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Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration
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Comparison of methods for multivariate gene-based association tests for complex diseases using common variants.

Jaeyoon Chung1,2, Gyungah R Jun2,3,4, Josée Dupuis4

  • 1Bioinformatics Graduate Program, Boston University, Boston, MA, USA.

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Summary

Combining multivariate and gene-based association tests improves power for complex diseases. The MultiPhen and GATES combination showed higher power for low phenotype correlations in Alzheimer's disease genetics.

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Area of Science:

  • Genetics
  • Biostatistics
  • Complex Disease Analysis

Background:

  • Complex diseases involve multiple correlated phenotypes, often not fully captured by single-trait analyses.
  • Joint analysis of multiple phenotypes in genetic studies can enhance the power to detect associations with common single nucleotide polymorphisms (SNPs).
  • Gene-based tests identify genes with multiple weakly associated risk variants for univariate traits.

Purpose of the Study:

  • To evaluate the performance of six multivariate gene-based association methods by combining three multivariate tests (O'Brien, TATES, MultiPhen) with two gene-based tests (GATES, VEGAS).
  • To compare type I error rates and statistical power using simulated genetic and phenotypic data.
  • To apply the most effective methods to a real-world Alzheimer's disease dataset.

Main Methods:

  • Simulated genetic sequence and correlated phenotype data for 2000 individuals, varying causal variant proportions and phenotype correlations.
  • Performance evaluation of six combined multivariate gene-based methods (O'Brien/GATES, O'Brien/VEGAS, TATES/GATES, TATES/VEGAS, MultiPhen/GATES, MultiPhen/VEGAS).
  • Application of selected methods to a Genome-Wide Association Study (GWAS) dataset of Alzheimer's disease neuropathological traits from 3500 autopsied brains.

Main Results:

  • TATES and MultiPhen paired with VEGAS showed inflated type I error rates across all simulated scenarios.
  • All three multivariate tests (O'Brien, TATES, MultiPhen) paired with GATES maintained correct type I error rates.
  • The MultiPhen and GATES combination demonstrated superior power when phenotype correlations were low (r < 0.57).
  • Analysis of the Alzheimer's dataset identified significant gene-level associations (P < 2.7x10^-6) in the TRAPPC12, TRAPPC12-AS1, and ADI1 gene region using O'Brien and VEGAS.
  • Univariate gene-based tests did not yield significant associations in the Alzheimer's dataset.

Conclusions:

  • The combination of multivariate association tests with the GATES gene-based test offers a robust approach for analyzing complex diseases, maintaining correct type I error.
  • The MultiPhen and GATES method is particularly powerful for detecting genetic associations when underlying phenotypic correlations are low.
  • Multivariate gene-based approaches can uncover significant genetic findings for complex diseases like Alzheimer's, which may be missed by univariate methods.