Evidence map›Paper›PMID 39134575›Full record

ArticleScientific reports2024

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 and 23 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

33 authors.

Hui-Yi LinBiostatistics and Data Science Program, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, LA, 70112, USA. hlin1@lsuhsc.edu.
Harun MazumderBiostatistics and Data Science Program, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, LA, 70112, USA.
Indrani SarkarBiostatistics and Data Science Program, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, LA, 70112, USA.
Po-Yu HuangInformation and Communications Research Laboratories, Industrial Technology Research Institute, Hsinchu, Taiwan.
Rosalind A EelesThe Institute of Cancer Research, London, SM2 5NG, UK.
Zsofia Kote-JaraiThe Institute of Cancer Research, London, SM2 5NG, UK.
Kenneth R MuirDivision of Population Health, Health Services Research and Primary Care, University of Manchester, Oxford Road, Manchester, M13 9PL, UK.
UKGPCS collaborators
Johanna SchleutkerInstitute of Biomedicine, University of Turku, Turku, Finland.
Nora PashayanDepartment of Applied Health Research, University College London, London, WC1E 7HB, UK.
Jyotsna BatraAustralian Prostate Cancer Research Centre-Qld, Institute of Health and Biomedical Innovation and School of Biomedical Science, Queensland University of Technology, Brisbane, QLD, 4059, Australia.
APCB (Australian Prostate Cancer BioResource)
David E NealNuffield Department of Surgical Sciences, University of Oxford, John Radcliffe Hospital, Room 6603, Level 6, Headley Way, Headington, Oxford, OX3 9DU, UK.
Sune F NielsenFaculty of Health and Medical Sciences, University of Copenhagen, 2200, Copenhagen, Denmark.
Børge G NordestgaardFaculty of Health and Medical Sciences, University of Copenhagen, 2200, Copenhagen, Denmark.
Henrik GrönbergDepartment of Medical Epidemiology and Biostatistics, Karolinska Institute, 171 77, Stockholm, Sweden.
Fredrik WiklundDepartment of Medical Epidemiology and Biostatistics, Karolinska Institute, 171 77, Stockholm, Sweden.
Robert J MacInnisCancer Epidemiology Division, Cancer Council Victoria, 200 Victoria Parade, East Melbourne, 3002, Australia.
Christopher A HaimanCenter for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA, 90015, USA.
Ruth C TravisCancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, UK.
Janet L StanfordDivision of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, 98109-1024, USA.
Adam S KibelDivision of Urologic Surgery, Brigham and Womens Hospital, 75 Francis Street, Boston, MA, 02115, USA.
Cezary CybulskiInternational Hereditary Cancer Center, Department of Genetics and Pathology, Pomeranian Medical University, 70-115, Szczecin, Poland.
Kay-Tee KhawClinical Gerontology Unit, University of Cambridge, Cambridge, CB2 2QQ, UK.
Christiane MaierHumangenetik Tuebingen, Paul-Ehrlich-Str 23, 72076, Tuebingen, Germany.
Stephen N ThibodeauDepartment of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, 55905, USA.
Manuel R TeixeiraDepartment of Laboratory Genetics, Portuguese Oncology Institute of Porto (IPO Porto)/Porto Comprehensive Cancer Center, Porto, Portugal.
Lisa Cannon-AlbrightDivision of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, 84132, USA.
Hermann BrennerDivision of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany.
Radka KanevaMolecular Medicine Center, Department of Medical Chemistry and Biochemistry, Medical University of Sofia, Sofia, 2 Zdrave Str., 1431, Sofia, Bulgaria.
Hardev PandhaThe University of Surrey, Guildford, Surrey, GU2 7XH, UK.
PRACTICAL consortium
Jong Y ParkDepartment of Cancer Epidemiology, Moffitt Cancer Center, 12902 Magnolia Drive, Tampa, FL, 33612, USA.

Funding

Cancer Research UK 17528U.S. Department of Defense PC220560
6 · The paper itself

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.

Indexed as

Multifactorial InheritancePolymorphism, Single NucleotideCluster AnalysisEpistasis, GeneticGene FrequencyGenome-Wide Association StudyHumansModels, GeneticClusterFalse positivitySimulationSNP interaction

Identifiers

PMID39134575
PMCPMC11319716

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.