Statistical Properties of Single-Marker Tests for Rare Variants. (June 2014)

• Record Type: Journal Article
• Authors: Bigdeli, T. Bernard; Neale, Benjamin M.; Neale, Michael C.
• Is Part Of: Twin research and human genetics. Volume 17:Number 3(2014)
• Page Start: 143
• Page End: 150
• Publication Date: 2014-06
• Subjects: Twins -- Periodicals; Multiple birth -- Periodicals; 618.25
• DOI: 10.1017/thg.2014.17
• Languages: English
• ISSNs: 1832-4274

Abstract

<abstract abstract-type="normal"> <title> <x content-type="archive" xml:space="preserve">Abstract</x> </title> <p>With the dramatic technological developments of genome-wide association single-nucleotide polymorphism (SNP) chips and next generation sequencing, human geneticists now have the ability to assay genetic variation at ever-rarer allele frequencies. To fully understand the impact of these rare variants on common, complex diseases, we must be able to accurately assess their statistical significance. However, it is well established that classical association tests are not appropriate for the analysis of low-frequency variation, giving spurious findings when observed counts are too few. To further our understanding of the asymptotic properties of traditional association tests, we conducted a range of simulations of a typical rare variant (~1%) under the null hypothesis and tested the allelic χ<sup>2</sup>, Cochran–Armitage trend, Wald, and Fisher's exact tests. We demonstrate that rare variation shows marked deviation from the expected distributional behavior for each test, with fewer minor alleles corresponding to a greater degree of test statistics deflation. The effect becomes more pronounced at progressively smaller α levels. We also show that the Wald test is particularly deflated at α levels consistent with genome-wide association significance, much more so than the other association tests considered. In general, these classical association tests are inappropriate for the analysis of variants for which the minor allele is observed fewer than 80 times, largely irrespective of sample size.</p> </abstract>