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Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

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Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
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Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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PhenoSpD: an integrated toolkit for phenotypic correlation estimation and multiple testing correction using GWAS

Jie Zheng1, Tom G Richardson1, Louise A C Millard1,2

  • 1MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Bristol, BS8 2BN, UK.

Gigascience
|August 31, 2018
PubMed
Summary
This summary is machine-generated.

PhenoSpD estimates phenotypic correlations from genome-wide association study (GWAS) summary statistics, reducing dimensionality for complex human traits. This toolkit offers a valuable method for large-scale biobank studies where individual-level data is restricted.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical genetics

Background:

  • Estimating phenotypic correlations across the human phenome is challenging due to restricted access to individual-level phenotype data.
  • Genome-wide association study (GWAS) summary results offer an alternative for estimating phenotypic correlations.
  • Existing methods like metaCCA and LD score regression provide approaches using GWAS summary statistics.

Purpose of the Study:

  • To present PhenoSpD, an integrated R toolkit for estimating phenotypic correlations using LD score regression from GWAS summary statistics.
  • To utilize estimated phenotypic correlations for correcting multiple testing in complex human traits via spectral decomposition (SpD).
  • To provide a method that extends the use of summary-level GWAS results for dimensionality reduction.

Main Methods:

  • Utilizes LD score regression to estimate phenotypic correlations from GWAS summary statistics.
  • Applies spectral decomposition (SpD) of matrices to correct for multiple testing based on estimated correlations.
  • Evaluated through simulations and case studies using UK Biobank and Kettunen's metabolite GWAS data.

Main Results:

  • Simulations indicate that PhenoSpD can identify non-independence of phenotypes even with partially overlapping samples.
  • PhenoSpD analysis of UK Biobank data suggested 399.6 independent tests among 487 human traits, closely matching true correlations.
  • Application to 107 metabolites revealed an equivalent of 33.5 independent tests, demonstrating utility in metabolite-trait associations.

Conclusions:

  • PhenoSpD provides a simple and conservative method for dimensionality reduction of complex human traits using GWAS summary statistics.
  • The toolkit is particularly valuable for large-scale biobank and consortia studies with accessible GWAS results but limited individual-level data.
  • Extends the utility of summary-level data for robust genetic and phenotypic correlation analyses.