A new mechanism for a familiar mutation - bovine DGAT1 K232A modulates gene expression through multi-junction exon splice enhancement

Functional impact of splicing variants in the elaboration of complex traits in cattle

GWAS conducted directly on imputed whole genome sequence have led to the identification of numerous genetic variants associated with agronomic traits in cattle. However, such variants are often simply markers in linkage disequilibrium with the actual causal variants, which is a limiting factor for the development of accurate genomic predictions. It is possible to identify causal variants by integrating information on how variants impact gene expression into GWAS output. RNA splicing plays a major role in regulating gene expression. Thus, assessing the effect of variants on RNA splicing may explain their function. Here, we use a high-throughput strategy to functionally analyse putative splice-disrupting variants in the bovine genome. Using GWAS, massively parallel reporter assay and deep learning algorithms designed to predict splice-disrupting variants, we identify 38 splice-disrupting variants associated with complex traits in cattle, three of which could be classified as causal. Our results indicate that splice-disrupting variants are widely found in the quantitative trait loci related to these phenotypes. Using our combined approach, we also assess the validity of splicing predictors originally developed to analyse human variants in the context of the bovine genome.

Comparison of genomic prediction accuracies in dairy cattle lactation traits using five classes of functional variants versus generic SNP

BACKGROUND

Genomic selection, typically employing genetic markers from SNP chips, is routine in modern dairy cattle breeding. This study assessed the impact of functional sequence variants on genomic prediction accuracy relative to 50 k SNP chip markers for fat percent, protein percent, milk volume, fat yield, and protein yield in lactating dairy cattle. The functional variants were identified through GWAS, RNA-seq, Histone modification ChIP-seq, ATAC-seq, or were coding variants. The genomic prediction accuracy obtained using each class of functional variants was compared with matched numbers of SNPs randomly selected from the Illumina 50 k SNP chip.

RESULTS

The investigation revealed that variants identified by GWAS or RNA-seq, significantly improved the prediction accuracy across all five traits. Contributions from ChIP-seq, ATAC-seq, and coding variants varied. Some variants identified using ChIP-seq showed marked improvements, while others reduced accuracy in protein yield predictions. Relative to a matched number of 32,595 SNPs from the SNP chip, pooling all the functional variants demonstrated prediction accuracy increases of 1.76% for fat percent, 2.97% for protein percent, 0.51% for milk volume, and 0.26% for fat yield, but with a slight decrease of 0.43% in protein yield.

CONCLUSION

The study demonstrates that functional variants can improve prediction accuracy relative to equivalent numbers of variants from a generic SNP panel, with percent traits showing more significant gains than yield traits. The main advantage of using functional variants for genomic prediction was achievement of comparable accuracy using a smaller, more selective set of loci. This is particularly evident in trait-specific scenarios. Our findings indicate that specific combinations of functional variants comprising 16 k variants can achieve genomic prediction accuracy comparable to employing a standard panel of twice the size (32.6 k), especially for percent traits. This highlights the potential for the development of more efficient, trait-focused SNP panels utilizing functional variants.

Autophagy Gene BECN1 Intronic Variant rs9890617 Predisposes Individuals to Hepatitis B Virus Infection

Beclin 1 protein encoded by the BECN1 gene plays a critical role in the autophagy pathway which is utilized by the Hepatitis B virus (HBV) for its replication. HBV is known for the subversion of the host's autophagy process for its multiplication. The aim of this study was to determine the role of BECN1 intronic variants in HBV susceptibility. Intronic region variant rs9890617 was analyzed using Human splicing finder v3.1 and was found to alter splicing signals. A total of 712 individuals (494 HBV infected and 218 healthy controls) were recruited in the study and genotyped by applying Polymerase Chain Reaction-Restriction Fragment Length Polymorphism (PCR-RFLP). Statistical analysis revealed that the mutant allele T of rs9890617 was significantly associated with the overall disease risk in the allelic model (OR 1.41; 95%CI 1.00-1.99, p = 0.04). On stratifying the data based on the different stages of HBV infection, the mutant genotype showed a significant association with the chronic group in allelic (OR 1.62; 95%CI 1.11-2.39, p = 0.01), dominant (OR 1.64; 95%CI 1.07-2.52, p = 0.02), and co-dominant (OR 1.55; 95%CI 1.00-2.40, p = 0.04) models. Overall, this is the first study regarding beclin 1 variant rs9890617 and we found a significant association of the mutant T allele with the genetic predisposition to HBV infection.

Identification and characterization of whole blood gene expression and splicing quantitative trait loci during early to mid-lactation of dairy cattle

BACKGROUND

Characterization of regulatory variants (e.g., gene expression quantitative trait loci, eQTL; gene splicing QTL, sQTL) is crucial for biologically interpreting molecular mechanisms underlying loci associated with complex traits. However, regulatory variants in dairy cattle, particularly in specific biological contexts (e.g., distinct lactation stages), remain largely unknown. In this study, we explored regulatory variants in whole blood samples collected during early to mid-lactation (22-150 days after calving) of 101 Holstein cows and analyzed them to decipher the regulatory mechanisms underlying complex traits in dairy cattle.

RESULTS

We identified 14,303 genes and 227,705 intron clusters expressed in the white blood cells of 101 cattle. The average heritability of gene expression and intron excision ratio explained by cis-SNPs is 0.28 ± 0.13 and 0.25 ± 0.13, respectively. We identified 23,485 SNP-gene expression pairs and 18,166 SNP-intron cluster pairs in dairy cattle during early to mid-lactation. Compared with the 2,380,457 cis-eQTLs reported to be present in blood in the Cattle Genotype-Tissue Expression atlas (CattleGTEx), only 6,114 cis-eQTLs (P < 0.05) were detected in the present study. By conducting colocalization analysis between cis-e/sQTL and the results of genome-wide association studies (GWAS) from four traits, we identified a cis-e/sQTL (rs109421300) of the DGAT1 gene that might be a key marker in early to mid-lactation for milk yield, fat yield, protein yield, and somatic cell score (PP4 > 0.6). Finally, transcriptome-wide association studies (TWAS) revealed certain genes (e.g., FAM83H and TBC1D17) whose expression in white blood cells was significantly (P < 0.05) associated with complex traits.

CONCLUSIONS

This study investigated the genetic regulation of gene expression and alternative splicing in dairy cows during early to mid-lactation and provided new insights into the regulatory mechanisms underlying complex traits of economic importance.

Autophagy related gene 5 polymorphism rs17587319 (C/G) in asthmatic patients in North Indian population

Objective: Genetic background and environmental stimuli play an important role in asthma, which is an individual's hyper-responsiveness to these stimuli leading to airway inflammation. Autophagy Related Gene 5 (ATG5) plays a critical role in the autophagy pathway and has been shown to be involved in asthma. The genetic polymorphisms in the ATG5 have been reported to predispose individuals to asthma. The role of single nucleotide polymorphism rs17587319 (C/G) of ATG5 in asthma has not been studied so far. Materials and methods: In this study, we in silico analysed rs17587319 (C/G) using web-based tools Human Splice Finder (HSF) and RegulomeDB and further a case-control study was conducted that included 187 blood samples (94 asthmatic and 93 healthy controls). Results: In silico analysis suggested alteration of splicing signals by this intronic variant. The samples were genotyped by applying the PCR-RFLP method. The MAF obtained was 0.022 and 0.043 in healthy controls and asthmatic individuals, respectively. The statistical analysis revealed no association (allelic model, OR = 2.02, 95%CI = 0.59-6.83, p = 0.25; co-dominant model, OR = 2.06, 95%CI = 0.6-7.12, p = 0.24) of rs17587319 (C/G) with the susceptibility to asthma in the north Indian population. Conclusions: In conclusion, rs17587319 (C/G) of ATG5 does not predispose individuals to asthma in our part of the world. Further studies are needed including more number of samples to ascertain the role of this polymorphism in asthma.

An organism-wide ATAC-seq peak catalog for the bovine and its use to identify regulatory variants

We report the generation of an organism-wide catalog of 976,813 cis-acting regulatory elements for the bovine detected by the assay for transposase accessible chromatin using sequencing (ATAC-seq). We regroup these regulatory elements in 16 components by nonnegative matrix factorization. Correlation between the genome-wide density of peaks and transcription start sites, correlation between peak accessibility and expression of neighboring genes, and enrichment in transcription factor binding motifs support their regulatory potential. Using a previously established catalog of 12,736,643 variants, we show that the proportion of single-nucleotide polymorphisms mapping to ATAC-seq peaks is higher than expected and that this is owing to an approximately 1.3-fold higher mutation rate within peaks. Their site frequency spectrum indicates that variants in ATAC-seq peaks are subject to purifying selection. We generate eQTL data sets for liver and blood and show that variants that drive eQTL fall into liver- and blood-specific ATAC-seq peaks more often than expected by chance. We combine ATAC-seq and eQTL data to estimate that the proportion of regulatory variants mapping to ATAC-seq peaks is approximately one in three and that the proportion of variants mapping to ATAC-seq peaks that are regulatory is approximately one in 25. We discuss the implication of these findings on the utility of ATAC-seq information to improve the accuracy of genomic selection.

Successful editing and maintenance of lactogenic gene expression in primary bovine mammary epithelial cells

In vitro investigation of bovine lactation processes is limited by a lack of physiologically representative cell models. This deficiency is most evident through the minimal or absent expression of lactation-specific genes in cultured bovine mammary tissues. Primary bovine mammary epithelial cells (pbMECs) extracted from lactating mammary tissue and grown in culture initially express milk protein transcripts at relatively representative levels. However, expression drops dramatically after only three or four passages, which greatly reduces the utility of primary cells to model and further examine lactogenesis. To investigate the effects of alternate alleles in pbMECs including effects on transcription, we have developed methods to deliver CRISPR-Cas9 gene editing reagents to primary mammary cells, resulting in very high editing efficiencies. We have also found that culturing the cells on an imitation basement membrane composed of Matrigel, results in the restoration of a more representative lactogenic gene expression profile and the cells forming three-dimensional structures in vitro. Here, we present data from four pbMEC lines recovered from pregnant cows and detail the expression profile of five key milk synthesis genes in these MECs grown on Matrigel. Additionally, we describe an optimised method for preferentially selecting CRISPR-Cas9-edited cells conferring a knock-out of DGAT1, using fluorescence-activated cell sorting (FACS). The combination of these techniques facilitates the use of pbMECs as a model to investigate the effects of gene introgressions and genetic variation in lactating mammary tissue.

Analysis of the impact of DGAT1 p.M435L and p.K232A variants on pre-mRNA splicing in a full-length gene assay

DGAT1 is playing a major role in fat metabolism and triacylglyceride synthesis. Only two DGAT1 loss-of-function variants altering milk production traits in cattle have been reported to date, namely p.M435L and p.K232A. The p.M435L variant is a rare alteration and has been associated with skipping of exon 16 which results in a non-functional truncated protein, and the p.K232A-containing haplotype has been associated with modifications of the splicing rate of several DGAT1 introns. In particular, the direct causality of the p.K232A variant in decreasing the splicing rate of the intron 7 junction was validated using a minigene assay in MAC-T cells. As both these DGAT1 variants were shown to be spliceogenic, we developed a full-length gene assay (FLGA) to re-analyse p.M435L and p.K232A variants in HEK293T and MAC-T cells. Qualitative RT-PCR analysis of cells transfected with the full-length DGAT1 expression construct carrying the p.M435L variant highlighted complete skipping of exon 16. The same analysis performed using the construct carrying the p.K232A variant showed moderate differences compared to the wild-type construct, suggesting a possible effect of this variant on the splicing of intron 7. Finally, quantitative RT-PCR analyses of cells transfected with the p.K232A-carrying construct did not show any significant modification on the splicing rate of introns 1, 2 and 7. In conclusion, the DGAT1 FLGA confirmed the p.M435L impact previously observed in vivo, but invalidated the hypothesis whereby the p.K232A variant strongly decreased the splicing rate of intron 7.

Using QTL to Identify Genes and Pathways Underlying the Regulation and Production of Milk Components in Cattle

Milk is a complex liquid, and the concentrations of many of its components are under genetic control. Many genes and pathways are known to regulate milk composition, and the purpose of this review is to highlight how the discoveries of quantitative trait loci (QTL) for milk phenotypes can elucidate these pathways. The main body of this review focuses primarily on QTL discovered in cattle (Bos taurus) as a model species for the biology of lactation, and there are occasional references to sheep genetics. The following section describes a range of techniques that can be used to help identify the causative genes underlying QTL when the underlying mechanism involves the regulation of gene expression. As genotype and phenotype databases continue to grow and diversify, new QTL will continue to be discovered, and although proving the causality of underlying genes and variants remains difficult, these new data sets will further enhance our understanding of the biology of lactation.

Strong evidence for association between K232A polymorphism of the DGAT1 gene and milk fat and protein contents: A meta-analysis

The relationship between K232A polymorphism of the DGAT1 gene and milk yield and composition was evaluated by meta-analysis of pooled data of more than 10,000 genotyped cattle. Four genetic models, including dominant (AA+KA vs. KK), recessive (AA vs. KA+KK), additive (AA vs. KK), and co-dominant (AA+KK vs. KA) were used to analyze the data. The standardized mean difference (SMD) was used to measure the size of the effects of the A and K alleles of K232A polymorphism on milk-related traits. The results showed that additive model was the best model for describing the effects of K232A polymorphism on studied traits. Under additive model, milk fat content was strongly decreased in cows having the AA genotype (SMD = -1.320). Furthermore, the AA genotype reduced the protein content of milk (SMD = -0.400). A significant difference in daily milk yield (SMD = 0.225) and lactation yield (SMD = 0.697) was found between cows carrying AA and KK genotypes, suggesting the positive effects of the K allele on these traits. Cook's distance measurement suggested some studies as outliers and sensitivity analyses by removing influential studies revealed that the results of meta-analyses for daily milk yield, fat content and protein content were not sensitive to outliers. However, the outcome of the meta-analysis for lactation yield was strongly influenced by outlier studies. Egger's test and Begg's funnel plots showed no evidence of publication bias in included studies. In conclusion, the K allele of K232A polymorphism showed a tremendous effect on increasing fat and protein contents in the milk of cattle, especially when 2 copies of this allele are inherited together, whereas the A allele of K232A polymorphism had negative effects on these traits.

Association of DGAT1 With Cattle, Buffalo, Goat, and Sheep Milk and Meat Production Traits

Milk fatty acids are essential for many dairy product productions, while intramuscular fat (IMF) is associated with the quality of meat. The triacylglycerols (TAGs) are the major components of IMF and milk fat. Therefore, understanding the polymorphisms and genes linked to fat synthesis is important for animal production. Identifying quantitative trait loci (QTLs) and genes associated with milk and meat production traits has been the objective of various mapping studies in the last decade. Consistently, the QTLs on chromosomes 14, 15, and 9 have been found to be associated with milk and meat production traits in cattle, goat, and buffalo and sheep, respectively. Diacylglycerol O-acyltransferase 1 (DGAT1) gene has been reported on chromosomes 14, 15, and 9 in cattle, goat, and buffalo and sheep, respectively. Being a key role in fat metabolism and TAG synthesis, the DGAT1 has obtained considerable attention especially in animal milk production. In addition to milk production, DGAT1 has also been a subject of interest in animal meat production. Several polymorphisms have been documented in DGAT1 in various animal species including cattle, buffalo, goat, and sheep for their association with milk production traits. In addition, the DGAT1 has also been studied for their role in meat production traits in cattle, sheep, and goat. However, very limited studies have been conducted in cattle for association of DGAT1 with meat production traits in cattle. Moreover, not a single study reported the association of DGAT1 with meat production traits in buffalo; thus, further studies are warranted to fulfill this huge gap. Keeping in view the important role of DGAT1 in animal production, the current review article was designed to highlight the major development and new insights on DGAT1 effect on milk and meat production traits in cattle, buffalo, sheep, and goat. Moreover, we have also highlighted the possible future contributions of DGAT1 for the studied species.

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

BACKGROUND

Fourier-transform mid-infrared (FT-MIR) spectroscopy provides a high-throughput and inexpensive method for predicting milk composition and other novel traits from milk samples. While there have been many genome-wide association studies (GWAS) conducted on FT-MIR predicted traits, there have been few GWAS for individual FT-MIR wavenumbers. Using imputed whole-genome sequence for 38,085 mixed-breed New Zealand dairy cattle, we conducted GWAS on 895 individual FT-MIR wavenumber phenotypes, and assessed the value of these direct phenotypes for identifying candidate causal genes and variants, and improving our understanding of the physico-chemical properties of milk.

RESULTS

Separate GWAS conducted for each of 895 individual FT-MIR wavenumber phenotypes, identified 450 1-Mbp genomic regions with significant FT-MIR wavenumber QTL, compared to 246 1-Mbp genomic regions with QTL identified for FT-MIR predicted milk composition traits. Use of mammary RNA-seq data and gene annotation information identified 38 co-localized and co-segregating expression QTL (eQTL), and 31 protein-sequence mutations for FT-MIR wavenumber phenotypes, the latter including a null mutation in the ABO gene that has a potential role in changing milk oligosaccharide profiles. For the candidate causative genes implicated in these analyses, we examined the strength of association between relevant loci and each wavenumber across the mid-infrared spectrum. This revealed shared association patterns for groups of genomically-distant loci, highlighting clusters of loci linked through their biological roles in lactation and their presumed impacts on the chemical composition of milk.

CONCLUSIONS

This study demonstrates the utility of FT-MIR wavenumber phenotypes for improving our understanding of milk composition, presenting a larger number of QTL and putative causative genes and variants than found from FT-MIR predicted composition traits. Examining patterns of significance across the mid-infrared spectrum for loci of interest further highlighted commonalities of association, which likely reflects the physico-chemical properties of milk constituents.

Feeding System Resizes the Effects of DGAT1 Polymorphism on Milk Traits and Fatty Acids Composition in Modicana Cows

Simple Summary

Genetic selection for single-locus polymorphisms could offer suitable opportunities to rapidly improve milk traits in local unselected cattle breeds characterized by low production levels. Since these hardy breeds are generally raised in traditional extensive and semi-intensive systems, which make wide use of grazing resources, the interactive effect between genotype and feeding system is worthy of investigation. In Modicana cattle breed, milk composition and fatty acid profile were influenced by both genetic polymorphisms at the DGAT1 K232A locus and feeding systems. The milk from homozygous AA cows was associated with a more favorable fatty acid composition due to a lower percentage of total saturated fatty acids, saturated to unsaturated ratio, atherogenic index, and a greater presence of oleic acid and total unsaturated fatty acids. Our finding confirmed the important role of pasture feeding on milk composition: the high nutritional and healthy value of milk obtained in extensive systems by pasture-fed cows. The interaction between the two experimental factors also appears to play a role: in our experimental condition, it seems that high pasture feeding can resize the effect of the DGAT1 genotype on milk traits and fatty acid composition in Modicana cows.

Abstract

The interaction between genetic polymorphism and feeding system on milk traits and fatty acid composition was investigated in Modicana cows. Two DGAT1 K232A genotypes (AK and AA) and two feeding regimes, extensive system (EX) with 8 h of grazing without concentrate (EX) and semi-intensive systems (SI) with 2 h of grazing with concentrate, were investigated. DGAT1 genotype did not influence milk yield and composition. The feeding system affected milk composition: protein was significantly higher in SI and lactose in the EX system. A significant genotype × feeding system interaction was observed: the protein and casein levels of AK cows were higher in the SI compared to the EX system. Milk fatty acids profile, total saturated to total unsaturated fatty acids, n-6 to n-3 ratios, and atherogenic index were affected by the feeding system, improving the healthy properties of milk from animals reared in the extensive system. DGAT1 genotype influenced the fatty acid composition: milk from AA cows had a more favorable fatty acid composition due to lower total saturated fatty acids, saturated to unsaturated ratio, atherogenic index, and higher levels of oleic acid and total unsaturated fatty acids. Furthermore, an interaction genotype x feeding system was observed: the AK milk was richer in short-chain FAs (C4:0–C8:0) and C10:0 only in the EX but not in the SI system. Our data suggest that a high amount of green forage in the diet of Modicana cows can resize the effect of the DGAT1 genotype on milk traits and fatty acids composition.

Non-additive association analysis using proxy phenotypes identifies novel cattle syndromes

Mammalian species carry ~100 loss-of-function variants per individual^1,2, where ~1-5 of these impact essential genes and cause embryonic lethality or severe disease when homozygous³. The functions of the remainder are more difficult to resolve, although the assumption is that these variants impact fitness in less manifest ways. Here we report one of the largest sequence-resolution screens of cattle to date, targeting discovery and validation of non-additive effects in 130,725 animals. We highlight six novel recessive loci with impacts generally exceeding the largest-effect variants identified from additive genome-wide association studies, presenting analogs of human diseases and hitherto-unrecognized disorders. These loci present compelling missense (PLCD4, MTRF1 and DPF2), premature stop (MUS81) and splice-disrupting (GALNT2 and FGD4) mutations, together explaining substantial proportions of inbreeding depression. These results demonstrate that the frequency distribution of deleterious alleles segregating in selected species can afford sufficient power to directly map novel disorders, presenting selection opportunities to minimize the incidence of genetic disease.

Evaluation of non-synonym mutation in DGAT1 K232A as a marker for milk production traits in Ongole cattle and Murrah buffalo from Southern India

Various candidate genes have been reported to affect milk yield and composition in dairy cattle. A non-synonymous mutation in the DGAT1 gene, i.e., K232A was reported to have a strong association with milk yield and milk composition of Bos taurus. A study has been undertaken on 502 unrelated individuals belonging to indigenous Ongole cattle, crossbred cattle, and Murrah buffaloes from the Indian sub-continent with the objective to determine the polymorphism of the K232A locus and their association with milk yield and composition. Typing DGAT1 K232A allelic variation by PCR-RFLP using CfrI restriction enzyme revealed three genotypes in crossbred cattle. Genotype KK was more prevalent (0.60) in Jersey crossbred, whereas in Holstein Friesian crossbred it was KA genotype (0.48). In Ongole cattle and Murrah buffaloes, the locus did not exhibit polymorphism. The least-square mean of milk yields pooled over lactations across the DGAT1 variants was significantly (P < 0.05) higher among the homozygous (AA) genotypes, both in Jersey crossbred and HF crossbred cattle after adjusting for the effects of farm, parity, and season. The fat, SNF, and protein content values of AA genotypes were less than the KK genotypes in both the genetic groups (P > 0.05). The fixation of the DGAT1^K allele at the locus in Bos indicus cattle and Bubalus bubalis in the present study did not support its use as a reliable universal marker for milk production and composition traits.

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

Context Functional genomics studies have highlighted genomic regions with regulatory and evolutionary significance. Such information independent of association analysis may benefit fine-mapping and genomic selection of economically important traits. However, systematic evaluation of the use of functional information in mapping, and genomic selection of cattle traits, is lacking. Also, single-nucleotide polymorphisms (SNPs) from the high-density (HD) panel are known to tag informative variants, but the performance of genomic prediction using HD SNPs together with variants supported by different functional genomics is unknown. Aims We selected six sets of functionally important variants and modelled each set together with HD SNPs in Bayesian models to map and predict protein, fat and milk yield as well as mastitis, somatic cell count and temperament of dairy cattle. Methods Two models were used, namely (1) BayesR, which includes priors of four distribution of variant effects, and (2) BayesRC, which includes additional priors of different functional classes of variants. Bayesian models were trained in three breeds of 28 000 cows of Holstein, Jersey and Australian Red and predicted into 2600 independent bulls. Key results Adding functionally important variants significantly increased the enrichment of genetic variance explained for mapped variants, suggesting improved genome-wide mapping precision. Such improvement was significantly higher when the same set of variants was modelled by BayesRC than by BayesR. Combining functional variant sets with HD SNPs improves genomic prediction accuracy in the majority of the cases and such improvement was more common and stronger for non-Holstein breeds and traits such as mastitis, somatic cell count and temperament. In contrast, adding a large number of random sequence variants to HD SNPs reduces mapping precision and has a worse or similar prediction accuracy, compared with using HD SNPs alone to map or predict. While BayesRC tended to have better genomic prediction accuracy than did BayesR, the overall difference in prediction accuracy between the two models was insignificant. Conclusions Our findings demonstrated the usefulness of functional data in genomic mapping and prediction. Implications We have highlighted the need for effective tools exploiting complex functional datasets to improve genomic prediction.