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The Metabochip, a Custom Genotyping Array for Genetic Studies of Metabolic, Cardiovascular, and Anthropometric Traits
PMCID: PMC3410907This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Bayesian Model Comparison in Genetic Association Analysis: Linear Mixed Modeling and SNP Set Testing
We consider the problems of hypothesis testing and model comparison under a
flexible Bayesian linear regression model whose formulation is closely
connected with the linear mixed effect model and the parametric models for SNP
set analysis in genetic association studies. We derive a class of analytic
approximate Bayes factors and illustrate their connections with a variety of
frequentist test statistics, including the Wald statistic and the variance
component score statistic. Taking advantage of Bayesian model averaging and
hierarchical modeling, we demonstrate some distinct advantages and
flexibilities in the approaches utilizing the derived Bayes factors in the
context of genetic association studies. We demonstrate our proposed methods
using real or simulated numerical examples in applications of single SNP
association testing, multi-locus fine-mapping and SNP set association testing
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Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes.
We aggregated coding variant data for 81,412 type 2 diabetes cases and 370,832 controls of diverse ancestry, identifying 40 coding variant association signals (P < 2.2 × 10-7); of these, 16 map outside known risk-associated loci. We make two important observations. First, only five of these signals are driven by low-frequency variants: even for these, effect sizes are modest (odds ratio ≤1.29). Second, when we used large-scale genome-wide association data to fine-map the associated variants in their regional context, accounting for the global enrichment of complex trait associations in coding sequence, compelling evidence for coding variant causality was obtained for only 16 signals. At 13 others, the associated coding variants clearly represent 'false leads' with potential to generate erroneous mechanistic inference. Coding variant associations offer a direct route to biological insight for complex diseases and identification of validated therapeutic targets; however, appropriate mechanistic inference requires careful specification of their causal contribution to disease predisposition
Modeling and Testing for Joint Association Using a Genetic Random Field Model
Substantial progress has been made in identifying single genetic variants
predisposing to common complex diseases. Nonetheless, the genetic etiology of
human diseases remains largely unknown. Human complex diseases are likely
influenced by the joint effect of a large number of genetic variants instead of
a single variant. The joint analysis of multiple genetic variants considering
linkage disequilibrium (LD) and potential interactions can further enhance the
discovery process, leading to the identification of new disease-susceptibility
genetic variants. Motivated by the recent development in spatial statistics, we
propose a new statistical model based on the random field theory, referred to
as a genetic random field model (GenRF), for joint association analysis with
the consideration of possible gene-gene interactions and LD. Using a
pseudo-likelihood approach, a GenRF test for the joint association of multiple
genetic variants is developed, which has the following advantages: 1.
considering complex interactions for improved performance; 2. natural dimension
reduction; 3. boosting power in the presence of LD; 4. computationally
efficient. Simulation studies are conducted under various scenarios. Compared
with a commonly adopted kernel machine approach, SKAT, GenRF shows overall
comparable performance and better performance in the presence of complex
interactions. The method is further illustrated by an application to the Dallas
Heart Study.Comment: 17 pages, 4 tables, the paper has been published on Biometric
A Latent Variable Approach to Multivariate Quantitative Trait Loci
A novel approach based on latent variable modelling is presented for the analysis of multivariate quantitative and qualitative trait loci. The approach is general in the sense that it enables the joint analysis of many kinds of quantitative and qualitative traits (including count data and censored traits) in a single modelling framework. In the framework, the observations are modelled as functions of latent variables, which are then affected by quantitative trait loci. Separating the analysis in this way means that measurement errors in the phenotypic observations can be included easily in the model, providing robust inferences. The performance of the method is illustrated using two real multivariate datasets, from barley and Scots pine
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