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Errors In Hypothesis Tests01:14

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Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
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An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
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Properties of permutation-based gene tests and controlling type 1 error using a summary statistic based gene test.

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New statistical methods analyze genomic regions for disease associations, overcoming limitations of permutation tests. These approaches improve power and do not require raw data, only summary statistics and marker correlations.

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Area of Science:

  • Genetics and Genomics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Genome-wide association studies (GWAS) identify novel disease-single nucleotide polymorphism (SNP) associations.
  • Existing statistical methods primarily focus on SNP-level associations, with fewer methods analyzing entire genes or genomic regions.
  • Permutation-based tests for regional association can have variable power depending on linkage disequilibrium (LD) structure.

Purpose of the Study:

  • To develop novel statistical methods for quantifying the association between genomic regions and outcomes, independent of LD structure.
  • To address the limitations of permutation-based tests by creating methods with consistent power across varying LD patterns.
  • To provide methods that can utilize summary statistics, reducing reliance on raw genetic data.

Main Methods:

  • Developed a dimension reduction method to filter redundant information in regions with significant LD.
  • Developed a summary-statistic test that scales marker Z-statistics using the correlation matrix of markers to control for LD.
  • Modified the summary-statistic test to maintain type 1 error rate when marker correlation structure is misspecified.

Main Results:

  • Applied the dimension reduction and summary-statistic tests to oral cleft sequence data.
  • Identified a significant association between the 8q24 region and oral cleft using the dimension reduction approach.
  • Observed a borderline significant association with oral cleft using the summary-statistic approach.

Conclusions:

  • The developed methods offer robust approaches for regional genetic association studies.
  • The summary-statistic test is versatile and can be applied using summary data from existing GWAS, such as for Chronic Obstructive Pulmonary Disease (COPD).
  • The modification of the summary-statistic test is effective even when marker correlation structure is imperfectly known.