Uncovering the complex genetic architecture of human plasma lipidome using machine learning methods

Bibliographic Details
Title:	Uncovering the complex genetic architecture of human plasma lipidome using machine learning methods
Authors:	Miikael Lehtimäki, Binisha H. Mishra, Coral Del-Val, Leo-Pekka Lyytikäinen, Mika Kähönen, C. Robert Cloninger, Olli T. Raitakari, Reijo Laaksonen, Igor Zwir, Terho Lehtimäki, Pashupati P. Mishra
Source:	Scientific Reports, Vol 13, Iss 1, Pp 1-12 (2023)
Publisher Information:	Nature Portfolio, 2023.
Publication Year:	2023
Collection:	LCC:Medicine LCC:Science
Subject Terms:	Medicine, Science
More Details:	Abstract Genetic architecture of plasma lipidome provides insights into regulation of lipid metabolism and related diseases. We applied an unsupervised machine learning method, PGMRA, to discover phenotype-genotype many-to-many relations between genotype and plasma lipidome (phenotype) in order to identify the genetic architecture of plasma lipidome profiled from 1,426 Finnish individuals aged 30–45 years. PGMRA involves biclustering genotype and lipidome data independently followed by their inter-domain integration based on hypergeometric tests of the number of shared individuals. Pathway enrichment analysis was performed on the SNP sets to identify their associated biological processes. We identified 93 statistically significant (hypergeometric p-value
Document Type:	article
File Description:	electronic resource
Language:	English
ISSN:	2045-2322
Relation:	https://doaj.org/toc/2045-2322
DOI:	10.1038/s41598-023-30168-z
Access URL:	https://doaj.org/article/30ca82718ab54e95962a8cd0d4d51402
Accession Number:	edsdoj.30ca82718ab54e95962a8cd0d4d51402
Database:	Directory of Open Access Journals
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More Details
ISSN:	20452322
DOI:	10.1038/s41598-023-30168-z
Published in:	Scientific Reports
Language:	English