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Inferring function from constrained coding regions in the human genome

Banff International Research Station for Mathematical Innovation and Discovery

An established approach to revealing essential genes and critical protein domains is measuring the degree of genomic conservation between species. More recently, statistical models have been developed to estimate functional “constraint” on each human gene by assessing the extent and frequency of genetic variation among thousands of human exomes. While gene-wide predictions of constraint are valuable, a single measure does not capture the often-extreme variability in constraint within a gene. Clearly, constraint can vary dramatically depending on the specific function and structural properties of the resulting protein structure. We have developed a statistical model to identify significantly constrained coding regions (CCRs) by leveraging the genetic variation observed among 60,706 exomes from the Exome Aggregation Consortium. Constrained coding regions identified by our model are significantly enriched for pathogenic mutations in Mendelian disorders and developmental delay, demonstrating its power to capture true biological constraint. I will present our efforts to use regions with similar degrees of constraint to infer function in poorly understood genes. Furthermore, I will illustrate how CCRs can be used to identify critical protein domains not previously identified from phylogenetic conservation. Lastly, I will discuss ongoing efforts to leverage CCRs in the clinical interpretation of variants in Long QT syndrome genes.

Mathematics; Statistics; Biology and other natural sciences

video/mp4

Author affiliation: University of Utah

2017-09-24

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/63097

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/4.0/