Biomedical subjects

Jan Bressler

Publications and source records attributed to Jan Bressler.

6 recordsLinked to original sources

PubMed2026

Estimating population structure using epigenome-wide methylation data.

Population stratification is one of the source of inflation in epigenome-wide association studies (EWAS) when not properly accounted for. To address this, we developed methylation population scores (MPSs) to predict genetic principal components (GPCs) using a feature selection approach. We used multi-ethnic DNA methylation data from Illumina EPIC arrays across five cohorts, including MESA (n = 929), CARDIA (n = 1123), JHS (n = 1365), ARIC (n = 2338), and HCHS/SOL (n = 1475), randomly splitting participants into training (85%) and test (15%) sets. Within each cohort, associations between GPCs and CpG sites were estimated using linear regression adjusting for age, sex, smoking and alcohol use, race/ethnicity, body mass index, and cell type proportions, followed by meta-analysis and selection of CpGs with FDR <0.05. We then applied a two-stage weighted least squares Lasso regression to construct MPSs, adjusting for the aforementioned covariates. In the test dataset, MPSs showed strong correlation with GPCs, with R² ranging from 0.27 (MPS7 vs. GPC7) to 0.98 (MPS1 vs. GPC1). Visualization demonstrated that MPSs recapitulated the pattern shown by GPCs in differentiating self-reported White, Black, and Hispanic/Latino groups and outperformed methylation-based principal components constructed using alternative published methods. Additionally, MPSs showed comparable performance to GPCs in reducing inflation in EWAS. Overall, MPSs uses supervised learning with covariate adjustment to capture genetic structure across diverse populations, and provide a reliable estimate of population structure in the data and can complement GPCs when genetic data are absent.

Humans↗

PubMed2025

Estimating population structure using epigenome-wide methylation data.

INTRODUCTION: In epigenome-wide association analysis (EWAS), unaddressed population stratification often leads to inflation. We aimed to compute methylation population scores (MPSs) that predict genetic principal components (GPCs) using a feature selection and regression approach. METHODS: We used multi-ethnic methylation data (Illumina 450K/EPIC array) from unrelated MESA (n=929), CARDIA (n=1123), JHS (n=1365), ARIC (n=2338), and HCHS/SOL (n=1475) individuals, randomly assigning 85% of participants from each cohort to a training dataset and the remaining 15% to a test dataset. First, we estimated the associations of GPCs with each available CpG methylation site using linear regression within each cohort, adjusting for age, sex, smoking status, race/ethnic background (as a proxy for background information associated with lifestyle and other environmental exposures that may impact methylation), alcohol use status, body mass index, and cell type proportions. We meta-analyzed the associations across cohorts and selected CpG sites with association FDR-adjusted q-value <0.05. We next aggregated individuallevel data across the cohort-specific training datasets, and applied two-stage weighted least squares Lasso regression, with the GPCs as the outcomes and the selected CpG sites as penalized predictors, adjusting for the aforementioned covariates. The developed MPSs are the weighted sum of selected CpG sites from the Lasso. To evaluate the developed MPSs, we constructed them in the test dataset, and compared them with GPCs, and with MPSs constructed based on a previously-published paper. Comparison was based on correlation analysis and data visualization. We demonstrate the use of the MPSs in EWAS. RESULTS: In the test dataset, the MPSs were highly correlated with GPCs, with correlation decreasing, though not monotonically, for later components. Specifically, MPS1 and GPC1 had R2= 0.99, while MPS7 and GPC7 had R2=0.27 (the lowest observed correlation). In data visualization, MPSs had similar patterns as GPCs in differentiating self-reported White, Black, and Hispanic/Latino groups, while outperforming MPC constructed using alternative published methods. MPSs showed comparable performance to GPCs in reducing some of the inflation in EWAS. CONCLUSIONS: Methylation-based population scores provide a reliable estimate of population structure in the data and can complement GPCs when genetic data are absent. Unlike previous methods based on unsupervised methylation PCA, MPSs uses supervised learning with covariate adjustment to capture genetic structure across diverse populations. The weights for each GPCs derived in our study can be applied to generate MPSs in other studies.

Journal Article↗

PubMed2006

Mouse imprinting defect mutations that model Angelman syndrome.

Prader-Willi syndrome (PWS) and Angelman syndrome (AS) are neurobehavioral disorders resulting from deficiency of imprinted gene expression from paternal or maternal chromosome 15q11-15q13, respectively. In humans, expression of the imprinted genes is under control of a bipartite cis-acting imprinting center (IC). Families with deletions causing PWS imprinting defects localize the PWS-IC to 4.3 kb overlapping with SNRPN exon 1. Families with deletions causing AS imprinting defects localize the AS-IC to 880 bp 35 kb upstream of the PWS-IC. We report two mouse mutations resulting in defects similar to that seen in AS patients with deletion of the AS-IC. An insertion/duplication mutation 13 kb upstream of Snrpn exon 1 resulted in lack of methylation at the maternal Snrpn promoter, activation of maternally repressed genes, and decreased expression of paternally repressed genes. The acquisition of a paternal epigenotype on the maternal chromosome in the mutant mice was demonstrated by the ability to rescue the lethality and growth retardation in a mouse model of a PWS imprinting defect. A second mutation, an 80-kb deletion extending upstream of the first mutation, caused a similar imprinting defect with variable penetrance. These results suggest that there is a mouse functional equivalent to the human AS-IC.

Angelman Syndrome↗

PubMed2004

A mixed epigenetic/genetic model for oligogenic inheritance of autism with a limited role for UBE3A.

The genetic contribution to autism is often attributed to the combined effects of many loci (ten or more). This conclusion is based in part on the much lower concordance for dizygotic (DZ) than for monozygotic (MZ) twins, and is consistent with the failure to find strong evidence for linkage in genome-wide studies. We propose that the twin data are compatible with oligogenic inheritance combined with a major, genetic or epigenetic, de novo component to the etiology. Based on evidence that maternal but not paternal duplications of chromosome 15q cause autism, we attempted to test the hypothesis that autism involves oligogenic inheritance (two or more loci) and that the Angelman gene (UBE3A), which encodes the E6-AP ubiquitin ligase, is one of the contributing genes. A search for epigenetic abnormalities led to the discovery of a tissue-specific differentially methylated region (DMR) downstream of the UBE3A coding exons, but the region was not abnormal in autism lymphoblasts or brain samples. Based on evidence for allele sharing in 15q among sib-pairs, abnormal DNA methylation at the 5'-CpG island of UBE3A in one of 17 autism brains, and decreased E6-AP protein in some autism brains, we propose a mixed epigenetic and genetic model for autism with both de novo and inherited contributions. The role of UBE3A may be quantitatively modest, but interacting proteins such as those ubiquitinated by UBE3A may be candidates for a larger role in an oligogenic model. A mixed epigenetic and genetic and mixed de novo and inherited (MEGDI) model could be relevant to other "complex disease traits".

Alleles↗

PubMed2004

Epigenetics and human disease.

Epigenetics is comprised of the stable and heritable (or potentially heritable) changes in gene expression that do not entail a change in DNA sequence. The role of epigenetics in the etiology of human disease is increasingly recognized with the most obvious evidence found for genes subject to genomic imprinting. Mutations and epimutations in imprinted genes can give rise to genetic and epigenetic phenotypes, respectively; uniparental disomy and imprinting defects represent epigenetic disease phenotypes. There are also genetic disorders that affect chromatin structure and remodeling. These disorders can affect chromatin in trans or in cis, as well as expression of both imprinted and nonimprinted genes. Data from Angelman and Beckwith-Wiedemann syndromes and other disorders indicate that a monogenic or oligogenic phenotype can be caused by a mixed epigenetic and genetic and mixed de novo and inherited (MEGDI) model. The MEGDI model may apply to some complex disease traits and could explain negative results in genome-wide genetic scans.

Alleles↗

PubMed2003

Disruption of the genomic imprint in trans with homologous recombination at Snrpn in ES cells.

In gene targeting studies of the Prader-Willi syndrome (PWS)/Angelman syndrome (AS) domain in mouse ES cells, we recovered only recombinants with the paternal allele for constructs at exons 2 or 3 of the imprinted, maternally silenced Snurf-Snrpn gene. These sites lie close to the imprinting center (IC) for this domain. In contrast, recombinants for Ube3a within the same imprinted domain were recovered with equal frequency on the maternal and paternal alleles. In addition, gene targeting of the paternal allele for Snurf-Snrpn resulted in partial or complete demethylation in trans with activation of expression for the previously silenced maternal allele. The imprint switching of the maternal allele in trans is not readily explained by competition for trans-acting factors and adds to a growing body of evidence indicating homologous association of oppositely imprinted chromatin domains in somatic mammalian cells.

Alleles↗