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Non-alcoholic fatty liver disease (NAFLD) is a prevalent chronic liverdisorder characterized by the excessive accumulation of fat in the liver inindividuals who do not consume significant amounts of alcohol, including riskfactors like obesity, insulin resistance, type 2 diabetes, etc. We aim toidentify subgroups of NAFLD patients based on demographic, clinical, andgenetic characteristics for precision medicine. The genomic and phenotypic data(3,408 cases and 4,739 controls) for this study were gathered from participantsin Mayo Clinic Tapestry Study (IRB#19-1) and their electric healthrecords, including their demographic, clinical, and comorbidity data, and thegenotype information through whole exome sequencing performed at Helix usingthe Exome+$^\circledR$ Assay according to standard procedure(www$.$helix$.$com). Factors highly relevant to NAFLD were determined by thechi-square test and stepwise backward-forward regression model. Latent classanalysis (LCA) was performed on NAFLD cases using significant indicatorvariables to identify subgroups. The optimal clustering revealed 5 latentsubgroups from 2,013 NAFLD patients (mean age 60.6 years and 62.1% women),while a polygenic risk score based on 6 single-nucleotide polymorphism (SNP)variants and disease outcomes were used to analyze the subgroups. The groupsare characterized by metabolic syndrome, obesity, different comorbidities,psychoneurological factors, and genetic factors. Odds ratios were utilized tocompare the risk of complex diseases, such as fibrosis, cirrhosis, andhepatocellular carcinoma (HCC), as well as liver failure between the clusters.Cluster 2 has a significantly higher complex disease outcome compared to otherclusters. Keywords: Fatty liver disease; Polygenic risk score; Precisionmedicine; Deep phenotyping; NAFLD comorbidities; Latent class analysis.

Deep Phenotyping of Non-Alcoholic Fatty Liver Disease Patients with Genetic Factors for Insights into the Complex Disease