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pcadapt: an R package to perform genome scans for selection based on principal component analysis.


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.


Authors+Show Affiliations


Laboratoire TIMC-IMAG, UMR 5525, CNRS, Université Grenoble Alpes, Grenoble, France.


Laboratoire d'Ecologie Alpine UMR 5553, CNRS, Université Grenoble Alpes, Grenoble, France.

Laboratoire TIMC-IMAG, UMR 5525, CNRS, Université Grenoble Alpes, Grenoble, France.


Molecular ecology resources 17:1 2017 Jan pg 67-77


Adaptation, Biological
Computational Biology
Genetics, Population
Principal Component Analysis
Selection, Genetic

Pub Type(s)

Comparative Study
Evaluation Studies
Journal Article



PubMed ID