Noncoding DNA regions have central roles in human biology, evolution, and disease. ChromHMM helps to annotate the noncoding genome using epigenomic information across one or multiple cell types. It combines multiple genome-wide epigenomic maps, and uses combinatorial and spatial mark patterns to infer a complete annotation for each cell type. ChromHMM learns chromatin-state signatures using a multivariate hidden Markov model (HMM) that explicitly models the combinatorial presence or absence of each mark. ChromHMM uses these signatures to generate a genome-wide annotation for each cell type by calculating the most probable state for each genomic segment. ChromHMM provides an automated enrichment analysis of the resulting annotations to facilitate the functional interpretations of each chromatin state. ChromHMM is distinguished by its modeling emphasis on combinations of marks, its tight integration with downstream functional enrichment analyses, its speed, and its ease of use. Chromatin states are learned, annotations are produced, and enrichments are computed within 1 d.

Chromatin-state discovery and genome annotation with ChromHMM

): Genome-wide association studies have identified many noncoding variants associated with common diseases and traits. We show that these variants are concentrated in regulatory DNA marked by deoxyribonuclease I (DNase I) hypersensitive sites (DHSs). Eighty-eight percent of such DHSs are active during fetal development and are enriched in variants associated with gestational exposure–related phenotypes. We identified distant gene targets for hundreds of variant-containing DHSs that may explain phenotype associations. Disease-associated variants systematically perturb transcription factor recognition sequences, frequently alter allelic chromatin states, and form regulatory networks. We also demonstrated tissue-selective enrichment of more weakly disease-associated variants within DHSs and the de novo identification of pathogenic cell types for Crohn’s disease, multiple sclerosis, and an electrocardiogram trait, without prior knowledge of physiological mechanisms. Our results suggest pervasive involvement of regulatory DNA variation in common human disease and provide pathogenic insights into diverse disorders.

Systematic Localization of Common Disease-Associated Variation in

Gene expression and its regulation can vary substantially across tissue types. In order to generate knowledge about gene expression in human tissues, the Genotype-Tissue Expression (GTEx) program has collected transcriptome data in a wide variety of tissue types from post-mortem donors. However, many tissue types are difficult to access and are not collected in every GTEx individual. Furthermore, in non-GTEx studies, the accessibility of certain tissue types greatly limits the feasibility and scale of studies of multi-tissue expression. In this work, we developed multi-tissue imputation methods to impute gene expression in uncollected or inaccessible tissues. Via simulation studies, we showed that the proposed methods outperform existing imputation methods in multi-tissue expression imputation and that incorporating imputed expression data can improve power to detect phenotype-expression correlations. By analyzing data from nine selected tissue types in the GTEx pilot project, we demonstrated that harnessing expression quantitative trait loci (eQTLs) and tissue-tissue expression-level correlations can aid imputation of transcriptome data from uncollected GTEx tissues. More importantly, we showed that by using GTEx data as a reference, one can impute expression levels in inaccessible tissues in non-GTEx expression studies.

Imputing gene expression in uncollected tissues within and beyond GTEx

Complex diseases are mediated via transcriptional dysregulation in multiple tissues. Thus, knowing an individual's tissue-specific gene expression can provide critical information about her health. Unfortunately, for most tissues, the transcriptome cannot be obtained without invasive procedures. Could we, however, infer an individual's tissue-specific expression from her whole blood transcriptome? Here, we rigorously address this question. We find that an individual's whole blood transcriptome can significantly predict tissue-specific expression levels for ~60% of the genes on average across 32 tissues, with up to 81% of the genes in skeletal muscle. The tissue-specific expression inferred from the blood transcriptome is almost as good as the actual measured tissue expression in predicting disease state for six different complex disorders, including hypertension and type 2 diabetes, substantially surpassing the blood transcriptome. The code for tissue-specific gene expression prediction, TEEBoT, is provided, enabling others to study its potential translational value in other indications.

https://advances.sciencemag.org/content/advances/7/14/eabd6991.full.pdf

Predicting tissue-specific gene expression from whole blood transcriptome.

As the most complex organ of the human body, the brain is composed of diverse regions, each consisting of distinct cell types and their respective cellular interactions. Human brain development involves a finely tuned cascade of interactive events. These include spatiotemporal gene expression changes and dynamic alterations in cell-type composition. However, our understanding of this process is still largely incomplete owing to the difficulty of brain spatiotemporal transcriptome collection. In this study, we developed a tensor-based approach to impute gene expression on a transcriptome-wide level. After rigorous computational benchmarking, we applied our approach to infer missing data points in the widely used BrainSpan resource and completed the entire grid of spatiotemporal transcriptomics. Next, we conducted deconvolutional analyses to comprehensively characterize major cell-type dynamics across the entire BrainSpan resource to estimate the cellular temporal changes and distinct neocortical areas across development. Moreover, integration of these results with GWAS summary statistics for 13 brain-associated traits revealed multiple novel trait-cell-type associations and trait-spatiotemporal relationships. In summary, our imputed BrainSpan transcriptomic data provide a valuable resource for the research community and our findings help further studies of the transcriptional and cellular dynamics of the human brain and related diseases.

/pdf/gene-expression-imputation-and-cell-type-deconvolution-in-2dga04fs2q.pdf

Gene expression imputation and cell-type deconvolution in human brain with spatiotemporal precision and its implications for brain-related disorders.

Motivation Gene expression profiling is widely used in basic and cancer research but still not feasible in many clinical applications because tissues, such as brain samples, are difficult and not ethnical to collect. Gene expression in uncollected tissues can be computationally inferred using genotype and expression quantitative trait loci. No methods can infer unmeasured gene expression of multiple tissues with single tissue gene expression profile as input. Results Here, we present a Bayesian ridge regression-based method (B-GEX) to infer gene expression profiles of multiple tissues from blood gene expression profile. For each gene in a tissue, a low-dimensional feature vector was extracted from whole blood gene expression profile by feature selection. We used GTEx RNAseq data of 16 tissues to train inference models to capture the cross-tissue expression correlations between each target gene in a tissue and its preselected feature genes in peripheral blood. We compared B-GEX with least square regression, LASSO regression and ridge regression. B-GEX outperforms the other three models in most tissues in terms of mean absolute error, Pearson correlation coefficient and root-mean-squared error. Moreover, B-GEX infers expression level of tissue-specific genes as well as those of non-tissue-specific genes in all tissues. Unlike previous methods, which require genomic features or gene expression profiles of multiple tissues, our model only requires whole blood expression profile as input. B-GEX helps gain insights into gene expressions of uncollected tissues from more accessible data of blood. Availability and implementation B-GEX is available at https://github.com/xuwenjian85/B-GEX. Supplementary information Supplementary data are available at Bioinformatics online.

Blood-based multi-tissue gene expression inference with Bayesian ridge regression

Autism spectrum disorders (ASD) is a highly heterogeneous neurodevelopmental condition characterized by atypical social interaction and communication together with repetitive behaviors and restricted interests. The prevalence of ASD has been increased these years. Compelling evidence has shown that genetic factors contribute largely to the development of ASD. However, knowledge about its genetic etiology and pathogenesis is limited. Broad applications of genomics studies have revealed the importance of gene mutations at protein-coding regions as well as the interrupted non-coding regions in the development of ASD. In this review, we summarize the current evidence for the known molecular genetic basis and possible pathological mechanisms as well as the risk genes and loci of ASD. Functional studies for the underlying mechanisms are also implicated. The understanding of the genetics and genomics of ASD is important for the genetic diagnosis and intervention for this condition.

/pdf/biological-implications-of-genetic-variations-in-autism-10782aza5p.pdf

Biological implications of genetic variations in autism spectrum disorders from genomics studies.

Genome-wide association studies (GWAS) have contributed significantly to predisposing the disease etiology by associating single nucleotide polymorphisms (SNPs) with complex diseases. However, most GWAS-SNPs are in the noncoding regions that may affect distal genes via long range enhancer-promoter interactions. Thus, the common practice on GWAS discoveries cannot fully reveal the molecular mechanisms underpinning complex diseases. It is known that perturbations of topological associated domains (TADs) lead to long range interactions which underlie disease etiology. To identify the probable long range interactions in noncoding regions via GWAS and TADs perturbed by deletions, we integrated datasets from GWAS-SNPs, enhancers, TADs, and deletions. After ranking and clustering, we prioritized 201,132 high confident pairs of GWAS-SNPs and target genes. In this study, we performed a systematic inference on noncoding regions via GWAS-SNPs and deletion-perturbed TADs to boost GWAS discovery power. The high confident pairs of GWAS-SNPs and target genes (SE-Gs) provide the promising candidates to understand the molecular mechanisms underlying complex diseases with emphasis on the three-dimensional genome.

Prioritizing long range interactions in noncoding regions using GWAS and deletions perturbed TADs

Albinism is a group of inherited disorders mainly affecting skin, hair and eyes. Here we identify a de novo point mutation, p.R210C, in the TPCN2 gene which encodes Two Pore Channel 2 (TPC2) from a patient with albinism. TPC2 is an endolysosome and melanosome localized non-selective cation channel involved in regulating pigment production. Through inside-out recording of plasma membrane targeted TPC2 and direct recording of enlarged endolysosomal vacuoles, we reveal that the R210C mutant displays constitutive channel activation and markedly increased affinity to PI(3,5)P2. Mice harboring the homologous mutation, R194C, also exhibit hypopigmentation in the fur and skin, as well as less pigment and melanosomes in the retina in a dominant inheritance manner. Moreover, mouse embryonic fibroblasts carrying the R194C mutation show enlarged endolysosomes, enhanced lysosomal Ca2+ release and hyper-acidification. Our data suggest that R210C is a pathogenic gain-of-function TPC2 variant that underlies an unusual dominant type of albinism. 

/pdf/a-gain-of-function-tpc2-variant-r210c-increases-affinity-to-1g0j9zn5.pdf

A gain-of-function TPC2 variant R210C increases affinity to PI(3,5)P2 and causes lysosome acidification and hypopigmentation

The parallel measurement of transcriptome and proteome revealed unmatched profiles. Since proteomic analysis is more expensive and challenging than transcriptomic analysis, the question of how to use messenger RNA (mRNA) expression data to predict protein level is extremely important. Here, we comprehensively evaluated 13 machine learning models on inferring protein expression levels using RNA expression profile. A total of 20 proteogenomic datasets from three mainstream proteomic platforms with >2500 samples of 13 human tissues were collected for model evaluation. Our results highlighted that the appropriate feature selection methods combined with classical machine learning models could achieve excellent predictive performance. The voting ensemble model outperformed other candidate models across datasets. Adding the mRNA proxy model to the regression model further improved the prediction performance. The dataset and gene characteristics could affect the prediction performance. Finally, we applied the model to the brain transcriptome of cerebral cortex regions to infer the protein profile for better understanding the functional characteristics of the brain regions. This benchmarking work not only provides useful hints on the inherent correlation between transcriptome and proteome, but also has practical value of the transcriptome-based prediction of protein expression levels.

Wenjian Xu

Papers

Blood-based multi-tissue gene expression inference with Bayesian ridge regression

Biological implications of genetic variations in autism spectrum disorders from genomics studies.

Prioritizing long range interactions in noncoding regions using GWAS and deletions perturbed TADs

A gain-of-function TPC2 variant R210C increases affinity to PI(3,5)P2 and causes lysosome acidification and hypopigmentation

Evaluation of machine learning models on protein level inference from prioritized RNA features