A statistical framework for mapping risk genes from de novo mutations in whole-genome-sequencing studies

dc.citation.epage1047en_US
dc.citation.issueNumber6en_US
dc.citation.spage1031en_US
dc.citation.volumeNumber102en_US
dc.contributor.authorLiu, Y.en_US
dc.contributor.authorLiang, Y.en_US
dc.contributor.authorÇiçek, A. Ercümenten_US
dc.contributor.authorLi, Z.en_US
dc.contributor.authorLi, J.en_US
dc.contributor.authorMuhle, R. A.en_US
dc.contributor.authorKrenzer, M.en_US
dc.contributor.authorMei, Y.en_US
dc.contributor.authorWang Y.en_US
dc.contributor.authorKnoblauch, N.en_US
dc.contributor.authorMorrison, J.en_US
dc.contributor.authorZhao, S.en_US
dc.contributor.authorJiang, Y.en_US
dc.contributor.authorGeller, E.en_US
dc.contributor.authorIonita-Laza, I.en_US
dc.contributor.authorWu, J.en_US
dc.contributor.authorXia, K.en_US
dc.contributor.authorNoonan, J. P.en_US
dc.contributor.authorSun, Z. S.en_US
dc.contributor.authorHe, X.en_US
dc.contributor.bilkentauthorÇiçek, A. Ercüment
dc.date.accessioned2019-02-21T16:01:22Z
dc.date.available2019-02-21T16:01:22Z
dc.date.issued2018en_US
dc.departmentDepartment of Computer Engineeringen_US
dc.description.abstractAnalysis of de novo mutations (DNMs) from sequencing data of nuclear families has identified risk genes for many complex diseases, including multiple neurodevelopmental and psychiatric disorders. Most of these efforts have focused on mutations in protein-coding sequences. Evidence from genome-wide association studies (GWASs) strongly suggests that variants important to human diseases often lie in non-coding regions. Extending DNM-based approaches to non-coding sequences is challenging, however, because the functional significance of non-coding mutations is difficult to predict. We propose a statistical framework for analyzing DNMs from whole-genome sequencing (WGS) data. This method, TADA-Annotations (TADA-A), is a major advance of the TADA method we developed earlier for DNM analysis in coding regions. TADA-A is able to incorporate many functional annotations such as conservation and enhancer marks, to learn from data which annotations are informative of pathogenic mutations, and to combine both coding and non-coding mutations at the gene level to detect risk genes. It also supports meta-analysis of multiple DNM studies, while adjusting for study-specific technical effects. We applied TADA-A to WGS data of ∼300 autism-affected family trios across five studies and discovered several autism risk genes. The software is freely available for all research uses.
dc.description.sponsorshipThis work was supported by National Institutes of Health grant ( 1R01MH110531 ) and Simons Foundation award (SFARI Award ID 385027 ) to X.H.
dc.identifier.doi10.1016/j.ajhg.2018.03.023
dc.identifier.eissn1537-6605en_US
dc.identifier.issn0002-9297
dc.identifier.urihttp://hdl.handle.net/11693/49831
dc.language.isoEnglish
dc.publisherCell Press
dc.relation.isversionofhttps://doi.org/10.1016/j.ajhg.2018.03.023
dc.relation.projectNational Institutes of Health, NIH: 1R01MH110531 - Simons Foundation: ID 385027
dc.rightsinfo:eu-repo/semantics/openAccess
dc.source.titleAmerican Journal of Human Geneticsen_US
dc.subjectAutismen_US
dc.subjectDe novo mutationsen_US
dc.subjectEpigenomicsen_US
dc.subjectNoncoding sequencesen_US
dc.subjectPsychiatric disordersen_US
dc.subjectStatistical modelen_US
dc.subjectNoncoding sequencesen_US
dc.subjectEpigenomicsen_US
dc.titleA statistical framework for mapping risk genes from de novo mutations in whole-genome-sequencing studiesen_US
dc.typeArticleen_US
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