Meta-Analysis

. 2026 Feb;58(2):307-316.

doi: 10.1038/s41588-025-02483-w. Epub 2026 Feb 5.

Global multi-ancestry genome-wide analyses identify genes and biological pathways associated with thyroid cancer and benign thyroid diseases

Samantha L White¹, Maizy S Brasher¹, Jack Pattee², Wei Zhou^{3

4

5}, Sinéad Chapman⁶, Yon Ho Jee⁷, Caitlin C Bell⁸, Taylor L Jamil⁹, Martin Barrio¹⁰, Christopher H Arehart^{11

12}, Luke M Evans^{11

12}, Jibril Hirbo^{13

14}, Nancy J Cox^{13

14}, Peter Straub^{13

14}, Shinichi Namba^{15

16

17}, Emily Bertucci-Richter¹⁸, Lindsay Guare¹⁹, Ahmed Edris²⁰, Sam Morris²⁰, Ashley J Mulford²¹, Haoyu Zhang¹, Brian Fennessy²², Martin D Tobin^{23

24}, Jing Chen²³, Alexander T Williams²³, Catherine John^{23

24}, David A van Heel²⁵, Rohini Mathur²⁶, Sarah Finer²⁶, Marta R Moksnes^{27

28}, Ben M Brumpton^{27

29

30}, Bjørn Olav Åsvold^{27

29

31}, Raitis Peculis³², Vita Rovite³², Ilze Konrade³³, Ying Wang³, Kristy Crooks³⁴, Sameer Chavan³⁴, Matthew J Fisher³⁴, Nicholas Rafaels³⁴, Meng Lin^{1

34}, Jonathan A Shortt^{1

34}, Alan R Sanders^{21

35}, David C Whiteman³⁶, Stuart MacGregor³⁶, Sarah E Medland³⁶, Unnur Thorsteinsdóttir^{37

38}, Kári Stefánsson^{37

38}, Tugce Karaderi³⁹, Kathleen M Egan⁴⁰, Therese Bocklage^{41

42}, Hilary C McCrary^{43

44}, Gregory Riedlinger⁴⁵, Bodour Salhia^{46

47}, Craig Shriver⁴⁸, Minh D Phan^{49

50}, Janice L Farlow⁵¹, Stephen Edge^{52

53}, Varinder Kaur^{54

55}, Michelle L Churchman⁵⁶, Robert J Rounbehler⁵⁶, Pamela L Brock⁵⁷, Matthew D Ringel^{57

58}, Milton Pividori^{1

34

59}, Rebecca Schweppe^{8

60}, Christopher D Raeburn¹⁰, Robin G Walters²⁰, Zhengming Chen²⁰, Liming Li^{61

62

63}, Koichi Matsuda^{64

65}, Yukinori Okada^{15

16

17}, Sebastian Zöllner¹⁸, Anurag Verma¹⁹; Penn Medicine BioBank; Michael H Preuss²², Eimear Kenny²², Audrey E Hendricks¹, Lauren Fishbein^{1

8

60

66}, Peter Kraft^{7

67}, Mark J Daly^{3

4

68}, Benjamin M Neale^{3

4

68}; Virtual Thyroid Biopsy Consortium; Colorado Center for Personalized Medicine; Genes & Health Research Team; BioBank Japan Project; Alicia R Martin^{3

4

68

69}, Joanne B Cole^{1

59}, Bryan R Haugen^{8

60}; Global Biobank Meta-analysis Initiative; Christopher R Gignoux^{70

71

72

73}, Nikita Pozdeyev^{74

75

76

77}

Collaborators, Affiliations

Collaborators

Genes & Health Research Team:
David A van Heel

Affiliations

¹ Department of Biomedical Informatics, University of Colorado Anschutz, Aurora, CO, USA.
² Center for Innovative Design & Analysis, Colorado School of Public Health, University of Colorado Anschutz, Aurora, CO, USA.
³ Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
⁴ Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
⁵ Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
⁶ The Broad Institute, Cambridge, MA, USA.
⁷ Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
⁸ Division of Endocrinology, Diabetes and Metabolism, University of Colorado Anschutz, Aurora, CO, USA.
⁹ Department of Otolaryngology, Head and Neck Surgery, University of Colorado Anschutz, Aurora, CO, USA.
¹⁰ Division of GI, Trauma, and Endocrine Surgery, Department of Surgery, University of Colorado Anschutz, Aurora, CO, USA.
¹¹ Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA.
¹² Department of Ecology & Evolutionary Biology, University of Colorado Boulder, Boulder, CO, USA.
¹³ Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
¹⁴ Vanderbilt Genetic Institute, Vanderbilt University Medical Center, Nashville, TN, USA.
¹⁵ Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
¹⁶ Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan.
¹⁷ Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
¹⁸ Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
¹⁹ Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
²⁰ Nuffield Department of Population Health, University of Oxford, Oxford, UK.
²¹ Genomic Health Initiative, Endeavor Health Research Institute, Evanston, IL, USA.
²² The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
²³ Division of Public Health and Epidemiology, University of Leicester, Leicester, UK.
²⁴ University Hospitals of Leicester NHS Trust, Leicester, UK.
²⁵ Blizard Institute, Queen Mary University of London, London, UK.
²⁶ Wolfson Institute of Population Health, Queen Mary University of London, London, UK.
²⁷ HUNT Center for Molecular and Clinical Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway.
²⁸ Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway.
²⁹ HUNT Research Centre, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway.
³⁰ Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway.
³¹ Department of Endocrinology, Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway.
³² Latvian Biomedical Research and Study Centre, Riga, Latvia.
³³ Department of Internal Medicine, Riga Stradins University, Riga, Latvia.
³⁴ Colorado Center for Personalized Medicine, University of Colorado Anschutz, Aurora, CO, USA.
³⁵ Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, USA.
³⁶ QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia.
³⁷ deCODE genetics/Amgen, Inc., Reykjavik, Iceland.
³⁸ Faculty of Medicine, University of Iceland, Reykjavik, Iceland.
³⁹ Department of Public Health, Faculty of Health and Medical Sciences, Center for Health Data Science, Section for Health Data Science and Artificial Intelligence, University of Copenhagen, Copenhagen, Denmark.
⁴⁰ Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
⁴¹ University of Kentucky Markey Cancer Center, Lexington, KY, USA.
⁴² University of Kentucky-Department of Pathology and Laboratory Medicine, University of Kentucky-Chandler Medical Center, Lexington, KY, USA.
⁴³ University of Utah Huntsman Cancer Institute, Salt Lake City, UT, USA.
⁴⁴ Department of Otolaryngology-Head and Neck Surgery, School of Medicine, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA.
⁴⁵ Rutgers Cancer Institute, New Brunswick, NJ, USA.
⁴⁶ Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
⁴⁷ Department of Translational Genomics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
⁴⁸ Murtha Cancer Center, Uniformed Services University/Walter Reed National Military Medical Center, Bethesda, MD, USA.
⁴⁹ University of Oklahoma Stephenson Cancer Center, Oklahoma City, OK, USA.
⁵⁰ Medical Oncology/Head and Neck Oncology, Stephenson Cancer Center, University of Oklahoma, Oklahoma City, OK, USA.
⁵¹ Indiana University School of Medicine, Indianapolis, IN, USA.
⁵² Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA.
⁵³ Departments of Surgical Oncology and Cancer Prevention and Control, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA.
⁵⁴ University of Virginia Cancer Center, Charlottesville, VA, USA.
⁵⁵ Department of Internal Medicine, Division of Hematology & Oncology, University of Virginia Health, Charlottesville, VA, USA.
⁵⁶ Aster Insights, Hudson, FL, USA.
⁵⁷ The Ohio State University Comprehensive Cancer Center and the Ohio State University College of Medicine, Columbus, OH, USA.
⁵⁸ Department of Molecular Medicine and Therapeutics, The Ohio State University Comprehensive Cancer Center and the Ohio State University College of Medicine, Columbus, OH, USA.
⁵⁹ Human Medical Genetics and Genomics Program, University of Colorado Anschutz, Aurora, CO, USA.
⁶⁰ University of Colorado Cancer Center, University of Colorado Anschutz, Aurora, CO, USA.
⁶¹ Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China.
⁶² Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China.
⁶³ Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China.
⁶⁴ Laboratory of Genome Technology, Human Genome Center, Institute of Medical Science, The University of Tokyo, Tokyo, Japan.
⁶⁵ Laboratory of Clinical Genome Sequencing, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan.
⁶⁶ Research Service, Rocky Mountain Regional VA Medical Center, Aurora, CO, USA.
⁶⁷ Transdivisional Research Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
⁶⁸ Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
⁶⁹ Department of Medicine, Harvard Medical School, Boston, MA, USA.
⁷⁰ Department of Biomedical Informatics, University of Colorado Anschutz, Aurora, CO, USA. chris.gignoux@cuanschutz.edu.
⁷¹ Colorado Center for Personalized Medicine, University of Colorado Anschutz, Aurora, CO, USA. chris.gignoux@cuanschutz.edu.
⁷² Human Medical Genetics and Genomics Program, University of Colorado Anschutz, Aurora, CO, USA. chris.gignoux@cuanschutz.edu.
⁷³ University of Colorado Cancer Center, University of Colorado Anschutz, Aurora, CO, USA. chris.gignoux@cuanschutz.edu.
⁷⁴ Department of Biomedical Informatics, University of Colorado Anschutz, Aurora, CO, USA. nikita.pozdeyev@cuanschutz.edu.
⁷⁵ Division of Endocrinology, Diabetes and Metabolism, University of Colorado Anschutz, Aurora, CO, USA. nikita.pozdeyev@cuanschutz.edu.
⁷⁶ Colorado Center for Personalized Medicine, University of Colorado Anschutz, Aurora, CO, USA. nikita.pozdeyev@cuanschutz.edu.
⁷⁷ University of Colorado Cancer Center, University of Colorado Anschutz, Aurora, CO, USA. nikita.pozdeyev@cuanschutz.edu.

PMID: 41644669
PMCID: PMC12900643
DOI: 10.1038/s41588-025-02483-w

Meta-Analysis

Global multi-ancestry genome-wide analyses identify genes and biological pathways associated with thyroid cancer and benign thyroid diseases

Samantha L White et al. Nat Genet. 2026 Feb.

. 2026 Feb;58(2):307-316.

doi: 10.1038/s41588-025-02483-w. Epub 2026 Feb 5.

Authors

Collaborators

Genes & Health Research Team:
David A van Heel

Affiliations

¹ Department of Biomedical Informatics, University of Colorado Anschutz, Aurora, CO, USA.
² Center for Innovative Design & Analysis, Colorado School of Public Health, University of Colorado Anschutz, Aurora, CO, USA.
³ Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
⁴ Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
⁵ Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
⁶ The Broad Institute, Cambridge, MA, USA.
⁷ Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
⁸ Division of Endocrinology, Diabetes and Metabolism, University of Colorado Anschutz, Aurora, CO, USA.
⁹ Department of Otolaryngology, Head and Neck Surgery, University of Colorado Anschutz, Aurora, CO, USA.
¹⁰ Division of GI, Trauma, and Endocrine Surgery, Department of Surgery, University of Colorado Anschutz, Aurora, CO, USA.
¹¹ Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA.
¹² Department of Ecology & Evolutionary Biology, University of Colorado Boulder, Boulder, CO, USA.
¹³ Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
¹⁴ Vanderbilt Genetic Institute, Vanderbilt University Medical Center, Nashville, TN, USA.
¹⁵ Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
¹⁶ Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan.
¹⁷ Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
¹⁸ Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
¹⁹ Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
²⁰ Nuffield Department of Population Health, University of Oxford, Oxford, UK.
²¹ Genomic Health Initiative, Endeavor Health Research Institute, Evanston, IL, USA.
²² The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
²³ Division of Public Health and Epidemiology, University of Leicester, Leicester, UK.
²⁴ University Hospitals of Leicester NHS Trust, Leicester, UK.
²⁵ Blizard Institute, Queen Mary University of London, London, UK.
²⁶ Wolfson Institute of Population Health, Queen Mary University of London, London, UK.
²⁷ HUNT Center for Molecular and Clinical Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway.
²⁸ Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway.
²⁹ HUNT Research Centre, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway.
³⁰ Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway.
³¹ Department of Endocrinology, Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway.
³² Latvian Biomedical Research and Study Centre, Riga, Latvia.
³³ Department of Internal Medicine, Riga Stradins University, Riga, Latvia.
³⁴ Colorado Center for Personalized Medicine, University of Colorado Anschutz, Aurora, CO, USA.
³⁵ Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, USA.
³⁶ QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia.
³⁷ deCODE genetics/Amgen, Inc., Reykjavik, Iceland.
³⁸ Faculty of Medicine, University of Iceland, Reykjavik, Iceland.
³⁹ Department of Public Health, Faculty of Health and Medical Sciences, Center for Health Data Science, Section for Health Data Science and Artificial Intelligence, University of Copenhagen, Copenhagen, Denmark.
⁴⁰ Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
⁴¹ University of Kentucky Markey Cancer Center, Lexington, KY, USA.
⁴² University of Kentucky-Department of Pathology and Laboratory Medicine, University of Kentucky-Chandler Medical Center, Lexington, KY, USA.
⁴³ University of Utah Huntsman Cancer Institute, Salt Lake City, UT, USA.
⁴⁴ Department of Otolaryngology-Head and Neck Surgery, School of Medicine, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA.
⁴⁵ Rutgers Cancer Institute, New Brunswick, NJ, USA.
⁴⁶ Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
⁴⁷ Department of Translational Genomics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
⁴⁸ Murtha Cancer Center, Uniformed Services University/Walter Reed National Military Medical Center, Bethesda, MD, USA.
⁴⁹ University of Oklahoma Stephenson Cancer Center, Oklahoma City, OK, USA.
⁵⁰ Medical Oncology/Head and Neck Oncology, Stephenson Cancer Center, University of Oklahoma, Oklahoma City, OK, USA.
⁵¹ Indiana University School of Medicine, Indianapolis, IN, USA.
⁵² Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA.
⁵³ Departments of Surgical Oncology and Cancer Prevention and Control, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA.
⁵⁴ University of Virginia Cancer Center, Charlottesville, VA, USA.
⁵⁵ Department of Internal Medicine, Division of Hematology & Oncology, University of Virginia Health, Charlottesville, VA, USA.
⁵⁶ Aster Insights, Hudson, FL, USA.
⁵⁷ The Ohio State University Comprehensive Cancer Center and the Ohio State University College of Medicine, Columbus, OH, USA.
⁵⁸ Department of Molecular Medicine and Therapeutics, The Ohio State University Comprehensive Cancer Center and the Ohio State University College of Medicine, Columbus, OH, USA.
⁵⁹ Human Medical Genetics and Genomics Program, University of Colorado Anschutz, Aurora, CO, USA.
⁶⁰ University of Colorado Cancer Center, University of Colorado Anschutz, Aurora, CO, USA.
⁶¹ Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China.
⁶² Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China.
⁶³ Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China.
⁶⁴ Laboratory of Genome Technology, Human Genome Center, Institute of Medical Science, The University of Tokyo, Tokyo, Japan.
⁶⁵ Laboratory of Clinical Genome Sequencing, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan.
⁶⁶ Research Service, Rocky Mountain Regional VA Medical Center, Aurora, CO, USA.
⁶⁷ Transdivisional Research Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
⁶⁸ Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
⁶⁹ Department of Medicine, Harvard Medical School, Boston, MA, USA.
⁷⁰ Department of Biomedical Informatics, University of Colorado Anschutz, Aurora, CO, USA. chris.gignoux@cuanschutz.edu.
⁷¹ Colorado Center for Personalized Medicine, University of Colorado Anschutz, Aurora, CO, USA. chris.gignoux@cuanschutz.edu.
⁷² Human Medical Genetics and Genomics Program, University of Colorado Anschutz, Aurora, CO, USA. chris.gignoux@cuanschutz.edu.
⁷³ University of Colorado Cancer Center, University of Colorado Anschutz, Aurora, CO, USA. chris.gignoux@cuanschutz.edu.
⁷⁴ Department of Biomedical Informatics, University of Colorado Anschutz, Aurora, CO, USA. nikita.pozdeyev@cuanschutz.edu.
⁷⁵ Division of Endocrinology, Diabetes and Metabolism, University of Colorado Anschutz, Aurora, CO, USA. nikita.pozdeyev@cuanschutz.edu.
⁷⁶ Colorado Center for Personalized Medicine, University of Colorado Anschutz, Aurora, CO, USA. nikita.pozdeyev@cuanschutz.edu.
⁷⁷ University of Colorado Cancer Center, University of Colorado Anschutz, Aurora, CO, USA. nikita.pozdeyev@cuanschutz.edu.

PMID: 41644669
PMCID: PMC12900643
DOI: 10.1038/s41588-025-02483-w

Abstract

Thyroid diseases are common and highly heritable. We performed a meta-analysis of genome-wide association studies from 19 biobanks for five thyroid diseases: thyroid cancer (ThC), benign nodular goiter, Graves' disease, lymphocytic thyroiditis and primary hypothyroidism. We analyzed genetic association data from ~2.9 million genomes and identified 313 known and 570 new independent loci linked to thyroid diseases. We discovered genetic correlations between ThC, benign nodular goiter and autoimmune thyroid diseases (rg = 0.16-0.97). Telomere maintenance genes contributed to benign and malignant thyroid nodular disease risk, whereas cell cycle, DNA repair and damage response genes were associated with ThC. We propose a paradigm that explains genetic predisposition to benign and malignant thyroid nodules. We found polygenic risk score associations with ThC risk of structural disease recurrence, tumor size, multifocality, lymph node metastases and extranodal extension. Polygenic risk scores identified individuals with aggressive ThC in a biobank, creating an opportunity for genetically informed population screening.

PubMed Disclaimer

Conflict of interest statement

Competing interests: N.P. and B.R.H. received research support from Veracyte, unrelated to this study. B.R.H. is the Clinical Liaison for ThyroSeq at Sonic Healthcare USA. N.P., S.L.W., B.R.H. and C.R.G. filed a provisional patent application 151077-00053PR2 with the United States Patent and Trademark Office dedicated to the use of a PRS for ThC diagnosis. The other authors declare no competing interests.

Figures

**Fig. 1. Study design.**
I. The VTB Consortium was established within the framework of the GBMI. The participating biobanks performed GWAS for five thyroid diseases. II. An inverse-variance-weighted meta-analysis was conducted after quality control procedures. Previously known and new independent genetic associations were identified. III. Functional inference studies included genetic correlation analysis with cov-LDSC. Asterisks denote Benjamini–Hochberg false discovery rate (FDR) < 0.05. IV. TWAS (FUSION and S-PrediXcan). V. Pathway (KEGG and Reactome) and gene expression analyses (TCGA and ORIEN AVATAR). VI. PRS were developed for ThC, benign thyroid diseases and to distinguish malignant and benign thyroid nodules. VII. PRS were tested for association with thyroid diseases and aggressive ThC features extracted from clinical charts and surgical histopathology reports.

**Fig. 2. Pleiotropic and phenotype-specific loci associated with thyroid diseases in the meta-analysis of GWAS.**
The heatmap illustrates the genetic correlation (rg) between thyroid phenotypes, which was estimated using cov-LDSC. The asterisks denote significance at a Benjamini–Hochberg FDR < 0.05. Circular plots highlight loci significantly associated with ThC and BNG (right) and autoimmune thyroid diseases (left). Right, The red and blue dots, along with the gene labels, indicate loci predominantly associated with ThC and BNG, respectively. Left, The red dots indicate loci significantly associated with GD but not with LT or primary hypothyroidism. *PTCSC2* (right, yellow) is the only locus inversely associated with ThC and BNG (Supplementary Tables 6.1–6.6 list all loci).

**Fig. 3. The ThC PRS.**
Two ThC PRS were developed: PRS_{ThC versus All} to identify individuals at risk in a population and PRS_{ThC versus BNG} for the clinically relevant task of discriminating malignant and benign thyroid nodules. PRS were tested in the CCPM population, which was not used for PRS development. a, AUCs (n = 94,561; 1,343 ThCs). b, ThC risk according to PRS decile. The error bars denote the 95% CI calculated as ± s.e. × 1.96 surrounding the odds ratio (OR). c, PRS association with features of aggressive ThC. P values were calculated using a two-sided Wald test. Asterisks indicate ThC risk features significantly associated with PRS at a nominal (black; *P ≤ 0.05) or Bonferroni-corrected (blue; **P ≤ 1.7 × 10⁻³) significance threshold. Raw PRS and ThC risk features are listed in Supplementary Table 16.

**Fig. 4. Germline genetic susceptibility to ThC and BNG.**
We hypothesize that two biological processes with distinct genetic architecture cause thyroid nodules: (1) hyperplasia, a polyclonal follicular cell proliferation with no malignant potential; and (2) neoplasia, a clonal growth driven by somatic genetic alterations. Neoplastic nodules can be benign or malignant, and the mismatch between biological mechanisms (hyperplasia and neoplasia) and GWAS phenotype definitions (benign and malignant thyroid nodules) has led to apparent genetic pleiotropy. The pathway and genes associated with BNG but not ThC in the GWAS meta-analysis (for example, the insulin-like growth factor 1 (*IGF1*) and fibroblast growth factor (*FGF*) signaling pathways) predispose to benign nodules. Pathways and genes associated with both BNG and ThC (for example, telomere maintenance) predispose to neoplastic thyroid nodules, either benign or malignant. In the absence of other genetic risk factors, patients develop benign adenomas or low-risk ThCs. Alternatively, genetic alterations in cell cycle and DNA damage response genes (associated predominantly with ThC but not BNG in the GWAS meta-analysis) predispose to high-risk ThC.

**Extended Data Fig. 1. Virtual Thyroid Biopsy Consortium.**
The Consortium aggregated data from 19 biobanks, 10 countries, four continents, and ~2.9 million participants.

**Extended Data Fig. 2. Prevalence of thyroid diseases across biobanks.**
Genetic ancestry was estimated from GWAS summary data using Summix2.

**Extended Data Fig. 3. Genetic correlation analysis for thyroid diseases in EUR-like GWAS meta-analysis.**
Genetic correlations were estimated using covariate-adjusted linkage disequilibrium score regression. The asterisks denote Benjamini-Hochberg false discovery rate (FDR) < 0.05; p-values were generated using a two-sided Wald test.

**Extended Data Fig. 4. mRNA expression of thyroid cancer-associated genes in normal thyroid tissue and thyroid cancer.**
Genes were identified from ANNOVAR annotations of genome-wide significant variants in thyroid cancer GWAS meta-analysis and FUSION TWAS cis-eQTL analysis. The dark blue color indicates genes with high expression in normal thyroid tissue, where the thyroid is among the top three tissues with the highest expression in pan-tissue transcriptome analysis from the NCBI Gene database (https://www.ncbi.nlm.nih.gov/gene). Positive (red) and negative (light blue) significant associations of mRNA expression with high-risk thyroid cancer features, such as earlier age at diagnosis, higher ERK score and lower thyroid differentiation score, etc., are shown. P-values were derived from a two-tailed t-test for linear regression (continuous variables) and a two-sided Wald test for logistic/ordinal regression (binary/ordinal variables). All regression analyses were adjusted for the major somatic oncogenic drivers, including *BRAF* V600E and *N/H/KRAS*. Significance threshold was adjusted using Bonferroni correction (p-value ≤ 1e-04). ORIEN - Oncology Research Information Exchange Network; TCGA – The Cancer Genome Atlas; AJCC - American Joint Committee on Cancer.

**Extended Data Fig. 5. Scatterplot of effect sizes of the variants in *PTCSC2* locus significantly (p-value < 5e-8) associated with thyroid cancer and benign nodular goiter.**
ThC – thyroid cancer. BNG – benign nodular goiter. $ρ$ - Spearman correlation. Shading highlights the regression line’s 95% confidence interval. P-value was calculated with a two-tailed t-test.

**Extended Data Fig. 6. All of Us Research Program whole genome sequencing data analysis pipeline.**
Variants (SNPs and indels) from participating biobanks GWAS summary data and the Polygenic Score Catalog (https://www.pgscatalog.org) were extracted from the All of Us Research Program Hail variant dataset v7 object.

**Extended Data Fig. 7. Variant overlap in GWAS from participating Biobanks.**
A fraction of variants that are identical by chromosome, position, reference and alternate allele in the harmonized GWAS summary are shown. The All of Us Research Program GWAS (top row) was performed on whole-genome sequencing data and was designed to maximize variant overlap with other biobanks.

**Extended Data Fig. 8. Post-GWAS quality control pipeline.**
AF – allele frequency. AC – allele count, cov-LDSC – covariate-adjusted linkage disequilibrium score regression. QQ plot – quantile-quantile plot.

Extended Data Fig. 9. Correlation of major continental ancestry fractions estimated by Summix2 (y-axis) and published by the Million Veteran Program, Colorado Center for Personalized Medicine and All of Us Research Program Biobanks (x-axis).
Multi-ancestry GWAS summary data were used for this analysis. Shading highlights the regression line’s 95% confidence interval. Pearson correlation coefficient p-value was calculated with a two-tailed t-test. MVP – Million Veteran Program. CCPM – Colorado Center for Personalized Medicine. AoU – All of Us Research Program.

See this image and copyright information in PMC

Update of

Global multi-ancestry genetic study elucidates genes and biological pathways associated with thyroid cancer and benign thyroid diseases.
White SL, Brasher MS, Pattee J, Zhou W, Chapman S, Jee YH, Bell CC, Jamil TL, Barrio M, Hirbo J, Cox NJ, Straub P, Namba S, Bertucci-Richter E, Guare L, EdrisMohammed A, Morris S, Mulford AJ, Zhang H, Fennessy B, Tobin MD, Chen J, Williams AT, John C, van Heel DA, Mathur R, Finer S, Moksnes MR, Brumpton B, Åsvold BO, Peculis R, Rovite V, Konrade I, Wang Y, Crooks K, Chavan S, Fisher MJ, Rafaels N, Lin M, Shortt J, Sanders AR, Whiteman D, MacGregor S, Medland S, Thorsteinsdóttir U, Stefánsson K, Karaderi T, Egan KM, Bocklage T, McCrary HC, Riedlingeer G, Salhia B, Shriver C, Phan MD, Farlow JL, Edge S, Kaur V, Churchman M, Rounbehler RJ, Brock PL, Ringel MD, Pividori M, Schweppe R, Raeburn CD, Walters R, Chen Z, Li L, Matsuda K, Okada Y, Zoellner S, Verma A, Preuss M, Kenny E, Hendricks A, Fishbein L, Kraft P, Daly M, Neale B; biobank at the Colorado Center for Personalized Medicine; Genes & Health Research Team; BioBank Japan Project; Martin A, Cole JB, Haugen BR, Gignoux CR, Pozdeyev N. White SL, et al. medRxiv [Preprint]. 2025 May 16:2025.05.15.25327513. doi: 10.1101/2025.05.15.25327513. medRxiv. 2025. Update in: Nat Genet. 2026 Feb;58(2):307-316. doi: 10.1038/s41588-025-02483-w. PMID: 40463558 Free PMC article. Updated. Preprint.

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Global multi-ancestry genome-wide analyses identify genes and biological pathways associated with thyroid cancer and benign thyroid diseases

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Global multi-ancestry genome-wide analyses identify genes and biological pathways associated with thyroid cancer and benign thyroid diseases

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