Coherent synthesis of genomic associations with phenotypes and home environments

Abstract

Local adaptation is often studied via (i) multiple common garden experiments comparing performance of genotypes in different environments and (ii) sequencing genotypes from multiple locations and characterizing geographic patterns in allele frequency.Both approaches aim to characterize the same pattern (local adaptation), yet the complementary information from each has not yet been coherently integrated. Here, we develop a genome-wide association model of genotype interactions with continuous environmental gradients (G 9 E), that is reaction norms. We present an approach to impute relative fitness, allowing us to coherently synthesize evidence from common garden and genome-environment associations. Our approach identifies loci exhibiting environmental clines where alleles are associated with higher fitness in home environments. Simulations show our approach can increase power to detect loci causing local adaptation. In a case study on Arabidopsis thaliana, most identified SNPs exhibited home allele advantage and fitness trade-offs along climate gradients, suggesting selective gradients can maintain allelic clines. SNPs exhibiting G 9 E associations with fitness were enriched in genic regions, putative partial selective sweeps and associations with an adaptive phenotype (flowering time plasticity). We discuss extensions for situations where only adaptive phenotypes other than fitness are available. Many types of data may point towards the loci underlying G 9 E and local adaptation; coherent models of diverse data provide a principled basis for synthesis.

Corrigendum to “Coherent synthesis of genomic associations with phenotypes and home environments”

“…Additionally, reanalysis showed a weaker, but significant enrichment of flowering time G×E SNPs in SNPs associated with change in fitness across gradients of growing season variability in precipitation. As in Lasky et al (), SNPs associated with change in fitness across gradients of annual aridity or winter cold were not enriched in flowering time G×E SNPs.…”

“…We fixed the error, converting the Li et al () SNPs from TAIR8 to TAIR9 and reanalyzing the enrichments. This reanalysis showed (as in Lasky et al, ) that SNPs associated with flowering time response to experimental warming (G×E, Li et al, ) were significantly and most strongly enriched in SNPs associated with change in fitness across gradients of growing season minimum temperatures (Table here, which supersedes the relevant row from Table 2 in Lasky et al, ). Additionally, reanalysis showed a weaker, but significant enrichment of flowering time G×E SNPs in SNPs associated with change in fitness across gradients of growing season variability in precipitation.…”

“…We discovered an error in our analyses (Lasky, Forester, & Reimherr, ). The specific error was in the analysis of enrichment of (a) SNPs associated with G×E for fitness with (b) SNPs associated with G×E for flowering time under experimental warming (Li, Cheng, Spokas, Palmer, & Borevitz, ).…”

“…The specific error was in the analysis of enrichment of (a) SNPs associated with G×E for fitness with (b) SNPs associated with G×E for flowering time under experimental warming (Li, Cheng, Spokas, Palmer, & Borevitz, ). The error resulted from a failure to convert SNPs from two different versions of the Arabidopsis reference genome assembly: from TAIR8 (used in Li et al, supplemental materials, which we reanalyzed in our paper) to TAIR9 (used in our analyses of G×E for fitness, Lasky et al, ).…”

Recent advances in conservation and population genomics data analysis

“…Landscape genomics is sometimes conflated with genotype–environment association (GEA) analysis, which includes a wide variety of statistical approaches for identifying candidate adaptive loci that covary with environmental predictors (Rellstab, Gugerli, Eckert, Hancock, & Holderegger, 2015). However, landscape genomics includes many other techniques for identifying and analyzing spatially structured, selection‐driven variation, including GWAS across multiple environments, simulation studies, experimental approaches such as environmentally stratified common gardens, epigenetic and transcriptomic studies, and innovative approaches that combine analytical techniques (Berg & Coop, 2014; Lasky, Forester, & Reimherr, 2018; Storfer, Antolin, Manel, Epperson, & Scribner, 2015). …”