A Powerful Nonparametric Statistical Framework for Family-Based Association Analyses
نویسندگان
چکیده
منابع مشابه
A powerful nonparametric statistical framework for family-based association analyses.
Family-based study design is commonly used in genetic research. It has many ideal features, including being robust to population stratification (PS). With the advance of high-throughput technologies and ever-decreasing genotyping cost, it has become common for family studies to examine a large number of variants for their associations with disease phenotypes. The yield from the analysis of thes...
متن کاملTitle: A powerful non-parametric statistical framework for family-based association analyses
The Framingham Heart Study dataset used for the analyses was obtained from the National Center for Biotechnology Information database of genotypes and phenotypes (NCBI dbGaP) through accession number phs000128.v3.p3. ABSTRACT Family-based study design is commonly used in genetic research. It has many ideal features, including being robust to population stratification (PS). With the advance of h...
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BACKGROUND The role of the microbiota in human health and disease has been increasingly studied, gathering momentum through the use of high-throughput technologies. Further identification of the roles of specific microbes is necessary to better understand the mechanisms involved in diseases related to microbiome perturbations. METHODS Here, we introduce a new microbiome-based group associatio...
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Integrative genomics offers a promising approach to more powerful genetic association studies. The hope is that combining outcome and genotype data with other types of genomic information can lead to more powerful SNP detection. We present a new association test based on a statistical model that explicitly assumes that genetic variations affect the outcome through perturbing gene expression lev...
متن کاملWeb - based Supplementary Materials for “ More powerful genetic association testing via a new statistical framework for integrative genomics ”
In Section 3 we showed that our method can have more power to detect o-eSNPs. Here we discuss its power to detect SNPs whose functional mechanisms have non-regulatory components. For simplicity we again consider only continuous Yi, Gi, and a single SNP Si in the ordinary linear model, where the variables have all been centered. We now consider the outcome model Yi = G T i αG + αSSi + i1, where ...
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ژورنال
عنوان ژورنال: Genetics
سال: 2015
ISSN: 1943-2631
DOI: 10.1534/genetics.115.175174