Analysis of Rare, Exonic Variation amongst Subjects with Autism Spectrum Disorders and Population Controls
Open accessAbstract
We report on results from whole-exome sequencing (WES) of 1,039 subjects diagnosed with autism spectrum disorders (ASD) and 870 controls selected from the NIMH repository to be of similar ancestry to cases. The WES data came from two centers using different methods to produce sequence and to call variants from it. Therefore, an initial goal was to ensure the distribution of rare variation was similar for data from different centers. This proved straightforward by filtering called variants by fraction of missing data, read depth, and balance of alternative to reference reads. Results were evaluated using seven samples sequenced at both centers and by results from the association study. Next we addressed how the data and/or results from the centers should be combined. Gene-based analyses of association was an obvious choice, but should statistics for association be combined across centers (meta-analysis) or should data be combined and then analyzed (mega-analysis)? Because of the nature of many gene-based tests, we showed by theory and simulations that mega-analysis has better power than meta-analysis. Finally, before analyzing the data for association, we explored the impact of population structure on rare variant analysis in these data. Like other recent studies, we found evidence that population structure can confound case-control studies by the clustering of rare variants in ancestry space; yet, unlike some recent studies, for these data we found that principal component-based analyses were sufficient to control for ancestry and produce test statistics with appropriate distributions. After using a variety of gene-based tests and both meta- and mega-analysis, we found no new risk genes for ASD in this sample. Our results suggest that standard gene-based tests will require much larger samples of cases and controls before being effective for gene discovery, even for a disorder like ASD.
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The overall inflation of results due to population stratification is negligible.
“The overall inflation of the results due to population stratification is negligible: a modified [7] genomic control factor [10] (see Text S1).”
From Integrated Model of De Novo and Inherited Genetic Variants Yields Greater Power to Identify Risk Genes (2013) · cites this paper as [7]
The AASC sample is of European ancestry, but a portion of it, largely from Portugal, carries some sub-Saharan alleles.
“While the AASC sample is of European ancestry, a portion of it, largely from Portugal, carries some sub-Saharan alleles [7].”
From Integrated Model of De Novo and Inherited Genetic Variants Yields Greater Power to Identify Risk Genes (2013) · cites this paper as [7]
For a sample of nearly 1000 ASD cases and well matched controls, the ARRA ASD sequencing consortium (AASC) found no significant associations, except for variation acting recessively.
“For a sample of nearly 1000 ASD case and well matched controls the ARRA ASD sequencing consortium (AASC) found no significant associations [7], except for variation acting recessively [6].”
From Integrated Model of De Novo and Inherited Genetic Variants Yields Greater Power to Identify Risk Genes (2013) · cites this paper as [7]
The case-control data have been carefully harmonized to minimize batch effects by using stringent quality control filters.
“the case-control data have been carefully harmonized to minimize batch effects by using stringent quality control filters [7]”
From Integrated Model of De Novo and Inherited Genetic Variants Yields Greater Power to Identify Risk Genes (2013) · cites this paper as [7]
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- Authors
- Liu, L., Sabo, A., Neale, B. M., Nagaswamy, U., Stevens, C., Lim, E., Bodea, C. A., Muzny, D., Reid, J. G., Banks, E., Coon, H., DePristo, M., Dinh, H., Fennel, T., Flannick, J., Gabriel, S., Garimella, K., Gross, S., Hawes, A., Lewis, L., Makarov, V., Maguire, J., Newsham, I., Poplin, R., Ripke, S., Shakir, K., Samocha, K. E., Wu, Y., Boerwinkle, E., Buxbaum, J. D., Cook, E. H., Devlin, B., Schellenberg, G. D., Sutcliffe, J. S., Daly, M. J., Gibbs, R. A., & Roeder, K.
- Affiliations
- Carnegie Mellon University (US)
- Baylor College of Medicine (US)
- Broad Institute (US)
- Icahn School of Medicine at Mount Sinai (US)
- The University of Texas MD Anderson Cancer Center (US)
- University of Illinois Chicago (US)
- University of Pittsburgh (US)
- University of Pennsylvania (US)
- Vanderbilt University (US)
- Corresponding author
- Roeder, Kathryn
- ORCID
- Liu, Li
- Sabo, Aniko
- Neale, Benjamin M.
- Stevens, Christine
- Lim, Elaine
- Muzny, Donna
- Reid, Jeffrey G.
- Banks, Eric
- Dinh, Huyen
- Flannick, Jason
- Gabriel, Stacey
- Garimella, Kiran
- Makarov, Vladimir
- Newsham, Irene
- Ripke, Stephan
- Samocha, Kaitlin E.
- Wu, Yuanqing
- Boerwinkle, Eric
- Buxbaum, Joseph D.
- Cook, Edwin H.
- Devlin, Bernie
- Schellenberg, Gerard D.
- Sutcliffe, James S.
- Daly, Mark J.
- Gibbs, Richard A.
- Roeder, Kathryn
- Venue
- PLoS Genetics
- Publisher
- Public Library of Science (PLoS)
- Published
- 2013-04-11
- Volume
- 9
- Issue
- 4
- Pages
- e1003443
- ISSN
- 1553-7390 (ISSN-L)
- 1553-7404
- PMC
- PMC3623759
- OpenAlex
- W2111528486
- Cited by
- 135
- References
- 53
- Citations by year
- 2026: 1 · 2025: 2 · 2023: 1 · 2022: 1 · 2021: 5 · 2020: 44 · 2019: 5 · 2018: 1 · 2017: 10 · 2016: 12 · 2015: 17 · 2014: 27 · 2013: 8
- FWCI
- 11.65
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS Genetics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- D-Scholarship@Pitt (University of Pittsburgh) (acceptedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Funders
- Autism Speaks
- National Institutes of Health (MH057881, HD015052, HD055751, RR024975)
- National Institute of Mental Health (R01MH061009, R01MH089025, R01MH089175, R01MH094400, R01MH089004, R37MH057881, R01MH089208, R01MH057881, R01MH089482)
- National Human Genome Research Institute (U54HG003067, U54HG003273)
- National Center for Research Resources (UL1RR024975)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (P30HD015052, P50HD055751)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Autism Spectrum Disorder Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Keywords
- autism spectrum disorder, gene-based association tests, mega-analysis, meta-analysis, population structure, principal component analysis
- MeSH terms
- Child, Child Development Disorders, Pervasive, Humans, Population Control, Software, Genetic Variation, Case-Control Studies, Sequence Analysis, DNA, Genetic Predisposition to Disease, Genome-Wide Association Study, Exome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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