A c c e p t e d M a n u sc r i p t

Context: Biological and translational insights from large-scale, array-based genetic studies of fat distribution, a key determinant of metabolic health, have been limited by the difficulty in linking predominantly non-coding variants to specific gene targets. Rare coding variant analyses provide greater confidence that a specific gene is involved, but do not necessarily indicate whether gain or loss-of-function (LoF) would be of most therapeutic benefit. Objective, Design and Setting: To identify genes/proteins involved in determining fat distribution, we combined the power of genome-wide analysis of array-based rare, non-synonymous variants in 450,562 individuals of UK Biobank with exome-sequence-based rare loss of function gene burden testing in 184,246 individuals. Results: The data indicates that loss-of-function of four genes ( PLIN1 [LoF variants, p =5.86×10 -7 ] , INSR [LoF variants, p =6.21×10 -7 ] , ACVR1C [ LoF + Moderate impact variants, p =1.68×10 -7 ; Moderate impact variants, p =4.57×10 -7 ] and PDE3B [LoF variants, p =1.41×10 -6 ]) is associated with a beneficial impact on WHR adjBMI and increased gluteofemoral fat mass, whereas LoF of PLIN4 [LoF variants, p =5.86×10 -7 ] adversely affects these parameters. Phenotypic follow-up suggests that LoF of PLIN1 , PDE3B and ACVR1C favourably affects metabolic phenotypes (e.g. triglyceride [TG] and HDL cholesterol concentrations) and reduces the risk of cardiovascular disease, whereas PLIN4 LoF has adverse health consequences. INSR LoF is associated with lower TG and HDL levels but may increase the risk of type 2 diabetes. Conclusion: This study robustly implicates these genes in the regulation of fat distribution, providing new and in some cases somewhat counter-intuitive insight into the potential consequences of targeting these molecules therapeutically. lipolysis it does adipocytes HEK293 cells were seeded at a density of 150,000 cells per well in 24-well tissue culture plates pre-treated with poly-D-lysine. On the following day, medium was replaced with Opti-MEM I Reduced Serum medium and a total of 550 ng of plasmid DNA; this included different pcDNA3.1 based ACVR1C constructs listed in the table below, as well as constructs encoding receptor components (ACVR-IIB and CRIPTO) along with firefly (consisting of the SMAD binding elements) and Renilla (control) luciferase reporter plasmids. Lipofectamine 3000 Reagent was used for the transfection according to the manufacturer‟s protocol. Opti-MEM I Reduced Serum medium was then replaced with DMEM growth medium 6 hours post transfection. of a freeze-thaw process. Cell lysates were cleared of cell debris by centrifugation at 21,130 x g for one minute. The assay was conducted in a 96-well plate format. In each assay, 20 µl of cleared supernatant was pre-dispensed, followed by sequential measurement of firefly and Renilla luciferase using a Tecan Spark 10M plate reader (Tecan, Switzerland). Firefly luciferase activity was normalised for Renilla luciferase activity, and then further normalised with values from non-stimulated cells transfected with empty pcDNA3.1 vector (EV). We also studied a constitutively active (ACVR1C p.T194D) mutant and a kinase dead (ACVR1C p.K222R) mutant for comparison 71,72 . The experiment was repeated with fresh transfections on three separate occasions and statistically analyzed as described in the figure legend. cells transfected with empty vector (EV) was set to 1. A constitutively active (CA) ACVR1C variant T194D and a kinase dead (KD) variant K222R were included for comparison. Results from three independent experiments are presented as mean ± SD. Statistical significance was evaluated by one-way ANOVA with Tukey‟s post hoc test for multiple comparisons between pairs.