2.8 | Association of loci with traits
To detect loci associated with each of the eight traits, we examined
genome-wide association (GWA) of SNP genotypes (association mapping) and
GWA of local ancestry (admixture mapping) at ddRAD loci with the
phenotypic values using mixed linear models. Despite the similarities
between association mapping and admixture mapping, some differences
between them in terms of aims and methods result in both merits and
demerits of each (Buerkle and Lexer 2008). Association mapping can deal
with complicated background of ancestry and variation within ancestral
lineages underlying phenotypes using more loci densely distributed
across a genome, while admixture mapping can work with fewer loci
sparsely distributed owing to larger blocks of linkage disequilibrium in
recombinants of distinct ancestries (Buerkle and Lexer 2008).
Phenotypic values of individual trees with the same Qd ancestry
were expected to differ between the inland and coastal sites due to
phenotypic plasticity. To exclude such confounding effects with
environment and plasticity and to conduct GWA in both sites together, we
scaled phenotypic values (mean = 0 and variance = 1) in each site
separately and applied a mixed linear model to pooled data of the scaled
values in both sites. Environmental heterogeneity within the sites were
incorporated as random errors in the models.
To conduct GWA, we used single-locus GWA (Yu et al. 2006) implemented in
the package rrBLUP 4.5 (Endelman 2011) in R 3.3.2. To discriminate
confounding factors with genetic structure of sampled trees in both
sites, we used the Q + K model with a
population structure matrix Q calculated from
probabilistic principal component analysis using the function ppca in
the package pcaMethods (Stacklies et al. 2007) and a kinship matrix
K calculated from realized additive relationship using
the function A.mat in rrBLUP. The first four principal components were
used for the Q matrix. All parameters in the GWA were
set at the default values. Significant associations were regarded as
< 0.05 false discovery rate (FDR) of –log10p calculated using the function p.adjust in R 3.3.2. Q-Q plots of
expected and observed –log10 p values were
examined for associations of SNP genotypes and local ancestry.
To search for genes involved in the adaptation to coastal environment
around the trait-associated loci, we selected genes (protein ID) nearest
to the loci detected by association mapping and within regions including
the loci detected by admixture mapping from PM1N v2.3 Q. roburannotation database (https://urgi.versailles.inra.fr/OakMine_PM1N)
(Plomion et al. 2018). We obtained gene ontology (GO) terms and protein
descriptions of the genes.