A new mechanism for a familiar mutation – bovineDGAT1K232A modulates gene expression through multi-junction exon splice enhancement

Abstract

The DGAT1 gene encodes an enzyme responsible for catalysing the terminal reaction in mammary triglyceride synthesis, and underpins a well-known pleiotropic quantitative trait locus (QTL) with a large influence on milk composition phenotypes. Since first described over 15 years ago, a protein-coding variant K232A has been assumed as the causative variant underlying these effects, following in-vitro studies that demonstrated differing levels of triglyceride synthesis between the two protein isoforms. In the current study, we used a large RNAseq dataset to re-examine the underlying mechanisms of this large milk production QTL, and hereby report novel expression-based functions of the chr14 g.1802265AA>GC variant that encodes the DGAT1 K232A substitution. Using expression QTL (eQTL) mapping, we demonstrate a highly-significant mammary eQTL for DGAT1, where the K232A mutation appears as one of the top associated variants for this effect. By conducting in vitro expression and splicing experiments in bovine mammary cell culture, we further show modulation of splicing efficiency by this mutation, likely through disruption of an exon splice enhancer as a consequence of the allele encoding the 232A variant. Although the relative contributions of the enzymatic and transcription-based mechanisms now attributed to K232A remain unclear, these results suggest that transcriptional impacts contribute to the diversity of lactation effects observed at this locus.A lysine to alanine amino acid substitution (K232A) encoded by a mutation in the diacylglyercol O-acyltransferase 1 (DGAT1) gene has major impacts on bovine lactation traits, the most substantial being its impact on milk fat percentage [1,2]. This substitution results from an AA to GC dinucleotide substitution in exon eight of DGAT1, and likely constitutes the most widely studied and validated variant in association analyses of bovine milk characteristics (initially described by Grisart et al (2002), with >1000 Google Scholar citations to date). The DGAT1 gene encodes an enzyme responsible for catalysing the terminal reaction in the mammary triglyceride synthesis pathway [3], and the DGAT1 K allele has been shown [4] to synthesise more triglycerides in vitro when compared to the A allele. Aside from the DGAT1 K232A mutation, an additional polymorphism 5′ of the transcription start site of the gene has also been shown to associate with milk fat percentage [5]. This variant, a variable number tandem repeat (VNTR) expansion, was speculated to play a role in bovine milk composition by increasing the number of putative transcription factor binding sites [5]. However, functional testing of the VNTR variant did not show any differences in DGAT1 expression between QTL genotypes in cell culture [6]. This finding largely put the competing, gene expression-based hypothesis of the DGAT1 milk fat effect to rest, with enzymatic differences deriving from the K232A mutation now widely assumed as the underlying mechanism.Since these initial analyses >10 years ago, further functional charact...

Genetic score omics regression and multi-trait meta-analysis detect widespread cis-regulatory effects shaping bovine complex traits

“…Based on the strongest statistical evidence from both GSOR and SMR for major traits of dairy cattle (non-linear assessment traits), we found that some traditionally recognised large-effect “single-gene” loci actually contain several adjacent genes or underlying spliced introns potentially causally linked to complex traits (Table 1 and Supplementary Data 1). We confirm the regulatory effects on milk production, gestation length, and height of several known causal loci, including DGAT1 10 via both gene expression and splicing, MGST1 25,28 and MATN3 29 via gene expression, and CSF2RB 30,31 and MUC1 32 via splicing. However, importantly, our evidence supports multiple regulatory loci underlying these major QTL.…”

“…In animals, only a few causal QTL are identified and one of the most extraordinary QTL is a mutation in the gene for diacylglycerol O-acyltransferase 1 (DGAT1) in cattle. This single QTL explains 30%-40% of the phenotypic variance of milk production traits 9,10 . While this QTL was previously identified to be caused by a protein-coding mutation 9,11,12 , more recent studies indicated regulatory effects 10,13 , possibly due to multiple causal mutations.…”

“…This single QTL explains 30%-40% of the phenotypic variance of milk production traits 9,10 . While this QTL was previously identified to be caused by a protein-coding mutation 9,11,12 , more recent studies indicated regulatory effects 10,13 , possibly due to multiple causal mutations. The new CattleGTEx 14 and the FAANG consortium 15 provide opportunities to explore the causal regulatory mechanisms behind this QTL.…”

Sequence-based genome-wide association study of individual milk mid-infrared wavenumbers in mixed-breed dairy cattle

“…Iterations are defined relative to the base GWAS, with the base GWAS represented as iteration 0. N of hits: number of wavenumbers for which the variant was selected as the representative (most significant) tag variant for a peak may be due in part to an expression-based mechanism [64]. For the effects observed in the ABCG2, PAEP and DGAT1 genes, the p-values for the most significant FT-MIR wavenumber were always more significant than the comparable values for any of the milk composition traits.…”

Bayesian genome-wide analysis of cattle traits using variants with functional and evolutionary significance

“…Additionally, a few thousand variants affecting the concentration of milk fat metabolites, i.e., metabolic mQTLs, also had significantly higher variance than SNPs in the 50k panel for cattle traits. Millions of variants that change gene expression levels (geQTLs) or RNA splicing (sQTLs) are also enriched with complex trait QTL (Xiang et al, 2018, Li et al, 2016, Silva et al, 2020, Fink et al, 2020, Lopdell et al, 2017). However, recent studies showed that variants close to genes with high or specific expression patterns had limited improvement in prediction accuracy (Fang et al, 2020, de Las Heras-Saldana et al, 2020).…”