{"doi":"10.1002/hsr2.71092","title":"An Atlas of Genetic Correlations Between Thyroid Hormone Levels and Human Health‐Related Traits","abstract":"Thyroid hormones are essential for normal human development and the regulation of metabolism [1, 2]. These hormones, including thyroid stimulating hormone (TSH), free triiodothyronine (FT3), total triiodothyronine (TT3), and free thyroxine (FT4), are routinely used in clinical practice as markers for screening and diagnosing thyroid dysfunction [3]; TSH serves as the primary screening test for patients with suspected thyroid disorders, while thyroid tests that measure FT3, TT3, and FT4 levels are used to diagnose thyroid diseases [3, 4]. Previous epidemiological studies have shown that thyroid hormone levels are associated with many diseases and health conditions in humans, including cardiovascular diseases [5, 6], psychiatric disorders [7, 8], sleep duration [9], and cancers at various body sites [10-12]. However, the etiology underlying these associations is not fully understood. Genetic correlation has emerged as a metric to quantify the degree of similarity between two traits based on shared genetic variations identified in genome-wide association studies (GWAS) and has enhanced our understanding of the etiology underlying many complex traits [13-15]. Though large-scale GWAS have helped in mapping the genetic bases of many human diseases and health-related traits [16-20], GWAS of thyroid hormones have been historically limited by lower sample sizes [21, 22] with the exception of a recent GWAS involving up to 271,040 individuals of European ancestry that identified 413 independent genetic variants associated with levels of several thyroid hormones [23]. Thus far, the shared genetic bases between thyroid hormones and other health-related traits and diseases has not been fully explored. In this study, we aimed to systematically compute the genetic correlation between each of four thyroid hormones (TSH, TT3, FT3, FT4) and numerous health-related traits such as aging, cancer, gastrointestinal disease, psychiatric-neurologic disorders, and blood-related, cardiometabolic, immune-related, and anthropometric traits. GWAS summary statistics for TSH, TT3, FT3, and FT4 were obtained from Sterenborg et al. [23] and were based on 271,040, 15,829, 59,061, and 119,120 individuals, respectively. GWAS summary statistics for 72 health-related traits and diseases that were not directly related to thyroid function or thyroid-related disease [18, 24-40] were obtained and processed as described in Barbeira et al. [41] from 85 previously conducted GWAS, and involved individuals across 18 consortia listed in Supporting Information S1: Table 1. We generally classified these traits/diseases into the following 10 categories: aging, cancer, gastrointestinal disease, psychiatric-neurological disease, skeletal disease, and anthropometric, blood-related, cardiometabolic, immune-related, and morphological traits (Supporting Information S1: Table 1). We selected these 72 traits for several reasons. First, these traits are all complex traits that encompass a wide range of physiological and disease-related states, thus allowing for a comprehensive evaluation of genetic correlations between thyroid hormones and human health and disease. In addition, previous literature has provided evidence suggesting that each of these traits and diseases has some genetic basis, with nearly all these traits having reported genome-wide significant loci [18, 24-40]. Lastly, these GWAS were harmonized and processed using identical protocols including variant imputation and standardization, and therefore had consistent imputation quality across traits [25, 41]; this consistency in post-GWAS harmonization enabled standardized comparison across traits in the genetic correlation analyses conducted in this study. All original studies involved in these GWAS received ethical approval, and participants provided informed consent. We utilized the LDSC package [13] to compute the genetic correlations between each thyroid hormone (TSH, TT3, FT3, and FT4) and each of the 72 health-related t","journal":"Health Science Reports","year":2025,"id":570656,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9609,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1357868,"name":"Yijia Sun","orcid":null,"position":1,"is_corresponding":false},{"id":493593,"name":"James Li","orcid":"0000-0002-1248-6592","position":0,"is_corresponding":true}],"reference_count":55,"raw_metadata":null,"created_at":"2026-07-19T02:57:11.713851Z","pmid":"40692564","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}