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pcadapt : an R package to perform genome scans for selection based on principal component analysis
Author(s) -
Luu Keurcien,
Bazin Eric,
Blum Michael G. B.
Publication year - 2017
Publication title -
molecular ecology resources
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.96
H-Index - 136
eISSN - 1755-0998
pISSN - 1755-098X
DOI - 10.1111/1755-0998.12592
Subject(s) - mahalanobis distance , principal component analysis , biology , population , outlier , statistic , selection (genetic algorithm) , genome , data set , computational biology , statistics , computer science , artificial intelligence , genetics , mathematics , gene , demography , sociology
The R package pcadapt performs genome scans to detect genes under selection based on population genomic data. It assumes that candidate markers are outliers with respect to how they are related to population structure. Because population structure is ascertained with principal component analysis, the package is fast and works with large‐scale data. It can handle missing data and pooled sequencing data. By contrast to population‐based approaches, the package handle admixed individuals and does not require grouping individuals into populations. Since its first release, pcadapt has evolved in terms of both statistical approach and software implementation. We present results obtained with robust Mahalanobis distance, which is a new statistic for genome scans available in the 2.0 and later versions of the package. When hierarchical population structure occurs, Mahalanobis distance is more powerful than the communality statistic that was implemented in the first version of the package. Using simulated data, we compare pcadapt to other computer programs for genome scans ( BayeScan , hapflk , OutFLANK , sNMF ). We find that the proportion of false discoveries is around a nominal false discovery rate set at 10% with the exception of BayeScan that generates 40% of false discoveries. We also find that the power of BayeScan is severely impacted by the presence of admixed individuals whereas pcadapt is not impacted. Last, we find that pcadapt and hapflk are the most powerful in scenarios of population divergence and range expansion. Because pcadapt handles next‐generation sequencing data, it is a valuable tool for data analysis in molecular ecology.