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

Figure 1

Flow chart of electronic medical record (EMR) data mining and the genome-wide association studies (GWAS) (GWAS) pipeline. We enrolled two cohorts: one with 1,850 hyperthyroidism patients and 21,499 age and sex matched individuals and a replication cohort with 917 hyperthyroidism patients and 10,743 age and sex matched individuals.

Figure 2

Genome-wide association studies (GWAS) and phenome-wide association studies (PheWAS). (A) A GWAS begins with a phenotype of interest and systematically analyzes variants across the entire genome (i.e., “genome-wide”) for association to the phenotype. GWAS can identify multiple genetic associations to a phenotype in complex or polygenic traits. (B) A PheWAS begins with a genetic variant of interest and systematically analyzes many phenotypes (i.e., “phenome-wide”) for association to the genotype. PheWAS has the ability to identify pleiotropy or multiple independent phenotypes associated with a single genetic variant.

Figure 3

Association of genome-wide variants with hyperthyroidism diagnosed in discovery batch (A,B) and replication (C,D) batch using Manhattan plot (A,C) and QQ plot (B,D) analysis. In Manhattan plot analysis, single nucleotide polymorphism (SNP) that passed quality control are plotted on the X-axis according to their chromosomal positions against Y-axis (- log₁₀ p-value). The upper and lower dotted lines indicate the genome-wide significance threshold (p = 5.0×10⁻⁸) and the cut-off level for selecting SNPs for replication study (p = 1.75×10⁻⁵), respectively.