Genome-wide analyses identify a role for SLC17A4 and AADAT in thyroid hormone regulation

Abstract

Thyroid dysfunction is an important public health problem, which affects 10% of the general population and increases the risk of cardiovascular morbidity and mortality. Many aspects of thyroid hormone regulation have only partly been elucidated, including its transport, metabolism, and genetic determinants. Here we report a large meta-analysis of genome-wide association studies for thyroid function and dysfunction, testing 8 million genetic variants in up to 72,167 individuals. One-hundred-and-nine independent genetic variants are associated with these traits. A genetic risk score, calculated to assess their combined effects on clinical end points, shows significant associations with increased risk of both overt (Graves’ disease) and subclinical thyroid disease, as well as clinical complications. By functional follow-up on selected signals, we identify a novel thyroid hormone transporter (SLC17A4) and a metabolizing enzyme (AADAT). Together, these results provide new knowledge about thyroid hormone physiology and disease, opening new possibilities for therapeutic targets.

Mendelian Randomization analyses reveal a causal effect of thyroid function on stroke via atrial fibrillation

“…We performed two-sample MR analysis by using summary genetic data from the largest GWAS studies available for thyroid function (18), CAD(19) and stroke(20). The exposures of interest included: normal range TSH and FT4 levels, GD and HD.…”

“…In order to cover the entire spectrum of thyroid (dys)function, for secondary analyses we assessed the causal effect of mild thyroid dysfunction on these two outcomes by using variants and summary data derived from two separate case–control GWAS meta-analyses of increased TSH levels (i.e., (subclinical and overt) hypothyroidism) and decreased TSH levels (i.e., (subclinical and overt) hyperthyroidism), including cases with TSH levels above and below the cohort-specific reference range, respectively, and controls with TSH levels within the reference range(18). One condition of MR is that exposure-related SNPs (the instrumental variables) must not be in Linkage Disequilibrium (LD) with each other, as that can result in confounding (30).…”

“…Summary statistics for TSH, FT4, increased and decreased TSH associated variants were extracted from the currently largest GWAS meta-analysis for thyroid function (18). For HD and GD risk variants we used summary associations derived from UKBB, as described above.…”

“…To estimate the power of our study, we calculated minimal odds ratio (OR) of the outcome variable (CAD and stroke) per standard deviation (SD) of the exposure variable (TSH and FT4 levels) detectable (power = 0.8, α = 0.05) in our study, using a non-centrality parameter-based approach (39), implemented in a publicly available mRnd web tool (http://cnsgenomics.com/shiny/mRnd/). Proportion of total variance in TSH and FT4 levels explained by the genetic variants used as instruments (9.4% and 4.8%, respectively) was established based on the data from Teumer et al (18).…”

“…Recently, the ThyroidOmics consortium reported the largest meta-analysis of genome-wide association studies (GWAS) on thyroid function in over 72,000 subjects which more than doubled the number of loci associated with thyroid function (18). These findings paved the way to conduct well-powered MR studies to test the causality of the observed associations between thyroid function and CVD.…”