Research output: Contribution to journal › Article › peer-review
Use of Genotypes of Common Variants for Genome-Wide Regional Association Analysis. / Kirichenko, A. V.; Zorkoltseva, I. V.; Belonogova, N. M. et al.
In: Russian Journal of Genetics, Vol. 54, No. 2, 01.02.2018, p. 250-258.Research output: Contribution to journal › Article › peer-review
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TY - JOUR
T1 - Use of Genotypes of Common Variants for Genome-Wide Regional Association Analysis
AU - Kirichenko, A. V.
AU - Zorkoltseva, I. V.
AU - Belonogova, N. M.
AU - Axenovich, T. I.
PY - 2018/2/1
Y1 - 2018/2/1
N2 - 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.
AB - 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.
KW - common genetic variants
KW - inflation factor
KW - quantitative traits
KW - regional association analysis
KW - simulation
KW - single nucleotide polymorphic markers
KW - type I error
UR - http://www.scopus.com/inward/record.url?scp=85043482439&partnerID=8YFLogxK
U2 - 10.1134/S1022795418010076
DO - 10.1134/S1022795418010076
M3 - Article
AN - SCOPUS:85043482439
VL - 54
SP - 250
EP - 258
JO - Russian Journal of Genetics
JF - Russian Journal of Genetics
SN - 1022-7954
IS - 2
ER -
ID: 10426868