EigenGWAS: finding loci under selection through genome-wide association studies of eigenvectors in structured populations.

Détails

ID Serval
serval:BIB_79D93185D45A
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
EigenGWAS: finding loci under selection through genome-wide association studies of eigenvectors in structured populations.
Périodique
Heredity
Auteur(s)
Chen G.B., Lee S.H., Zhu Z.X., Benyamin B., Robinson M.R.
ISSN
1365-2540 (Electronic)
ISSN-L
0018-067X
Statut éditorial
Publié
Date de publication
2016
Peer-reviewed
Oui
Volume
117
Numéro
1
Pages
51-61
Langue
anglais
Résumé
We develop a novel approach to identify regions of the genome underlying population genetic differentiation in any genetic data where the underlying population structure is unknown, or where the interest is assessing divergence along a gradient. By combining the statistical framework for genome-wide association studies (GWASs) with eigenvector decomposition (EigenGWAS), which is commonly used in population genetics to characterize the structure of genetic data, loci under selection can be identified without a requirement for discrete populations. We show through theory and simulation that our approach can identify regions under selection along gradients of ancestry, and in real data we confirm this by demonstrating LCT to be under selection between HapMap CEU-TSI cohorts, and we then validate this selection signal across European countries in the POPRES samples. HERC2 was also found to be differentiated between both the CEU-TSI cohort and within the POPRES sample, reflecting the likely anthropological differences in skin and hair colour between northern and southern European populations. Controlling for population stratification is of great importance in any quantitative genetic study and our approach also provides a simple, fast and accurate way of predicting principal components in independent samples. With ever increasing sample sizes across many fields, this approach is likely to be greatly utilized to gain individual-level eigenvectors avoiding the computational challenges associated with conducting singular value decomposition in large data sets. We have developed freely available software, Genetic Analysis Repository (GEAR), to facilitate the application of the methods.

Mots-clé
Algorithms, Computational Biology/methods, Evolution, Molecular, Genetics, Population, Genome-Wide Association Study/methods, Genomics/methods, Phenotype, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Reproducibility of Results, Selection, Genetic
Pubmed
Web of science
Création de la notice
06/12/2017 13:42
Dernière modification de la notice
03/03/2018 18:32
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