The five SSR markers detected previously by single-marker regression analysis were within this QTL region.
These included QTL for average daily gain, body weight, milk yield, milk lactose yield, and milk fat percentage.> > In our analysis, we calculated the proportion of additive genetic variance explained by each SNP for a Bonferroni adjusted P-value cut off threshold of 0.01, detected by single-marker regression analysis.
However, this result was achieved by adding variances contributed by markers from distinct single marker regression models, which may give a misleading picture of the variance captured by markers because LD between them may overemphasize the contribution of significant markers [ 49].
In order to deal with potential problems raised by multiple testing in single marker regression, the p-value for individual testing was set to 1.316×10−5 to obtain a 0.05 genome-wide type I error rate using the Šidák's correction.
The ROH4Mb effect of a SNP was estimated by regressing ROH4Mb of a SNP on the same phenotype as single marker regression and gradient boosted machine and therefore the additive effect explained by the EBV was removed from the phenotype.
single marker regression.
For 144 RILs, single marker regression based on a GLM was completed to examine the association between each SNP and the anaerobic response index.
The single marker regression was also done fixing the top marker (higher F statistic) for each trait.
At each marker, the mean fertility, scored as a Z-score, was compared between genotypes using single marker regression.
Grapes et al. [ 3] and Zhao et al. [ 4] demonstrated that the single marker regression model is as powerful and precise as other more sophisticated techniques, such as multiple regression, regression on haplotypes, or the IBD method proposed by Meuwissen and Goddard [ 5].
