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Power Analysis for Genetic Association Test (PAGEANT) provides insights to challenges for rare variant association studies
Author(s) -
Andriy Derkach,
Haoyu Zhang,
Nilanjan Chatterjee
Publication year - 2017
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btx770
Subject(s) - genetic association , genome wide association study , computer science , association test , genetic architecture , statistical power , association (psychology) , association mapping , trait , data mining , computational biology , variance (accounting) , quantitative trait locus , genetics , statistics , biology , single nucleotide polymorphism , mathematics , genotype , psychology , gene , psychotherapist , accounting , business , programming language
Genome-wide association studies are now shifting focus from analysis of common to rare variants. As power for association testing for individual rare variants may often be low, various aggregate level association tests have been proposed to detect genetic loci. Typically, power calculations for such tests require specification of large number of parameters, including effect sizes and allele frequencies of individual variants, making them difficult to use in practice. We propose to approximate power to a varying degree of accuracy using a smaller number of key parameters, including the total genetic variance explained by multiple variants within a locus.

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