A framework for the interpretation of de novo mutation in human disease
Spontaneously arising (‘de novo’) mutations play an important role in medical genetics. For diseases with extensive locus heterogeneity – such as autism spectrum disorders (ASDs) – the signal from de novo mutations (DNMs) is distributed across many genes, making it difficult to distinguish disease-r...
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| Format: | Online |
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2014
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4222185/ |
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pubmed-42221852015-03-01 A framework for the interpretation of de novo mutation in human disease Samocha, Kaitlin E. Robinson, Elise B. Sanders, Stephan J. Stevens, Christine Sabo, Aniko McGrath, Lauren M. Kosmicki, Jack A. Rehnström, Karola Mallick, Swapan Kirby, Andrew Wall, Dennis P. MacArthur, Daniel G. Gabriel, Stacey B. dePristo, Mark Purcell, Shaun M. Palotie, Aarno Boerwinkle, Eric Buxbaum, Joseph D. Cook, Edwin H. Gibbs, Richard A. Schellenberg, Gerard D. Sutcliffe, James S. Devlin, Bernie Roeder, Kathryn Neale, Benjamin M. Daly, Mark J. Article Spontaneously arising (‘de novo’) mutations play an important role in medical genetics. For diseases with extensive locus heterogeneity – such as autism spectrum disorders (ASDs) – the signal from de novo mutations (DNMs) is distributed across many genes, making it difficult to distinguish disease-relevant mutations from background variation. We provide a statistical framework for the analysis of DNM excesses per gene and gene set by calibrating a model of de novo mutation. We applied this framework to DNMs collected from 1,078 ASD trios and – while affirming a significant role for loss-of-function (LoF) mutations – found no excess of de novo LoF mutations in cases with IQ above 100, suggesting that the role of DNMs in ASD may reside in fundamental neurodevelopmental processes. We also used our model to identify ~1,000 genes that are significantly lacking functional coding variation in non-ASD samples and are enriched for de novo LoF mutations identified in ASD cases. 2014-08-03 2014-09 /pmc/articles/PMC4222185/ /pubmed/25086666 http://dx.doi.org/10.1038/ng.3050 Text en http://www.nature.com/authors/editorial_policies/license.html#terms Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:http://www.nature.com/authors/editorial_policies/license.html#terms |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Samocha, Kaitlin E. Robinson, Elise B. Sanders, Stephan J. Stevens, Christine Sabo, Aniko McGrath, Lauren M. Kosmicki, Jack A. Rehnström, Karola Mallick, Swapan Kirby, Andrew Wall, Dennis P. MacArthur, Daniel G. Gabriel, Stacey B. dePristo, Mark Purcell, Shaun M. Palotie, Aarno Boerwinkle, Eric Buxbaum, Joseph D. Cook, Edwin H. Gibbs, Richard A. Schellenberg, Gerard D. Sutcliffe, James S. Devlin, Bernie Roeder, Kathryn Neale, Benjamin M. Daly, Mark J. |
| spellingShingle |
Samocha, Kaitlin E. Robinson, Elise B. Sanders, Stephan J. Stevens, Christine Sabo, Aniko McGrath, Lauren M. Kosmicki, Jack A. Rehnström, Karola Mallick, Swapan Kirby, Andrew Wall, Dennis P. MacArthur, Daniel G. Gabriel, Stacey B. dePristo, Mark Purcell, Shaun M. Palotie, Aarno Boerwinkle, Eric Buxbaum, Joseph D. Cook, Edwin H. Gibbs, Richard A. Schellenberg, Gerard D. Sutcliffe, James S. Devlin, Bernie Roeder, Kathryn Neale, Benjamin M. Daly, Mark J. A framework for the interpretation of de novo mutation in human disease |
| author_facet |
Samocha, Kaitlin E. Robinson, Elise B. Sanders, Stephan J. Stevens, Christine Sabo, Aniko McGrath, Lauren M. Kosmicki, Jack A. Rehnström, Karola Mallick, Swapan Kirby, Andrew Wall, Dennis P. MacArthur, Daniel G. Gabriel, Stacey B. dePristo, Mark Purcell, Shaun M. Palotie, Aarno Boerwinkle, Eric Buxbaum, Joseph D. Cook, Edwin H. Gibbs, Richard A. Schellenberg, Gerard D. Sutcliffe, James S. Devlin, Bernie Roeder, Kathryn Neale, Benjamin M. Daly, Mark J. |
| author_sort |
Samocha, Kaitlin E. |
| title |
A framework for the interpretation of de novo mutation in human disease |
| title_short |
A framework for the interpretation of de novo mutation in human disease |
| title_full |
A framework for the interpretation of de novo mutation in human disease |
| title_fullStr |
A framework for the interpretation of de novo mutation in human disease |
| title_full_unstemmed |
A framework for the interpretation of de novo mutation in human disease |
| title_sort |
framework for the interpretation of de novo mutation in human disease |
| description |
Spontaneously arising (‘de novo’) mutations play an important role in medical genetics. For diseases with extensive locus heterogeneity – such as autism spectrum disorders (ASDs) – the signal from de novo mutations (DNMs) is distributed across many genes, making it difficult to distinguish disease-relevant mutations from background variation. We provide a statistical framework for the analysis of DNM excesses per gene and gene set by calibrating a model of de novo mutation. We applied this framework to DNMs collected from 1,078 ASD trios and – while affirming a significant role for loss-of-function (LoF) mutations – found no excess of de novo LoF mutations in cases with IQ above 100, suggesting that the role of DNMs in ASD may reside in fundamental neurodevelopmental processes. We also used our model to identify ~1,000 genes that are significantly lacking functional coding variation in non-ASD samples and are enriched for de novo LoF mutations identified in ASD cases. |
| publishDate |
2014 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4222185/ |
| _version_ |
1613152948473298944 |