Analysis of predicted loss-of-function variants in UK Biobank identifies variants protective for disease

Cyclic nucleotide phosphodiesterases: New targets in the metabolic syndrome?

Metabolic diseases have a tremendous impact on human morbidity and mortality. Numerous targets regulating adenosine monophosphate kinase (AMPK) have been identified for treating the metabolic syndrome (MetS), and many compounds are being used or developed to increase AMPK activity. In parallel, the cyclic nucleotide phosphodiesterase families (PDEs) have emerged as new therapeutic targets in cardiovascular diseases, as well as in non-resolved pathologies. Since some PDE subfamilies inactivate cAMP into 5'-AMP, while the beneficial effects in MetS are related to 5'-AMP-dependent activation of AMPK, an analysis of the various controversial relationships between PDEs and AMPK in MetS appears interesting. The present review will describe the various PDE families, AMPK and molecular mechanisms in the MetS and discuss the PDEs/PDE modulators related to the tissues involved, thus supporting the discovery of original molecules and the design of new therapeutic approaches in MetS.

Metabolites downstream of predicted loss-of-function variants inform relationship to disease

A small minority (< 3%) of protein-coding genetic variants are predicted to lead to loss of protein function. However, these predicted loss-of-function (pLOF) variants can provide insight into mode of transcriptional effect. To examine how these changes are propagated to phenotype, we determined associations with downstream metabolites. We performed association analyses of 37 pLOF variants - previously reported to be significantly associated with disease in >400,000 subjects in UK Biobank - with metabolites. We conducted these analyses in three community-based cohorts: the Framingham Heart Study (FHS) Offspring Cohort, FHS Generation 3, and the KORA F4 cohort. We identified 19 new low-frequency or rare (minor allele frequency (MAF) <5%) pLOF variant-metabolite associations, and 12 new common (MAF > 5%) pLOF variant-metabolite associations. Rare pLOF variants in the genes BTN3A2, ENPEP, and GEM that have been associated with blood pressure in UK Biobank, were associated with vasoactive metabolites indoxyl sulfate, asymmetric dimethylarginine (ADMA), and with niacinamide, respectively. A common pLOF variant in gene CCHCR1, associated with asthma in UK Biobank, was associated with histamine and niacinamide in FHS Generation 3, both reported to play a role in this disease. Common variants in olfactory receptor gene OX4C11 that associated with blood pressure in UK Biobank were associated with the nicotine metabolite cotinine, suggesting an interaction between altered olfaction, smoking behaviour, and blood pressure. These findings provide biological validity for pLOF variant-disease associations, and point to the effector roles of common metabolites. Such an approach may provide novel disease markers and therapeutic targets.

Target discovery using biobanks and human genetics

Large-scale biobanks can yield unprecedented insights into our health and provide discoveries of new and potentially targetable biomarkers. Several protective loss-of-function alleles have been identified, including variants that protect against cardiovascular disease, obesity, type 2 diabetes, and asthma and allergic diseases. These alleles serve as indicators of efficacy, mimicking the effects of drugs and suggesting that inhibiting these genes could provide therapeutic benefit, as has been observed for PCSK9. We provide a context for these findings through a multifaceted review covering the use of genetics in drug discovery efforts through genome-wide and phenome-wide association studies, linking deep mutation scanning data to molecular function and highlighting some additional tools that might help in the interpretation of newly discovered variants.

Components of genetic associations across 2,138 phenotypes in the UK Biobank highlight adipocyte biology

Population-based biobanks with genomic and dense phenotype data provide opportunities for generating effective therapeutic hypotheses and understanding the genomic role in disease predisposition. To characterize latent components of genetic associations, we apply truncated singular value decomposition (DeGAs) to matrices of summary statistics derived from genome-wide association analyses across 2,138 phenotypes measured in 337,199 White British individuals in the UK Biobank study. We systematically identify key components of genetic associations and the contributions of variants, genes, and phenotypes to each component. As an illustration of the utility of the approach to inform downstream experiments, we report putative loss of function variants, rs114285050 (GPR151) and rs150090666 (PDE3B), that substantially contribute to obesity-related traits and experimentally demonstrate the role of these genes in adipocyte biology. Our approach to dissect components of genetic associations across the human phenome will accelerate biomedical hypothesis generation by providing insights on previously unexplored latent structures.

Genomic Predictors of Asthma Phenotypes and Treatment Response

Asthma is a complex respiratory disease considered as the most common chronic condition in children. A large genetic contribution to asthma susceptibility is predicted by the clustering of asthma and allergy symptoms among relatives and the large disease heritability estimated from twin studies, ranging from 55 to 90%. Genetic basis of asthma has been extensively investigated in the past 40 years using linkage analysis and candidate-gene association studies. However, the development of dense arrays for polymorphism genotyping has enabled the transition toward genome-wide association studies (GWAS), which have led the discovery of several unanticipated asthma genes in the last 11 years. Despite this, currently known risk variants identified using many thousand samples from distinct ethnicities only explain a small proportion of asthma heritability. This review examines the main findings of the last 2 years in genomic studies of asthma using GWAS and admixture mapping studies, as well as the direction of studies fostering integrative perspectives involving omics data. Additionally, we discuss the need for assessing the whole spectrum of genetic variation in association studies of asthma susceptibility, severity, and treatment response in order to further improve our knowledge of asthma genes and predictive biomarkers. Leveraging the individual's genetic information will allow a better understanding of asthma pathogenesis and will facilitate the transition toward a more precise diagnosis and treatment.

Genomic insights into the causes of type 2 diabetes

Genome-wide association studies have implicated around 250 genomic regions in predisposition to type 2 diabetes, with evidence for causal variants and genes emerging for several of these regions. Understanding of the underlying mechanisms, including the interplay between β-cell failure, insulin sensitivity, appetite regulation, and adipose storage has been facilitated by the integration of multidimensional data for diabetes-related intermediate phenotypes, detailed genomic annotations, functional experiments, and now multiomic molecular features. Studies in diverse ethnic groups and examples from population isolates have shown the value and need for a broad genomic approach to this global disease. Transethnic discovery efforts and large-scale biobanks in diverse populations and ancestries could help to address some of the Eurocentric bias. Despite rapid progress in the discovery of the highly polygenic architecture of type 2 diabetes, dominated by common alleles with small, cumulative effects on disease risk, these insights have been of little clinical use in terms of disease prediction or prevention, and have made only small contributions to subtype classification or stratified approaches to treatment. Successful development of academia-industry partnerships for exome or genome sequencing in large biobanks could help to deliver economies of scale, with implications for the future of genomics-focused research.