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000292290 1001_ $$aLin, Hui-Yi$$b0
000292290 245__ $$aCluster effect for SNP-SNP interaction pairs for predicting complex traits.
000292290 260__ $$a[London]$$bMacmillan Publishers Limited, part of Springer Nature$$c2024
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000292290 520__ $$aSingle nucleotide polymorphism (SNP) interactions are the key to improving polygenic risk scores. Previous studies reported several significant SNP-SNP interaction pairs that shared a common SNP to form a cluster, but some identified pairs might be false positives. This study aims to identify factors associated with the cluster effect of false positivity and develop strategies to enhance the accuracy of SNP-SNP interactions. The results showed the cluster effect is a major cause of false-positive findings of SNP-SNP interactions. This cluster effect is due to high correlations between a causal pair and null pairs in a cluster. The clusters with a hub SNP with a significant main effect and a large minor allele frequency (MAF) tended to have a higher false-positive rate. In addition, peripheral null SNPs in a cluster with a small MAF tended to enhance false positivity. We also demonstrated that using the modified significance criterion based on the 3 p-value rules and the bootstrap approach (3pRule + bootstrap) can reduce false positivity and maintain high true positivity. In addition, our results also showed that a pair without a significant main effect tends to have weak or no interaction. This study identified the cluster effect and suggested using the 3pRule + bootstrap approach to enhance SNP-SNP interaction detection accuracy.
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000292290 650_7 $$2Other$$aCluster
000292290 650_7 $$2Other$$aFalse positivity
000292290 650_7 $$2Other$$aSNP interaction
000292290 650_7 $$2Other$$aSimulation
000292290 650_2 $$2MeSH$$aPolymorphism, Single Nucleotide
000292290 650_2 $$2MeSH$$aHumans
000292290 650_2 $$2MeSH$$aMultifactorial Inheritance: genetics
000292290 650_2 $$2MeSH$$aGene Frequency
000292290 650_2 $$2MeSH$$aGenome-Wide Association Study: methods
000292290 650_2 $$2MeSH$$aCluster Analysis
000292290 650_2 $$2MeSH$$aModels, Genetic
000292290 650_2 $$2MeSH$$aEpistasis, Genetic
000292290 7001_ $$aMazumder, Harun$$b1
000292290 7001_ $$aSarkar, Indrani$$b2
000292290 7001_ $$aHuang, Po-Yu$$b3
000292290 7001_ $$aEeles, Rosalind A$$b4
000292290 7001_ $$aKote-Jarai, Zsofia$$b5
000292290 7001_ $$aMuir, Kenneth R$$b6
000292290 7001_ $$aUKGPCS collaborators$$b7$$eCollaboration Author
000292290 7001_ $$aSchleutker, Johanna$$b8
000292290 7001_ $$aPashayan, Nora$$b9
000292290 7001_ $$aBatra, Jyotsna$$b10
000292290 7001_ $$aAPCB$$b11$$eCollaboration Author
000292290 7001_ $$aNeal, David E$$b12
000292290 7001_ $$aNielsen, Sune F$$b13
000292290 7001_ $$aNordestgaard, Børge G$$b14
000292290 7001_ $$aGrönberg, Henrik$$b15
000292290 7001_ $$aWiklund, Fredrik$$b16
000292290 7001_ $$aMacInnis, Robert J$$b17
000292290 7001_ $$aHaiman, Christopher A$$b18
000292290 7001_ $$aTravis, Ruth C$$b19
000292290 7001_ $$aStanford, Janet L$$b20
000292290 7001_ $$aKibel, Adam S$$b21
000292290 7001_ $$aCybulski, Cezary$$b22
000292290 7001_ $$aKhaw, Kay-Tee$$b23
000292290 7001_ $$aMaier, Christiane$$b24
000292290 7001_ $$aThibodeau, Stephen N$$b25
000292290 7001_ $$aTeixeira, Manuel R$$b26
000292290 7001_ $$aCannon-Albright, Lisa$$b27
000292290 7001_ $$0P:(DE-He78)90d5535ff896e70eed81f4a4f6f22ae2$$aBrenner, Hermann$$b28$$udkfz
000292290 7001_ $$aKaneva, Radka$$b29
000292290 7001_ $$aPandha, Hardev$$b30
000292290 7001_ $$aconsortium, PRACTICAL$$b31$$eCollaboration Author
000292290 7001_ $$aPark, Jong Y$$b32
000292290 773__ $$0PERI:(DE-600)2615211-3$$a10.1038/s41598-024-66311-7$$gVol. 14, no. 1, p. 18677$$n1$$p18677$$tScientific reports$$v14$$x2045-2322$$y2024
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