<scp>rehh</scp> 2.0: a reimplementation of the R package <scp>rehh</scp> to detect positive selection from haplotype structure

TLDR

Identifying genomic regions with unusually high local haplotype homozygosity is a powerful strategy to characterize candidate genes responding to natural or artificial positive selection, and statistics such as EHH, iHS, Rsb, and XP‑EHH have been proposed for this purpose; the original rehh package facilitated genome‑wide scans of selection based on long‑range haplotypes. We propose a major upgrade of the rehh package to enable iHS, Rsb, or XP‑EHH based scans on large data sets. The upgrade improves input file processing, introduces a faster haplotype enumeration algorithm, and adds multithreading. The improvements reduce computation time by more than an order of magnitude on large human haplotype data sets, and the rehh 2.0 package is available from CRAN with help files and a detailed manual.

Abstract

Abstract Identifying genomic regions with unusually high local haplotype homozygosity represents a powerful strategy to characterize candidate genes responding to natural or artificial positive selection. To that end, statistics measuring the extent of haplotype homozygosity within (e.g. EHH , iHS ) and between (Rsb or XP ‐ EHH ) populations have been proposed in the literature. The rehh package for r was previously developed to facilitate genome‐wide scans of selection, based on the analysis of long‐range haplotypes. However, its performance was not sufficient to cope with the growing size of available data sets. Here, we propose a major upgrade of the rehh package, which includes an improved processing of the input files, a faster algorithm to enumerate haplotypes, as well as multithreading. As illustrated with the analysis of large human haplotype data sets, these improvements decrease the computation time by more than one order of magnitude. This new version of rehh will thus allow performing iHS ‐, Rsb‐ or XP ‐ EHH ‐based scans on large data sets. The package rehh 2.0 is available from the CRAN repository ( http://cran.r-project.org/web/packages/rehh/index.html ) together with help files and a detailed manual.