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

被引:0
|
作者
Kirichenko, A. V. [1 ]
Zorkoltseva, I. V. [1 ]
Belonogova, N. M. [1 ]
Axenovich, T. I. [1 ,2 ]
机构
[1] Russian Acad Sci, Siberian Branch, Fed Res Ctr Inst Cytol & Genet, Novosibirsk 630090, Russia
[2] Novosibirsk State Univ, Dept Cytol & Genet, Novosibirsk 630090, Russia
基金
俄罗斯基础研究基金会;
关键词
regional association analysis; quantitative traits; type I error; inflation factor; simulation; common genetic variants; single nucleotide polymorphic markers; MISSING HERITABILITY; RARE VARIANTS; HUMAN HEIGHT; DISEASES; POPULATION; STRATEGIES; TRAITS; LOCI; GWAS; SET;
D O I
10.1134/S1022795418010076
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
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.
引用
收藏
页码:250 / 258
页数:9
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