Large-scale Genome Sequencing Research Articles

Complete genomic characterization in patients with cancer of unknown primary origin in routine diagnostics

In ∼3%-5% of patients with metastatic disease, tumor origin remains unknown despite modern imaging techniques and extensive pathology work-up. With long diagnostic delays and limited and ineffective therapy options, the clinical outcome of patients with cancer of unknown primary (CUP) remains poor. Large-scale genome sequencing studies have revealed that tumor types can be predicted based on distinct patterns of somatic variants and other genomic characteristics. Moreover, actionable genomic events are present in almost half of CUP patients. This study investigated the clinical value of whole genome sequencing (WGS) in terms of primary tumor identification and detection of actionable events, in the routine diagnostic work-up of CUP patients. A WGS-based tumor type 'cancer of unknown primary prediction algorithm' (CUPPA) was developed based on previously described principles and validated on a large pan-cancer WGS database of metastatic cancer patients (>4000 samples) and 254 independent patients, respectively. We assessed the clinical value of this prediction algorithm as part of routine WGS-based diagnostic work-up for 72 CUP patients. CUPPA correctly predicted the primary tumor type in 78% of samples in the independent validation cohort (194/254 patients). High-confidence predictions (>95% precision) were obtained for 162/254 patients (64%). When integrated in the diagnostic work-up of CUP patients, CUPPA could identify a primary tumor type for 49/72 patients (68%). Most common diagnoses included non-small-cell lung (n= 7), gastroesophageal (n= 4), pancreatic (n= 4), and colorectal cancer (n= 3). Actionable events with matched therapy options in clinical trials were identified in 47% of patients. Genome-based tumor type prediction can predict cancer diagnoses with high accuracy when integrated in the routine diagnostic work-up of patients with metastatic cancer. With identification of the primary tumor type in the majority of patients and detection of actionable events, WGS is a valuable diagnostic tool for patients with CUP.

Predicting functional effect of missense variants using graph attention neural networks.

Accurate prediction of damaging missense variants is critically important for interpreting a genome sequence. Although many methods have been developed, their performance has been limited. Recent advances in machine learning and the availability of large-scale population genomic sequencing data provide new opportunities to considerably improve computational predictions. Here we describe the graphical missense variant pathogenicity predictor (gMVP), a new method based on graph attention neural networks. Its main component is a graph with nodes that capture predictive features of amino acids and edges weighted by co-evolution strength, enabling effective pooling of information from the local protein context and functionally correlated distal positions. Evaluation of deep mutational scan data shows that gMVP outperforms other published methods in identifying damaging variants in TP53, PTEN, BRCA1 and MSH2. Furthermore, it achieves the best separation of de novo missense variants in neuro developmental disorder cases from those in controls. Finally, the model supports transfer learning to optimize gain- and loss-of-function predictions in sodium and calcium channels. In summary, we demonstrate that gMVP can improve interpretation of missense variants in clinical testing and genetic studies.

Managing sample metadata for biodiversity: considerations from the Darwin Tree of Life project

Large-scale reference genome sequencing projects for all of biodiversity are underway and common standards have been in place for some years to enable the understanding and sharing of sequence data. However, the metadata that describes the collection, processing and management of samples, and link to the associated sequencing and genome data, are not yet adequately developed and standardised for these projects. At the time of writing, the Darwin Tree of Life (DToL) Project is over two years into its ten-year ambition to sequence all described eukaryotic species in Britain and Ireland. We have sought consensus from a wide range of scientists across taxonomic domains to determine the minimal set of metadata that we collectively deem as critically important to accompany each sequenced specimen. These metadata are made available throughout the subsequent laboratory processes, and once collected, need to be adequately managed to fulfil the requirements of good data management practice. Due to the size and scale of management required, software tools are needed. These tools need to implement rigorous development pathways and change management procedures to ensure that effective research data management of key project and sample metadata is maintained. Tracking of sample properties through the sequencing process is handled by Lab Information Management Systems (LIMS), so publication of the sequenced data is achieved via technical integration of LIMS and data management tools. Discussions with community members on how metadata standards need to be managed within large-scale programmes is a priority in the planning process. Here we report on the standards we developed with respect to a robust and reusable mechanism of metadata collection, in the hopes that other projects forthcoming or underway will adopt these practices for metadata.

CuGenDBv2: an updated database for cucurbit genomics.

The Cucurbitaceae (cucurbit) family consists of about 1,000 species in 95 genera, including many economically important and popular fruit and vegetable crops. During the past several years, reference genomes have been generated for >20 cucurbit species, and variome and transcriptome profiling data have been rapidly accumulated for cucurbits. To efficiently mine, analyze and disseminate these large-scale datasets, we have developed an updated version of Cucurbit Genomics Database. The updated database, CuGenDBv2 (http://cucurbitgenomics.org/v2), currently hosts 34 reference genomes from 27 cucurbit species/subspecies belonging to 10 different genera. Protein-coding genes from these genomes have been comprehensively annotated by comparing their protein sequences to various public protein and domain databases. A novel 'Genotype' module has been implemented to facilitate mining and analysis of the functionally annotated variome data including SNPs and small indels from large-scale genome sequencing projects. An updated 'Expression' module has been developed to provide a comprehensive gene expression atlas for cucurbits. Furthermore, synteny blocks between any two and within each of the 34 genomes, representing a total of 595 pair-wise genome comparisons, have been identified and can be explored and visualized in the database.

Discovery and Validation of Clinically Relevant Long Non-Coding RNAs in Colorectal Cancer.

Simple SummaryRecent efforts in biomedical research have focused on the identification of molecular biomarkers to improve the diagnosis, prognosis and eventually treatment of the most common human diseases worldwide, including cancer. In this context, a large number of studies point to a pivotal role of long non-coding RNAs (lncRNAs) in the pathophysiology of carcinogenesis, suggesting diagnostic or therapeutic potential. However, for most of them, supporting evidence is scarce and often based on a single large-scale analysis. Here, focusing on colorectal cancer (CRC), we present an overview of the main approaches for discovering and validating lncRNA candidate molecules, and provide a curated list of the most promising lncRNAs associated with this malignancy.Colorectal cancer (CRC) is the third most prevalent cancer worldwide, with nearly two million newly diagnosed cases each year. The survival of patients with CRC greatly depends on the cancer stage at the time of diagnosis, with worse prognosis for more advanced cases. Consequently, considerable effort has been directed towards improving population screening programs for early diagnosis and identifying prognostic markers that can better inform treatment strategies. In recent years, long non-coding RNAs (lncRNAs) have been recognized as promising molecules, with diagnostic and prognostic potential in many cancers, including CRC. Although large-scale genome and transcriptome sequencing surveys have identified many lncRNAs that are altered in CRC, most of their roles in disease onset and progression remain poorly understood. Here, we critically review the variety of detection methods and types of supporting evidence for the involvement of lncRNAs in CRC. In addition, we provide a reference catalog that features the most clinically relevant lncRNAs in CRC. These lncRNAs were selected based on recent studies sorted by stringent criteria for both supporting experimental evidence and reproducibility.

Extreme purifying selection against point mutations in the human genome

Large-scale genome sequencing has enabled the measurement of strong purifying selection in protein-coding genes. Here we describe a new method, called ExtRaINSIGHT, for measuring such selection in noncoding as well as coding regions of the human genome. ExtRaINSIGHT estimates the prevalence of “ultraselection” by the fractional depletion of rare single-nucleotide variants, after controlling for variation in mutation rates. Applying ExtRaINSIGHT to 71,702 whole genome sequences from gnomAD v3, we find abundant ultraselection in evolutionarily ancient miRNAs and neuronal protein-coding genes, as well as at splice sites. By contrast, we find much less ultraselection in other noncoding RNAs and transcription factor binding sites, and only modest levels in ultraconserved elements. We estimate that ~0.4–0.7% of the human genome is ultraselected, implying ~ 0.26–0.51 strongly deleterious mutations per generation. Overall, our study sheds new light on the genome-wide distribution of fitness effects by combining deep sequencing data and classical theory from population genetics.

Contrasting Distribution of SARS-CoV-2 Lineages across Multiple Rounds of Pandemic Waves in West Bengal, the Gateway of East and North-East States of India.

ABSTRACTThe evolution of viral variants and their impact on viral transmission have been an area of considerable importance in this pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We analyzed the viral variants in different phases of the pandemic in West Bengal, a state in India that is important geographically, and compared the variants with other states like Delhi, Maharashtra, and Karnataka, located in other regions of the country. We have identified 57 pango-lineages in 3,198 SARS-CoV-2 genomes, alteration in their distribution, as well as contrasting profiles of amino acid mutational dynamics across different waves in different states. The evolving characteristics of Delta (B.1.617.2) sublineages and alterations in hydrophobicity profiles of the viral proteins caused by these mutations were also studied. Additionally, implications of predictive host miRNA binding/unbinding to emerging spike or nucleocapsid mutations were highlighted. Our results throw considerable light on interesting aspects of the viral genomic variation and provide valuable information for improved understanding of wave-defining mutations in unfolding the pandemic.IMPORTANCE Multiple waves of infection were observed in many states in India during the coronavirus disease 2019 (COVID19) pandemic. Fine-scale evolution of major SARS-CoV-2 lineages and sublineages during four wave-window categories: Pre-Wave 1, Wave 1, Pre-Wave 2, and Wave 2 in four major states of India: Delhi (North), Maharashtra (West), Karnataka (South), and West Bengal (East) was studied using large-scale virus genome sequencing data. Our comprehensive analysis reveals contrasting molecular profiles of the wave-defining mutations and their implications in host miRNA binding/unbinding of the lineages in the major states of India.

Abstract 1938: Large-scale identification of mutation activity and drug sensitivity in BRAF via a novel multi-label multi-task CNN model

Abstract Precision medicine has allowed for many drugs to be developed for frequently occurring well studied oncogenic mutations such as NTRK fusions or EGFR exon 19 mutations. However, large scale genomic sequencing of patient samples shows that tumors harbor many mutations classified as Variants of Uncertain Significance (VUS). Such mutations could have a significant role in tumor progression and can thus serve as potential drug targets. Therefore, understanding and characterizing the functional significance of VUSs and their response to targeted agents is essential. Here we present a novel machine learning (ML) model consisting of a multi-label, multi-task deep convolutional neural network followed by a decision tree-based regression model. Focusing on alterations in BRAF, we used data from a cell-based assay that measures the activity of signaling pathway activation. This is done using fluorescent imaging of cells expressing a mutated protein together with a fluorescently labeled signaling pathway reporter, providing the input to our model. We trained the model on 3 types of cell images: cells transfected with WT BRAF, BRAF V600E, and BRAF V600E treated with a high dose of Vemurafenib. We use two datasets to evaluate our performance: a set of 17 known active BRAF fusions, as well as a set of 16 known active non-V600E mutations. We also compare our performance to previously published single-task ML model that aims to detect both activity and response. The two methodologies are compared via two criteria: ability to detect activity of the mutations in the dataset, as well as ability to predict response to Vemurafenib or to FORE8394, a drug previously unseen by the model. We show that while both single-task and multi-task models identify all 17 known active fusions as oncogenic, the multi-task does slightly better on the non-V600E mutations, correctly identifying 15/16 of the active mutations vs 10/16 for the single-task model. Comparing drug response, the multi-task model has higher sensitivity in detecting active mutations as responsive to Vemurafenib or FORE8394, including all V600 mutations which are known responders to Vemurafenib and for whom the single-task model does not capture response. Following the training and validation, given a dataset of >300 previously unseen BRAF mutations, the multi-task model is then able to predict both the mutation’s oncogenicity level as well as its expected response to the given drug. Interestingly, the multi-task model also suggests a different drug response profile for vemurafenib compared to FORE8394. We conclude that our novel multi-task model provides an accurate and efficient method for uncovering the actionable and treatable mutational landscape of a drug for patients with mutations in BRAF. It can thus be viewed as a step forward in developing sensitive methodologies for determining patients that are more susceptible to benefit from a potential drug. Citation Format: Ilona Kifer, Arie Aizenman, Natalie Fillipov-Levy, Zohar Barbash, Michael Vidne, Gabi Tarcic. Large-scale identification of mutation activity and drug sensitivity in BRAF via a novel multi-label multi-task CNN model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1938.

Opportunities and challenges for the use of common controls in sequencing studies.

Genome-wide association studies using large-scale genome and exome sequencing data have become increasingly valuable in identifying associations between genetic variants and disease, transforming basic research and translational medicine. However, this progress has not been equally shared across all people and conditions, in part due to limited resources. Leveraging publicly available sequencing data as external common controls, rather than sequencing new controls for every study, can better allocate resources by augmenting control sample sizes or providing controls where none existed. However, common control studies must be carefully planned and executed as even small differences in sample ascertainment and processing can result in substantial bias. Here, we discuss challenges and opportunities for the robust use of common controls in high-throughput sequencing studies, including study design, quality control and statistical approaches. Thoughtful generation and use of large and valuable genetic sequencing data sets will enable investigation of a broader and more representative set of conditions, environments and genetic ancestries than otherwise possible.

Large‐scale profiling of physiologically relevant naturally occurring rare GPCR variants using the bioSensAll® technology

G protein‐coupled receptors (GPCRs) represent the largest class of membrane‐bound proteins which regulate diverse physiological processes by engaging multiple signaling pathways. GPCR signaling can be differentially modulated by specific ligands to selectively promote the engagement of different subsets of signaling pathways, an outcome known as biased signaling. Although dysregulation of GPCR activity has been linked to pathological consequences, the involvement of their signaling pathways toward specific clinical repercussions remain unresolved. Thus, linking the role of GPCR signaling pathways to distinct physiological processes is not only important for understanding GPCR biology, but also essential for successfully targeting these receptors as therapeutic avenues. Here, to exploit the potential of naturally occurring GPCR variants emerging from large scale exome and genome sequencing efforts, we assessed the signaling profiles of rare variants across different GPCRs found in human populations with documented clinical phenotypes. Signaling profiles were assessed through an automated platform using our bioSensAll® technology, which measures the activity of various signaling pathways using a panel of 16 selective bioluminescence resonance energy transfer (BRET)‐based biosensors that monitor the activation of heterotrimeric G proteins (Gαs, Gαi1, Gαi2, GαoA, GαoB, Gαz, Gαq, Gα11, Gα14, Gα15/16, Gα12, Gα13) or recruitment of β‐arrestins (β‐arrestin 1 and β‐arrestin 2) to the plasma membrane upon agonist stimulation. To achieve the large‐scale screen in a high throughput system, bioSensAll® assays were miniaturized and adapted to a 384‐well format. HEK293 cells were co‐transfected with receptors and biosensors, and a scheduling software was used to coordinate the interaction between various instruments via a robotic microplate mover. Analysis was performed through an automated tool to generate and compile signaling profiles for each variant normalized to wild‐type receptor, revealing gain‐ or loss‐of‐function properties induced by variants across different signaling pathways, thus also allowing us to identify genetic alterations that impart biased signaling profiles. By pairing the multiparametric signaling profiles of variants found in human populations generated through our automated bioSensAll® platform with information on their associated clinical phenotypes, we can uncover associations between altered receptor pharmacology and clinical outcomes to accelerate the discovery of disease‐relevant targets and improve translation from bench to clinic.

A Sequence Obfuscation Method for Protecting Personal Genomic Privacy.

With the technological advances in recent decades, determining whole genome sequencing of a person has become feasible and affordable. As a result, large-scale individual genomic sequences are produced and collected for genetic medical diagnoses and cancer drug discovery, which, however, simultaneously poses serious challenges to the protection of personal genomic privacy. It is highly urgent to develop methods which make the personal genomic data both utilizable and confidential. Existing genomic privacy-protection methods are either time-consuming for encryption or with low accuracy of data recovery. To tackle these problems, this paper proposes a sequence similarity-based obfuscation method, namely IterMegaBLAST, for fast and reliable protection of personal genomic privacy. Specifically, given a randomly selected sequence from a dataset of genomic sequences, we first use MegaBLAST to find its most similar sequence from the dataset. These two aligned sequences form a cluster, for which an obfuscated sequence was generated via a DNA generalization lattice scheme. These procedures are iteratively performed until all of the sequences in the dataset are clustered and their obfuscated sequences are generated. Experimental results on benchmark datasets demonstrate that under the same degree of anonymity, IterMegaBLAST significantly outperforms existing state-of-the-art approaches in terms of both utility accuracy and time complexity.

Development of a versatile resource for post-genomic research through consolidating and characterizing 1500 diverse wild and cultivated soybean genomes

BackgroundWith advances in next-generation sequencing technologies, an unprecedented amount of soybean accessions has been sequenced by many individual studies and made available as raw sequencing reads for post-genomic research.ResultsTo develop a consolidated and user-friendly genomic resource for post-genomic research, we consolidated the raw resequencing data of 1465 soybean genomes available in the public and 91 highly diverse wild soybean genomes newly sequenced. These altogether provided a collection of 1556 sequenced genomes of 1501 diverse accessions (1.5 K). The collection comprises of wild, landraces and elite cultivars of soybean that were grown in East Asia or major soybean cultivating areas around the world. Our extensive sequence analysis discovered 32 million single nucleotide polymorphisms (32mSNPs) and revealed a SNP density of 30 SNPs/kb and 12 non-synonymous SNPs/gene reflecting a high structural and functional genomic diversity of the new collection. Each SNP was annotated with 30 categories of structural and/or functional information. We further identified paired accessions between the 1.5 K and 20,087 (20 K) accessions in US collection as genomic “equivalent” accessions sharing the highest genomic identity for minimizing the barriers in soybean germplasm exchange between countries. We also exemplified the utility of 32mSNPs in enhancing post-genomics research through in-silico genotyping, high-resolution GWAS, discovering and/or characterizing genes and alleles/mutations, identifying germplasms containing beneficial alleles that are potentially experiencing artificial selection.ConclusionThe comprehensive analysis of publicly available large-scale genome sequencing data of diverse cultivated accessions and the newly in-house sequenced wild accessions greatly increased the soybean genome-wide variation resolution. This could facilitate a variety of genetic and molecular-level analyses in soybean. The 32mSNPs and 1.5 K accessions with their comprehensive annotation have been made available at the SoyBase and Ag Data Commons. The dataset could further serve as a versatile and expandable core resource for exploring the exponentially increasing genome sequencing data for a variety of post-genomic research.

Characterizing mobile element insertions in 5675 genomes.

Mobile element insertions (MEIs) are a major class of structural variants (SVs) and have been linked to many human genetic disorders, including hemophilia, neurofibromatosis, and various cancers. However, human MEI resources from large-scale genome sequencing are still lacking compared to those for SNPs and SVs. Here, we report a comprehensive map of 36 699 non-reference MEIs constructed from 5675 genomes, comprising 2998 Chinese samples (∼26.2×, NyuWa) and 2677 samples from the 1000 Genomes Project (∼7.4×, 1KGP). We discovered that LINE-1 insertions were highly enriched in centromere regions, implying the role of chromosome context in retroelement insertion. After functional annotation, we estimated that MEIs are responsible for about 9.3% of all protein-truncating events per genome. Finally, we built a companion database named HMEID for public use. This resource represents the latest and largest genomewide study on MEIs and will have broad utility for exploration of human MEI findings.

Insights into National Laboratory Newborn Screening and Future Prospects

Newborn screening (NBS) is a group of tests that check all newborns for certain rare conditions, covering several genetic or metabolic disorders. The laboratory NBS is performed through blood testing. However, the conditions that newborn babies are screened for vary from one country to another. Since NBS began in the 1960s, technological advances have enabled its expansion to include an increasing number of disorders, and there is a national trend to further expand the NBS program. The use of mass spectrometry (MS) for the diagnosis of inborn errors of metabolism (IEM) obviously helps in the expansion of the screening panels. This technology allows the detection of different metabolic disorders at one run, replacing the use of traditional techniques. Analysis of the targeted pathogenic gene variant is a routine application in the molecular techniques for the NBS program as a confirmatory testing to the positive laboratory screening results. Recently, a lot of molecular investigations, such as next generation sequencing (NGS), have been introduced in the routine NBS program. Nowadays, NGS techniques are widely used in the diagnosis of IMD where its results are rapid, confirmed and reliable, but, due to its uncertainties and the nature of IEM, it necessitates a holistic approach for diagnosis. However, various characteristics found in NGS make it a potentially powerful tool for NBS. A range of disorders can be analyzed with a single assay directly, and samples can reduce costs and can largely be automated. For the implementation of a robust technology such as NGS in a mass NBS program, the main focus should not be just technologically biased; it should also be tested for its long- and short-term impact on the family and the child. The crucial question here is whether large-scale genomic sequencing can provide useful medical information beyond what current NBS is already providing and at what economical and emotional cost? Currently, the topic of newborn genome sequencing as a public health initiative remains argumentative. Thus, this article seeks the answer to the question: NGS for newborn screening- are we there yet?

A large-scale genome and transcriptome sequencing analysis reveals the mutation landscapes induced by high-activity adenine base editors in plants

BackgroundThe high-activity adenine base editors (ABEs), engineered with the recently-developed tRNA adenosine deaminases (TadA8e and TadA9), show robust base editing activity but raise concerns about off-target effects.ResultsIn this study, we perform a comprehensive evaluation of ABE8e- and ABE9-induced DNA and RNA mutations in Oryza sativa. Whole-genome sequencing analysis of plants transformed with four ABEs, including SpCas9n-TadA8e, SpCas9n-TadA9, SpCas9n-NG-TadA8e, and SpCas9n-NG-TadA9, reveal that ABEs harboring TadA9 lead to a higher number of off-target A-to-G (A>G) single-nucleotide variants (SNVs), and that those harboring CRISPR/SpCas9n-NG lead to a higher total number of off-target SNVs in the rice genome. An analysis of the T-DNAs carrying the ABEs indicates that the on-target mutations could be introduced before and/or after T-DNA integration into plant genomes, with more off-target A>G SNVs forming after the ABEs had integrated into the genome. Furthermore, we detect off-target A>G RNA mutations in plants with high expression of ABEs but not in plants with low expression of ABEs. The off-target A>G RNA mutations tend to cluster, while off-target A>G DNA mutations rarely clustered.ConclusionOur findings that Cas proteins, TadA variants, temporal expression of ABEs, and expression levels of ABEs contribute to ABE specificity in rice provide insight into the specificity of ABEs and suggest alternative ways to increase ABE specificity besides engineering TadA variants.

Spatio-temporal dynamics of intra-host variability in SARS-CoV-2 genomes.

During the course of the COVID-19 pandemic, large-scale genome sequencing of SARS-CoV-2 has been useful in tracking its spread and in identifying variants of concern (VOC). Viral and host factors could contribute to variability within a host that can be captured in next-generation sequencing reads as intra-host single nucleotide variations (iSNVs). Analysing 1347 samples collected till June 2020, we recorded 16 410 iSNV sites throughout the SARS-CoV-2 genome. We found ∼42% of the iSNV sites to be reported as SNVs by 30 September 2020 in consensus sequences submitted to GISAID, which increased to ∼80% by 30th June 2021. Following this, analysis of another set of 1774 samples sequenced in India between November 2020 and May 2021 revealed that majority of the Delta (B.1.617.2) and Kappa (B.1.617.1) lineage-defining variations appeared as iSNVs before getting fixed in the population. Besides, mutations in RdRp as well as RNA-editing by APOBEC and ADAR deaminases seem to contribute to the differential prevalence of iSNVs in hosts. We also observe hyper-variability at functionally critical residues in Spike protein that could alter the antigenicity and may contribute to immune escape. Thus, tracking and functional annotation of iSNVs in ongoing genome surveillance programs could be important for early identification of potential variants of concern and actionable interventions.

Ethical, legal, and social issues in the Earth BioGenome Project

The Earth BioGenome Project (EBP) is an audacious endeavor to obtain whole-genome sequences of representatives from all eukaryotic species on Earth. In addition to the project’s technical and organizational challenges, it also faces complicated ethical, legal, and social issues. This paper, from members of the EBP’s Ethical, Legal, and Social Issues (ELSI) Committee, catalogs these ELSI concerns arising from EBP. These include legal issues, such as sample collection and permitting; the applicability of international treaties, such as the Convention on Biological Diversity and the Nagoya Protocol; intellectual property; sample accessioning; and biosecurity and ethical issues, such as sampling from the territories of Indigenous peoples and local communities, the protection of endangered species, and cross-border collections, among several others. We also comment on the intersection of digital sequence information and data rights. More broadly, this list of ethical, legal, and social issues for large-scale genomic sequencing projects may be useful in the consideration of ethical frameworks for future projects. While we do not—and cannot—provide simple, overarching solutions for all the issues raised here, we conclude our perspective by beginning to chart a path forward for EBP’s work.

The Role of Microtubule Associated Serine/Threonine Kinase 3 Variants in Neurodevelopmental Diseases: Genotype-Phenotype Association.

Objective: To prove microtubule associated serine/threonine kinase 3 (MAST3) gene is associated with neurodevelopmental diseases (NDD) and the genotype-phenotype correlation.Methods: Trio exome sequencing (trio ES) was performed on four NDD trios. Bioinformatic analysis was conducted based on large-scale genome sequencing data and human brain transcriptomic data. Further in vivo zebrafish studies were performed.Results: In our study, we identified four de novo MAST3 variants (NM_015016.1: c.302C > T:p.Ser101Phe; c.311C > T:p.Ser104Leu; c.1543G > A:p.Gly515Ser; and c.1547T > C:p.Leu516Pro) in four patients with developmental and epileptic encephalopathy (DEE) separately. Clinical heterogeneities were observed in patients carrying variants in domain of unknown function (DUF) and serine-threonine kinase (STK) domain separately. Using the published large-scale exome sequencing data, higher CADD scores of missense variants in DUF domain were found in NDD cohort compared with gnomAD database. In addition, we obtained an excess of missense variants in DUF domain when compared autistic spectrum disorder (ASD) cohort with gnomAD database, similarly an excess of missense variants in STK domain when compared DEE cohort with gnomAD database. Based on Brainspan datasets, we showed that MAST3 expression was significantly upregulated in ASD and DEE-related brain regions and was functionally linked with DEE genes. In zebrafish model, abnormal morphology of central nervous system was observed in mast3a/b crispants.Conclusion: Our results support the possibility that MAST3 is a novel gene associated with NDD which could expand the genetic spectrum for NDD. The genotype-phenotype correlation may contribute to future genetic counseling.

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.