Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity

Abstract

Genome-wide association studies (GWAS) have identified >250 loci for body mass index (BMI), implicating pathways related to neuronal biology. Most GWAS loci represent clusters of common, non-coding variants from which pinpointing causal genes remains challenging. Here, we combined data from 718,734 individuals to discover rare and low-frequency (MAF<5%) coding variants associated with BMI. We identified 14 coding variants in 13 genes, of which eight in genes (ZBTB7B, ACHE, RAPGEF3, RAB21, ZFHX3, ENTPD6, ZFR2, ZNF169) newly implicated in human obesity, two (MC4R, KSR2) previously observed in extreme obesity, and two variants in GIPR. Effect sizes of rare variants are ~10 times larger than of common variants, with the largest effect observed in carriers of an MC4R stop-codon (p.Tyr35Ter, MAF=0.01%), weighing ~7kg more than non-carriers. Pathway analyses confirmed enrichment of neuronal genes and provide new evidence for adipocyte and energy expenditure biology, widening the potential of genetically-supported therapeutic targets to treat obesity.

Obesity genetics and cardiometabolic health: Potential for risk prediction

“…The carriers of a rare (MAF 0.01) MC4R mutation (Tyr35Ter) weighed 7 kg more than the non-carriers in that study. 37 Exome sequencing in a family-based design has detected novel functional variants for predicting childhood obesity in peroxisome biogenesis factor (PEX-1). PEX-1, an earlier identified gene by GWAS, is involved in childhood obesity through a novel mechanism of peroxisomal biogenesis and metabolism.…”

Exautomate: A user-friendly tool for region-based rare variant association analysis (RVAA)

“…One method frequently used for region-based RVAA is the sequence kernel association test (SKAT) [15][16][17][18] . While the method has been useful in revealing rare variants and genomic loci of interest in complex traits and diseases-such as cardiovascular disease, body-mass index, height, and neurodegenerative diseases [19][20][21][22] -reproducing these results can be difficult. SKAT is challenging to implement for exome-and genome-scale analyses as it requires significant data preprocessing involving additional software dependencies and variables that can complicate reproducibility 23 .…”

Adjustment for covariates using summary statistics of genome‐wide association studies

“…Together, these efforts have already yielded the successful discovery of over thousands of disease associated loci (Ehret et al, 2016; Ehret et al, 2011; Evangelou, 2018; Hoffmann et al, 2017; Lane et al, 2016; Liang et al, 2017; Liu et al, 2016; Marouli et al, 2017; Turcot et al, 2018). The commonly-used approach for analyzing a continuous trait still relies on a simple regression model.…”

“…Genome-wide association studies (GWAS), whole-exome sequencing studies and whole-genome sequencing studies have produced many large data sets of genetic variation in hundreds of thousands to millions of human subjects (Boyle, Li, & Pritchard, 2017), which has motivated the development of new statistical methods to analyze these data sets for addressing important biological questions. Together, these efforts have already yielded the successful discovery of over thousands of disease-associated loci (Ehret et al, 2011;Ehret et al, 2016;Evangelou, Warren & Mosen-Ansorena, 2017;Hoffmann et al, 2017;Lane et al, 2016;Liang et al, 2017;Liu et al, 2016;Marouli et al, 2017;Turcot et al, 2018). The commonly used approach for analyzing a continuous trait still relies on a simple regression model.…”