A common regulatory haplotype doubles lactoferrin concentration in milk.

BACKGROUND: Bovine lactoferrin (Lf) is an iron absorbing whey protein with antibacterial, antiviral, and antifungal activity. Lactoferrin is economically valuable and has an extremely variable concentration in milk, partly driven by environmental influences such as milking frequency, involution, or mastitis. A significant genetic influence has also been previously observed to regulate lactoferrin content in milk. Here, we conducted genetic mapping of lactoferrin protein concentration in conjunction with RNA-seq, ChIP-seq, and ATAC-seq data to pinpoint candidate causative variants that regulate lactoferrin concentrations in milk. RESULTS: We identified a highly-significant lactoferrin protein quantitative trait locus (pQTL), as well as a cis lactotransferrin (LTF) expression QTL (cis-eQTL) mapping to the LTF locus. Using ChIP-seq and ATAC-seq datasets representing lactating mammary tissue samples, we also report a number of regions where the openness of chromatin is under genetic influence. Several of these also show highly significant QTL with genetic signatures similar to those highlighted through pQTL and eQTL analysis. By performing correlation analysis between these QTL, we revealed an ATAC-seq peak in the putative promotor region of LTF, that highlights a set of 115 high-frequency variants that are potentially responsible for these effects. One of the 115 variants (rs110000337), which maps within the ATAC-seq peak, was predicted to alter binding sites of transcription factors known to be involved in lactation-related pathways. CONCLUSIONS: Here, we report a regulatory haplotype of 115 variants with conspicuously large impacts on milk lactoferrin concentration. These findings could enable the selection of animals for high-producing specialist herds.

Allele specific binding of histone modifications and a transcription factor does not predict allele specific expression in correlated ChIP-seq peak-exon pairs.

Allele specific expression (ASE) is widespread in many species including cows. Therefore, regulatory regions which control gene expression should show cis-regulatory variation which mirrors this differential expression within the animal. ChIP-seq peaks for histone modifications and transcription factors measure activity at functional regions and the height of some peaks have been shown to correlate across tissues with the expression of particular genes, suggesting these peaks are putative regulatory regions. In this study we identified ASE in the bovine genome in multiple tissues and investigated whether ChIP-seq peaks for four histone modifications and the transcription factor CTCF show allele specific binding (ASB) differences in the same tissues. We then investigate whether peak height and gene expression, which correlates across tissues, also correlates within the animal by investigating whether the direction of ASB in putative regulatory regions, mirrors that of the ASE in the genes they are putatively regulating. We found that ASE and ASB were widespread in the bovine genome but vary in extent between tissues. However, even when the height of a peak was positively correlated across tissues with expression of an exon, ASE of the exon and ASB of the peak were in the same direction only half the time. A likely explanation for this finding is that the correlations between peak height and exon expression do not indicate that the height of the peak causes the extent of exon expression, at least in some cases.

Genetic variation in histone modifications and gene expression identifies regulatory variants in the mammary gland of cattle.

BACKGROUND: Causal variants for complex traits, such as eQTL are often found in non-coding regions of the genome, where they are hypothesised to influence phenotypes by regulating gene expression. Many regulatory regions are marked by histone modifications, which can be assayed by chromatin immunoprecipitation followed by sequencing (ChIP-seq). Sequence reads from ChIP-seq form peaks at putative regulatory regions, which may reflect the amount of regulatory activity at this region. Therefore, eQTL which are also associated with differences in histone modifications are excellent candidate causal variants. RESULTS: We assayed the histone modifications H3K4Me3, H3K4Me1 and H3K27ac and mRNA in the mammary gland of up to 400 animals. We identified QTL for peak height (histone QTL), exon expression (eeQTL), allele specific expression (aseQTL) and allele specific binding (asbQTL). By intersecting these results, we identify variants which may influence gene expression by altering regulatory regions of the genome, and may be causal variants for other traits. Lastly, we find that these variants are found in putative transcription factor binding sites, identifying a mechanism for the effect of many eQTL. CONCLUSIONS: We find that allele specific and traditional QTL analysis often identify the same genetic variants and provide evidence that many eQTL are regulatory variants which alter activity at regulatory regions of the bovine genome. Our work provides methodological and biological updates on how regulatory mechanisms interplay at multi-omics levels.

Corrigendum: Putative Causal Variants Are Enriched in Annotated Functional Regions From Six Bovine Tissues.

[This corrects the article DOI: 10.3389/fgene.2021.664379.].

Putative Causal Variants Are Enriched in Annotated Functional Regions From Six Bovine Tissues.

Genetic variants which affect complex traits (causal variants) are thought to be found in functional regions of the genome. Identifying causal variants would be useful for predicting complex trait phenotypes in dairy cows, however, functional regions are poorly annotated in the bovine genome. Functional regions can be identified on a genome-wide scale by assaying for post-translational modifications to histone proteins (histone modifications) and proteins interacting with the genome (e.g., transcription factors) using a method called Chromatin immunoprecipitation followed by sequencing (ChIP-seq). In this study ChIP-seq was performed to find functional regions in the bovine genome by assaying for four histone modifications (H3K4Me1, H3K4Me3, H3K27ac, and H3K27Me3) and one transcription factor (CTCF) in 6 tissues (heart, kidney, liver, lung, mammary and spleen) from 2 to 3 lactating dairy cows. Eighty-six ChIP-seq samples were generated in this study, identifying millions of functional regions in the bovine genome. Combinations of histone modifications and CTCF were found using ChromHMM and annotated by comparing with active and inactive genes across the genome. Functional marks differed between tissues highlighting areas which might be particularly important to tissue-specific regulation. Supporting the cis-regulatory role of functional regions, the read counts in some ChIP peaks correlated with nearby gene expression. The functional regions identified in this study were enriched for putative causal variants as seen in other species. Interestingly, regions which correlated with gene expression were particularly enriched for potential causal variants. This supports the hypothesis that complex traits are regulated by variants that alter gene expression. This study provides one of the largest ChIP-seq annotation resources in cattle including, for the first time, in the mammary gland of lactating cows. By linking regulatory regions to expression QTL and trait QTL we demonstrate a new strategy for identifying causal variants in cattle.

Expression quantitative trait loci in sheep liver and muscle contribute to variations in meat traits.

BACKGROUND: Variants that regulate transcription, such as expression quantitative trait loci (eQTL), have shown enrichment in genome-wide association studies (GWAS) for mammalian complex traits. However, no study has reported eQTL in sheep, although it is an important agricultural species for which many GWAS of complex meat traits have been conducted. Using RNA sequence data produced from liver and muscle from 149 sheep and imputed whole-genome single nucleotide polymorphisms (SNPs), our aim was to dissect the genetic architecture of the transcriptome by associating sheep genotypes with three major molecular phenotypes including gene expression (geQTL), exon expression (eeQTL) and RNA splicing (sQTL). We also examined these three types of eQTL for their enrichment in GWAS of multi-meat traits and fatty acid profiles. RESULTS: Whereas a relatively small number of molecular phenotypes were significantly heritable (h2 > 0, P < 0.05), their mean heritability ranged from 0.67 to 0.73 for liver and from 0.71 to 0.77 for muscle. Association analysis between molecular phenotypes and SNPs within ± 1 Mb identified many significant cis-eQTL (false discovery rate, FDR < 0.01). The median distance between the eQTL and transcription start sites (TSS) ranged from 68 to 153 kb across the three eQTL types. The number of common variants between geQTL, eeQTL and sQTL within each tissue, and the number of common variants between liver and muscle within each eQTL type were all significantly (P < 0.05) larger than expected by chance. The identified eQTL were significantly (P < 0.05) enriched in GWAS hits associated with 56 carcass traits and fatty acid profiles. For example, several geQTL in muscle mapped to the FAM184B gene, hundreds of sQTL in liver and muscle mapped to the CAST gene, and hundreds of sQTL in liver mapped to the C6 gene. These three genes are associated with body composition or fatty acid profiles. CONCLUSIONS: We detected a large number of significant eQTL and found that the overlap of variants between eQTL types and tissues was prevalent. Many eQTL were also QTL for meat traits. Our study fills a gap in the knowledge on the regulatory variants and their role in complex traits for the sheep model.

Mitochondrial protein gene expression and the oxidative phosphorylation pathway associated with feed efficiency and energy balance in dairy cattle.

Feed efficiency and energy balance are important traits underpinning profitability and environmental sustainability in animal production. They are complex traits, and our understanding of their underlying biology is currently limited. One measure of feed efficiency is residual feed intake (RFI), which is the difference between actual and predicted intake. Variation in RFI among individuals is attributable to the metabolic efficiency of energy utilization. High RFI (H_RFI) animals require more energy per unit of weight gain or milk produced compared with low RFI (L_RFI) animals. Energy balance (EB) is a closely related trait calculated very similarly to RFI. Cellular energy metabolism in mitochondria involves mitochondrial protein (MiP) encoded by both nuclear (NuMiP) and mitochondrial (MtMiP) genomes. We hypothesized that MiP genes are differentially expressed (DE) between H_RFI and L_RFI animal groups and similarly between negative and positive EB groups. Our study aimed to characterize MiP gene expression in white blood cells of H_RFI and L_RFI cows using RNA sequencing to identify genes and biological pathways associated with feed efficiency in dairy cattle. We used the top and bottom 14 cows ranked for RFI and EB out of 109 animals as H_RFI and L_RFI, and positive and negative EB groups, respectively. The gene expression counts across all nuclear and mitochondrial genes for animals in each group were used for differential gene expression analyses, weighted gene correlation network analysis, functional enrichment, and identification of hub genes. Out of 244 DE genes between RFI groups, 38 were MiP genes. The DE genes were enriched for the oxidative phosphorylation (OXPHOS) and ribosome pathways. The DE MiP genes were underexpressed in L_RFI (and negative EB) compared with the H_RFI (and positive EB) groups, suggestive of reduced mitochondrial activity in the L_RFI group. None of the MtMiP genes were among the DE MiP genes between the groups, which suggests a non-rate limiting role of MtMiP genes in feed efficiency and warrants further investigation. The role of MiP, particularly the NuMiP and OXPHOS pathways in RFI, was also supported by our gene correlation network analysis and the hub gene identification. We validated the findings in an independent data set. Overall, our study suggested that differences in feed efficiency in dairy cows may be linked to differences in cellular energy demand. This study broadens our knowledge of the biology of feed efficiency in dairy cattle.

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

Many genome variants shaping mammalian phenotype are hypothesized to regulate gene transcription and/or to be under selection. However, most of the evidence to support this hypothesis comes from human studies. Systematic evidence for regulatory and evolutionary signals contributing to complex traits in a different mammalian model is needed. Sequence variants associated with gene expression (expression quantitative trait loci [eQTLs]) and concentration of metabolites (metabolic quantitative trait loci [mQTLs]) and under histone-modification marks in several tissues were discovered from multiomics data of over 400 cattle. Variants under selection and evolutionary constraint were identified using genome databases of multiple species. These analyses defined 30 sets of variants, and for each set, we estimated the genetic variance the set explained across 34 complex traits in 11,923 bulls and 32,347 cows with 17,669,372 imputed variants. The per-variant trait heritability of these sets across traits was highly consistent (r > 0.94) between bulls and cows. Based on the per-variant heritability, conserved sites across 100 vertebrate species and mQTLs ranked the highest, followed by eQTLs, young variants, those under histone-modification marks, and selection signatures. From these results, we defined a Functional-And-Evolutionary Trait Heritability (FAETH) score indicating the functionality and predicted heritability of each variant. In additional 7,551 cattle, the high FAETH-ranking variants had significantly increased genetic variances and genomic prediction accuracies in 3 production traits compared to the low FAETH-ranking variants. The FAETH framework combines the information of gene regulation, evolution, and trait heritability to rank variants, and the publicly available FAETH data provide a set of biological priors for cattle genomic selection worldwide.

Accuracy of imputation to whole-genome sequence in sheep.

BACKGROUND: The use of whole-genome sequence (WGS) data for genomic prediction and association studies is highly desirable because the causal mutations should be present in the data. The sequencing of 935 sheep from a range of breeds provides the opportunity to impute sheep genotyped with single nucleotide polymorphism (SNP) arrays to WGS. This study evaluated the accuracy of imputation from SNP genotypes to WGS using this reference population of 935 sequenced sheep. RESULTS: The accuracy of imputation from the Ovine Infinium® HD BeadChip SNP (~ 500 k) to WGS was assessed for three target breeds: Merino, Poll Dorset and F1 Border Leicester × Merino. Imputation accuracy was highest for the Poll Dorset breed, although there were more Merino individuals in the sequenced reference population than Poll Dorset individuals. In addition, empirical imputation accuracies were higher (by up to 1.7%) when using larger multi-breed reference populations compared to using a smaller single-breed reference population. The mean accuracy of imputation across target breeds using the Minimac3 or the FImpute software was 0.94. The empirical imputation accuracy varied considerably across the genome; six chromosomes carried regions of one or more Mb with a mean imputation accuracy of < 0.7. Imputation accuracy in five variant annotation classes ranged from 0.87 (missense) up to 0.94 (intronic variants), where lower accuracy corresponded to higher proportions of rare alleles. The imputation quality statistic reported from Minimac3 (R2) had a clear positive relationship with the empirical imputation accuracy. Therefore, by first discarding imputed variants with an R2 below 0.4, the mean empirical accuracy across target breeds increased to 0.97. Although accuracy of genomic prediction was less affected by filtering on R2 in a multi-breed population of sheep with imputed WGS, the genomic heritability clearly tended to be lower when using variants with an R2 ≤ 0.4. CONCLUSIONS: The mean imputation accuracy was high for all target breeds and was increased by combining smaller breed sets into a multi-breed reference. We found that the Minimac3 software imputation quality statistic (R2) was a useful indicator of empirical imputation accuracy, enabling removal of very poorly imputed variants before downstream analyses.

Genome variants associated with RNA splicing variations in bovine are extensively shared between tissues.

BACKGROUND: Mammalian phenotypes are shaped by numerous genome variants, many of which may regulate gene transcription or RNA splicing. To identify variants with regulatory functions in cattle, an important economic and model species, we used sequence variants to map a type of expression quantitative trait loci (expression QTLs) that are associated with variations in the RNA splicing, i.e., sQTLs. To further the understanding of regulatory variants, sQTLs were compare with other two types of expression QTLs, 1) variants associated with variations in gene expression, i.e., geQTLs and 2) variants associated with variations in exon expression, i.e., eeQTLs, in different tissues. RESULTS: Using whole genome and RNA sequence data from four tissues of over 200 cattle, sQTLs identified using exon inclusion ratios were verified by matching their effects on adjacent intron excision ratios. sQTLs contained the highest percentage of variants that are within the intronic region of genes and contained the lowest percentage of variants that are within intergenic regions, compared to eeQTLs and geQTLs. Many geQTLs and sQTLs are also detected as eeQTLs. Many expression QTLs, including sQTLs, were significant in all four tissues and had a similar effect in each tissue. To verify such expression QTL sharing between tissues, variants surrounding (±1 Mb) the exon or gene were used to build local genomic relationship matrices (LGRM) and estimated genetic correlations between tissues. For many exons, the splicing and expression level was determined by the same cis additive genetic variance in different tissues. Thus, an effective but simple-to-implement meta-analysis combining information from three tissues is introduced to increase power to detect and validate sQTLs. sQTLs and eeQTLs together were more enriched for variants associated with cattle complex traits, compared to geQTLs. Several putative causal mutations were identified, including an sQTL at Chr6:87392580 within the 5th exon of kappa casein (CSN3) associated with milk production traits. CONCLUSIONS: Using novel analytical approaches, we report the first identification of numerous bovine sQTLs which are extensively shared between multiple tissue types. The significant overlaps between bovine sQTLs and complex traits QTL highlight the contribution of regulatory mutations to phenotypic variations.