Distinct epigenomic patterns are associated with haploinsufficiency and predict risk genes of developmental disorders

Nature Communications
Xinwei HanYufeng Shen

Abstract

Haploinsufficiency is a major mechanism of genetic risk in developmental disorders. Accurate prediction of haploinsufficient genes is essential for prioritizing and interpreting deleterious variants in genetic studies. Current methods based on mutation intolerance in population data suffer from inadequate power for genes with short transcripts. Here we show haploinsufficiency is strongly associated with epigenomic patterns, and develop a computational method (Episcore) to predict haploinsufficiency leveraging epigenomic data from a broad range of tissue and cell types by machine learning methods. Based on data from recent exome sequencing studies on developmental disorders, Episcore achieves better performance in prioritizing likely-gene-disrupting (LGD) de novo variants than current methods. We further show that Episcore is less-biased by gene size, and complementary to mutation intolerance metrics for prioritizing LGD variants. Our approach enables new applications of epigenomic data and facilitates discovery and interpretation of novel risk variants implicated in developmental disorders.

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Citations

Apr 27, 2019·Autism Research : Official Journal of the International Society for Autism Research·Emily L CasanovaManuel F Casanova
Jan 18, 2019·PLoS Genetics·Matthew Jensen, Santhosh Girirajan
Jul 10, 2020·European Journal of Human Genetics : EJHG·Jiayao WangWendy K Chung
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Nov 5, 2021·Frontiers in Genetics·Shasha HuangYongyi Yuan
Nov 22, 2020·Seminars in Cancer Biology·Parvez KhanMohd Wasim Nasser

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Methods Mentioned

BETA
exome sequencing
ChIP-seq
RNA-Seq
Hi-C.

Software Mentioned

Epitensor
Random
R
GREAT
ClinGen
R package “ glmnet ”
pLI
Episcore
Ensembl
Ensembl BioMart

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