Genome-wide association studies (GWAS) have identified genetic variants at 34 loci contributing to age-related macular degeneration (AMD)1-3. We generated transcriptional profiles of postmortem retinas from 453 controls and cases at distinct stages of AMD and integrated retinal transcriptomes, covering 13,662 protein-coding and 1,462 noncoding genes, with genotypes at more than 9 million common SNPs for expression quantitative trait loci (eQTL) analysis of a tissue not included in Genotype-Tissue Expression (GTEx) and other large datasets4,5. Cis-eQTL analysis identified 10,474 genes under genetic regulation, including 4,541 eQTLs detected only in the retina. Integrated analysis of AMD-GWAS with eQTLs ascertained likely target genes at six reported loci. Using transcriptome-wide association analysis (TWAS), we identified three additional genes, RLBP1, HIC1 and PARP12, after Bonferroni correction. Our studies expand the genetic landscape of AMD and establish the Eye Genotype Expression (EyeGEx) database as a resource for post-GWAS interpretation of multifactorial ocular traits.

Hepatocellular carcinoma (HCC) is one of the most common malignancy tumors with insidious onset, rapid development and metastasis, and poor prognosis. Therefore, it is necessary to understand molecular mechanisms of HCC and identify clinically useful biomarkers for it. This study aimed to investigate the role of retinoblastoma binding protein 5 (RBBP5) in HCC. The expression level of RBBP5 was examined by immunohistochemistry and western blot. The effect of RBBP5 on cell cycle, proliferation, apoptosis, and drug sensitivity was analyzed. RBBP5 was significantly upregulated in HCC tissues and cells. High RBBP5 expression was significantly associated with elevated level of AFP, advanced TNM stage, high Ki-67 expression, larger tumor size, and poor prognosis. Knockdown of RBBP5 significantly inhibited proliferation of HCC cells through cell cycle arrest. In addition, inhibition of RBBP5 increased the sensitivity of HCC cells to doxorubicin. In conclusion, our findings suggest that RBBP5 plays an important role in the progression of HCC and may serve as a novel biomarker and potential therapeutic target for HCC.

To describe cumulative radiation exposure in a large single-center cohort of children with congenital heart disease (CHD) and identify risk factors for greater exposure.

RNA sequencing (RNA-seq) enables characterization and quantification of individual transcriptomes as well as detection of patterns of allelic expression and alternative splicing. Current RNA-seq protocols depend on high-throughput short-read sequencing of cDNA. However, as ongoing advances are rapidly yielding increasing read lengths, a technical hurdle remains in identifying the degree to which differences in read length influence various transcriptome analyses. In this study, we generated two paired-end RNA-seq datasets of differing read lengths (2×75 bp and 2×262 bp) for lymphoblastoid cell line GM12878 and compared the effect of read length on transcriptome analyses, including read-mapping performance, gene and transcript quantification, and detection of allele-specific expression (ASE) and allele-specific alternative splicing (ASAS) patterns. Our results indicate that, while the current long-read protocol is considerably more expensive than short-read sequencing, there are important benefits that can only be achieved with longer read length, including lower mapping bias and reduced ambiguity in assigning reads to genomic elements, such as mRNA transcript. We show that these benefits ultimately lead to improved detection of cis-acting regulatory and splicing variation effects within individuals.

Understanding the consequences of regulatory variation in the human genome remains a major challenge, with important implications for understanding gene regulation and interpreting the many disease-risk variants that fall outside of protein-coding regions. Here, we provide a direct window into the regulatory consequences of genetic variation by sequencing RNA from 922 genotyped individuals. We present a comprehensive description of the distribution of regulatory variation--by the specific expression phenotypes altered, the properties of affected genes, and the genomic characteristics of regulatory variants. We detect variants influencing expression of over ten thousand genes, and through the enhanced resolution offered by RNA-sequencing, for the first time we identify thousands of variants associated with specific phenotypes including splicing and allelic expression. Evaluating the effects of both long-range intra-chromosomal and trans (cross-chromosomal) regulation, we observe modularity in the regulatory network, with three-dimensional chromosomal configuration playing a particular role in regulatory modules within each chromosome. We also observe a significant depletion of regulatory variants affecting central and critical genes, along with a trend of reduced effect sizes as variant frequency increases, providing evidence that purifying selection and buffering have limited the deleterious impact of regulatory variation on the cell. Further, generalizing beyond observed variants, we have analyzed the genomic properties of variants associated with expression and splicing and developed a Bayesian model to predict regulatory consequences of genetic variants, applicable to the interpretation of individual genomes and disease studies. Together, these results represent a critical step toward characterizing the complete landscape of human regulatory variation.

Synthesis of star-shaped block copolymer with oxalyl chloride and preparation of micelles to assess the prospect for drug-carrier applications.

Structural variants (SVs) are an important source of human genetic diversity, but their contribution to traits, disease and gene regulation remains unclear. We mapped cis expression quantitative trait loci (eQTLs) in 13 tissues via joint analysis of SVs, single-nucleotide variants (SNVs) and short insertion/deletion (indel) variants from deep whole-genome sequencing (WGS). We estimated that SVs are causal at 3.5-6.8% of eQTLs-a substantially higher fraction than prior estimates-and that expression-altering SVs have larger effect sizes than do SNVs and indels. We identified 789 putative causal SVs predicted to directly alter gene expression: most (88.3%) were noncoding variants enriched at enhancers and other regulatory elements, and 52 were linked to genome-wide association study loci. We observed a notable abundance of rare high-impact SVs associated with aberrant expression of nearby genes. These results suggest that comprehensive WGS-based SV analyses will increase the power of common- and rare-variant association studies.

Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of disease.

High-throughput quantitative genetic interaction (GI) measurements provide detailed information regarding the structure of the underlying biological pathways by reporting on functional dependencies between genes. However, the analytical tools for fully exploiting such information lag behind the ability to collect these data. We present a novel Bayesian learning method that uses quantitative phenotypes of double knockout organisms to automatically reconstruct detailed pathway structures. We applied our method to a recent data set that measures GIs for endoplasmic reticulum (ER) genes, using the unfolded protein response as a quantitative phenotype. The results provided reconstructions of known functional pathways including N-linked glycosylation and ER-associated protein degradation. It also contained novel relationships, such as the placement of SGT2 in the tail-anchored biogenesis pathway, a finding that we experimentally validated. Our approach should be readily applicable to the next generation of quantitative GI data sets, as assays become available for additional phenotypes and eventually higher-level organisms.

Genetic studies of complex traits have mainly identified associations with noncoding variants. To further determine the contribution of regulatory variation, we combined whole-genome and transcriptome data for 624 individuals from Sardinia to identify common and rare variants that influence gene expression and splicing. We identified 21,183 expression quantitative trait loci (eQTLs) and 6,768 splicing quantitative trait loci (sQTLs), including 619 new QTLs. We identified high-frequency QTLs and found evidence of selection near genes involved in malarial resistance and increased multiple sclerosis risk, reflecting the epidemiological history of Sardinia. Using family relationships, we identified 809 segregating expression outliers (median z score of 2.97), averaging 13.3 genes per individual. Outlier genes were enriched for proximal rare variants, providing a new approach to study large-effect regulatory variants and their relevance to traits. Our results provide insight into the effects of regulatory variants and their relationship to population history and individual genetic risk.

Compared with paclitaxel, sirolimus has been more used in the treatment of vascular restenosis gradually as an anti-proliferative drug, but few basic studies have elucidated its mechanism. The anti-proliferative effects of sirolimus or paclitaxel have been demonstrated by numerous studies under normoxia, but few studies have been achieved focusing hypoxia. In this study, porcine carotid artery injury model and classical cobalt chloride hypoxia cell model were established. Sirolimus nanoparticles (SRM-NPs), paclitaxel nanoparticles (PTX-NPs) and blank nanoparticles (Blank-NPs) were prepared respectively. The effect of RPM-NPs on the degree of stenosis, proliferative index and the expression of PCNA after 28 days of porcine carotid artery injury model was evaluated. Compared with saline group and SRM groups, SRM-NPs group suppressed vascular stenosis, proliferative index and the expression of PCNA (P < 0.01 and P < 0.05). Endothelial cell (EC) and smooth muscle cell (SMC) were pre-treated with cobaltous chloride, followed by SRM-NPs, PTX-NPs, Blank-NPs or PBS control treating, the effects on cell proliferation, HIF-1 expression and glycolysis were detected. SRM-NPs could inhibit EC and SMC proliferation under hypoxia, while PTX-NPs couldn't (P < 0.001). Significant differences between sirolimus and paclitaxel NPs in anti-proliferation effect under normoxia and hypoxia may due to the different inhibitory effects on HIF-1α expression and glycolysis. In conclusion, these results suggest that sirolimus can inhibit the proliferation of hypoxic cells more effectively than paclitaxel. These observations may provide a basis for understanding clinical vascular stenosis therapeutic differences between rapamycin and paclitaxel.

Neutrophil-platelet interactions are responsible for thrombosis as well as inflammatory responses following acute myocardial infarction (AMI). While histamine has been shown to play a crucial role in many physiological and pathological processes, its effects on neutrophil-platelet interactions in thromboinflammatory complications of AMI remain elusive. In this study, we show a previously unknown mechanism by which neutrophil-derived histamine protects the infarcted heart from excessive neutrophil-platelet interactions and redundant arterial thrombosis. Using histamine-deficient (histidine decarboxylase knockout, HDC-/- ) and wild-type murine AMI models, we demonstrate that histamine deficiency increases the number of microthrombosis after AMI, in accordance with depressed cardiac function. Histamine-producing myeloid cells, mainly Ly6G+ neutrophils, directly participate in arteriole thrombosis. Histamine deficiency elevates platelet activation and aggregation by enhancing Akt phosphorylation and leads to dysfunctional characteristics in neutrophils which was confirmed by high levels of reactive oxygen species production and CD11b expression. Furthermore, HDC-/- platelets were shown to elicit neutrophil extracellular nucleosomes release, provoke neutrophil-platelet interactions and promote HDC-expressing neutrophils recruitment in arteriole thrombosis in vivo. In conclusion, we provide evidence that histamine deficiency promotes coronary microthrombosis and deteriorates cardiac function post-AMI, which is associated with the enhanced platelets/neutrophils function and neutrophil-platelet interactions.

Trait-associated genetic variants affect complex phenotypes primarily via regulatory mechanisms on the transcriptome. To investigate the genetics of gene expression, we performed cis- and trans-expression quantitative trait locus (eQTL) analyses using blood-derived expression from 31,684 individuals through the eQTLGen Consortium. We detected cis-eQTL for 88% of genes, and these were replicable in numerous tissues. Distal trans-eQTL (detected for 37% of 10,317 trait-associated variants tested) showed lower replication rates, partially due to low replication power and confounding by cell type composition. However, replication analyses in single-cell RNA-seq data prioritized intracellular trans-eQTL. Trans-eQTL exerted their effects via several mechanisms, primarily through regulation by transcription factors. Expression of 13% of the genes correlated with polygenic scores for 1,263 phenotypes, pinpointing potential drivers for those traits. In summary, this work represents a large eQTL resource, and its results serve as a starting point for in-depth interpretation of complex phenotypes.

Practically all studies of gene expression in humans to date have been performed in a relatively small number of adult tissues. Gene regulation is highly dynamic and context-dependent. In order to better understand the connection between gene regulation and complex phenotypes, including disease, we need to be able to study gene expression in more cell types, tissues, and states that are relevant to human phenotypes. In particular, we need to characterize gene expression in early development cell types, as mutations that affect developmental processes may be of particular relevance to complex traits. To address this challenge, we propose to use embryoid bodies (EBs), which are organoids that contain a multitude of cell types in dynamic states. EBs provide a system in which one can study dynamic regulatory processes at an unprecedentedly high resolution. To explore the utility of EBs, we systematically explored cellular and gene expression heterogeneity in EBs from multiple individuals. We characterized the various cell types that arise from EBs, the extent to which they recapitulate gene expression in vivo, and the relative contribution of technical and biological factors to variability in gene expression, cell composition, and differentiation efficiency. Our results highlight the utility of EBs as a new model system for mapping dynamic inter-individual regulatory differences in a large variety of cell types.

Hereditary spastic paraplegia (HSP) represents a group of rare inherited neurodegenerative conditions and is characterized by progressive lower limb spasticity. Ubiquitin-associated protein 1 (UBAP1)-related HSP is classified as spastic paraplegia-80 (SPG80), which is an autosomal-dominant (AD) juvenile-onset neurologic disorder and mainly affects the lower limbs. We described the clinical and genetic features of two patients in the same family caused by heterozygous mutation of the UBAP1 gene. The proband was a 34-year-old woman with progressive spasticity and hyperreflexia in the lower limbs for 26 years. Her mother also had similar symptoms since the age of 6. The proband and her mother only had motor dysfunctions, such as unsteady gait, hypertonia, and hyperreflexia of lower limbs. Other system functions (sensory, urinary, visual, and cognitive impairments) were not involved. WES disclosed a frameshift mutation (c.371dupT) in the UBAP1 gene, which was predicted to be "likely pathogenic" and was co-segregated in the pedigree. c.371dupT, encoding the truncated UBAP1 protein with 72.6% missing of the normal amino acid sequence, is responsible for the spastic paraplegia (SPG) in this family. In combination with clinical characteristics, genetic testing results, and co-segregation analysis, the diagnosis is considered to be pure spastic paraplegia-80 (SPG80), which is an AD disease. By retrospectively analyzing the documented cases, we comprehensively review the phenotypic features and summarize the genotype spectrum of SPG80 to enhance earlier recognition and therapeutic strategies.

The availability of long reads is revolutionizing studies of structural variants (SVs). However, because SVs vary across individuals and are discovered through imprecise read technologies and methods, they can be difficult to compare. Addressing this, we present Jasmine and Iris ( https://github.com/mkirsche/Jasmine/ ), for fast and accurate SV refinement, comparison and population analysis. Using an SV proximity graph, Jasmine outperforms six widely used comparison methods, including reducing the rate of Mendelian discordance in trio datasets by more than fivefold, and reveals a set of high-confidence de novo SVs confirmed by multiple technologies. We also present a unified callset of 122,813 SVs and 82,379 indels from 31 samples of diverse ancestry sequenced with long reads. We genotype these variants in 1,317 samples from the 1000 Genomes Project and the Genotype-Tissue Expression project with DNA and RNA-sequencing data and assess their widespread impact on gene expression, including within medically relevant genes.

Gene co-expression networks capture biological relationships between genes and are important tools in predicting gene function and understanding disease mechanisms. We show that technical and biological artifacts in gene expression data confound commonly used network reconstruction algorithms. We demonstrate theoretically, in simulation, and empirically, that principal component correction of gene expression measurements prior to network inference can reduce false discoveries. Using data from the GTEx project in multiple tissues, we show that this approach reduces false discoveries beyond correcting only for known confounders.

It is estimated that 350 million individuals worldwide suffer from rare diseases, which are predominantly caused by mutation in a single gene1. The current molecular diagnostic rate is estimated at 50%, with whole-exome sequencing (WES) among the most successful approaches2-5. For patients in whom WES is uninformative, RNA sequencing (RNA-seq) has shown diagnostic utility in specific tissues and diseases6-8. This includes muscle biopsies from patients with undiagnosed rare muscle disorders6,9, and cultured fibroblasts from patients with mitochondrial disorders7. However, for many individuals, biopsies are not performed for clinical care, and tissues are difficult to access. We sought to assess the utility of RNA-seq from blood as a diagnostic tool for rare diseases of different pathophysiologies. We generated whole-blood RNA-seq from 94 individuals with undiagnosed rare diseases spanning 16 diverse disease categories. We developed a robust approach to compare data from these individuals with large sets of RNA-seq data for controls (n = 1,594 unrelated controls and n = 49 family members) and demonstrated the impacts of expression, splicing, gene and variant filtering strategies on disease gene identification. Across our cohort, we observed that RNA-seq yields a 7.5% diagnostic rate, and an additional 16.7% with improved candidate gene resolution.

RNA editing plays a key role in organelle gene expression. Little is known about how RNA editing factors influence soybean plant development. Here, we report the isolation and characterization of a soybean yl (yellow leaf) mutant. The yl plants showed decreased chlorophyll accumulation, lower PS II activity, an impaired net photosynthesis rate, and an altered chloroplast ultrastructure. Fine mapping of YL uncovered a point mutation in Glyma.20G187000, which encodes a chloroplast-localized protein homologous to Arabidopsis thaliana (Arabidopsis) ORRM1. YL is mainly expressed in trifoliate leaves, and its deficiency affects the editing of multiple chloroplast RNA sites, leading to inferior photosynthesis in soybean. Taken together, these results demonstrate the importance of the soybean YL protein in chloroplast RNA editing and photosynthesis.

Large scale genetic association studies have identified many trait-associated variants and understanding the role of these variants in the downstream regulation of gene-expressions can uncover important mediating biological mechanisms. Here we propose ARCHIE, a summary statistic based sparse canonical correlation analysis method to identify sets of gene-expressions trans-regulated by sets of known trait-related genetic variants. Simulation studies show that compared to standard methods, ARCHIE is better suited to identify "core"-like genes through which effects of many other genes may be mediated and can capture disease-specific patterns of genetic associations. By applying ARCHIE to publicly available summary statistics from the eQTLGen consortium, we identify gene sets which have significant evidence of trans-association with groups of known genetic variants across 29 complex traits. Around half (50.7%) of the selected genes do not have any strong trans-associations and are not detected by standard methods. We provide further evidence for causal basis of the target genes through a series of follow-up analyses. These results show ARCHIE is a powerful tool for identifying sets of genes whose trans-regulation may be related to specific complex traits.