Research output: Contribution to journal › Article › peer-review
Reconstruction of a Matrix of Genotypic Correlations between Variants within a Gene for Joint Analysis of Imputed and Sequenced Data. / Svishcheva, G. R.; Kirichenko, A. V.; Belonogova, N. M. et al.
In: Russian Journal of Genetics, Vol. 60, No. 7, 07.2024, p. 969-976.Research output: Contribution to journal › Article › peer-review
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TY - JOUR
T1 - Reconstruction of a Matrix of Genotypic Correlations between Variants within a Gene for Joint Analysis of Imputed and Sequenced Data
AU - Svishcheva, G. R.
AU - Kirichenko, A. V.
AU - Belonogova, N. M.
AU - Elgaeva, E. E.
AU - Tsepilov, Ya A.
AU - Zorkoltseva, I. V.
AU - Axenovich, T. I.
N1 - Work by G.R. Svishcheva, I.V. Zorkoltseva, N.M. Belonogova, and E.E. Elgaeva was supported from the Russian Science Foundation (RSF), grant no. 23-25-00209.
PY - 2024/7
Y1 - 2024/7
N2 - When combining imputed and sequenced data in a single gene-based association analysis, the problem of reconstructing genetic correlation matrices arises. It is related to the fact that the correlations between genotypes of all imputed variants and the correlations between genotypes of all sequenced variants are known for a gene but we do not know the correlations between genotypes of variants, one of which is imputed, and the other is sequenced. To recover these correlations, we propose an efficient method based on maximising the determinant of the matrix. This method has a number of useful properties and an analytical solution for our task. Approbation of the proposed method was performed by comparing reconstructed and real correlation matrices constructed on individual genotypes from the UK Biobank. Comparison of the results of gene-based association analysis performed by the SKAT, BT, and PCA methods on reconstructed and real matrices using modelled summary statistics and calculated summary statistics on real phenotypes showed high quality of reconstruction and robustness of the method to different gene structures.
AB - When combining imputed and sequenced data in a single gene-based association analysis, the problem of reconstructing genetic correlation matrices arises. It is related to the fact that the correlations between genotypes of all imputed variants and the correlations between genotypes of all sequenced variants are known for a gene but we do not know the correlations between genotypes of variants, one of which is imputed, and the other is sequenced. To recover these correlations, we propose an efficient method based on maximising the determinant of the matrix. This method has a number of useful properties and an analytical solution for our task. Approbation of the proposed method was performed by comparing reconstructed and real correlation matrices constructed on individual genotypes from the UK Biobank. Comparison of the results of gene-based association analysis performed by the SKAT, BT, and PCA methods on reconstructed and real matrices using modelled summary statistics and calculated summary statistics on real phenotypes showed high quality of reconstruction and robustness of the method to different gene structures.
KW - gene-based association analysis
KW - genetic variants
KW - imputed and sequenced genotypes
KW - summary statistics
UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85200040006&origin=inward&txGid=a8998d046f9d6e6d13800310642d72b2
UR - https://www.mendeley.com/catalogue/ab6aae15-9b42-3133-95c5-c035b1a5835d/
U2 - 10.1134/S1022795424700418
DO - 10.1134/S1022795424700418
M3 - Article
VL - 60
SP - 969
EP - 976
JO - Russian Journal of Genetics
JF - Russian Journal of Genetics
SN - 1022-7954
IS - 7
ER -
ID: 60862676