Dataset Information

Shared genetic and experimental links between obesity-related traits and asthma subtypes in UK Biobank.

ABSTRACT:

Background

Clinical and epidemiologic studies have shown that obesity is associated with asthma and that these associations differ by asthma subtype. Little is known about the shared genetic components between obesity and asthma.

Objective

We sought to identify shared genetic associations between obesity-related traits and asthma subtypes in adults.

Methods

A cross-trait genome-wide association study (GWAS) was performed using 457,822 subjects of European ancestry from the UK Biobank. Experimental evidence to support the role of genes significantly associated with both obesity-related traits and asthma through a GWAS was sought by using results from obese versus lean mouse RNA sequencing and RT-PCR experiments.

Results

We found a substantial positive genetic correlation between body mass index and later-onset asthma defined by asthma age of onset at 16 years or greater (Rg = 0.25, P = 9.56 × 10^-22). Mendelian randomization analysis provided strong evidence in support of body mass index causally increasing asthma risk. Cross-trait meta-analysis identified 34 shared loci among 3 obesity-related traits and 2 asthma subtypes. GWAS functional analyses identified potential causal relationships between the shared loci and Genotype-Tissue Expression (GTEx) quantitative trait loci and shared immune- and cell differentiation-related pathways between obesity and asthma. Finally, RNA sequencing data from lungs of obese versus control mice found that 2 genes (acyl-coenzyme A oxidase-like [ACOXL] and myosin light chain 6 [MYL6]) from the cross-trait meta-analysis were differentially expressed, and these findings were validated by using RT-PCR in an independent set of mice.

Conclusions

Our work identified shared genetic components between obesity-related traits and specific asthma subtypes, reinforcing the hypothesis that obesity causally increases the risk of asthma and identifying molecular pathways that might underlie both obesity and asthma.

SUBMITTER: Zhu Z

PROVIDER: S-EPMC7010560 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Publications

Shared genetic and experimental links between obesity-related traits and asthma subtypes in UK Biobank.

Zhu Zhaozhong Z Guo Yanjun Y Shi Huwenbo H Liu Cong-Lin CL Panganiban Ronald Allan RA Chung Wonil W O'Connor Luke J LJ Himes Blanca E BE Gazal Steven S Hasegawa Kohei K Camargo Carlos A CA Qi Lu L Moffatt Miriam F MF Hu Frank B FB Lu Quan Q Cookson William O C WOC Liang Liming L

The Journal of allergy and clinical immunology 20191024 2

<h4>Background</h4>Clinical and epidemiologic studies have shown that obesity is associated with asthma and that these associations differ by asthma subtype. Little is known about the shared genetic components between obesity and asthma.<h4>Objective</h4>We sought to identify shared genetic associations between obesity-related traits and asthma subtypes in adults.<h4>Methods</h4>A cross-trait genome-wide association study (GWAS) was performed using 457,822 subjects of European ancestry from the ...[more]

PMID: 31669095

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Project description:ObjectiveTo determine whether there is a correlation between obesity-related variants and regional brain volumes.MethodsBased on a mixed linear model (MLM), we analyzed the association between 1,498 obesity-related SNPs in the GWAS Catalog and 164 regional brain volumes from 29,420 participants (discovery cohort N = 19,997, validation cohort N = 9,423) in UK Biobank. The statistically significant brain regions in association analysis were classified into 6 major neural networks (dopamine (DA) motive system, central autonomic network (CAN), cognitive emotion regulation, visual object recognition network, auditory object recognition network, and sensorimotor system). We summarized the association between obesity-related variants (metabolically healthy obesity variants, metabolically unhealthy obesity variants, and unclassified obesity-related variants) and neural networks.ResultsFrom association analysis, we determined that 17 obesity-related SNPs were associated with 51 regional brain volumes. Several single SNPs (e.g., rs13107325-T (SLC39A8), rs1876829-C (CRHR1), and rs1538170-T (CENPW)) were associated with multiple regional brain volumes. In addition, several single brain regions (e.g., the white matter, the grey matter in the putamen, subcallosal cortex, and insular cortex) were associated with multiple obesity-related variants. The metabolically healthy obesity variants were mainly associated with the regional brain volumes in the DA motive system, sensorimotor system and cognitive emotion regulation neural networks, while metabolically unhealthy obesity variants were mainly associated with regional brain volumes in the CAN and total tissue volumes. In addition, unclassified obesity-related variants were mainly associated with auditory object recognition network and total tissue volumes. The results of MeSH (medical subject headings) enrichment analysis showed that obesity genes associated with brain structure pointed to the functional relatedness with 5-Hydroxytryptamine receptor 4 (5-HT4), growth differentiation factor 5 (GDF5), and high mobility group protein AT-hook 2 (HMGA2 protein).ConclusionIn summary, we found that obesity-related variants were associated with different brain volume measures. On the basis of the multiple SNPs, we found that metabolically healthy and unhealthy obesity-related SNPs were associated with different brain neural networks. Based on our enrichment analysis, modifications of the 5-HT4 pathway might be a promising therapeutic strategy for obesity.

Project description:Abstract Objectives Epidemiological studies suggest that coffee intake (CI) is protective against body weight gain. We explored whether genetic determinants of CI are associated with obesity-related phenotypic traits, primarily body mass Index (BMI). Methods We leveraged information from ∼354,000 individuals in the UKBB database (https://genetics.opentargets.org/) searching for genetic variants associated with CI (cutoff P < 0.5E-8). We further explored the association of these variants with BMI and other obesity-related traits (body fat percentage-BFP, obesity or waist circumference-WC) using summarized data from Neale's lab (http://www.nealelab.is/uk-biobank/). Results Twenty seven variants were significantly associated with CI, including rs2472297-CYP1A1/2 (P = 3.4E-116, beta(b) = 0.047) and rs4410790-AHR- (P = 3.2E-95, b = 0.039), which were previously reported to be associated with CI. Seventeen variants showed significant associations with BMI in the same direction (i.e., rs2472297- CYP1A1/2, P = 1.2E-6, b = 0.06; rs4410790-AHR, P = 1.7E-4, b = 0.04; rs589500-SEC16B, P = 2.5E-59, b = 0.22; rs1260326-GCKR, P = 8.0E-6, b = 0.05; rs3814424-MEF2C, P = 6.3E-20, b = 0.14; rs1189470082-AL355997.1, P = 4.3E-8, b = 0.07; rs9398171-LINC00222/FOXO3, P = 6.1E-10, b = 0.08; rs370535199-KBTBD2, P = 9.2E-5, b = 0.045; rs1057868-POR, P = 9.9E-6, b = 0.055; rs56094641-FTO, P = 2.1E-219, b = 0.26). The remaining 9 variants showed no associations with any obesity-related trait. One variant (rs57918684-MED13) showed a marginal and opposite effect. Beta coefficients for CI and BMI were significantly correlated (Spearman R: 0.69, P < 0.0001), which is compatible with a significant genetic correlation between both traits (rg = 0.24 ± 0.02, P = 4.06E-23). The positive association between CI and BMI is biologically supported by genetic correlations between CI and food intake (rg = 0.26 ± 0.07, P = 1.0E-4), BFP (rg = 0.16 ± 0.02, P = 2.54E-13) and WC (rg = 0.23 ± 0.02, P = 2.0E-22). Conclusions Variants associated with CI present direct pleiotropic effects on obesity-related traits such as BMI, BFP, and WC. If these are not causal relations, then from a Mendelian Randomization point of view, CI has an undesirable effect. Funding Sources Supported by grants from the National Agency for Scientific and Technological Promotion and the National Scientific and Technical Research Council (Argentina).

Dataset Information

Shared genetic and experimental links between obesity-related traits and asthma subtypes in UK Biobank.

Background

Objective

Methods

Results

Conclusions

Publications

Shared genetic and experimental links between obesity-related traits and asthma subtypes in UK Biobank.

Similar Datasets

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