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

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.

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Publication Details

Journal
Scientific Reports
Published
2024-08-12
DOI
https://doi.org/10.1038/s41598-024-66311-7
Primary Topic
Genetic Associations and Epidemiology
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Rosalind A. Eeles, Henrik Grönberg, Hui‐Yi Lin, Kenneth Muir et al.
2 links
Scientific Reports
Genetic Associations and Epidemiology
article

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

Rosalind A. Eeles, Henrik Grönberg, Hui‐Yi Lin, Kenneth Muir, Ruth C. Travis, Po-Yu Huang, Kay‐Tee Khaw, Jyotsna Batra, Harun Mazumder, Manuel R. Teixeira, Lisa Cannon‐Albright, Christopher A. Haiman, Robert J. MacInnis, Hardev Pandha, Jong Y. Park, Adam S. Kibel, Johanna Schleutker, Indrani Sarkar, Hermann Brenner, Børge Grønne Nordestgaard, Radka Kaneva, Stephen N. Thibodeau, David E. Neal, Nora Pashayan, Fredrik Wiklund, Janet L. Stanford, Sune F. Nielsen, Christiane Maier, Zsofia Kote-Jarai, Cezary Cybulski
article en

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.

Scientific ReportsVol. 14(1)
University of Southern California (US), University of Copenhagen (DK), Brigham and Women's Hospital (US), Translational Research Institute (AU), Royal Marsden NHS Foundation Trust (GB), Institute of Cancer Research (GB), University of Turku (FI), Queensland University of Technology (AU), The University of Melbourne (AU), German Cancer Research Center (DE), Universidade do Porto (PT), Cancer Council Victoria (AU), University of Washington (US), University of Utah (US), Heidelberg University (DE), University of Cambridge (GB), Manchester Academic Health Science Centre (GB), Cancer Research UK (GB), Turku University Hospital (FI), Gentofte Hospital (DK), John Radcliffe Hospital (GB), Karolinska Institutet (SE), University of Surrey (GB), University of Manchester (GB), Medical University of Sofia (BG), Moffitt Cancer Center (US), University of Oxford (GB), Fred Hutch Cancer Center (US), Instituto Português de Oncologia Francisco Gentil (PT), IPO Porto (PT), National Center for Tumor Diseases (DE), Praxis für Humangenetik Tübingen (DE), Mayo Clinic in Arizona (US), Industrial Technology Research Institute (TW), Addenbrooke's Hospital (GB), MRC Epidemiology Unit (GB), International Hereditary Cancer Center (PL), George E. Wahlen Department of VA Medical Center (US), University College London (GB), Louisiana State University Health Sciences Center New Orleans (US), Pomeranian Medical University (PL)
U.S. Department of Defense, Louisiana State University
Openalex Percentile: Top 85%
Genetic Associations and Epidemiology
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