Human Genome Project Research Articles

More than the SRY: The Non-Coding Landscape of the Y Chromosome and Its Importance in Human Disease

Historically, the Y chromosome has presented challenges to classical methodology and philosophy of understanding the differences between males and females. A genetic unsolved puzzle, the Y chromosome was the last chromosome to be fully sequenced. With the advent of the Human Genome Project came a realization that the human genome is more than just genes encoding proteins, and an entire universe of RNA was discovered. This dark matter of biology and the black box surrounding the Y chromosome have collided over the last few years, as increasing numbers of non-coding RNAs have been identified across the length of the Y chromosome, many of which have played significant roles in disease. In this review, we will uncover what is known about the connections between the Y chromosome and the non-coding RNA universe that originates from it, particularly as it relates to long non-coding RNAs, microRNAs and circular RNAs.

Exploring non-coding genetic variability in ACE2: Functional annotation and in vitro validation of regulatory variants

The surge in human whole-genome sequencing data has facilitated the study of non-coding region variations, yet understanding their biological significance remains a challenge. We used a computational workflow to assess the regulatory potential of non-coding variants, with a particular focus on the Angiotensin Converting Enzyme 2 (ACE2) gene. This gene is crucial in physiological processes and serves as the entry point for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus causing coronavirus disease 19 (COVID-19). In our analysis, using data from the gnomAD population database and functional annotation, we identified 17 significant Single Nucleotide Variants (SNVs) in ACE2, particularly in its enhancers, promoters, and 3′ untranslated regions (UTRs). We found preliminary evidence supporting the regulatory impact of some of these variants on ACE2 expression. Our detailed examination of two SNVs, rs147718775 and rs140394675, in the ACE2 promoter revealed that these co-occurring SNVs, when mutated, significantly enhance promoter activity, suggesting a possible increase in specific ACE2 isoform expression. This method proves effective in identifying and interpreting impactful non-coding variants, aiding in further studies and enhancing understanding of molecular bases of monogenic and complex traits.

Meta-Research: Understudied genes are lost in a leaky pipeline between genome-wide assays and reporting of results.

Present-day publications on human genes primarily feature genes that already appeared in many publications prior to completion of the Human Genome Project in 2003. These patterns persist despite the subsequent adoption of high-throughput technologies, which routinely identify novel genes associated with biological processes and disease. Although several hypotheses for bias in the selection of genes as research targets have been proposed, their explanatory powers have not yet been compared. Our analysis suggests that understudied genes are systematically abandoned in favor of better-studied genes between the completion of -omics experiments and the reporting of results. Understudied genes remain abandoned by studies that cite these -omics experiments. Conversely, we find that publications on understudied genes may even accrue a greater number of citations. Among 45 biological and experimental factors previously proposed to affect which genes are being studied, we find that 33 are significantly associated with the choice of hit genes presented in titles and abstracts of -omics studies. To promote the investigation of understudied genes, we condense our insights into a tool, find my understudied genes (FMUG), that allows scientists to engage with potential bias during the selection of hits. We demonstrate the utility of FMUG through the identification of genes that remain understudied in vertebrate aging. FMUG is developed in Flutter and is available for download at fmug.amaral.northwestern.edu as a MacOS/Windows app.

Meta-Research: Understudied genes are lost in a leaky pipeline between genome-wide assays and reporting of results

Present-day publications on human genes primarily feature genes that already appeared in many publications prior to completion of the Human Genome Project in 2003. These patterns persist despite the subsequent adoption of high-throughput technologies, which routinely identify novel genes associated with biological processes and disease. Although several hypotheses for bias in the selection of genes as research targets have been proposed, their explanatory powers have not yet been compared. Our analysis suggests that understudied genes are systematically abandoned in favor of better-studied genes between the completion of -omics experiments and the reporting of results. Understudied genes remain abandoned by studies that cite these -omics experiments. Conversely, we find that publications on understudied genes may even accrue a greater number of citations. Among 45 biological and experimental factors previously proposed to affect which genes are being studied, we find that 33 are significantly associated with the choice of hit genes presented in titles and abstracts of -omics studies. To promote the investigation of understudied genes, we condense our insights into a tool, find my understudied genes (FMUG), that allows scientists to engage with potential bias during the selection of hits. We demonstrate the utility of FMUG through the identification of genes that remain understudied in vertebrate aging. FMUG is developed in Flutter and is available for download at fmug.amaral.northwestern.edu as a MacOS/Windows app.

NGS Technique for Palindromic Sequencing of DNA Through Effective PST-PCR

DNA Sequencing technologies have been in use since 1970 and has diversified to much more effective transformations till the date. Initially due to certain drawbacks like cost, time period and requirement of toxic and radioactive elements for the compilation of the process, it remained unintroduced to research setting for complex data in earlier times. One of a technique named as the Sanger technique had more practical approach for sequencing the desired data of the fragments. But the need of DNA sequencing surged after the commencement of the Human Genome Project (HGP) which was a 13 year long collaboration to sequence human genome for understanding its applicable uses.1 At the current stage, the progress moved towards Next Generation Sequencing (NGS) to   sequence the fragments of DNA for a better acknowledgement but somehow this did not reported for a fair outcome for palindromic DNA while sequencing. In this rugged sequencing platforms, Roche’s 454 with emulsion PCR technique amplified the templates through beads which later went through pyrosequencing with DNA Polymerase gave the desired output but had a major drawback of high cost.2 Therefore, this study deals with effective PST – PCR technique for studying palindromic DNA in NGS and how it can be considered to fit in all advantages for the above task.

Ethical considerations in utilizing artificial intelligence for analyzing the NHGRI’s History of Genomics and Human Genome Project archives

Understanding “how to optimize the production of scientific knowledge” is paramount to those who support scientific research—funders as well as research institutions—to the communities served, and to researchers. Structured archives can help all involved to learn what decisions and processes help or hinder the production of new knowledge. Using artificial intelligence (AI) and large language models (LLMs), we recently created the first structured digital representation of the historic archives of the National Human Genome Research Institute (NHGRI), part of the National Institutes of Health. This work yielded a digital knowledge base of entities, topics, and documents that can be used to probe the inner workings of the Human Genome Project, a massive international public-private effort to sequence the human genome, and several of its offshoots like The Cancer Genome Atlas (TCGA) and the Encyclopedia of DNA Elements (ENCODE). The resulting knowledge base will be instrumental in understanding not only how the Human Genome Project and genomics research developed collaboratively, but also how scientific goals come to be formulated and evolve. Given the diverse and rich data used in this project, we evaluated the ethical implications of employing AI and LLMs to process and analyze this valuable archive. As the first computational investigation of the internal archives of a massive collaborative project with multiple funders and institutions, this study will inform future efforts to conduct similar investigations while also considering and minimizing ethical challenges. Our methodology and risk-mitigating measures could also inform future initiatives in developing standards for project planning, policymaking, enhancing transparency, and ensuring ethical utilization of artificial intelligence technologies and large language models in archive exploration.Author Contributions: Mohammad Hosseini: Investigation; Project Administration; Writing – original draft; Writing – review & editing. Spencer Hong: Conceptualization, Data curation, Investigation, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing. Thomas Stoeger: Conceptualization; Investigation; Project Administration; Supervision; Writing – original draft; Writing – review & editing. Kristi Holmes: Funding acquisition, Supervision, Writing – review & editing. Luis A. Nunes Amaral: Funding acquisition, Supervision, Writing – review & editing. Christopher Donohue: Conceptualization, Project administration, Resources, Supervision, Writing – original draft, Writing – review & editing. Kris Wetterstrand: Conceptualization, Funding acquisition, Project administration.

Artificial Intelligence with Great Potential in Medical Informatics: A Brief Review

In the 1950s and 1960s, in molecular biology, information technology was mainly applied to the molecular evolution of proteins and DNA, and later expanded to multiple fields such as sequence alignment, protein structure prediction, and gene splicing. Entering the 21st century, the completion of the Human Genome Project marks the arrival of the era of biomedical big data, providing a large amount of data for the application of artificial intelligence in this field. Especially in recent years, the continuous accumulation of medical data has pushed the application of artificial intelligence in the medical field to a broader and more practical level. This paper briefly introduces the applications of artificial intelligence in genomics, proteomics, transcriptomics, epigenetics, drug development, and other fields. I hope this review can clearly introduce which biomedical fields artificial intelligence can be applied to, and also promote doctors and related scholars to actively use artificial intelligence technology to solve specific biomedical problems.

Are non-protein coding RNAs junk or treasure?: An attempt to explain and reconcile opposing viewpoints of whether the human genome is mostly transcribed into non-functional or functional RNAs.

The human genome project's lasting legacies are the emerging insights into human physiology and disease, and the ascendance of biology as the dominant science of the 21st century. Sequencing revealed that >90% of the human genome is not coding for proteins, as originally thought, but rather is overwhelmingly transcribed into non-protein coding, or non-coding, RNAs (ncRNAs). This discovery initially led to the hypothesis that most genomic DNA is "junk", a term still championed by some geneticists and evolutionary biologists. In contrast, molecular biologists and biochemists studying the vast number of transcripts produced from most of this genome "junk" often surmise that these ncRNAs have biological significance. What gives? This essay contrasts the two opposing, extant viewpoints, aiming to explain their bases, which arise from distinct reference frames of the underlying scientific disciplines. Finally, it aims to reconcile these divergent mindsets in hopes of stimulating synergy between scientific fields.

Precision Population Cancer Medicine in Cancer of the Uterine Cervix: A Potential Roadmap to Eradicate Cervical Cancer.

With the success of the Human Genome Project, the era of genomic medicine (GM) was born. Later on, as GM made progress, there was a feeling of exhilaration that GM could help resolve many disease processes. It also led to the conviction that personalized medicine was possible, and a relatively synonymous word, precision medicine (PM), was coined. However, the influence of environmental factors and social determinants of diseases was only partially given their due importance in the definition of PM, although more recently, this has been recognized. With the rapid advances in GM, big data, data mining, wearable devices for health monitoring, telemedicine, etc., PM can be more easily extended to population-level health care in disease management, prevention, early screening, and so on.and the term precision population medicine (PPM) more aptly describes it. PPM's potential in cancer care was posited earlier,and the current authors planned a series of cancer disease-specific follow-up articles. These papers are mainly aimed at helping emerging students in health sciences (medicine, pharmacy, nursing, dentistry, public health, population health), healthcare management (health-focused business administration, nonprofit administration, public institutional administration, etc.), and policy-making (e.g., political science), although not exclusively. This first disease-specific report focuses on the cancer of the uterine cervix (CC). It describes how recent breakthroughs can be leveraged as force multipliers to improve outcomes in CC - by improving early detection, better screening for CC, potential GM-based interventions during the stage of persistent Human papillomavirus (HPV) infection and treatment interventions - especially among the disadvantaged and resource-scarce populations. This work is a tiny step in our attempts to improve outcomes in CC and ultimately eradicate CC from the face of the earth.

A Quantitative Approach in Identifying Natural Selection Signals on Biallelic Single Nucleotide Polymorphisms of BRCA1 Gene in Diverse Populations

Population-specific studies reveal that cancer-related mechanisms of BRCA1 gene mutations may vary by ethnicity. The wealth of public genomic data provides insight into the functional roles of BRCA1 in diverse populations. In this study, we performed population differentiation analysis on biallelic SNPs located in the BRCA1 region using variant-calling data from the 1000 Human Genome Project. First, we conducted an Analysis of Molecular Variance (AMOVA) in global populations to infer a differentiation of BRCA1 gene in three population-related hierarchical levels. An FST-based approach was also conducted for each defined locus in the gene. Moreover, the signals of the natural selection in BRCA1 gene were computed using integrated Haplotype Score (iHS) per locus. The results demonstrated that BRCA1 gene differentiation can be attributed to the continental difference, for example, the genetic difference between Asian and African superpopulations accounts for 25% of the total variance. Imposing the iHS computation, we found that only two East Asian populations that underwent a positive selection, in which only benign variants were observed. In addition, those putative variants are only found in the non-coding regions: intron and 3’ UTR. Our study is expected to ignite research interest in cancer-related genes for underrepresented populations.

Did the human genome project affect research on Schizophrenia?

The Human Genome Project was undertaken primarily to discover genetic causes and better treatments for human diseases. Schizophrenia was targeted since three of the project`s principal architects had a personal interest and also because, based on family, adoption, and twin studies, schizophrenia was widely believed to be a genetic disorder. Extensive studies using linkage analysis, candidate genes, genome wide association studies [GWAS], copy number variants, exome sequencing and other approaches have failed to identify causal genes. Instead, they identified almost 300 single nucleotide polymorphisms [SNPs] associated with altered risks of developing schizophrenia as well as some rare variants associated with increased risk in a small number of individuals. Risk genes play a role in the clinical expression of most diseases but do not cause the disease in the absence of other factors. Increasingly, observers question whether schizophrenia is strictly a genetic disorder. Beginning in 1996 NIMH began shifting its research resources from clinical studies to basic research based on the promise of the Human Genome Project. Consequently, three decades later NIMH's genetics investment has yielded almost nothing clinically useful for individuals currently affected. It is time to review NIMH`s schizophrenia research portfolio.

SENNET CONSORTIUM: OPPORTUNITIES AND CHALLENGES

Abstract The Cellular Senescence Network (SenNet) Program was established in 2021 by the NIH Director’s Office Common Fund with the goal of comprehensively identifying and characterizing senescent cells across human lifespan in numerous organs. Mapping senescent cells in mice was added in 2022 to leverage genetic and pharmacologic approaches to drive, report, and ablate senescence in vivo. Senescent cells are implicated in driving most if not all age-related chronic diseases as well as accelerated aging caused by chronic diseases and their treatments (e.g., cancer chemotherapy). Furthermore, therapeutics specifically targeting senescent cells (senotherapeutics) have been developed and are currently being tested in clinical trials for multiple disease and geriatric endpoints. Thus, identifying and characterizing senescent cells throughout human aging and disease states has the potential to significantly impact human health. The SenNet Consortium is a highly collaborative network of scientists across the globe jointly benchmarking tools, technologies, and data analytic approaches, and aligning their activities with other NIH Common Fund initiatives, such as HuBMAP. While mapping senescent cells holds great biomedical promise, there are challenges such as the absence of specific biomarker to identify senescent cells, the appearance of senescence under physiological and pathological conditions, and little knowledge of what drives senescence in vivo. Analogous to the Human Genome Project, to achieve SenNet goals, innovative tools and technologies for single cell and spatial tissue analysis must be developed. Products of SenNet, in addition these new approaches, will include common nomenclature to define senescent cells, novel biomarkers of senescent cells, and publicly accessible databases and 4D atlases of senescent cells in 18 human and murine organs that enables 4D visualization.

Omics and AI in precision medicine: Maintaining socio-technical imaginaries by transforming technological assemblages

We understand precision medicine as a socio-technical imaginary according to Jasanoff. After briefly outlining how the imaginary of precision medicine emerged from the Human Genome Project and spread across national contexts, we raise the question of why the imaginary and the expectations and promises associated with it persist despite regular disappointment among practitioners about the failure of personalized healthcare. We argue that short-term technological hypes enable stakeholders to renew and maintain the promises of precision medicine. In our view, these hypes are around transformations of a technological assemblage. We discuss this in detail for omics and AI technologies and evaluate the recent transformations in light of the long-term imaginary of precision medicine.

A Global Survey of Precision Medicine in Emergency General Surgery

Abstract Background Since first sequencing the human genome, precision medicine has become a modern-day reality. However, translation to all areas of medicine has not been equal. This study aimed to provide a ‘status update’ of precision medicine in EGS to guide future priority setting. Methodology An observational study was conducted utilising two curated global on-line databases: 1. Pubmed publications from 2001 & 2. Clinical trials registered on ClinicalTrials.gov. The top 10 presenting conditions in EGS, Oncology, and Acute Medicine were identified from literature review. Publications/trials in these areas were identified using associated keywords: precision, personalised, genomics, genome-wide association studies, exome studies, epigenetics, transcriptomics, proteomics, metabolomics, microbiomics, and pharmacogenomics. Duplicated publications/trials were removed prior to final analysis and only active, recruiting or completed trials were included. Temporal trends were compared and annual growth rates were calculated. Results There were 209,423 EGS publications, 1807 (0.9%) being precision-focused. Oncology had 1,137,396 publications, with 127,556 precision-focused (11.2%), and Acute Medicine had 1,647,046 publications, with 80,240 (4.9%) precision-focused. The rate of rise of annual publications since 2001 (human genome project publication) was significantly (p<0.05) lower for EGS (x2.9) versus oncology (x3.5) and medicine (x6.0). Only 7 precision-focused clinical trials were identified for EGS compared to 96 for Oncology and 39 for Medicine. x10 fewer precision-focused EGS publications and trials exist over 20 years, compared to covid-19 over 2 years. Conclusion Research and implementation of precision medicine in EGS remains inadequate. Increased investment and improved global implementation are warranted to address the widening gap in translation.

Preimplantation Genetic Testing for Single Gene Conditions and Aneuploidy

It is now over 30 years since the first pregnancies and live births following IVF, embryo biopsy and preimplantation genetic testing (PGT) were reported in 1990. Since then, methods for genetic analysis at the single-cell level have evolved alongside the rapid advances in human genetics and sequencing of the human genome. With the development of methods for whole genome amplification, such as multiple displacement amplification (MDA), genome-wide analysis is now possible. This has led to the development of microarray or next generation sequencing (NGS) based methods for genome-wide haplotyping of parental chromosomes in embryo samples and enabled universal linkage-based testing for monogenic diseases combined with molecular cytogenetic analysis of chromosome abnormalities. These comprehensive methods have also recently been exploited to calculate polygenic risk scores for common multifactorial conditions, such as diabetes, cardiovascular disease, and cancer. Although controversial, polygenic risk scoring may have a role for reducing residual risk of breast cancer, for example, in combination with PGT for pathogenic variants. Finally, there is now evidence that cell-free DNA in spent culture medium at the blastocyst stage may originate from nuclear DNA shedding from the trophectoderm. In the future, therefore, it may be possible to use this cell-free DNA for genome-wide analysis and comprehensive PGT to avoid embryo biopsy.

Genomics education for advance practice nurses: Staying cutting edge.

As we celebrate the 20th anniversary of sequencing of the human genome and the rapid integration of genetics in health care, we pause to reflect on the status of genomic competency in nursing. The literature provides evidence that nurses do not feel prepared or confident in genomics. Genomic education for nurses and other health care professionals can support access and equity in the integration of genomics into practice. Resources are provided to support genomic education and clinical support.

A substituição nua: o apoio de geneticistas e biotecnólogos à eugenia da Segunda Guerra Mundial ao Projeto Genoma Humano

ABSTRACT Using a combination of archival and secondary sources, this article argues that scientists and bioethicists after the Second World War advocated a wide variety of eugenic practices and strongly supported the development of eugenics, strengthened by advances in medicine, human genetics, and population genetics. I detail extensive research in both the Curt Stern and the American Eugenics Society Papers (American Philosophical Society in Philadelphia), uncovering novel defenses of eugenics and its integration in sciences after the Second World War by key figures such as Curt Stern and Theodosius Dobzhansky. Moreover, I relate to Richard Lewontin’s participation in a Princeton conference sponsored by the American Eugenics Society in 1965. This article is also the first to not only describe geneticists’ defense and development of eugenic ideas but also details bioethicists’ defense of this inhumane and unscientific ideology until the dawn of the Human Genome Project in the late 1980s.

A conceptual analysis of public opinion regarding genome research in Japan

In the 20 years since the completion of the Human Genome Project, the gap between scientific development and public understanding of genome research has been widening. While genome research has been increasingly utilized for social and clinical purposes in a multifaceted manner, this has resulted in an increase in the potential risks associated with genomic data. In this context, our study aims to consider the nature of public perceptions of genome research, primarily by using as a case study the results of previous public surveys relevant to donations for social benefits in Japan. We explored certain types of awareness, attitude, and intention (A-A-I) in such surveys and discussed the resultant key findings through the cultural transmission framework. Reframing the public’s response toward genome research based on A-A-I analysis and behavioral science may contribute to developing more systematic communication approaches with the public. With a view to establishing such approaches, our perspective suggests some new insights to discuss the science–society gap in genome research internationally.

Genomic insights and prognostic significance of novel biomarkers in pancreatic ductal adenocarcinoma: A comprehensive analysis

Pancreatic ductal adenocarcinoma (PDAC) is a highly prevalent digestive system malignancy, with a significant impact on public health, especially in the elderly population. The advent of the Human Genome Project has opened new avenues for precision medicine, allowing researchers to explore genetic markers and molecular targets for cancer diagnosis and treatment. Despite significant advances in genomic research, early diagnosis of pancreatic cancer remains elusive due to the lack of highly sensitive and specific markers. Therefore, there is a need for in-depth research to identify more precise and reliable diagnostic markers for pancreatic cancer. In this study, we utilized a combination of public databases from different sources to meticulously screen genes associated with prognosis in pancreatic cancer. We used gene differential analysis, univariate cox regression analysis, least absolute selection and shrinkage operator (LASSO) regression, and multivariate cox regression analysis to identify genes associated with prognosis. Subsequently, we constructed a scoring system, validated its validity using survival analysis and ROC analysis, and further confirmed its reliability by nomogram and decision curve analysis (DCA). We evaluated the diagnostic value of this scoring system for pancreatic cancer prognosis and validated the function of the genes using single cell data analysis. Our analysis identifies six genes, including GABRA3, IL20RB, CDK1, GPR87, TTYH3, and KCNA2, that were strongly associated with PDAC prognosis. Clinical prognostic models based on these genes showed strong predictive power not only in the training set but also in external datasets. Functional enrichment analysis revealed significant differences between high- and low-risk groups mainly in immune-related functions. Additionally, we explored the potential of the risk score as a marker for immunotherapy response and identified key factors within the tumor microenvironment. The single-cell RNA sequencing analysis further enriched our understanding of cell clusters and six hub genes expressions. This comprehensive investigation provides valuable insights into pancreatic PDAC and its intricate immune landscape. The identified genes and their functional significance underscore the importance of continued research into improving diagnosis and treatment strategies for PDAC.

Comprehensive genomic profiling for oncological advancements by precision medicine.

Considerable advancements in next generation sequencing (NGS) techniques have sparked the use of comprehensive genomic profiling (CGP) as a guiding tool for precision-centered oncological treatments. The past two decades have seen the completion of the human genome project, and the consequential invention of NGS. High-throughput sequencing technologies support the discovery and commonplace use of individualized cancer treatments, specifically immune-centered checkpoint inhibitor therapies, and oncogene and tumor suppressor gene targeted therapies. Nevertheless, CGP is not commonly used in all clinical settings. This review investigates the clinically relevant applications of CGP. Studies published between the years 2000-2023 have shown substantial evidence of the benefits of integrating CGP into routine care practice, while also making important comparisons to current-standard oncological treatment strategies. Findings of a comprehensive genomic profile includes predictive, prognostic, and diagnostic biomarkers, together with somatic mutation identification which can indicate the efficacy of immunotherapies and molecularly guided therapies. This review highlights the importance of CGP in identifying driver mutations in tumors that subsequently can be effectively targeted with molecular therapeutics and lead to drug discovery, allowing for increased precision in treating tumors selectively based on their specific genetic mutations, thereby improving patient outcomes.