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Genome-wide Association Studies-GWAS01:11

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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.
GWAS does not require the identification of the target gene involved in...
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Statistical power of transcriptome-wide association studies.

Ruoyu He1,2, Haoran Xue2, Wei Pan2

  • 1School of Statistics, University of Minnesota, Minneapolis, Minnesota, USA.

Genetic Epidemiology
|June 29, 2022
PubMed
Summary

Transcriptome-Wide Association Studies (TWAS) power depends on gene expression heritability, not just sample size. Multivariate TWAS (MV-TWAS) may increase or decrease power compared to univariate TWAS (UV-TWAS), depending on gene relationships.

Keywords:
2SLSAlzheimer's diseaseTWAScausal inferencesample size

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Transcriptome-Wide Association Studies (TWAS) identify genes linked to complex traits using expression quantitative trait loci (eQTL) and genome-wide association study (GWAS) data.
  • Existing TWAS methods often use small eQTL datasets and have low model R-squared values, raising questions about statistical power.
  • The emergence of multivariate TWAS (MV-TWAS) necessitates an understanding of its power relative to univariate TWAS (UV-TWAS).

Purpose of the Study:

  • To develop a general method for sample size and power calculations in two-sample TWAS.
  • To empirically assess the impact of eQTL sample size and model R-squared on TWAS power.
  • To compare the statistical power of MV-TWAS versus UV-TWAS.

Main Methods:

  • Developed a general sample size/power calculation method for two-sample TWAS.
  • Utilized Alzheimer's Disease Neuroimaging Initiative (ADNI) and Genotype-Tissue Expression (GTEx) eQTL data for stage 1.
  • Employed International Genomics of Alzheimer's Project (IGAP) AD GWAS and UK Biobank (UKB) data for stage 2.

Main Results:

  • A stage 1 sample size of approximately 8000 is sufficient for TWAS power, which is primarily determined by cis-heritability of gene expression.
  • MV-TWAS power can be higher or lower than UV-TWAS, contingent on correlations and effect sizes among the trait and multiple genes.
  • Significant power gains were observed in MV-TWAS for several genes previously associated with Alzheimer's disease.

Conclusions:

  • TWAS power is more influenced by gene expression heritability than by stage 1 sample size alone.
  • The choice between MV-TWAS and UV-TWAS depends on the genetic architecture of the trait and the relationships between genes.
  • The developed methods and findings are valuable for designing future TWAS and related omics studies.