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A comparison of multiple testing adjustment methods with block-correlation positively-dependent tests.

ABSTRACT: In high dimensional data analysis (such as gene expression, spatial epidemiology, or brain imaging studies), we often test thousands or more hypotheses simultaneously. As the number of tests increases, the chance of observing some statistically significant tests is very high even when all null hypotheses are true. Consequently, we could reach incorrect conclusions regarding the hypotheses. Researchers frequently use multiplicity adjustment methods to control type I error rates-primarily the family-wise error rate (FWER) or the false discovery rate (FDR)-while still desiring high statistical power. In practice, such studies may have dependent test statistics (or p-values) as tests can be dependent on each other. However, some commonly-used multiplicity adjustment methods assume independent tests. We perform a simulation study comparing several of the most common adjustment methods involved in multiple hypothesis testing, under varying degrees of block-correlation positive dependence among tests.

SUBMITTER: Stevens JR

PROVIDER: S-EPMC5409054 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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A comparison of multiple testing adjustment methods with block-correlation positively-dependent tests.

Stevens John R JR Al Masud Abdullah A Suyundikov Anvar A

PloS one 20170428 4

In high dimensional data analysis (such as gene expression, spatial epidemiology, or brain imaging studies), we often test thousands or more hypotheses simultaneously. As the number of tests increases, the chance of observing some statistically significant tests is very high even when all null hypotheses are true. Consequently, we could reach incorrect conclusions regarding the hypotheses. Researchers frequently use multiplicity adjustment methods to control type I error rates-primarily the fami ...[more]

PMID: 28453517

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Project description:It has been suggested that pathway analysis can complement single-SNP analysis in exploring genomewide association data. Pathway analysis incorporates the available biological knowledge of genes and SNPs and is expected to improve the chances of revealing the underlying genetic architecture of complex traits. Methods for pathway analysis can be classified as competitive (enrichment) or self-contained (association) according to the hypothesis tested. Although association tests are statistically more powerful than enrichment tests they can be difficult to calibrate because biases in analysis accumulate across multiple SNPs or genes. Furthermore, enrichment tests can be more scientifically relevant than association tests, as they detect pathways with relatively more evidence for association than the remaining genes. Here we show how some well known association tests can be simply adapted to test for enrichment, and compare their performance to some established enrichment tests. We propose versions of the Adaptive Rank Truncated Product (ARTP), Tail Strength Measure and Fisher's combination of p-values for testing the enrichment null hypothesis. We compare the behaviour of these proposed methods with the established Hypergeometric Test and Gene-Set Enrichment Analysis (GSEA). The results of the simulation study show that the modified version of the ARTP method has generally the best performance across the situations considered. The methods were also applied for finding enriched pathways for body mass index (BMI) and platelet function phenotypes. The pathway analysis of BMI identified the Vasoactive Intestinal Peptide pathway as significantly associated with BMI. This pathway has been previously reported as associated with BMI and the risk of obesity. The ARTP method was the method that identified the largest number of enriched pathways across all tested pathway databases and phenotypes. The simulation and data application results are in agreement with previous work on association tests and suggests that the ARTP should be preferred for both enrichment and association testing.

Dataset Information

A comparison of multiple testing adjustment methods with block-correlation positively-dependent tests.

Publications

A comparison of multiple testing adjustment methods with block-correlation positively-dependent tests.

Similar Datasets

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