TY - JOUR

T1 - Joint regression analysis of multiple traits based on genetic relationships

AU - Buchardt, Ann-Sophie

AU - Zhou, Xiang

AU - Ekstrøm, Claus Thorn

N1 - © The Author(s) 2024. Published by Oxford University Press.

PY - 2024

Y1 - 2024

N2 - MOTIVATION: Polygenic scores (PGSs) are widely available and employed in genomic data analyses for predicting and understanding genetic architectures. Existing approaches either require information on SNP level, do not infer clusters of traits sharing genetic characteristic, or do not have any immediate predictive properties.RESULTS: Here, we present geneJAM, which is a novel clustering and estimation method using PGSs for inferring a genetic relationship among multiple, simultaneously measured and potentially correlated traits in a multivariate GWAS.Using graphical lasso, we estimate a sparse covariance matrix of the PGSs and obtain clusters of traits sharing genetic characteristics. We use the clusters to specify the structure of the error covariance matrix of a generalized least squares (GLS) model and use the feasible GLS estimator for estimating a linear regression model with a certain unknown degree of correlation between the residuals.The method suits many biology studies well with traits embedded in some genetic functioning groups and facilitates development of the PGS research. We compare the method with fully parametric techniques on simulated data and illustrate the utility of the methods by examining a heterogeneous stock mouse data set from the Wellcome Trust Centre for Human Genetics. We demonstrate that the method successfully identifies clusters of traits and increases precision, power, and computational efficiency.AVAILABILITY AND IMPLEMENTATION: GeneJAM is implemented in R and available at: https://github.com/abuchardt/geneJAM.

AB - MOTIVATION: Polygenic scores (PGSs) are widely available and employed in genomic data analyses for predicting and understanding genetic architectures. Existing approaches either require information on SNP level, do not infer clusters of traits sharing genetic characteristic, or do not have any immediate predictive properties.RESULTS: Here, we present geneJAM, which is a novel clustering and estimation method using PGSs for inferring a genetic relationship among multiple, simultaneously measured and potentially correlated traits in a multivariate GWAS.Using graphical lasso, we estimate a sparse covariance matrix of the PGSs and obtain clusters of traits sharing genetic characteristics. We use the clusters to specify the structure of the error covariance matrix of a generalized least squares (GLS) model and use the feasible GLS estimator for estimating a linear regression model with a certain unknown degree of correlation between the residuals.The method suits many biology studies well with traits embedded in some genetic functioning groups and facilitates development of the PGS research. We compare the method with fully parametric techniques on simulated data and illustrate the utility of the methods by examining a heterogeneous stock mouse data set from the Wellcome Trust Centre for Human Genetics. We demonstrate that the method successfully identifies clusters of traits and increases precision, power, and computational efficiency.AVAILABILITY AND IMPLEMENTATION: GeneJAM is implemented in R and available at: https://github.com/abuchardt/geneJAM.

U2 - 10.1093/bioadv/vbad192

DO - 10.1093/bioadv/vbad192

M3 - Journal article

C2 - 38264461

VL - 4

JO - Bioinformatics Advances

JF - Bioinformatics Advances

SN - 2635-0041

IS - 1

M1 - vbad192

ER -