2.4.1. Single Marker Regression (SMR)

PLINK 1.9 [32] was used to perform an association analysis between SNP markers and DEBVs for each trait, which was tested with a single marker regression as follows:

where y is a vector of DEBVs for each trait (MY, FY, PY, and SCS); μ is overall mean; X is a design matrix allocating records to the marker effect; g is the effect of the SNP marker; and e is a vector of the random deviate eij~N0,σe2, where σe2 is the error variance. In this additive model, the marker effect is treated as a fixed effect (0, 1, and 2). The results were also clumped based on LD between SNPs using—clump flag in PLINK 1.9 with the default option (index variants were formed with p-values < 0.0001, and SNPs which are less than 250 kb away from an index variant and have r² larger than 0.5 with it were removed). The significance threshold of the −log₁₀ p-value ≥ 6.61 was determined based on Bonferroni correction. In addition, Quantile-Quantile (Q-Q) plots for each phenotype were performed to identify population stratification or cryptic relatedness.