2.6 | Genome-Wide Association Studies (GWAS)
GWAS were performed on the two different phenotypic traits (DLA-R and
DLA-S) estimated for each isolate in
(Dumartinet et al.,
2019) using two different statistical models: the multi-locus mixed
linear model (MLMM,
(Segura et al., 2012))
and the settlement of MLM under progressively exclusive relationship
(SUPER) (Wen et al.,
2018) both implemented in GAPIT software
(Lipka et al., 2012).
The first model accounts for the linkage disequilibrium between loci
associated with traits while the second model is known to have higher
statistical power than regular mixed linear models. All GWAS were
corrected for population genetics by estimating a distance matrix
between all isolates, and all single-marker p-values were corrected
using the Benjamini–Hochberg FDR procedure
(Benjamini &
Hochberg, 1995). The above-mentioned local score procedure was also
applied to GWAS p-values to increase GWAS resolution to detect clusters
of loci with low p-values, as in Bonhomme et al. 2019.