Document Detail


Empirical power of very rare variants for common traits and disease: results from sanger sequencing 1998 individuals.
MedLine Citation:
PMID:  23321613     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
The optimal study design for identifying rare variants associated with common disease is not yet clear and researchers have to decide whether to prioritize lower sequencing coverage on larger sample sizes, or higher coverage on smaller sample sizes. High-coverage sequencing affords several advantages, such as genotype accuracy and improved identification of very rare variants, but this comes at increased cost. However, the magnitude of the contribution of very rare variants to the statistical power of gene-based association tests is unknown. By using Sanger sequence data on seven genes from 1998 subjects with simulated phenotypes, we provide evidence that excluding very rare variants, in general, reduces the statistical power of rare variant association tests only modestly. However, if the probability of being causal and the effect size of the causal variants are inversely related to the minor allele frequency, then very rare variants do contribute to some power, however the absolute power remains low. As very rare variants constitute the majority of variants identified in sequencing studies, these findings suggest that careful attention need to be placed on the plausible relationship that exist between very rare variants and common disease.
Authors:
Martin Ladouceur; Hou-Feng Zheng; Celia M T Greenwood; J Brent Richards
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't     Date:  2013-01-16
Journal Detail:
Title:  European journal of human genetics : EJHG     Volume:  21     ISSN:  1476-5438     ISO Abbreviation:  Eur. J. Hum. Genet.     Publication Date:  2013 Sep 
Date Detail:
Created Date:  2013-08-16     Completed Date:  2014-03-24     Revised Date:  2014-09-02    
Medline Journal Info:
Nlm Unique ID:  9302235     Medline TA:  Eur J Hum Genet     Country:  England    
Other Details:
Languages:  eng     Pagination:  1027-30     Citation Subset:  IM    
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MeSH Terms
Descriptor/Qualifier:
Data Interpretation, Statistical
Empirical Research
Genes
Genetic Association Studies / methods*
Genetic Predisposition to Disease
Genetic Variation
Humans
Models, Genetic
Sequence Analysis, DNA*
Grant Support
ID/Acronym/Agency:
//Canadian Institutes of Health Research
Comments/Corrections

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