Validation of markers with non-additive effects on milk yield and fertility in Holstein and Jersey cows

Abstract

BackgroundIt has been suggested that traits with low heritability, such as fertility, may have proportionately more genetic variation arising from non-additive effects than traits with higher heritability, such as milk yield. Here, we performed a large genome scan with 408,255 single nucleotide polymorphism (SNP) markers to identify chromosomal regions associated with additive, dominance and epistatic (pairwise additive × additive) variability in milk yield and a measure of fertility, calving interval, using records from a population of 7,055 Holstein cows. The results were subsequently validated in an independent set of 3,795 Jerseys.ResultsWe identified genomic regions with validated additive effects on milk yield on Bos taurus autosomes (BTA) 5, 14 and 20, whereas SNPs with suggestive additive effects on fertility were observed on BTA 5, 9, 11, 18, 22, 27, 29 and the X chromosome. We also confirmed genome regions with suggestive dominance effects for milk yield (BTA 2, 3, 5, 26 and 27) and for fertility (BTA 1, 2, 3, 7, 23, 25 and 28). A number of significant epistatic effects for milk yield on BTA 14 were found across breeds. However on close inspection, these were likely to be associated with the mutation in the diacylglycerol O-acyltransferase 1 (DGAT1) gene, given that the associations were no longer significant when the additive effect of the DGAT1 mutation was included in the epistatic model.ConclusionsIn general, we observed a low statistical power (high false discovery rates and small number of significant SNPs) for non-additive genetic effects compared with additive effects for both traits which could be an artefact of higher dependence on linkage disequilibrium between markers and causative mutations or smaller size of non-additive effects relative to additive effects. The results of our study suggest that individual non-additive effects make a small contribution to the genetic variation of milk yield and fertility. Although we found no individual mutation with large dominance effect for both traits under investigation, a contribution to genetic variance is still possible from a large number of small dominance effects, so methods that simultaneously incorporate genotypes across all loci are suggested to test the variance explained by dominance gene actions.Electronic supplementary materialThe online version of this article (doi:10.1186/s12863-015-0241-9) contains supplementary material, which is available to authorized users.

Including nonadditive genetic effects in mating programs to maximize dairy farm profitability

“…Significant inbreeding depression can also happen even without large dominance variances if the dominance deviations are small across many loci (Bolormaa et al, 2015). Aliloo et al (2015) implemented a large genome scan on milk yield and CaI with a similar data set to the one in the present study and found that the dominance effects of markers with suggestive dominance gene actions were very small and distributed all over the genome. This is in agreement with the findings of the present study where the effect of average heterozygosity on trait phenotypes was significant for all traits, but the dominance variance was not significant after correcting for average heterozygosity.…”

Benefits of Dominance over Additive Models for the Estimation of Average Effects in the Presence of Dominance

“…Only few studies have used the AD-model in GWAS to explicitly estimate $a$ and $d$ ( e.g. , Lopes et al 2014; Aliloo et al 2015; Huang et al 2015; Bennewitz et al 2017) and, to our knowledge, none have investigated differences in accuracy of estimated average effects between the A-model and AD-model. The effects of sampling genotypes on $\stackrel{true^}{\alpha }$ shown in this study apply to ${\stackrel{true^}{\alpha }}_m$ in GWAS, because ${\stackrel{true^}{\alpha }}_m$ are usually estimated by ordinary least squares.…”

The genetic regulatory network centered on Pto-Wuschela and its targets involved in wood formation revealed by association studies

“…Here, 435 unrelated poplar individuals, whose LD is much lower than animals, were used for identifying epistasis effects, which may reduce the false positive rate in our study. However, further work should be performed to validate the significant epistasis effects 35 . Also, genome-wide deep sequencing and computer analysis software will make SNP-based association genetics analysis more powerful for construction of GRNs.…”