The genetic basis of phenotypic variability is the fundamental underpinning of evolutionary biology and key in understanding factors that define speciation, biogeographical distributions and fitness under natural conditions (Stapley et al., 2010; Savolainen et al., 2013). Achieving such understanding is becoming more attainable as the ability to cast a wider net for gene discovery in traits of interest emerges. In plant biology, the integration of extensive genetic and phenotypic data is finding application in development and improvement of crop species, but is also extending our understanding of the genetics underlying traits of evolutionary and ecological importance (Ingvarsson et al., 2008; Eckert et al., 2009, 2010, 2012; Fournier-Level et al., 2011; Parchman et al., 2012; Olson et al., 2013). Genome-wide association studies (GWAS) can be powerful for identifying putative causal genes, or suites of genes, underlying phenotypic variation, particularly in traits with complex genetic architecture (Vandenkoornhuyse et al., 2010; Ingvarsson & Street, 2011; Savolainen et al., 2013; Sork et al., 2013). Where traits are complex (i.e. involving a number of genes or gene networks), GWAS using high genome coverage of single nucleotide polymorphisms (SNP) markers has been very effective for identifying the genetic architecture underlying variability in these traits (Eckert et al., 2012; Parchman et al., 2012; Riedelsheimer et al., 2012; Morris et al., 2013; Porth et al., 2013a). GWAS can also uncover loci with potential pleiotropic effects that may be important to natural variation within species and their capacity for adaptation (Mackay et al., 2009; Stapley et al., 2010; Porth et al., 2014).
Defining the roles of genotypic and phenotypic variability in adaptation across a landscape are key to understanding the evolution and adaptability of species (Sork et al., 2013). Within tree species, phenotypic variability is influenced by wide geographic distributions and numerous traits are considered to be under polygenic control (Savolainen et al., 2007; Ingvarsson & Street, 2011; Cooke et al., 2012; Sork et al., 2013). High genetic complexity is reported for many adaptive traits in trees, such as cold hardiness, bud break, bud set, cone serotiny, disease resistance and growth (Ruttink et al., 2007; Holliday et al., 2008, 2010; Ingvarsson et al., 2008; Eckert et al., 2009; Ibáñez et al., 2010; Ma et al., 2010; Rohde et al., 2010; Keller et al., 2012; Parchman et al., 2012; La Mantia et al., 2013; Olson et al., 2013). By comparison, the underlying genetic variability for numerous physiological traits considered important in range-wide adaptation of tree species, such as nutrient uptake, leaf anatomy, photosynthetic rate and water-use efficiency (cf. Soolanayakanahally et al., 2009; Chamaillard et al., 2011; Keller et al., 2011; McKown et al., 2014), is only beginning to be explored (González-Martínez et al., 2008; Cumbie et al., 2011).
In this study, we focused on the genetics underlying phenotypic trait variation in black cottonwood (Populus trichocarpa), a species of high ecological, scientific and economic value (Cronk, 2005; Tuskan et al., 2006). Like many poplars, P. trichocarpa trees are outbreeding, fast growing and often function as pioneers and/or constitute major canopy-forming components of riparian forest ecosystems (Farrar, 1995; Braatne et al., 1996). The species is common throughout the Pacific Northwest of North America and has high natural phenotypic variation relating to its geographical distribution spanning environmental and climatic gradients (Gornall & Guy, 2007; McKown et al., 2014). Trait variation within P. trichocarpa relates primarily to its latitudinal distribution and gradients in photoperiodic regime (daylength) and/or temperature across its natural range (McKown et al., 2014). Furthermore, heritability is generally highest in traits that co-vary strongly with these ecological and geographical gradients.
Extensive genomic tools available for P. trichocarpa (Tuskan et al., 2006; Geraldes et al., 2013) and high intraspecific variability in traits (McKown et al., 2014) support using the GWAS approach to provide significant insights into the genetic architecture of ecologically important phenotypic variation (Eckert et al., 2010, 2012; Parchman et al., 2012; Morris et al., 2013; Porth et al., 2013a; La Mantia et al., 2013; Olson et al., 2013). Nevertheless, GWAS is challenging to implement using natural populations across a landscape (Ingvarsson & Street, 2011; Neale & Kremer, 2011; Sork et al., 2013). As genetic structure reflects the effects of family relatedness, demography and adaptive history, model-fitting in GWAS as a corrective measure is necessary to balance the risk of false-positives with that of false-negatives (Balding, 2006; Ingvarsson & Street, 2011; Sork et al., 2013). However, attempts to minimize the loss of some associations where relationships exist between loci, demography and geography should be made by assessing corrective measures on a trait-by-trait basis (La Mantia et al., 2013; Porth et al., 2013a,b).
Using accessions originating from wild populations of P. trichocarpa, we investigated the genetic basis of intraspecific variation in 40 biomass, ecophysiology and phenology traits in an association genetics framework. We employed GWAS, integrating extensive biological information on quantitative variation in these traits assayed within a common garden over multiple years (McKown et al., 2013, 2014) and SNP genotype data from the same trees obtained using an Illumina iSelect Infinium 34K Populus SNP genotyping array developed for P. trichocarpa (Geraldes et al., 2013). We predicted that certain traits considered genetically complex, such as growth or bud set, might retrieve multiple associations underscoring the genetic complexity of the trait. Additionally, we expected that genes underlying trait variation would associate repeatedly with the same trait when phenotyped over multiple years. Finally, we expected that the same loci would associate with multiple traits where traits are genetically correlated. Based on the results from our GWAS, we propose numerous key loci for further testing in trait variation, highlighting these as important in the evolution and ecology of P. trichocarpa.