Regional fat depot masses are influenced by protein-coding gene variants

Waist-to-hip ratio (WHR) is a prominent cardiometabolic risk factor that increases cardio-metabolic disease risk independently of BMI and for which multiple genetic loci have been identified. However, WHR is a relatively crude proxy for fat distribution and it does not capture all variation in fat distribution. We here present a study of the role of coding genetic variants on fat mass in 6 distinct regions of the body, based on dual-energy X-ray absorptiometry imaging on more than 17k participants. We find that the missense variant CCDC92(S70C), previously associated with WHR, is associated specifically increased leg fat mass and reduced visceral but not subcutaneous central fat. The minor allele-carrying transcript of CCDC92 is constitutively more highly expressed in adipose tissue samples. In addition, we identify two coding variants in SPATA20 and UQCC1 that are associated with arm fat mass. SPATA20(K422R) is a low-frequency variant with a large effect on arm fat only, and UQCC1(R51Q) is a common variant reaching significance for arm but showing similar trends in other subcutaneous fat depots. Our findings support the notion that different fat compartments are regulated by distinct genetic factors.Peer reviewe

Large scale phenotype imputation and in vivo functional validation implicate ADAMTS14 as an adiposity gene

K.A.K. acknowledges funding from the MRC Doctoral Training Programme in Precision Medicine (MR/N013166/1). L.K. was supported by an RCUK Innovation Fellowship from the National Productivity Investment Fund (MR/R026408/1). Z.K. was supported by the Swiss National Science Foundation (310030-189147). J.F.W. acknowledges funding from the MRC Human Genetics Unit programme grant Quantitative Traits in Health and Disease (U. MC_UU_00007/10). N.M.M. was supported by a Wellcome Trust New Investigator Award (100981/Z/13/Z). We kindly thank Alain Colige and colleagues at the University of Liege for the provision of Adamts14+/– mouse sperm. We would also like to thank the researchers, funders and participants of all the contributing cohorts. Specifically, we thank the UK Biobank Resource, approved under application 19655. ORCADES was supported by the Chief Scientist Office of the Scottish Government (CZB/4/276, CZB/4/710), the Royal Society, the MRC Human Genetics Unit, Arthritis Research UK and the European Union framework program 6 EUROSPAN project (contract no. LSHG-CT-2006-018947). DNA extractions were performed at the Clinical Research Facility in Edinburgh. We would like to acknowledge the invaluable contributions of the research nurses in Orkney, the administrative team in Edinburgh and the people of Orkney. The EPIC-Norfolk study (https://doi.org/10.22025/2019.10.105.00004) has received funding from the Medical Research Council (MR/N003284/1 and MC-UU_12015/1) and Cancer Research UK (C864/A14136). The genetics work in the EPIC-Norfolk study was funded by the Medical Research Council (MC_PC_13048). We are grateful to all the participants who have been part of the project and to the many members of the study teams at the University of Cambridge who have enabled this research. The Fenland Study (10.22025/2017.10.101.00001) is funded by the Medical Research Council (MC_UU_12015/1). We are grateful to all the volunteers and to the General Practitioners and practice staff for assistance with recruitment. We thank the Fenland Study Investigators, Fenland Study Co-ordination team and the Epidemiology Field, Data and Laboratory teams. We further acknowledge support for genomics from the Medical Research Council (MC_PC_13046).Peer reviewedPublisher PD

Exome-Derived Adiponectin-Associated Variants Implicate Obesity and Lipid Biology

Circulating levels of adiponectin, an adipocyte-secreted protein associated with cardiovascular and metabolic risk, are highly heritable. To gain insights into the biology that regulates adiponectin levels, we performed an exome array meta-analysis of 265,780 genetic variants in 67,739 individuals of European, Hispanic, African American, and East Asian ancestry. We identified 20 loci associated with adiponectin, including 11 that had been reported previously (p .60) spanning as much as 900 kb. To identify potential genes and mechanisms through which the previously unreported association signals act to affect adiponectin levels, we assessed cross-trait associations, expression quantitative trait loci in subcutaneous adipose, and biological pathways of nearby genes. Eight of the nine loci were also associated (p <1 x 10(-4)) with at least one obesity or lipid trait. Candidate genes include PRKAR2A, PTH1R, and HDAC9, which have been suggested to play roles in adipocyte differentiation or bone marrow adipose tissue. Taken together, these findings provide further insights into the processes that influence circulating adiponectin levels.Peer reviewe

Genetic Studies of Leptin Concentrations Implicate Leptin in the Regulation of Early Adiposity.

Leptin influences food intake by informing the brain about the status of body fat stores. Rare LEP mutations associated with congenital leptin deficiency cause severe early-onset obesity that can be mitigated by administering leptin. However, the role of genetic regulation of leptin in polygenic obesity remains poorly understood. We performed an exome-based analysis in up to 57,232 individuals of diverse ancestries to identify genetic variants that influence adiposity-adjusted leptin concentrations. We identify five novel variants, including four missense variants, in LEP, ZNF800, KLHL31, and ACTL9, and one intergenic variant near KLF14. The missense variant Val94Met (rs17151919) in LEP was common in individuals of African ancestry only, and its association with lower leptin concentrations was specific to this ancestry (P = 2 × 10-16, n = 3,901). Using in vitro analyses, we show that the Met94 allele decreases leptin secretion. We also show that the Met94 allele is associated with higher BMI in young African-ancestry children but not in adults, suggesting that leptin regulates early adiposity

Evidence from men for ovary-independent effects of genetic risk factors for polycystic ovary syndrome

Abstract Context: Polycystic ovary syndrome (PCOS) is characterized by ovulatory dysfunction and hyperandrogenism and can be associated with cardiometabolic dysfunction, but it remains unclear which of these features are inciting causes and which are secondary consequences. Objective: To determine whether ovarian function is necessary for genetic risk factors for PCOS to produce nonreproductive phenotypes. Design, Setting, and Participants: Cohort of 176 360 men in the UK Biobank and replication cohort of 37 348 men in the Estonian Biobank. Main Outcome Measures: We calculated individual PCOS polygenic risk scores (PRS), tested for association of these PRS with PCOS-related phenotypes using linear and logistic regression and performed mediation analysis. Results: For every 1 SD increase in the PCOS PRS, men had increased odds of obesity (odds ratio [OR]: 1.09; 95% CI, 1.08-1.10; P = 1 × 10-49), type 2 diabetes mellitus (T2DM) (OR: 1.08; 95% CI, 1.05-1.10; P = 3 × 10-12), coronary artery disease (CAD) (OR: 1.03; 95% CI, 1.01-1.04; P = 0.0029), and marked androgenic alopecia (OR: 1.03; 95% CI, 1.02-1.05; P = 3 × 10-5). Body mass index (BMI), hemoglobin A1c, triglycerides, and free androgen index increased as the PRS increased, whereas high-density lipoprotein cholesterol and SHBG decreased (all P &lt; .0001). The association between the PRS and CAD appeared to be completely mediated by BMI, whereas the associations with T2DM and marked androgenic alopecia appeared to be partially mediated by BMI. Conclusions: Genetic risk factors for PCOS have phenotypic consequences in men, indicating that they can act independently of ovarian function. Thus, PCOS in women may not always be a primary disorder of the ovaries

Insights into genetic variants associated with NASH-fibrosis from metabolite profiling.

Several genetic discoveries robustly implicate five single-nucleotide variants in the progression of non-alcoholic fatty liver disease to non-alcoholic steatohepatitis and fibrosis (NASH-fibrosis), including a recently identified variant in MTARC1. To better understand these variants as potential therapeutic targets, we aimed to characterize their impact on metabolism using comprehensive metabolomics data from two population-based studies. A total of 9135 participants from the Fenland study and 9902 participants from the EPIC-Norfolk cohort were included in the study. We identified individuals with risk alleles associated with NASH-fibrosis: rs738409C>G in PNPLA3, rs58542926C>T in TM6SF2, rs641738C>T near MBOAT7, rs72613567TA>T in HSD17B13 and rs2642438A>G in MTARC1. Circulating levels of 1449 metabolites were measured using targeted and untargeted metabolomics. Associations between NASH-fibrosis variants and metabolites were assessed using linear regression. The specificity of variant-metabolite associations were compared to metabolite associations with ultrasound-defined steatosis, gene variants linked to liver fat (in GCKR, PPP1R3B and LYPLAL1) and gene variants linked to cirrhosis (in HFE and SERPINA1). Each NASH-fibrosis variant demonstrated a specific metabolite profile with little overlap (8/97 metabolites) comprising diverse aspects of lipid metabolism. Risk alleles in PNPLA3 and HSD17B13 were both associated with higher 3-methylglutarylcarnitine and three variants were associated with lower lysophosphatidylcholine C14:0. The risk allele in MTARC1 was associated with higher levels of sphingomyelins. There was no overlap with metabolites that associated with HFE or SERPINA1 variants. Our results suggest a link between the NASH-protective variant in MTARC1 to the metabolism of sphingomyelins and identify distinct molecular patterns associated with each of the NASH-fibrosis variants under investigation.This work was supported by the Medical Research Council (Lipid Profiling and Signalling, MC UP A90 1006 & Lipid Dynamics and Regulation, MC PC 13030) and grants to MRC Epidemiology Units (MC UU12015/1, MC UU 12015/5, MC_PC_13046, and MR/L00002/1) and the Innovative Medicines Initiative Joint Undertaking under EMIF grant agreement no. 115372, resources of which are composed of financial contributions from the European Union's Seventh Framework Programme (FP7/2007-2013). JPM is supported by a Wellcome Trust fellowship (216329/Z/19/Z) and a European Society for Paediatric Research Young Investigator Award. MP is supported by a fellowship from the German Research Foundation (DFG PI 1446/2-1)

Prioritising Risk Factors for Type 2 Diabetes: Causal Inference through Genetic Approaches

PURPOSE OF THE REVIEW: Causality has been demonstrated for few of the many putative risk factors for type 2 diabetes (T2D) emerging from observational epidemiology. Genetic approaches are increasingly being used to infer causality, and in this review, we discuss how genetic discoveries have shaped our understanding of the causal role of factors associated with T2D. RECENT FINDINGS: Genetic discoveries have led to the identification of novel potential aetiological factors of T2D, including the protective role of peripheral fat storage capacity and specific metabolic pathways, such as the branched-chain amino acid breakdown. Consideration of specific genetic mechanisms contributing to overall lipid levels has suggested that distinct physiological processes influencing lipid levels may influence diabetes risk differentially. Genetic approaches have also been used to investigate the role of T2D and related metabolic traits as causal risk factors for other disease outcomes, such as cancer, but comprehensive studies are lacking. Genome-wide association studies of T2D and metabolic traits coupled with high-throughput molecular phenotyping and in-depth characterisation and follow-up of individual loci have provided better understanding of aetiological factors contributing to T2D