Biology subjects

Bush, W.

Publications and source records attributed to Bush, W..

5 recordsLinked to original sources

bioRxiv2023

Predicting genetically regulated gene expression on the X chromosome

BackgroundThe X chromosome is often omitted in disease association studies despite containing thousands of genes which may provide insight into well-known sex differences in the risk of Alzheimers Disease. ObjectiveTo model the expression of X chromosome genes and evaluate their impact on Alzheimers Disease risk in a sex-stratified manner. MethodsUsing elastic net, we evaluated multiple modeling strategies in a set of 175 whole blood samples and 126 brain cortex samples, with whole genome sequencing and RNA-seq data. SNPs (MAF>0.05) within the cis-regulatory window were used to train tissue-specific models of each gene. We apply the best models in both tissues to sex-stratified summary statistics from a meta-analysis of Alzheimers disease Genetics Consortium (ADGC) studies to identify AD-related genes on the X chromosome. ResultsAcross different model parameters, sample sex, and tissue types, we modeled the expression of 217 genes (95 genes in blood and 135 genes in brain cortex). The average model R2 was 0.12 (range from 0.03 to 0.34). We also compared sex-stratified and sex-combined models on the X chromosome. We further investigated genes that escaped X chromosome inactivation (XCI) to determine if their genetic regulation patterns were distinct. We found ten genes associated with AD at p < 0.05, with only ARMCX6 in female brain cortex (p = 0.008) nearing the significance threshold after adjusting for multiple testing ( = 0.002). ConclusionsWe optimized the expression prediction of X chromosome genes, applied these models to sex-stratified AD GWAS summary statistics, and identified one putative AD risk gene, ARMCX6.

bioinformatics↗

bioRxiv2022

Somatic Loss of the Y Chromosome and Alzheimer's Disease Risk

Mosaic loss of the Y chromosome (LOY) is a somatic, age-related event that has been previously associated with a variety of diseases of aging. A prior study of European cohorts demonstrated an association between LOY and Alzheimers Disease and more recent molecular studies have shown that LOY can also occur within microglia, suggesting a potential functional role in AD pathogenesis. In this study, we further validate the association between LOY and AD via prospective analyses of 1,447 males, and perform Mendelian Randomization analysis on 10,013 males across 26 US cohorts. Significant results from these analyses provide further evidence for a role of LOY in the development of Alzheimers Disease.

genetics↗

bioRxiv2022

Allelic Transcription Factor binding shape transcriptional kinetics in human cell lines

Gene expression from bulk RNA-seq studies is an average measurement between two chromosomes and across cell populations. Both allelic and cell-to-cell heterogeneity in gene expression results from promoter bursting patterns that repeatedly alternate between an activated and inactivated state. Increased cell-to-cell heterogeneity in gene expression has been associated with aging and stem cell pluripotency. However, studies of bursting kinetics and their molecular mechanism are relatively limited in human cells compared to other species due to laborious single-molecule experiments. Here, we systematically investigate the regulatory effect of genetic variants and transcription factor (TF) binding on transcriptional kinetics at the single chromosome level with GM12878. We found that the transcription initiation rate and burst frequency correlate most with eQTL effect sizes among transcriptional kinetics, which suggests that eQTLs affect average gene expression mainly through altering burst kinetics. We further found that [~]90% of the variance of burst frequency can be explained by TF occupancy in phase with the core promoter. We identified and replicated several examples where eQTL or GWAS catalog loci perturb TF binding affinity and are consequently associated with the change of burst kinetics.

genetics↗

bioRxiv2021

Using attentive gated neural networks to quantify the impact of non-coding variants on transcription factor binding affinity

A large proportion of non-coding variants are present within binding sites of transcription factors(TFs), which play a significant role in gene regulation. Thus, deriving the impact of non-coding variants on TF binding is the first step towards unravelling their regulatory roles within their associated disease traits. Most of the modern algorithms used for this purpose are based on convolutional neural network(CNN) architectures. However, these models are incapable of capturing the positional effect of different sub-sequences within the TF binding sites on the binding affinity. In this paper, we utilize the attentive gated neural network(AGNet) architecture to build a set of TF-AGNet models for predicting in vivo TF binding intensities in the GM12878 lymphoblastoid cells. These models have novel layers capable of deriving the impact of relative positions of different DNA sub-sequences, within a binding site, on TF binding affinity, and of extracting the most relevant prediction features. We show that the TF-AGNet models are able to outperform conventional CNNs for predicting continuous values of TF binding affinity. We also train additional TF-AGNet models for 20 TFs using data from 4 other cell-lines to assess the generalizability of their prediction accuracy. Lastly, we show that the TF-AGNet based models more accurately classify non-coding variants that significantly affect TF binding compared to models based on 7 variant annotation tools. This accuracy can be leveraged to derive gene regulatory roles of millions of non-coding variants across the genome to further examine their mechanistic associations with complex disease traits.

bioinformatics↗

bioRxiv2021

Detecting global influence of transcription factor interactions on gene expression in lymphoblastoid cells using neural network models

BackgroundTranscription factor(TF) interactions are known to regulate target gene(TG) expression in eukaryotes via TF regulatory modules(TRMs). Such interactions can be formed due to co-localizing TFs binding proximally to each other in the DNA sequence or over long distances between distally binding TFs via chromatin looping. While the former type of interaction has been characterized extensively, long distance TF interactions are still largely understudied. Furthermore, most prior approaches have focused on characterizing physical TF interactions without accounting for their effects on TG expression regulation. Understanding TRM based TG expression regulation could aid in understanding diseases caused by disruptions to these mechanisms. In this paper, we present a novel neural network based TRM detection approach that consists of using multi-omics TF based regulatory mechanism information to generate features for building non-linear multilayer perceptron TG expression prediction models in the GM12878 immortalized lymphoblastoid cells. ResultsWe estimated main effects of 149 individual TFs and interaction effects of 48 distinct combinations of TFs forming TRMs based on their influence on TG expression. We identified several well-known and discovered multiple previously uncharacterized TF interactions within our detected set of TRMs. We further characterized the pairwise TRMs using long distance chromatin looping and motif co-occurrence data. We found that nearly all the TFs constituting TRMs detected by our approach interacted via chromatin looping, and that these TFs further interacted with promoters to influence TG expression through one of four possible regulatory configurations. ConclusionHere, we have provided a framework for detecting TRMs using neural network models containing multi-omics TF based regulatory features. We have also described these TRMs based on their regulatory potential along with presenting evidence for the possibility of TF interactions forming the TRMs occurring via chromatin looping.

bioinformatics↗