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Trait selection strategy in multi-trait GWAS: Boosting SNP discoverability.

Yuka Suzuki1, Hervé Ménager2, Bryan Brancotte2

  • 1Institut Pasteur, Université Paris Cité, Department of Computational Biology, 75015 Paris, France.

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|June 14, 2024
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Summary
This summary is machine-generated.

Multi-trait genome-wide association studies (GWAS) enhance variant detection by analyzing multiple traits together. Key genetic features like heritability and correlation predict power gains, outperforming traditional trait selection methods.

Keywords:
Complex traitsGenetic ArchitectureMulti-trait GWASStatistical powerVariant mapping

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Human Phenotypes

Background:

  • Genome-wide association studies (GWAS) have identified numerous variant-trait associations.
  • Univariate testing for complex traits requires large sample sizes, limiting comprehensive genetic mapping.
  • Multi-trait GWAS leverages joint genetic architecture to improve statistical power and overcome sample size limitations.

Purpose of the Study:

  • To investigate the impact of trait selection strategies on multi-trait GWAS power.
  • To identify genetic features that predict increased variant detection in multi-trait analyses.
  • To develop data-driven models for optimal trait set selection in GWAS.

Main Methods:

  • Conducted multi-trait GWAS on ~20,000 combinations of 72 traits using an omnibus test (Joint Analysis of Summary Statistics).
  • Assessed genetic features (heritability, number of traits, genetic correlation) associated with variant detection gains compared to univariate screening.
  • Developed predictive models using these features and compared performance against an alternative multi-trait approach (Multi-Trait Analysis of GWAS) and clinical similarity-based selection.

Main Results:

  • Heritability, number of traits, and genetic correlation were identified as key drivers of multi-trait GWAS power gain.
  • Predictive models based on these features captured a significant fraction of the observed power gain (r=0.43).
  • Data-driven trait set selection systematically outperformed selection based on clinical similarity and an alternative multi-trait method.

Conclusions:

  • The study provides a comprehensive understanding of factors influencing multi-trait GWAS statistical power.
  • Identified key genetic features that can guide trait selection for maximizing discovery in multi-trait GWAS.
  • Outlines practical, data-driven strategies for enhancing the efficiency and power of multi-trait GWAS.