Outlier loci highlight the direction of introgression in oaks

Abstract

Loci considered to be under selection are generally avoided in attempts to infer past demographic processes as they do not fit neutral model assumptions. However, opportunities to better reconstruct some aspects of past demography might thus be missed. Here we examined genetic differentiation between two sympatric European oak species with contrasting ecological dynamics (Quercus robur and Quercus petraea) with both outlier (i.e. loci possibly affected by divergent selection between species or by hitchhiking effects with genomic regions under selection) and nonoutlier loci. We sampled 855 individuals in six mixed forests in France and genotyped them with a set of 262 SNPs enriched with markers showing high interspecific differentiation, resulting in accurate species delimitation. We identified between 13 and 74 interspecific outlier loci, depending on the coalescent simulation models and parameters used. Greater genetic diversity was predicted in Q. petraea (a late-successional species) than in Q. robur (an early successional species) as introgression should theoretically occur predominantly from the resident species to the invading species. Remarkably, this prediction was verified with outlier loci but not with nonoutlier loci. We suggest that the lower effective interspecific gene flow at loci showing high interspecific divergence has better preserved the signal of past asymmetric introgression towards Q. petraea caused by the species' contrasting dynamics. Using markers under selection to reconstruct past demographic processes could therefore have broader potential than generally recognized.

The loops of the N-SH2 binding cleft do not serve as allosteric switch in SHP2 activation

“…Bodénès et al 2016, see Table 1). Leaves were sampled, stored in silica gel and sent to INRA (Cestas, France) for DNA extraction following Guichoux et al (2013). DNA quality and concentration were assessed with a Nanodrop spectrophotometer (NanoDrop Technologies,Wilmington,152 DE,USA) and by separating samples in 1% agarose gels stained with ethidium bromide.…”

A linkage disequilibrium perspective on the genetic mosaic of speciation in two hybridizing Mediterranean white oaks

“…That percentage corresponds to the mean admixture from second-generation backcrosses (BC2); it is large enough to avoid statistical noise (mean admixture in BC3 would be 6.25%), and has been previously used to identify pure species among hybridizing oaks (Goicoechea et al, 2012;Guichoux et al, 2013).…”

The population genomic signature of environmental selection in the widespread insect-pollinated tree species Frangula alnus at different geographical scales

“…Although restricting the study to SNPs derived only from coding regions may have overestimated the proportion of outlier loci to some extent, outlier detection studies in other tree species revealed similar proportions of outliers at a large scale (4400 km), for example in Picea glauca (5.5% outliers, F ST = 0.006, genic SNPs) (Namroud et al, 2008), A. glutinosa (3.7% outliers, F ST = 0.057, amplified fragment length polymorphisms; and 2.86%, F ST = 0.01, SNPs) (Cox et al, 2011;De Kort et al, 2014), and Quercus robur and Q. petraea (4% and 5% outliers resp., F ST = 0.013 and 0.015 resp., genic SNPs) (Guichoux et al, 2013). Nevertheless, many of these findings should be interpreted with considerable caution due to the use of candidate loci (except for those of Cox et al, 2011, De Kort et al, 2014, which tend to overestimate neutral distributions and therefore underestimate the true percentage of loci under selection, the latter being expected to be high when using loci related to adaptive functions or traits.…”

“…More specifically, a standard Bayesian population genomic outlier screen (BAYESCAN, Foll & Gaggiotti, 2008) was complemented with individual-based latent factor mixed modeling (LFMM, Frichot et al, 2013), and multivariate redundancy analyses (RDA, Borcard et al, 2011), to identify (i) SNPs putatively under selection and (ii) the main environmental drivers of population differentiation while accounting for spatial autocorrelation in allele frequencies. Although the use of candidate SNPs has been the standard method to find loci putatively under selection (for example, Namroud et al, 2008;Alberto et al, 2013;Guichoux et al, 2013;Olson et al, 2013), they may compromise the relevance of classical outlier detection methods due to overestimation of neutral distributions. This stresses the importance of integrating landscape genomic methods to reliably reveal loci putatively under selection and the selective agents underlying the observed patterns (De Villemereuil et al, 2014;Weinig et al, 2014).…”