Genome-wide association study of hyperthyroidism based on electronic medical record from Taiwan

Ting-Yuan Liu; Wen-Ling Liao; Tzu-Yuan Wang; Chia-Jung Chan; Jan-Gowth Chang; Yu-Chia Chen; Hsing-Fang Lu; Hsien-Hui Yang; Shih-Yin Chen; Fuu-Jen Tsai

doi:10.3389/fmed.2022.830621

Genome-wide association study of hyperthyroidism based on electronic medical record from Taiwan

Front Med (Lausanne). 2022 Jul 27:9:830621. doi: 10.3389/fmed.2022.830621. eCollection 2022.

Authors

Ting-Yuan Liu^{1

2}, Wen-Ling Liao^{3

4

5}, Tzu-Yuan Wang^{6

7}, Chia-Jung Chan⁵, Jan-Gowth Chang¹, Yu-Chia Chen^{1

2}, Hsing-Fang Lu², Hsien-Hui Yang⁸, Shih-Yin Chen^{5

9}, Fuu-Jen Tsai^{5

9

10}

Affiliations

¹ Center for Precision Medicine, China Medical University Hospital, Taichung, Taiwan.
² Million-Person Precision Medicine Initiative, Department of Medical Research, China Medical University Hospital, Taichung, Taiwan.
³ College of Chinese Medicine, Graduate Institute of Integrated Medicine, China Medical University, Taichung, Taiwan.
⁴ Center for Personalized Medicine, China Medical University Hospital, Taichung, Taiwan.
⁵ Genetics Center, Medical Research, China Medical University Hospital, Taichung, Taiwan.
⁶ Department of Internal Medicine, College of Medicine, China Medical University, Taichung, Taiwan.
⁷ Division of Endocrinology, China Medical University Hospital, Taichung, Taiwan.
⁸ China Medical University Beigang Campus, Yunlin, Taiwan.
⁹ School of Chinese Medicine, China Medical University, Taichung, Taiwan.
¹⁰ Department of Medical Genetics, China Medical University Hospital, Taichung, Taiwan.

Abstract

Excess thyroid hormones have complex metabolic effects, particularly hyperthyroidism, and are associated with various cardiovascular risk factors. Previous candidate gene studies have indicated that genetic variants may contribute to this variable response. Electronic medical record (EMR) biobanks containing clinical and genomic data on large numbers of individuals have great potential to inform the disease comorbidity development. In this study, we combined electronic medical record (EMR) -derived phenotypes and genotype information to conduct a genome-wide analysis of hyperthyroidism in a 35,009-patient cohort in Taiwan. Diagnostic codes were used to identify 2,767 patients with hyperthyroidism. Our genome-wide association study (GWAS) identified 44 novel genomic risk markers in 10 loci on chromosomes 2, 6, and 14 (P < 5 × 10-14), including CTLA4, HCP5, HLA-B, POU5F1, CCHCR1, HLA-DRA, HLA-DRB9, TSHR, RPL17P3, and CEP128. We further conducted a comorbidity analysis of our results, and the data revealed a strong correlation between hyperthyroidism patients with thyroid storm and stroke. In this study, we demonstrated application of the PheWAS using large EMR biobanks to inform the comorbidity development in hyperthyroidism patients. Our data suggest significant common genetic risk factors in patients with hyperthyroidism. Additionally, our results show that sex, body mass index (BMI), and thyroid storm are associated with an increased risk of stroke in subjects with hyperthyroidism.

Keywords: electronic medical record (EMR); genome-wide association study (GWAS); hyperthyroidism; phenome-wide association studies (PheWAS); stroke.