| Title: | Revealing the genetic complexity of hypothyroidism: integrating complementary association methods |
| Journal: | Frontiers in Genetics |
| Published: | 11 Jun 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/38919955/ |
| DOI: | https://doi.org/10.3389/fgene.2024.1409226 |
| URL: | https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2024.1409226/pdf |
Publication 11877
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Abstract
Hypothyroidism is a common endocrine disorder whose prevalence increases with age. The disease manifests itself when the thyroid gland fails to produce sufficient thyroid hormones. The disorder includes cases of congenital hypothyroidism (CH), but most cases exhibit hormonal feedback dysregulation and destruction of the thyroid gland by autoantibodies. In this study, we sought to identify causal genes for hypothyroidism in large populations. The study used the UK-Biobank (UKB) database, reporting on 13,687 cases of European ancestry. We used GWAS compilation from Open Targets (OT) and tuned protocols focusing on genes and coding regions, along with complementary association methods of PWAS (proteome-based) and TWAS (transcriptome-based). Comparing summary statistics from numerous GWAS revealed a limited number of variants associated with thyroid development. The proteome-wide association study method identified 77 statistically significant genes, half of which are located within the Chr6-MHC locus and are enriched with autoimmunity-related genes. While coding GWAS and PWAS highlighted the centrality of immune-related genes, OT and transcriptome-wide association study mostly identified genes involved in thyroid developmental programs. We used independent populations from Finland (FinnGen) and the Taiwan cohort to validate the PWAS results. The higher prevalence in females relative to males is substantiated as the polygenic risk score prediction of hypothyroidism relied mostly from the female group genetics. Comparing results from OT, TWAS, and PWAS revealed the complementary facets of hypothyroidism's etiology. This study underscores the significance of synthesizing gene-phenotype association methods for this common, intricate disease. We propose that the integration of established association methods enhances interpretability and clinical utility.</p>
5 Authors
- Roei Zucker
- Michael Kovalerchik
- Amos Stern
- Hadasa Kaufman
- Michal Linial
1 Application
| Application ID | Title |
|---|---|
| 26664 | Novel machine-learning framework for improved inference in GWAS |
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