Accurate diagnosis of atopic dermatitis by combining transcriptome and microbiota data with supervised machine learning.

Atopic dermatitis (AD) is a common skin disease in childhood whose diagnosis requires expertise in dermatology. Recent studies have indicated that host genes-microbial interactions in the gut contribute to human diseases including AD. We sought to develop an accurate and automated pipeline for AD diagnosis based on transcriptome and microbiota data. Using these data of 161 subjects including AD patients and healthy controls, we trained a machine learning classifier to predict the risk of AD. We found that the classifier could accurately differentiate subjects with AD and healthy individuals based on the omics data with an average F1-score of 0.84. With this classifier, we also identified a set of 35 genes and 50 microbiota features that are predictive for AD. Among the selected features, we discovered at least three genes and three microorganisms directly or indirectly associated with AD. Although further replications in other cohorts are needed, our findings suggest that these genes and microbiota features may provide novel biological insights and may be developed into useful biomarkers of AD prediction.

Rare variants regulate expression of nearby individual genes in multiple tissues.

The rapid decrease in sequencing cost has enabled genetic studies to discover rare variants associated with complex diseases and traits. Once this association is identified, the next step is to understand the genetic mechanism of rare variants on how the variants influence diseases. Similar to the hypothesis of common variants, rare variants may affect diseases by regulating gene expression, and recently, several studies have identified the effects of rare variants on gene expression using heritability and expression outlier analyses. However, identifying individual genes whose expression is regulated by rare variants has been challenging due to the relatively small sample size of expression quantitative trait loci studies and statistical approaches not optimized to detect the effects of rare variants. In this study, we analyze whole-genome sequencing and RNA-seq data of 681 European individuals collected for the Genotype-Tissue Expression (GTEx) project (v8) to identify individual genes in 49 human tissues whose expression is regulated by rare variants. To improve statistical power, we develop an approach based on a likelihood ratio test that combines effects of multiple rare variants in a nonlinear manner and has higher power than previous approaches. Using GTEx data, we identify many genes regulated by rare variants, and some of them are only regulated by rare variants and not by common variants. We also find that genes regulated by rare variants are enriched for expression outliers and disease-causing genes. These results suggest the regulatory effects of rare variants, which would be important in interpreting associations of rare variants with complex traits.

Long-range chromatin interactions in pathogenic gene expression control.

A large number of distal cis-regulatory elements (cREs) have been annotated in the human genome, which plays a central role in orchestrating spatiotemporal gene expression. Since many cREs regulate non-adjacent genes, long-range cRE-promoter interactions are an important factor in the functional characterization of the engaged cREs. In this regard, recent studies have demonstrated that identification of long-range target genes can decipher the effect of genetic mutations residing within cREs on abnormal gene expression. In addition, investigation of altered long-range cREs-promoter interactions induced by chromosomal rearrangements has revealed their critical roles in pathogenic gene expression. In this review, we briefly discuss how the analysis of 3D chromatin structure can help us understand the functional impact of cREs harboring disease-associated genetic variants and how chromosomal rearrangements disrupting topologically associating domains can lead to pathogenic gene expression.