Identifying recombination hotspots using population genetic data

TL;DR

This paper introduces a method to identify recombination hotspots in mammalian genomes using linkage disequilibrium patterns, demonstrating moderate detection power and manageable false positive rates through simulations.

Contribution

The authors develop a novel approach for locating recombination hotspots from population genetic data, improving detection capabilities over previous methods.

Findings

Hotspot detection power of 50-60% depending on hotspot strength

False positive rate between 0.24 and 0.56 per Mb in simulated human data

Method available at http://github.com/auton1/LDhot

Abstract

Motivation: Recombination rates vary considerably at the fine scale within mammalian genomes, with the majority of recombination occurring within hotspots of ~2 kb in width. We present a method for inferring the location of recombination hotspots from patterns of linkage disequilibrium within samples of population genetic data. Results: Using simulations, we show that our method has hotspot detection power of approximately 50-60%, but depending on the magnitude of the hotspot. The false positive rate is between 0.24 and 0.56 false positives per Mb for data typical of humans. Availability: http://github.com/auton1/LDhot