Protein-Coding Variants Implicate Novel Genes Related to Lipid Homeostasis Contributing to Body Fat Distribution

Abstract

Body fat distribution is a heritable risk factor for a range of adverse health consequences, including hyperlipidemia and type 2 diabetes. To identify protein-coding variants associated with body fat distribution, assessed by waist-to-hip ratio adjusted for body mass index, we analyzed 228,985 predicted coding and splice site variants available on exome arrays in up to 344,369 individuals from five major ancestries for discovery and 132,177 independent European-ancestry individuals for validation. We identified 15 common (minor allele frequency, MAF≥5%) and 9 low frequency or rare (MAF<5%) coding variants that have not been reported previously. Pathway/gene set enrichment analyses of all associated variants highlight lipid particle, adiponectin level, abnormal white adipose tissue physiology, and bone development and morphology as processes affecting fat distribution and body shape. Furthermore, the cross-trait associations and the analyses of variant and gene function highlight a strong connection to lipids, cardiovascular traits, and type 2 diabetes. In functional follow-up analyses, specifically in Drosophila RNAi-knockdown crosses, we observed a significant increase in the total body triglyceride levels for two genes (DNAH10 and PLXND1). By examining variants often poorly tagged or entirely missed by genome-wide association studies, we implicate novel genes in fat distribution, stressing the importance of interrogating low-frequency and protein-coding variants.

The ChinaMAP analytics of deep whole genome sequences in 10,588 individuals

“…Novel association between the coding variant (rs35169799) of PLCB3 and type 2 diabetes and body-fat distribution were reported recently. 41 , 42 Furthermore, we identified the MAFA ( P = 1.34 × 10 −11 ), MTMR9 ( P = 4.45 × 10 −7 ) and PAX6 ( P = 3.39 × 10 −15 ), ANGPTL4 ( P = 1.26 × 10 −6 ), and SOX4 ( P = 9.46 × 10 −7 ) in the analysis of FBG and 2h-PBG (Fig. 7b, c ).…”

Skeletal muscle enhancer interactions identify genes controlling whole-body metabolism

“…Notably, we did not detect a physical link between the UQCC1 intronic enhancers and the UQCC1promoter, nor did UQCC1 change expression by TNFα. A recent study has shown that human UQCC1 coding variants are associated with WHR 61 . Interestingly, eQTL analysis indicates that these variants associate not only with the expression levels of UQCC1, but also EIF6 61 , suggesting that several genes within this locus could contribute to the modulation of WHR in humans.…”

Obesity and eating behavior from the perspective of twin and genetic research

“…A GWA study of body fat percentage found overlap between SNPs previously found to be associated with BMI; however, four novel SNPs having genome-wide significance were also identified and the effect sizes of overlapping SNPs were different on body fat percentage and BMI (Lu et al, 2016). Additionally, a large GWA study on waist-to-hip ratio (WHR) adjusted for BMI identified several novel SNPs (Justice et al, 2019). These results suggest that even when there are a set of genes affecting general body fatness, there are separate genes regulating fat distribution.…”

“…Thus, there is strong evidence for the role of behavioral factors -especially eating behaviorbehind the mechanisms between genes and BMI, but the reverse causality from BMI to behavior through the brain anatomy can also contribute to this association, possibly even reinforcing the effect of genes. On the other hand, SNPs associated with WHR adjusted for BMI seem to be especially related to lipid metabolism and adipose tissue regulation, also showing considerable differences in the effect sizes between males and females (Justice et al, 2019). This suggests that when the genetic factors of general adiposity affect through behavioral factors, genes regulating fat distribution influence through other mechanisms.…”