A computationally efficient Bayesian seemingly unrelated regressions model for high-dimensional quantitative trait loci discovery. ...

• Main Authors: Bottolo, Leonardo, Banterle, Marco, Richardson, Sylvia, Ala-Korpela, Mika, Järvelin, Marjo-Riitta, Lewin, Alex
• Format: Article in Journal/Newspaper
• Language: English
• Published: Oxford University Press (OUP) 2021
• Subjects: Bayesian computation; Markov chain Monte Carlo; covariance reparametrisation; graphical models; metabolomics; quantitative trait loci; Northern Finland
• Online Access: https://dx.doi.org/10.17863/cam.66284; https://www.repository.cam.ac.uk/handle/1810/319166

Our work is motivated by the search for metabolite quantitative trait loci (QTL) in a cohort of more than 5000 people. There are 158 metabolites measured by NMR spectroscopy in the 31-year follow-up of the Northern Finland Birth Cohort 1966 (NFBC66). These metabolites, as with many multivariate phenotypes produced by high-throughput biomarker technology, exhibit strong correlation structures. Existing approaches for combining such data with genetic variants for multivariate QTL analysis generally ignore phenotypic correlations or make restrictive assumptions about the associations between phenotypes and genetic loci. We present a computationally efficient Bayesian seemingly unrelated regressions model for high-dimensional data, with cell-sparse variable selection and sparse graphical structure for covariance selection. Cell sparsity allows different phenotype responses to be associated with different genetic predictors and the graphical structure is used to represent the conditional dependencies between ...