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Exome sequencing (ES) has been used in a variety of clinical settings but there are limited data on its utility for diagnosis and/or prediction of monogenic liver diseases. We analyzed ES data in 758 patients with liver diseases., We developed a curated list of 502 genes for monogenic disorders associated with liver phenotypes and analyzed ES data for these genes in 758 patients with chronic liver diseases (CLD). For comparison, we examined ES data in 7,856 self-declared healthy controls (HC), and 2,187 patients with chronic kidney disease (CKD). Candidate pathogenic (P) or likely pathogenic (LP) variants were initially identified in 19.9% of participants, most of which were attributable to previously reported pathogenic variants with implausibly high allele frequencies. After variant annotation and stringent filtering based on population minor allele frequency, we detected a significant enrichment of P/LP variants in the CLD cohort compared to the HC cohort (X2test OR: 5.00, 95% CI:3.06-8.18,, p-value=4.5 e-12). A second-level manual annotation was necessary to capture true pathogenic variants that were removed by stringent allele frequency filters. After these sequential steps, the diagnostic rate of monogenic disorders was 5.7% in the CLD cohort, attributable to P/LP variants in 25 genes. We also identified concordant liver disease phenotypes for 15/22 kidney disease patients with P/LP variants in liver genes, mostly associated with cystic liver disease phenotypes. Clinically confirmed sequencing results had many implications for clinical management, including familial testing for early diagnosis and management, preventative screening for associated comorbidities, and in some cases for therapy. Exome sequencing provided a 5.7% diagnostic rate in CLD patients and required multiple rounds of review to reduce both false positive and false negative findings. The identification of concordant phenotypes in many patients with P/LP variants and no known liver disease also indicates a potential for predictive testing for selected monogenic liver disorders.

ObjectivesNonalcoholic Fatty Liver Disease (NAFLD) is the most common global cause of chronic liver disease. Therapeutic interventions are rapidly advancing for its inflammatory phenotype, nonalcoholic steatohepatitis (NASH) at all stages of disease. Diagnosis codes alone fail to accurately recognize and stratify at-risk patients. Our work aims to rapidly identify NAFLD patients within large electronic health record (EHR) databases for automated stratification and targeted intervention based on clinically relevant phenotypes.MethodsWe present a rule-based phenotyping algorithm for the rapid identification of NAFLD patients developed using EHRs from 6.4 million patients at Columbia University Irving Medical Center (CUIMC) and validated at two independent healthcare centers. The algorithm uses the Observational Medical Outcomes Partnership (OMOP) Common Data Model and queries multiple structured and unstructured data elements, including diagnosis codes, laboratory measurements, radiology and pathology modalities.ResultsOur approach identified 16,006 CUIMC NAFLD patients, 10,753 (67%) of whom were previously unidentifiable by NAFLD diagnosis codes. Fibrosis scoring on patients without histology identified 943 subjects with scores indicative of advanced fibrosis (FIB-4, APRI, NAFLD–FS). The algorithm was validated at two independent healthcare systems, University of Pennsylvania Health System (UPHS) and Vanderbilt Medical Center (VUMC), where 20,779 and 19,575 NAFLD patients were identified, respectively. Clinical chart review identified a high positive predictive value (PPV) across all healthcare systems: 91% at CUIMC, 75% at UPHS, and 85% at VUMC, and a sensitivity of 79.6%.ConclusionsOur rule-based algorithm provides an accurate, automated approach for rapidly identifying, stratifying, and sub-phenotyping NAFLD patients within a large EHR system.Study HighlightsWHAT IS KNOWNNAFLD is the leading form of chronic liver disease with a rising prevalence in the population.NAFLD is often under-recognized in at-risk individuals, including within healthcare settings.Current means of identification and stratification are complex and dependent on provider recognition of clinical risk factors.WHAT IS NEW HEREAn accurate, validated rule-based algorithm for the high-throughput and rapid EHR identification of NAFLD patients.Rapid discovery of a NAFLD cohort from diverse EHR systems comprising approximately 12.1 million patients.Our algorithm has high performance (mean PPV=85%, sensitivity=79.6%) in NAFLD patient discovery.The majority of algorithmically derived NAFLD patients were previously unidentified within healthcare systems.Computational stratification of individuals with advanced fibrosis can be achieved rapidly.

Background & AimsA common genetic variant near MBOAT7 (rs641738C>T) has been previously associated with hepatic fat and advanced histology in non-alcoholic fatty liver disease (NAFLD), however, these findings have not been consistently replicated in the literature. We aimed to establish whether rs641738C>T is a risk factor across the spectrum of NAFLD and characterize its role in the regulation of related metabolic phenotypes through meta-analysis.MethodsWe performed meta-analysis of studies with data on the association between rs641738C>T genotype and: liver fat, NAFLD histology, and serum ALT, lipids, or insulin. These included directly genotyped studies and population-level data from genome-wide association studies (GWAS). We performed random effects meta-analysis using recessive, additive, and dominant genetic models.ResultsData from 1,047,265 participants (8,303 with liver biopsies) across 42 studies was included in the meta-analysis. rs641738C>T was associated with higher liver fat on CT/MRI (+0.03 standard deviations [95% CI: 0.02 - 0.05]) and diagnosis of NAFLD (OR 1.22 [95% CI 1.08 - 1.39]) in Caucasian adults. The variant was also positively associated with presence of severe steatosis, NASH, and advanced fibrosis (OR: 1.32 [95% CI: 1.06 - 1.63]) in Caucasian adults using a recessive model of inheritance (CC+CT vs. TT). Meta-analysis of data from previous GWAS found the variant to be associated with higher ALT (Pz=0.002) and lower serum triglycerides (Pz=1.5×10−4). rs641738C>T was not associated with fasting insulin and no effect was observed in children with NAFLD.ConclusionOur study validates rs641738C>T near MBOAT7 as a risk factor for the presence and severity of NAFLD in individuals of European descent.