Abstract
Polymorphic inversions contribute to adaptation and phenotypic variation. However, large multi-centric association studies of inversions remain challenging. We present scoreInvHap, a method to genotype inversions from SNP data for genome-wide association studies (GWASs), overcoming important limitations of current methods and outperforming them in accuracy and applicability. scoreInvHap calls individual inversion-genotypes from a similarity score to the SNPs of experimentally validated references. It can be used on different sources of SNP data, including those with low SNP coverage such as exome sequencing, and is easily adaptable to genotype new inversions, either in humans or in other species. We present 20 human inversions that can be reliably and easily genotyped with scoreInvHap to discover their role in complex human traits, and illustrate a first genome-wide association study of experimentally-validated human inversions. scoreInvHap is implemented in R and it is freely available from Bioconductor.
Highlights
Frequent polymorphic inversions contribute to adaptation and phenotypic variation [1,2]
We present 20 human inversions that can be reliably and genotyped with scoreInvHap to discover their role in complex human traits, and illustrate a first genome-wide association study of experimentally-validated human inversions. scoreInvHap is implemented in R and it is freely available from Bioconductor
Chromosomal inversions are structural variants consisting on an orientation change of a chromosome segment
Summary
Frequent polymorphic inversions contribute to adaptation and phenotypic variation [1,2]. The genotypes of the rest of the subjects are inferred by haplotype-genotype cluster membership [6] This unsupervised inference, with posterior experimental labeling of the clusters, has allowed the genotyping of inversions in large cohorts [6,7,8]. This approach is still very limited because individual inferences are based on the analysis of entire population samples, making them computationally inefficient [9] and forcing the reanalysis of the whole dataset when new individuals are included. Current methods do not address the needs required for the meta-analyses of inversion association studies that include efficient and reliable genotyping in large population samples and inversion-genotype harmonization across different sources of SNP data
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