1. Uncovering the complex genetic architecture of human plasma lipidome using machine learning methods
- Author
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Lehtimäki, Miikael, H. Mishra, Binisha, Del-Val, Coral, Lyytikäinen, Leo Pekka, Kähönen, Mika, Cloninger, C. Robert, Raitakari, Olli T., Laaksonen, Reijo, Zwir, Igor, Lehtimäki, Terho, Mishra, Pashupati P., Tampere University, Department of Clinical Chemistry, Clinical Medicine, and Department of Clinical Physiology and Nuclear Medicine
- Subjects
Multidisciplinary ,3111 Biomedicine ,3121 Internal medicine - 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., Research Council of Finland, Social Insurance Institution of Finland, Competitive State Research Financing of Expert Responsibility area of Kuopio, Tampere and Turku University Hospitals, Juho Vainio Foundation, Paavo Nurmi Foundation, Finnish Foundation for Cardiovascular Research, Finnish Cultural Foundation Finnish IT center for science, Sigrid Juselius Foundation, Tampere Tuberculosis Foundation, Emil Aaltonen Foundation, Yrjo Jahnsson Foundation, Signe and Ane Gyllenberg Foundation, 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, Horizon 2020, European Research Council (ERC) European Commission 349708, Tampere University Hospital Supporting Foundation, Finnish Society of Clinical Chemistry, Spanish Government RTI2018-098983-B-100, Laboratoriolaaketieteen Edistamissaatio~Sr, Ida Montinin saatio, Kalle Kaiharin saatio, Aarne Koskelon saatio, Faculty of Medicine and Health Technology, Tampere University, Project HPC-EUROPA3 X51001 50191928, EC Research Innovation Action under H2020 Programme 755320
- Published
- 2023