Genome-wide association studies have identified thousands of loci for common diseases, but, for the majority of these, the mechanisms underlying disease susceptibility remain unknown. Most associated variants are not correlated with protein-coding changes, suggesting that polymorphisms in regulatory regions probably contribute to many disease phenotypes. Here we describe the Genotype-Tissue Expression (GTEx) project, which will establish a resource database and associated tissue bank for the scientific community to study the relationship between genetic variation and gene expression in human tissues

Understanding the functional consequences of genetic variation, and how it affects complex human disease and quantitative traits, remains a critical challenge for biomedicine. We present an analysis of RNA sequencing data from 1641 samples across 43 tissues from 175 individuals, generated as part of the pilot phase of the Genotype-Tissue Expression (GTEx) project. We describe the landscape of gene expression across tissues, catalog thousands of tissue-specific and shared regulatory expression quantitative trait loci (eQTL) variants, describe complex network relationships, and identify signals from genome-wide association studies explained by eQTLs. These findings provide a systematic understanding of the cellular and biological consequences of human genetic variation and of the heterogeneity of such effects among a diverse set of human tissues.

With the goal of providing a comprehensive, high-quality resource for both plant transcription factors (TFs) and their regulatory interactions with target genes, we upgraded plant TF database PlantTFDB to version 4.0 (http://planttfdb.cbi.pku.edu.cn/). In the new version, we identified 320 370 TFs from 165 species, presenting a more comprehensive genomic TF repertoires of green plants. Besides updating the pre-existing abundant functional and evolutionary annotation for identified TFs, we generated three new types of annotation which provide more directly clues to investigate functional mechanisms underlying: (i) a set of high-quality, non-redundant TF binding motifs derived from experiments; (ii) multiple types of regulatory elements identified from high-throughput sequencing data; (iii) regulatory interactions curated from literature and inferred by combining TF binding motifs and regulatory elements. In addition, we upgraded previous TF prediction server, and set up four novel tools for regulation prediction and functional enrichment analyses. Finally, we set up a novel companion portal PlantRegMap (http://plantregmap.cbi.pku.edu.cn) for users to access the regulation resource and analysis tools conveniently.

The recycling of the amyloid precursor protein (APP) from the cell surface via the endocytic pathways plays a key role in the generation of amyloid β-peptide (Aβ) in Alzheimer's Disease (AD). We report here that inherited variants in the SORL1 neuronal sorting receptor are associated with late-onset AD. These variants, which occur in at least two different clusters of intronic sequences may regulate tissue-specific expression of SORL1. We also show that SORL1 directs trafficking of APP into recycling pathways, and that when SORL1 is under-expressed, APP is sorted into Aβ-generating compartments. These data suggest that inherited or acquired changes in SORL1 expression or function are mechanistically involved in causing AD.

To identify susceptibility loci for bipolar disorder, we tested 1.8 million variants in 4,387 cases and 6,209 controls and identified a region of strong association (rs10994336, P = 9.1 × 10-9) in ANK3 (ankyrin G). We also found further support for the previously reported CACNA1C (alpha 1C subunit of the L-type voltage-gated calcium channel; combined P = 7.0 × 10-8, rs1006737). Our results suggest that ion channelopathies may be involved in the pathogenesis of bipolar disorder.

With advances in next-generation sequencing technologies, numerous novel transcripts in a large number of organisms have been identified. With the goal of fast, accurate assessment of the coding ability of RNA transcripts, we upgraded the coding potential calculator CPC1 to CPC2. CPC2 runs ∼1000 times faster than CPC1 and exhibits superior accuracy compared with CPC1, especially for long non-coding transcripts. Moreover, the model of CPC2 is species-neutral, making it feasible for ever-growing non-model organism transcriptomes. A mobile-friendly web server, as well as a downloadable standalone package, is freely available at http://cpc2.cbi.pku.edu.cn.

With the goal of charting plant transcriptional regulatory maps (i.e. transcription factors (TFs), cis-elements and interactions between them), we have upgraded the TF-centred database PlantTFDB (http://planttfdb.cbi.pku.edu.cn/) to a plant regulatory data and analysis platform PlantRegMap (http://plantregmap.cbi.pku.edu.cn/) over the past three years. In this version, we updated the annotations for the previously collected TFs and set up a new section, ‘extended TF repertoires’ (TFext), to allow users prompt access to the TF repertoires of newly sequenced species. In addition to our regular TF updates, we are dedicated to updating the data on cis-elements and functional interactions between TFs and cis-elements. We established genome-wide conservation landscapes for 63 representative plants and then developed an algorithm, FunTFBS, to screen for functional regulatory elements and interactions by coupling the base-varied binding affinities of TFs with the evolutionary footprints on their binding sites. Using the FunTFBS algorithm and the conservation landscapes, we further identified over 20 million functional TF binding sites (TFBSs) and two million functional interactions for 21 346 TFs, charting the functional regulatory maps of these 63 plants. These resources are publicly available at PlantRegMap (http://plantregmap.cbi.pku.edu.cn/) and a cloud-based mirror (http://plantregmap.gao-lab.org/), providing the plant research community with valuable resources for decoding plant transcriptional regulatory systems.

BackgroundAt present, the severity of patients infected with severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) has been a focal point.MethodsTo assess the factors associated with severity and prognosis of patients infected with SARS‐CoV‐2, we retrospectively investigated the clinical, imaging and laboratory characteristics of confirmed 280 cases of novel coronavirus disease (COVID‐19) from 20 January to 20 February 2020.ResultsThe median age of patients in the mild group was 37.55 years, whilst that in the severe group was 63.04 years. The proportion of patients aged over 65 years in the severe group was significantly higher than that of the mild group (59.04% vs. 10.15%, P < 0.05). 85.54% of severe patients had diabetes or cardiovascular diseases, which was significantly higher than that of the mild group (51.81% vs. 7.11%, P = 0.025; 33.73% vs. 3.05%, P = 0.042). Patients in the mild group experienced earlier initiation of antiviral treatment (1.19 ± 0.45 vs. 2.65 ± 1.06 days in the severe group, P < 0.001). Our study showed that comorbidity, time from illness onset to antiviral treatment and age >=65 were three major risk factors for COVID‐19 progression, whilst comorbidity and time from illness onset to antiviral treatment were two major risk factors for COVID‐19 recovery.ConclusionsThe elderly and patients with underlying diseases are more likely to experience a severe progression of COVID‐19. It is recommended that timely antiviral treatment should be initiated to slow the disease progression and improve the prognosis.