Duplicated gene networks promote 'hopeful' phenotypic variation.

The consequences of whole-genome duplication (WGD) remain elusive. A new study by Ebadi et al. simulating duplicated gene networks predicts that WGD immediately generates autopolyploids with extreme phenotypes and increases phenotypic variance. Such theoretical work calls for new experimental studies addressing to what extent WGD may be beneficial under environmental changes.

Biased Retention of Environment-Responsive Genes Following Genome Fractionation.

The molecular underpinnings and consequences of cycles of whole-genome duplication (WGD) and subsequent gene loss through subgenome fractionation remain largely elusive. Endogenous drivers, such as transposable elements (TEs), have been postulated to shape genome-wide dominance and biased fractionation, leading to a conserved least-fractionated (LF) subgenome and a degenerated most-fractionated (MF) subgenome. In contrast, the role of exogenous factors, such as those induced by environmental stresses, has been overlooked. In this study, a chromosome-scale assembly of the alpine buckler mustard (Biscutella laevigata; Brassicaceae) that underwent a WGD event about 11 million years ago is coupled with transcriptional responses to heat, cold, drought, and herbivory to assess how gene expression is associated with differential gene retention across the MF and LF subgenomes. Counteracting the impact of TEs in reducing the expression and retention of nearby genes across the MF subgenome, dosage balance is highlighted as a main endogenous promoter of the retention of duplicated gene products under purifying selection. Consistent with the "turn a hobby into a job" model, about one-third of environment-responsive duplicates exhibit novel expression patterns, with one copy typically remaining conditionally expressed, whereas the other copy has evolved constitutive expression, highlighting exogenous factors as a major driver of gene retention. Showing uneven patterns of fractionation, with regions remaining unbiased, but with others showing high bias and significant enrichment in environment-responsive genes, this mesopolyploid genome presents evolutionary signatures consistent with an interplay of endogenous and exogenous factors having driven gene content following WGD-fractionation cycles.

Integrating very high resolution environmental proxies in genotype-environment association studies.

Landscape genomic analyses associating genetic variation with environmental variables are powerful tools for studying molecular signatures of species' local adaptation and for detecting candidate genes under selection. The development of landscape genomics over the past decade has been spurred by improvements in resolutions of genomic and environmental datasets, allegedly increasing the power to identify putative genes underlying local adaptation in non-model organisms. Although these associations have been successfully applied to numerous species across a diverse array of taxa, the spatial scale of environmental predictor variables has been largely overlooked, potentially limiting conclusions to be reached with these methods. To address this knowledge gap, we systematically evaluated performances of genotype-environment association (GEA) models using predictor variables at multiple spatial resolutions. Specifically, we used multivariate redundancy analyses to associate whole-genome sequence data from the plant Arabis alpina L. collected across four neighboring valleys in the western Swiss Alps, with very high-resolution topographic variables derived from digital elevation models of grain sizes between 0.5 m and 16 m. These comparisons highlight the sensitivity of landscape genomic models to spatial resolution, where the optimal grain sizes were specific to variable type, terrain characteristics, and study extent. To assist in selecting variables at appropriate spatial resolutions, we demonstrate a practical approach to produce, select, and integrate multiscale variables into GEA models. After generalizing fine-grained variables to multiple spatial resolutions, a forward selection procedure is applied to retain only the most relevant variables for a particular context. Depending on the spatial resolution, the relevance for topographic variables in GEA studies calls for integrating multiple spatial scales into landscape genomic models. By carefully considering spatial resolutions, candidate genes under selection by a more realistic range of pressures can be detected for downstream analyses, with important applied implications for experimental research and conservation management of natural populations.