiments.

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