Biology subjects

Gottlieb, A.

Publications and source records attributed to Gottlieb, A..

3 recordsLinked to original sources

bioRxiv2018

TF-TWAS: Transcription-factor polymorphism associated with tissue-specific gene expression

Transcriptional regulation is associated with a broad range of diseases. Methods associating genetic polymorphism with gene transcription levels offer key insights for understanding the transcriptional regulation plan. The majority of gene imputation methods focus on modeling polymorphism in the cis regions of the gene, partially owing to the large genetic search space. We hypothesize that polymorphism within transcription factors (TFs) may help explain transcription levels of their transcribed genes.\n\nHere, we test this hypothesis by developing TF-TWAS: imputation models that integrate transcription factor information with transcription-wide association study methodology. By comparing TF-TWAS models to base models that use only gene cis information, we are able to estimate possible mechanisms of the TF polymorphism effect - TF expression or binding affinity within four tissues - whole blood, liver, brain hippocampus and coronary artery.\n\nWe identified 48 genes where the TF-TWAS models explain significantly better their expression than cis models alone in at least one of the four tissues. Sixteen of these genes are associated with various diseases, including cancer, neurological, psychiatric and rare genetic diseases. Our method is a new expansion to transcriptome-wide association studies and enables the identification of new associations between polymorphism in transcription factor and gene transcription levels.

genomics↗

bioRxiv2017

Estimating Effects Of Second Line Therapy For Type 2 Diabetes Mellitus: Retrospective Cohort Study

ObjectiveMetformin is the recommended initial drug treatment in type 2 Diabetes Mellitus, but there is no clearly preferred choice for an additional drug when indicated. We use electronic health records to infer the counterfactual drug effectiveness in reducing HbA1c levels and effect on body-mass index (BMI) of four second line diabetes drug classes.\n\nStudy design and settingRetrospective analysis of the electronic health records of US-based patients in the Explorys database using causal inference methodology to adjust for censored patients and confounders.\n\nParticipants and ExposuresOur cohort consisted of roughly 25,000 patients with type 2 diabetes, prescribed metformin along with a drug out of four second line drug classes - sulfonylureas, thiazolidinediones, DPP-4 inhibitors and GLP-1 agonists, during the years 2000-2013.\n\nMain outcome measuresGlycated hemoglobin (HbA1c) and BMI of these patients after six and twelve months of treatment.\n\nResultsWe show that all four drug classes reduce glycated hemoglobin levels, but the effect of sulfonylureas after 12 months of treatment is less pronounced compared to other classes. We also predict that thiazolidinediones increase body weight while DPP-4 inhibitors decrease it.\n\nConclusionOur results are in line with current knowledge on second line drug effectiveness and effect on BMI. They demonstrate that causal inference from Electronic health records is an effective way for conducting multi-treatment causal inference studies.

epidemiology↗

bioRxiv2017

Population-specific imputation of gene expression improves prediction of pharmacogenomic traits for African Americans

Genome-wide association studies (GWAS) are useful for discovering genotype-phenotype associations but are limited because they require large cohorts to identify a signal, which can be population-specific. Mapping genetic variation to genes improves power, and allows the effects of both protein coding variation as well as variation in expression to be combined into \"gene level\" effects.\n\nPrevious work has shown that warfarin dose can be predicted using information from genetic variation that affects protein coding regions. Here, we introduce a method that improves the predicted dose by integrating tissue-specific gene expression. In particular, we use drug pathways and expression quantitative trait loci knowledge to impute gene expression--on the assumption that differential expression of key pathway genes may impact dose requirement. We focus on 116 genes from the pharmacokinetic (PK) and pharmacodynamic (PD) pathways of warfarin within training and validation sets comprising both European and African-descent individuals. We build gene-tissue signatures associated with warfarin dose, and identify a signature of eleven gene-tissue pairs that significantly augment the International Warfarin Pharmacogenetics Consortium dosage-prediction algorithm in both populations. Our results demonstrate that imputed expression can improve dose prediction, in a population-specific manner.

genomics↗