[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124434-en":3,"doc-seo-124434-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124434,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","Uncovering the complex genetic architecture of human plasma lipidome using machine learning methods","Genetic architecture of the human plasma lipidome informs regulation of lipid metabolism and cardiovascular disease risk. The study uses an unsupervised machine-learning framework, PGMRA, to uncover phenotype-genotype many-to-many relationships between genotype and lipidome measured in 1,426 Finnish individuals aged 30–45. Genotype and lipidome biclusters are derived independently and integrated via hypergeometric tests, followed by pathway enrichment on associated SNP sets. Results reveal 93 significant relations, forming 29 distinct genotype–lipidome subgroups supported by enriched biological processes, relevant for precision medicine.","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nUncovering the complex genetic architecture of human plasma lipidome using machine learning methods  \nMiikael Lehtimäki1,2,3,12, Binisha H. Mishra1,2,3,12, Coral Del‑Val4,11,  \nLeo‑Pekka Lyytikäinen1,2,3, Mika Kähönen2,5, C. Robert Cloninger6, OlliT. Raitakari7,8,9, Reijo Laaksonen1,2,10, Igor Zwir4,6,11, Terho Lehtimäki1,2,3 & Pashupati P. Mishra1,2,3*  \nGenetic 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 ofthe 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 \u003C 0.01) lipidome‑ genotype 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 SNPsand 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.  \nAtherosclerosis, the underlying pathology behind many cardiovascular diseases (CVDs), is a heterogeneous lipid accumulation and inflammation related disease with roots including genetics1, personality2, and lifestyle factors3. Previous lipidomic analyses have revealed several ceramides and phospholipids associated with key atherosclerosis processes such as uptake and aggregation of lipoproteins, accumulation of cholesterol within macrophages, production of superoxide anions, expression of cytokines and inflammation4–6. Similarly, genetic studies of traditional lipids such as total cholesterol (TC), HDL-cholesterol (HDL-C), LDL-cholesterol (LDL-C), non-HDL-cholesterol and triglycerides have identified about 1000 genomic loci and improved our understanding of lipid metabolism7–10. Some studies have reported genetic associations for subsets of lipidome11–13 and metabolome 13–20. Only few genome-wide association studies (GWASs) of lipidome involving 141–596 lipid  \n1Department of Clinical Chemistry, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland. 2Faculty of Medicine and Health Technology, Finnish Cardiovascular Research Center Tampere, Tampere University, Tampere, Finland. 3Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland. 4Department of Computer Science and Artificial Intelligence, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain. 5Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland. 6Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA. 7Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland. 8Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland. 9Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland. 10Zora Biosciences Oy, Espoo, Finland. 11Instituto de Investigación Biosanitaria ibs. GRANADA, Comple","cbCaigz6j12h3Uvk","https://ap.wps.com/l/cbCaigz6j12h3Uvk","pdf",1985802,1,12,"English","en",105,"# Abstract\n## Study goal and dataset\n## Method: PGMRA and biclustering integration\n## Statistical findings and enriched biological processes\n## Implications for precision medicine","[{\"question\":\"What is the main aim of the study on human plasma lipidome genetics?\",\"answer\":\"To identify phenotype-genotype many-to-many relationships that reveal the genetic architecture regulating plasma lipid metabolism and its disease-related trajectories.\"},{\"question\":\"How does PGMRA work in this research?\",\"answer\":\"PGMRA performs biclustering of genotype and lipidome data independently, then integrates cross-domain relationships using hypergeometric tests based on shared individuals, and applies pathway enrichment on SNP sets.\"},{\"question\":\"What key results were obtained from the Finnish cohort?\",\"answer\":\"The study identified 93 statistically significant lipidome-genotype relations, containing genotype biclusters spanning 3164 genes, which further define 29 distinct genotype–lipidome subgroups with enriched biological processes.\"}]","Uncovering the complex genetic architecture of human plasma lipidome using machine learning methods | 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is the main aim of the study on human plasma lipidome genetics?","Question",{"text":75,"@type":76},"To identify phenotype-genotype many-to-many relationships that reveal the genetic architecture regulating plasma lipid metabolism and its disease-related trajectories.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does PGMRA work in this research?",{"text":80,"@type":76},"PGMRA performs biclustering of genotype and lipidome data independently, then integrates cross-domain relationships using hypergeometric tests based on shared individuals, and applies pathway enrichment on SNP sets.",{"name":82,"@type":73,"acceptedAnswer":83},"What key results were obtained from the Finnish cohort?",{"text":84,"@type":76},"The study identified 93 statistically significant lipidome-genotype relations, containing genotype biclusters spanning 3164 genes, which further define 29 distinct genotype–lipidome subgroups with enriched biological 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