Choline deficiency causes hepatic fat accumulation, and is associated with a higher risk of nonalcoholic fatty liver disease (NAFLD) and more advanced NAFLD-related hepatic fibrosis. Reduced expression of hepatic phosphatidylethanolamine N-methyltransferase (PEMT), which catalyzes the production of phosphatidylcholine, causes steatosis, inflammation, and fibrosis in mice. In humans, common PEMT variants impair phosphatidylcholine synthesis, and are associated with NAFLD risk. We investigated hepatic PEMT expression in a large cohort of patients representing the spectrum of NAFLD, and examined the relationship between PEMT genetic variants and gene expression. Hepatic PEMT expression was reduced in NAFLD patients with inflammation and fibrosis (i.e., nonalcoholic steatohepatitis or NASH) compared to participants with normal liver histology (β = −1.497; p = 0.005). PEMT levels also declined with increasing severity of fibrosis with cirrhosis < incomplete cirrhosis < bridging fibrosis (β = −1.185; p = 0.011). Hepatic PEMT expression was reduced in postmenopausal women with NASH compared to those with normal liver histology (β = −3.698; p = 0.030). We detected a suggestive association between rs7946 and hepatic fibrosis (p = 0.083). Although none of the tested variants were associated with hepatic PEMT expression, computational fine mapping analysis indicated that rs4646385 may impact PEMT levels in the liver. Hepatic PEMT expression decreases with increasing severity of NAFLD in obese individuals and postmenopausal women, and may contribute to disease pathogenesis in a subset of NASH patients.

Large-scale genome-wide association studies (GWAS) strongly suggest that most traits and diseases have a polygenic component. This observation has motivated the development of disease-specific “polygenic scores (PGS)” that are weighted sums of the effects of disease-associated variants identified from GWAS that correlate with an individual’s likelihood of expressing a specific phenotype. Although most GWAS have been pursued on disease traits, leading to the creation of refined “Polygenic Risk Scores” (PRS) that quantify risk to diseases, many GWAS have also been pursued on extreme human longevity, general fitness, health span, and other health-positive traits. These GWAS have discovered many genetic variants seemingly protective from disease and are often different from disease-associated variants (i.e., they are not just alternative alleles at disease-associated loci) and suggest that many health-positive traits also have a polygenic basis. This observation has led to an interest in “polygenic longevity scores (PLS)” that quantify the “risk” or genetic predisposition of an individual towards health. We derived 11 different PLS from 4 different available GWAS on lifespan and then investigated the properties of these PLS using data from the UK Biobank (UKB). Tests of association between the PLS and population structure, parental lifespan, and several cancerous and non-cancerous diseases, including death from COVID-19, were performed. Based on the results of our analyses, we argue that PLS are made up of variants not only robustly associated with parental lifespan, but that also contribute to the genetic architecture of disease susceptibility, morbidity, and mortality.

Metabolic dysfunction-associated steatotic liver disease (MASLD) occurs across a wide spectrum of body weights, yet the genetic determinants underlying hepatic steatosis in individuals with normal BMI remain underexplored. This study aimed to identify genetic variants associated with liver fat fraction in normal-weight individuals. We performed a genome-wide association study (GWAS) using magnetic resonance imaging-proton density fat fraction (MRI-PDFF) data from 10,918 normal-weight participants (BMI <25 kg/m²) of European ancestry in the UK Biobank. Hepatic steatosis and liver fat content were assessed using both case–control (CC; 815 cases with MRI-PDFF ≥5% vs. 10,103 controls with MRI-PDFF <5%) and quantitative trait (QT; N = 10,918, with MRI-PDFF as a continuous outcome) designs. Fine mapping prioritized potential causal variants. Gene-level associations were evaluated using multi-marker analysis of genomic annotation (MAGMA), and liver-specific gene expression was imputed for transcriptome-wide association studies (TWAS). We identified 241 genome-wide significant variants in the CC-GWAS and 418 in the QT-GWAS, with most located on chromosomes 19 and 22, including known loci such as PNPLA3, TM6SF2, and SAMM50. Fine-mapping analyses prioritized three candidate causal variants in SUGP1, GATAD2A, and MAU2. MAGMA identified eight genes in CC-GWAS and 19 in QT-GWAS, including a novel association with RFXANK. TWAS supported the involvement of MBOAT7 and SAMM50, with fine mapping further implicating SAMM50 as a likely causal gene. This study, one of the first to detect genome-wide associations for hepatic steatosis in normal-weight individuals, identified both novel and established genetic loci. These findings highlight the role of genetic susceptibility independent of obesity-related pathways and may inform targeted strategies for MASLD prevention and treatment in this understudied population. This study provides new insights into the genetic risk factors underlying metabolic dysfunction-associated steatotic liver disease in individuals with a normal BMI, a group often under-represented in steatotic liver disease research. Leveraging large-scale genomic and imaging data from the UK Biobank, we identified both known and novel variants associated with liver fat accumulation, emphasizing that genetic predisposition can drive hepatic steatosis independently of excess adiposity. While the study is based on individuals of European ancestry, future research should assess the relevance of these findings in more diverse populations to ensure broader clinical applicability. These results may help inform future strategies for early risk stratification and targeted prevention in metabolically vulnerable, normal-weight individuals.

Background: Polygenic risk scores (PRS) have been developed to predict individual cancer risk and their potential clinical utility is receiving a great deal of attention. However, the degree to which the predictive utility of individual cancer-specific PRS may be augmented or refined by the incorporation of other cancer PRS, non-cancer disease PRS, or the protective effects of health and longevity-associated variants, is largely unexplored. Methods: We constructed PRS for different cancers from public domain data as well as genetic scores for longevity (‘Polygenic Longevity Scores’ or ‘PLS’) for individuals in the UK Biobank. We then explored the relationships of these multiple PRS and PLS among those with and without various cancers. Results: We found statistically significant associations between some PLS and individual cancers, even after accounting for cancer-specific PRS. None of the PLS in their current form had an effect pronounced enough to motivate clinical cancer risk stratification based on its combined use with cancer PRS. A few variants at loci used in the PLS had known associations with Alzheimer’s disease and other diseases. Conclusion: Underlying heterogeneity behind cancer susceptibility in the population at large is not captured by PRS derived from analytical models that only consider marginal associations of individual variants with cancer diagnoses. Our results have implications for the derivation and calculation of PRS and their use in clinical and biomedical research settings. Impact: Extensions of analyses like ours could result in a more refined understanding of cancer biology and how to construct PRS for cancer.

Alzheimer’s disease and related disorders (ADRD), as well as general age-related cognitive decline, are known to be multifactorial with heterogeneous etiologies. Identifying and accommodating heterogeneity in any one data set can be pursued using different analytical techniques, each with different assumptions or purposes. Whereas a great deal of research has explored clustering individuals of variables that exhibit greater similarity in some way, little research has explored evidence for heterogeneity in the relationships between relevant outcomes, such as a performance on a memory test, and risk factors such as environmental exposures, behaviors or genetic factors among individuals. Heterogeneity in relationships between outcomes and risk factors could compromise the development of accurate risk prediction models. We explored evidence of heterogeneity in the relationships between ability on a memory test and multiple social and demographic risk factors using the large MindCrowd database (n>90,000 individuals). We focused on mixtures of regression models, but compared models assuming many interaction effects among independent variables as well as random effects. We ultimately find substantial evidence for heterogeneity and offer an intuitive explanation for this heterogeneity involving motivation for participation. We provide suggestions to validate and understand these claims using companion or ancillary studies, such as those being pursued as part of the Precision Aging Network (PAN) consortium. Our results also motivate caution in the use of large epidemiological study or survey-oriented data sets to build predictive models of clinical or subclinical pathologies without exploring or accommodating heterogeneity.

Introduction: Tumor mutation burden (TMB), defined as the number of somatic gene mutations per megabase in a tumor genome, is used clinically to identify cancer patients that may respond to immune checkpoint inhibitors. Recent studies suggest that patient ancestry can influence TMB, where individuals of African ancestry were found to have elevated TMB values based on tumor-only sequencing analysis. However, the impact of patient ancestry on germline mutational burden and implications for interpretation of tumor-only mutational burden data is largely unexplored. Methods: We examined the influence of patient ancestry on germline and tumor mutation burden using tumor-normal whole exome sequencing (WES) data from a pan-cancer cohort of 1228 individuals from a single institution. Genetic ancestry was estimated from constitutional WES data using single nucleotide polymorphism weights from external reference panels. Variant calling was performed using constitutional, tumor-only, and paired tumor-normal workflows. Total and loss-of-function burden scores were calculated for each participant from each workflow based on the total number of mutations detected in each sample and the total number of predicted loss-of-function mutations, based on snpEff annotations, respectively. Results: Genetic ancestry analysis found that one-third of this pan-cancer cohort was of non-European ancestry. 9.4% of individuals were of Eastern Asian ancestry, 5.3% of African ancestry, 1.0% of South Asian ancestry, 0.2% of Native American ancestry, and 17.1% of admixed ancestry, predominantly European and Native American admixed ancestry (15.6%). Total and loss-of-function germline burden scores varied across ancestral groups, with individuals of African ancestry showing significantly increased germline burden scores compared to other ancestral groups (p<0.0001). Tumor-only analysis also showed increased mutation burden for individuals of African ancestry. However, when private and rare germline variation was taken into account using paired tumor-normal analysis, tumor mutation burden did not differ based on patient ancestry, suggesting the differences seen in tumor-only analysis are due to germline variation rather than differences in true somatic mutation burden. Conclusion: Our study reveals that the paucity of knowledge of ancestry-specific reference genomes leads to unacceptable health disparities in cancer, specifically in patients with African ancestry. We show that germline mutational burden varies by ancestral background, with individuals of African ancestry displaying increased genetic variation compared to other ancestral populations. These results suggest that patient ancestry should be considered when interpreting tumor mutational burden values, particularly from tumor-only analysis. Approaches utilizing paired tumor-normal analysis or ancestry-specific reference genomes can aid in more accurate assessment of TMB, thereby avoiding futile treatments targeted at presumed high mutational burden tumors specifically in African American populations. Citation Format: Janith Don, Sara A. Byron, Guangfa Zhang, Tyler Izatt, Jiaming Zhang, Bethany Davis, Bryce Turner, Jonathan J. Keats, Jeffrey M. Trent, Lorna Rodriguez-Rodriguez, Nicholas J. Schork. Increased germline mutational burden in individuals of African ancestry: Implications for interpretation of tumor mutation burden [abstract]. In: Proceedings of the 15th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2022 Sep 16-19; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr A006.