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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

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Published on: June 21, 2018

Gene-based multiple trait analysis for exome sequencing data.

Jingyuan Zhao1, Anbupalam Thalamuthu

  • 1Human Genetics, Genome Institute of Singapore, 60 Biopolis Street 02-01, Singapore 138672. anbupalamt@gis.a-star.edu.sg.

BMC Proceedings
|March 1, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a new gene-level association test for multiple traits, accounting for trait correlations. The method effectively identifies pleiotropic genes by jointly analyzing common and rare variants, crucial for understanding complex diseases.

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Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Genome-wide association studies (GWAS) explain limited genetic risk for complex diseases.
  • Next-generation sequencing (NGS) detects rare variants, potentially explaining missing heritability.
  • Multiple correlated traits offer opportunities to enhance genetic association study power.

Purpose of the Study:

  • To develop a gene-level association test for multiple traits that accounts for trait correlations.
  • To jointly analyze common and rare variants within genes for association testing.
  • To identify pleiotropic genetic variants influencing multiple traits.

Main Methods:

  • Proposed a novel gene-level association test for multiple traits.
  • Incorporated a weighted-sum pooling method to test joint association of common and rare variants within a gene.
  • Applied the method to simulated mini-exome data from the Genetic Association Workshop 17 (GAW17).

Main Results:

  • Multiple-trait analysis did not show increased power compared to single-trait analysis due to the GAW17 simulation model.
  • Multiple-trait analysis did not result in a substantial loss of power.
  • The method demonstrated utility in analyzing multiple traits and identifying pleiotropic genes.

Conclusions:

  • The proposed gene-level association test is a valuable tool for identifying pleiotropic genes.
  • Accounting for trait correlations in gene-level analysis is important for complex disease genetics.
  • This approach aids in dissecting the genetic architecture of complex diseases by considering multiple traits simultaneously.