Use of Genotypes of Common Variants for Genome-Wide Regional Association Analysis

A. V. Kirichenko, I. V. Zorkoltseva, N. M. Belonogova, T. I. Axenovich

Research output: Contribution to journalArticlepeer-review

Abstract

Regional association analysis is a new statistical method which simultaneously considers all variants in a selected genome region. This method was created for the analysis of rare genetic variants, whose genotypes are determined by exome or genome sequencing. The gene is usually considered as a region. It was also proposed to use a regional analysis for testing of the association between a complex trait and a set of common variants genotyped by the panels developed for genome-wide association analysis. In this case, overlapping genome regions (sliding windows) are usually considered as a region. Since the size of such regions can be rather large, there is a risk of overestimation (inflation) of the test statistic and an increase in the type I error. In this work, the effect of the size of the region on the type I error was studied for traits with different heritability. The results of simulating experiments demonstrated that the physical size of the region but not the number of genetic variants in it is a limiting factor. The higher the trait heritability, the greater the type I error differs from the declared value. The analysis of a large number of real traits confirmed these conclusions. It is necessary to take into account these results during the interpretation of the results of regional association analysis conducted on large regions using common genetic variants.

Original languageEnglish
Pages (from-to)250-258
Number of pages9
JournalRussian Journal of Genetics
Volume54
Issue number2
DOIs
Publication statusPublished - 1 Feb 2018

Keywords

  • common genetic variants
  • inflation factor
  • quantitative traits
  • regional association analysis
  • simulation
  • single nucleotide polymorphic markers
  • type I error

Fingerprint

Dive into the research topics of 'Use of Genotypes of Common Variants for Genome-Wide Regional Association Analysis'. Together they form a unique fingerprint.

Cite this