Translational genomics and precision medicine: Moving from the lab to the clinic

A roadmap for genome projects to foster psychosocial and economic evidence to further policy and practice

Advances in genomic sequencing (GS) have transformed personalised treatment strategies for genetic diseases across a diverse array of clinical indications, resulting in notable public health progress. However, limited evidence on the broader psychosocial and economic impacts hinders its widespread adoption in healthcare systems. The launch of genome projects offers an opportunity to address the unmet needs of a wide range of genetic diseases. This Perspective examines the multi-dimensional effectiveness of GS and summarises indicators and measurement tools for psychosocial and economic outcomes. It highlights priority areas identified by the Clinical Sequencing Exploratory Research Consortium. Drawing on initiatives such as the Genomics England 100,000 Genomes Project and Australian Genomics initiative, this article showcases best practices in selecting outcome measures for assessing the effectiveness of GS in policy and practice. This Perspective intends to equip future studies with a strategic and sustainable approach for outcome-oriented research within genome projects, facilitating evidence-based clinical implementation of GS in an appropriate, equitable and efficient manner.

Genetic Variants Associated with Suspected Neonatal Hypoxic Ischaemic Encephalopathy: A Study in a South African Context

Neonatal encephalopathy suspected to be due to hypoxic ischaemic encephalopathy (NESHIE) carries the risk of death or severe disability (cognitive defects and cerebral palsy). Previous genetic studies on NESHIE have predominantly focused on exomes or targeted genes. The objective of this study was to identify genetic variants associated with moderate-severe NESHIE through whole-genome, unbiased analysis. Variant filtering and prioritization were performed, followed by association testing both on a case-control basis and to compare the grades of severity and/or progression. Association testing on neonates with NESHIE (N = 172) and ancestry-matched controls (N = 288) produced 71 significant genetic variants (false discovery rate corrected p-value < 6.2 × 10-4), all located in non-coding regions and not previously implicated in NESHIE. Disease-associated variants in non-coding regions are considered to affect regulatory functions, possibly by modifying gene expression, promoters, enhancers, or DNA structure. The most significant variant was at position 6:162010973 in the Parkin RBR E3 ubiquitin protein ligase (PRKN) intron. Intronic variants were also identified in genes involved in inflammatory processes (SLCO3A1), DNA repair (ZGRF1), synaptogenesis (CNTN5), haematopoiesis (ASXL2), and the transcriptional response to hypoxia (PADI4). Ten variants were associated with a higher severity or lack of improvement in NESHIE, including one in ADAMTS3, which encodes a procollagen amino protease with a role in angiogenesis and lymphangiogenesis. This analysis represents one of the first efforts to analyze whole-genome data to investigate the genetic complexity of NESHIE in diverse ethnolinguistic groups of African origin and provides direction for further study.

De novo familial adenomatous polyposis with germline double heterozygosity of APC/BRCA2: a case report and literature review

BACKGROUND

The widespread application of colonoscopy screening and genetic testing in colorectal cancer (CRC) treatment has led to the identification of a subset of familial adenomatous polyposis (FAP) patients who lack a family history of the disease but harbor germline gene mutations. Moreover, distinct genotypes may be associated with varied clinical presentations and therapeutic options. This case report describes a male patient with de novo FAP who harbored germline double heterozygosity (GDH) for APC and BRCA2 mutations. The patient underwent total colectomy, and genetic testing enabled personalized surveillance and management strategies for his family members.

CASE PRESENTATION

A 43-year-old male with no family history of cancer presented to the outpatient clinic of the Colorectal Surgery Department with complaints of constipation and hematochezia. Colonoscopy revealed hundreds of polyps throughout the colon and a rectal adenocarcinoma located 5 cm from the anal verge. Gastroduodenal endoscopy did not detect any upper gastrointestinal adenomas. The patient underwent laparoscopic total colectomy with abdominoperineal resection of the rectum and end ileostomy. With the consent of the patient and his family, genetic testing was performed. The index patient was found to carry an APC splicing site mutation (exon 15: c.1744-1G > A) and a BRCA2 missense mutation (exon 17: c.7976G > A: p.R2659K). His daughter was found to have inherited the same germline BRCA2 variant. Additionally, the rectal cancer exhibited proficient DNA mismatch repair (pMMR) status, ERBB2 copy number amplification, and a missense mutation, while the KRAS, NRAS, and BRAF genes were wild-type. Based on the genetic testing results and clinical manifestations, the index patient was diagnosed with familial adenomatous polyposis (FAP) and rectal cancer. Personalized surveillance and management strategies were implemented for the patient and his family, focusing on the risks of extra-colonic diseases and potential malignancies in the prostate, pancreas, breast, and ovaries.

CONCLUSION

De novo FAP with double germline mutations in APC and BRCA2, along with somatic ERBB2 mutations, is exceptionally rare among hereditary cancer cases. With the rapid advancements in genomics, the detection of multiple gene variants in individuals or families has become increasingly common. Additionally, the application of artificial intelligence (AI) in medical research may provide powerful tools for genetic analysis and clinical decision-making. Consequently, a comprehensive evaluation of family history, a deep understanding of hereditary cancer syndromes, and precise interpretation of genetic mutations are essential for personalized clinical management in the era of precision medicine. However, these tasks pose significant challenges for clinicians and genetic counselors alike.

The pharmacogenomic landscape in the Chinese: An analytics of pharmacogenetic variants in 206,640 individuals

Pharmacogenomic landscapes and related databases are important for identifying the biomarkers of drug response and toxicity. However, these data are still lacking for the Chinese population. In this study, we constructed a pharmacogenomic landscape and an associated database using whole-genome sequencing data generated by non-invasive prenatal testing in 206,640 Chinese individuals. In total, 1,577,513 variants (including 331,610 novel variants) were identified among 3,538 pharmacogenes related to 2,086 drugs. We found that the variant spectrum in the Chinese population differed among the seven major regions. Regional differences also exist among provinces in China. The average numbers of drug enzyme, transporter, and receptor variants were 258, 557, and 632, respectively. Subsequent correlation analysis indicated that the pharmacogenes affecting multiple drugs had fewer variants. Among the 16 categories of drugs, we found that nervous system, cardiovascular system, and genitourinary system/sex hormone drugs were more likely to be affected by variants of pharmacogenes. Characteristics of the variants in the enzyme, transporter, and receptor subfamilies showed specificity. To explore the clinical utility of these data, a genetic association study was conducted on 1,019 lung cancer patients. Two novel variants, AKT2 chr19:40770621 C>G and SLC19A1 chr21:46934171 A>C, were identified as novel platinum response biomarkers. Finally, a pharmacogenomic database, named the Chinese Pharmacogenomic Knowledge Base (CNPKB: http://www.cnpkb.com.cn/), was constructed to collect all the data. In summary, a pharmacogenomic landscape and database for the Chinese population were constructed in this study, which could support personalized Chinese medicine in the future.

Live-cell imaging of human liver fibrosis using hepatic micro-organoids

Due to the limitations of available in vitro systems and animal models, we lack a detailed understanding of the pathogenetic mechanisms of and have minimal treatment options for liver fibrosis. Therefore, we engineered a live-cell imaging system that assessed fibrosis in a human multilineage hepatic organoid in a microwell (i.e., microHOs). Transcriptomic analysis revealed that TGFB converted mesenchymal cells in microHOs into myofibroblast-like cells resembling those in fibrotic human liver tissue. When pro-fibrotic intracellular signaling pathways were examined, the antifibrotic effect of receptor-specific tyrosine kinase inhibitors was limited to the fibrosis induced by the corresponding growth factor, which indicates their antifibrotic efficacy would be limited to fibrotic diseases solely mediated by that growth factor. Based upon transcriptomic and transcription factor activation analyses in microHOs, glycogen synthase kinase 3β and p38 MAPK inhibitors were identified as potential new broad-spectrum therapies for liver fibrosis. Other new therapies could subsequently be identified using the microHO system.

Promises and challenges of crop translational genomics

Crop translational genomics applies breeding techniques based on genomic datasets to improve crops. Technological breakthroughs in the past ten years have made it possible to sequence the genomes of increasing numbers of crop varieties and have assisted in the genetic dissection of crop performance. However, translating research findings to breeding applications remains challenging. Here we review recent progress and future prospects for crop translational genomics in bringing results from the laboratory to the field. Genetic mapping, genomic selection and sequence-assisted characterization and deployment of plant genetic resources utilize rapid genotyping of large populations. These approaches have all had an impact on breeding for qualitative traits, where single genes with large phenotypic effects exert their influence. Characterization of the complex genetic architectures that underlie quantitative traits such as yield and flowering time, especially in newly domesticated crops, will require further basic research, including research into regulation and interactions of genes and the integration of genomic approaches and high-throughput phenotyping, before targeted interventions can be designed. Future priorities for translation include supporting genomics-assisted breeding in low-income countries and adaptation of crops to changing environments.

Role of artificial intelligence in critical care nutrition support and research

Nutrition plays a key role in the comprehensive care of critically ill patients. Determining optimal nutrition strategy, however, remains a subject of intense debate. Artificial intelligence (AI) applications are becoming increasingly common in medicine, and specifically in critical care, driven by the data-rich environment of intensive care units. In this review, we will examine the evidence regarding the application of AI in critical care nutrition. As of now, the use of AI in critical care nutrition is relatively limited, with its primary emphasis on malnutrition screening and tolerance of enteral nutrition. Despite the current scarcity of evidence, the potential for AI for more personalized nutrition management for critically ill patients is substantial. This stems from the ability of AI to integrate multiple data streams reflecting patients' changing needs while addressing inherent heterogeneity. The application of AI in critical care nutrition holds promise for optimizing patient outcomes through tailored and adaptive nutrition interventions. A successful implementation of AI, however, necessitates a multidisciplinary approach, coupled with careful consideration of challenges related to data management, financial aspects, and patient privacy.

The Influencing Factors of Psychosocial Adaptation of Cancer Patients: A Systematic Review and Meta-Analysis

Purpose

The psycho-social adaptation of cancer patients is very important, which affects the treatment, rehabilitation process and prognosis of patients, and is closely related to the subjective well-being and quality of life of patients. However, the key factors affecting the psycho-social adjustment of cancer patients are not clear yet. This study aims to evaluate the psycho-social adaptation of cancer patients and its influencing factors based on a meta-analysis.

Basic procedures

The Systematic review was conducted following the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) checklist and guided by the society-to-cell model framework. Literature retrieval was conducted from the construction of the library to December 2023.

Main findings

Fourteen pieces of literature were included in this study, with a total sample size of 2922 cases. Among the 14 literatures included, 9 were in English and 5 were in Chinese, published between 1991 and 2021. All of the 14 literatures were cross-sectional studies. According to the society-to-cells model framework, the influencing factors are divided into 5 levels: society, community, family, individual, and physiology. However, studies related to the cellular level are lacking.

Principal conclusions

The psychosocial adaptation of cancer patients is affected by physiology, individual, family, community and society, among which age, education level, disease uncertainty, hope level, psychological pain, self-efficacy, social support, coping styles (facing, avoidance, submission, and emotion-oriented) are the main factors affecting the psychosocial adaptation of cancer patients. However, studies related to the cellular level are lacking. This may be due to the fact that most of the factors from the individual to the society level are intervenable, and most studies focus more on the mining of these levels of factors. However, the biological basis is crucial to the occurrence and development of diseases, and needs to be paid attention to by nursing staff, and further research on this level needs to be strengthened in the future.

Asymmetrical genetic attributions for the presence and absence of health problems

OBJECTIVE

Recent research has suggested that people more readily make genetic attributions for positively valenced or desirable traits than for negatively valenced or undesirable traits-an asymmetry that may be mediated by perceptions that positive characteristics are more 'natural' than negative ones. This research sought to examine whether a similar asymmetry in genetic attributions would emerge between positive and negative health outcomes.

DESIGN

Across seven experiments, participants were randomly assigned to read a short vignette describing an individual experiencing a health problem (e.g. hypertension) or a corresponding healthy state (e.g. normal blood pressure).

MAIN OUTCOME MEASURES

All participants provided ratings of naturalness and genetic attributions for the outcome described in their assigned vignette.

RESULTS

For diagnoses other than addictive disorders, participants rated the presence of a diagnosis as less genetically caused than its absence; for addictive disorders, the presence of a diagnosis was rated as more genetically caused than its absence. Participants consistently rated the presence of a health problem as less natural than its absence.

CONCLUSION

Even within a single domain of health, people ascribe differing degrees of 'naturalness' and genetic causation to positive versus negative health outcomes, which could impact their preferences for treatment and prevention strategies.

A Novel Macrophage Subpopulation Conveys Increased Genetic Risk of Coronary Artery Disease

BACKGROUND

Coronary artery disease (CAD), the leading cause of death worldwide, is influenced by both environmental and genetic factors. Although over 250 genetic risk loci have been identified through genome-wide association studies, the specific causal variants and their regulatory mechanisms are still largely unknown, particularly in disease-relevant cell types such as macrophages.

METHODS

We utilized single-cell RNA-seq and single-cell multiomics approaches in primary human monocyte-derived macrophages to explore the transcriptional regulatory network involved in a critical pathogenic event of coronary atherosclerosis-the formation of lipid-laden foam cells. The relative genetic contribution to CAD was assessed by partitioning disease heritability across different macrophage subpopulations. Meta-analysis of single-cell RNA-seq data sets from 38 human atherosclerotic samples was conducted to provide high-resolution cross-referencing to macrophage subpopulations in vivo.

RESULTS

We identified 18 782 cis-regulatory elements by jointly profiling the gene expression and chromatin accessibility of >5000 macrophages. Integration with CAD genome-wide association study data prioritized 121 CAD-related genetic variants and 56 candidate causal genes. We showed that CAD heritability was not uniformly distributed and was particularly enriched in the gene programs of a novel CD52-hi lipid-handling macrophage subpopulation. These CD52-hi macrophages displayed significantly less lipoprotein accumulation and were also found in human atherosclerotic plaques. We investigated the cis-regulatory effect of a risk variant rs10488763 on FDX1, implicating the recruitment of AP-1 and C/EBP-β in the causal mechanisms at this locus.

CONCLUSIONS

Our results provide genetic evidence of the divergent roles of macrophage subsets in atherogenesis and highlight lipid-handling macrophages as a key subpopulation through which genetic variants operate to influence disease. These findings provide an unbiased framework for functional fine-mapping of genome-wide association study results using single-cell multiomics and offer new insights into the genotype-environment interactions underlying atherosclerotic disease.

Targeted delivery strategies: The interactions and applications of nanoparticles in liver diseases

In recent years, nanoparticles have been broadly utilized in various drugs delivery formulations. Nanodelivery systems have shown promise in solving problems associated with the distribution of hydrophobic drugs and have promoted the accumulation of nanomedicines in the circulation or in organs. However, the injection dose of nanoparticles (NPs) is much greater than that needed by diseased tissues or organs. In other words, most of the NPs are localized off-target and do not reach the desired tissue or organs. With the rapid development of biodegradable and biosafety nanomaterials, the nanovectors represent assurance of safety. However, the off-target effects also induce concerns about the application of NPs, especially in the delivery of gene editing tools. Therefore, a complete understanding of the biological responses to NPs in the body will clearly guide the design of targeted delivery of NPs. The different properties of various nanodelivery systems may induce diverse interactions between carriers and organs. In this review, we describe the relationship between the liver, the most influenced organ of systemic administration of NPs, and targeted delivery nanoplatforms. Various transport vehicles have adopted multiple delivery strategies for the targeted delivery to the cells in the homeostasis liver and in diseased liver. Additionally, nanodelivery systems provide a novel strategy for treating incurable diseases. The appearance of a targeted delivery has profoundly improved the application of NPs to liver diseases.

CardioHotspots: a database of mutational hotspots for cardiac disorders

Mutational hotspots are DNA regions with an abnormally high frequency of genetic variants. Identifying whether a variant is located in a mutational hotspot is critical for determining the variant's role in disorder predisposition, development, and treatment response. Despite their significance, current databases on mutational hotspots are limited to the oncology domain. However, identifying mutational hotspots is critical for any disorder in which genetics plays a role. This is true for the world's leading cause of death: cardiac disorders. In this work, we present CardioHotspots, a literature-based database of manually curated hotspots for cardiac diseases. This is the only database we know of that provides high-quality and easily accessible information about hotspots associated with cardiac disorders. CardioHotspots is publicly accessible via a web-based platform (https://genomics-hub.pros.dsic.upv.es:3099/). Database URL: https://genomics-hub.pros.dsic.upv.es:3099/.

The transition from genomics to phenomics in personalized population health

Modern health care faces several serious challenges, including an ageing population and its inherent burden of chronic diseases, rising costs and marginal quality metrics. By assessing and optimizing the health trajectory of each individual using a data-driven personalized approach that reflects their genetics, behaviour and environment, we can start to address these challenges. This assessment includes longitudinal phenome measures, such as the blood proteome and metabolome, gut microbiome composition and function, and lifestyle and behaviour through wearables and questionnaires. Here, we review ongoing large-scale genomics and longitudinal phenomics efforts and the powerful insights they provide into wellness. We describe our vision for the transformation of the current health care from disease-oriented to data-driven, wellness-oriented and personalized population health.

Emerging applications of machine learning in genomic medicine and healthcare

The integration of artificial intelligence technologies has propelled the progress of clinical and genomic medicine in recent years. The significant increase in computing power has facilitated the ability of artificial intelligence models to analyze and extract features from extensive medical data and images, thereby contributing to the advancement of intelligent diagnostic tools. Artificial intelligence (AI) models have been utilized in the field of personalized medicine to integrate clinical data and genomic information of patients. This integration allows for the identification of customized treatment recommendations, ultimately leading to enhanced patient outcomes. Notwithstanding the notable advancements, the application of artificial intelligence (AI) in the field of medicine is impeded by various obstacles such as the limited availability of clinical and genomic data, the diversity of datasets, ethical implications, and the inconclusive interpretation of AI models' results. In this review, a comprehensive evaluation of multiple machine learning algorithms utilized in the fields of clinical and genomic medicine is conducted. Furthermore, we present an overview of the implementation of artificial intelligence (AI) in the fields of clinical medicine, drug discovery, and genomic medicine. Finally, a number of constraints pertaining to the implementation of artificial intelligence within the healthcare industry are examined.

Precision Medicine in Nephrology: An Integrative Framework of Multidimensional Data in the Kidney Precision Medicine Project

Chronic kidney disease (CKD) and acute kidney injury (AKI) are heterogeneous syndromes defined clinically by serial measures of kidney function. Each condition possesses strong histopathologic associations, including glomerular obsolescence or acute tubular necrosis, respectively. Despite such characterization, there remains wide variation in patient outcomes and treatment responses. Precision medicine efforts, as exemplified by the Kidney Precision Medicine Project (KPMP), have begun to establish evolving, spatially anchored, cellular and molecular atlases of the cell types, states, and niches of the kidney in health and disease. The KPMP atlas provides molecular context for CKD and AKI disease drivers and will help define subtypes of disease that are not readily apparent from canonical functional or histopathologic characterization but instead are appreciable through advanced clinical phenotyping, pathomic, transcriptomic, proteomic, epigenomic, and metabolomic interrogation of kidney biopsy samples. This perspective outlines the structure of the KPMP, its approach to the integration of these diverse datasets, and its major outputs relevant to future patient care.

Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases

Phenotype-guided gene prioritizers have proved a highly efficient approach to identifying causal genes for Mendelian diseases. In our previous study, we preliminarily evaluated the performance of ten prioritizers. However, all the selected software was run based on default settings and singleton mode. With a large-scale family dataset from Deciphering Developmental Disorders (DDD) project (N = 305) and an in-house trio cohort (N = 152), the four optimal performers in our prior study including Exomiser, PhenIX, AMELIE, and LIRCIAL were further assessed through parameter optimization and/or the utilization of trio mode. The in-depth assessment revealed high diagnostic yields of the four prioritizers with refined preferences, each alone or together: (1) 83.3-91.8% of the causal genes were presented among the first ten candidates in the final ranking lists of the four tools; (2) Over 97.7% of the causal genes were successfully captured within the top 50 by either of the four software. Exomiser did best in directly hitting the target (ranking the causal gene at the very top) while LIRICAL displayed a predominant overall detection capability. Besides, cases affected by low-penetrance and high-frequency pathogenic variants were found misjudged during the automated prioritization process. The discovery of the limitations shed light on the specific directions of future enhancement for causal-gene ranking tools.

Potential of CDC25 phosphatases in cancer research and treatment: key to precision medicine

The global burden of cancer continues to rise, underscoring the urgency of developing more effective and precisely targeted therapies. This comprehensive review explores the confluence of precision medicine and CDC25 phosphatases in the context of cancer research. Precision medicine, alternatively referred to as customized medicine, aims to customize medical interventions by taking into account the genetic, genomic, and epigenetic characteristics of individual patients. The identification of particular genetic and molecular drivers driving cancer helps both diagnostic accuracy and treatment selection. Precision medicine utilizes sophisticated technology such as genome sequencing and bioinformatics to elucidate genetic differences that underlie the proliferation of cancer cells, hence facilitating the development of customized therapeutic interventions. CDC25 phosphatases, which play a crucial role in governing the progression of the cell cycle, have garnered significant attention as potential targets for cancer treatment. The dysregulation of CDC25 is a characteristic feature observed in various types of malignancies, hence classifying them as proto-oncogenes. The proteins in question, which operate as phosphatases, play a role in the activation of Cyclin-dependent kinases (CDKs), so promoting the advancement of the cell cycle. CDC25 inhibitors demonstrate potential as therapeutic drugs for cancer treatment by specifically blocking the activity of CDKs and modulating the cell cycle in malignant cells. In brief, precision medicine presents a potentially fruitful option for augmenting cancer research, diagnosis, and treatment, with an emphasis on individualized care predicated upon patients' genetic and molecular profiles. The review highlights the significance of CDC25 phosphatases in the advancement of cancer and identifies them as promising candidates for therapeutic intervention. This statement underscores the significance of doing thorough molecular profiling in order to uncover the complex molecular characteristics of cancer cells.

Evaluation of noninvasive biospecimens for transcriptome studies

Transcriptome studies disentangle functional mechanisms of gene expression regulation and may elucidate the underlying biology of disease processes. However, the types of tissues currently collected typically assay a single post-mortem timepoint or are limited to investigating cell types found in blood. Noninvasive tissues may improve disease-relevant discovery by enabling more complex longitudinal study designs, by capturing different and potentially more applicable cell types, and by increasing sample sizes due to reduced collection costs and possible higher enrollment from vulnerable populations. Here, we develop methods for sampling noninvasive biospecimens, investigate their performance across commercial and in-house library preparations, characterize their biology, and assess the feasibility of using noninvasive tissues in a multitude of transcriptomic applications. We collected buccal swabs, hair follicles, saliva, and urine cell pellets from 19 individuals over three to four timepoints, for a total of 300 unique biological samples, which we then prepared with replicates across three library preparations, for a final tally of 472 transcriptomes. Of the four tissues we studied, we found hair follicles and urine cell pellets to be most promising due to the consistency of sample quality, the cell types and expression profiles we observed, and their performance in disease-relevant applications. This is the first study to thoroughly delineate biological and technical features of noninvasive samples and demonstrate their use in a wide array of transcriptomic and clinical analyses. We anticipate future use of these biospecimens will facilitate discovery and development of clinical applications.

Proof-of-concept recall-by-genotype study of extremely low and high Alzheimer's polygenic risk reveals autobiographical deficits and cingulate cortex correlates

BACKGROUND

Genome-wide association studies demonstrate that Alzheimer's disease (AD) has a highly polygenic architecture, where thousands of independent genetic variants explain risk with high classification accuracy. This AD polygenic risk score (AD-PRS) has been previously linked to preclinical cognitive and neuroimaging features observed in asymptomatic individuals. However, shared variance between AD-PRS and neurocognitive features are small, suggesting limited preclinical utility.

METHODS

Here, we recruited sixteen clinically asymptomatic individuals (mean age 67; range 58-76) with either extremely low / high AD-PRS (defined as at least 2 standard deviations from the wider sample mean (N = 4504; N EFFECTIVE = 90)) with comparable age sex and education level. We assessed group differences in autobiographical memory and T1-weighted structural neuroimaging features.

RESULTS

We observed marked reductions in autobiographical recollection (Cohen's d =  - 1.66; P FDR = 0.014) and midline structure (cingulate) thickness (Cohen's d =  - 1.55, P FDR = 0.05), with no difference in hippocampal volume (P > 0.3). We further confirm the negative association between AD-PRS and cingulate thickness in a larger study with a comparable age (N = 31,966, β =  - 0.002, P = 0.011), supporting the validity of our approach.

CONCLUSIONS

These observations conform with multiple streams of prior evidence suggesting alterations in cingulate structures may occur in individuals with higher AD genetic risk. We were able to use a genetically informed research design strategy that significantly improved the efficiency and power of the study. Thus, we further demonstrate that the recall-by-genotype of AD-PRS from wider samples is a promising approach for the detection, assessment, and intervention in specific individuals with increased AD genetic risk.

Pretreatment with Lovastatin Improves Depression-Like Behavior After Traumatic Brain Injury Through Activation of the AMPK Pathway

BACKGROUND

The beneficial effect of pretreatment with statins on traumatic brain injury (TBI)-induced depression and anxiety and its mechanism of action remain unclear. In this study, we combined epidemiological and experimental animal data to clarify this issue.

METHODS

We used the Taiwan National Health Insurance database to identify patients who were diagnosed with TBI from 2000 to 2013 and compared patients with and without statin treatment matched by age, sex, and underlying comorbidities in a 1:1 ratio. The risk of developing depression and/or anxiety was compared between patients with and without a statin using Cox proportional hazards regression. We also used a rat model to assess the effect of lovastatin pretreatment on neurobehavioral and neuropathological changes following TBI.

RESULTS

The risk of developing depression was lower in the 41,803 patients in the statin cohort than nonstatin cohort (adjusted hazard ratio, 0.91 [95% confidence interval, 0.83-0.99]). In animal models, the lovastatin group had significantly reduced infarct volume, decreased immobility time and latency to eat, a reduced number of Fluoro- Jade-positive cells and levels of glial fibrillary acidic protein and tumor necrosis factor-alpha, and increased adenosine monophosphate -activated protein kinase (AMPK) and its upstream kinase liver kinase B1 in the hippocampal dentate gyrus. These effects were blocked in AMPK inhibitor-pretreated TBI rats.

CONCLUSIONS

Our epidemiological data showed that a decreased risk of depression was associated with statin pretreatment, which was supported by an animal study. The underlying mechanism for this appears to involve AMPK activation in the statin pretreatment-induced alleviation of TBI.

Update on Surgical Management of FAP

Familial adenomatous polyposis (FAP) is an autosomal dominant disease caused by pathogenic germline adenomatous polyposis coli mutation, and characterized with multiple adenomas in the colon and the rectum. Various genetic variants have been confirmed to be associated with corresponding FAP phenotypes, which play important roles in the diagnosis and surgical treatment of FAP. Generally, proctocolectomy is recommended for FAP patients at the age of 20s. Exceptionally, for patients with attenuated FAP, high-risk of desmoid, chemoprevention therapy, or other circumstances, surgery can be postponed. With the wide application of minimal invasive surgery in colorectal cancer, laparoscopic, robotic surgery, and natural orifice specimen extraction are proved to be feasible for FAP patients, but high-level evidences are needed to confirm their safety and advantages. In the times of precise medicine, the surgical management of FAP should vary with individuals based on genotype, phenotype, and clinical practice. Therefore, in addition to innovation in surgical procedures, investigation in links between genetic features and phenotypes will be helpful to optimize the surgical management of FAP in the future.

XMAP: Cross-population fine-mapping by leveraging genetic diversity and accounting for confounding bias

Fine-mapping prioritizes risk variants identified by genome-wide association studies (GWASs), serving as a critical step to uncover biological mechanisms underlying complex traits. However, several major challenges still remain for existing fine-mapping methods. First, the strong linkage disequilibrium among variants can limit the statistical power and resolution of fine-mapping. Second, it is computationally expensive to simultaneously search for multiple causal variants. Third, the confounding bias hidden in GWAS summary statistics can produce spurious signals. To address these challenges, we develop a statistical method for cross-population fine-mapping (XMAP) by leveraging genetic diversity and accounting for confounding bias. By using cross-population GWAS summary statistics from global biobanks and genomic consortia, we show that XMAP can achieve greater statistical power, better control of false positive rate, and substantially higher computational efficiency for identifying multiple causal signals, compared to existing methods. Importantly, we show that the output of XMAP can be integrated with single-cell datasets, which greatly improves the interpretation of putative causal variants in their cellular context at single-cell resolution.

Automated machine learning for genome wide association studies

MOTIVATION

Genome-wide association studies (GWAS) present several computational and statistical challenges for their data analysis, including knowledge discovery, interpretability, and translation to clinical practice.

RESULTS

We develop, apply, and comparatively evaluate an automated machine learning (AutoML) approach, customized for genomic data that delivers reliable predictive and diagnostic models, the set of genetic variants that are important for predictions (called a biosignature), and an estimate of the out-of-sample predictive power. This AutoML approach discovers variants with higher predictive performance compared to standard GWAS methods, computes an individual risk prediction score, generalizes to new, unseen data, is shown to better differentiate causal variants from other highly correlated variants, and enhances knowledge discovery and interpretability by reporting multiple equivalent biosignatures.

AVAILABILITY AND IMPLEMENTATION

Code for this study is available at: https://github.com/mensxmachina/autoML-GWAS. JADBio offers a free version at: https://jadbio.com/sign-up/. SNP data can be downloaded from the EGA repository (https://ega-archive.org/). PRS data are found at: https://www.aicrowd.com/challenges/opensnp-height-prediction. Simulation data to study population structure can be found at: https://easygwas.ethz.ch/data/public/dataset/view/1/.

Progress and Perspective of CRISPR-Cas9 Technology in Translational Medicine

Translational medicine aims to improve human health by exploring potential treatment methods developed during basic scientific research and applying them to the treatment of patients in clinical settings. The advanced perceptions of gene functions have remarkably revolutionized clinical treatment strategies for target agents. However, the progress in gene editing therapy has been hindered due to the severe off-target effects and limited editing sites. Fortunately, the development in the clustered regularly interspaced short palindromic repeats associated protein 9 (CRISPR-Cas9) system has renewed hope for gene therapy field. The CRISPR-Cas9 system can fulfill various simple or complex purposes, including gene knockout, knock-in, activation, interference, base editing, and sequence detection. Accordingly, the CRISPR-Cas9 system is adaptable to translational medicine, which calls for the alteration of genomic sequences. This review aims to present the latest CRISPR-Cas9 technology achievements and prospect to translational medicine advances. The principle and characterization of the CRISPR-Cas9 system are firstly introduced. The authors then focus on recent pre-clinical and clinical research directions, including the construction of disease models, disease-related gene screening and regulation, and disease treatment and diagnosis for multiple refractory diseases. Finally, some clinical challenges including off-target effects, in vivo vectors, and ethical problems, and future perspective are also discussed.

Meta-analysis of the diagnostic and clinical utility of exome and genome sequencing in pediatric and adult patients with rare diseases across diverse populations

PURPOSE

This meta-analysis aims to compare the diagnostic and clinical utility of exome sequencing (ES) vs genome sequencing (GS) in pediatric and adult patients with rare diseases across diverse populations.

METHODS

A meta-analysis was conducted to identify studies from 2011 to 2021.

RESULTS

One hundred sixty-one studies across 31 countries/regions were eligible, featuring 50,417 probands of diverse populations. Diagnostic rates of ES (0.38, 95% CI 0.36-0.40) and GS (0.34, 95% CI 0.30-0.38) were similar (P = .1). Within-cohort comparison illustrated 1.2-times odds of diagnosis by GS over ES (95% CI 0.79-1.83, P = .38). GS studies discovered a higher range of novel genes than ES studies; yet, the rate of variant of unknown significance did not differ (P = .78). Among high-quality studies, clinical utility of GS (0.77, 95% CI 0.64-0.90) was higher than that of ES (0.44, 95% CI 0.30-0.58) (P < .01).

CONCLUSION

This meta-analysis provides an important update to demonstrate the similar diagnostic rates between ES and GS and the higher clinical utility of GS over ES. With the newly published recommendations for clinical interpretation of variants found in noncoding regions of the genome and the trend of decreasing variant of unknown significance and GS cost, it is expected that GS will be more widely used in clinical settings.

The challenges of access to innovative medicines with limited evidence in the European Union

The European Medicines Agency (EMA) fosters access to innovative medicines through accelerated procedures and flexibility in the authorization requirements for diseases with unmet medical needs, such as many rare diseases as well as oncological diseases. However, the resulting increase of medicines being marketed with conditional authorizations and in exceptional circumstances has lead to higher clinical uncertainty about their efficacy and safety than when the standard authorizations are applied. This uncertainty has significant implications for clinical practice and the negotiation of pricing and reimbursement, particularly as high prices are based on assumptions of high value, supported by regulatory prioritization. The burden of clinical development is often shifted towards public healthcare systems, resulting in increased spending budgets and opportunity costs. Effective management of uncertainty, through appropriate testing and evaluation, and fair reflection of costs and risks in prices, is crucial. However, it is important not to sacrifice essential elements of evidence-based healthcare for the sake of access to new treatments. Balancing sensitive and rational access to new treatments, ensuring their safety, efficacy, and affordability to healthcare systems requires thoughtful decision-making. Ultimately, a responsible approach to timely access to innovative medicines that balances the needs of patients with healthcare systems' concerns is necessary. This approach emphasizes the importance of evidence-based decision-making and fair pricing and reimbursement.

A FinnGen pilot clinical recall study for Alzheimer's disease

Successful development of novel therapies requires that clinical trials are conducted in patient cohorts with the highest benefit-to-risk ratio. Population-based biobanks with comprehensive health and genetic data from large numbers of individuals hold promise to facilitate identification of trial participants, particularly when interventions need to start while symptoms are still mild, such as for Alzheimer's disease (AD). This study describes a process for clinical recall studies from FinnGen. We demonstrate the feasibility to systematically ascertain customized clinical data from FinnGen participants with ICD10 diagnosis of AD or mild cognitive disorder (MCD) in a single-center cross-sectional study testing blood-based biomarkers and cognitive functioning in-person, computer-based and remote. As a result, 19% (27/140) of a pre-specified FinnGen subcohort were successfully recalled and completed the study. Hospital records largely validated registry entries. For 8/12 MCD patients, other reasons than AD were identified as underlying diagnosis. Cognitive measures correlated across platforms, with highest consistencies for dementia screening (r = 0.818) and semantic fluency (r = 0.764), respectively, for in-person versus telephone-administered tests. Glial fibrillary acidic protein (GFAP) (p < 0.002) and phosphorylated-tau 181 (pTau-181) (p < 0.020) most reliably differentiated AD from MCD participants. We conclude that informative, customized clinical recall studies from FinnGen are feasible.

Applications of multi-omics analysis in human diseases

Multi-omics usually refers to the crossover application of multiple high-throughput screening technologies represented by genomics, transcriptomics, single-cell transcriptomics, proteomics and metabolomics, spatial transcriptomics, and so on, which play a great role in promoting the study of human diseases. Most of the current reviews focus on describing the development of multi-omics technologies, data integration, and application to a particular disease; however, few of them provide a comprehensive and systematic introduction of multi-omics. This review outlines the existing technical categories of multi-omics, cautions for experimental design, focuses on the integrated analysis methods of multi-omics, especially the approach of machine learning and deep learning in multi-omics data integration and the corresponding tools, and the application of multi-omics in medical researches (e.g., cancer, neurodegenerative diseases, aging, and drug target discovery) as well as the corresponding open-source analysis tools and databases, and finally, discusses the challenges and future directions of multi-omics integration and application in precision medicine. With the development of high-throughput technologies and data integration algorithms, as important directions of multi-omics for future disease research, single-cell multi-omics and spatial multi-omics also provided a detailed introduction. This review will provide important guidance for researchers, especially who are just entering into multi-omics medical research.

Biomarkers Associated With Severe COVID-19 Among Populations With High Cardiometabolic Risk: A 2-Sample Mendelian Randomization Study

Importance

Cardiometabolic parameters are established risk factors for COVID-19 severity. The identification of causal or protective biomarkers for COVID-19 severity may facilitate the development of novel therapies.

Objective

To identify protein biomarkers that promote or reduce COVID-19 severity and that mediate the association of cardiometabolic risk factors with COVID-19 severity.

Design, Setting, and Participants

This genetic association study using 2-sample mendelian randomization (MR) was conducted in 2022 to investigate associations among cardiometabolic risk factors, circulating biomarkers, and COVID-19 hospitalization. Inputs for MR included genetic and proteomic data from 4147 participants with dysglycemia and cardiovascular risk factors collected through the Outcome Reduction With Initial Glargine Intervention (ORIGIN) trial. Genome-wide association study summary statistics were obtained from (1) 3 additional independent plasma proteome studies, (2) genetic consortia for selected cardiometabolic risk factors (including body mass index [BMI], type 2 diabetes, type 1 diabetes, and systolic blood pressure; all n >10 000), and (3) the COVID-19 Host Genetics Initiative (n = 5773 hospitalized and 15 497 nonhospitalized case participants with COVID-19). Data analysis was performed in July 2022.

Exposures

Genetically determined concentrations of 235 circulating proteins assayed with a multiplex biomarker panel from the ORIGIN trial for the initial analysis.

Main Outcomes and Measures

Hospitalization status of individuals from the COVID-19 Host Genetics Initiative with a positive COVID-19 test result.

Results

Among 235 biomarkers tested in samples totaling 22 101 individuals, MR analysis showed that higher kidney injury molecule-1 (KIM-1) levels reduced the likelihood of COVID-19 hospitalization (odds ratio [OR] per SD increase in KIM-1 levels, 0.86 [95% CI, 0.79-0.93]). A meta-analysis validated the protective association with no observed directional pleiotropy (OR per SD increase in KIM-1 levels, 0.91 [95% CI, 0.88-0.95]). Of the cardiometabolic risk factors studied, only BMI was associated with KIM-1 levels (0.17 SD increase in biomarker level per 1 kg/m2 [95% CI, 0.08-0.26]) and COVID-19 hospitalization (OR per 1-SD biomarker level, 1.33 [95% CI, 1.18-1.50]). Multivariable MR analysis also revealed that KIM-1 partially mitigated the association of BMI with COVID-19 hospitalization, reducing it by 10 percentage points (OR adjusted for KIM-1 level per 1 kg/m2, 1.23 [95% CI, 1.06-1.43]).

Conclusions and Relevance

In this genetic association study, KIM-1 was identified as a potential mitigator of COVID-19 severity, possibly attenuating the increased risk of COVID-19 hospitalization among individuals with high BMI. Further studies are required to better understand the underlying biological mechanisms.

Unlocking Genetic Profiles with a Programmable DNA-Powered Decoding Circuit

Human genetic architecture provides remarkable insights into disease risk prediction and personalized medication. Advances in genomics have boosted the fine-mapping of disease-associated genetic variants across human genome. In healthcare practice, interpreting intricate genetic profiles into actionable medical decisions can improve health outcomes but remains challenging. Here an intelligent genetic decoder is engineered with programmable DNA computation to automate clinical analyses and interpretations. The DNA-based decoder recognizes multiplex genetic information by one-pot ligase-dependent reactions and interprets implicit genetic profiles into explicit decision reports. It is shown that the DNA decoder implements intended computation on genetic profiles and outputs a corresponding answer within hours. Effectiveness in 30 human genomic samples is validated and it is shown that it achieves desirable performance on the interpretation of CYP2C19 genetic profiles into drug responses, with accuracy equivalent to that of Sanger sequencing. Circuit modules of the DNA decoder can also be readily reprogrammed to interpret another pharmacogenetics genes, provide drug dosing recommendations, and implement reliable molecular calculation of polygenic risk score (PRS) and PRS-informed cancer risk assessment. The DNA-powered intelligent decoder provides a general solution to the translation of complex genetic profiles into actionable healthcare decisions and will facilitate personalized healthcare in primary care.

Genomic Innovation in Early Life Cardiovascular Disease Prevention and Treatment

Cardiovascular disease (CVD) is a leading cause of morbidity and mortality globally. Although CVD events do not typically manifest until older adulthood, CVD develops gradually across the life-course, beginning with the elevation of risk factors observed as early as childhood or adolescence and the emergence of subclinical disease that can occur in young adulthood or midlife. Genomic background, which is determined at zygote formation, is among the earliest risk factors for CVD. With major advances in molecular technology, including the emergence of gene-editing techniques, along with deep whole-genome sequencing and high-throughput array-based genotyping, scientists now have the opportunity to not only discover genomic mechanisms underlying CVD but use this knowledge for the life-course prevention and treatment of these conditions. The current review focuses on innovations in the field of genomics and their applications to monogenic and polygenic CVD prevention and treatment. With respect to monogenic CVD, we discuss how the emergence of whole-genome sequencing technology has accelerated the discovery of disease-causing variants, allowing comprehensive screening and early, aggressive CVD mitigation strategies in patients and their families. We further describe advances in gene editing technology, which might soon make possible cures for CVD conditions once thought untreatable. In relation to polygenic CVD, we focus on recent innovations that leverage findings of genome-wide association studies to identify druggable gene targets and develop predictive genomic models of disease, which are already facilitating breakthroughs in the life-course treatment and prevention of CVD. Gaps in current research and future directions of genomics studies are also discussed. In aggregate, we hope to underline the value of leveraging genomics and broader multiomics information for characterizing CVD conditions, work which promises to expand precision approaches for the life-course prevention and treatment of CVD.

A personalized pharmaco-epistatic network model of precision medicine

Precision medicine, the utilization of targeted treatments to address an individual's disease, relies on knowledge about the genetic cause of that individual's drug response. Here, we present a functional graph (FunGraph) theory to chart comprehensive pharmacogenetic architecture for each and every patient. FunGraph is the combination of functional mapping - a dynamic model for genetic mapping and evolutionary game theory guiding interactive strategies. It coalesces all pharmacogenetic factors into multilayer and multiplex networks that fully capture bidirectional, signed and weighted epistasis. It can visualize and interrogate how epistasis moves in the cell and how this movement leads to patient- and context-specific genetic architecture in response to organismic physiology. We discuss the future implementation of FunGraph to achieve precision medicine. Teaser: We present a functional graph (FunGraph) theory to draw a complete picture of pharmacogenetic architecture underlying interindividual variability in drug response. FunGraph can characterize how each gene acts and interacts with every other gene to mediate therapeutic response.

Genome-wide polygenic risk score for major osteoporotic fractures in postmenopausal women using associated single nucleotide polymorphisms

BACKGROUND

Osteoporosis is highly polygenic and heritable, with heritability ranging from 50 to 80%; most inherited susceptibility is associated with the cumulative effect of many common genetic variants. However, existing genetic risk scores (GRS) only provide a few percent predictive power for osteoporotic fracture.

METHODS

We derived and validated a novel genome-wide polygenic score (GPS) comprised of 103,155 common genetic variants to quantify this susceptibility and tested this GPS prediction ability in an independent dataset (n = 15,776).

RESULTS

Among postmenopausal women, we found a fivefold gradient in the risk of major osteoporotic fracture (MOF) (p < 0.001) and a 15.25-fold increased risk of severe osteoporosis (p < 0.001) across the GPS deciles. Compared with the remainder of the GPS distribution, the top GPS decile was associated with a 3.59-, 2.48-, 1.92-, and 1.58-fold increased risk of any fracture, MOF, hip fracture, and spine fracture, respectively. The top GPS decile also identified nearly twofold more high-risk osteoporotic patients than the top decile of conventional GRS based on 1103 conditionally independent genome-wide significant SNPs. Although the relative risk of severe osteoporosis for postmenopausal women at around 50 is relatively similar, the cumulative incident at 20-year follow-up is significantly different between the top GPS decile (13.7%) and the bottom decile (< 1%). In the subgroup analysis, the GPS transferability in non-Hispanic White is better than in other racial/ethnic groups.

CONCLUSIONS

This new method to quantify inherited susceptibility to osteoporosis and osteoporotic fracture affords new opportunities for clinical prevention and risk assessment.

Exploring human biology with N-of-1 clinical trials

Studies on humans that exploit contemporary data-intensive, high-throughput 'omic' assay technologies, such as genomics, transcriptomics, proteomics and metabolomics, have unequivocally revealed that humans differ greatly at the molecular level. These differences, which are compounded by each individual's distinct behavioral and environmental exposures, impact individual responses to health interventions such as diet and drugs. Questions about the best way to tailor health interventions to individuals based on their nuanced genomic, physiologic, behavioral, etc. profiles have motivated the current emphasis on 'precision' medicine. This review's purpose is to describe how the design and execution of N-of-1 (or personalized) multivariate clinical trials can advance the field. Such trials focus on individual responses to health interventions from a whole-person perspective, leverage emerging health monitoring technologies, and can be used to address the most relevant questions in the precision medicine era. This includes how to validate biomarkers that may indicate appropriate activity of an intervention as well as how to identify likely beneficial interventions for an individual. We also argue that multivariate N-of-1 and aggregated N-of-1 trials are ideal vehicles for advancing biomedical and translational science in the precision medicine era since the insights gained from them can not only shed light on how to treat or prevent diseases generally, but also provide insight into how to provide real-time care to the very individuals who are seeking attention for their health concerns in the first place.

Genomic medicine in Africa: a need for molecular genetics and pharmacogenomics experts

The large-scale implementation of genomic medicine in Africa has not been actualized. This overview describes how routine molecular genetics and advanced protein engineering/structural biotechnology could accelerate the implementation of genomic medicine. By using data-mining and analysis approaches, we analyzed relevant information obtained from public genomic databases on pharmacogenomics biomarkers and reviewed published studies to discuss the ideas. The results showed that only 68 very important pharmacogenes currently exist, while 867 drug label annotations, 201 curated functional pathways, and 746 annotated drugs have been catalogued on the largest pharmacogenomics database (PharmGKB). Only about 5009 variants of the reported ∼25,000 have been clinically annotated. Predominantly, the genetic variants were derived from 43 genes that contribute to 2318 clinically relevant variations in 57 diseases. Majority (∼60%) of the clinically relevant genetic variations in the pharmacogenes are missense variants (1390). The enrichment analysis showed that 15 pharmacogenes are connected biologically and are involved in the metabolism of cardiovascular and cancer drugs. The review of studies showed that cardiovascular diseases are the most frequent non-communicable diseases responsible for approximately 13% of all deaths in Africa. Also, warfarin pharmacogenomics is the most studied drug on the continent, while CYP2D6, CYP2C9, DPD, and TPMT are the most investigated pharmacogenes with allele activities indicated in African and considered to be intermediate metaboliser for DPD and TPMT (8.4% and 11%). In summary, we highlighted a framework for implementing genomic medicine starting from the available resources on ground.

Insights from multi-omics integration in complex disease primary tissues

Genome-wide association studies (GWAS) have provided insights into the genetic basis of complex diseases. In the next step, integrative multi-omics approaches can characterize molecular profiles in relevant primary tissues to reveal the mechanisms that underlie disease development. Here, we highlight recent progress in four examples of complex diseases generated by integrative studies: type 2 diabetes (T2D), osteoarthritis, Alzheimer's disease (AD), and systemic lupus erythematosus (SLE). High-resolution methodologies such as single-cell and spatial omics techniques will become even more important in the future. Furthermore, we emphasize the urgent need to include as yet understudied cell types and increase the diversity of studied populations.

3D genome organization links non-coding disease-associated variants to genes

Genome sequencing has revealed over 300 million genetic variations in human populations. Over 90% of variants are single nucleotide polymorphisms (SNPs), the remainder include short deletions or insertions, and small numbers of structural variants. Hundreds of thousands of these variants have been associated with specific phenotypic traits and diseases through genome wide association studies which link significant differences in variant frequencies with specific phenotypes among large groups of individuals. Only 5% of disease-associated SNPs are located in gene coding sequences, with the potential to disrupt gene expression or alter of the function of encoded proteins. The remaining 95% of disease-associated SNPs are located in non-coding DNA sequences which make up 98% of the genome. The role of non-coding, disease-associated SNPs, many of which are located at considerable distances from any gene, was at first a mystery until the discovery that gene promoters regularly interact with distal regulatory elements to control gene expression. Disease-associated SNPs are enriched at the millions of gene regulatory elements that are dispersed throughout the non-coding sequences of the genome, suggesting they function as gene regulation variants. Assigning specific regulatory elements to the genes they control is not straightforward since they can be millions of base pairs apart. In this review we describe how understanding 3D genome organization can identify specific interactions between gene promoters and distal regulatory elements and how 3D genomics can link disease-associated SNPs to their target genes. Understanding which gene or genes contribute to a specific disease is the first step in designing rational therapeutic interventions.

Framework and baseline examination of the German National Cohort (NAKO)

The German National Cohort (NAKO) is a multidisciplinary, population-based prospective cohort study that aims to investigate the causes of widespread diseases, identify risk factors and improve early detection and prevention of disease. Specifically, NAKO is designed to identify novel and better characterize established risk and protection factors for the development of cardiovascular diseases, cancer, diabetes, neurodegenerative and psychiatric diseases, musculoskeletal diseases, respiratory and infectious diseases in a random sample of the general population. Between 2014 and 2019, a total of 205,415 men and women aged 19–74 years were recruited and examined in 18 study centres in Germany. The baseline assessment included a face-to-face interview, self-administered questionnaires and a wide range of biomedical examinations. Biomaterials were collected from all participants including serum, EDTA plasma, buffy coats, RNA and erythrocytes, urine, saliva, nasal swabs and stool. In 56,971 participants, an intensified examination programme was implemented. Whole-body 3T magnetic resonance imaging was performed in 30,861 participants on dedicated scanners. NAKO collects follow-up information on incident diseases through a combination of active follow-up using self-report via written questionnaires at 2–3 year intervals and passive follow-up via record linkages. All study participants are invited for re-examinations at the study centres in 4–5 year intervals. Thereby, longitudinal information on changes in risk factor profiles and in vascular, cardiac, metabolic, neurocognitive, pulmonary and sensory function is collected. NAKO is a major resource for population-based epidemiology to identify new and tailored strategies for early detection, prediction, prevention and treatment of major diseases for the next 30 years.

The promise of new anti-obesity therapies arising from knowledge of genetic obesity traits

Obesity is a multifactorial and complex disease that often manifests in early childhood with a lifelong burden. Polygenic and monogenic obesity are driven by the interaction between genetic predisposition and environmental factors. Polygenic variants are frequent and confer small effect sizes. Rare monogenic obesity syndromes are caused by defined pathogenic variants in single genes with large effect sizes. Most of these genes are involved in the central nervous regulation of body weight; for example, genes of the leptin-melanocortin pathway. Clinically, patients with monogenic obesity present with impaired satiety, hyperphagia and pronounced food-seeking behaviour in early childhood, which leads to severe early-onset obesity. With the advent of novel pharmacological treatment options emerging for monogenic obesity syndromes that target the central melanocortin pathway, genetic testing is recommended for patients with rapid weight gain in infancy and additional clinical suggestive features. Likewise, patients with obesity associated with hypothalamic damage or other forms of syndromic obesity involving energy regulatory circuits could benefit from these novel pharmacological treatment options. Early identification of patients affected by syndromic obesity will lead to appropriate treatment, thereby preventing the development of obesity sequelae, avoiding failure of conservative treatment approaches and alleviating stigmatization of patients and their families.

Identifying disease-critical cell types and cellular processes by integrating single-cell RNA-sequencing and human genetics

Genome-wide association studies provide a powerful means of identifying loci and genes contributing to disease, but in many cases, the related cell types/states through which genes confer disease risk remain unknown. Deciphering such relationships is important for identifying pathogenic processes and developing therapeutics. In the present study, we introduce sc-linker, a framework for integrating single-cell RNA-sequencing, epigenomic SNP-to-gene maps and genome-wide association study summary statistics to infer the underlying cell types and processes by which genetic variants influence disease. The inferred disease enrichments recapitulated known biology and highlighted notable cell-disease relationships, including γ-aminobutyric acid-ergic neurons in major depressive disorder, a disease-dependent M-cell program in ulcerative colitis and a disease-specific complement cascade process in multiple sclerosis. In autoimmune disease, both healthy and disease-dependent immune cell-type programs were associated, whereas only disease-dependent epithelial cell programs were prominent, suggesting a role in disease response rather than initiation. Our framework provides a powerful approach for identifying the cell types and cellular processes by which genetic variants influence disease.

Transcriptomics and RNA-Based Therapeutics as Potential Approaches to Manage SARS-CoV-2 Infection

SARS-CoV-2 is a coronavirus family member that appeared in China in December 2019 and caused the disease called COVID-19, which was declared a pandemic in 2020 by the World Health Organization. In recent months, great efforts have been made in the field of basic and clinical research to understand the biology and infection processes of SARS-CoV-2. In particular, transcriptome analysis has contributed to generating new knowledge of the viral sequences and intracellular signaling pathways that regulate the infection and pathogenesis of SARS-CoV-2, generating new information about its biology. Furthermore, transcriptomics approaches including spatial transcriptomics, single-cell transcriptomics and direct RNA sequencing have been used for clinical applications in monitoring, detection, diagnosis, and treatment to generate new clinical predictive models for SARS-CoV-2. Consequently, RNA-based therapeutics and their relationship with SARS-CoV-2 have emerged as promising strategies to battle the SARS-CoV-2 pandemic with the assistance of novel approaches such as CRISPR-CAS, ASOs, and siRNA systems. Lastly, we discuss the importance of precision public health in the management of patients infected with SARS-CoV-2 and establish that the fusion of transcriptomics, RNA-based therapeutics, and precision public health will allow a linkage for developing health systems that facilitate the acquisition of relevant clinical strategies for rapid decision making to assist in the management and treatment of the SARS-CoV-2-infected population to combat this global public health problem.

Ethical, legal and social/societal implications (ELSI) of recall-by-genotype (RbG) and genotype-driven-research (GDR) approaches: a scoping review

Recall by Genotype (RbG), Genotype-driven-recall (GDR), and Genotype-based-recall (GBR) strategies are increasingly used to conduct genomic or biobanking sub-studies that single out participants as eligible because of their specific individual genotypic information. However, existing regulatory and governance frameworks do not apply to all aspects of genotype-driven research approaches. The recall strategies disclose or withhold personal genotypic information with uncertain clinical utility. Accordingly, this scoping review aims to identify peculiar, explicit and implicit ethical, legal, and societal/social implications (ELSI) of RbG study designs. We conducted a systematic literature search of three electronic databases from November 2020 to February 2021. We investigated qualitative and quantitative research methods used to report ELSI aspects in RbG research. Congruent with other research findings, we identified a lack of qualitative research investigating the particular ELSI challenges with RbG. We included and analysed the content of twenty-five publications. We found a consensus on RbG posing significant ethical issues, dilemmas, barriers, concerns and societal challenges. However, we found that the approaches to disclosure and study-specific recall and communication strategies employed consent models and Return of Research Results (RoRR) policies varied considerably. Furthermore, we identified a high heterogeneity in perspectives of participants and experts about ELSI of study-specific RbG policies. Therefore, further fine-mapping through qualitative and empirical research is needed to draw conclusions and re-fine ELSI frameworks.

A Systematic Review of Polygenic Models for Predicting Drug Outcomes

Polygenic models have emerged as promising prediction tools for the prediction of complex traits. Currently, the majority of polygenic models are developed in the context of predicting disease risk, but polygenic models may also prove useful in predicting drug outcomes. This study sought to understand how polygenic models incorporating pharmacogenetic variants are being used in the prediction of drug outcomes. A systematic review was conducted with the aim of gaining insights into the methods used to construct polygenic models, as well as their performance in drug outcome prediction. The search uncovered 89 papers that incorporated pharmacogenetic variants in the development of polygenic models. It was found that the most common polygenic models were constructed for drug dosing predictions in anticoagulant therapies (n = 27). While nearly all studies found a significant association with their polygenic model and the investigated drug outcome (93.3%), less than half (47.2%) compared the performance of the polygenic model against clinical predictors, and even fewer (40.4%) sought to validate model predictions in an independent cohort. Additionally, the heterogeneity of reported performance measures makes the comparison of models across studies challenging. These findings highlight key considerations for future work in developing polygenic models in pharmacogenomic research.

Managing the introduction of genomic applications into the National Health Service: A special challenge for health technology assessment in Italy

In recent years, the rapid proliferation of genomic tests for use in clinical practice has prompted healthcare systems to use a health technology assessment (HTA) approach to distinguish valuable from unwarranted applications. In this study, we narratively review the Italian HTA mechanisms for medical devices (MDs), both at the national and regional levels, and discuss the opportunity and benefits of extending them to genomic technologies, for which a dedicated assessment path was advocated by the National Plan for Public Health Genomics in 2017. We found that the National Health Technology Assessment Program for MDs, completed in 2019, had developed a structured pathway for the HTA of MDs; it established a hub-and-spoke structure, run by a governmental institution, and put in place transparent methodological procedures to cover all four HTA phases (i.e., proposal and prioritization, assessment, appraisal, and dissemination). However, several factors have hindered its adoption, and the regions are at different stages of its implementation. For these reasons, efforts should be made to ensure its effective deployment, both at national and regional levels. In addition, we argue that to harmonize the institutional roles and methodological procedures adopted, the time has come to concentrate resources on a single pathway for the assessment of all technologies that include both MDs and genomic applications.

Genomic instability genes in lung and colon adenocarcinoma indicate organ specificity of transcriptomic impact on Copy Number Alterations

Genomic instability (GI) in cancer facilitates cancer evolution and is an exploitable target for therapy purposes. However, specific genes involved in cancer GI remain elusive. Causal genes for GI via expressions have not been comprehensively identified in colorectal cancers (CRCs). To fill the gap in knowledge, we developed a data mining strategy (Gene Expression to Copy Number Alterations; "GE-CNA"). Here we applied the GE-CNA approach to 592 TCGA CRC datasets, and identified 500 genes whose expression levels associate with CNA. Among these, 18 were survival-critical (i.e., expression levels correlate with significant differences in patients' survival). Comparison with previous results indicated striking differences between lung adenocarcinoma and CRC: (a) less involvement of overexpression of mitotic genes in generating genomic instability in the colon and (b) the presence of CNA-suppressing pathways, including immune-surveillance, was only partly similar to those in the lung. Following 13 genes (TIGD6, TMED6, APOBEC3D, EP400NL, B3GNT4, ZNF683, FOXD4, FOXD4L1, PKIB, DDB2, MT1G, CLCN3, CAPS) were evaluated as potential drug development targets (hazard ratio [> 1.3 or < 0.5]). Identification of specific CRC genomic instability genes enables researchers to develop GI targeting approach. The new results suggest that the "targeting genomic instability and/or aneuploidy" approach must be tailored for specific organs.