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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration
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Using genome-wide complex trait analysis to quantify 'missing heritability' in Parkinson's disease.

Margaux F Keller1, Mohamad Saad, Jose Bras

  • 1Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD 20892, USA.

Human Molecular Genetics
|August 16, 2012
PubMed
Summary

Genome-wide complex trait analysis (GCTA) reveals significant missing heritability in Parkinson's disease (PD). This study highlights that common genetic variants, not typically found by Genome-wide association studies (GWASs), explain substantial phenotypic variance in PD.

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

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Published on: July 27, 2021

Area of Science:

  • Genetics
  • Neuroscience
  • Statistical Genomics

Background:

  • Genome-wide association studies (GWASs) identify common variants linked to traits, but much heritability remains unexplained.
  • Genome-wide complex trait analysis (GCTA) uses linear mixed models to estimate phenotypic variance explained by all genome-wide SNPs.

Purpose of the Study:

  • To investigate the "missing heritability" in Parkinson's disease (PD) using GCTA.
  • To quantify the proportion of phenotypic variance attributable to common SNPs in PD.

Main Methods:

  • Applied GCTA to 8 cohorts (7096 cases, 19455 controls) of European ancestry.
  • Meta-analyzed results to obtain robust heritability estimates for different PD types.

Main Results:

  • GCTA identified 27% of phenotypic variance for all PD types (P = 8.08E-08).
  • Early-onset PD showed 15% variance (P = 0.09), while late-onset PD showed 31% (P = 1.34E-05).
  • This is a significant increase compared to the 3-5% variance identified by top GWAS hits alone.

Conclusions:

  • Common variants not detected by GWASs contribute substantially to PD heritability.
  • More risk loci for complex diseases like PD likely exist among common variants with small effects.