High performance computation of landscape genomic models integrating local indices of spatial association

TL;DR

This paper introduces Samβada, a software tool that rapidly analyzes genome-environment associations and spatial patterns in landscape genomics, aiding in detecting local adaptation signatures from large genomic datasets.

Contribution

The paper presents Samβada, a novel integrated method combining genome-environment association analysis with spatial association measures for landscape genomics.

Findings

Samβada effectively identifies candidate adaptive loci.

It compares favorably with existing methods like BayEnv and LFMM.

Application to Ugandan cattle data reveals local adaptation signatures.

Abstract

Since its introduction, landscape genomics has developed quickly with the increasing availability of both molecular and topo-climatic data. The current challenges of the field mainly involve processing large numbers of models and disentangling selection from demography. Several methods address the latter, either by estimating a neutral model from population structure or by inferring simultaneously environmental and demographic effects. Here we present Sam$\beta$ada, an integrated approach to study signatures of local adaptation, providing rapid processing of whole genome data and enabling assessment of spatial association using molecular markers. Specifically, candidate loci to adaptation are identified by automatically assessing genome-environment associations. In complement, measuring the Local Indicators of Spatial Association (LISA) for these candidate loci allows to detect whether similar genotypes tend to gather in space, which constitutes a useful indication of the possible kinship relationship between individuals. In this paper, we also analyze SNP data from Ugandan cattle to detect signatures of local adaptation with Sam$\beta$ada, BayEnv, LFMM and an outlier method (FDIST approach in Arlequin) and compare their results. Sam$\beta$ada is an open source software for Windows, Linux and MacOS X available at \url{http://lasig.epfl.ch/sambada}