Biology subjects

Mezey, J.

Publications and source records attributed to Mezey, J..

4 recordsLinked to original sources

bioRxiv2024

Genetic variation modulates susceptibility to aberrant DNA hypomethylation and imprint deregulation in naive pluripotent stem cells

Naive pluripotent stem cells (nPSC) frequently undergo pathological and not readily reversible loss of DNA methylation marks at imprinted gene loci. This abnormality poses a hurdle for using pluripotent cell lines in biomedical applications and underscores the need to identify the causes of imprint instability in these cells. We show that nPSCs from inbred mouse strains exhibit pronounced strain-specific susceptibility to locus-specific deregulation of imprinting marks during reprogramming to pluripotency and upon culture with MAP kinase inhibitors, a common approach to maintain naive pluripotency. Analysis of genetically highly diverse nPSCs from the Diversity Outbred (DO) stock confirms that genetic variation is a major determinant of epigenome stability in pluripotent cells. We leverage the variable DNA hypomethylation in DO lines to identify several trans-acting quantitative trait loci (QTLs) that determine epigenome stability at either specific target loci or genome-wide. Candidate factors encoded by two multi-target QTLs on chromosomes 4 and 17 suggest specific transcriptional regulators that contribute to DNA methylation maintenance in nPSCs. We propose that genetic variants represent candidate biomarkers to identify pluripotent cell lines with desirable properties and might serve as entry points for the targeted engineering of nPSCs with stable epigenomes. HighlightsO_LINaive pluripotent stem cells from distinct inbred mouse strains exhibit variable DNA methylation levels at imprinted gene loci. C_LIO_LIThe vulnerability of pluripotent stem cells to loss of genomic imprinting caused by MAP kinase inhibition strongly differs between inbred mouse strains. C_LIO_LIGenetically diverse pluripotent stem cell lines from Diversity Outbred mouse stock allow the identification of quantitative trait loci controlling DNA methylation stability. C_LIO_LIGenetic variants may serve as biomarkers to identify naive pluripotent stem cell lines that are epigenetically stable in specific culture conditions. C_LI

developmental biology↗

bioRxiv2022

Benchmarking Polygenic Risk Score Model Assumptions: towards more accurate risk assessment

Polygenic risk scores represent an individuals genetic susceptibility to a phenotype. Like with any models, statistical models commonly employed to fit polygenic risk scores and assess their accuracy contain several assumptions. The effects of these assumptions on models of polygenic risk score have not been thoroughly assessed. We assessed 26 variations of the traditional polygenic risk score model, each of which mitigate assumptions in one of five facets of disease modelling: representation of age (6 variations), censorship (3 variations), competing risks (7 variations), formation of disease labels (6 variations), and selection of covariates (4 variations). With data from the UK Biobank, each model variation included age, sex, and a polygenic risk score derived from the PGS Catalog. Each of the 26 model variations were fitted to predict 18 diseases. Compared to the plain model that contained all five facets of assumptions, the model variations often fit the data better and generated predictions that largely differed from the predictions of the plain model. The statistic Roystons R2 measured a models goodness of fit, and thereby determined if the model was an enhancement upon the plain model. For 15 of the 26 model variations Roystons R2 was greater than that of the plain model for >50% of diseases. Reclassification rates, defined as the fraction of individuals in the top five percentiles of the plain models predictions who are not in the top five percentiles of a model variations predictions, was used to determine if the variation led to significantly different predictions. For 20 of the 26 model variations the median reclassification rate calculated across the 18 diseases was greater than 10%. Comparisons of accuracy statistics further illustrated how much each model variations predictions differed from the plain models predictions. Models containing polygenic risk scores appear to be significantly affected by many common modelling assumptions. Therefore, future investigations should consider taking some action to mitigate modelling assumptions. Author SummaryAn individuals genetics can increase their risk of experiencing a disease. The exact magnitude of the increased risk is estimated within a statistical model. The traditional model type employed in this process is relatively plain and contains several assumptions. The predicted risk estimates from this plain model may be unnecessarily inaccurate. To test this possibility, we searched the literature for model variations that reduce the assumptions of the plain model, ultimately creating 26 distinct model variations that may improve upon the plain model. Each model variation was fit with data from the UK Biobank to predict 18 diseases. We found that 15 of the 26 models variations fit the data better than the plain model for a majority of diseases. Goodness of fit was measured with Roystons R2 statistic. Further calculations found that the predictions of the model variations were often significantly more or less accurate than the predictions of the plain model. We believe these results indicate that future investigations of polygenic risk scores should not employ the plain model, as unreliable risk predictions will likely result.

genomics↗

bioRxiv2019

Age, Sex, and Genetics Influence the Abundance of Infiltrating Immune Cells in Human Tissues

Despite infiltrating immune cells playing an essential role in human disease and the patient response to treatment, the central mechanisms influencing variability in infiltration patterns are unclear. Using bulk RNA-seq data from 53 GTEx tissues, we applied cell-type deconvolution algorithms to evaluate the immune landscape across the healthy human body. We first performed a differential expression analysis of inflamed versus non-inflamed samples to identify essential pathways and regulators of infiltration. Next, we found 21 of 73 infiltration-related phenotypes to be associated with either age or sex (FDR < 0.1). Through our genetic analysis, we discovered 13 infiltration-related phenotypes have genome-wide significant associations (iQTLs) (P < 5.0 x 10-8), with a significant enrichment of tissue-specific expression quantitative trait loci in suggested iQTLs (P < 10-5). We highlight an association between neutrophil content in lung tissue and a variant near the CUX1 transcription factor gene (P = 9.7 x 10-11), which has been previously linked to neutrophil infiltration, inflammatory mechanisms, and the regulation of several immune response genes. Together, our results identify key factors influencing inter-individual variability of specific tissue infiltration patterns, which could provide insights on therapeutic targets for shifting infiltration profiles to a more favorable one.

genetics↗

bioRxiv2019

Identifying novel associations in GWAS by hierarchical Bayesian latent variable detection of differentially misclassified phenotypes

Heterogeneity in definition and measurement of complex diseases in Genome-Wide Association Studies (GWAS) may lead to misdiagnoses and misclassification errors that can significantly impact discovery of disease loci. While well appreciated, almost all analyses of GWAS data consider reported disease phenotype values as is without accounting for potential misclassification. Here, we introduce Phenotype Latent variable Extraction of disease misdiagnosis (PheLEx), a GWAS analysis framework that learns and corrects misclassified phenotypes using structured genotype associations within a dataset. PheLEx consists of a hierarchical Bayesian latent variable model, where inference of differential misclassification is accomplished using filtered genotypes while implementing a full mixed model to account for population structure and genetic relatedness in study populations. Through simulations, we show that the PheLEx framework dramatically improves recovery of the correct disease state when considering realistic allele effect sizes compared to existing methodologies designed for Bayesian recovery of disease phenotypes. We also demonstrate the potential of PheLEx for extracting new candidate loci from existing GWAS data by analyzing epilepsy and bipolar disorder phenotypes available from the UK Biobank dataset, where we identify new candidate disease loci not previously reported for these datasets that have biological connections to the disease phenotypes and/or were identified in independent GWAS. In the discussion, we consider both the broader consequences and importance of careful interpretation of misclassification correction in GWAS phenotypes, as well as potential of PheLEx for re-analyzing existing GWAS data to make novel discoveries. Author SummaryPrevalent misdiagnosis of diseases due to lack of understanding and/or gold-standard diagnostic measures can impact any analytics that follow. These misdiagnosis errors are especially significant in the domain of psychiatric or psychological disorders where the definition of disease and/or their diagnostic tools are always in flux or under further improvement. Here, we propose a method to extract misdiagnosis from disease and infer the correct disease phenotype. We examined the performance of this method on rigorous simulations and real disease phenotypes obtained from the UK Biobank database. We found that this method successfully recovered misdiagnosed individuals in simulations using a carefully designed hierarchical Bayesian latent variable model framework. For real disease phenotypes, epilepsy and bipolar disorder, this method not only suggested an alternate phenotype but results from this method were also used to discover new genomic loci that have been previously showed to be associated with the respective phenotypes, suggesting that this method can be further used to reanalyze large-scale genetic datasets to discover novel loci that might be ignored using traditional methodologies.

genetics↗