2014/05/31 by Sylvie Stucki, Pablo Orozco-terWengel, Pablo Orozco‐terWengel +11
Biochemistry, Genetics and Molecular Biology · #Adaptation (eye) #Association mapping #Biology #Computational biology #Computer science #Data mining #Genetic Mapping and Diversity in Plants and Animals #Genetic and phenotypic traits in livestock #Genetic association #Genetic diversity and population structure #Genetics #Genome #Genomics #Genotype #Local adaptation #Machine learning #Population #Population genomics #Selection (genetic algorithm) #Single-nucleotide polymorphism #q-bio.PE
paper · pdf · doi:10.1111/1755-0998.12629
1 figure in text, 1 figure in supplementary material The structure of the article was modified and some explanations were updated. The methods and results presented are the same as in the previous version
arxiv created 2014/11/20 · arxiv updated 2016/11/08 · openalex publication_date 2016/11/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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 sam β ada , an approach designed to study signatures of local adaptation, with special emphasis on high performance computing of large‐scale genetic and environmental data sets. sam β ada 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, sam β ada 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 sam β ada , bayenv , lfmm and an F ST outlier method ( FDIST approach in arlequin ) and compare their results. sam β ada – 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.