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Promoter CpG Density Predicts Downstream Gene Loss-of-Function Intolerance.

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Authors
Boukas, Leandros
Bjornsson, Hans T
Hansen, Kasper D
Issue Date
2020-08-14

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Boukas L, Bjornsson HT, Hansen KD. Promoter CpG Density Predicts Downstream Gene Loss-of-Function Intolerance. Am J Hum Genet. 2020 Sep 3;107(3):487-498. doi: 10.1016/j.ajhg.2020.07.014. Epub 2020 Aug 14. PMID: 32800095; PMCID: PMC7477270.
Abstract
The aggregation and joint analysis of large numbers of exome sequences has recently made it possible to derive estimates of intolerance to loss-of-function (LoF) variation for human genes. Here, we demonstrate strong and widespread coupling between genic LoF intolerance and promoter CpG density across the human genome. Genes downstream of the most CpG-rich promoters (top 10% CpG density) have a 67.2% probability of being highly LoF intolerant, using the LOEUF metric from gnomAD. This is in contrast to 7.4% of genes downstream of the most CpG-poor (bottom 10% CpG density) promoters. Combining promoter CpG density with exonic and promoter conservation explains 33.4% of the variation in LOEUF, and the contribution of CpG density exceeds the individual contributions of exonic and promoter conservation. We leverage this to train a simple and easily interpretable predictive model that outperforms other existing predictors and allows us to classify 1,760 genes-which are currently unascertained in gnomAD-as highly LoF intolerant or not. These predictions have the potential to aid in the interpretation of novel variants in the clinical setting. Moreover, our results reveal that high CpG density is not merely a generic feature of human promoters but is preferentially encountered at the promoters of the most selectively constrained genes, calling into question the prevailing view that CpG islands are not subject to selection. Keywords: CpG density; CpG islands; GC content; dosage sensitivity; epigenetics; gnomAD; haploinsufficiency; loss-of-function; promoters; selection.
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https://www.sciencedirect.com/science/article/pii/S0002929720302445?via%3Dihub
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Copyright © 2020. Published by Elsevier Inc.
ae974a485f413a2113503eed53cd6c53
10.1016/j.ajhg.2020.07.014
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