Digital Commons Network^™

Gene-By-Environment Interaction In Non-Alcoholic Fatty Liver Disease And Depression: The Role Of Hepatic Transaminases, Eron G. Manusov, Vincent P. Diego, Edward Abrego, Kathryn Herklotz, Marcio A. Almeida, Xi Mao, Sandra L. Laston, John Blangero, Sarah Williams-Blangero

School of Medicine Publications and Presentations

Non-alcoholic fatty liver disease (NAFLD) encompasses a range of liver conditions, from benign fatty accumulation to severe fibrosis. The global prevalence of NAFLD has risen to 25-30%, with variations across ethnic groups. NAFLD may advance to hepatocellular carcinoma, increases cardiovascular risk, is associated with chronic kidney disease, and is an independent metabolic disease risk factor. Assessment methods for liver health include liver biopsy, magnetic resonance imaging, ultrasound, and vibration-controlled transient elastography (VCTE by FibroScan). Hepatic transaminases are cost-effective and minimally invasive liver health assessment methods options.

This study focuses on the interaction between genetic factors underlying the traits (hepatic transaminases …

Focal Adhesion Is Associated With Lithium Response In Bipolar Disorder: Evidence From A Network-Based Multi-Omics Analysis, Vipavee Niemsiri, Sara Brin Rosenthal, Caroline M. Nievergelt, Adam X. Maihofer, Maria C. Marchetto, Renata Santos, Tatyana Shekhtman, Ney Alliey-Rodriguez, Amit Anand, Yokesh Balaraman

School of Medicine Publications and Presentations

Lithium (Li) is one of the most effective drugs for treating bipolar disorder (BD), however, there is presently no way to predict response to guide treatment. The aim of this study is to identify functional genes and pathways that distinguish BD Li responders (LR) from BD Li non-responders (NR). An initial Pharmacogenomics of Bipolar Disorder study (PGBD) GWAS of lithium response did not provide any significant results. As a result, we then employed network-based integrative analysis of transcriptomic and genomic data. In transcriptomic study of iPSC-derived neurons, 41 significantly differentially expressed (DE) genes were identified in LR vs NR regardless …

Cocktail-Party Listening And Cognitive Abilities Show Strong Pleiotropy, Samuel R. Mathias, Emma M. Knowles, Josephine Mollon, Peter T. Fox, Rene L. Olvera, Juan M. Peralta, Satish Kumar, Harald H. H. Goring, Ravi Duggirala, Joanne E. Curran, John Blangero, David C. Glahn

School of Medicine Publications and Presentations

Introduction: The cocktail-party problem refers to the difficulty listeners face when trying to attend to relevant sounds that are mixed with irrelevant ones. Previous studies have shown that solving these problems relies on perceptual as well as cognitive processes. Previously, we showed that speech-reception thresholds (SRTs) on a cocktail-party listening task were influenced by genetic factors. Here, we estimated the degree to which these genetic factors overlapped with those influencing cognitive abilities.

Methods: We measured SRTs and hearing thresholds (HTs) in 493 listeners, who ranged in age from 18 to 91 years old. The same individuals completed a cognitive test …

Potential Mirna Biomarkers And Therapeutic Targets For Early Atherosclerotic Lesions, Genesio M. Karere, Jeremy P. Glenn, Ge Li, Ayati Konar, John L. Vandeberg, Laura A. Cox

School of Medicine Publications and Presentations

Identification of potential therapeutic targets and biomarkers indicative of burden of early atherosclerosis that occur prior to advancement to life-threatening unstable plaques is the key to eradication of CAD prevalence and incidences. We challenged 16 baboons with a high cholesterol, high fat diet for 2 years and evaluated early-stage atherosclerotic lesions (fatty streaks, FS, and fibrous plaques, FP) in formalin-fixed common iliac arteries (CIA). We used small RNA sequencing to identify expressed miRNAs in CIA and in baseline blood samples of the same animals. We found 412 expressed miRNAs in CIA and 356 in blood samples. Eight miRNAs (miR-7975, -486-5p, …

Uncovering The Complex Genetic Architecture Of Human Plasma Lipidome Using Machine Learning Methods, 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

School of Medicine Publications and Presentations

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) 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 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.