An unexpectedly large number of human autosomal genes are subject to monoallelic expression (MAE). Our analysis of 4,227 such genes reveals surprisingly high genetic variation across human populations. This increased diversity is unlikely to reflect relaxed purifying selection. Remarkably, MAE genes exhibit elevated recombination rate and increased density of hypermutable sequence contexts. However, these factors do not fully account for the increased diversity. We find that the elevated nucleotide diversity of MAE genes is also associated with greater allelic age: their variants tend to be older and are enriched in polymorphisms shared with Neanderthals and chimpanzees. Both synonymous and nonsynonymous alleles in MAE genes have elevated average population frequencies. We also observed strong enrichment of the MAE signature among genes reported to evolve under balancing selection. We propose that an important biological function of widespread MAE might be generation of cell-to-cell heterogeneity; the increased genetic variation contributes to this heterogeneity.

PurposeTo describe an artificial intelligence platform that detects thyroid eye disease (TED).DesignDevelopment of a deep learning model.Methods1944 photographs from a clinical database were used to train a deep learning model. 344 additional images (‘test set’) were used to calculate performance metrics. Receiver operating characteristic, precision–recall curves and heatmaps were generated. From the test set, 50 images were randomly selected (‘survey set’) and used to compare model performance with ophthalmologist performance. 222 images obtained from a separate clinical database were used to assess model recall and to quantitate model performance with respect to disease stage and grade.ResultsThe model achieved test set accuracy of 89.2%, specificity 86.9%, recall 93.4%, precision 79.7% and an F1 score of 86.0%. Heatmaps demonstrated that the model identified pixels corresponding to clinical features of TED. On the survey set, the ensemble model achieved accuracy, specificity, recall, precision and F1 score of 86%, 84%, 89%, 77% and 82%, respectively. 27 ophthalmologists achieved mean performance of 75%, 82%, 63%, 72% and 66%, respectively. On the second test set, the model achieved recall of 91.9%, with higher recall for moderate to severe (98.2%, n=55) and active disease (98.3%, n=60), as compared with mild (86.8%, n=68) or stable disease (85.7%, n=63).ConclusionsThe deep learning classifier is a novel approach to identify TED and is a first step in the development of tools to improve diagnostic accuracy and lower barriers to specialist evaluation.

Reactive oxygen species (ROS) have been shown to play crucial roles in regulating various cellular functions, e.g. focal adhesion (FA) dynamics and cell migration upon growth factor stimulation. However, it is not clear how ROS are regulated at subcellular FA sites to impact cell migration. We have developed a biosensor capable of monitoring ROS production at FA sites in live cells with high sensitivity and specificity, utilizing fluorescence resonance energy transfer (FRET). The results revealed that platelet derived growth factor (PDGF) can induce ROS production at FA sites, which is mediated by Rac1 activation. In contrast, integrins, specifically integrin αvβ3, inhibits this local ROS production. The RhoA activity can mediate this inhibitory role of integrins in regulating ROS production. Therefore, PDGF and integrin αvβ3 coordinate to have an antagonistic effect in the ROS production at FA sites to regulate cell adhesion and migration.

MotivationThere is recent interest in using gene expression data to contextualize findings from traditional genome-wide association studies (GWAS). Conditioned on a tissue, expression quantitative trait loci (eQTLs) are genetic variants associated with gene expression, and eGenes are genes whose expression levels are associated with genetic variants. eQTLs and eGenes provide great supporting evidence for GWAS hits and important insights into the regulatory pathways involved in many diseases. When a significant variant or a candidate gene identified by GWAS is also an eQTL or eGene, there is strong evidence to further study this variant or gene. Multi-tissue gene expression datasets like the Gene Tissue Expression (GTEx) data are used to find eQTLs and eGenes. Unfortunately, these datasets often have small sample sizes in some tissues. For this reason, there have been many meta-analysis methods designed to combine gene expression data across many tissues to increase power for finding eQTLs and eGenes. However, these existing techniques are not scalable to datasets containing many tissues, like the GTEx data. Furthermore, these methods ignore a biological insight that the same variant may be associated with the same gene across similar tissues.ResultsWe introduce a meta-analysis model that addresses these problems in existing methods. We focus on the problem of finding eGenes in gene expression data from many tissues, and show that our model is better than other types of meta-analyses.Availability and ImplementationSource code is at https://github.com/datduong/RECOV.Supplementary information Supplementary data are available at Bioinformatics online.