Polygenic adaptation of rosette growth in Arabidopsis thaliana

Abstract

The rate at which plants grow is a major functional trait in plant ecology. However, little is known about its evolution in natural populations. Here, we investigate evolutionary and environmental factors shaping variation in the growth rate of Arabidopsis thaliana. We used plant diameter as a proxy to monitor plant growth over time in environments that mimicked latitudinal differences in the intensity of natural light radiation, across a set of 278 genotypes sampled within four broad regions, including an outgroup set of genotypes from China. A field experiment conducted under natural conditions confirmed the ecological relevance of the observed variation. All genotypes markedly expanded their rosette diameter when the light supply was decreased, demonstrating that environmental plasticity is a predominant source of variation to adapt plant size to prevailing light conditions. Yet, we detected significant levels of genetic variation both in growth rate and growth plasticity. Genome-wide association studies revealed that only 2 single nucleotide polymorphisms associate with genetic variation for growth above Bonferroni confidence levels. However, marginally associated variants were significantly enriched among genes with an annotated role in growth and stress reactions. Polygenic scores computed from marginally associated variants confirmed the polygenic basis of growth variation. For both light regimes, phenotypic divergence between the most distantly related population (China) and the various regions in Europe is smaller than the variation observed within Europe, indicating that the evolution of growth rate is likely to be constrained by stabilizing selection. We observed that Spanish genotypes, however, reach a significantly larger size than Northern European genotypes. Tests of adaptive divergence and analysis of the individual burden of deleterious mutations reveal that adaptive processes have played a more important role in shaping regional differences in rosette growth than maladaptive evolution.

Unravelling the complex genetic basis of growth in New Zealand silver trevally (Pseudocaranx georgianus)

“…GWAS identified 113 associations with growth, corresponding to 93 different SNPs spanning 22 LGs, further supporting the hypothesis of a polygenic basis of growth-related traits in trevally. Growth is considered as a complex trait and has been found to be polygenic across the tree of life, in very diverse taxa from plants, like in the model species Arabidopsis thaliana (Wieters, Steige et al 2021), to vertebrates like humans (Sinnott-Armstrong, Tanigawa et al 2021) and other fish species (e.g. Liu, Sun et al 2014, Yang, Wu et al 2020, Debes, Piavchenko et al 2021).…”

Genetic architecture of variation in Arabidopsis thaliana rosettes

“…We have previously characterized rosette morphology in 19 Arabidopsis ecotypes using image based approaches during growth and development [ 22 ] and other studies have used similar descriptors for screening large Arabidopsis populations and mutants, tracking morphological changes over time and allowing a more precise dissection of developmental timing of plant growth and development [ 59 – 63 ]. Image analysis can quantify size and shape variation due to defined genetic lesions in rosette plants [ 64 ] and used to identify QTL for variation in rosette area, revealing a number of candidate genes for growth and size traits [ 3 , 4 , 62 , 63 , 65 – 67 ]. More recently, a multi-scale approach was used with the purpose of linking genes to plant shape at several scales, from the whole plant to cells and tissues attending to quantitative measurements extracted from digital images and models [ 50 ].…”

Using singleton densities to detect recent selection inBos taurus

“…The release of these data spurred a series of studies and new methods designed specifically to detect polygenic selection. These methods usually involve determining which SNPs affecting a phenotype show correlated changes in frequency (Berg & Coop, 2014; Racimo et al ., 2018; Sanjak et al ., 2018; Josephs et al ., 2019; Berg et al ., 2019a, 2019b; Uricchio et al ., 2019; Edge & Coop, 2019; Kreiner et al ., 2020; Wieters et al ., 2021; Gramlich et al ., 2021); which sets of alleles are associated with certain environmental or climatic variations (Coop et al ., 2010; Turchin et al ., 2012; Robinson et al ., 2015; Yeaman et al ., 2016; Exposito-Alonso et al ., 2018; Zan & Carlborg, 2018; Exposito-Alonso et al ., 2019; MacLachlan et al ., 2021; Ehrlich et al ., 2021; Fuhrmann et al ., 2021; Rowan et al ., 2021); or determining which SNPs or genetic regions explain a large fraction of phenotypic variance and trait heritability (Zhou et al ., 2013; Yang et al ., 2015; Gazal et al ., 2017; Zeng et al ., 2018; Schoech et al ., 2019; Exposito-Alonso et al ., 2020; Duntsch et al ., 2020; Zeng et al ., 2021).…”