Polygenic adaptation and negative selection across traits, years and environments in a long-lived plant species (Pinus pinasterAit., Pinaceae)

Abstract

27Results from a decade of association studies in different organisms suggest that most complex 28 traits are polygenic, that is, their genetic architectures are determined by numerous causal loci 29 distributed across the genome each with small effect-size. Thus, determining the degree of 30 polygenicity is a central goal to understand the genetic basis of phenotypic variation. 31Recently, multi-loci methods able to detect variants associated with a phenotype of interest 32 despite the subtle allele frequency changes between populations usually observed have been 33 developed. In this study, we applied two multi-loci methods to estimate the degree of 34 polygenicity of fitness-related traits in a long-lived plant species (maritime pine) and to 35 analyze how polygenicity changes across years and environments. For this purpose, we 36 evaluated five categories of fitness related traits, such as, height, survival, phenology-related, 37 biotic-stress resistance and functional traits in a clonal common garden network planted in 38 contrasted environments. Most of the analyzed traits showed evidences of local adaptation. 39We observed a remarkably stable degree of polygenicity (average 6%) across traits, 40 environments and years. Additionally, some of the measured traits showed evidences of 41 negative selection that could explain the observed degree of polygenicity, as previously 42 suggested in humans. The observed genetic architecture of fitness-related traits in maritime 43 pine is in accordance with the polygenic adaptation model. Because polygenic adaptation can 44 take place rapidly, our results can contribute to improve the predictions about the capacity of 45

Combining climatic and genomic data improves range-wide tree height growth prediction in a forest tree

“…However, these studies are based on young plants and genetic variation could be expressed at later ontogenic stages in favorable environments. is is supported by the increasing heritability estimates in the French Atlantic common garden (Bordeaux) across years reported in de Miguel et al (2020). In this line, a meta-analysis for wild animal populations reported higher heritability for morphometric traits in favorable environments (but not for life-history traits; Charmantier and Garant 2005).…”

“…in a particular environment), three regional GWAS were performed using separately data from the Iberian Atlantic common gardens (Asturias and Portugal), the French Atlantic common garden (Bordeaux) and the Mediterranean common gardens (Madrid and Cáceres). All GWAS were performed following the Bayesian variable selection regression (BVSR) methodology implemented in the piMASS software (Guan and Stephens 2011), including corrections for population structure and using the height BLUPs reported in de Miguel et al (2020). For each of the four GWAS, we selected the 350 SNPs with the highest absolute estimates of the posterior effect size (i.e.…”

“…For each of the four GWAS, we selected the 350 SNPs with the highest absolute estimates of the posterior e ect size (i.e. Rao-Blackwellized estimates), corresponding approximately to the levels of polygenicity estimated for height in maritime pine (de Miguel et al 2020).…”

“…For example, Mahony et al (2020) used counts of alleles positively associated with the traits of interest (PEAs) to describe patterns and identify drivers of local adaptation in lodgepole pine. Recent studies have shown that most quantitative traits are highly polygenic (see reviews in Pritchard et al 2010, Barghi et al 2020; and de Miguel et al 2020 for maritime pine) and that the effect of trait-associated alleles may vary across environments (Anderson et al 2013, Tiffin and Ross-Ibarra 2014), which complicates the use of genomic information in trait prediction. In addition, patterns in allele frequencies induced by population demographic history are often correlated with environmental gradients (Latta 2009, Alberto et al 2013, Nadeau et al 2016), which makes difficult to separate the signature of population structure from that of adaptive processes (Sella and Barton 2019, Sohail et al 2019).…”

“…However, forest trees have also large and complex genomes (especially conifers; Mackay et al 2012), that show a rapid decay of linkage disequilibrium (Olson et al 2010), and extensive genotyping would be needed to identify all (most) relevant polymorphisms underlying (highly polygenic) adaptive traits (Neale and Savolainen 2004, Jaramillo-Correa et al 2015). For example, de Miguel et al (2020) found that the average proportion of genome-wide variants with non-zero effects on height growth in maritime pine, i.e. the degree of polygenicity, could be as high as ~7%.…”