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

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.

SUBMITTER: Han X

PROVIDER: S-EPMC5976622 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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

Han Xinwei X Chen Siying S Flynn Elise E Wu Shuang S Wintner Dana D Shen Yufeng Y

Nature communications 20180530 1

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 haploinsufficie ...[more]

PMID: 29849042

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Project description:OBJECTIVE:Neurodevelopmental disorders presumably involve events that occur during brain development. The authors hypothesized that neuropsychiatric disorders considered to be developmental in etiology are associated with susceptibility genes that are relatively upregulated during fetal life (i.e., differentially expressed). METHOD:The authors investigated the presence of prenatal expression enrichment of susceptibility genes systematically, as composite gene sets associated with six neuropsychiatric disorders in the microarray-based "BrainCloud" dorsolateral prefrontal cortex transcriptome. RESULTS:Using a fetal/postnatal log2-fold change threshold of 0.5, genes associated with syndromic neurodevelopmental disorders (N=31 genes, p=3.37×10-3), intellectual disability (N=88 genes, p=5.53×10-3), and autism spectrum disorder (N=242 genes, p=3.45×10-4) were relatively enriched in prenatal transcript abundance, compared with the overall transcriptome. Genes associated with schizophrenia by genome-wide association studies were not preferentially fetally expressed (N=106 genes, p=0.46), nor were genes associated with schizophrenia by exome sequencing (N=212 genes, p=0.21), but specific genes within copy-number variant regions associated with schizophrenia were relatively enriched in prenatal transcript abundance, and genes associated with schizophrenia by meta-analysis were functionally enriched for some neurodevelopmental processes. In contrast, genes associated with neurodegenerative disorders were significantly underexpressed during fetal life (N=46 genes, p=1.67×10-3). CONCLUSIONS:The authors found evidence for relative prenatal enrichment of putative susceptibility genes for syndromic neurodevelopmental disorders, intellectual disability, and autism spectrum disorder. Future transcriptome-level association studies should evaluate regions other than the dorsolateral prefrontal cortex, at other time points, and incorporate further RNA sequencing analyses.

Dataset Information

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

Publications

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

Similar Datasets

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