Genetic variants associated with ALT elevation from therapeutic acetaminophen.

BACKGROUND: Several studies have suggested genetic variants associated with acetaminophen induced liver injury (DILI) following overdose. Genetic variation associated with acetaminophen-induced alanine aminotransferase elevation during therapeutic dosing has not been examined. METHODS: We performed genetic analyses on patients that ingested therapeutic doses of 4 grams of acetaminophen for up to 16 days. We examined 20 genes previously implicated in the metabolism of acetaminophen or the development of immune-mediated DILI using the Illumina Multi-Ethnic Global Array 2. Autosomes were aligned and imputed using TOPMed. A candidate gene region analysis was performed by testing each gene individually using linkage disequilibrium (LD) pruned variants with the adaptive sum of powered scores (aSPU) test from the aSPU R package. The highest measured ALT during therapy, the maximum ALT, was used as the outcome. RESULTS: 192 subjects taking therapeutic APAP were included in the genetic analysis. 136 (70.8%) were female, 133 (69.2%) were Caucasian race, and the median age was 34 years (IQR: 26, 46). Age > 50 years was the only clinical factor associated with maximum ALT increase. Variants in SULT1E1, the gene responsible for Sulfotransferase Family 1E Member 1 enzyme production, were associated with maximum ALT. No single variant drove this association, but rather the association was due to the additive effects of numerous variants within the gene. No other genes were associated with maximum ALT increase in this cohort. CONCLUSION: Acetaminophen induced ALT elevation at therapeutic doses was not associated with variation in most genes associated with acetaminophen metabolism or immune-induced DILI in this cohort. The role of SULT1E1 polymorphism in acetaminophen-induced elevated ALT needs further examination.

Summix: A method for detecting and adjusting for population structure in genetic summary data.

Publicly available genetic summary data have high utility in research and the clinic, including prioritizing putative causal variants, polygenic scoring, and leveraging common controls. However, summarizing individual-level data can mask population structure, resulting in confounding, reduced power, and incorrect prioritization of putative causal variants. This limits the utility of publicly available data, especially for understudied or admixed populations where additional research and resources are most needed. Although several methods exist to estimate ancestry in individual-level data, methods to estimate ancestry proportions in summary data are lacking. Here, we present Summix, a method to efficiently deconvolute ancestry and provide ancestry-adjusted allele frequencies (AFs) from summary data. Using continental reference ancestry, African (AFR), non-Finnish European (EUR), East Asian (EAS), Indigenous American (IAM), South Asian (SAS), we obtain accurate and precise estimates (within 0.1%) for all simulation scenarios. We apply Summix to gnomAD v.2.1 exome and genome groups and subgroups, finding heterogeneous continental ancestry for several groups, including African/African American (∼84% AFR, ∼14% EUR) and American/Latinx (∼4% AFR, ∼5% EAS, ∼43% EUR, ∼46% IAM). Compared to the unadjusted gnomAD AFs, Summix's ancestry-adjusted AFs more closely match respective African and Latinx reference samples. Even on modern, dense panels of summary statistics, Summix yields results in seconds, allowing for estimation of confidence intervals via block bootstrap. Given an accompanying R package, Summix increases the utility and equity of public genetic resources, empowering novel research opportunities.