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Related Concept Videos

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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Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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Some statistical consideration in transcriptome-wide association studies.

Haoran Xue1, Wei Pan2,

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

Genetic Epidemiology
|December 11, 2019
PubMed
Summary
This summary is machine-generated.

Standard transcriptome-wide association studies (TWAS) using two-stage least squares (2SLS) effectively identify causal genes for complex traits. This method, integrating eQTL and GWAS data, is recommended for practical applications with large sample sizes.

Keywords:
2SLS2SPS2SRIMendelian randomizationTWAScausal inferenceinstrumental variables

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Area of Science:

  • Genetics
  • Statistical genetics
  • Bioinformatics

Background:

  • Transcriptome-wide association studies (TWAS) integrate expression quantitative trait loci (eQTL) and genome-wide association studies (GWAS) data.
  • TWAS aims to identify causal genes and elucidate biological pathways linking genetic variants to traits.
  • Standard TWAS employs a two-stage least squares (2SLS) method for causal inference.

Purpose of the Study:

  • To evaluate the necessity and efficacy of two-stage residual inclusion (2SRI) compared to standard 2SLS in TWAS.
  • To assess the impact of measurement error in imputed gene expression on standard error estimates in TWAS.
  • To compare one-sample and two-sample 2SLS methodologies within the TWAS framework.

Main Methods:

  • Utilized Alzheimer's Disease Neuroimaging Initiative (ADNI) data and simulated datasets.
  • Applied and compared standard two-stage least squares (2SLS) and two-stage residual inclusion (2SRI) methods.
  • Investigated the performance of one-sample versus two-sample 2SLS for transcriptome-wide association studies.

Main Results:

  • Standard TWAS using 2SLS demonstrates robust performance in practice, particularly with large sample sizes and small genetic effect sizes.
  • The potential issues associated with measurement error in imputed gene expression and the need for corrections were examined.
  • Comparisons between 2SLS and 2SRI, as well as one-sample and two-sample approaches, were conducted.

Conclusions:

  • Standard TWAS methodology is recommended for practical applications due to its reliable performance.
  • The study provides insights into the statistical considerations and methodological choices in TWAS for causal gene discovery.
  • Findings support the continued use and application of standard TWAS in genetic research.