Academic Journal
A Guide for Exploring Pleiotropic Associations in Genome-Wide Association Studies Using Summary Statistics.
| Τίτλος: | A Guide for Exploring Pleiotropic Associations in Genome-Wide Association Studies Using Summary Statistics. |
|---|---|
| Συγγραφείς: | Feng CY; School of Mathematical and Physical Sciences, Macquarie University, Sydney, New South Wales, Australia., Sugier PE; Laboratoire de Mathématiques et de leurs Applications, UMR CNRS 5142, Université de Pau et des Pays de l'Adour, E2S-UPPA, Pau, France.; Université Paris Cité, CNRS, MAP5, Paris, France., Zou N; School of Mathematical and Physical Sciences, Macquarie University, Sydney, New South Wales, Australia., Liquet B; School of Mathematical and Physical Sciences, Macquarie University, Sydney, New South Wales, Australia.; Laboratoire de Mathématiques et de leurs Applications, UMR CNRS 5142, Université de Pau et des Pays de l'Adour, E2S-UPPA, Pau, France. |
| Πηγή: | Statistics in medicine [Stat Med] 2026 Sep; Vol. 45 (20-22), pp. e70717. |
| Τύπος έκδοσης: | Journal Article; Review |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Wiley Country of Publication: England NLM ID: 8215016 Publication Model: Print Cited Medium: Internet ISSN: 1097-0258 (Electronic) Linking ISSN: 02776715 NLM ISO Abbreviation: Stat Med Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Chichester ; New York : Wiley, c1982- |
| Ιατρικοί όροι (MeSH): | Genome-Wide Association Study*/methods , Genome-Wide Association Study*/statistics & numerical data , Genetic Pleiotropy*, Breast Neoplasms/genetics ; Thyroid Neoplasms/genetics ; Humans ; Polymorphism, Single Nucleotide ; Meta-Analysis as Topic ; Phenotype ; Female ; Models, Statistical ; Data Interpretation, Statistical |
| Περίληψη: | Genome-wide association studies (GWAS) have shown that pleiotropy, whereby a single genetic variant or gene influences multiple traits, is common in complex human diseases. Detecting cross-phenotype associations from GWAS summary statistics remains challenging because of small effect sizes, extensive multiple testing, heterogeneous effects, and possible differences in effect direction across traits. Methods that jointly analyze multiple traits can improve the ability to detect pleiotropic signals while retaining the practical advantages of summary statistic-based analyses. Although a range of statistical approaches has been developed for this purpose, practical guidance on their application, assumptions, and interpretation remains limited. This tutorial reviews several widely used methods for pleiotropy detection from GWAS summary statistics, including ASSET, PLACO, GPA, CPBayes, and GCPBayes, and demonstrates their application using breast and thyroid cancer datasets. We also highlight the importance of accounting for effect heterogeneity, correlation, and biological group structure at the gene and pathway levels in the detection and interpretation of pleiotropic association signals. (© 2026 The Author(s). Statistics in Medicine published by John Wiley & Sons Ltd.) |
| References: | E. Uffelmann, “Genome‐Wide Association Studies,” Nature Reviews Methods Primers 1, no. 1 (2021): 1–21, https://doi.org/10.1038/s43586‐021‐00056‐9. S. Sivakumaran, “Abundant Pleiotropy in Human Complex Diseases and Traits,” American Journal of Human Genetics 89, no. 5 (2011): 607–618, https://doi.org/10.1016/j.ajhg.2011.10.004. N. Solovieff, C. Cotsapas, P. H. Lee, S. M. Purcell, and J. W. Smoller, “Pleiotropy in Complex Traits: Challenges and Strategies,” Nature Reviews Genetics 14, no. 7 (2013): 483–495, https://doi.org/10.1038/nrg3461. P. M. Visscher and J. Yang, “A Plethora of Pleiotropy Across Complex Traits,” Nature Genetics 48, no. 7 (2016): 707–708, https://doi.org/10.1038/ng.3604. S. Hackinger and E. Zeggini, “Statistical Methods to Detect Pleiotropy in Human Complex Traits,” Open Biology 7, no. 11 (2017): 170125, https://doi.org/10.1098/rsob.170125. Y. D. Salinas, Z. Wang, and A. T. DeWan, “Statistical Analysis of Multiple Phenotypes in Genetic Epidemiologic Studies: From Cross‐Phenotype Associations to Pleiotropy,” American Journal of Epidemiology 187, no. 4 (2018): 855–863, https://doi.org/10.1093/aje/kwx296. C. Cotsapas, “Pervasive Sharing of Genetic Effects in Autoimmune Disease,” PLoS Genetics 7, no. 8 (2011): e1002254, https://doi.org/10.1371/journal.pgen.1002254. A. E. Shikov, R. K. Skitchenko, A. V. Predeus, and Y. A. Barbitoff, “Phenome‐Wide Functional Dissection of Pleiotropic Effects Highlights Key Molecular Pathways for Human Complex Traits,” Scientific Reports 10, no. 1 (2020): 1037, https://doi.org/10.1038/s41598‐020‐58040‐4. P. F. Sullivan, M. J. Daly, and M. O'Donovan, “Genetic Architectures of Psychiatric Disorders: The Emerging Picture and Its Implications,” Nature Reviews Genetics 13, no. 8 (2012): 537–551, https://doi.org/10.1038/nrg3240. S. H. Lee, “Genetic Relationship Between Five Psychiatric Disorders Estimated From Genome‐Wide SNPs,” Nature Genetics 45, no. 9 (2013): 984–994, https://doi.org/10.1038/ng.2711. H. Lu, “A Comprehensive Gene‐Centric Pleiotropic Association Analysis for 14 Psychiatric Disorders With GWAS Summary Statistics,” BMC Medicine 19, no. 1 (2021): 314, https://doi.org/10.1186/s12916‐021‐02186‐z. D. Ray and N. Chatterjee, “A Powerful Method for Pleiotropic Analysis Under Composite Null Hypothesis Identifies Novel Shared Loci Between Type 2 Diabetes and Prostate Cancer,” PLoS Genetics 16, no. 12 (2020): e1009218, https://doi.org/10.1371/journal.pgen.1009218. Y. Zeng, “GWA‐Based Pleiotropic Analysis Identified Potential SNPs and Genes Related to Type 2 Diabetes and Obesity,” Journal of Human Genetics 66, no. 3 (2021): 297–306, https://doi.org/10.1038/s10038‐020‐00843‐4. P. M. Visscher, “10 Years of GWAS Discovery: Biology, Function, and Translation,” American Journal of Human Genetics 101, no. 1 (2017): 5–22, https://doi.org/10.1016/j.ajhg.2017.06.005. S. H. Lee, N. R. Wray, M. E. Goddard, and P. M. Visscher, “Estimating Missing Heritability for Disease From Genome‐Wide Association Studies,” American Journal of Human Genetics 88, no. 3 (2011): 294–305, https://doi.org/10.1016/j.ajhg.2011.02.002. B. Maher, “Personal Genomes: The Case of the Missing Heritability,” Nature 456, no. 7218 (2008): 18–21, https://doi.org/10.1038/456018a. S. Bhattacharjee, “A Subset‐Based Approach Improves Power and Interpretation for the Combined Analysis of Genetic Association Studies of Heterogeneous Traits,” American Journal of Human Genetics 90, no. 5 (2012): 821–835, https://doi.org/10.1016/j.ajhg.2012.03.015. D. Chung, C. Yang, C. Li, J. Gelernter, and H. Zhao, “GPA: A Statistical Approach to Prioritizing GWAS Results by Integrating Pleiotropy and Annotation,” PLoS Genetics 10, no. 11 (2014): e1004787, https://doi.org/10.1371/journal.pgen.1004787. A. Majumdar, T. Haldar, S. Bhattacharya, and J. S. Witte, “An Efficient Bayesian Meta‐Analysis Approach for Studying Cross‐Phenotype Genetic Associations,” PLoS Genetics 14, no. 2 (2018): e1007139, https://doi.org/10.1371/journal.pgen.1007139. T. Baghfalaki, “Bayesian Meta‐Analysis Models for Cross Cancer Genomic Investigation of Pleiotropic Effects Using Group Structure,” Statistics in Medicine 40, no. 6 (2021): 1498–1518, https://doi.org/10.1002/sim.8855. P.‐E. Sugier, Y. Asgari, M. Sedki, T. Truong, and B. Liquet, “Meta‐Analysis Models With Group Structure for Pleiotropy Detection at Gene and Variant Level Using Summary Statistics From Multiple Datasets,” Biostatistics 26, no. 1 (2025): kxaf037, https://doi.org/10.1093/biostatistics/kxaf037. S. E. Eynard, J. J. Windig, G. Leroy, R. van Binsbergen, and M. P. L. Calus, “The Effect of Rare Alleles on Estimated Genomic Relationships From Whole Genome Sequence Data,” BMC Genetics 16 (2015): 24, https://doi.org/10.1186/s12863‐015‐0185‐0. L. Kruglyak and D. A. Nickerson, “Variation Is the Spice of Life,” Nature Genetics 27, no. 3 (2001): 234–236, https://doi.org/10.1038/85776. M. R. Nelson, “Large‐Scale Validation of Single Nucleotide Polymorphisms in Gene Regions,” Genome Research 14, no. 8 (2004): 1664–1668, https://doi.org/10.1101/gr.2421604. A. T. Kraja, D. I. Chasman, K. E. North, et al., “Pleiotropic Genes for Metabolic Syndrome and Inflammation,” Molecular Genetics and Metabolism 112, no. 4 (2014): 317–338, https://doi.org/10.1016/j.ymgme.2014.04.007. J. Y. Ong and J. Z. Torres, “Dissecting the Mechanisms of Cell Division,” Journal of Biological Chemistry 294, no. 30 (2019): 11382–11390, https://doi.org/10.1074/jbc.AW119.008149. arcOGEN Consortium and arcOGEN Collaborators, “Identification of New Susceptibility Loci for Osteoarthritis (arcOGEN): A Genome‐Wide Association Study,” Lancet 380, no. 9844 (2012): 815–823, https://doi.org/10.1016/S0140‐6736(12)60681‐3. V. Anttila, “Genome‐Wide Meta‐Analysis Identifies New Susceptibility Loci for Migraine,” Nature Genetics 45, no. 8 (2013): 912–917, https://doi.org/10.1038/ng.2676. C. Bycroft, “The UK Biobank Resource With Deep Phenotyping and Genomic Data,” Nature 562, no. 7726 (2018): 203–209, https://doi.org/10.1038/s41586‐018‐0579‐z. A. Buniello, “The NHGRI‐EBI GWAS Catalog of Published Genome‐Wide Association Studies, Targeted Arrays and Summary Statistics 2019,” Nucleic Acids Research 47, no. D1 (2019): D1005–D1012, https://doi.org/10.1093/nar/gky1120. G. R. Franke, “Multicollinearity,” in Wiley International Encyclopedia of Marketing (John Wiley & Sons, Ltd, 2010). M. Carlsen, G. Fu, S. Bushman, and C. Corcoran, “Exploiting Linkage Disequilibrium for Ultrahigh‐Dimensional Genome‐Wide Data With an Integrated Statistical Approach,” Genetics 202, no. 2 (2016): 411–426, https://doi.org/10.1534/genetics.115.179507. A. Gelman, J. B. Carlin, H. S. Stern, et al., Bayesian Data Analysis, 3rd ed. (Chapman and Hall/CRC, 2013), https://doi.org/10.1201/b16018. B. J. Reich and S. K. Ghosh, Bayesian Statistical Methods (Chapman and Hall/CRC, 2019). O. A. Andreassen, “Improved Detection of Common Variants Associated With Schizophrenia by Leveraging Pleiotropy With Cardiovascular‐Disease Risk Factors,” American Journal of Human Genetics 92, no. 2 (2013): 197–209, https://doi.org/10.1016/j.ajhg.2013.01.001. M. E. Glickman, S. R. Rao, and M. R. Schultz, “False Discovery Rate Control Is a Recommended Alternative to Bonferroni‐Type Adjustments in Health Studies,” Journal of Clinical Epidemiology 67, no. 8 (2014): 850–857, https://doi.org/10.1016/j.jclinepi.2014.03.012. D. Ellinghaus, “Analysis of Five Chronic Inflammatory Diseases Identifies 27 New Associations and Highlights Disease‐Specific Patterns at Shared Loci,” Nature Genetics 48, no. 5 (2016): 510–518, https://doi.org/10.1038/ng.3528. F. Gu, “Common Genetic Variants in the 9p21 Region and Their Associations With Multiple Tumours,” British Journal of Cancer 108, no. 6 (2013): 1378–1386, https://doi.org/10.1038/bjc.2013.7. S. P. Kar, “Genome‐Wide Meta‐Analyses of Breast, Ovarian, and Prostate Cancer Association Studies Identify Multiple New Susceptibility Loci Shared by at Least Two Cancer Types,” Cancer Discovery 6, no. 9 (2016): 1052–1067, https://doi.org/10.1158/2159‐8290.CD‐15‐1227. J. E. Taylor, K. J. Worsley, and F. Gosselin, “Maxima of Discretely Sampled Random Fields, With an Application to ‘Bubbles’,” Biometrika 94 (2007): 1–18, https://doi.org/10.1093/biomet/asm004. S. Bhattacharjee, G. Qi, N. Chatterjee, and W. Wheeler, “ASSET: An R Package for Subset‐Based Association Analysis of Heterogeneous Traits and Subtypes,” R package version 2.29.0, (2025), https://bioconductor.org/packages/ASSET. G. Qi, “Genome‐Wide Large‐Scale Multi‐Trait Analysis Characterizes Global Patterns of Pleiotropy and Unique Trait‐Specific Variants,” Nature Communications 15 (2024): 6985, https://doi.org/10.1038/s41467‐024‐51075‐5. D. Ray, “PLACO: Pleiotropic Analysis Under Composite,” R package Version 0.2.0. (2025), https://github.com/RayDebashree/PLACO. J. Park and D. Ray, “A Robust Pleiotropy Method With Applications to Lipid Traits and to Inflammatory Bowel Disease Subtypes With Sample Overlap,” Human Genetics and Genomics Advances 7, no. 1 (2026): 100501, https://doi.org/10.1016/j.xhgg.2025.100501. A. C. Chung and D. Kortemeier, “GPA: Genetic Analysis Incorporating Pleiotropy and Annotation,” R package Version 1.22.0. (2025), https://bioconductor.org/packages/GPA. A. J. Schork, “All SNPs Are Not Created Equal: Genome‐Wide Association Studies Reveal a Consistent Pattern of Enrichment Among Functionally Annotated SNPs,” PLoS Genetics 9, no. 4 (2013): e1003449, https://doi.org/10.1371/journal.pgen.1003449. C. Yang, C. Li, Q. Wang, D. Chung, and H. Zhao, “Implications of Pleiotropy: Challenges and Opportunities for Mining Big Data in Biomedicine,” Frontiers in Genetics 6 (2015): 229, https://doi.org/10.3389/fgene.2015.00229. A. Majumdar, T. Haldar, and J. Witte, “CPBayes: Bayesian Meta Analysis for Studying Cross‐Phenotype Genetic Associations,” R package Version 1.1.0. (2020), https://github.com/ArunabhaCodes/CPBayes. T. J. Mitchell and J. J. Beauchamp, “Bayesian Variable Selection in Linear Regression: Rejoinder,” Journal of the American Statistical Association 83, no. 404 (1988): 1035–1036, https://doi.org/10.2307/2290132. C. Kanduri, C. Bock, S. Gundersen, E. Hovig, and G. K. Sandve, “Colocalization Analyses of Genomic Elements: Approaches, Recommendations and Challenges,” Bioinformatics 35, no. 9 (2019): 1615–1624, https://doi.org/10.1093/bioinformatics/bty835. Y. Asgari, “GCPBayes: Group Level Bayesian Meta Analysis for Cross Phenotype Genetic Association,” R package Version 4.2.0, (2024), https://github.com/tbaghfalaki/GCPBayes. X. Xu and M. Ghosh, “Bayesian Variable Selection and Estimation for Group Lasso,” Bayesian Analysis 10, no. 4 (2015): 909–936, ISSN: 1936‐0975, 1931‐6690, https://doi.org/10.1214/14‐BA929. Y. Asgari, “GCPBayes Pipeline: A Tool for Exploring Pleiotropy at the Gene Level,” NAR Genomics and Bioinformatics 5, no. 3 (2023): lqad065, https://doi.org/10.1093/nargab/lqad065. L. Dong, J. Lu, B. Zhao, W. Wang, and Y. Zhao, “Review of the Possible Association Between Thyroid and Breast Carcinoma,” World Journal of Surgical Oncology 16, no. 1 (2018): 130, https://doi.org/10.1186/s12957‐018‐1436‐0. E. L. Bolf, B. L. Sprague, and F. E. Carr, “A Linkage Between Thyroid and Breast Cancer: A Common Etiology?,” Cancer Epidemiology, Biomarkers & Prevention: A Publication of the American Association for Cancer Research, Cosponsored by the American Society of Preventive Oncology 28, no. 4 (2019): 643–649, https://doi.org/10.1158/1055‐9965.EPI‐18‐0877. S. M. Nielsen, “The Breast‐Thyroid Cancer Link: A Systematic Review and Meta‐Analysis,” Cancer Epidemiology, Biomarkers & Prevention: A Publication of the American Association for Cancer Research, Cosponsored by the American Society of Preventive Oncology 25, no. 2 (2016): 231–238, https://doi.org/10.1158/1055‐9965.EPI‐15‐0833. M. Sutton, P.‐E. Sugier, T. Truong, and B. Liquet, “Leveraging Pleiotropic Association Using Sparse Group Variable Selection in Genomics Data,” BMC Medical Research Methodology 22, no. 1 (2022): 9, https://doi.org/10.1186/s12874‐021‐01491‐8. C. Broc, T. Truong, and B. Liquet, “Penalized Partial Least Squares for Pleiotropy,” BMC Bioinformatics 22, no. 1 (2021): 86, https://doi.org/10.1186/s12859‐021‐03968‐1. D. Bachelet, “Breast Cancer and Exposure to Organochlorines in the CECILE Study: Associations With Plasma Levels Measured at the Time of Diagnosis and Estimated During Adolescence,” International Journal of Environmental Research and Public Health 16, no. 2 (2019): 271, https://doi.org/10.3390/ijerph16020271. F. Clavel‐Chapelon, G. Guillas, L. Tondeur, C. Kernaleguen, and M.‐C. Boutron‐Ruault, “Risk of Differentiated Thyroid Cancer in Relation to Adult Weight, Height and Body Shape Over Life: The French E3N Cohort,” International Journal of Cancer 126, no. 12 (2010): 2984–2990, https://doi.org/10.1002/ijc.25066. E. Cordina‐Duverger, “Hormonal and Reproductive Risk Factors of Papillary Thyroid Cancer: A Population‐Based Case‐Control Study in France,” Cancer Epidemiology 48 (2017): 78–84, https://doi.org/10.1016/j.canep.2017.04.001. L. Lécuyer, “Adapted Dietary Inflammatory Index and Differentiated Thyroid Carcinoma Risk in Two French Population‐Based Case–Control Studies,” European Journal of Nutrition 61, no. 2 (2022): 1097–1108, https://doi.org/10.1007/s00394‐021‐02721‐x. M.‐X. Li, J. M. Y. Yeung, S. S. Cherny, and P. C. Sham, “Evaluating the Effective Numbers of Independent Tests and Significant p Value Thresholds in Commercial Genotyping Arrays and Public Imputation Reference Datasets,” Human Genetics 131, no. 5 (2012): 747–756, https://doi.org/10.1007/s00439‐011‐1118‐2. A. Majumdar, T. Haldar, and J. S. Witte, “Determining Which Phenotypes Underlie a Pleiotropic Signal,” Genetic Epidemiology 40, no. 5 (2016): 366–381, https://doi.org/10.1002/gepi.21973. S. R. Rashkin, “Pan‐Cancer Study Detects Genetic Risk Variants and Shared Genetic Basis in Two Large Cohorts,” Nature Communications 11, no. 1 (2020): 4423, https://doi.org/10.1038/s41467‐020‐18246‐6. R. E. Kass and A. E. Raftery. “Bayes Factors,” Journal of the American Statistical Association 90, no. 430 (1995): 773–795, https://doi.org/10.1080/01621459.1995.10476572. N. Yi, Z. Tang, X. Zhang, and B. Guo, “BhGLM: Bayesian Hierarchical GLMs and Survival Models, With Applications to Genomics and Epidemiology,” Bioinformatics 35, no. 8 (2018): 1419–1421, https://doi.org/10.1093/bioinformatics/bty803. S. A. Pendergrass, “Phenome‐Wide Association Study (PheWAS) for Detection of Pleiotropy Within the Population Architecture Using Genomics and Epidemiology (PAGE) Network,” PLoS Genetics 9, no. 1 (2013): e1003087, ISSN: 1553‐7404, https://doi.org/10.1371/journal.pgen.1003087. L. M. Federmann, “Neurobiological Correlates of Schizophrenia‐Specific and Highly Pleiotropic Genetic Risk Scores for Neuropsychiatric Disorders,” Translational Psychiatry 15, no. 1 (2025): 230, ISSN: 2158‐3188, https://doi.org/10.1038/s41398‐025‐03440‐1. K. Esoh, A. Yalcouyé, and A. Wonkam, “Parasite‐Resistant Variants in African Genomes: Implications for Global Genetic Medicine,” Trends in Parasitology 41, no. 7 (2025): 536–546. issn: 1471‐4922, 1471‐5007, https://doi.org/10.1016/j.pt.2025.05.007. Y.‐J. Hu, “Meta‐Analysis of Gene‐Level Associations for Rare Variants Based on Single‐Variant Statistics,” American Journal of Human Genetics 93, no. 2 (2013): 236–248, https://doi.org/10.1016/j.ajhg.2013.06.011. 1000 Genomes Project Consortium, “A Global Reference for Human Genetic Variation,” Nature 526, no. 7571 (2015): 68–74, https://doi.org/10.1038/nature15393. W. Fu, “Analysis of 6,515 Exomes Reveals the Recent Origin of Most Human Protein‐Coding Variants,” Nature 493, no. 7431 (2013): 216–220, https://doi.org/10.1038/nature11690. M. C. Wu, “Rare‐Variant Association Testing for Sequencing Data With the Sequence Kernel Association Test,” American Journal of Human Genetics 89, no. 1 (2011): 82–93, https://doi.org/10.1016/j.ajhg.2011.05.029. S. Lee, “Optimal Unified Approach for Rare‐Variant Association Testing With Application to Small‐Sample Case‐Control Whole‐Exome Sequencing Studies,” American Journal of Human Genetics 91, no. 2 (2012): 224–237, https://doi.org/10.1016/j.ajhg.2012.06.007. K. A. Broadaway, “A Statistical Approach for Testing Cross‐Phenotype Effects of Rare Variants,” American Journal of Human Genetics 98, no. 3 (2016): 525–540, https://doi.org/10.1016/j.ajhg.2016.01.017. B. Wu and J. S. Pankow, “Sequence Kernel Association Test of Multiple Continuous Phenotypes,” Genetic Epidemiology 40, no. 2 (2016): 91–100, https://doi.org/10.1002/gepi.21945. D. Dutta, L. Scott, M. Boehnke, and S. Lee, “Multi‐SKAT: General Framework to Test for Rare Variant Association With Multiple Phenotypes,” Genetic Epidemiology 43, no. 1 (2019): 4–23, https://doi.org/10.1002/gepi.22156. L. Luo, “Multi‐Trait Analysis of Rare‐Variant Association Summary Statistics Using MTAR,” Nature Communications 11, no. 1 (2020): 2850, https://doi.org/10.1038/s41467‐020‐16591‐0. M. G. Levin, “Genome‐Wide Assessment of Pleiotropy Across >1000 Traits From Global Biobanks,” medRxiv [Preprint]: The Preprint Server for Health Sciences (2025): 2025.04.18.25326074, https://doi.org/10.1101/2025.04.18.25326074. |
| Contributed Indexing: | Keywords: GWAS; cross‐phenotype association; meta‐analysis; pleiotropy; summary statistics |
| Entry Date(s): | Date Created: 20260828 Date Completed: 20260828 Latest Revision: 20260911 |
| Update Code: | 20260911 |
| PubMed Central ID: | PMC13523090 |
| DOI: | 10.1002/sim.70717 |
| PMID: | 42663118 |
| Βάση Δεδομένων: | MEDLINE |
καταχωρήστε σχόλιο πρώτοι!