@misc{10481/82736,
year = {2023},
month = {2},
url = {https://hdl.handle.net/10481/82736},
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 < 0.01) lipidomegenotype
relations. Genotype biclusters in these 93 relations contained 5977 SNPs across 3164 genes.
Twenty nine of the 93 relations contained genotype biclusters with more than 50% unique SNPs
and participants, thus representing most distinct subgroups. We identified 30 significantly enriched
biological processes among the SNPs involved in 21 of these 29 most distinct genotype-lipidome
subgroups through which the identified genetic variants can influence and regulate plasma lipid
related metabolism and profiles. This study identified 29 distinct genotype-lipidome subgroups in the
studied Finnish population that may have distinct disease trajectories and therefore could be useful in
precision medicine research.},
organization = {Research Council of Finland},
organization = {Social Insurance Institution of Finland},
organization = {Competitive State Research Financing of Expert Responsibility area of Kuopio, Tampere and Turku University Hospitals},
organization = {Juho Vainio Foundation},
organization = {Paavo Nurmi Foundation},
organization = {Finnish Foundation for Cardiovascular Research},
organization = {Finnish Cultural Foundation
Finnish IT center for science},
organization = {Sigrid Juselius Foundation},
organization = {Tampere Tuberculosis Foundation},
organization = {Emil Aaltonen Foundation},
organization = {Yrjo Jahnsson Foundation},
organization = {Signe and Ane Gyllenberg Foundation},
organization = {Diabetes Research Foundation of Finnish Diabetes Association	322098
286284
134309
126925
121584
124282
255381
256474
283115
319060
320297
314389
338395
330809
104821
129378
117797
141071
INFRAIA-2016-1-730897},
organization = {Horizon 2020},
organization = {European Research Council (ERC)
European Commission	349708},
organization = {Tampere University Hospital Supporting Foundation},
organization = {Finnish Society of Clinical Chemistry},
organization = {Spanish Government	RTI2018-098983-B-100},
organization = {Laboratoriolaaketieteen Edistamissaatio~Sr},
organization = {Ida Montinin saatio},
organization = {Kalle Kaiharin saatio},
organization = {Aarne Koskelon saatio},
organization = {Faculty of Medicine and Health Technology, Tampere University},
organization = {Project HPC-EUROPA3	X51001
50191928},
organization = {EC Research Innovation Action under H2020 Programme	755320},
publisher = {SpringerNature},
title = {Uncovering the complex genetic architecture of human plasma lipidome using machine learning methods},
doi = {10.1038/s41598-023-30168-z},
author = {Lehtimäki, Miikael and Val Muñoz, María Coral Del and Zwir Nawrocki, Jorge Sergio Igor},
}