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 -