Variance explained by whole genome sequence variants in coding and regulatory genome annotations for six dairy traits

Abstract

BackgroundThere are an exceedingly large number of sequence variants discovered through whole genome sequencing in most populations, including cattle. Deciphering which of these affect complex traits is a major challenge. In this study we hypothesize that variants in some functional classes, such as splice site regions, coding regions, DNA methylated regions and long noncoding RNA will explain more variance in complex traits than others. Two variance component approaches were used to test this hypothesis – the first determines if variants in a functional class capture a greater proportion of the variance, than expected by chance, the second uses the proportion of variance explained when variants in all annotations are fitted simultaneously.ResultsOur data set consisted of 28.3 million imputed whole genome sequence variants in 16,581 dairy cattle with records for 6 complex trait phenotypes, including production and fertility. We found that sequence variants in splice site regions and synonymous classes captured the greatest proportion of the variance, explaining up to 50% of the variance across all traits. We also found sequence variants in target sites for DNA methylation (genomic regions that are found be highly methylated in bovine placentas), captured a significant proportion of the variance. Per sequence variant, splice site variants explain the highest proportion of variance in this study. The proportion of variance captured by the missense predicted deleterious (from SIFT) and missense tolerated classes was relatively small.ConclusionThe results demonstrate using functional annotations to filter whole genome sequence variants into more informative subsets could be useful for prioritization of the variants that are more likely to be associated with complex traits. In addition to variants found in splice sites and protein coding genes regulatory variants and those found in DNA methylated regions, explained considerable variation in milk production and fertility traits. In our analysis synonymous variants captured a significant proportion of the variance, which raises the possible explanation that synonymous mutations might have some effects, or more likely that these variants are miss-annotated, or alternatively the results reflect imperfect imputation of the actual causative variants.Electronic supplementary materialThe online version of this article (10.1186/s12864-018-4617-x) contains supplementary material, which is available to authorized users.

Genetic architecture of quantitative traits in beef cattle revealed by genome wide association studies of imputed whole genome sequence variants: I: feed efficiency and component traits

“…These results are in agreement with the report by Koufariotis et al [40] that the intron and intergenic variants explained the lowest proportion of the genetic variance per SNP basis for milk and fertility traits in dairy cattle, which is likely due to much larger number of DNA variants in the class and majority of them have small or zero effects on the traits. Notable, 3'UTR and synonymous variants captured a greater amount of additive genetic variance per sequence variant for all the traits, which are in line with the results reported by Koufariotis et al [41] for milk production traits in dairy cattle. These results indicate a greater role of 3'UTR variants in affecting the complex traits, which is also supported by previous studies that have shown that microRNAs interact with target sites in 3'UTR of mRNA to regulate their gene expressions [42,43].…”

“…1. At the suggestive significant level of P-value < 0.005, 41,248,46,455,44,746, and 47,923 SNPs (i.e. sequence variants) were found to be associated with RFI, DMI, ADG, and MWT, respectively (Table 2).…”

Quantifying the contribution of sequence variants with regulatory and evolutionary significance to 34 bovine complex traits

“…Variants annotated as coding related, of which the majority of variants are missense and synonymous (SI Appendix, Table S1), had relatively low ranking of genetic importance to complex traits (Figure 3). It seems a surprising result, but Koufariotis et al also reported similar observations in cattle (50). Perhaps coding variants that influence phenotype are subject to purifying selection and hence have low heterozygosity and hence low contribution to variance.…”

“…The variant annotation sets of non-coding related and splice sites ranked relatively high for their contribution to trait variation ( Figure 3). Previously, variants annotated as splice sites had a high ranking of genetic importance to cattle complex traits (50). The majority of the variants from the non-coding related set are 'non_coding_transcript_exon_variant' (SI Appendix, Table S1) which is 'a sequence variant that changes non-coding exon sequence in a non-coding transcript' according to VEP (28).…”

DNA methylation pattern of the goat &lt;i&gt;PITX1&lt;/i&gt; gene and its effects on milk performance

“…Lactation performance is a complex quantitative trait affected by several crucial factors including genetic background, nutrition, and environment (Carcangiu et al, 2018). Over the past decade, the influences of genetic variations on milk performance in animals have been extensively studied (Koufariotis et al, 2018). Recently an increasing number of studies have demonstrated that nutritional or environmental changes contribute to changes in inheritable epigenetic modifications, such as DNA methylation (Lan et al, 2013a;Chavatte-Palmer et al, 2018;Kwan et al, 2018;Yuan et al, 2018).…”