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

Hui-Yi Lin1, Harun Mazumder2, Indrani Sarkar2

  • 1Biostatistics and Data Science Program, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, LA, 70112, USA. hlin1@lsuhsc.edu.

Scientific Reports
|August 12, 2024
PubMed
Summary
This summary is machine-generated.

False positives in SNP-SNP interaction analysis are often caused by a cluster effect. A new method, 3pRule + bootstrap, effectively reduces false positives while maintaining accuracy in detecting genetic interactions.

Keywords:
ClusterFalse positivitySNP interactionSimulation

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Single nucleotide polymorphism (SNP) interactions are crucial for enhancing polygenic risk scores.
  • Previous studies identified SNP-SNP interaction clusters but faced challenges with false positives.

Purpose of the Study:

  • To investigate factors contributing to the cluster effect in false-positive SNP-SNP interactions.
  • To develop strategies for improving the accuracy of SNP-SNP interaction detection.

Main Methods:

  • Analysis of factors associated with false positivity in SNP-SNP interaction clusters.
  • Development and application of a modified significance criterion (3pRule + bootstrap).

Main Results:

  • The cluster effect, driven by correlations between causal and null SNP pairs, is a significant source of false positives.
  • Hub SNPs with large minor allele frequencies (MAF) and peripheral null SNPs with small MAFs exacerbate false positivity.
  • The 3pRule + bootstrap approach effectively reduces false positives while preserving true positives.

Conclusions:

  • The cluster effect significantly impacts the reliability of SNP-SNP interaction findings.
  • The 3pRule + bootstrap method offers a robust strategy to enhance the accuracy of detecting true SNP-SNP interactions.