Robust identification of local adaptation from allele frequencies
Torsten Günther, Graham Coop
(Submitted on 13 Sep 2012)
Comparing allele frequencies among populations that differ in environment has long been a tool for detecting loci involved in local adaptation. However, such analyses are complicated by an imperfect knowledge of population allele frequencies and neutral correlations of allele frequencies among populations due to shared population history and gene flow. Here we develop a set of methods to robustly test for unusual allele frequency patterns, and correlations between environmental variables and allele frequencies while accounting for these complications based on a Bayesian model previously implemented in the software Bayenv. Using this model, we calculate a set of `standardized allele frequencies’ that allows investigators to apply tests of their choice to multiple populations, while accounting for sampling and covariance due to population history. We illustrate this first by showing that these standardized frequencies can be used to calculate powerful tests to detect non-parametric correlations with environmental variables, which are also less prone to spurious results due to outlier populations. We then demonstrate how these standardized allele frequencies can be used to construct a test to detect SNPs that deviate strongly from neutral population structure. This test is conceptually related to FST but should be more powerful as we account for population history. We also extend the model to next-generation sequencing of population pools, which is a cost-efficient way to estimate population allele frequencies, but it implies an additional level of sampling noise. The utility of these methods is demonstrated in simulations and by re-analyzing human SNP data from the HGDP populations. An implementation of our method will be available from this http URL.
Complex patterns of local adaptation in teosinte
Tanja Pyhäjärvi, Matthew B. Hufford, Sofiane Mezmouk, Jeffrey Ross-Ibarra
(Submitted on 3 Aug 2012)
Populations of widely distributed species often encounter and adapt to specific environmental conditions. However, comprehensive characterization of the genetic basis of adaptation is demanding, requiring genome-wide genotype data, multiple sampled populations, and a good understanding of population structure. We have used environmental and high-density genotype data to describe the genetic basis of local adaptation in 21 populations of teosinte, the wild ancestor of maize. We found that altitude, dispersal events and admixture among subspecies formed a complex hierarchical genetic structure within teosinte. Patterns of linkage disequilibrium revealed four mega-base scale inversions that segregated among populations and had altitudinal clines. Based on patterns of differentiation and correlation with environmental variation, inversions and nongenic regions play an important role in local adaptation of teosinte. Further, we note that strongly differentiated individual populations can bias the identification of adaptive loci. The role of inversions in local adaptation has been predicted by theory and requires attention as genome-wide data become available for additional plant species. These results also suggest a potentially important role for noncoding variation, especially in large plant genomes in which the gene space represents a fraction of the entire genome.
Detection of correlation between genotypes and environmental variables. A fast computational approach for genomewide studies
(Submitted on 5 Jun 2012)
Genomic regions displaying outstanding correlation with some environmental variables are likely to be under selection and this is the rationale of recent methods of identifying selected loci and retrieve functional information about them. To be efficient, such methods need to be able to disentangle the potential effect of environmental variables from the confounding effect of population history. For the routine analysis of genomewide data-sets, one also need fast inference and model selection algorithms. We describe a method based on an explicit spatial model that builds on the theoretical and computational framework developed by Rue et al. (2009) and Lindgren et al. (2011}. The methods allows one to quantify correlation between genotypes and environmental variables and to rank loci accordingly. It works for SNP and AFLP data obtained either at the individual or at the population level. We provide R scripts with detailed comments that can be used readily for the analysis of real data without specific prior knowledge of the R language.