Background: Obesity is a major risk factor for T2D, cardiovascular disease, and some cancers. Genome-wide association studies (GWAS) identified hundreds of common variants associated with BMI and obesity. Here, we aimed to identify rare (MAF <1%) variants associated with BMI and obesity leveraging whole exome sequencing data of 300,790 individuals from NHLBI-Trans-Omics for precision Medicine (TOPMed), UK Biobank and BioMe.
Methods: We performed ancestry-specific single variant (SV) and gene-based (Burden and SKAT) association analysis of BMI and obesity within Europeans (N=234,719), Africans (N=31,452), Hispanics (n=21,687) and Asians (n=8,437) and in other populations (N=3,375). We defined obesity as either a) individuals with obesity (BMI  30) vs individuals without obesity (BMI < 30) or b) individuals with obesity (BMI >30) vs individuals with normal weight (18.5  BMI < 25). We restricted analyses to protein truncating and missense variants with MAF<1% and MAC >10. Summary statistics were meta-analyzed across datasets using fixed effects. Analyses were performed in GENESIS and regenie.
Results: Our gene-based analyses identified 16 genes associated with BMI and/or obesity including known genes MC4R (P=1.6x10-16) and GIPR (P=7.6x10-8). We also identify a rare variant burden in other genes that have been associated with anthropometric traits in GWAS studies such as ZNF423 within the African ancestry (obesity; P=1.6x10-6) or genes involved in the central nervous system such as GRM7 in the all-ancestry meta-analyses (obesity, P=4x10-7). For the SV analyses, none of the variants reached the genome-wide significance threshold. However, among our most significant results are rare variants in several biologically relevant genes. For example, p.Arg35Gln in WDTC1 (MAFAFR=0.5%) is associated with obesity (OR=2.4, P=4x10-7) in the African-ancestry population, whereas this variant was much rarer in other ancestries and monomorphic in the European population. As wdtc1 plays a role in adipogenesis and lipid accumulation in mouse models, the gene may present as a new candidate for human obesity. We note that the well-known monogenic obesity mutation, p.Tyr35Ter in MC4R (MAF=0.01%, P=8.5x10-6; BetaBMI=0.74) and the previously reported p.Gly245Arg in SPARC (MAF 0.04%; P=4.9x10-6; BetaBMI=0.07) just missed our corrected P-value threshold.
Conclusions: We identified 14 novel genes associated with BMI and/or obesity in 300,000 exome-sequenced individuals. We also identify several variants that did not make the significance threshold but that lie in genes for which there is functional evidence for a role in obesity/adipogenesis or in which common variants are associated with anthropometric traits and therefore warrant further consideration in follow-up analyses.

Introduction
Height is heritable and provides insights into the genetic architecture of human traits. Thousands of common and low-frequency variants have been identified with height using GWAS. However, these common variants only explain a limited fraction of heritability. A recent study shows that rare variants (RVs) are a major source of the missing heritability of height. Large-scale whole-genome sequencing (WGS) studies, such as the multi-ethnic NHLBI Trans-Omics Precision Medicine (TOPMed) Program, enable the assessment of associations between height and rare variants across the genome, especially for the noncoding genome.

Hypothesis
Rare variant aggregations are associated with height.

Methods
We applied our newly developed STAARpipeline workflow to rare variant (MAF < 0.01) association analyses using 87,652 individuals from TOPMed Freeze 8 WGS data, including gene-centric analysis and non-gene-centric analysis using a variety of coding and noncoding masks. The gene-centric analysis provides five coding and eight noncoding functional categories. The non-gene-centric analysis includes sliding window analysis with fixed sizes and dynamic window analysis with data-adaptive sizes.

Results
In the gene-centric analysis of coding RVs, we identified 7 genome-wide significant associations at the Bonferroni-corrected level 5.00E-07 (=0.05/20,000/5). After conditioning on known height-associated variants, the association between missense RVs in ACAN remained significant at the same level 5.00E-07. In the gene-centric analysis of noncoding RVs, we identified 8 genome-wide significant associations at the Bonferroni-corrected level 3.12E-07 (=0.05/20,000/8). The association of RVs in the promoter of HMGA1 remained significant at the same level 3.12E-07 in conditional analysis by adjusting for known height-associated variants. In 2-kb sliding window analysis, we identified 25 genome-wide significant associations at the Bonferroni-corrected level 1.88E-08 (=0.05/2.66E06). After conditioning on known height-associated variants, the strengths of all associations reduced and 4 of these associations remained significant at level 1.00E-05. Two of them are in the coding region of ACAN, and the other two are in the upstream region of HMGA1. The results of dynamic window analysis are similar to sliding window analysis. We identified 11 genome-wide significant associations, and 4 of these associations remained significant at level 1.00E-05 in conditional analysis.

Summary
Two new RV associations, missense RVs in ACAN and RVs in the promoter of HMGA1 with height, were identified using the TOPMed WGS Freeze 8 data through STAARpipeline.

Background: Obesity is a major risk factor for T2D and CVD. GWA studies identified hundreds of common variants associated with BMI and obesity. In contrast, exome-chip analyses by the GIANT consortium in 718,734 individuals suggest that large-scale sequencing data is required to identify truly rare variants. Here, we aimed to identify rare (MAF <1%) variants associated with BMI and obesity leveraging whole exome sequencing data on 300,790 individuals from NHLBI-Trans-Omics for precision Medicine (TOPMed), UK Biobank and BioMe.
Methods: We performed ancestry-specific single variant (SV) association analysis of BMI and obesity within Europeans (N=234,719), Africans (N=31,452), Hispanics (n=21,687), Asians (n=8,437), and other ancestry groups (N=3,375). We restricted analyses to protein truncating & missense variants with MAF<1% and MAC >10. We analyzed 1.2 million variants and used a Bonferroni-corrected P-value threshold of 4x10-8 for significance. Analyses were performed using GENESIS and regenie. Summary statistics were meta-analyzed across datasets using fixed effects. Gene-based analyses is underway.
Results: None of the variants reached the Bonferroni-corrected threshold for our SV analysis. However, among our most significant results, we identify rare variants in thirteen biologically relevant genes. For example, p.Arg35Gln in WDTC1 (MAFAFR=0.5%) is associated with obesity (OR=2.4, P=4x10-7) in the African-ancestry population, whereas this variant was much rarer in other ancestries and monomorphic in Europeans. As WDTC1 plays a role in adipogenesis and lipid accumulation in mouse models, the gene may present as a new candidate for human obesity. Also p.Gly85Glu in MMP20 (MAFEUR: 0.08%) was associated with obesity (OR=1.5, P=6x10-7). MMP20 belongs to the matrix metalloproteinase family, which includes several other proteins implicated in obesity. Additionally, we observe many novel rare variants (P<5x10-6) in genes (TNRC18, ZNF687, PARG1, USH2A and GEM) in which common variants have been associated with BMI, extreme obesity or height. We note that the well-known monogenic obesity mutation, p.Tyr35Ter in MC4R (MAF=0.01%, P=8.5x10-6; BetaBMI=0.74) and the previously reported p.Gly245Arg in SPARC (MAF 0.04%; P=4.9x10-6; BetaBMI=0.07) did not reach our corrected P-value threshold.
Conclusions: We observed thirteen rare variant associations that failed to reach the exome-wide Bonferroni-corrected P-value but that lie in genes that have a functional evidence for a role in obesity/adipogenesis or in which common variants are associated with BMI or a related trait and therefore warrant further consideration in follow-up analyses.