Abstract
Current work for multivariate analysis of phenotypes in genome-wide association studies often requires that genetic similarity matrices be inverted or decomposed. This can be a computational bottleneck when many phenotypes are presented, each with a different missingness pattern. A usual method in this case is to perform decompositions on subsets of the kinship matrix for each phenotype, with each subset corresponding to the set of observed samples for that phenotype. We provide a new method for decomposing these kinship matrices that can reduce the computational complexity by an order of magnitude by propagating low-rank modifications along a tree spanning the phenotypes. We demonstrate that our method provides speed improvements of around 40% under reasonable conditions.
Highlights
U nderstanding the etiology and biological pathways involved in health and disease requires a multivariate analysis of phenotypic data in genome-wide association studies (GWAS)
Further multivariate analysis is required for the study of complex diseases such as cancer (Knox, 2010) and groups of complex phenotypes such as brain imaging phenotypes (Elliott et al, 2018), and the analysis of large consortia involving multimodal data such as the U.K
The use of multiphenotype data often requires that a scaled version of the kinship matrix K be used as a covariance matrix of a multivariate distribution or matrix
Summary
U nderstanding the etiology and biological pathways involved in health and disease requires a multivariate analysis of phenotypic data in genome-wide association studies (GWAS). This sort of analysis is becoming more common due to increased compute power (Cox et al, 2018). In this figure, genetic similarity matrices are simulated by drawing from a Wishart distribution with means given by the identity matrix and with. 1j1
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
