Scientific Reports (Aug 2024)
Cluster effect for SNP–SNP interaction pairs for predicting complex traits
- Hui-Yi Lin,
- Harun Mazumder,
- Indrani Sarkar,
- Po-Yu Huang,
- Rosalind A. Eeles,
- Zsofia Kote-Jarai,
- Kenneth R. Muir,
- UKGPCS collaborators,
- Johanna Schleutker,
- Nora Pashayan,
- Jyotsna Batra,
- APCB (Australian Prostate Cancer BioResource),
- David E. Neal,
- Sune F. Nielsen,
- Børge G. Nordestgaard,
- Henrik Grönberg,
- Fredrik Wiklund,
- Robert J. MacInnis,
- Christopher A. Haiman,
- Ruth C. Travis,
- Janet L. Stanford,
- Adam S. Kibel,
- Cezary Cybulski,
- Kay-Tee Khaw,
- Christiane Maier,
- Stephen N. Thibodeau,
- Manuel R. Teixeira,
- Lisa Cannon-Albright,
- Hermann Brenner,
- Radka Kaneva,
- Hardev Pandha,
- The PRACTICAL consortium,
- Jong Y. Park
Affiliations
- Hui-Yi Lin
- Biostatistics and Data Science Program, School of Public Health, Louisiana State University Health Sciences Center
- Harun Mazumder
- Biostatistics and Data Science Program, School of Public Health, Louisiana State University Health Sciences Center
- Indrani Sarkar
- Biostatistics and Data Science Program, School of Public Health, Louisiana State University Health Sciences Center
- Po-Yu Huang
- Information and Communications Research Laboratories, Industrial Technology Research Institute
- Rosalind A. Eeles
- The Institute of Cancer Research
- Zsofia Kote-Jarai
- The Institute of Cancer Research
- Kenneth R. Muir
- Division of Population Health, Health Services Research and Primary Care, University of Manchester
- UKGPCS collaborators
- Johanna Schleutker
- Institute of Biomedicine, University of Turku
- Nora Pashayan
- Department of Applied Health Research, University College London
- Jyotsna Batra
- Australian Prostate Cancer Research Centre-Qld, Institute of Health and Biomedical Innovation and School of Biomedical Science, Queensland University of Technology
- APCB (Australian Prostate Cancer BioResource)
- Australian Prostate Cancer Research Centre-QLD, Queensland University of Technology
- David E. Neal
- Nuffield Department of Surgical Sciences, University of Oxford, John Radcliffe Hospital
- Sune F. Nielsen
- Faculty of Health and Medical Sciences, University of Copenhagen
- Børge G. Nordestgaard
- Faculty of Health and Medical Sciences, University of Copenhagen
- Henrik Grönberg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute
- Fredrik Wiklund
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute
- Robert J. MacInnis
- Cancer Epidemiology Division, Cancer Council Victoria
- Christopher A. Haiman
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center
- Ruth C. Travis
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford
- Janet L. Stanford
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center
- Adam S. Kibel
- Division of Urologic Surgery, Brigham and Womens Hospital
- Cezary Cybulski
- International Hereditary Cancer Center, Department of Genetics and Pathology, Pomeranian Medical University
- Kay-Tee Khaw
- Clinical Gerontology Unit, University of Cambridge
- Christiane Maier
- Humangenetik Tuebingen
- Stephen N. Thibodeau
- Department of Laboratory Medicine and Pathology, Mayo Clinic
- Manuel R. Teixeira
- Department of Laboratory Genetics, Portuguese Oncology Institute of Porto (IPO Porto)/Porto Comprehensive Cancer Center
- Lisa Cannon-Albright
- Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine
- Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ)
- Radka Kaneva
- Molecular Medicine Center, Department of Medical Chemistry and Biochemistry, Medical University of Sofia
- Hardev Pandha
- The University of Surrey
- The PRACTICAL consortium
- Jong Y. Park
- Department of Cancer Epidemiology, Moffitt Cancer Center
- DOI
- https://doi.org/10.1038/s41598-024-66311-7
- Journal volume & issue
-
Vol. 14,
no. 1
pp. 1 – 14
Abstract
Abstract Single nucleotide polymorphism (SNP) interactions are the key to improving polygenic risk scores. Previous studies reported several significant SNP–SNP interaction pairs that shared a common SNP to form a cluster, but some identified pairs might be false positives. This study aims to identify factors associated with the cluster effect of false positivity and develop strategies to enhance the accuracy of SNP–SNP interactions. The results showed the cluster effect is a major cause of false-positive findings of SNP–SNP interactions. This cluster effect is due to high correlations between a causal pair and null pairs in a cluster. The clusters with a hub SNP with a significant main effect and a large minor allele frequency (MAF) tended to have a higher false-positive rate. In addition, peripheral null SNPs in a cluster with a small MAF tended to enhance false positivity. We also demonstrated that using the modified significance criterion based on the 3 p-value rules and the bootstrap approach (3pRule + bootstrap) can reduce false positivity and maintain high true positivity. In addition, our results also showed that a pair without a significant main effect tends to have weak or no interaction. This study identified the cluster effect and suggested using the 3pRule + bootstrap approach to enhance SNP–SNP interaction detection accuracy.
Keywords
