Pleiotropic effect of SNP on plant height and seed weight
GWAS summary statistic data for both traits were obtained from above
GWAS analysis and were used for common factor analysis. Pleiotropic
effect of each SNP on plant height and seed weight were estimated with a
common factor model (Grotzinger et al. 2019). The common factor model
included the two traits plant height and seed weight, assuming each SNP
assert effect on both traits. The effect of each SNP on each
trait was estimated within a genomic structural equation modelling
framework (Grotzinger et al., 2019). The estimate of the SNP
effect and model test was implemented with GenomicSEM (Grotzinger
et al., 2019). As the experimental data currently are not compatible
with the hypothesis that every mutation (or gene) affects every trait
(Wagner & Zhang 2011), and large data size (e.g. >10,000
in this case) could, meanwhile, cause spurious correlation (Lin et al.,
2013; Kaplan et al. 2014), we identified the outliers that are deviated
from the general pattern of relationship between the SNP effects on
plant height and that on seed seeds as pleiotropic SNPs. A bivariate
linear regression between the SNP effects on plant height and that on
seeds weight was implemented, and residues of the regression were then
obtained. The SNPs with residues of the regression beyond the 95%
confident zone as outliers using a Z-score method (Z-score
>1.96, or Z-score < - 1.96).
Basic summary statistics were carried out using PAST v3 (Hammer et al.,
2011). The R package ggplot2 (Wickham, 2016) was used to plot
model results including the kolmogorov-smirnov plots. Regression slopes
were compared using estimated marginal means with the R package
“emmeans (Lenth et al., 2017). Genomic diversity (π, nucleotide
diversity) was calculated using TASSEL v5 (Bradbury et al., 2007).
Significance was taken at p < 0.05 for the null
hypothesis.