Investigation of the geographical scale of adaptive phenological variation and its underlying genetics in Arabidopsis thaliana

Abstract

Despite the increasing number of genomic tools, identifying the genetics underlying adaptive complex traits remains challenging in the model species Arabidopsis thaliana. This is due, at least in part, to the lack of data on the geographical scale of adaptive phenotypic variation. The aims of this study were (i) to tease apart the historical roles of adaptive and nonselective processes in shaping phenological variation in A. thaliana in France and (ii) to gain insights into the spatial scale of adaptive variation by identifying the putative selective agents responsible for this selection. Forty-nine natural stands from four climatically contrasted French regions were characterized (i) phenologically for six traits, (ii) genetically using 135 SNP markers and (iii) ecologically for 42 variables. Up to 63% of phenological variation could be explained by neutral genetic diversity. The remaining phenological variation displayed stronger associations with ecological variation within regions than among regions, suggesting the importance of local selective agents in shaping adaptive phenological variation. Although climatic conditions have often been suggested as the main selective agents acting on phenology in A. thaliana, both edaphic conditions and interspecific competition appear to be strong selective agents in some regions. In a first attempt to identify the genetics of phenological variation at different geographical scales, we phenotyped worldwide accessions and local polymorphic populations from the French RegMap in a genome-wide association (GWA) mapping study. The genomic regions associated with phenological variation depended upon the geographical scale considered, stressing the need to account for the scale of adaptive phenotypic variation when choosing accession panels for GWAS.

The roles of genetic drift and natural selection in quantitative trait divergence along an altitudinal gradient in Arabidopsis thaliana

“…Nonetheless, Q ST -F ST comparisons have been experimentally shown to be valid in A. thaliana (Porcher et al, 2006). Overall F ST was 0.73 in this study, a value in the upper range of regional F ST of A. thaliana reported for Scandinavia, France and Spain and calculated from either SSRs or SNPs (Kuittinen et al, 1997;Le Corre, 2005;Brachi et al, 2013;Mendez-Vigo et al, 2013). However, F ST calculated using SSRs, as we do here, can be lower than F ST calculated using SNPs because of the high mutation rate of SSRs combined with very low migration rates (Edelaar et al, 2011;MendezVigo et al, 2013), but it is not clear whether this is a general pattern (Leinonen et al, 2013).…”

Marker-Based Estimation of Heritability in Immortal Populations

“…We considered four subpopulations: 298 Swedish accessions [Swedish RegMap (Horton et al 2012;Long et al 2013)], 204 French accessions [French RegMap (Horton et al 2012;Brachi et al 2013)], 350 accessions from the HapMap population (Li et al 2010), and a subset of 250 accessions that we refer to as the structured RegMap (accession IDs are given in Supporting Information, Table S1). The structured RegMap accessions were chosen to have large differences in genetic relatedness (i.e., variation in kinship coefficients, across pairs of accessions).…”

Common garden experiments in the genomic era: new perspectives and opportunities

“…Here, the number of populations is also of importance, as it will determine the power to detect significance for the parameter β B . Note that Brachi et al (2013) used a different approach of multiscale (local to worldwide variation) analysis and found very different results depending on the studied scale of local adaptation. The approach that is probably the most typical of the genomic era is to scan genomes for signal of selection (mostly selective sweeps and local adaptation).…”

“…As a result, combining common garden experiments of non-model species with genome-wide association studies provides opportunity for multiple-population genome-wide association studies (Brachi et al, 2013;Slavov et al, 2014). For a locally adapted trait, it would even be possible to differentiate markers explaining among-population phenotypic variability (by testing for among-population effects) from markers explaining within-population variability (by testing for within-population effects).…”

“…Because it enables to unravel the genetic basis of complex phenotypes across various populations without the confounding effects of the corresponding environment, the common garden experiment is used to test for local adaptation signal in traits of interest such as life history traits (Kawakami et al, 2011), phenology (Brachi et al, 2013) and allometric relationships (Gonda et al, 2011). Local adaptation might be suspected because of the existence of an environmental gradient such as latitude (Toräng et al, 2015) or altitude (Alberto et al, 2011), or because of the existence of several contrasting environments, such as sea and fresh water (DeFaveri and Merilä, 2014).…”