| Title: | Variants in JAZF1 are associated with asthma, type 2 diabetes, and height in the United Kingdom biobank population |
| Journal: | Frontiers in Genetics |
| Published: | 12 Jun 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/37377600/ |
| DOI: | https://doi.org/10.3389/fgene.2023.1129389 |
| URL: | https://www.frontiersin.org/articles/10.3389/fgene.2023.1129389/pdf |
Publication 10509
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Abstract
Background: Asthma, type 2 diabetes (T2D), and anthropometric measures are correlated complex traits that all have a major genetic component. Objective: To investigate the overlap in genetic variants associated with these complex traits. Methods: Using United Kingdom Biobank data, we performed univariate association analysis, fine-mapping, and mediation analysis to identify and dissect shared genomic regions associated with asthma, T2D, height, weight, body mass index (BMI), and waist circumference (WC). Results: We found several genome-wide significant variants in and around the JAZF1 gene that are associated with asthma, T2D, or height with two of these variants shared by the three phenotypes. We also observed an association in this region with WC when adjusted for BMI. However, there was no association with WC when it was not adjusted for BMI or weight. Additionally, only suggestive associations between variants in this region and BMI were observed. Fine-mapping analyses suggested that within JAZF1 there are non-overlapping regions harboring causal susceptibility variants for asthma, T2D, and height. Mediation analyses supported the conclusion that these are independent associations. Conclusion: Our findings indicate that variants in the JAZF1 are associated with asthma, T2D, and height, but the associated causal variant(s) are different for each of the three phenotypes.</p>
9 Authors
- Andrew T. DeWan
- Megan E. Cahill
- Diana M. Cornejo-Sanchez
- Yining Li
- Zihan Dong
- Tabassum Fabiha
- Hao Sun
- Gao Wang
- Suzanne M. Leal
1 Application
| Application ID | Title |
|---|---|
| 32285 | Detecting pleiotropic effects through integration of omics data |
Enabling scientific discoveries that improve human health