Unified inference of missense variant effects and gene constraints in the human genome

Huang, Yifei

doi:10.1101/757468

Cited by 2 publications

(4 citation statements)

References 91 publications

(114 reference statements)

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“…In this work, I have introduced the MK regression, the first evolutionary model for jointly estimating the effects of multiple, potentially correlated genomic features on the rate of adaptive substitutions. Based on similar ideas, my colleagues and I have previously developed statistical approaches to infer negative selection on genetic variants (Huang et al ., 2017; Huang and Siepel, 2019; Huang, 2020) and the evolutionary turnover of cis-regulatory elements (Dukler et al ., 2020). Thus, unifying generalized linear models and evolutionary models may be a powerful strategy to address a variety of statistical problems in evolutionary biology.…”

Section: Discussionmentioning

confidence: 99%

“…Third, the MK regression currently can only accommodate the fixed effects of genomic features. It is tempting to extend the MK regression by introducing a gene-level random effect (Huang, 2020), which may allow for estimating the rate of adaptation at the gene level. Fourth, the MK regression currently can only estimate the effects of genomic features on ω a .…”

Section: Discussionmentioning

confidence: 99%

“…Also, I utilized interspecies divergence in the chimpanzee lineage to construct a map of local mutation rates in the panTro4 assembly. To do so, I converted putatively neutral regions defined in Huang (2020) from hg19 to panTro4 using liftOver. Then, I computed the density of chimpanzee-specific substitutions in putatively neutral regions using a 100 Kb non-overlapping sliding window, which was used as a proxy of local mutation rates.…”

Section: Methodsmentioning

confidence: 99%

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Dissecting genomic determinants of positive selection with an evolution-guided regression model

Huang

2020

Preprint

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In evolutionary genomics, it is fundamentally important to understand how characteristics of genomic sequences, such as the expression level of a gene, determine the rate of adaptive evolution. While numerous statistical methods, such as the McDonald-Kreitman test, are available to examine the association between genomic features and positive selection, we currently lack a statistical approach to disentangle the direct effects of genomic features from the indirect effects mediated by confounding factors. To address this problem, we present a novel statistical model, the MK regression, which augments the McDonald-Kreitman test with a generalized linear model. Analogous to the classic multiple regression model, the MK regression can analyze multiple genomic features simultaneously to distinguish between direct and indirect effects on positive selection. Using the MK regression, we identify numerous genomic features responsible for positive selection in chimpanzees, including local mutation rate, residue exposure level, gene expression level, tissue specificity, and metabolic genes. In particular, we show that highly expressed genes have a higher rate of adaptation than their weakly expressed counterparts, even though a higher expression level may impose stronger negative selection on protein sequences. Also, we observe that metabolic genes tend to have a higher rate of adaptation than their non-metabolic counterparts, possibly due to recent changes in diet in primate evolution. Overall, the MK regression is a powerful approach to elucidate the genomic basis of adaptation.Significance StatementThe McDonald-Kreitman test is a powerful method to detect genomic features associated with positive selection, but it cannot disentangle the direct effects of genomic features from the indirect effects mediated by confounding factors. Here we introduce the MK regression that augments the McDonald-Kreitman test with a generalized linear model. Analogous to the classic multiple regression model, the MK regression enables quantification of the direct effects of genomic features by controlling for potential confounders. It is particularly useful to analyze data with multiple correlated features and species with low levels of polymorphism. Using the MK regression, we identify numerous genomic features responsible for positive selection in chimpanzees, which provides novel insights into the adaptive evolution of our closest relatives.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Dissecting genomic determinants of positive selection with an evolution-guided regression model

Huang

2020

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“…In clinical genetic testing, many of missense variants in well-established risk genes are classified as variants of uncertain significance, unless they are highly recurrent in patients. Previously published in silico prediction methods have facilitated the interpretation of missense variants, such as CADD 8 , VEST3 9 , MetaSVM 10 , M-CAP 11 , REVEL 12 , PrimateAI 13 , and UNEE-CON 14 . However, based on recent de novo mutation data, they all have limited performance with low positive predictive value (Supplementary Data 1), especially in non-constrained genes (defined as ExAC 15 pLI < 0.5).…”

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confidence: 99%

MVP predicts the pathogenicity of missense variants by deep learning

et al. 2021

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Accurate pathogenicity prediction of missense variants is critically important in genetic studies and clinical diagnosis. Previously published prediction methods have facilitated the interpretation of missense variants but have limited performance. Here, we describe MVP (Missense Variant Pathogenicity prediction), a new prediction method that uses deep residual network to leverage large training data sets and many correlated predictors. We train the model separately in genes that are intolerant of loss of function variants and the ones that are tolerant in order to take account of potentially different genetic effect size and mode of action. We compile cancer mutation hotspots and de novo variants from developmental disorders for benchmarking. Overall, MVP achieves better performance in prioritizing pathogenic missense variants than previous methods, especially in genes tolerant of loss of function variants. Finally, using MVP, we estimate that de novo coding variants contribute to 7.8% of isolated congenital heart disease, nearly doubling previous estimates.

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Unified inference of missense variant effects and gene constraints in the human genome

Cited by 2 publications

References 91 publications

Dissecting genomic determinants of positive selection with an evolution-guided regression model

Dissecting genomic determinants of positive selection with an evolution-guided regression model

MVP predicts the pathogenicity of missense variants by deep learning

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