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Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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Discovering non-additive heritability using additive GWAS summary statistics.

Samuel Pattillo Smith1,2,3,4, Gregory Darnell1,5, Dana Udwin6

  • 1Center for Computational Molecular Biology, Brown University, Providence, United States.

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|June 24, 2024
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Summary

Interaction-LD score (i-LDSC) regression extends genome-wide association study (GWAS) methods to capture genetic variance from variant interactions. This new approach, applied to large biobanks, identifies additional genetic contributions to complex traits.

Keywords:
geneticsgenomicsheritabilityhumaninteractionsnon-additive effectssummary statistics

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Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical Genomics

Background:

  • LD score regression (LDSC) estimates heritability from GWAS summary statistics but does not account for genetic interactions.
  • Complex traits are influenced by non-additive genetic effects, including interactions between variants.
  • Existing methods may underestimate the total genetic contribution to complex traits.

Purpose of the Study:

  • To introduce interaction-LD score (i-LDSC) regression, an extension of LDSC that incorporates genetic interactions.
  • To evaluate the performance of i-LDSC in simulations and real-world genetic data.
  • To identify genetic variance missed by standard LDSC due to interactions.

Main Methods:

  • Developed the interaction-LD score (i-LDSC) regression framework.
  • Conducted simulations using various generative models to test i-LDSC.
  • Applied i-LDSC to analyze 25 quantitative phenotypes from UK Biobank (349,468 individuals) and BioBank Japan (up to 159,095 individuals).

Main Results:

  • Simulations demonstrated i-LDSC's ability to recover genetic variance from interactions.
  • Re-analysis of 25 traits revealed that i-LDSC detects additional genetic variation not captured by standard LDSC.
  • The inclusion of cis-interaction scores significantly improved heritability estimates for all analyzed traits.

Conclusions:

  • i-LDSC regression is a powerful extension of LDSC for estimating heritability, particularly when considering genetic interactions.
  • The i-LDSC software successfully identifies contributions from non-additive genetic effects in large-scale biobank data.
  • This method advances the understanding of the genetic architecture of complex traits by accounting for variant interactions.