GWAS


An Expanded View of Complex Traits: From Polygenic to Omnigenic

Our next meeting will be at 2:30 on September 1st, in room 4160 of the Discovery building. Our Selected paper is An Expanded View of Complex Traits: From Polygenic to Omnigenic.
The abstract is as follows.

A central goal of genetics is to understand the links between genetic variation and disease. Intuitively, one might expect disease-causing variants to cluster into key pathways that drive disease etiology. But for complex traits, association signals tend to be spread across most of the genome—including near many genes without an obvious connection to disease. We propose that gene regulatory networks are sufficiently interconnected such that all genes expressed in disease-relevant cells are liable to affect the functions of core disease-related genes and that most heritability can be explained by effects on genes outside core pathways. We refer to this hypothesis as an “omnigenic” model.

We welcome all who can join us for this discussion. Feel free to begin that discussion in the comments section below.


Tensor decomposition for multiple-tissue gene expression experiments

Our next meeting will be at 12:30 on August 15th, in room 3160 of the Discovery building. Our Selected paper is Tensor decomposition for multiple-tissue gene expression experiments.
The abstract is as follows.

Genome-wide association studies of gene expression traits and other cellular phenotypes have successfully identified links between genetic variation and biological processes. The majority of discoveries have uncovered cis–expression quantitative trait locus (eQTL) effects via mass univariate testing of SNPs against gene expression in single tissues. Here we present a Bayesian method for multiple-tissue experiments focusing on uncovering gene networks linked to genetic variation. Our method decomposes the 3D array (or tensor) of gene expression measurements into a set of latent components. We identify sparse gene networks that can then be tested for association against genetic variation across the genome. We apply our method to a data set of 845 individuals from the TwinsUK cohort with gene expression measured via RNA-seq analysis in adipose, lymphoblastoid cell lines (LCLs) and skin. We uncover several gene networks with a genetic basis and clear biological and statistical significance. Extensions of this approach will allow integration of different omics, environmental and phenotypic data sets.

We welcome all who can join us for this discussion. Feel free to begin that discussion in the comments section below.