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A modified false discovery rate multiple‐comparisons procedure for discrete data, applied to human immunodeficiency virus genetics
Author(s) -
Gilbert Peter B.
Publication year - 2005
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/j.1467-9876.2005.00475.x
Subject(s) - false discovery rate , human immunodeficiency virus (hiv) , mutation , mutation rate , multiple comparisons problem , statistical power , computational biology , statistical hypothesis testing , computer science , statistics , biology , virology , mathematics , genetics , gene
Summary. To help to design vaccines for acquired immune deficiency syndrome that protect broadly against many genetic variants of the human immunodeficiency virus, the mutation rates at 118 positions in HIV amino‐acid sequences of subtype C versus those of subtype B were compared. The false discovery rate (FDR) multiple‐comparisons procedure can be used to determine statistical significance. When the test statistics have discrete distributions, the FDR procedure can be made more powerful by a simple modification. The paper develops a modified FDR procedure for discrete data and applies it to the human immunodeficiency virus data. The new procedure detects 15 positions with significantly different mutation rates compared with 11 that are detected by the original FDR method. Simulations delineate conditions under which the modified FDR procedure confers large gains in power over the original technique. In general FDR adjustment methods can be improved for discrete data by incorporating the modification proposed.