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Joint analysis of multiple phenotypes for extremely unbalanced case-control association studies using multi-layer

Hongjing Xie1, Xuewei Cao1, Shuanglin Zhang1

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Summary

This study introduces a Multi-Layer Network with Omnibus (MLN-O) method for analyzing genome-wide associations with multiple, unbalanced phenotypes. MLN-O effectively controls type I error rates and enhances power, identifying more significant single nucleotide polymorphisms (SNPs) in real-world biobank data.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Genome-wide association studies (GWAS) are crucial for identifying relationships between phenotypes and single nucleotide polymorphisms (SNPs).
  • Large biobanks often contain binary phenotypes that are highly unbalanced, leading to inflated type I error rates in standard association tests.
  • Joint analysis of multiple phenotypes is challenging due to data imbalance and the need for robust statistical methods.

Purpose of the Study:

  • To develop a novel method, Multi-Layer Network with Omnibus (MLN-O), for joint analysis of multiple, unbalanced phenotypes in GWAS.
  • To improve the control of type I error rates and increase the statistical power of association tests in large biobanks.
  • To provide a computationally efficient approach for identifying significant SNPs associated with complex trait sets.

Main Methods:

  • Constructing a Multi-Layer Network (MLN) using individuals with at least one case status across all phenotypes.
  • Employing a community detection algorithm to cluster related phenotypes based on the MLN.
  • Applying a score test for individual merged phenotypes within clusters and an Omnibus test for overall association with a SNP.

Main Results:

  • Extensive simulations demonstrate that MLN-O effectively controls type I error rates and outperforms existing methods in terms of power.
  • Application to UK Biobank data, specifically for musculoskeletal and connective tissue diseases, revealed MLN-O identified more significant SNPs compared to other methods.
  • The MLN-O approach proved robust and powerful in analyzing real-world, imbalanced phenotype data.

Conclusions:

  • MLN-O offers a statistically sound and powerful approach for joint phenotype-SNP association analysis in large-scale biobanks with unbalanced data.
  • The method's ability to control type I errors and enhance power makes it a valuable tool for genetic discovery.
  • MLN-O successfully identified novel genetic associations in a real-world dataset, highlighting its practical utility in complex trait genetics.