The influence of rare variants in circulating metabolic biomarkers

Merging metabolomics and genomics provides a catalog of genetic factors that influence molecular phenotypes in pigs linking relevant metabolic pathways

BACKGROUND

Metabolomics opens novel avenues to study the basic biological mechanisms underlying complex traits, starting from characterization of metabolites. Metabolites and their levels in a biofluid represent simple molecular phenotypes (metabotypes) that are direct products of enzyme activities and relate to all metabolic pathways, including catabolism and anabolism of nutrients. In this study, we demonstrated the utility of merging metabolomics and genomics in pigs to uncover a large list of genetic factors that influence mammalian metabolism.

RESULTS

We obtained targeted characterization of the plasma metabolome of more than 1300 pigs from two populations of Large White and Duroc pig breeds. The metabolomic profiles of these pigs were used to identify genetically influenced metabolites by estimating the heritability of the level of 188 metabolites. Then, combining breed-specific genome-wide association studies of single metabolites and their ratios and across breed meta-analyses, we identified a total of 97 metabolite quantitative trait loci (mQTL), associated with 126 metabolites. Using these results, we constructed a human-pig comparative catalog of genetic factors influencing the metabolomic profile. Whole genome resequencing data identified several putative causative mutations for these mQTL. Additionally, based on a major mQTL for kynurenine level, we designed a nutrigenetic study feeding piglets that carried different genotypes at the candidate gene kynurenine 3-monooxygenase (KMO) varying levels of tryptophan and demonstrated the effect of this genetic factor on the kynurenine pathway. Furthermore, we used metabolomic profiles of Large White and Duroc pigs to reconstruct metabolic pathways using Gaussian Graphical Models, which included perturbation of the identified mQTL.

CONCLUSIONS

This study has provided the first catalog of genetic factors affecting molecular phenotypes that describe the pig blood metabolome, with links to important metabolic pathways, opening novel avenues to merge genetics and nutrition in this livestock species. The obtained results are relevant for basic and applied biology and to evaluate the pig as a biomedical model. Genetically influenced metabolites can be further exploited in nutrigenetic approaches in pigs. The described molecular phenotypes can be useful to dissect complex traits and design novel feeding, breeding and selection programs in pigs.

The contribution of genetic determinants of blood gene expression and splicing to molecular phenotypes and health outcomes

The biological mechanisms through which most nonprotein-coding genetic variants affect disease risk are unknown. To investigate gene-regulatory mechanisms, we mapped blood gene expression and splicing quantitative trait loci (QTLs) through bulk RNA sequencing in 4,732 participants and integrated protein, metabolite and lipid data from the same individuals. We identified cis-QTLs for the expression of 17,233 genes and 29,514 splicing events (in 6,853 genes). Colocalization analyses revealed 3,430 proteomic and metabolomic traits with a shared association signal with either gene expression or splicing. We quantified the relative contribution of the genetic effects at loci with shared etiology, observing 222 molecular phenotypes significantly mediated by gene expression or splicing. We uncovered gene-regulatory mechanisms at disease loci with therapeutic implications, such as WARS1 in hypertension, IL7R in dermatitis and IFNAR2 in COVID-19. Our study provides an open-access resource on the shared genetic etiology across transcriptional phenotypes, molecular traits and health outcomes in humans ( https://IntervalRNA.org.uk ).

Open Targets Platform: facilitating therapeutic hypotheses building in drug discovery

The Open Targets Platform (https://platform.opentargets.org) is a unique, open-source, publicly-available knowledge base providing data and tooling for systematic drug target identification, annotation, and prioritisation. Since our last report, we have expanded the scope of the Platform through a number of significant enhancements and data updates, with the aim to enable our users to formulate more flexible and impactful therapeutic hypotheses. In this context, we have completely revamped our target-disease associations page with more interactive facets and built-in functionalities to empower users with additional control over their experience using the Platform, and added a new Target Prioritisation view. This enables users to prioritise targets based upon clinical precedence, tractability, doability and safety attributes. We have also implemented a direction of effect assessment for eight sources of target-disease association evidence, showing the effect of genetic variation on the function of a target is associated with risk or protection for a trait to inform on potential mechanisms of modulation suitable for disease treatment. These enhancements and the introduction of new back and front-end technologies to support them have increased the impact and usability of our resource within the drug discovery community.

Coupling metabolomics and exome sequencing reveals graded effects of rare damaging heterozygous variants on gene function and human traits

Genetic studies of the metabolome can uncover enzymatic and transport processes shaping human metabolism. Using rare variant aggregation testing based on whole-exome sequencing data to detect genes associated with levels of 1,294 plasma and 1,396 urine metabolites, we discovered 235 gene-metabolite associations, many previously unreported. Complementary approaches (genetic, computational (in silico gene knockouts in whole-body models of human metabolism) and one experimental proof of principle) provided orthogonal evidence that studies of rare, damaging variants in the heterozygous state permit inferences concordant with those from inborn errors of metabolism. Allelic series of functional variants in transporters responsible for transcellular sulfate reabsorption (SLC13A1, SLC26A1) exhibited graded effects on plasma sulfate and human height and pinpointed alleles associated with increased odds of diverse musculoskeletal traits and diseases in the population. This integrative approach can identify new players in incompletely characterized human metabolic reactions and reveal metabolic readouts informative of human traits and diseases.

Genome-wide association studies and multi-omics integrative analysis reveal novel loci and their molecular mechanisms for circulating polyunsaturated, monounsaturated, and saturated fatty acids

Previous genome-wide association studies (GWAS) have identified genetic loci associated with the circulating levels of FAs, but the biological mechanisms of these genetic associations remain largely unexplored. Here, we conducted GWAS to identify additional genetic loci for 19 circulating fatty acid (FA) traits in UK Biobank participants of European ancestry (N = 239,268) and five other ancestries (N = 508 - 4,663). We leveraged the GWAS findings to characterize genetic correlations and colocalized regions among FAs, explore sex differences, examine FA loci influenced by lipoprotein metabolism, and apply statistical fine-mapping to pinpoint putative causal variants. We integrated GWAS signals with multi-omics quantitative trait loci (QTL) to reveal intermediate molecular phenotypes mediating the associations between the genetic loci and FA levels. Altogether, we identified 215 significant loci for polyunsaturated fatty acids (PUFAs)-related traits in European participants, 163 loci for monounsaturated fatty acids (MUFAs)-related traits, and 119 loci for saturated fatty acids (SFAs)-related traits, including 70, 61, and 54 novel loci, respectively. A novel locus for total FAs, the percentage of omega-6 PUFAs in total FAs, and total MUFAs (around genes GSTT1/2/2B) overlapped with QTL signals for all six molecular phenotypes examined, including gene expression, protein abundance, DNA methylation, splicing, histone modification, and chromatin accessibility. Across 19 FA traits, 65% of GWAS loci overlapped with QTL signals for at least one molecular phenotype. Our study identifies novel genetic loci for circulating FA levels and systematically uncovers their underlying molecular mechanisms.

Human Genetics and Genomics for Drug Target Identification and Prioritization: Open Targets' Perspective

Open Targets, a consortium among academic and industry partners, focuses on using human genetics and genomics to provide insights to key questions that build therapeutic hypotheses. Large-scale experiments generate foundational data, and open-source informatic platforms systematically integrate evidence for target-disease relationships and provide dynamic tooling for target prioritization. A locus-to-gene machine learning model uses evidence from genome-wide association studies (GWAS Catalog, UK BioBank, and FinnGen), functional genomic studies, epigenetic studies, and variant effect prediction to predict potential drug targets for complex diseases. These predictions are combined with genetic evidence from gene burden analyses, rare disease genetics, somatic mutations, perturbation assays, pathway analyses, scientific literature, differential expression, and mouse models to systematically build target-disease associations (https://platform.opentargets.org). Scored target attributes such as clinical precedence, tractability, and safety guide target prioritization. Here we provide our perspective on the value and impact of human genetics and genomics for generating therapeutic hypotheses.

Investigating the genetic characteristics of CAD: Is there a role for myocardial perfusion imaging techniques?

Several environmental and genetic factors have been found to influence the development and progression of coronary artery disease (CAD). Although the effects of the environmental hazards on CAD pathophysiology are well documented, the genetic architecture of the disease remains quite unclear. A number of single-nucleotide polymorphisms have been identified based on the results of the genome-wide association studies. However, there is a lack of strong evidence regarding molecular causality. The minority of the reported predisposing variants can be related to the conventional risk factors of CAD, while most of the polymorphisms occur in non-protein-coding regions of the DNA. However, independently of the specific underlying mechanisms, genetic information could lead to the identification of a population at higher genetic risk for the long-term development of CAD. Myocardial single-photon emission computed tomography (SPECT) and positron emission tomography (PET) are functional imaging techniques that can evaluate directly myocardial perfusion, and detect vascular and/or endothelial dysfunction. Therefore, these techniques could have a role in the investigation of the underlying mechanisms associated with the identified predisposing variants, advancing our understanding regarding molecular causality. In the population at higher genetic risk, myocardial SPECT or PET could provide important evidence through the early depiction of sub-clinical dysfunctions, well before any atherosclerosis marker could be identified. Notably, SPECT and PET techniques have been already used for the investigation of the functional consequences of several CAD-related polymorphisms, as well as the response to certain treatments (statins). Furthermore, therefore, in the clinical setting, the combination of genetic evidence with the findings of myocardial SPECT, or PET, functional imaging techniques could lead to more efficient screening methods and may improve decision making with regard to the diagnostic investigation and patients' management.

Whole-exome sequencing identifies rare genetic variants associated with human plasma metabolites

Metabolite levels measured in the human population are endophenotypes for biological processes. We combined sequencing data for 3,924 (whole-exome sequencing, WES, discovery) and 2,805 (whole-genome sequencing, WGS, replication) donors from a prospective cohort of blood donors in England. We used multiple approaches to select and aggregate rare genetic variants (minor allele frequency [MAF] < 0.1%) in protein-coding regions and tested their associations with 995 metabolites measured in plasma by using ultra-high-performance liquid chromatography-tandem mass spectrometry. We identified 40 novel associations implicating rare coding variants (27 genes and 38 metabolites), of which 28 (15 genes and 28 metabolites) were replicated. We developed algorithms to prioritize putative driver variants at each locus and used mediation and Mendelian randomization analyses to test directionality at associations of metabolite and protein levels at the ACY1 locus. Overall, 66% of reported associations implicate gene targets of approved drugs or bioactive drug-like compounds, contributing to drug targets' validating efforts.

Contribution of rare whole-genome sequencing variants to plasma protein levels and the missing heritability

Despite the success of genome-wide association studies, much of the genetic contribution to complex traits remains unexplained. Here, we analyse high coverage whole-genome sequencing data, to evaluate the contribution of rare genetic variants to 414 plasma proteins. The frequency distribution of genetic variants is skewed towards the rare spectrum, and damaging variants are more often rare. We estimate that less than 4.3% of the narrow-sense heritability is expected to be explained by rare variants in our cohort. Using a gene-based approach, we identify Cis-associations for 237 of the proteins, which is slightly more compared to a GWAS (N = 213), and we identify 34 associated loci in Trans. Several associations are driven by rare variants, which have larger effects, on average. We therefore conclude that rare variants could be of importance for precision medicine applications, but have a more limited contribution to the missing heritability of complex diseases.

Despite the success of genome-wide association studies, much of the genetic contribution to complex traits remains unexplained. Here, the authors identify effects by rare variants on plasma proteins, and estimate the contribution of rare variants to the heritability.

Molecular Quantitative Trait Locus Mapping in Human Complex Diseases

Mapping quantitative trait loci (QTLs) for molecular traits from chromatin to metabolites (i.e., xQTLs) provides insight into the locations and effect modes of genetic variants that influence these molecular phenotypes and the propagation of functional consequences of each variant. xQTL studies indirectly interrogate the functional landscape of the molecular basis of complex diseases, including the impact of non-coding regulatory variants, the tissue specificity of regulatory elements, and their contribution to disease by integrating with genome-wide association studies (GWAS). We summarize a variety of molecular xQTL studies in human tissues and cells. In addition, using the Alzheimer's Disease Sequencing Project (ADSP) as an example, we describe the ADSP xQTL project, a collaborative effort across the ADSP Functional Genomics Consortium (ADSP-FGC). The project's ultimate goal is a reference map of Alzheimer's-related QTLs using existing datasets from multiple omics layers to help us study the consequences of genetic variants identified in the ADSP. xQTL studies enable the identification of the causal genes and pathways in GWAS loci, which will likely aid in the discovery of novel biomarkers and therapeutic targets for complex diseases. © 2022 Wiley Periodicals LLC.