Large-scale Genome Sequencing Research Articles

A genetic history of migration, diversification, and admixture in Asia

L.L. Cavalli-Sforza spearheaded early efforts to study the genetic history of humans, recognizing the importance of sampling diverse populations worldwide. He supported research on human evolutionary genetics in Asia, with research on human dispersal into Asia and genetic distances between present-day East Asians in the late 20th century. Since then, great strides have been made in understanding the genetic history of humans in Asia, through large-scale genomic sequencing of present-day humans and targeted sequencing of DNA from ancient humans. In this review, I survey the genetic prehistory of humans in Asia, based on research using sequence data from humans who lived in Asia as early as 45,000 years ago. Genetic studies comparing present-day Australasians and Asians show that they likely derived from a single dispersal out of Africa, rapidly differentiating into three main lineages: one that persists partially in South Asia, one that is primarily found today in Australasia, and one that is widely represented across Siberia, East Asia, and Southeast Asia. Studies of ancient DNA from human remains in Asia dating from as far back as 45,000 years has greatly increased our understanding of the population dynamics leading to the current Asian populations. Based on "Jin L, Underhill PA, Doctor V, Davis RW, Shen P, Cavalli-Sforza LL, Oefner PJ. Distribution of haplotypes from a chromosome 21 region distinguishes multiple prehistoric human migrations. Proc Natl Acad Sci U S A. 1999;96(7):3796-3800”.

Pan-cancer assessment of mutational landscape in intrinsically disordered hotspots reveals potential driver genes.

Large-scale cancer genome sequencing has enabled the catalogs of somatic mutations; however, the mutational impact on intrinsically disordered protein regions (IDRs) has not been systematically investigated to date. Here, we comprehensively characterized the mutational landscapes of IDRs and found that IDRs have higher mutation frequencies across diverse cancers. We thus developed a computational method, ROI-Driver, to identify putative driver genes enriching IDR and domain hotspots in cancer. Numerous well-known cancer-related oncogenes or tumor suppressors that play important roles in cancer signaling regulation, development and immune response were identified at a higher resolution. In particular, the incorporation of IDR structures helps in the identification of novel potential driver genes that play central roles in human protein–protein interaction networks. Interestingly, we found that the putative driver genes with IDR hotspots were significantly enriched with predicted phase separation propensities, suggesting that IDR mutations disrupt phase separation in key cellular pathways. We also identified an appreciable number of clinically relevant genes enriching IDR mutational hotspots that exhibited differential expression patterns and are associated with cancer patient survival. In summary, combinations of mutational effects on IDRs significantly increase the sensitivity of driver detection and are likely to open new therapeutic avenues for various cancers.

Large-Scale Whole Genome Sequencing Study Reveals Genetic Architecture and Key Variants for Breast Muscle Weight in Native Chickens.

Breast muscle weight (BrW) is one of the most important economic traits in chicken, and directional breeding for that results in both phenotypic and genetic changes. The Jingxing yellow chicken, including an original (without human-driven selection) line and a selected line (based on selection for increased intramuscular fat content), were used to dissect the genetic architecture and key variants associated with BrW. We detected 1069 high-impact single nucleotide polymorphisms (SNPs) with high conserved score and significant frequency difference between two lines. Based on the annotation result, the ECM-receptor interaction and fatty acid biosynthesis were enriched, and muscle-related genes, including MYOD1, were detected. By performing genome-wide association study for the BrW trait, we defined a major haplotype and two conserved SNPs that affected BrW. By integrated genomic and transcriptomic analysis, IGF2BP1 was identified as the crucial gene associated with BrW. In conclusion, these results offer a new insight into chicken directional selection and provide target genetic markers by which to improve chicken BrW.

An accessible, efficient and global approach for the large-scale sequencing of bacterial genomes

We have developed an efficient and inexpensive pipeline for streamlining large-scale collection and genome sequencing of bacterial isolates. Evaluation of this method involved a worldwide research collaboration focused on the model organism Salmonella enterica, the 10KSG consortium. Following the optimization of a logistics pipeline that involved shipping isolates as thermolysates in ambient conditions, the project assembled a diverse collection of 10,419 isolates from low- and middle-income countries. The genomes were sequenced using the LITE pipeline for library construction, with a total reagent cost of less than USD$10 per genome. Our method can be applied to other large bacterial collections to underpin global collaborations.

Copy number variation (CNV) identification and association study on 3,928 Alzheimer's disease whole genome sequencing data from the Alzheimer's Disease Sequencing Project (ADSP).

Estimation of heritability of Alzheimer's Disease (AD) ranges from 49-79%, but the conventional single nucleotide variants (SNVs) identified to date account for <50% of the AD heritability. The involvement of genetic variants, such as SNVs and copy number variations (CNVs), has been proposed to explain part of the missing heritability. To accelerate AD genetic discovery, the Alzheimer's Disease Sequencing Project (ADSP) sequenced cases and cognitively normal elder controls from multiple ethnic groups. Leveraging this large-scale Whole Genome Sequence (WGS) collection, we could detect and conduct AD genetic analyses in full-spectrum of CNVs (small or large, common or rare, and coding or non-coding in genomic regions). We developed a bioinformatics pipeline consisting of four steps. First, we applied three calling algorithms, CNVnator, JAX-CNV, and Smoove, on each sample. Results of each tool were then merged by Svimmer. Second, we employed GraphTyper for genotyping. GraphTyper is a graph-pangenome-based method that may mitigate bias of human references. Finally, we resolved issues of multiple CNVs overlapping in a region by bedtools. Once the CNV callset was obtained, we conducted an association study to identify CNV regions that contribute to AD. We consider conventional association methods as well as a new developed CNV-curve-centric strategy which models CNVs as piecewise constant curves to assess the CNV effect on AD. For each sample, we identified an average of 7,959 CNVs by three algorithms. After merging CNVs of all samples, we obtained 56,316 deletions and 16,390 duplications for GraphTyper joint genotyping. The preliminary results of PLINK permutation-based test on the chromosome 19 indicated significant CNVs (p-value < 0.05 after adjusting for multiple tests) on genes UNC13A, IGSF23, KMT2B, LIN37, PSENEN, IGFLR1, and U2AF1L4. UNC13A, KMT2B, and PSENEN have been discovered to be associated in frontotemporal dementia, dystonia, and AD. Further validations of those CNVs are necessary. We developed a scalable CNV detection pipeline and applied it to 3,928 ADSP WGS samples. The preliminary results of the association study validate some known AD, dementia, and dystonia genes. To the best of our knowledge, this is the first large-scale CNV investigation of AD using WGS data.

Similar pattern, different paths: tracing the biogeographical history of Megaloptera (Insecta: Neuropterida) using mitochondrial phylogenomics.

The sequential breakup of the supercontinent Pangaea since the Middle Jurassic is one of the crucial factors that has driven the biogeographical patterns of terrestrial biotas. Despite decades of effort searching for concordant patterns between diversification and continental fragmentation among taxonomic groups, increasing evidence has revealed more complex and idiosyncratic scenarios resulting from a mixture of vicariance, dispersal and extinction. Aquatic insects with discreet ecological requirements, low vagility and disjunct distributions represent a valuable model for testing biogeographical hypotheses by reconstructing their distribution patterns and temporal divergences. Insects of the order Megaloptera have exclusively aquatic larvae, their adults have low vagility, and the group has a highly disjunct geographical distribution. Here we present a comprehensive phylogeny of Megaloptera based on a large-scale mitochondrial genome sequencing of 99 species representing >90% of the world genera from all major biogeographical regions. Molecular dating suggests that the deep divergence within Megaloptera pre-dates the breakup of Pangaea. Subsequently, the intergeneric divergences within Corydalinae (dobsonflies), Chauliodinae (fishflies) and Sialidae (alderflies) might have been driven by both vicariance and dispersal correlated with the shifting continent during the Cretaceous, but with strikingly different and incongruent biogeographical signals. The austral distribution of many corydalids appears to be a result of colonization from Eurasia through southward dispersal across Europe and Africa during the Cretaceous, whereas a nearly contemporaneous dispersal via northward rafting of Gondwanan landmasses may account for the colonization of extant Eurasian alderflies from the south.

Abstract 11332: Integration of Protein Interactome Networks with Congenital Heart Disease Variants Reveals Candidate Disease Genes

Congenital heart disease (CHD) is present in 1% of live births, yet despite large-scale genomic sequencing efforts, identification of causal mutations remains a challenge. We hypothesized that genetic determinants for CHDs may lie in the protein interactomes of GATA4 and TBX5, two transcription factors whose mutation cause CHDs. Defining the GATA4 or TBX5 interactomes in human cardiac progenitors via affinity purification-mass spectrometry and integrating the results with genetic data from the Pediatric Cardiac Genomic Consortium revealed an enrichment of de novo variants associated with CHD. A consolidative score that prioritized interactome members based on variant, gene, and proband features identified likely CHD-causing genes, including the epigenetic reader GLYR1. GLYR1 and GATA4 widely co-occupied and co-activated cardiac developmental genes, and the GLYR1 missense variant identified disrupted interaction with GATA4 and impaired transcriptional co-regulation in cardiomyocyte differentiation in vitro and cardiogenesis in vivo. This integrative proteomic and genetic approach provides a framework for prioritizing and interrogating the contribution of genetic variants in disease.

RmvPFBAM: Removing Primers from BAM Files Based on Amplicon-Based Next-Generation Sequencing and Cloud Computing When Analyzing Personal Genome Data

Personal genomic data constitute one important part of personal health data. However, due to the large amount of personal genomic data obtained by the next-generation sequencing technology, special tools are needed to analyze these data. In this article, we will explore a tool analyzing cloud-based large-scale genome sequencing data. Analyzing and identifying genomic variations from amplicon-based next-generation sequencing data are necessary for the clinical diagnosis and treatment of cancer patients. When processing the amplicon-based next-generation sequencing data, one essential step is removing primer sequences from the reads to avoid detecting false-positive mutations introduced by nonspecific primer binding and primer extension reactions. At present, the removing primer tools usually discard primer sequences from the FASTQ file instead of BAM file, but this method could cause some downstream analysis problems. Only one tool (BAMClipper) removes primer sequences from BAM files, but it only modified the CIGAR value of the BAM file, and false-positive mutations falling in the primer region could still be detected based on its processed BAM file. So, we developed one cutting primer tool (rmvPFBAM) removing primer sequences from the BAM file, and the mutations detected based on the processed BAM file by rmvPFBAM are highly credible. Besides that, rmvPFBAM runs faster than other tools, such as cutPrimers and BAMClipper.

Molecular diagnoses in the congenital malformations caused by ciliopathies cohort of the 100,000 Genomes Project

BackgroundPrimary ciliopathies represent a group of inherited disorders due to defects in the primary cilium, the ‘cell’s antenna’. The 100,000 Genomes Project was launched in 2012 by Genomics England (GEL),...

Dead-End Hybridization in Walnut Trees Revealed by Large-Scale Genomic Sequence Data.

Although hybridization plays a large role in speciation, some unknown fraction of hybrid individuals never reproduces, instead remaining as genetic dead-ends. We investigated a morphologically distinct and culturally important Chinese walnut, Juglans hopeiensis, suspected to have arisen from hybridization of Persian walnut (J. regia) with Asian butternuts (J. cathayensis, J. mandshurica, and hybrids between J. cathayensis and J. mandshurica). Based on 151 whole-genome sequences of the relevant taxa, we discovered that all J. hopeiensis individuals are first-generation hybrids, with the time for the onset of gene flow estimated as 370,000 years, implying both strong postzygotic barriers and the presence of J. regia in China by that time. Six inversion regions enriched for genes associated with pollen germination and pollen tube growth may be involved in the postzygotic barriers that prevent sexual reproduction in the hybrids. Despite its long-recurrent origination and distinct traits, J. hopeiensis does not appear on the way to speciation.

Genetic Drivers of Ileal Neuroendocrine Tumors.

Simple SummaryAlthough ileal neuroendocrine tumors are the most common tumors of the small intestine, they are not well-defined at the genetic level. Unlike most cancers, they have an unusually low number of mutations, and also lack recurrently mutated genes. Moreover ileal NETs have been difficult to study in the laboratory because there were no animal models and because cell lines were generally unavailable. But recent advances, including the first ileal NET mouse model as well as methods for culturing patient tumor samples, have been described and have already helped to identify IGF2 and CDK4 as two of the genetic drivers for this tumor type. These advances may help in the development of new treatments for patients.The genetic causes of ileal neuroendocrine tumors (ileal NETs, or I-NETs) have been a mystery. For most types of tumors, key genes were revealed by large scale genomic sequencing that demonstrated recurrent mutations of specific oncogenes or tumor suppressors. In contrast, genomic sequencing of ileal NETs demonstrated a distinct lack of recurrently mutated genes, suggesting that the mechanisms that drive the formation of I-NETs may be quite different than the cell-intrinsic mutations that drive the formation of other tumor types. However, recent mouse studies have identified the IGF2 and RB1 pathways in the formation of ileal NETs, which is supported by the subsequent analysis of patient samples. Thus, ileal NETs no longer appear to be a cancer without genetic causes.

Pathogenicity of VHL variants in families with non-syndromic von Hippel-Lindau phenotypes: An integrated evaluation of germline and somatic genomic results

Von Hippel-Lindau (VHL) syndrome is a hereditary tumor syndrome associated with germline loss-of-function pathogenic variants (PVs) in the VHL gene. VHL is classically associated with a high penetrance for many different tumor types. The same tumors may be sporadic in the setting of somatic VHL PVs. With more large-scale genome sequencing, variants with low penetrance or variable expressivity are identified. This has introduced challenges in patient management and the clinical interpretation of germline VHL variants identified in non-classic families. Herein, we report individuals from 3 non-classic families with VHL variants who presented with unexpected or non-syndromic phenotypes, but often with a VHL component tumor. In family 1, two siblings, age 61, with pathogenic VHL p.Leu188Val presented with clear cell renal cell carcinoma and lobular breast cancer. In family 2, the proband, age 82, was found to have pathogenic germline VHL p.Tyr98His on testing for metastatic bladder cancer. In family 3, four members carried germline VHL p.Pro81Ser (variant of uncertain significance), after the proband, age 40, presented with cerebellar hemangioblastoma. None of the individuals in the above three families met clinical criteria of classic VHL, suggesting germline VHL p.Leu188Val, p.Y98H, and p.Tyr98His may be low penetrant variants. Large studies are needed to evaluate penetrance and possible effect of genetic and non-genetic modifiers. Somatic sequencing performed on their respective tumors could help discern the etiology of the component tumors, highlighting the role of somatic evaluation in these cases. Paired examination of somatic and germline findings provided a more complete landscape of genome alterations in cancer development.

Development of a Multiplex Tandem PCR (MT-PCR) Assay for the Detection of Emerging SARS-CoV-2 Variants.

The emergence of variants of SARS-CoV-2 has created challenges for the testing infrastructure. Although large-scale genome sequencing of SARS-CoV-2 has facilitated hospital and public health responses, access to sequencing facilities globally is variable and turnaround times can be significant, so there is a requirement for rapid and cost-effective alternatives. Applying a polymerase chain reaction (PCR)-based single nucleotide polymorphism (SNP) approach enables rapid (<4 h) identification of SARS-CoV-2 lineages from nucleic acid extracts, through the presence or absence of a panel of defined of genomic polymorphisms. For example, the B.1.1.7 lineage (“UK”, “Alpha”, or “Kent” variant) is characterised by 23 mutations compared to the reference strain, and the most biologically significant of these are found in the S gene. We have developed a SARS-CoV-2 typing assay focused on five positions in the S gene (HV69/70, N501, K417, E484 and P681). This configuration can identify a range of variants, including all the “Variants of Concern” currently designated by national and international public health bodies. The panel has been evaluated using a range of clinical isolates and standardised control materials at four UK hospitals and shows excellent concordance with the known lineage information derived from full sequence analysis. The assay has a turnaround time of about three hours for a set of up to 24 samples and has been utilised to identify emerging variants in a clinical setting.

Exploring variable-length features (motifs) for predicting binding sites through interpretable deep neural networks

Transcription factor binding sites (TFBS) and RNA-binding proteins (RBP) plays a key role in gene regulation, transcription, RNA editing. Identifying and locating these potential sites is essential for detecting pathogenic variation in many biological processes. Some portions of binding sites are recognized by biological experiments that are time-intensive and expensive. Many computational approaches are considered as possible alternative solutions and few deep learning methods are recently developed for fast and accurate prediction of binding sites. Although existing approaches achieve competent performance, many methods requires specialized feature set and moreover interpretability remains challenging. To overcome these problems, we propose an interpretable deep learning technique called protein binding variable pattern predictor (PBVPP), which uses a wide variety of experimental data and performance metrics to predict binding sites. The novelty of our proposed method is based on three key factors: (i) PBVPP along with its variant has the capability to extract vital features from large-scale genomic sequences obtained by high throughput technology to predict the occurrence of TFBS and RBP sites. (ii) The proposed interpretable model reveals how to mine vital features, and also extract variable length patterns for accurate prediction of binding sites. (iii) The obtained motifs are validated against the TFBSshape DNA (JASPAR) database’s known target motifs. The proposed model has shown an improvement of 5.88%, 5.01% over state-of-the-art methods in terms of receiver operating curve for TFBS, RBP and also shown tremendous improvement of 60% in precision recall curve for TFBS prediction.

Controlled Complexity: Optimized Systems to Study the Role of the Gut Microbiome in Host Physiology.

The profound impact of the gut microbiome on host health has led to a revolution in biomedical research, motivating researchers from disparate fields to define the specific molecular mechanisms that mediate host-beneficial effects. The advent of genomic technologies allied to the use of model microbiomes in gnotobiotic mouse models has transformed our understanding of intestinal microbial ecology and the impact of the microbiome on the host. However, despite incredible advances, our understanding of the host-microbiome dialogue that shapes host physiology is still in its infancy. Progress has been limited by challenges associated with developing model systems that are both tractable enough to provide key mechanistic insights while also reflecting the enormous complexity of the gut ecosystem. Simplified model microbiomes have facilitated detailed interrogation of transcriptional and metabolic functions of the microbiome but do not recapitulate the interactions seen in complex communities. Conversely, intact complex communities from mice or humans provide a more physiologically relevant community type, but can limit our ability to uncover high-resolution insights into microbiome function. Moreover, complex microbiomes from lab-derived mice or humans often do not readily imprint human-like phenotypes. Therefore, improved model microbiomes that are highly defined and tractable, but that more accurately recapitulate human microbiome-induced phenotypic variation are required to improve understanding of fundamental processes governing host-microbiome mutualism. This improved understanding will enhance the translational relevance of studies that address how the microbiome promotes host health and influences disease states. Microbial exposures in wild mice, both symbiotic and infectious in nature, have recently been established to more readily recapitulate human-like phenotypes. The development of synthetic model communities from such “wild mice” therefore represents an attractive strategy to overcome the limitations of current approaches. Advances in microbial culturing approaches that allow for the generation of large and diverse libraries of isolates, coupled to ever more affordable large-scale genomic sequencing, mean that we are now ideally positioned to develop such systems. Furthermore, the development of sophisticated in vitro systems is allowing for detailed insights into host-microbiome interactions to be obtained. Here we discuss the need to leverage such approaches and highlight key challenges that remain to be addressed.

1133P Whole genome sequencing can classify diagnostically challenging tumors

Cancer of Unknown Primary (CUP) accounts for approximately 3% of all metastatic cancers. Despite modern imaging techniques and extensive pathology work-up in CUPs, tumor origin remains unknown in the majority of these patients. Large-scale genome sequencing studies have revealed that tumor types can be classified based on distinct patterns of somatic variants and other genomic characteristics. Here, we investigated the clinical value of genome-driven tumor type prediction using whole genome sequencing (WGS) in the diagnostic workup of patients with CUPs and other tumors that are challenging to diagnose.

Pathogenic Mechanisms of Hypertrophic Cardiomyopathy beyond Sarcomere Dysfunction.

Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiovascular disorder, affecting 1 in 500 people in the general population. Although characterized by asymmetric left ventricular hypertrophy, cardiomyocyte disarray, and cardiac fibrosis, HCM is in fact a highly complex disease with heterogenous clinical presentation, onset, and complications. While HCM is generally accepted as a disease of the sarcomere, variable penetrance in families with identical genetic mutations challenges the monogenic origin of HCM and instead implies a multifactorial cause. Furthermore, large-scale genome sequencing studies revealed that many genes previously reported as causative of HCM in fact have little or no evidence of disease association. These findings thus call for a re-evaluation of the sarcomere-centered view of HCM pathogenesis. Here, we summarize our current understanding of sarcomere-independent mechanisms of cardiomyocyte hypertrophy, highlight the role of extracellular signals in cardiac fibrosis, and propose an alternative but integrated model of HCM pathogenesis.

Population sequencing data reveal a compendium of mutational processes in the human germ line.

Mechanistic processes underlying human germline mutations remain largely unknown.Variation in mutation rate and spectra along the genome is informative about the biological mechanisms. We statistically decompose this variation into separate processes using a blind source separation technique. The analysis of a large-scale whole genome sequencing dataset (TOPMed) reveals nine processes that explain the variation in mutation properties between loci. Seven of these processes lend themselves to a biological interpretation. One process is driven by bulky DNA lesions that resolve asymmetrically with respect to transcription and replication. Two processes independently track direction of replication fork and replication timing. We identify a mutagenic effect of active demethylation primarily acting in regulatory regions. We also demonstrate that a recently discovered mutagenic process specific to oocytes can be localized solely from population sequencing data. This process is spread across all chromosomes and is highly asymmetric with respect to the direction of transcription, suggesting a major role of DNA damage.

Build a better bootstrap and the RAWR shall beat a random path to your door: phylogenetic support estimation revisited.

MotivationThe standard bootstrap method is used throughout science and engineering to perform general-purpose non-parametric resampling and re-estimation. Among the most widely cited and widely used such applications is the phylogenetic bootstrap method, which Felsenstein proposed in 1985 as a means to place statistical confidence intervals on an estimated phylogeny (or estimate ‘phylogenetic support’). A key simplifying assumption of the bootstrap method is that input data are independent and identically distributed (i.i.d.). However, the i.i.d. assumption is an over-simplification for biomolecular sequence analysis, as Felsenstein noted.ResultsIn this study, we introduce a new sequence-aware non-parametric resampling technique, which we refer to as RAWR (‘RAndom Walk Resampling’). RAWR consists of random walks that synthesize and extend the standard bootstrap method and the ‘mirrored inputs’ idea of Landan and Graur. We apply RAWR to the task of phylogenetic support estimation. RAWR’s performance is compared to the state-of-the-art using synthetic and empirical data that span a range of dataset sizes and evolutionary divergence. We show that RAWR support estimates offer comparable or typically superior type I and type II error compared to phylogenetic bootstrap support. We also conduct a re-analysis of large-scale genomic sequence data from a recent study of Darwin’s finches. Our findings clarify phylogenetic uncertainty in a charismatic clade that serves as an important model for complex adaptive evolution.Availability and implementationData and software are publicly available under open-source software and open data licenses at: https://gitlab.msu.edu/liulab/RAWR-study-datasets-and-scripts.

Protein phosphatase 2A - structure, function and role in neurodevelopmental disorders.

Neurodevelopmental disorders (NDDs), including intellectual disability (ID), autism and schizophrenia, have high socioeconomic impact, yet poorly understood etiologies. A recent surge of large-scale genome or exome sequencing studies has identified a multitude of mostly de novo mutations in subunits of the protein phosphatase 2A (PP2A) holoenzyme that are strongly associated with NDDs. PP2A is responsible for at least 50% of total Ser/Thr dephosphorylation in most cell types and is predominantly found as trimeric holoenzymes composed of catalytic (C), scaffolding (A) and variable regulatory (B) subunits. PP2A can exist in nearly 100 different subunit combinations in mammalian cells, dictating distinct localizations, substrates and regulatory mechanisms. PP2A is well established as a regulator of cell division, growth, and differentiation, and the roles of PP2A in cancer and various neurodegenerative disorders, such as Alzheimer's disease, have been reviewed in detail. This Review summarizes and discusses recent reports on NDDs associated with mutations of PP2A subunits and PP2A-associated proteins. We also discuss the potential impact of these mutations on the structure and function of the PP2A holoenzymes and the etiology of NDDs.