LFMM 2: Fast and Accurate Inference of Gene-Environment Associations in Genome-Wide Studies.

Gene-environment association (GEA) studies are essential to understand the past and ongoing adaptations of organisms to their environment, but those studies are complicated by confounding due to unobserved demographic factors. Although the confounding problem has recently received considerable attention, the proposed approaches do not scale with the high-dimensionality of genomic data. Here, we present a new estimation method for latent factor mixed models (LFMMs) implemented in an upgraded version of the corresponding computer program.

Naturalgwas: An R package for evaluating genomewide association methods with empirical data.

Association studies of polygenic traits are notoriously difficult when those studies are conducted at large geographic scales. The difficulty arises as genotype frequencies often vary in geographic space and across distinct environments. Those large-scale variations are known to yield false positives in standard association testing approaches.

Identifying outlier loci in admixed and in continuous populations using ancestral population differentiation statistics.

Finding genetic signatures of local adaptation is of great interest for many population genetic studies. Common approaches to sorting selective loci from their genomic background focus on the extreme values of the fixation index, F , across loci. However, the computation of the fixation index becomes challenging when the population is genetically continuous, when predefining subpopulations is a difficult task, and in the presence of admixed individuals in the sample.

Controlling false discoveries in genome scans for selection.

Population differentiation (PD) and ecological association (EA) tests have recently emerged as prominent statistical methods to investigate signatures of local adaptation using population genomic data. Based on statistical models, these genomewide testing procedures have attracted considerable attention as tools to identify loci potentially targeted by natural selection. An important issue with PD and EA tests is that incorrect model specification can generate large numbers of false-positive associations.

TESS3: fast inference of spatial population structure and genome scans for selection.

Geography and landscape are important determinants of genetic variation in natural populations, and several ancestry estimation methods have been proposed to investigate population structure using genetic and geographic data simultaneously. Those approaches are often based on computer-intensive stochastic simulations and do not scale with the dimensions of the data sets generated by high-throughput sequencing technologies. There is a growing demand for faster algorithms able to analyse genomewide patterns of population genetic variation in their geographic context.