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Background Understanding the variation between well and poorly adapted cattle breeds to local environments and pathogens is essential for breeding cattle with improved climate and disease-resistant phenotypes. Although considerable progress has been made towards identifying genetic differences between breeds, variation at the epigenetic and chromatin levels remains poorly characterized. Here, we generate, sequence and analyse over 150 libraries at base-pair resolution to explore the dynamics of DNA methylation and chromatin accessibility of the bovine immune system across three distinct cattle lineages. Results We find extensive epigenetic divergence between the taurine and indicine cattle breeds across immune cell types, which is linked to the levels of local DNA sequence divergence between the two cattle sub-species. The unique cell type profiles enable the deconvolution of complex cellular mixtures using digital cytometry approaches. Finally, we show distinct sub-categories of CpG islands based on their chromatin and methylation profiles that discriminate between classes of distal and gene proximal islands linked to discrete transcriptional states. Conclusions Our study provides a comprehensive resource of DNA methylation, chromatin accessibility and RNA expression profiles of three diverse cattle populations. The findings have important implications, from understanding how genetic editing across breeds, and consequently regulatory backgrounds, may have distinct impacts to designing effective cattle epigenome-wide association studies in non-European breeds.

Background Differential isoform usage is an important driver of inter-individual phenotypic diversity and is linked to various diseases and traits. However, accurately detecting the differential usage of different gene transcripts between groups can be difficult, in particular in less well annotated genomes where the spectrum of transcript isoforms is largely unknown. Results We investigated whether machine learning approaches can detect differential isoform usage based purely on the distribution of reads across a gene region. We illustrate that gradient boosting and elastic net approaches can successfully identify large numbers of genes showing potential differential isoform usage between Europeans and Africans, that are enriched among relevant biological pathways and significantly overlap those identified by previous approaches. We demonstrate that diversity at the 3′ and 5′ ends of genes are primary drivers of these differences between populations. Conclusion Machine learning methods can effectively detect differential isoform usage from read fraction data, and can provide novel insights into the biological differences between groups.

Background Structural variants (SVs) are genetic polymorphisms including deletions, insertions, inversions, and duplications, with potential to influence traits through impacts on gene function and expression. SVs have not been widely utilized in genetic analysis, owing to the challenge of their accurate detection and genotyping. Addressing this general issue, and the broader demand to understand their role in commercially important taxa, we report a comprehensive analysis of SVs in the genome of European seabass ( Dicentrarchus labrax ), the most commercially important fish in the Mediterranean region. Results Using whole genome sequencing from a farmed population ( n  = 90 samples), 21,428 SVs were identified using a comprehensive detection and genotyping strategy involving manual curation of every variant. This high-confidence SV atlas was annotated to predict impacts on genes and evolutionarily conserved sequences. We explored the overlap between SVs and repeats, identified heterogeneity in SV density across the genome, and tested if the coding genes disrupted by SVs are enriched for specific biological processes or conserved protein domains. SVs impacting evolutionarily conserved genomic regions were enriched in genes with nervous system and developmental functions. Finally, we performed a comparative analysis incorporating 38,408 high-confidence SVs identified independently for three wild populations ( n  = 80 samples) using identical methods. An analysis of 41,336 SVs merged across the two datasets provides insights into genes and biological functions targeted during aquaculture domestication, with evidence of shifts in allele frequency for SVs located within or near genes controlling behaviour, enriched for forebrain and synaptic functions, and specifically expressed in the brain. Conclusions This study sheds light on the global organisation of SVs across the European seabass genome, revealing a potential role in aquaculture domestication. The reported datasets provide a novel, high-quality reference for future genetic investigations of both farmed and wild European seabass.

Background Whole genome sequencing (WGS), despite its advantages, is yet to replace methods for genotyping single nucleotide variants (SNVs) such as SNP arrays and targeted genotyping assays. Structural variants (SVs) have larger effects on traits than SNVs, but are more challenging to accurately genotype. Using low-coverage WGS with genotype imputation offers a cost-effective strategy to achieve genome-wide variant coverage, but is yet to be tested for SVs. Methods Here, we investigate combined SNV and SV imputation with low-coverage WGS data in Atlantic salmon ( Salmo salar ). As the reference panel, we used genotypes for high-confidence SVs and SNVs for n = 365 wild individuals sampled from diverse populations. We also generated 15 × WGS data (n = 20 samples) for a commercial population external to the reference panel, and called SVs and SNVs with gold-standard approaches. An imputation method selected for its established performance using low-coverage sequencing data (GLIMPSE) was tested at WGS depths of 1 × , 2 × , 3 × , and 4 × for samples within and external to the reference panel. Results SNVs were imputed with high accuracy and recall across all WGS depths, including for samples out-with the reference panel. For SVs, we compared imputation based purely on linkage disequilibrium (LD) with SNVs, to that supplemented with SV genotype likelihoods (GLs) from low-coverage WGS. Including SV GLs increased imputation accuracy, but as a trade-off with recall, requiring 3–4 × depth for best performance. Combining strategies allowed us to capture 84% of the reference panel deletions with 87% accuracy at 1 × depth. We also show that SV length affects imputation performance, with provision of SV GLs greatly enhancing accuracy for the longest SVs in the dataset. Conclusions This study highlights the promise of reference panel imputation using low-coverage WGS, including novel opportunities to enhance the resolution of genome-wide association studies by capturing SVs.

Second generation sequencing has prompted a number of groups to re-interrogate the transcriptomes of several bacterial and archaeal species. One of the central findings has been the identification of complex networks of small non-coding RNAs that play central roles in transcriptional regulation in all growth conditions and for the pathogen's interaction with and survival within host cells. Legionella pneumophila is a gram-negative facultative intracellular human pathogen with a distinct biphasic lifestyle. One of its primary environmental hosts in the free-living amoeba Acanthamoeba castellanii and its infection by L. pneumophila mimics that seen in human macrophages. Here we present analysis of strand specific sequencing of the transcriptional response of L. pneumophila during exponential and post-exponential broth growth and during the replicative and transmissive phase of infection inside A. castellanii. We extend previous microarray based studies as well as uncovering evidence of a complex regulatory architecture underpinned by numerous non-coding RNAs. Over seventy new non-coding RNAs could be identified; many of them appear to be strain specific and in configurations not previously reported. We discover a family of non-coding RNAs preferentially expressed during infection conditions and identify a second copy of 6S RNA in L. pneumophila. We show that the newly discovered putative 6S RNA as well as a number of other non-coding RNAs show evidence for antisense transcription. The nature and extent of the non-coding RNAs and their expression patterns suggests that these may well play central roles in the regulation of Legionella spp. specific traits and offer clues as to how L. pneumophila adapts to its intracellular niche. The expression profiles outlined in the study have been deposited into Genbank's Gene Expression Omnibus (GEO) database under the series accession GSE27232.

Despite the key role genetic mutations play in shaping phenotypic differences between species, little is currently known about the evolution of germline mutation spectra across mammals. Domesticated species are likely particularly interesting case studies because of their high mutation rates and complex evolutionary histories, which can span multiple founding events and genetic bottlenecks. Here we have developed a new reusable workflow, nSPECTRa, that can undertake the key steps in characterising mutation spectra, from determining ancestral alleles to characterising multiple forms of variation. We apply nSPECTRa to seven species, including several that have undergone domestication, and highlight how nSPECTRa can provide important insights into mutation rate evolution. While mutation spectra most often show marked differences between species and even breeds, certain mutation types have risen to a high frequency in subpopulations of different species, indicative of convergent evolution in mutation rates. This includes the previously characterized TCC- > TTC change enriched among European humans, which is also enriched among East Asian cattle. We show Indicine cattle are particularly interesting examples of how different mutation spectra segregate within a population and subsequently spread across the globe. Together, this work has important implications for understanding the control and evolution of mammalian mutation rates. Cross-species analysis of germline mutation spectra uncovers convergent mutation patterns and shows how domestication has shaped genome evolution.

Large-scale gene expression datasets are providing an increasing understanding of the location of cis-eQTLs in the human genome and their role in disease. However, little is currently known regarding the extent of regulatory site-sharing between genes. This is despite it having potentially wide-ranging implications, from the determination of the way in which genetic variants may shape multiple phenotypes to the understanding of the evolution of human gene order. By first identifying the location of non-redundant cis-eQTLs, we show that regulatory site-sharing is a relatively common phenomenon in the human genome, with over 10% of non-redundant regulatory variants linked to the expression of multiple nearby genes. We show that these shared, local regulatory sites are linked to high levels of chromatin looping between the regulatory sites and their associated genes. In addition, these co-regulated gene modules are found to be strongly conserved across mammalian species, suggesting that shared regulatory sites have played an important role in shaping human gene order. The association of these shared cis-eQTLs with multiple genes means they also appear to be unusually important in understanding the genetics of human phenotypes and pleiotropy, with shared regulatory sites more often linked to multiple human phenotypes than other regulatory variants. This study shows that regulatory site-sharing is likely an underappreciated aspect of gene regulation and has important implications for the understanding of various biological phenomena, including how the two and three dimensional structures of the genome have been shaped and the potential causes of disease pleiotropy outside coding regions.

Background In cattle, genome-wide association studies (GWAS) have largely focused on European or Asian breeds, using genotyping arrays that were primarily designed for European cattle. Because there is growing interest in performing GWAS in African breeds, we have assessed the performance of 23 commercial bovine genotyping arrays for capturing the diversity across African breeds and performing imputation. We used 409 whole-genome sequences (WGS) spanning global cattle breeds, and a real cohort of 2481 individuals (including African breeds) that were genotyped with the Illumina high-density (HD) array and the GeneSeek bovine 50 k array. Results We found that commercially available arrays were not effective in capturing variants that segregate among African indicine animals. Only 6% of these variants in high linkage disequilibrium (LD) (r 2  > 0.8) were on the best performing arrays, which contrasts with the 17% and 25% in African and European taurine cattle, respectively. However, imputation from available HD arrays can successfully capture most variants (accuracies up to 0.93), mainly when using a global, not continent-specific, reference panel, which partially reflects the unusually high levels of admixture on the continent. When considering functional variants, the GGPF250 array performed best for tagging WGS variants and imputation. Finally, we show that imputation from low-density arrays can perform almost as well as HD arrays, if a two-stage imputation approach is adopted, i.e. first imputing to HD and then to WGS, which can potentially reduce the costs of GWAS. Conclusions Our results show that the choice of an array should be based on a balance between the objective of the study and the breed/population considered, with the HD and BOS1 arrays being the best choice for both taurine and indicine breeds when performing GWAS, and the GGPF250 being preferable for fine-mapping studies. Moreover, our results suggest that there is no advantage to using the indicus-specific arrays for indicus breeds, regardless of the objective. Finally, we show that using a reference panel that better represents global bovine diversity improves imputation accuracy, particularly for non-European taurine populations.

Despite the clear potential of livestock models of human functional variants to provide important insights into the biological mechanisms driving human diseases and traits, their use to date has been limited. Generating such models via genome editing is costly and time consuming, and it is unclear which variants will have conserved effects across species. In this study we address these issues by studying naturally occurring livestock models of human functional variants. We show that orthologues of over 1.6 million human variants are already segregating in domesticated mammalian species, including several hundred previously directly linked to human traits and diseases. Models of variants linked to particular phenotypes, including metabolomic disorders and height, are preferentially shared across species, meaning studying the genetic basis of these phenotypes is particularly tractable in livestock. Using machine learning we demonstrate it is possible to identify human variants that are more likely to have an existing livestock orthologue, and, importantly, we show that the effects of functional variants are often conserved in livestock, acting on orthologous genes with the same direction of effect. Consequently, this work demonstrates the substantial potential of naturally occurring livestock carriers of orthologues of human functional variants to disentangle their functional impacts. An investigation of genetic variants that exist across human and livestock species supports the clear potential of livestock models in providing insights into the mechanisms driving human diseases and traits.

The diversity in genome resources is fundamental to designing genomic strategies for local breed improvement and utilisation. These resources also support gene discovery and enhance our understanding of the mechanisms of resilience with applications beyond local breeds. Here, we report the genome sequences of 555 cattle (208 of which comprise new data) and high-density (HD) array genotyping of 1,082 samples (537 new samples) from indigenous African cattle populations. The new sequences have an average genome coverage of ~30X, three times higher than the average (~10X) of the over 300 sequences already in the public domain. Following variant quality checks, we identified approximately 32.3 million sequence variants and 661,943 HD autosomal variants mapped to the Bos taurus reference genome (ARS-UCD1.2). The new datasets were generated as part of the Centre for Tropical Livestock Genetics and Health (CTLGH) Genomic Reference Resource for African Cattle (GRRFAC) initiative, which aspires to facilitate the generation of this livestock resource and hopes for its utilisation for complete indigenous breed characterisation and sustainable global livestock improvement.