  Non-alcoholic fatty liver disease (NAFLD) is a prevalent chronic liver
disorder characterized by the excessive accumulation of fat in the liver in
individuals who do not consume significant amounts of alcohol, including risk
factors like obesity, insulin resistance, type 2 diabetes, etc. We aim to
identify subgroups of NAFLD patients based on demographic, clinical, and
genetic characteristics for precision medicine. The genomic and phenotypic data
(3,408 cases and 4,739 controls) for this study were gathered from participants
in Mayo Clinic Tapestry Study (IRB#19-000001) and their electric health
records, including their demographic, clinical, and comorbidity data, and the
genotype information through whole exome sequencing performed at Helix using
the Exome+$^\circledR$ Assay according to standard procedure
(www$.$helix$.$com). Factors highly relevant to NAFLD were determined by the
chi-square test and stepwise backward-forward regression model. Latent class
analysis (LCA) was performed on NAFLD cases using significant indicator
variables to identify subgroups. The optimal clustering revealed 5 latent
subgroups 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 groups
are characterized by metabolic syndrome, obesity, different comorbidities,
psychoneurological factors, and genetic factors. Odds ratios were utilized to
compare the risk of complex diseases, such as fibrosis, cirrhosis, and
hepatocellular carcinoma (HCC), as well as liver failure between the clusters.
Cluster 2 has a significantly higher complex disease outcome compared to other
clusters. Keywords: Fatty liver disease; Polygenic risk score; Precision
medicine; Deep phenotyping; NAFLD comorbidities; Latent class analysis.
