High performance computation of landscape genomic models including local indicators of spatial association

Abstract

With the increasing availability of both molecular and topo-climatic data, the main challenges facing landscape genomics -that is the combination of landscape ecology with population genomics -include processing large numbers of models and distinguishing between selection and demographic processes (e.g. population structure). Several methods address the latter, either by estimating a null model of population history or by simultaneously inferring environmental and demographic effects. Here we present SAMbADA, an approach designed to study signatures of local adaptation, with special emphasis on high performance computing of large-scale genetic and environmental data sets. SAMbADA identifies candidate loci using genotype-environment associations while also incorporating multivariate analyses to assess the effect of many environmental predictor variables. This enables the inclusion of explanatory variables representing population structure into the models to lower the occurrences of spurious genotype-environment associations. In addition, SAMbADA calculates local indicators of spatial association for candidate loci to provide information on whether similar genotypes tend to cluster in space, which constitutes a useful indication of the possible kinship between individuals. To test the usefulness of this approach, we carried out a simulation study and analysed a data set from Ugandan cattle to detect signatures of local adaptation with SAMbADA, BAYENV, LFMM and an F ST outlier method (FDIST approach in ARLEQUIN) and compare their results. SAMbADA -an open source software for Windows, Linux and Mac OS X available at http://lasig.epfl.ch/sambada -outperforms other approaches and better suits whole-genome sequence data processing.

Disentangling the effects of isolation-by-distance and isolation-by-environment on genetic differentiation among Rhododendron lineages in the subgenus Tsutsusi

“…Associations of allelic frequencies of outliers identified with values of environmental variables were tested using spatial analysis method implemented in the software Samßada (Stucki et al 2014). Samßada applies multiple univariate logistic regressions to test for associations of allelic frequencies at outlier loci with values of the seven retained environmental variables.…”

Are there morphological and life‐history traits under climate‐dependent differential selection in S TunesianDiplotaxis harra(Forssk.) Boiss. (Brassicaceae) populations?

“…This was done with two methods (MCHEZA, Antao & Beaumont, 2011; BayeScan, Foll & Gaggiotti, 2008) that use locus‐wise measures of population differentiation ( F ST values) to find loci with differentiation values significantly larger than average as candidates for as being under divergent selection (“outlier loci”). Additionally, a third approach was used (as implemented in Samβada; Joost et al., 2007; Stucki et al., 2017) that searches for AFLP loci under selection by testing the strength of relationship between the presence or absence of an allele in a single‐individual genotype and environmental variables using logistic regression models. In a second step, we tested for geographical signals in the two nonoverlapping subsets of loci (“selected” vs. “neutral” ones) in order to detect isolation‐by‐distance (IBD) caused by demography and neutral history of populations (phylogeography) in the background of either natural selection along geographical gradients (“selected loci” with a significant IBD signal) or natural selection without a geographical motivation (“selected loci” without a significant IBD signal).…”

“…All other parameters were set to default. As above, we used a FDR of 0.05 for outlier detection.A third method to detect adaptive loci is the spatial analysis method as implemented in the program Samβada (Joost et al., 2007; Stucki et al., 2017). In contrast to the two above‐mentioned procedures, it does not analyze AFLP data in a population genetic framework but is based on logistic regression models testing the strength of relationships between the presence or absence of an allele/band in a single‐individual genotype and environmental variables.…”

Simple Rules for an Efficient Use of Geographic Information Systems in Molecular Ecology

“…While GeoDa is better to visualize the SA of one variable, it cannot be automated to calculate it for many. For a fast computation of both global and local SA on genetic data, Sambada is handful (Stucki et al, 2016). It can be easily programmed to compute SA on millions of genetic markers and so with different neighborhood sizes and weighting schemes.…”