If these results are used, please cite:
Turcot V. et al. 
Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity.
Nat Genet. 50(1):26-41. doi:10.1038/s41588-017-0011-x.

The discovery cohort consisted of 123 studies (163 datasets) comprising 526,508 adult (>=18yrs) 
individuals of the following ancestries : 1) European (N = 449,889), 2) South Asian (N = 29,398), 
3) African (N = 27,610), 4) East Asian (N = 8,839), and 5) Hispanic (N = 10,772). Discovery meta-analyses 
were carried out in each ancestry separately and in the All-ancestries combined group, for both sex-specific 
and sex-combined analyses.

Body mass index (BMI: weight [in kilograms] / height [in meters]2) was corrected for age, age2 and genomic 
principal components (PC, derived from GWAS data, the variants with MAF > 1% on ExomeChip, or ancestry 
informative markers available on the ExomeChip), as well as any additional study-specific covariates 
(e.g. recruiting center), in a linear regression model. For studies with non-related individuals, 
residuals were calculated separately by sex, whereas for family-based studies sex was included as a 
covariate in the model. Additionally, residuals for case/control studies were calculated separately. 
Finally, residuals were subject to inverse normal transformation.

Individual cohorts were analyzed separately for each ancestry, in sex-combined and sex-specific groups, 
with either RAREMETALWORKER (see URL links at the end of the Online Methods) or RVTEST, to associate 
inverse normal transformed BMI with genotype accounting for potential cryptic relatedness (kinship matrix) 
in a linear mixed model. These software tools are designed to perform score-statistics based rare-variant 
association analyses, can accommodate both unrelated and related individuals, and provide single-variant 
results and variance-covariance matrices. The covariance matrix captures linkage disequilibrium (LD) 
relationships between markers within 1 Mb, which is used for gene-level meta-analyses and conditional 
analyses. Single-variant analyses were performed for both additive and recessive models.

Meta-analyses were carried out by two different analysts at two sites in parallel. We excluded variants 
with a call rate < 95%, Hardy-Weinberg equilibrium P-value < 1×10-7, or large allele frequency deviations 
from reference populations (> 0.6 for all-ancestry analyses and > 0.3 for ancestry-specific population analyses). 
Significance for single-variant analyses was defined at the array-wide level (a Bonferroni-corrected threshold 
of P < 2×10-7 for ~250,000 SNVs). 

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, noncoding variants 
from which pinpointing causal genes remains challenging. Here we combined data from 718,734 individuals 
to discover rare and low-frequency (minor allele frequency (MAF) < 5%) coding variants associated with BMI. 
We identified 14 coding variants in 13 genes, of which 8 variants were in genes (ZBTB7B, ACHE, RAPGEF3, 
RAB21, ZFHX3, ENTPD6, ZFR2 and ZNF169) newly implicated in human obesity, 2 variants were in genes 
(MC4R and KSR2) previously observed to be mutated in extreme obesity and 2 variants were in GIPR. 
The effect sizes of rare variants are ~10 times larger than those of common variants, with the largest 
effect observed in carriers of an MC4R mutation introducing a stop codon (p.Tyr35Ter, MAF = 0.01%), 
who weighed ~7 kg more than non-carriers. Pathway analyses based on the variants associated with BMI 
confirm enrichment of neuronal genes and provide new evidence for adipocyte and energy expenditure
biology, widening the potential of genetically supported therapeutic targets in obesity.

Column formatting is similar across all of the results files. One exception is the minor allele frequency (MAF)
column. MAF is given for the respective ancestry background sample and matching ExAC database sample. In the 
All_ancestry file a global MAF and ExAC MAF is given.

Column heading example:

CHR	POS	REF	ALT	SNPNAME	GMAF	ExAC_MAF	beta	se	Pvalue