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and human gene essentiality estimations capture contrasting functional constraints. | LitMetric

and human gene essentiality estimations capture contrasting functional constraints.

NAR Genom Bioinform

MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA 02139, USA.

Published: September 2021

AI Article Synopsis

Article Abstract

Gene essentiality estimation is a popular empirical approach to link genotypes to phenotypes. In humans, essentiality is estimated based on loss-of-function (LoF) mutation intolerance, either from population exome sequencing () data or CRISPR-based perturbation experiments. Both approaches identify genes presumed to have detrimental consequences on the organism upon mutation. Are these genes constrained by having key cellular/organismal roles? Do and estimations equally recover these constraints? Insights into these questions have important implications in generalizing observations from cell models and interpreting disease risk genes. To empirically address these questions, we integrate genome-scale datasets and compare structural, functional and evolutionary features of essential genes versus genes with extremely high mutational tolerance. We found that essentiality estimates do recover functional constraints. However, the organismal or cellular context of estimation leads to functionally contrasting properties underlying the constraint. Our results suggest that depletion of LoF mutations in human populations effectively captures organismal-level functional constraints not experimentally accessible through CRISPR-based screens. Finally, we identify a set of genes (), which are mutationally intolerant but highly tolerant . These genes drive observed functional constraint differences and have an unexpected preference for nervous system expression.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8276763PMC
http://dx.doi.org/10.1093/nargab/lqab063DOI Listing

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