Genome-wide characterization of circulating metabolic biomarkers.

Genome-wide association analyses using high-throughput metabolomics platforms have led to novel insights into the biology of human metabolism1-7. This detailed knowledge of the genetic determinants of systemic metabolism has been pivotal for uncovering how genetic pathways influence biological mechanisms and complex diseases8-11. Here we present a genome-wide association study for 233 circulating metabolic traits quantified by nuclear magnetic resonance spectroscopy in up to 136,016 participants from 33 cohorts. We identify more than 400 independent loci and assign probable causal genes at two-thirds of these using manual curation of plausible biological candidates. We highlight the importance of sample and participant characteristics that can have significant effects on genetic associations. We use detailed metabolic profiling of lipoprotein- and lipid-associated variants to better characterize how known lipid loci and novel loci affect lipoprotein metabolism at a granular level. We demonstrate the translational utility of comprehensively phenotyped molecular data, characterizing the metabolic associations of intrahepatic cholestasis of pregnancy. Finally, we observe substantial genetic pleiotropy for multiple metabolic pathways and illustrate the importance of careful instrument selection in Mendelian randomization analysis, revealing a putative causal relationship between acetone and hypertension. Our publicly available results provide a foundational resource for the community to examine the role of metabolism across diverse diseases.

Association of genetic variants related to combined exposure to higher BMI and waist-to-hip ratio on lifelong cardiovascular risk in UK Biobank.

OBJECTIVE: This study examines the individual and combined association of BMI and waist-to-hip ratio (WHR) with CVD risk using genetic scores of the obesity measurements as proxies. DESIGN: A 2 × 2 factorial analysis approach was applied, with participants divided into four groups of lifetime exposure to low BMI and WHR, high BMI, high WHR, and high BMI and WHR based on weighted genetic risk scores. The difference in CVD risk across groups was evaluated using multivariable logistic regression. SETTING: Cohort study. PARTICIPANTS: A total of 408 003 participants were included from the prospective observational UK Biobank study. RESULTS: A total of 58 429 CVD events were recorded. Compared to the low BMI and WHR genetic scores group, higher BMI or higher WHR genetic scores were associated with an increase in CVD risk (high WHR: OR, 1·07; 95 % CI (1·04, 1·10)); high BMI: OR, 1·12; 95 % CI (1·09, 1·16). A weak additive effect on CVD risk was found between BMI and WHR (high BMI and WHR: OR, 1·16; 95 % CI (1·12, 1·19)). Subgroup analysis showed similar patterns between different sex, age (<65, ≥65 years old), smoking status, Townsend deprivation index, fasting glucose level and medication uses, but lower systolic blood pressure was associated with higher CVD risk in obese participants. CONCLUSIONS: High BMI and WHR were associated with increased CVD risk, and their effects are weakly additive. Even though there were overlapping of effect, both BMI and WHR are important in assessing the CVD risk in the general population.

Blood Pressure and Risk of Cardiovascular Disease in UK Biobank: A Mendelian Randomization Study.

This study aims to evaluate the causal association of blood pressure (BP) with cardiovascular diseases (CVDs). Two-sample Mendelian randomization was performed using a large genome-wide association study (n=299 024) and the UK Biobank cohort (n=375 256). We identified 327 and 364 single-nucleotide polymorphisms strongly and independently associated with systolic BP and diastolic BP, respectively, as genetic instruments to assess the causal association of BP with total CVD, CVD mortality, and 14 cardiovascular conditions. Nonlinearity was examined with nonlinear instrumental variable assumptions. Genetically predicted BP was significantly positively associated with total CVD (systolic BP, per 10 mm Hg: odds ratio [OR], 1.32 [95% CI, 1.25-1.40]; diastolic BP, per 5 mm Hg: OR, 1.20 [95% CI, 1.15-1.26]). Similar positive causal associations were observed for 14 cardiovascular conditions including ischemic heart disease (systolic BP, per 10 mm Hg: OR, 1.33 [95% CI, 1.24-1.41]; diastolic BP, per 5 mm Hg: OR, 1.20 [95% CI, 1.14-1.27]) and stroke (systolic BP, per 10 mm Hg: OR, 1.35 [95% CI, 1.24-1.48]; diastolic BP, per 5 mm Hg: OR, 1.20 [95% CI, 1.12-1.28]). Nonlinearity Mendelian randomization test demonstrated linear causal association of BP with these outcomes. Consistent estimates were observed in sensitivity analyses, suggesting robustness of the associations and minimal horizontal pleiotropy. The linear positive causal association of BP and CVD was consistent with previous findings that lower BP is better, thus consolidating clinical knowledge on hypertension management in CVD risk reduction.

Pathway-based meta-analysis for partially paired transcriptomics analysis.

Pathway-based differential expression analysis allows the incorporation of biological domain knowledge into transcriptomics analysis to enhance our understanding of disease mechanisms. To integrate information among multiple studies at the pathway level, pathway-based meta-analysis can be performed. Paired or partially paired samples are common in biomedical research. However, there are currently no existing pathway-based meta-analysis methods appropriate for paired or partially paired study designs. In this study, we developed a pathway-based meta-analysis approach for paired or partially paired samples. Meta-analysis on the transcriptomics profiles were conducted using p-value-based, rank-based, and effect size-based algorithms. The application of our approach was demonstrated using partially paired data from psoriasis transcriptomics studies. Upon combining six transcriptomics studies, genes related to the cell cycle and DNA replication pathways are found to be highly perturbed in psoriatic lesional skin samples. Results were validated externally with independent RNA-Seq data. Comparison with existing pathway meta-analysis methods revealed consistent results, with our method showing higher detection power. This study demonstrated the utility of our newly developed pathway-based meta-analysis that allows the incorporation of partially paired or paired samples. The proposed framework can be applied to omics data including but not limited to transcriptomics data.