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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
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A rare-variant test for high-dimensional data.

Marika Kaakinen1, Reedik Mägi2, Krista Fischer2

  • 1Department of Genomics of Common Disease, Imperial College London, London, UK.

European Journal of Human Genetics : EJHG
|May 25, 2017
PubMed
Summary

We introduce Multi-Phenotype Analysis of Rare Variants (MARV), a novel method to boost the discovery of genetic loci. MARV enhances the analysis of rare variants across multiple traits, improving power for complex disease genetics.

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Genome-wide association studies (GWAS) have identified numerous genetic loci but struggle with the 'missing heritability' for complex traits.
  • Current methods often lack the power to detect associations involving rare variants (RVs) and multiple phenotypes simultaneously.

Purpose of the Study:

  • To develop and validate a novel statistical method, Multi-Phenotype Analysis of Rare Variants (MARV), for enhanced genetic locus discovery.
  • To improve the power of association tests for rare variants by integrating information from multiple phenotypes.

Main Methods:

  • MARV employs a burden test framework extended to multiple phenotypes using a reverse regression technique.
  • It models the proportion of rare variants within a genomic region as a linear combination of various phenotypes (binary and continuous).
  • The method directly accommodates genotyped and imputed genetic data.

Main Results:

  • Simulations demonstrate that MARV controls type I error rates effectively, even with correlated phenotypes.
  • MARV significantly outperforms univariate burden tests in detecting associations for rare variants.
  • Application to lipid traits in the Northern Finland Birth Cohort identified known loci with stronger signals and suggested novel RV associations.

Conclusions:

  • MARV offers a powerful and robust approach for rare variant association analysis across multiple phenotypes.
  • This method enhances the discovery of genetic variants contributing to complex traits, addressing the missing heritability problem.
  • MARV provides a valuable tool for genetic research, particularly in large cohort studies.