Peer review of: GWAS of thyroid stimulating hormone highlights pleiotropic effects and inverse association (2009)

Reviewers' comments: 

Reviewer #1 (Remarks to the Author): 

Dr. Zhou and colleagues present a GWAS meta-analysis of TSH levels in the HUNT and MGI studies. 
They identify 17 novel loci, which are followed-up with fine-mapping and functional 
characterization. In addition, they perform a phenome-wide scan and a Mendelian randomization 
study to assess the causal effect of TSH levels on thyroid cancer. While the manuscript is easy to 
follow, there are some issues that I would like to bring to the Authors' attention. 

- Distribution and characteristics of the two study samples (HUNG and MGI) should be clearly 
described at the beginning of the Results (age, sex, ancestry, other characteristics). 

- I am confused by the GWAS design. Significance alpha for discovery is not defined. No criterion 
for replication is clearly stated. With HUNT and MGI together, the Authors had at their disposal 
>65K GWAS samples. The Authors could have followed a discovery-replication approach, using 
ThyroidOmics data as replication (testing 1-sided replication penalized by the number of multiple 
tests) or a joint analysis of HUNT and MGI and ThyroidOmics, which would have resulted in a more 
powerful single-stage analysis. Instead, the Authors' choice was half-a-way between the two 
solutions: they used HUNT+MIG for discovery, selected SNPs with p-value < 5e-06 and, on these, 
perform a meta-analysis with ThyroidOmics. The Authors claim 17 novel loci but, if all data were 
pooled in a single analysis encompassing 65K + 54K samples, the yield would have been probably 
much bigger. The rationale for such a choice,which looks like a missed opportunity, looks unclear. 
Please, provide convincing explanation or review the work following a more standard design. 

- On page 4, for several variants, MAF and effect size are given only for the HUNT study and not 
for the meta-analysis of Hunt and MGI (or Hunt-MGI-ThyroidOmics). Why? In addition, what does 
it mean that rs139352934) is 22 
times more frequent in HUNT than in other non-Finnish Europeans? Is it a Scandinavian variant 
then? 

- In Table 1, directions of the effects for each of the studies included in the ThyroidOmics meta-
analysis is probably also available. It would be very informative to add those effect directions as 
well, and compute an overall heterogeneity index that accounts for all ThyroidOmics studies, HUNT, 
and MGI. 

- Given the large sample size of the HUNT study, small differences in the laboratory method to 
assess TSH levels might affect association estimated, especially because laboratory methods are 
not randomized across participants (PMID 28374192). Did the Authors control for laboratory 
method in their GWAS? If not, an analysis should be performed to guarantee that no spurious 
stratification has arisen. 

- In both HUNT and MGI studies: what was the reason to adjust for just 4 PCs? They two studies 
have very different setting and genetic structure. The choice of exactly the same number of PCs 
sounds curious. 

- Is it correct that the Authors performed an inverse-variance based meta-analysis of HUNT, MGI 
and ThyroidOmics GWAS results? While in MGI and ThyroidOmics, the inverse normal 
transformation of TSH was analyzed, in the HUNT study, TSH was not transformed. Probabily a 
meta-analysis of p-values or z-scores weighted by sample size would be more appropriate. 

- The MGI is a study on pre-surgical individuals. It is a special study, as reflected, for instance, by 
the large proportion of individuals affected by any thyroid disease who have been excluded from 
the analysis. In their Mendelian randomizazion analyses, did the Authors consider the problem of 
different population background between exposure and outcome studies? In other words: in MR 

studies, it is assumed that exposure and outcome refer to a similar context (similar age, 
population characteristics, etc.). Has this been verified? How? 

- Would it make sense to perform bidirectional MR analysis to test whether there might be a causal 
effect of thyroid cancer on TSH levels? 

- In Fig. 1, please avoid barplots and use boxplots instead, showing the actual points. In the figure 
and in the text, please, define the number of independent statistical units and how you took 
multiple testing into account. Please, avoid p-value dichotomization using "*", and discuss the 
results in the light of the underlying statistical assumptions. 

- Fig 2: please, provide confidence interval for prevalence as well. In addition, use more 
appropriate ways to estimate confidence intervals for the odds ratios, as they cannot be >1. 
Finally, discuss the very different prevalence in Columbus. 

- I don't think ThyroidOmics can be defined a "cohort", as it was itself a consortium including many 
studies. 

- When describing the methods for the HUNT study and for the Phenome-wide scan, please, 
indicate which version of ICD codes was used (9, 10, 11?). 

- When describing the methods for the HUNT meta-analysis, I assume "the first four principal 
components" should read "the first four genetic principal components". 

- There are several typos / misprints throughout the document. Please, read carefully and fix. 
There are unexplained acronyms (eg: HRC) 

Reviewer #2 (Remarks to the Author): 

Zhou et al. conducted a GWAS on TSH levels using data from HUNT study and the MGI biobank. In 
the replication phase using data from THyroidOmics, 58 loci were replicated of which 17 were 
novel. In order to identify causal variants, fine-mapping analysis were performed and functional 
follow-up with a focus on the significant locus located in the TG gene. 

The authors also analyzed the pleiotropic effect of the top 85 TSH variants (not located in HLA 
region) with 1,283 human diseases and 274 continuous traits using the data from the UK Biobank 
and they identified 17 variants associated to 29 diseases and 29 variants associated to at least one 
quantitative traits. Analysis of the pleiotropic effects of these 85 variants with thyroid cancer risk 
was carried on using results from the meta-analysis of UK Biobank and deCODE data; identifying 
17 variants associated to both TSH levels and thyroid cancer risk. 

A TSH polygenic risk score was derived from the 85 variants highlighted by the meta-analysis of 
GWAS (HUNT study, MGI biobank and ThyroidOmics consortium) and a pheWAS analysis was 
conducted using data from the UK Biobank dataset (1283 diseases) and from FinnGen, showing a 
positive association with hypothyroidism risk and an inverse association with risk of goiter in both 
studies. An inverse association between the TSH PGS and thyroid cancer risk was also reported in 
the UK biobank, and replicated in the Columbus, deCODE and FinnGen studies. 

At last, in order to evaluate the association between TSH levels and thyroid cancer risk using a 
two-sample mendelian randomization approach, an instrumental variable of TSH levels was 
derived from the 83 significant variants from the meta-analysis of HUNT and the ThyroidOmics 
consortium and its association with TC risk was tested in the meta-analysis of UKBB, MGI and 
deCODE. The association between the instrumental variable for TSH levels was also tested in 
association with goiter risk using the UKBB data. These analyses suggested a protective effect of 

high TSH levels on the risk of thyroid cancer and goiter. 

This is a large GWAS study on TSH levels with analysis of its pleiotropic effects with thyroid 
conditions and disorders. The results are interesting, however I think some major information were 
missing making the article difficult to understand. 

Here are some major comments: 

1- Only data from HUNT and MGI are described. Description of deCODE, UK BB, FinnGen, 
Colombus and ThyroidOmics studies are missing. In particular, it would be important to describe 
how the phenotypes of interest (thyroid cancer, TSH levels, and thyroid disorders) were defined 
and how many individuals for each phenotype were available in these studies. If only summary 
statistics were available for these studies, this need to be mentioned. 

2- A descriptive table of the different studies with the number of cases for the phenotypes of 
interest and some indication on the age group and sex distribution would be helpful. Especially as 
age and sex have important effects on thyroid disorders. 

3- If definition of thyroid conditions differs across the studies (self-declared vs validated cases, 
information on histology of thyroid cancer,…) this need to be commented further as limits of the 
study in the discussion part. It is known that information on self-declared thyroid cancer can be 
very poor in some cohort studies, and also risk factors of medullary thyroid cancer are really 
different from papillary or vesicular thyroid cancer. 

4- In the TSH GWAS, data from HUNT and MGI studies were used. When several measurements of 
TSH levels were available for an individual, it is mentioned that in HUNT study, the last 
measurement was used, while in MGI study the authors used the average of the TSH levels. Why 
the authors did not used the same rule for both studies, this need to be justified/commented? 

5- In general, the tables, supplementary tables and figures titles are not explicit and there not 
enough explanation in the legend. Abbreviations in tables should be clarified and meaning of some 
columns are not clear. For instance, I don’t understand the column “direction” in Table 1. 

6- The authors should explain the principle of the SuSiE method used for fine-mapping. Also, it is 
unclear how to interpret the results shown in Supplementary Table 4. A footnote with more 
explanation is required. Also, only 51 loci appeared in this table while 58 loci were highlighted by 
the GWAS. The authors should explain this difference. 

7- The authors should explain why they focused on TG genes and not on other loci while they 
conducted the fine-mapping analysis. 

8- In page 6, the authors explained why that they limited the analyses of pleiotropic effect on the 
85 non HLA variants in the paragraph “Associations of PGS of TSH with other phenotypes”. This 
explanation should appear in the previous paragraph “Pleiotropic effects of TSH loci”. Also, it is not 
clear how many variants were removed from the analysis because they were located in the HLA 
region. 

9- It seems that the authors are confused with the difference between case-control and cohort 
studies while citing the Colombus cohort as a “case-control cohort” (on page 7). 
It does not make sense to look at the thyroid prevalence using the data from a case-control study. 
I do not understand the rationale to show both prevalence and OR of thyroid cancer in figure 2. If 
logistic regression were note adjusted on any factors, then both results (right and left panel) are 
similar but not on the same scale. Also, the results on prevalence are difficult to compare between 
studies. I suggest to remove the results on prevalence. 

10- The authors should give the weight of the SNPs that permit to construct the instrumental 
variable for TSH levels, as it seems that it is derived from different studies (HUNT and the 
ThyroidOmics consortium) than for the polygenic score (HUNT study, MGI biobank.and 
THyroidOmics). 
Also, they should explain why they did not use the same weight between the instrumental variable 
for TSH levels and the polygenic score. Also why FinnGen study was used to replicate the 
association between PGS and goiter but was not used for the MR analysis on goiter? 

11- 17 pleiotropic variants were reported between TSH levels and thyroid cancer risk. To my 
understanding, the instrumental variable included those variants in the mendelian randomisation 
(MR) analysis. As the presence of pleiotropic effect violates the condition of application of MR, a 
sensitivity analysis excluding these SNPs may be useful. 

12- In the discussion part, the authors should discuss the results of the TSH GWAS in regards to 
the results from previous GWAS. Is there any loci that were previously highlighted that were not 
replicated in this new GWAS? 

13- In page 8, it is stated that the causal direction between TSH levels and thyroid cancer risk is 
unexpected. I think the author should mentioned and discuss recent findings from epidemiological 
studies that showed an inverse association between TSH levels measures and papillary thyroid 
cancer. Here are some references: 
-Franceschi S, Rinaldi S. TSH, Thyroid Hormone, and PTC-Letter.Cancer Epidemiol Biomarkers Prev. 
2018 Feb;27(2):227. 
- Huang H, Rusiecki J, Zhao N, Chen Y, Ma S, Yu H, et al. Thyroid-stimulating hormone, thyroid 
hormones, and risk of papillary thyroid cancer: a nested case-control study. Cancer Epidemiol 
Biomarkers Prev 2017;26:1209–18. 
- Rinaldi S, Plummer M, Biessy C, Tsilidis KK, Ostergaard JN, Overvad K, et al. Thyroid-stimulating 
hormone, thyroglobulin, and thyroid hormones and risk of differentiated thyroid carcinoma: the 
EPIC study. J Natl Cancer Inst 2014. 

Minor comments 

1- Some studies were named differently along the manuscript. The Columbus study was names 
Ohio cohort in introduction, while deCODE study is sometime named Iceland study 
2- I think that the description of the GWAS analysis should not be in the studies description but 
should appear separately to make it clearer. 
3- The sentence “We performed genomic control correction was performed on HUNT and MGI…” 
“ (page 10) need to be corrected 
4- Page 11: the “materials” section need to be rephrase 
5- Order of table need to be revised, for instance Supp Table 13 is cited after Supp Table 14 in the 
text 

Reviewer #3 (Remarks to the Author): 

This study identifies 58 genome-wide significant loci for TSH levels, 17 of which are novel. These 
loci show pleiotropic effects on various diseases and traits, and provide genetic evidence for a 
causal relationship between TSH levels and the risk of thyroid cancer and goiter. Associations in 
the thyroglobulin (TG) locus are fine-mapped to two missense variants in TG, and functional work 
provides evidence for their role in thyroglobulin secretion. 

This interesting study contains several novel findings that significantly expands the current genetic 
knowledge in this field. However, I have several comments/concerns presented below in the same 
order as presented in the manuscript (from Results to the Methods section): 

Was there any evidence of inflation in the GWAS statistics due to reasons other than polygenicity 
(e.g. population structure)? It would be informative to investigate this using the univariate LD 
score regression intercept. 

Four rare variants (rs546738875, rs145153320, rs139352934, and rs121908872) that were 
associated with TSH levels in HUNT were not available in the ThyroidOmics dataset for replication. 
The authors explain that the frequency of the risk allele were somewhere between 11-22 times 
higher in HUNT than the other studies. However, how the authors are convinced that these results 
are not false positive findings in HUNT? In addition, were these variant genome-wide significant in 
the original analysis or only after performing the conditional analysis? If the latter, could this be an 
artifact due to the conditional analysis? 

Were the significantly associated rare variants as well as those included in the SKAT-O analysis 
imputed? If so, was their imputation quality reasonably high? Please provide their imputation 
quality scores in a supplementary Table. 

The authors mention: “An intronic variant (rs6544660) in the thyroid adenoma associated gene 
THADA was recently identified to be associated with TSH”. However, this variant has been reported 
as a novel discovery in this study in Table 1. 

Were there any sample overlaps between the discovery and target datasets used for the PGS and 
MR analyses? I noticed that this study by design attempted to avoid such sample overlaps; 
however, it would be ideal to ensure that the results are not biased due to hidden sample overlaps 
or relatedness between individuals in the discovery and targets datasets. This can be investigated, 
for instance, using the bivariate LD score regression intercept. 

Apart from thyroid cancer and goiter, did the authors investigate causality of the other phenotypes 
that were associated with TSH PGS? Performing MR for the traits that were significantly associated 
in PGS analyses may help understand whether these genetic links are consistent with pleiotropy or 
causality. I do not have a strong preference whether all these analyses should be included in this 
paper; however, considering the PGS results, some reader may be interested to know whether the 
genetically predicted TSH levels are causally associated with any of the other traits that came up 
in the PGS analyses. 

For the MR findings for thyroid cancer, although the CIs of the MR estimates overlap between the 
sensitivity tests, most of the test statistics are not significant (e.g. MR Egger, simple mode, and 
weighted mode in Suppl Table 15). Please discuss this in the text. Is this a power issue? 

It would be helpful to discuss briefly, what would be the clinical implication of showing evidence of 
causality of higher TSH levels on reduced risk of thyroid cancer? Is there any evidence showing 
that increasing TSH levels is capable of treating cancer? Alternatively, in terms of prevention 
strategies, is increasing TSH levels practical, or can this lead to thyroid dysfunction and adverse 
metabolic consequences? 

How well did the TSH PGS predict the phenotypes investigated? Providing r2 or AUC in a 
supplementary table can be informative. Although I would expect that, similar to the other 
complex traits, a good prediction accuracy requires much larger sample sizes. 

The authors mention that the previously identified 47 independent TSH-associated loci account for 
14% of TSH heritability. How much the novel loci add to the explained heritability? In addition, 
based on the other complex traits, explaining 14% of variance in heritability from only 47 SNPs 
seems to be too high. Is that because several common SNPs with large effect sizes are involved? 
Alternatively, please ensure that the explained heritability is estimated accurately. 

For the significant traits in PheWAS (e.g. for the 29 disease phenotypes), it would be interesting to 
know whether the genetic link with those traits also expand throughout the genome. The overall 
genetic correlation can be investigated using summary statistics using the LD score regression 
approach. 

Although having thyroid gland as the most significant tissue in the DEPICT tissue enrichment 
analysis is expected and interesting, none of the tissue enrichment associations were statistically 
significant after correcting for multiple testing. I suggest specifying this in the text. 

To get an unbiased understanding of genetic link between TSH and thyroid cancer, instead of using 
only SNPs that were also significant for thyroid cancer, it was better to include all the significant 
TSH SNPs in the analysis presented in supplementary Fig 6 as well as binomial tests for the 
direction of effect. In addition, adding correlation coefficient or variance explained to the 
supplementary Fig 6 would be helpful. Having said that, the caveat for using all the TSH significant 
SNPs in the binomial tests for the direction of effect would be that some SNPs might show 
unexpected direction of effect for thyroid cancer while their CIs overlap with the expected direction. 

The authors have inversed normalized TSH levels for each study separately, and report the 
association results on the SD scale per study. Was the SD similar across all the studies? Different 
SD across studies makes it difficult to convert the meta-analyzed effect estimates back on the 
actual TSH levels scale. 

The investigators specify that they have used different methods for measuring TSH levels across 
studies. Does this bias the results? Do they get similar results after adding the measurement 
method as a covariate in their regression analysis? 

The authors mention that “We computed polygenic score (PGS) for TSH based on the 84 non-HLA 
significant index variants weighted by the effect size estimates based on the meta-analysis results 
of the HUNT Study and the MGI Biobank and then with the GWAS results from the ThyroidOmics 
consortium”. Does this mean that PGS was calculated from the HUNT + MGI + ThyroidOmics 
meta-analysis, or was PGS based on ThyroidOmics estimates calculated separately for a 
comparison? 

For HUNT, the authors have noted that if multiple TSH measurements were available, they used 
the most recent one. However, for MGI they used the average of the TSH levels in the same 
scenario. Was this inconsistency justified, or is it due to different analysis plans per study prior to 
conducting the meta-analysis? 

It was not clear why the authors have only included significant variants from the meta-analysis of 
HUNT + MGI in their DEPICT analysis. Would the final meta-analyzed results including 
ThyroidOmics dataset be more powerful for their DEPICT analysis? 

Please provide details of the stepwise conditional analysis in the Methods section. Was this done 
using GWAS summary statistics or raw genotypes? 

The authors say: “For all genetic variants with P-value < 5x10-6, we conducted another meta-
analysis with up to 54,288 additional samples in ThyroidOmics”. Why the meta-analysis was not 
performed using all the SNPs in these three studies? Was this because of access restrictions to 
ThyroidOmics data?