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; PRACTICAL consortium; Jong Y Park

doi:10.1038/s41598-024-66311-7

Cluster effect for SNP-SNP interaction pairs for predicting complex traits

Sci Rep. 2024 Aug 12;14(1):18677. doi: 10.1038/s41598-024-66311-7.

Authors

Hui-Yi Lin¹, Harun Mazumder², Indrani Sarkar², Po-Yu Huang³, Rosalind A Eeles^{4

5}, Zsofia Kote-Jarai⁴, Kenneth R Muir⁶; UKGPCS collaborators; Johanna Schleutker^{7

8}, Nora Pashayan^{9

10}, Jyotsna Batra^{11

12}; APCB (Australian Prostate Cancer BioResource); David E Neal^{13

14

15}, Sune F Nielsen^{16

17}, Børge G Nordestgaard^{16

17}, Henrik Grönberg¹⁸, Fredrik Wiklund¹⁸, Robert J MacInnis^{19

20}, Christopher A Haiman²¹, Ruth C Travis²², Janet L Stanford^{23

24}, Adam S Kibel²⁵, Cezary Cybulski²⁶, Kay-Tee Khaw²⁷, Christiane Maier²⁸, Stephen N Thibodeau²⁹, Manuel R Teixeira^{30

31

32}, Lisa Cannon-Albright^{33

34}, Hermann Brenner^{35

36

37}, Radka Kaneva³⁸, Hardev Pandha³⁹; PRACTICAL consortium; Jong Y Park⁴⁰

Affiliations

¹ Biostatistics and Data Science Program, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, LA, 70112, USA. hlin1@lsuhsc.edu.
² Biostatistics and Data Science Program, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, LA, 70112, USA.
³ Information and Communications Research Laboratories, Industrial Technology Research Institute, Hsinchu, Taiwan.
⁴ The Institute of Cancer Research, London, SM2 5NG, UK.
⁵ Royal Marsden NHS Foundation Trust, London, SW3 6JJ, UK.
⁶ Division of Population Health, Health Services Research and Primary Care, University of Manchester, Oxford Road, Manchester, M13 9PL, UK.
⁷ Institute of Biomedicine, University of Turku, Turku, Finland.
⁸ Department of Medical Genetics, Genomics, Laboratory Division, Turku University Hospital, PO Box 52, 20521, Turku, Finland.
⁹ Department of Applied Health Research, University College London, London, WC1E 7HB, UK.
¹⁰ Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Strangeways Laboratory, Worts Causeway, Cambridge, CB1 8RN, UK.
¹¹ Australian Prostate Cancer Research Centre-Qld, Institute of Health and Biomedical Innovation and School of Biomedical Science, Queensland University of Technology, Brisbane, QLD, 4059, Australia.
¹² Translational Research Institute, Brisbane, QLD, 4102, Australia.
¹³ Nuffield Department of Surgical Sciences, University of Oxford, John Radcliffe Hospital, Room 6603, Level 6, Headley Way, Headington, Oxford, OX3 9DU, UK.
¹⁴ Department of Oncology, University of Cambridge, Addenbrooke's Hospital, Hills Road, Box 279, Cambridge, CB2 0QQ, UK.
¹⁵ Cancer Research UK, Cambridge Research Institute, Li Ka Shing Centre, Cambridge, CB2 0RE, UK.
¹⁶ Faculty of Health and Medical Sciences, University of Copenhagen, 2200, Copenhagen, Denmark.
¹⁷ Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, 2200, Copenhagen, Denmark.
¹⁸ Department of Medical Epidemiology and Biostatistics, Karolinska Institute, 171 77, Stockholm, Sweden.
¹⁹ Cancer Epidemiology Division, Cancer Council Victoria, 200 Victoria Parade, East Melbourne, 3002, Australia.
²⁰ Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Grattan Street, Parkville, VIC, 3010, Australia.
²¹ Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA, 90015, USA.
²² Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, UK.
²³ Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, 98109-1024, USA.
²⁴ Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, 98195, USA.
²⁵ Division of Urologic Surgery, Brigham and Womens Hospital, 75 Francis Street, Boston, MA, 02115, USA.
²⁶ International Hereditary Cancer Center, Department of Genetics and Pathology, Pomeranian Medical University, 70-115, Szczecin, Poland.
²⁷ Clinical Gerontology Unit, University of Cambridge, Cambridge, CB2 2QQ, UK.
²⁸ Humangenetik Tuebingen, Paul-Ehrlich-Str 23, 72076, Tuebingen, Germany.
²⁹ Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, 55905, USA.
³⁰ Department of Laboratory Genetics, Portuguese Oncology Institute of Porto (IPO Porto)/Porto Comprehensive Cancer Center, Porto, Portugal.
³¹ Cancer Genetics Group, IPO Porto Research Center (CI-IPOP)/RISE@CI-IPOP (Health Research Network), Portuguese Oncology Institute of Porto (IPO Porto)/Porto Comprehensive Cancer Center, Porto, Portugal.
³² School of Medicine and Biomedical Sciences (ICBAS), University of Porto, Porto, Portugal.
³³ Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, 84132, USA.
³⁴ George E. Wahlen Department of Veterans Affairs Medical Center, Salt Lake City, UT, 84148, USA.
³⁵ Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany.
³⁶ German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany.
³⁷ Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Im Neuenheimer Feld 460, 69120, Heidelberg, Germany.
³⁸ Molecular Medicine Center, Department of Medical Chemistry and Biochemistry, Medical University of Sofia, Sofia, 2 Zdrave Str., 1431, Sofia, Bulgaria.
³⁹ The University of Surrey, Guildford, Surrey, GU2 7XH, UK.
⁴⁰ Department of Cancer Epidemiology, Moffitt Cancer Center, 12902 Magnolia Drive, Tampa, FL, 33612, USA.

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: Cluster; False positivity; SNP interaction; Simulation.

MeSH terms

Cluster Analysis
Epistasis, Genetic
Gene Frequency
Genome-Wide Association Study / methods
Humans
Models, Genetic
Multifactorial Inheritance* / genetics
Polymorphism, Single Nucleotide*

Abstract

MeSH terms

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