Non-coding Sequence Variants Research Articles

Single-cell eQTL mapping in yeast reveals a tradeoff between growth and reproduction.

Expression quantitative trait loci (eQTLs) provide a key bridge between noncoding DNA sequence variants and organismal traits. The effects of eQTLs can differ among tissues, cell types, and cellular states, but these differences are obscured by gene expression measurements in bulk populations. We developed a one-pot approach to map eQTLs in Saccharomyces cerevisiae by single-cell RNA sequencing (scRNA-seq) and applied it to over 100,000 single cells from three crosses. We used scRNA-seq data to genotype each cell, measure gene expression, and classify the cells by cell-cycle stage. We mapped thousands of local and distant eQTLs and identified interactions between eQTL effects and cell-cycle stages. We took advantage of single-cell expression information to identify hundreds of genes with allele-specific effects on expression noise. We used cell-cycle stage classification to map 20 loci that influence cell-cycle progression. One of these loci influenced the expression of genes involved in the mating response. We showed that the effects of this locus arise from a common variant (W82R) in the gene GPA1 , which encodes a signaling protein that negatively regulates the mating pathway. The 82R allele increases mating efficiency at the cost of slower cell-cycle progression and is associated with a higher rate of outcrossing in nature. Our results provide a more granular picture of the effects of genetic variants on gene expression and downstream traits.

HiChIP-Based Epigenomic Footprinting Identifies a Promoter Variant of UXS1 That Confers Genetic Susceptibility to Gastroesophageal Cancer.

Genome-wide association studies (GWAS) have identified more than a hundred single nucleotide variants (SNV) associated with the risk of gastroesophageal cancer (GEC). The majority of the identified SNVs map to noncoding regions of the genome. Uncovering the causal SNVs and genes they modulate could help improve GEC prevention and treatment. Herein, we used HiChIP against histone 3 lysine 27 acetylation (H3K27ac) to simultaneously annotate active promoters and enhancers, identify the interactions between them, and detect nucleosome-free regions (NFR) harboring potential causal SNVs in a single assay. The application of H3K27ac HiChIP in GEC relevant models identified 61 potential functional SNVs that reside in NFRs and interact with 49 genes at 17 loci. The approach led to a 67% reduction in the number of SNVs in linkage disequilibrium at these 17 loci, and at 7 loci, a single putative causal SNV was identified. One SNV, rs147518036, located within the promoter of the UDP-glucuronate decarboxylase 1 (UXS1) gene, seemed to underlie the GEC risk association captured by the rs75460256 index SNV. The rs147518036 SNV creates a GABPA DNA recognition motif, resulting in increased promoter activity, and CRISPR-mediated inhibition of the UXS1 promoter reduced the viability of the GEC cells. These findings provide a framework that simplifies the identification of potentially functional regulatory SNVs and target genes underlying risk-associated loci. In addition, the study implicates increased expression of the enzyme UXS1 and activation of its metabolic pathway as a predisposition to gastric cancer, which highlights potential therapeutic avenues to treat this disease. Significance: Epigenomic footprinting using a histone posttranslational modification targeted 3D genomics methodology elucidates functional noncoding sequence variants and their target genes at cancer risk loci.

Unraveling noncoding DNA variants and epimutations: a paradigm shift in hereditary cancer research.

Exhaustive efforts have been dedicated to uncovering genomic aberrations linked to cancer susceptibility. Noncoding sequence variants and epigenetic alterations significantly influence gene regulation and could contribute to cancer development. However, exploring noncoding regions in hereditary cancer susceptibility demands cutting-edge methodologies for functionally characterizing genomic discoveries. Additionally, comprehending the impact on cancer development of variants in noncoding DNA and the epigenome necessitates integrating diverse data through bioinformatic analyses. As novel technologies and analytical methods continue to advance, this realm of research is rapidly gaining traction. Within this mini-review, we delve into future research domains concerning aberrations in noncoding DNA regions, such as pseudoexons, promoter variants and cis-epimutations.

Genome-wide patterns of noncoding and protein-coding sequence variation in the major fungal pathogen Aspergillus fumigatus.

Aspergillus fumigatus is a deadly fungal pathogen, responsible for >400,000 infections/year and high mortality rates. A. fumigatus strains exhibit variation in infection-relevant traits, including in their virulence. However, most A. fumigatus protein-coding genes, including those that modulate its virulence, are shared between A. fumigatus strains and closely related nonpathogenic relatives. We hypothesized that A. fumigatus genes exhibit substantial genetic variation in the noncoding regions immediately upstream to the start codons of genes, which could reflect differences in gene regulation between strains. To begin testing this hypothesis, we identified 5,812 single-copy orthologs across the genomes of 263 A. fumigatus strains. In general, A. fumigatus noncoding regions showed higher levels of sequence variation compared with their corresponding protein-coding regions. Focusing on 2,482 genes whose protein-coding sequence identity scores ranged between 75 and 99%, we identified 478 total genes with signatures of positive selection only in their noncoding regions and 65 total genes with signatures only in their protein-coding regions. Twenty-eight of the 478 noncoding regions and 5 of the 65 protein-coding regions under selection are associated with genes known to modulate A. fumigatus virulence. Noncoding region variation between A. fumigatus strains included single-nucleotide polymorphisms and insertions or deletions of at least a few nucleotides. These results show that noncoding regions of A. fumigatus genes harbor greater sequence variation than protein-coding regions, raising the hypothesis that this variation may contribute to A. fumigatus phenotypic heterogeneity.

Intergenic sequences harboring potential enhancer elements contribute to Axenfeld-Rieger syndrome by regulating PITX2.

Recent studies have uncovered that noncoding sequence variants may relate to Axenfeld-Rieger syndrome (ARS), a rare developmental anomaly with genetic heterogeneity. However, how these genomic regions are functionally and structurally associated with ARS is still unclear. In this study, we performed genome-wide linkage analysis and whole-genome sequencing in a Chinese family with ARS and identified a heterozygous deletion of about 570 kb (termed LOH-1) in the intergenic sequence between paired-like homeodomain transcription factor 2 (PITX2) and family with sequence similarity 241 member A. Knockout of LOH-1 homologous sequences caused ARS phenotypes in mice. RNA-Seq and real-time quantitative PCR revealed a significant reduction in Pitx2 gene expression in LOH-1-/- mice, while forkhead box C1 expression remained unchanged. ChIP-Seq and bioinformatics analysis identified a potential enhancer region (LOH-E1) within LOH-1. Deletion of LOH-E1 led to a substantial downregulation of the PITX2 gene. Mechanistically, we found a sequence (hg38 chr4:111,399,594-111,399,691) that is on LOH-E1 could regulate PITX2 by binding to RAD21, a critical component of the cohesin complex. Knockdown of RAD21 resulted in reduced PITX2 expression. Collectively, our findings indicate that a potential enhancer sequence that is within LOH-1 may regulate PITX2 expression remotely through cohesin-mediated loop domains, leading to ARS when absent.

Escherichia coli non-coding regulatory regions are highly conserved.

Microbial genome sequences are rapidly accumulating, enabling large-scale studies of sequence variation. Existing studies primarily focus on coding regions to study amino acid substitution patterns in proteins. However, non-coding regulatory regions also play a distinct role in determining physiologic responses. To investigate intergenic sequence variation on a large-scale, we identified non-coding regulatory region alleles across 2350 Escherichia coli strains. This 'alleleome' consists of 117781 unique alleles for 1169 reference regulatory regions (transcribing 1975 genes) at single base-pair resolution. We find that 64% of nucleotide positions are invariant, and variant positions vary in a median of just 0.6% of strains. Additionally, non-coding alleles are sufficient to recover E. coli phylogroups. We find that core promoter elements and transcription factor binding sites are significantly conserved, especially those located upstream of essential or highly-expressed genes. However, variability in conservation of transcription factor binding sites is significant both within and across regulons. Finally, we contrast mutations acquired during adaptive laboratory evolution with wild-type variation, finding that the former preferentially alter positions that the latter conserves. Overall, this analysis elucidates the wealth of information found in E. coli non-coding sequence variation and expands pangenomic studies to non-coding regulatory regions at single-nucleotide resolution.

Review: Genetic and protein variants of milk caseins in goats.

The milk casein genes in goats, are highly polymorphic genes with numerous synonymous and non-synonymous mutations. So far, 20 protein variants have been reported in goats for alpha-S1-casein, eight for beta-casein, 14 for alpha-S2-casein, and 24 for kappa-casein. This review provides a comprehensive overview on identified milk casein protein variants in goat and non-coding DNA sequence variants with some affecting the expression of the casein genes. The high frequency of some casein protein variants in different goat breeds and geographical regions might reflect specific breeding goals with respect to milk processing characteristics, properties for human nutrition and health, or adaptation to the environment. Because protein names, alongside the discovery of protein variants, go through a historical process, we linked old protein names with new ones that reveal more genetic variability. The haplotypes across the cluster of the four genetically linked casein genes are recommended as a valuable genetic tool for discrimination between breeds, managing genetic diversity within and between goat populations, and breeding strategies. The enormous variation in the casein proteins and genes is crucial for producing milk and dairy products with different properties for human health and nutrition, and for genetic improvement depending on local breeding goals.

Epigenetic Fine Mapping and Functional Dissection of Inherited Non-Coding Variation Impacting the Pharmacogenomics of Acute Lymphoblastic Leukemia Treatment

Acute lymphoblastic leukemia (ALL) is the most prevalent childhood cancer. Despite cure rates exceeding 90%, relapsed ALL is the 5th most common pediatric cancer. Although chemotherapeutic drug resistance remains the primary cause of treatment failure and relapse, it remains unclear to what extent non-coding inherited DNA sequence variants impact gene regulatory programs that contribute to differences in chemotherapeutic drug resistance and clinical outcome in childhood ALL. To address this knowledge gap we performed epigenetic fine mapping on inherited non-coding DNA sequence variants associated with ex vivo resistance to six antileukemic agents (dexamethasone, prednisolone, l-asparaginase, vincristine, 6-mercaptopurine and 6-thioguanine) in primary ALL cells from pediatric patients and inherited non-coding DNA sequence variants associated with clinical outcome (persistence of minimal residual disease and relapse) in pediatric patient cohorts using chromatin accessibility data from over 130 primary ALL cells, ALL cell lines and ALL xenografts. This analysis fine mapped over 1700 variants (sentinel variants and variants in high linkage disequilibrium; r2>0.8) to cis-regulatory elements in the ALL genome. To comprehensively dissect the functional effects of these fine mapped variants on gene regulatory activity we performed massively parallel reporter assays (MPRAs) in 10 human ALL cell lines of both B- and T-cell lineages. MPRAs utilized 23 DNA barcodes per variant allele and tested variants at bidirectional promoters using both sequence orientations. In total, 98,168 oligonucleotides each containing 175-bp of genomic sequence centered on each variant allele and a unique 10-bp barcode sequence were evaluated for gene regulatory activity. MPRA identified 558 variants with significant and concordant allele-specific activities in three or more ALL cell lines. Expression quantitative trait loci (eQTL) mapping and HiChIP three-dimensional promoter loop profiling identified candidate target genes for a subset of functional variants. We additionally evaluated the functional effects of top ranked variants using luciferase reporter assays and CRISPR/Cas9 genome editing. These variants were associated with the expression of B3GNT5, CACUL1, HERC1, KDM4C and VRK3. Notably, two of these functional variants (rs10411204 and rs1247117) impacted transcription factor (TF) consensus DNA motifs for AP-1 family TFs and PU.1. Collectively, these data suggest that many inherited non-coding variants associated with pharmacogenomic traits in childhood ALL have significant gene regulatory effects. Importantly, the identification of chemotherapeutic drug resistance-associated molecular pathways perturbed by inherited non-coding variants can improve our ability to circumvent drug resistance with novel therapeutic approaches.

Glucocorticoids unmask silent non-coding genetic risk variants for common diseases.

Understanding the function of non-coding genomic sequence variants represents a challenge for biomedicine. Many diseases are products of gene-by-environment interactions with complex mechanisms. This study addresses these themes by mechanistic characterization of non-coding variants that influence gene expression only after drug or hormone exposure. Using glucocorticoid signaling as a model system, we integrated genomic, transcriptomic, and epigenomic approaches to unravel mechanisms by which variant function could be revealed by hormones or drugs. Specifically, we identified cis-regulatory elements and 3D interactions underlying ligand-dependent associations between variants and gene expression. One-quarter of the glucocorticoid-modulated variants that we identified had already been associated with clinical phenotypes. However, their affected genes were 'unmasked' only after glucocorticoid exposure and often with function relevant to the disease phenotypes. These diseases involved glucocorticoids as risk factors or therapeutic agents and included autoimmunity, metabolic and mood disorders, osteoporosis and cancer. For example, we identified a novel breast cancer risk gene, MAST4, with expression that was repressed by glucocorticoids in cells carrying the risk genotype, repression that correlated with MAST4 expression in breast cancer and treatment outcomes. These observations provide a mechanistic framework for understanding non-coding genetic variant-chemical environment interactions and their role in disease risk and drug response.

Extensive non-coding sequence divergence between the major human pathogen Aspergillus fumigatus and its relatives.

Invasive aspergillosis is a deadly fungal disease; more than 400,000 patients are infected worldwide each year and the mortality rate can be as high as 50-95%. Of the ~450 species in the genus Aspergillus only a few are known to be clinically relevant, with the major pathogen Aspergillus fumigatus being responsible for ~50% of all invasive mold infections. Genomic comparisons between A. fumigatus and other Aspergillus species have historically focused on protein-coding regions. However, most A. fumigatus genes, including those that modulate its virulence, are also present in other pathogenic and non-pathogenic closely related species. Our hypothesis is that differential gene regulation - mediated through the non-coding regions upstream of genes' first codon - contributes to A. fumigatus pathogenicity. To begin testing this, we compared non-coding regions upstream of the first codon of single-copy orthologous genes from the two A. fumigatus reference strains Af293 and A1163 and eight closely related Aspergillus section Fumigati species. We found that these non-coding regions showed extensive sequence variation and lack of homology across species. By examining the evolutionary rates of both protein-coding and non-coding regions in a subset of orthologous genes with highly conserved non-coding regions across the phylogeny, we identified 418 genes, including 25 genes known to modulate A. fumigatus virulence, whose non-coding regions exhibit a different rate of evolution in A. fumigatus. Examination of sequence alignments of these non-coding regions revealed numerous instances of insertions, deletions, and other types of mutations of at least a few nucleotides in A. fumigatus compared to its close relatives. These results show that closely related Aspergillus species that vary greatly in their pathogenicity exhibit extensive non-coding sequence variation and identify numerous changes in non-coding regions of A. fumigatus genes known to contribute to virulence.

Machine Learning Prediction of Non-Coding Variant Impact in Human Retinal cis-Regulatory Elements.

PurposePrior studies have demonstrated the significance of specific cis-regulatory variants in retinal disease; however, determining the functional impact of regulatory variants remains a major challenge. In this study, we utilized a machine learning approach, trained on epigenomic data from the adult human retina, to systematically quantify the predicted impact of cis-regulatory variants.MethodsWe used human retinal DNA accessibility data (ATAC-seq) to determine a set of 18.9k high-confidence, putative cis-regulatory elements. Eighty percent of these elements were used to train a machine learning model utilizing a gapped k-mer support vector machine–based approach. In silico saturation mutagenesis and variant scoring was applied to predict the functional impact of all potential single nucleotide variants within cis-regulatory elements. Impact scores were tested in a 20% hold-out dataset and compared to allele population frequency, phylogenetic conservation, transcription factor (TF) binding motifs, and existing massively parallel reporter assay data.ResultsWe generated a model that distinguishes between human retinal regulatory elements and negative test sequences with 95% accuracy. Among a hold-out test set of 3.7k human retinal CREs, all possible single nucleotide variants were scored. Variants with negative impact scores correlated with higher phylogenetic conservation of the reference allele, disruption of predicted TF binding motifs, and massively parallel reporter expression.ConclusionsWe demonstrated the utility of human retinal epigenomic data to train a machine learning model for the purpose of predicting the impact of non-coding regulatory sequence variants. Our model accurately scored sequences and predicted putative transcription factor binding motifs. This approach has the potential to expedite the characterization of pathogenic non-coding sequence variants in the context of unexplained retinal disease.Translational RelevanceThis workflow and resulting dataset serve as a promising genomic tool to facilitate the clinical prioritization of functionally disruptive non-coding mutations in the retina.

Performance Comparison of Computational Methods for the Prediction of the Function and Pathogenicity of Non-coding Variants

Non-coding variants in the human genome significantly influence human traits and complex diseases via their regulation and modification effects. Hence, an increasing number of computational methods are developed to predict the effects of variants in human non-coding sequences. However, it is difficult for inexperienced users to select appropriate computational methods from dozens of available methods. To solve this issue, we assessed 12 performance metrics of 24 methods on four independent non-coding variant benchmark datasets: (1) rare germline variants from clinical relevant sequence variants (ClinVar), (2) rare somatic variants from Catalogue Of Somatic Mutations In Cancer (COSMIC), (3) common regulatory variants from curated expression quantitative trait locus (eQTL) data, and (4) disease-associated common variants from curated genome-wide association studies (GWAS). All 24 tested methods performed differently under various conditions, indicating varying strengths and weaknesses under different scenarios. Importantly, the performance of existing methods was acceptable for rare germline variants from ClinVar with the area under the receiver operating characteristic curve (AUROC) of 0.4481–0.8033 and poor for rare somatic variants from COSMIC (AUROC = 0.4984–0.7131), common regulatory variants from curated eQTL data (AUROC = 0.4837–0.6472), and disease-associated common variants from curated GWAS (AUROC = 0.4766–0.5188). We also compared the prediction performance of 24 methods for non-coding de novo mutations in autism spectrum disorder, and found that the combined annotation-dependent depletion (CADD) and context-dependent tolerance score (CDTS) methods showed better performance. Summarily, we assessed the performance of 24 computational methods under diverse scenarios, providing preliminary advice for proper tool selection and guiding the development of new techniques in interpreting non-coding variants.

Genetic Differentiation among Subspecies of Banksia nivea (Proteaceae) Associated with Expansion and Habitat Specialization

Subspecies are traditionally defined using phenotypic differences associated with different geographical areas. Yet patterns of morphological and genetic variation may not coincide and thereby fail to reflect species’ evolutionary history. The division of the shrub Banksia nivea Labill. into one widespread (B. nivea subsp. nivea) and two geographically localized subspecies (B. nivea subsp. uliginosa (A.S. George) A.R. Mast & K.R. Thiele and B. nivea subsp. Morangup (M. Pieroni 94/2)) in south-west Australia has been based mainly on variation in leaf shape and pistil length, although flowering time and habitat differences are also evident, and subsp. uliginosa occurs on a different substrate. To assess the genetic divergence of B. nivea subspecies, we genotyped representatives from each subspecies for nuclear microsatellite and non-coding chloroplast sequence variation. We used distance and parsimony-based methods to assess genetic relatedness. Patterns were consistent with the existing taxonomy of subsp. nivea and uliginosa but not subsp. Morangup. Phylogenetic analyses revealed evidence for a more recent divergence of subsp. uliginosa associated with expansion from dryer sandy soils into the winter-wet ironstone soils in the southwest of Western Australia, consistent with progressive long-term climatic drying. Nuclear microsatellites showed low to moderate diversity, high population differentiation overall, and genetic structuring of subspecies in different biogeographical areas. We propose this pattern reflects the predicted impact of a patchy distribution, small populations, and restrictions to gene flow driven by both distance and biogeographic differences in subspecies’ habitats.

Characterizing the Copy Number Variation of Non-Coding RNAs Reveals Potential Therapeutic Targets and Prognostic Markers of LUSC.

Lung squamous cell carcinoma (LUSC) has a poor clinical prognosis and a lack of available targeted therapies. Therefore, there is an urgent need to identify novel prognostic markers and therapeutic targets to assist in the diagnosis and treatment of LUSC. With the development of high-throughput sequencing technology, integrated analysis of multi-omics data will provide annotation of pathogenic non-coding variants and the role of non-coding sequence variants in cancers. Here, we integrated RNA-seq profiles and copy number variation (CNV) data to study the effects of non-coding variations on gene regulatory network. Furthermore, the 372 long non-coding RNAs (lncRNA) regulated by CNV were used as candidate genes, which could be used as biomarkers for clinical application. Nine lncRNAs including LINC00896, MCM8-AS1, LINC01251, LNX1-AS1, GPRC5D-AS1, CTD-2350J17.1, LINC01133, LINC01121, and AC073130.1 were recognized as prognostic markers for LUSC. By exploring the association of the prognosis-related lncRNAs (pr-lncRNAs) with immune cell infiltration, GPRC5D-AS1 and LINC01133 were highlighted as markers of the immunosuppressive microenvironment. Additionally, the cascade response of pr-lncRNA-CNV-mRNA-physiological functions was revealed. Taken together, the identification of prognostic markers and carcinogenic regulatory mechanisms will contribute to the individualized treatment for LUSC and promote the development of precision medicine.

Disease category-specific annotation of variants using an ensemble learning framework.

Understanding the impact of non-coding sequence variants on complex diseases is an essential problem. We present a novel ensemble learning framework-CASAVA, to predict genomic loci in terms of disease category-specific risk. Using disease-associated variants identified by GWAS as training data, and diverse sequencing-based genomics and epigenomics profiles as features, CASAVA provides risk prediction of 24 major categories of diseases throughout the human genome. Our studies showed that CASAVA scores at a genomic locus provide a reasonable prediction of the disease-specific and disease category-specific risk prediction for non-coding variants located within the locus. Taking MHC2TA and immune system diseases as an example, we demonstrate the potential of CASAVA in revealing variant-disease associations. A website (http://zhanglabtools.org/CASAVA) has been built to facilitate easily access to CASAVA scores.

Noncoding sequence variants define a novel regulatory element in the first intron of the N-acetylglutamate synthase gene.

N-acetylglutamate synthase deficiency is an autosomal recessive urea cycle disorder caused either by decreased expression of the NAGS gene or defective NAGS enzyme resulting in decreased production of N-acetylglutamate (NAG), an allosteric activator of carbamylphosphate synthetase 1 (CPS1). NAGSD is the only urea cycle disorder that can be effectively treated with a single drug, N-carbamylglutamate (NCG), a stable NAG analog, which activates CPS1 to restore ureagenesis. We describe three patients with NAGSD due to four novel noncoding sequence variants in the NAGS regulatory regions. All three patients had hyperammonemia that resolved upon treatment with NCG. Sequence variants NM_153006.2:c.427-222G>A and NM_153006.2:c.427-218A>C reside in the 547 bp-long first intron of NAGS and define a novel NAGS regulatory element that binds retinoic X receptor α. Sequence variants NC_000017.10:g.42078967A>T (NM_153006.2:c.-3065A>T) and NC_000017.10:g.42078934C>T (NM_153006.2:c.-3098C>T) reside in the NAGS enhancer, within known HNF1 and predicted glucocorticoid receptor binding sites, respectively. Reporter gene assays in HepG2 and HuH-7 cells demonstrated that all four substitutions could result in reduced expression of NAGS. These findings show that analyzing noncoding regions of NAGS and other urea cycle genes can reveal molecular causes of disease and identify novel regulators of ureagenesis.

SnapHiC: a computational pipeline to identify chromatin loops from single-cell Hi-C data

Single-cell Hi-C (scHi-C) analysis has been increasingly used to map chromatin architecture in diverse tissue contexts, but computational tools to define chromatin loops at high resolution from scHi-C data are still lacking. Here, we describe Single-Nucleus Analysis Pipeline for Hi-C (SnapHiC), a method that can identify chromatin loops at high resolution and accuracy from scHi-C data. Using scHi-C data from 742 mouse embryonic stem cells, we benchmark SnapHiC against a number of computational tools developed for mapping chromatin loops and interactions from bulk Hi-C. We further demonstrate its use by analyzing single-nucleus methyl-3C-seq data from 2,869 human prefrontal cortical cells, which uncovers cell type-specific chromatin loops and predicts putative target genes for noncoding sequence variants associated with neuropsychiatric disorders. Our results indicate that SnapHiC could facilitate the analysis of cell type-specific chromatin architecture and gene regulatory programs in complex tissues.

Prediction of disease-associated functional variants in noncoding regions through a comprehensive analysis by integrating datasets and features.

One of the greatest challenges in human genetics is deciphering the link between functional variants in noncoding sequences and the pathophysiology of complex diseases. To address this issue, many methods have been developed to sort functional single-nucleotide variants (SNVs) for neutral SNVs in noncoding regions. In this study, we integrated well-established features and commonly used datasets and merged them into large-scale datasets based on a random forest model, which yielded promising performance and outperformed some cutting-edge approaches. Our analyses of feature importance and data coverage also provide certain clues for future research in enhancing the prediction of functional noncoding SNVs.

Investigating the molecular guts of endoderm formation using zebrafish.

The vertebrate endoderm makes major contributions to the respiratory and gastrointestinal tracts and all associated organs. Zebrafish and humans share a high degree of genetic homology and strikingly similar endodermal organ systems. Combined with a multitude of experimental advantages, zebrafish are an attractive model organism to study endoderm development and disease. Recent functional genomics studies have shed considerable light on the gene regulatory programs governing early zebrafish endoderm development, while advances in biological and technological approaches stand to further revolutionize our ability to investigate endoderm formation, function and disease. Here, we discuss the present understanding of endoderm specification in zebrafish compared to other vertebrates, how current and emerging methods will allow refined and enhanced analysis of endoderm formation, and how integration with human data will allow modeling of the link between non-coding sequence variants and human disease.

Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program

The Trans-Omics for Precision Medicine (TOPMed) programme seeks to elucidate the genetic architecture and biology of heart, lung, blood and sleep disorders, with the ultimate goal of improving diagnosis, treatment and prevention of these diseases. The initial phases of the programme focused on whole-genome sequencing of individuals with rich phenotypic data and diverse backgrounds. Here we describe the TOPMed goals and design as well as the available resources and early insights obtained from the sequence data. The resources include a variant browser, a genotype imputation server, and genomic and phenotypic data that are available through dbGaP (Database of Genotypes and Phenotypes)1. In the first 53,831 TOPMed samples, we detected more than 400 million single-nucleotide and insertion or deletion variants after alignment with the reference genome. Additional previously undescribed variants were detected through assembly of unmapped reads and customized analysis in highly variable loci. Among the more than 400 million detected variants, 97% have frequencies of less than 1% and 46% are singletons that are present in only one individual (53% among unrelated individuals). These rare variants provide insights into mutational processes and recent human evolutionary history. The extensive catalogue of genetic variation in TOPMed studies provides unique opportunities for exploring the contributions of rare and noncoding sequence variants to phenotypic variation. Furthermore, combining TOPMed haplotypes with modern imputation methods improves the power and reach of genome-wide association studies to include variants down to a frequency of approximately 0.01%.