mutation

An Expanded View of Complex Traits: From Polygenic to Omnigenic

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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.

Mutation effects predicted from sequence co-variation

This entry was posted in mutation Uncategorized on by

Our next meeting will be at 3:00 on February 24th, in room 4160 of the Discovery building. Our Selected paper is Mutation effects predicted from sequence co-variation.
The abstract is as follows.

Many high-throughput experimental technologies have been developed to assess the effects of large numbers of mutations (variation) on phenotypes. However, designing functional assays for these methods is challenging, and systematic testing of all combinations is impossible, so robust methods to predict the effects of genetic variation are needed. Most prediction methods exploit evolutionary sequence conservation but do not consider the interdependencies of residues or bases. We present EVmutation, an unsupervised statistical method for predicting the effects of mutations that explicitly captures residue dependencies between positions. We validate EVmutation by comparing its predictions with outcomes of high-throughput mutagenesis experiments and measurements of human disease mutations and show that it outperforms methods that do not account for epistasis. EVmutation can be used to assess the quantitative effects of mutations in genes of any organism. We provide pre-computed predictions for ~7,000 human proteins at http://evmutation.org/.

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