Genome-Wide Mapping of Gene–Phenotype Relationships in Experimentally Evolved Populations

Abstract

Model organisms subjected to sustained experimental evolution often show levels of phenotypic differentiation that dramatically exceed the phenotypic differences observed in natural populations. Genome-wide sequencing of pooled populations then offers the opportunity to make inferences about the genes that are the cause of these phenotypic differences. We tested, through computer simulations, the efficacy of a statistical learning technique called the "fused lasso additive model" (FLAM). We focused on the ability of FLAM to distinguish between genes which are differentiated and directly affect a phenotype from differentiated genes which have no effect on the phenotype. FLAM can separate these two classes of genes even with relatively small samples (10 populations, in total). The efficacy of FLAM is improved with increased number of populations, reduced environmental phenotypic variation, and increased within-treatment among-replicate variation. FLAM was applied to SNP variation measured in both twenty-population and thirty-population studies of Drosophila subjected to selection for age-at-reproduction, to illustrate the application of the method.

Genome-Wide Architecture of Adaptation in Experimentally EvolvedDrosophila

“…Currently, we only have the full suite of genomics, transcriptomics, and phenotypic data for 20 populations. As shown in Mueller et al (11), a 20-population analysis is barely sufficient for detecting causal loci, and by no means will detect the full range of causally important sites in the genome. Ideally, the number of populations used in analyses of this kind should approach 100 populations.…”

“…This method has been shown to effectively identify causal loci of phenotypic variation in experimentally evolved populations that exhibit large phenotypic differences (11). In addition, FLAM has the ability to distinguish between these causal loci and those that show genetic differentiation between populations but are not causally related to the phenotype of interest (11). We also implemented a permutation procedure for expanding the list of causative loci (11), In this study, a total of 100 permutations of the columns of genetic data were done and the final list consisted of genetic variants which occurred at a frequency of at least 50% among these lists of causative loci.…”

“…To find gene regions that might be causally related to the phenotypes studied, we used a statistical learning method called the "fused lasso additive model" or "FLAM" (10). This method has been shown to effectively identify causal loci of phenotypic variation in experimentally evolved populations that exhibit large phenotypic differences (11). In addition, FLAM has the ability to distinguish between these causal loci and those that show genetic differentiation between populations but are not causally related to the phenotype of interest (11).…”

“…Genome-wide sequencing can provide extensive catalogs of changes in the frequency spectrum of genetic variants in lab evolved populations, as well as a portrait of their expression levels (7, 8). New statistical approaches have been developed that capitalize on genome-wide sequencing in experimental evolution (9, 10, 11). The hope is that the combination of high-throughput sequencing, statistical learning, and well-characterized life-history characters might help us to understand how genetic variation underpins adaptation generally.…”

“…In a similar vein, we use genomic data to infer which loci are causally linked to changes in gene expression across the transcriptome. These inferences are made using a statistical learning technique called fused lasso additive model, or FLAM (10), which theoretical work suggests is well suited to the task (11).…”