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Integrated Model of De Novo and Inherited Genetic Variants Yields Greater Power to Identify Risk Genes

De novo mutations affect risk for many diseases and disorders, especially those with early-onset. An example is autism spectrum disorders (ASD). Four recent whole-exome sequencing (WES) studies of ASD families revealed a handful of novel risk genes, based on independent de novo loss-of-function (LoF...

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Autores principales: He, Xin, Sanders, Stephan J., Liu, Li, De Rubeis, Silvia, Lim, Elaine T., Sutcliffe, James S., Schellenberg, Gerard D., Gibbs, Richard A., Daly, Mark J., Buxbaum, Joseph D., State, Matthew W., Devlin, Bernie, Roeder, Kathryn
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3744441/
https://www.ncbi.nlm.nih.gov/pubmed/23966865
http://dx.doi.org/10.1371/journal.pgen.1003671
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author He, Xin
Sanders, Stephan J.
Liu, Li
De Rubeis, Silvia
Lim, Elaine T.
Sutcliffe, James S.
Schellenberg, Gerard D.
Gibbs, Richard A.
Daly, Mark J.
Buxbaum, Joseph D.
State, Matthew W.
Devlin, Bernie
Roeder, Kathryn
author_facet He, Xin
Sanders, Stephan J.
Liu, Li
De Rubeis, Silvia
Lim, Elaine T.
Sutcliffe, James S.
Schellenberg, Gerard D.
Gibbs, Richard A.
Daly, Mark J.
Buxbaum, Joseph D.
State, Matthew W.
Devlin, Bernie
Roeder, Kathryn
author_sort He, Xin
collection PubMed
description De novo mutations affect risk for many diseases and disorders, especially those with early-onset. An example is autism spectrum disorders (ASD). Four recent whole-exome sequencing (WES) studies of ASD families revealed a handful of novel risk genes, based on independent de novo loss-of-function (LoF) mutations falling in the same gene, and found that de novo LoF mutations occurred at a twofold higher rate than expected by chance. However successful these studies were, they used only a small fraction of the data, excluding other types of de novo mutations and inherited rare variants. Moreover, such analyses cannot readily incorporate data from case-control studies. An important research challenge in gene discovery, therefore, is to develop statistical methods that accommodate a broader class of rare variation. We develop methods that can incorporate WES data regarding de novo mutations, inherited variants present, and variants identified within cases and controls. TADA, for Transmission And De novo Association, integrates these data by a gene-based likelihood model involving parameters for allele frequencies and gene-specific penetrances. Inference is based on a Hierarchical Bayes strategy that borrows information across all genes to infer parameters that would be difficult to estimate for individual genes. In addition to theoretical development we validated TADA using realistic simulations mimicking rare, large-effect mutations affecting risk for ASD and show it has dramatically better power than other common methods of analysis. Thus TADA's integration of various kinds of WES data can be a highly effective means of identifying novel risk genes. Indeed, application of TADA to WES data from subjects with ASD and their families, as well as from a study of ASD subjects and controls, revealed several novel and promising ASD candidate genes with strong statistical support.
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spelling pubmed-37444412013-08-21 Integrated Model of De Novo and Inherited Genetic Variants Yields Greater Power to Identify Risk Genes He, Xin Sanders, Stephan J. Liu, Li De Rubeis, Silvia Lim, Elaine T. Sutcliffe, James S. Schellenberg, Gerard D. Gibbs, Richard A. Daly, Mark J. Buxbaum, Joseph D. State, Matthew W. Devlin, Bernie Roeder, Kathryn PLoS Genet Research Article De novo mutations affect risk for many diseases and disorders, especially those with early-onset. An example is autism spectrum disorders (ASD). Four recent whole-exome sequencing (WES) studies of ASD families revealed a handful of novel risk genes, based on independent de novo loss-of-function (LoF) mutations falling in the same gene, and found that de novo LoF mutations occurred at a twofold higher rate than expected by chance. However successful these studies were, they used only a small fraction of the data, excluding other types of de novo mutations and inherited rare variants. Moreover, such analyses cannot readily incorporate data from case-control studies. An important research challenge in gene discovery, therefore, is to develop statistical methods that accommodate a broader class of rare variation. We develop methods that can incorporate WES data regarding de novo mutations, inherited variants present, and variants identified within cases and controls. TADA, for Transmission And De novo Association, integrates these data by a gene-based likelihood model involving parameters for allele frequencies and gene-specific penetrances. Inference is based on a Hierarchical Bayes strategy that borrows information across all genes to infer parameters that would be difficult to estimate for individual genes. In addition to theoretical development we validated TADA using realistic simulations mimicking rare, large-effect mutations affecting risk for ASD and show it has dramatically better power than other common methods of analysis. Thus TADA's integration of various kinds of WES data can be a highly effective means of identifying novel risk genes. Indeed, application of TADA to WES data from subjects with ASD and their families, as well as from a study of ASD subjects and controls, revealed several novel and promising ASD candidate genes with strong statistical support. Public Library of Science 2013-08-15 /pmc/articles/PMC3744441/ /pubmed/23966865 http://dx.doi.org/10.1371/journal.pgen.1003671 Text en © 2013 He et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
He, Xin
Sanders, Stephan J.
Liu, Li
De Rubeis, Silvia
Lim, Elaine T.
Sutcliffe, James S.
Schellenberg, Gerard D.
Gibbs, Richard A.
Daly, Mark J.
Buxbaum, Joseph D.
State, Matthew W.
Devlin, Bernie
Roeder, Kathryn
Integrated Model of De Novo and Inherited Genetic Variants Yields Greater Power to Identify Risk Genes
title Integrated Model of De Novo and Inherited Genetic Variants Yields Greater Power to Identify Risk Genes
title_full Integrated Model of De Novo and Inherited Genetic Variants Yields Greater Power to Identify Risk Genes
title_fullStr Integrated Model of De Novo and Inherited Genetic Variants Yields Greater Power to Identify Risk Genes
title_full_unstemmed Integrated Model of De Novo and Inherited Genetic Variants Yields Greater Power to Identify Risk Genes
title_short Integrated Model of De Novo and Inherited Genetic Variants Yields Greater Power to Identify Risk Genes
title_sort integrated model of de novo and inherited genetic variants yields greater power to identify risk genes
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3744441/
https://www.ncbi.nlm.nih.gov/pubmed/23966865
http://dx.doi.org/10.1371/journal.pgen.1003671
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