Rapid identification and interpretation of gene–environment associations using the new R.SamBada landscape genomics pipeline

Abstract

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The Quest for Genes Involved in Adaptation to Climate Change in Ruminant Livestock

“…This software has High Performance Computing (HPC) capacities to handle the large datasets created when several million SNPs, produced by high-throughput sequencing, are combined with hundreds of environmental variables. Samβada is also supported by R-SamBada [219], an R software package that provides a complete pipeline for landscape genomic analyses, from the retrieval of environmental variables at sampling locations to gene annotation using the Ensembl genome browser. Other landscape genomics software include BAYENV [220], which uses the Bayesian method to compute correlations between allele frequencies and ecological variables, taking into account differences in sample size and population structure; LFMM [211,221], which identifies gene-environment associations and SNPs with allele frequencies that correlate with clines of environmental variables; and SGLMM [222], which extends the BAYENV approach [223] by using a spatially explicit model and calculating inferences with an Integrated Nested Laplace Approximation and Stochastic Partial Differential Equation (SPDE).…”

Spatial areas of genotype probability: Predicting the spatial distribution of adaptive genetic variants under future climatic conditions

“…The latter is designed for high‐performance computation and is therefore attractive for whole genome‐scale genotyping. In addition, Samβada has recently been complemented with an R‐package facilitating the uptake of the method (R. Samβada, Duruz et al., 2019, with dedicated guidelines to boost statistical power via sampling strategy as proposed in Selmoni et al., 2020).…”

Smoothing technical and computational obstacles in gene‐environment associations

“…But it has also increased the complexity of the analysis and the computation time required. In this issue of Molecular Ecology Resources , Duruz et al () describe a new R‐package ( r.sambada ) which acts as an integrated pipeline for landscape genomic analysis. r.sambada includes all the steps of a landscape genomics analysis ‐ from the filtering of the genomic data (e.g., SNPs) and the retrieval of environmental data ‐ to the spatial visualization and annotation of potential candidate genes.…”

“…r.sambada includes all the steps of a landscape genomics analysis ‐ from the filtering of the genomic data (e.g., SNPs) and the retrieval of environmental data ‐ to the spatial visualization and annotation of potential candidate genes. Duruz et al () present a step forward in the rapid and simple detection of putative candidate genes in gene‐environment associations analysis. The usefulness of this pipeline was illustrated in their investigation into the signal of local adaptation in Moroccan sheep (Figure ) and Spanish cattle breeds, two species for which a large number of SNPs as well as a reference genome are available.…”

“…But it has also increased the complexity of the analysis and the computation time required. In this issue of Molecular Ecology Resources, Duruz et al (2019) integrating all the steps into one package, r.sambada will also save user time during the input-data formatting steps as this has to be done once only. Formatting and importing data into different R packages or other software programs can be a thankless and highly timeconsuming step for users.…”