highly significantP < 0.001
SAC effects were highly significant (inclusion of column processes: χ 2 (df = 2) = 179.7, P < 0.001) and explained 36% of the variance ( Fig. 3B ).
SAC effects were highly significant (inclusion of column processes: χ 2 (df = 2) = 179.7, P < 0.001) and explained 36% of the variance ( Fig. 3B ).
Addition of SAC resulted in a marginally nonsignificant improvement in model fit (inclusion of column and row processes: χ 2 (3) = 7.58, P = 0.056) and explained less than 3% of the variance, and this came mostly from the residual variance with very little change in heritability ( Table 2 ; Fig. 3D ).