Document Detail

The effect of genomic inversions on estimation of population genetic parameters from SNP data.
MedLine Citation:
PMID:  23150602     Owner:  NLM     Status:  MEDLINE    
In recent years it has emerged that structural variants have a substantial impact on genomic variation. Inversion polymorphisms represent a significant class of structural variant, and despite the challenges in their detection, data on inversions in the human genome are increasing rapidly. Statistical methods for inferring parameters such as the recombination rate and the selection coefficient have generally been developed without accounting for the presence of inversions. Here we exploit new software for simulating inversions in population genetic data, invertFREGENE, to assess the potential impact of inversions on such methods. Using data simulated by invertFREGENE, as well as real data from several sources, we test whether large inversions have a disruptive effect on widely applied population genetics methods for inferring recombination rates, for detecting selection, and for controlling for population structure in genome-wide association studies (GWAS). We find that recombination rates estimated by LDhat are biased downward at inversion loci relative to the true contemporary recombination rates at the loci but that recombination hotspots are not falsely inferred at inversion breakpoints as may have been expected. We find that the integrated haplotype score (iHS) method for detecting selection appears robust to the presence of inversions. Finally, we observe a strong bias in the genome-wide results of principal components analysis (PCA), used to control for population structure in GWAS, in the presence of even a single large inversion, confirming the necessity to thin SNPs by linkage disequilibrium at large physical distances to obtain unbiased results.
Nafisa-Katrin Seich Al Basatena; Clive J Hoggart; Lachlan J Coin; Paul F O'Reilly
Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't     Date:  2012-11-12
Journal Detail:
Title:  Genetics     Volume:  193     ISSN:  1943-2631     ISO Abbreviation:  Genetics     Publication Date:  2013 Jan 
Date Detail:
Created Date:  2012-12-31     Completed Date:  2013-06-07     Revised Date:  2014-01-09    
Medline Journal Info:
Nlm Unique ID:  0374636     Medline TA:  Genetics     Country:  United States    
Other Details:
Languages:  eng     Pagination:  243-53     Citation Subset:  IM    
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MeSH Terms
Genetics, Population*
Models, Genetic*
Polymorphism, Single Nucleotide*
Principal Component Analysis
Recombination, Genetic
Selection, Genetic
Grant Support
//Wellcome Trust

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