The predictive capacity of personal genome sequencing

An EMT-related genes signature as a prognostic biomarker for patients with endometrial cancer

BACKGROUND

The epithelial-mesenchymal transition (EMT) plays an indispensable role in the development and progression of Endometrial cancer (EC). Nevertheless, little evidence is reported to uncover the functionality and application of EMT-related molecules in the prognosis of EC. This study aims to develop novel molecular markers for prognosis prediction in patients with EC.

METHODS

RNA sequencing profiles of EC patients obtained from The Cancer Genome Atlas (TCGA) database were used to screen differential expression genes (DEGs) between tumors and normal tissues. The Cox regression model with the LASSO method was utilized to identify survival-related DEGs and to establish a prognostic signature whose performance was evaluated by Kaplan-Meier curve, receiver operating characteristic (ROC) and calibration curve. Eventually, functional enrichment analysis and cellular experiments were performed to reveal the roles of prognosis-related genes in EC progression.

RESULTS

A total of 540 EMT-related DEGs in EC were screened, and subsequently a four-gene risk signature comprising SIRT2, SIX1, CDKN2A and PGR was established to predict overall survival of EC. This risk signature could serve as a meaningfully independent indicator for EC prognosis via multivariate Cox regression (HR = 2.002, 95%CI = 1.433-2.798; P < 0.001). The nomogram integrating the risk signature and clinical characteristics exhibited robust validity and performance at predicting EC overall survival indicated by ROC and calibration curve. Functional enrichment analysis revealed that the EMT-related genes risk signature was associated with extracellular matrix organization, mesenchymal development and cellular component morphogenesis, suggesting its possible relevance to epithelial-mesenchymal transition and cancer progression. Functionally, we demonstrated that the silencing of SIX1, SIRT2 and CDKN2A expression could accelerate the migratory and invasive capacities of tumor cells, whereas the downregulation of PGR dramatically inhibited cancer cells migration and invasion.

CONCLUSIONS

Altogether, a novel four-EMT-related genes signature was a potential biomarker for EC prognosis. These findings might help to ameliorate the individualized prognostication and therapeutic treatment of EC patients.

Non-Pharmaceutical Interventions against COVID-19 Causing a Lower Trend in Age of LHON Onset

Leber hereditary optic neuropathy (LHON) is a monogenic but multifactorial disease vulnerable to environmental triggers. Little is known about how LHON onset changed during the COVID-19 pandemic and how non-pharmaceutical interventions (NPHIs) against COVID-19 impact LHON onset. One hundred and forty-seven LHON patients with the m.11778G>A mutation complaining of vision loss were involved between January 2017 and July 2022. The onset time points, age of onset, and possible risk factors were evaluated. Analyses were conducted among 96 LHON patients in the Pre-COVID-19 group and 51 in the COVID-19 group. The median (IQR) age of onset decreased significantly from 16.65 (13.739, 23.02) in pre-COVID-19 to 14.17 (8.87, 20.29) during COVID-19. Compared with the Pre-COVID-19 group, the COVID-19 group exhibited bimodal distribution with an additional peak at six; the first quarter of 2020 also witnessed a relatively denser onset, with no subsequent second spike. NPHIs against COVID-19 significantly changed patients' lifestyles, including higher secondhand smoke exposure (p < 0.001), adherence to masks (p < 0.001), reduction in time spent outdoors for leisure (p = 0.001), and prolonged screen time (p = 0.007). Multivariate logistic regression revealed that secondhand smoke exposure and mask-wearing were independent risk factors of younger LHON onset. Lower age of onset of LHON appeared after the breakout of the COVID-19 pandemic, and novel risk factors were detected, including secondhand exposure and long mask-wearing. Carriers of LHON mtDNA mutations, especially teenagers or children, should be advised to avoid secondhand smoke exposure and there are possible adverse outcomes of longer mask-wearing.

Gattaca as a lens on contemporary genetics: marking 25 years into the film's "not-too-distant" future

The 1997 film Gattaca has emerged as a canonical pop culture reference used to discuss modern controversies in genetics and bioethics. It appeared in theaters a few years prior to the announcement of the "completion" of the human genome (2000), as the science of human genetics was developing a renewed sense of its social implications. The story is set in a near-future world in which parents can, with technological assistance, influence the genetic composition of their offspring on the basis of predicted life outcomes. The current moment-25 years after the film's release-offers an opportunity to reflect on where society currently stands with respect to the ideas explored in Gattaca. Here, we review and discuss several active areas of genetic research-genetic prediction, embryo selection, forensic genetics, and others-that interface directly with scenes and concepts in the film. On its silver anniversary, we argue that Gattaca remains an important reflection of society's expectations and fears with respect to the ways that genetic science has manifested in the real world. In accompanying supplemental material, we offer some thought questions to guide group discussions inside and outside of the classroom.

DNA methylation-based predictors of health: applications and statistical considerations

DNA methylation data have become a valuable source of information for biomarker development, because, unlike static genetic risk estimates, DNA methylation varies dynamically in relation to diverse exogenous and endogenous factors, including environmental risk factors and complex disease pathology. Reliable methods for genome-wide measurement at scale have led to the proliferation of epigenome-wide association studies and subsequently to the development of DNA methylation-based predictors across a wide range of health-related applications, from the identification of risk factors or exposures, such as age and smoking, to early detection of disease or progression in cancer, cardiovascular and neurological disease. This Review evaluates the progress of existing DNA methylation-based predictors, including the contribution of machine learning techniques, and assesses the uptake of key statistical best practices needed to ensure their reliable performance, such as data-driven feature selection, elimination of data leakage in performance estimates and use of generalizable, adequately powered training samples.

Soft Statistical Mechanics for Biology

The multilevel organization of nature is self-evident: proteins do interact among them to give rise to an organized metabolism and the same hierarchical organization is in action for gene expression, tissue and organ architectures, and ecological systems.The still more common approach to such state of affairs is to think that causally relevant events originate from the lower level in the form of perturbations, that climb up the hierarchy reaching the ultimate layer of macroscopic behavior (e.g., causing a specific disease). Such rigid bottom-up causative model is unable to offer realistic models of many biological phenomena.Complex network approach allows to uncover the nature of multilevel organization, but in order to operationally define the organization principles of biological systems, we need to go further and complement network approach with sensible measures of order and organization. These measures, while keeping their original physical meaning, must not impose theoretical premises not verifiable in biological frameworks. We will show here how relatively simple and largely hypothesis-free multidimensional statistics tools can satisfactorily meet these criteria.

Evaluation of Maximum DNA Yield from a New Noninvasive Buccal Collection Device Following Various Extraction Protocols

Background: As the demand in genetic testing increases, various fields look toward collection methods that are noninvasive and efficient in recovering deoxyribonucleic acid (DNA) for testing that will allow for high first-pass success rates. Objective: Two extraction methods (PrepFiler™ Express Forensic Extraction and the Maxwell^® RSC Buccal Swab DNA Kits) were optimized to increase DNA yield from a buccal cell collection device (Gentueri's CollectEject™ Swab). Materials and Methods: Buccal swabs were processed under varying incubation parameters using a forensic workflow. The PrepFiler method was adjusted to test longer incubation times and more aggressive agitation. The Maxwell method was adjusted to test incubation temperatures and duration. Results: Quantitative results showed that increased agitation can yield more DNA through the PrepFiler extraction, but longer incubation times did not increase DNA recovery. The results from the Maxwell study showed no significant difference between incubation temperatures or times. Conclusions: The results indicate that various applied genetic fields can utilize a noninvasive, simple collection method using the CollectEject device in conjunction with extraction methods already implemented in laboratories to collect 5000 ng of DNA or greater from a buccal cell collection.

Epidemiological Characterization of a Directed and Weighted Disease Network Using Data From a Cohort of One Million Patients: Network Analysis

Background

In the past 20 years, various methods have been introduced to construct disease networks. However, established disease networks have not been clinically useful to date because of differences among demographic factors, as well as the temporal order and intensity among disease-disease associations.

Objective

This study sought to investigate the overall patterns of the associations among diseases; network properties, such as clustering, degree, and strength; and the relationship between the structure of disease networks and demographic factors.

Methods

We used National Health Insurance Service-National Sample Cohort (NHIS-NSC) data from the Republic of Korea, which included the time series insurance information of 1 million out of 50 million Korean (approximately 2%) patients obtained between 2002 and 2013. After setting the observation and outcome periods, we selected only 520 common Korean Classification of Disease, sixth revision codes that were the most prevalent diagnoses, making up approximately 80% of the cases, for statistical validity. Using these data, we constructed a directional and weighted temporal network that considered both demographic factors and network properties.

Results

Our disease network contained 294 nodes and 3085 edges, a relative risk value of more than 4, and a false discovery rate-adjusted P value of <.001. Interestingly, our network presented four large clusters. Analysis of the network topology revealed a stronger correlation between in-strength and out-strength than between in-degree and out-degree. Further, the mean age of each disease population was related to the position along the regression line of the out/in-strength plot. Conversely, clustering analysis suggested that our network boasted four large clusters with different sex, age, and disease categories.

Conclusions

We constructed a directional and weighted disease network visualizing demographic factors. Our proposed disease network model is expected to be a valuable tool for use by early clinical researchers seeking to explore the relationships among diseases in the future.

Payment Reform in the Era of Advanced Diagnostics, Artificial Intelligence, and Machine Learning

Health care is undergoing a profound transformation driven by an increase in new types of diagnostic data, increased data sharing enabled by interoperability, and improvements in our ability to interpret data through the application of artificial intelligence and machine learning. Paradoxically, we are also discovering that our current paradigms for implementing electronic health-care records and our ability to create new models for reforming the health-care system have fallen short of expectations. This article traces these shortcomings to two basic issues. The first is a reliance on highly centralized quality improvement and measurement strategies that fail to account for the high level of variation and complexity found in human disease. The second is a reliance on legacy payment systems that fail to reward the sharing of data and knowledge across the health-care system. To address these issues, and to better harness the advances in health care noted above, the health-care system must undertake a phased set of reforms. First, efforts must focus on improving both the diagnostic process and data sharing at the local level. These efforts should include the formation of diagnostic management teams and increased collaboration between pathologists and radiologists. Next, building off current efforts to develop national federated research databases, providers must be able to query national databases when information is needed to inform the care of a specific complex patient. In addition, providers, when treating a specific complex patient, should be enabled to consult nationally with other providers who have experience with similar patient issues. The goal of these efforts is to build a health-care system that is funded in part by a novel fee-for-knowledge-sharing paradigm that fosters a collaborative decentralized approach to patient care and financially incentivizes large-scale data and knowledge sharing.

GSAlign: an efficient sequence alignment tool for intra-species genomes

BACKGROUND

Personal genomics and comparative genomics are becoming more important in clinical practice and genome research. Both fields require sequence alignment to discover sequence conservation and variation. Though many methods have been developed, some are designed for small genome comparison while some are not efficient for large genome comparison. Moreover, most existing genome comparison tools have not been evaluated the correctness of sequence alignments systematically. A wrong sequence alignment would produce false sequence variants.

RESULTS

In this study, we present GSAlign that handles large genome sequence alignment efficiently and identifies sequence variants from the alignment result. GSAlign is an efficient sequence alignment tool for intra-species genomes. It identifies sequence variations from the sequence alignments. We estimate performance by measuring the correctness of predicted sequence variations. The experiment results demonstrated that GSAlign is not only faster than most existing state-of-the-art methods, but also identifies sequence variants with high accuracy.

CONCLUSIONS

As more genome sequences become available, the demand for genome comparison is increasing. Therefore an efficient and robust algorithm is most desirable. We believe GSAlign can be a useful tool. It exhibits the abilities of ultra-fast alignment as well as high accuracy and sensitivity for detecting sequence variations.

Mitonuclear conflict and cooperation govern the integration of genotypes, phenotypes and environments

The mitonuclear genome is the most successful co-evolved mutualism in the history of life on Earth. The cross-talk between the mitochondrial and nuclear genomes has been shaped by conflict and cooperation for more than 1.5 billion years, yet this system has adapted to countless genomic reorganizations by each partner, and done so under changing environments that have placed dramatic biochemical and physiological pressures on evolving lineages. From putative anaerobic origins, mitochondria emerged as the defining aerobic organelle. During this transition, the two genomes resolved rules for sex determination and transmission that made uniparental inheritance the dominant, but not a universal pattern. Mitochondria are much more than energy-producing organelles and play crucial roles in nutrient and stress signalling that can alter how nuclear genes are expressed as phenotypes. All of these interactions are examples of genotype-by-environment (GxE) interactions, gene-by-gene (GxG) interactions (epistasis) or more generally context-dependent effects on the link between genotype and phenotype. We provide evidence from our own studies in Drosophila, and from those of other systems, that mitonuclear interactions—either conflicting or cooperative—are common features of GxE and GxG. We argue that mitonuclear interactions are an important model for how to better understand the pervasive context-dependent effects underlying the architecture of complex phenotypes. Future research in this area should focus on the quantitative genetic concept of effect size to place mitochondrial links to phenotype in a proper context.

This article is part of the theme issue ‘Linking the mitochondrial genotype to phenotype: a complex endeavour’.

Sex-Stratified Polygenic Risk Score Identifies Individuals at Increased Risk of Basal Cell Carcinoma

The incidence of basal cell carcinoma (BCC) is higher among men than women. Susceptibility loci for BCC have been identified through genome-wide association studies, and two previous studies have found polygenic risk scores (PRS) to be significantly associated with the risk of BCC. However, to our knowledge, sex-stratified PRS analyses examining the genetic contribution to BCC risk among men and women have not been previously reported. To quantify the contribution of genetic variability on the BCC risk by sex, we derived a polygenic risk score and estimated the genetic relative risk distribution for men and women. Using 29 published single nucleotide polymorphisms, we found that the estimated relative risk of BCC increases with higher percentiles of the polygenic risk score. For men, the estimated risk of BCC is twice the average population risk at the 88th percentile, while for women, this occurs at the 99th percentile. Our findings indicate that there is a significant impact of genetic variation on the risk of developing BCC and that this impact may be greater for men than for women. Polygenic risk scores may be clinically useful tools for risk stratification, particularly in combination with other known risk factors for BCC development.

Ethical priority of the most actionable system of biomolecules: the metabolome

The metabolome is a system of small biomolecules (metabolites) and a direct result of human bioculture. Consequently, metabolomics is well poised to impact anthropological and biomedical research for the foreseeable future. Overall, we provide a perspective on the ethical, legal, and social implications (ELSI) of metabolomics, which we argue are often more alarming than those of genomics. Given the current mechanisms to fund research, ELSI beyond human DNA is stifled and in need of considerable attention.

Diving into the abyss of undiscovered kidney function-related factors

Meta-analyses and reintroduction of biological knowledge are 2 classic strategies to increase genomewide association study statistical power and overcome the burden of multiple testing. These strategies have empowered the nephrology community to discover new signals associated with kidney function and nephropathies. Here we discuss the current genomewide association study limitations and strategies to dive further into the abyss of yet-to-be discovered kidney function-related factors.

Repurposing large health insurance claims data to estimate genetic and environmental contributions in 560 phenotypes

We analysed a large health insurance dataset to assess the genetic and environmental contributions of 560 disease-related phenotypes in 56,396 twin pairs and 724,513 sibling pairs out of 44,859,462 individuals that live in the United States. We estimated the contribution of environmental risk factors (socioeconomic status (SES), air pollution and climate) in each phenotype. Mean heritability (h² = 0.311) and shared environmental variance (c² = 0.088) were higher than variance attributed to specific environmental factors such as zip-code-level SES (var_SES = 0.002), daily air quality (var_AQI = 0.0004), and average temperature (var_temp = 0.001) overall, as well as for individual phenotypes. We found significant heritability and shared environment for a number of comorbidities (h² = 0.433, c² = 0.241) and average monthly cost (h² = 0.290, c² = 0.302). All results are available using our Claims Analysis of Twin Correlation and Heritability (CaTCH) web application.

Cancer biomarker discovery and translation: proteomics and beyond

INTRODUCTION

Cancer is often diagnosed at late stages when the chance of cure is relatively low and although research initiatives in oncology discover many potential cancer biomarkers, few transition to clinical applications. This review addresses the current landscape of cancer biomarker discovery and translation with a focus on proteomics and beyond. Areas covered: The review examines proteomic and genomic techniques for cancer biomarker detection and outlines advantages and challenges of integrating multiple omics approaches to achieve optimal sensitivity and address tumor heterogeneity. This discussion is based on a systematic literature review and direct participation in translational studies. Expert commentary: Identifying aggressive cancers early on requires improved sensitivity and implementation of biomarkers representative of tumor heterogeneity. During the last decade of genomic and proteomic research, significant advancements have been made in next generation sequencing and mass spectrometry techniques. This in turn has led to a dramatic increase in identification of potential genomic and proteomic cancer biomarkers. However, limited successes have been shown with translation of these discoveries into clinical practice. We believe that the integration of these omics approaches is the most promising molecular tool for comprehensive cancer evaluation, early detection and transition to Precision Medicine in oncology.

Redefining environmental exposure for disease etiology

Etiological studies of human exposures to environmental factors typically rely on low-throughput methods that target only a few hundred chemicals or mixtures. In this Perspectives article, I outline how environmental exposure can be defined by the blood exposome—the totality of chemicals circulating in blood. The blood exposome consists of chemicals derived from both endogenous and exogenous sources. Endogenous chemicals are represented by the human proteome and metabolome, which establish homeostatic networks of functional molecules. Exogenous chemicals arise from diet, vitamins, drugs, pathogens, microbiota, pollution, and lifestyle factors, and can be measured in blood as subsets of the proteome, metabolome, metals, macromolecular adducts, and foreign DNA and RNA. To conduct ‘exposome-wide association studies’, blood samples should be obtained prospectively from subjects—preferably at critical stages of life—and then analyzed in incident disease cases and matched controls to find discriminating exposures. Results from recent metabolomic investigations of archived blood illustrate our ability to discover potentially causal exposures with current technologies.

Quantifying the Polygenic Contribution to Cutaneous Squamous Cell Carcinoma Risk

Genetic factors play an important role in cutaneous squamous cell carcinoma risk. Genome-wide association studies have identified 21 single nucleotide polymorphisms associated with cutaneous squamous cell carcinoma risk. Yet no studies have attempted to quantify the contribution of heritability to cutaneous squamous cell carcinoma risk by calculating the population attributable risk using a combination of all discovered genetic variants. Using an additive multi-locus linear logistic model, we determined the cumulative association of these 21 genetic regions to cutaneous squamous cell carcinoma population attributable risk. We computed a multi-locus population attributable risk of 62%, suggesting that if the effects of all the risk alleles were removed from a population, the cutaneous squamous cell carcinoma risk would drop by 62%. Using stratified analysis, we also examined the impact of sex on polygenic risk score, and found that men have an increased relative risk throughout the spectrum of the polygenic risk score. Quantifying the impact of genetic predisposition on the proportion of cancer cases can guide future research decisions and public health policy planning.

Recent insights into human bronchial proteomics - how are we progressing and what is next?

The human respiratory system is highly prone to diseases and complications. Many lung diseases, including lung cancer (LC), tuberculosis (TB), and chronic obstructive pulmonary disease (COPD) have been among the most common causes of death worldwide. Cystic fibrosis (CF), the most common genetic disease in Caucasians, has adverse impacts on the lungs. Bronchial proteomics plays a significant role in understanding the underlying mechanisms and pathogenicity of lung diseases and provides insights for biomarker and therapeutic target discoveries. Areas covered: We overview the recent achievements and discoveries in human bronchial proteomics by outlining how some of the different proteomic techniques/strategies are developed and applied in LC, TB, COPD, and CF. Also, the future roles of bronchial proteomics in predictive proteomics and precision medicine are discussed. Expert commentary: Much progress has been made in bronchial proteomics. Owing to the advances in proteomics, we now have better ability to isolate proteins from desired cellular compartments, greater protein separation methods, more powerful protein detection technologies, and more sophisticated bioinformatic techniques. These all contributed to our further understanding of lung diseases and for biomarker and therapeutic target discoveries.

Personalized medicine-a modern approach for the diagnosis and management of hypertension

The main goal of treating hypertension is to reduce blood pressure to physiological levels and thereby prevent risk of cardiovascular disease and hypertension-associated target organ damage. Despite reductions in major risk factors and the availability of a plethora of effective antihypertensive drugs, the control of blood pressure to target values is still poor due to multiple factors including apparent drug resistance and lack of adherence. An explanation for this problem is related to the current reductionist and ‘trial-and-error’ approach in the management of hypertension, as we may oversimplify the complex nature of the disease and not pay enough attention to the heterogeneity of the pathophysiology and clinical presentation of the disorder. Taking into account specific risk factors, genetic phenotype, pharmacokinetic characteristics, and other particular features unique to each patient, would allow a personalized approach to managing the disease. Personalized medicine therefore represents the tailoring of medical approach and treatment to the individual characteristics of each patient and is expected to become the paradigm of future healthcare. The advancement of systems biology research and the rapid development of high-throughput technologies, as well as the characterization of different –omics, have contributed to a shift in modern biological and medical research from traditional hypothesis-driven designs toward data-driven studies and have facilitated the evolution of personalized or precision medicine for chronic diseases such as hypertension.

Integrated Genomic Medicine: A Paradigm for Rare Diseases and Beyond

Individualized medicine, or the tailoring of therapeutic interventions to a patient's unique genetic, biochemical, physiological, exposure and behavioral profile, has been enhanced, if not enabled, by modern biomedical technologies such as high-throughput DNA sequencing platforms, induced pluripotent stem cell assays, biomarker discovery protocols, imaging modalities, and wireless monitoring devices. Despite successes in the isolated use of these technologies, however, it is arguable that their combined and integrated use in focused studies of individual patients is the best way to not only tailor interventions for those patients, but also shed light on treatment strategies for patients with similar conditions. This is particularly true for individuals with rare diseases since, by definition, they will require study without recourse to other individuals, or at least without recourse to many other individuals. Such integration and focus will require new biomedical scientific paradigms and infrastructure, including the creation of databases harboring study results, the formation of dedicated multidisciplinary research teams and new training programs. We consider the motivation and potential for such integration, point out areas in need of improvement, and argue for greater emphasis on improving patient health via technological innovations, not merely improving the technologies themselves. We also argue that the paradigm described can, in theory, be extended to the study of individuals with more common diseases.

Inference in the age of big data: Future perspectives on neuroscience

Neuroscience is undergoing faster changes than ever before. Over 100 years our field qualitatively described and invasively manipulated single or few organisms to gain anatomical, physiological, and pharmacological insights. In the last 10 years neuroscience spawned quantitative datasets of unprecedented breadth (e.g., microanatomy, synaptic connections, and optogenetic brain-behavior assays) and size (e.g., cognition, brain imaging, and genetics). While growing data availability and information granularity have been amply discussed, we direct attention to a less explored question: How will the unprecedented data richness shape data analysis practices? Statistical reasoning is becoming more important to distill neurobiological knowledge from healthy and pathological brain measurements. We argue that large-scale data analysis will use more statistical models that are non-parametric, generative, and mixing frequentist and Bayesian aspects, while supplementing classical hypothesis testing with out-of-sample predictions.

A new structural approach to genomic discovery of disease: example of adult-onset diabetes

This paper reports on an investigation of disease discovery from genomic data, by methods which depart substantially from customary practices found in the investigation of genome-wide association studies. Such data in general are composed of the genomic content from two contrasting phenotypes, e.g., disease versus control populations, and the analysis proceeds under the hypothesis that populational dissimilarities might reveal disease risk alleles. The proposed suite of new methods is in part based on information theory (Shannon in Bell Syst Tech J 27:379-423, 1948a; Bell Syst Tech J 27:623-656, 1948b; Jaynes in Phys Rev 106:620-630, 1957), and strong evidence will be given of the effectiveness of this new approach. The methodology extends naturally and successfully to predicting genomic disposition to disease arising from large collections of weakly contributing genomic loci. Evidence will be advanced that the example of adult-onset diabetes ("type 2 diabetes") is such a candidate disease, and in this case, probably for the first time, it can be demonstrated that disease prediction is possible. Another novel element of this study is the search and identification of potential beneficial genomic loci that may counter a disease. The generality of the methodology suggests that it might extend to other diseases.

The Significance of an Enhanced Concept of the Organism for Medicine

Recent developments in evolutionary biology, comparative embryology, and systems biology suggest the necessity of a conceptual shift in the way we think about organisms. It is becoming increasingly evident that molecular and genetic processes are subject to extremely refined regulation and control by the cell and the organism, so that it becomes hard to define single molecular functions or certain genes as primary causes of specific processes. Rather, the molecular level is integrated into highly regulated networks within the respective systems. This has consequences for medical research in general, especially for the basic concept of personalized medicine or precision medicine. Here an integrative systems concept is proposed that describes the organism as a multilevel, highly flexible, adaptable, and, in this sense, autonomous basis for a human individual. The hypothesis is developed that these properties of the organism, gained from scientific observation, will gradually make it necessary to rethink the conceptual framework of physiology and pathophysiology in medicine.

The Genetics of Success: How Single-Nucleotide Polymorphisms Associated With Educational Attainment Relate to Life-Course Development

A previous genome-wide association study (GWAS) of more than 100,000 individuals identified molecular-genetic predictors of educational attainment. We undertook in-depth life-course investigation of the polygenic score derived from this GWAS using the four-decade Dunedin Study (N = 918). There were five main findings. First, polygenic scores predicted adult economic outcomes even after accounting for educational attainments. Second, genes and environments were correlated: Children with higher polygenic scores were born into better-off homes. Third, children's polygenic scores predicted their adult outcomes even when analyses accounted for their social-class origins; social-mobility analysis showed that children with higher polygenic scores were more upwardly mobile than children with lower scores. Fourth, polygenic scores predicted behavior across the life course, from early acquisition of speech and reading skills through geographic mobility and mate choice and on to financial planning for retirement. Fifth, polygenic-score associations were mediated by psychological characteristics, including intelligence, self-control, and interpersonal skill. Effect sizes were small. Factors connecting DNA sequence with life outcomes may provide targets for interventions to promote population-wide positive development.

Genetic Factors Are Not the Major Causes of Chronic Diseases

The risk of acquiring a chronic disease is influenced by a person’s genetics (G) and exposures received during life (the ‘exposome’, E) plus their interactions (G×E). Yet, investigators use genome-wide association studies (GWAS) to characterize G while relying on self-reported information to classify E. If E and G×E dominate disease risks, this imbalance obscures important causal factors. To estimate proportions of disease risk attributable to G (plus shared exposures), published data from Western European monozygotic (MZ) twins were used to estimate population attributable fractions (PAFs) for 28 chronic diseases. Genetic PAFs ranged from 3.4% for leukemia to 48.6% for asthma with a median value of 18.5%. Cancers had the lowest PAFs (median = 8.26%) while neurological (median = 26.1%) and lung (median = 33.6%) diseases had the highest PAFs. These PAFs were then linked with Western European mortality statistics to estimate deaths attributable to G for heart disease and nine cancer types. Of 1.53 million Western European deaths in 2000, 0.25 million (16.4%) could be attributed to genetics plus shared exposures. Given the modest influences of G-related factors on the risks of chronic diseases in MZ twins, the disparity in coverage of G and E in etiological research is problematic. To discover causes of disease, GWAS should be complemented with exposome-wide association studies (EWAS) that profile chemicals in biospecimens from incident disease cases and matched controls.

Pragmatic medicine in solid cancer: a translational alternative to precision medicine

The precision medicine (PM) initiative is a response to the dismal outlook in solid cancer. Despite heterogeneity, common mechanistic denominators may exist across the spectrum of solid cancer. A shift from conventional research and development (R&D) toward PM will require conceptual and structural change. As individuals and as a society, we welcome innovation, but question change. We ask: In solid cancer, does PM identify and address the causes of prior failures, and, if so, are the proposed solutions feasible? And, when may we expect safer, more effective and affordable drugs in the clinic? Considerations that prompt a pragmatic rethink include a failure analysis of translational R&D in solid cancer suggesting that trials and regulations need to be aligned with the natural history of the disease. In successful therapeutic interventions in chronic, complex disease, surrogate markers and endpoints should be consistent with the Prentice's criteria. In solid cancer, drug induced tumor shrinkage, is a drug effect and not a disease response; tumor shrinkage does not reflect nor predict interruption of the disease. Overall, we support a pragmatic, multidisciplinary, and collaborative R&D, and suggest that direction be set by clinical need and utility, and by questions, not answers. PM will prove worthwhile if it could improve clinical outcomes. The lag in therapeutics relative to diagnostics is a cause for confusion. Overdiagnosis adds to fear and harm, especially in the absence of effective interventions. A revised initiative that prioritizes metastasis research could replicate the successful HIV/AIDS model in solid cancer. A pragmatic approach may further translational efforts toward meaningfully effective, generally available, and affordable solutions.

Reclassification of genetic-based risk predictions as GWAS data accumulate

Background

Disease risk assessments based on common genetic variation have gained widespread attention and use in recent years. The clinical utility of genetic risk profiles depends on the number and effect size of identified loci, and how stable the predicted risks are as additional loci are discovered. Changes in risk classification for individuals over time would undermine the validity of common genetic variation for risk prediction. In this analysis, we quantified reclassification of genetic risk based on past and anticipated future GWAS data.

Methods

We identified disease-associated SNPs via the NHGRI GWAS catalog and recent large scale genome-wide association study (GWAS). We calculated the genomic risk for a simulated cohort of 100,000 individuals based on a multiplicative odds ratio model using cumulative GWAS-identified SNPs at four time points: 2007, 2009, 2011, and 2013. Individuals were classified as Higher Risk (population adjusted odds >2), Average Risk (between 0.5 and 2), and Lower Risk (<0.5) for each time point and we compared classifications between time points for breast cancer (BrCa), prostate cancer (PrCa), diabetes mellitus type 2 (T2D), and cardiovascular heart disease (CHD). We estimated future reclassification using the anticipated number of undiscovered SNPs.

Results

Risk reclassification occurred for all four phenotypes from 2007 to 2013. During the most recent interval (2011-2013), the degree of risk reclassification ranged from 16.3 % for CHD to 24.4 % for PrCa. Many individuals classified as Higher Risk at earlier time points were subsequently reclassified into a lower risk category. From 2011 to 2013, the degree of such downward risk reclassification ranged from 24.9 % for T2D to 55 % for CHD. The percent of individuals classified as Higher Risk increased as more SNPs were discovered, ranging from an increase of 5 % for CHD to 9 % for PrCa from 2007 to 2013. Reclassification continued to occur when we modeled the discovery of anticipated SNPs based on doubling current sample size.

Conclusion

Risk estimates from common genetic variation show large reclassification rates. Identifying disease-associated SNPs facilitates the clinically relevant task of identifying higher-risk individuals. However, the large amount of reclassification that we demonstrated in individuals initially classified as Higher Risk but later as Average Risk or Lower Risk, suggests that caution is currently warranted in basing clinical decisions on common genetic variation for many complex diseases.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-016-0272-5) contains supplementary material, which is available to authorized users.

Cancer Prevention: Obstacles, Challenges and the Road Ahead

Approaches to reduce the global burden of cancer include two major strategies: screening and early detection and active preventive intervention. The latter is the topic of this Commentary and spans a broad range of activities. The genetic heterogeneity and complexity of advanced cancers strongly support the rationale for early interruption of the carcinogenic process and an enhanced focus on prevention as a priority strategy to reduce the burden of cancer; however, the focus of cancer prevention management should be on individuals at high risk and on primary localized disease in which screening and detection should also play a vital role. The timing and dose of (chemo-)preventive intervention also affects response. The intervention may be ineffective if the target population is very high risk or already presenting with preneoplastic lesions with cellular changes that cannot be reversed. The field needs to move beyond general concepts of carcinogenesis to targeted organ site prevention approaches in patients at high risk, as is currently being done for breast and colorectal cancers. Establishing the benefit of new cancer preventive interventions will take years and possibly decades, depending on the outcome being evaluated. We also propose that comparative effectiveness research designs and the value of information obtained from large-scale prevention studies are necessary in order for preventive interventions to become a routine part of cancer management.

Familial Risk and Heritability of Cancer Among Twins in Nordic Countries

IMPORTANCE

Estimates of familial cancer risk from population-based studies are essential components of cancer risk prediction.

OBJECTIVE

To estimate familial risk and heritability of cancer types in a large twin cohort.

DESIGN, SETTING, AND PARTICIPANTS

Prospective study of 80,309 monozygotic and 123,382 same-sex dizygotic twin individuals (N = 203,691) within the population-based registers of Denmark, Finland, Norway, and Sweden. Twins were followed up a median of 32 years between 1943 and 2010. There were 50,990 individuals who died of any cause, and 3804 who emigrated and were lost to follow-up.

EXPOSURES

Shared environmental and heritable risk factors among pairs of twins.

MAIN OUTCOMES AND MEASURES

The main outcome was incident cancer. Time-to-event analyses were used to estimate familial risk (risk of cancer in an individual given a twin's development of cancer) and heritability (proportion of variance in cancer risk due to interindividual genetic differences) with follow-up via cancer registries. Statistical models adjusted for age and follow-up time, and accounted for censoring and competing risk of death.

RESULTS

A total of 27,156 incident cancers were diagnosed in 23,980 individuals, translating to a cumulative incidence of 32%. Cancer was diagnosed in both twins among 1383 monozygotic (2766 individuals) and 1933 dizygotic (2866 individuals) pairs. Of these, 38% of monozygotic and 26% of dizygotic pairs were diagnosed with the same cancer type. There was an excess cancer risk in twins whose co-twin was diagnosed with cancer, with estimated cumulative risks that were an absolute 5% (95% CI, 4%-6%) higher in dizygotic (37%; 95% CI, 36%-38%) and an absolute 14% (95% CI, 12%-16%) higher in monozygotic twins (46%; 95% CI, 44%-48%) whose twin also developed cancer compared with the cumulative risk in the overall cohort (32%). For most cancer types, there were significant familial risks and the cumulative risks were higher in monozygotic than dizygotic twins. Heritability of cancer overall was 33% (95% CI, 30%-37%). Significant heritability was observed for the cancer types of skin melanoma (58%; 95% CI, 43%-73%), prostate (57%; 95% CI, 51%-63%), nonmelanoma skin (43%; 95% CI, 26%-59%), ovary (39%; 95% CI, 23%-55%), kidney (38%; 95% CI, 21%-55%), breast (31%; 95% CI, 11%-51%), and corpus uteri (27%; 95% CI, 11%-43%).

CONCLUSIONS AND RELEVANCE

In this long-term follow-up study among Nordic twins, there was significant excess familial risk for cancer overall and for specific types of cancer, including prostate, melanoma, breast, ovary, and uterus. This information about hereditary risks of cancers may be helpful in patient education and cancer risk counseling.

How should whole-genome sequencing be implemented in children? A consideration of the current limitations

In children, whole-genome sequencing (WGS) is envisioned as a tool to improve diagnosis of undiagnosed diseases and to improve population-based screening. Pilot applications have shown benefits: genomic information has been used as a diagnostic aid; pharmacogenomics can reduce medicine-related adverse events; advanced knowledge of the potential for later-onset disease can target tests and appropriate therapies. However, emerging technical, conceptual and ethical challenges may limit WGS from fulfilling the current vision for future applications. WGS platforms still struggle with reliability and accuracy. The role of the genome in long-term organismal function and disease is still being established. Ethical implications of WGS in both undiagnosed disease and population screening, particularly potential impacts of testing on children and their families are still unresolved.

Early life trauma, depression and the glucocorticoid receptor gene--an epigenetic perspective

BACKGROUND

Hopes to identify genetic susceptibility loci accounting for the heritability seen in unipolar depression have not been fully realized. Family history remains the 'gold standard' for both risk stratification and prognosis in complex phenotypes such as depression. Meanwhile, the physiological mechanisms underlying life-event triggers for depression remain opaque. Epigenetics, comprising heritable changes in gene expression other than alterations of the nucleotide sequence, may offer a way to deepen our understanding of the aetiology and pathophysiology of unipolar depression and optimize treatments. A heuristic target for exploring the relevance of epigenetic changes in unipolar depression is the hypothalamic-pituitary-adrenal (HPA) axis. The glucocorticoid receptor (GR) gene (NR3C1) has been found to be susceptible to epigenetic modification, specifically DNA methylation, in the context of environmental stress such as early life trauma, which is an established risk for depression later in life.

METHOD

In this paper we discuss the progress that has been made by studies that have investigated the relationship between depression, early trauma, the HPA axis and the NR3C1 gene. Difficulties with the design of these studies are also explored.

RESULTS

Future efforts will need to comprehensively address epigenetic natural histories at the population, tissue, cell and gene levels. The complex interactions between the epigenome, genome and environment, as well as ongoing nosological difficulties, also pose significant challenges.

CONCLUSIONS

The work that has been done so far is nevertheless encouraging and suggests potential mechanistic and biomarker roles for differential DNA methylation patterns in NR3C1 as well as novel therapeutic targets.

Can we afford to sequence every newborn baby's genome?

Whole-exome sequencing and whole-genome sequencing are gradually entering into the clinical arena. Drops in sequencing prices have led some to suggest that these analyses could be extended to the screening of whole populations or subsets thereof. Herein, we argue that this optimism is presently still unfounded. While cost estimates take into account the generation of sequence data, they fail to properly evaluate both the price of accurate and efficient interpretation and of the proper return of genomic information to the consulting individuals. Thus, short of inventing new, cost-effective ways of achieving these goals, the latter are likely to ruin our healthcare systems. We posit that due to lack of available resources, generalization of this practice remains, for the time being, unrealistic.

The complexity of epigenetic diseases

Over the past 30 years, a plethora of pathogenic mutations affecting enhancer regions and epigenetic regulators have been identified. Coupled with more recent genome‐wide association studies (GWAS) and epigenome‐wide association studies (EWAS) implicating major roles for regulatory mutations in disease, it is clear that epigenetic mechanisms represent important biomarkers for disease development and perhaps even therapeutic targets. Here, we discuss the diversity of disease‐causing mutations in enhancers and epigenetic regulators, with a particular focus on cancer. © 2015 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

Random monoallelic expression of autosomal genes: stochastic transcription and allele-level regulation

Random monoallelic expression (RME) of genes represents a striking example of how stochastic molecular processes can result in cellular heterogeneity. Recent transcriptome-wide studies have revealed both mitotically stable and cell-to-cell dynamic forms of autosomal RME, with the latter presumably resulting from burst-like stochastic transcription. Here, we discuss the distinguishing features of these two forms of RME and revisit literature on their nature, pervasiveness and regulation. Finally, we explore how RME may contribute to phenotypic variation, including the incomplete penetrance and variable expressivity often seen in genetic disease.

Functions of noncoding sequences in mammalian genomes

Most of the mammalian genome consists of nucleotide sequences not coding for proteins. Exons of genes make up only 3% of the human genome, while the significance of most other sequences remains unknown. Recent genome studies with high-throughput methods demonstrate that the so-called noncoding part of the genome may perform important functions. This hypothesis is supported by three groups of experimental data: 1) approximately 10% of the sequences, most of which are located in noncoding parts of the genome, is evolutionarily conserved and thus can be of functional importance; 2) up to 99% of the mammalian genome is being transcribed forming short and long noncoding RNAs in addition to common mRNA; and 3) mutations in noncoding parts of the genome can be accompanied by progression of pathological states of the organism. In the light of these data, in the review we consider the functional role of numerous known sequences of noncoding parts of the genome including introns, DNA methylation regions, enhancers and locus control regions, insulators, S/MAR sequences, pseudogenes, and genes of noncoding RNAs, as well as transposons and simple repeats of centromeric and telomeric regions of chromosomes. The assumption is made that the intergenic noncoding sequences without definite/clear functions can be involved in spatial organization of genetic loci in interphase nuclei.

Opioid Receptors

Opioids are the oldest and most potent drugs for the treatment of severe pain. Their clinical application is undisputed in acute (e.g., postoperative) and cancer pain, but their long-term use in chronic pain has met increasing scrutiny. This article reviews mechanisms underlying opioid analgesia and other opioid actions. It discusses the structure, function, and plasticity of opioid receptors; the central and peripheral sites of analgesic actions and side effects; endogenous and exogenous opioid receptor ligands; and conventional and novel opioid compounds. Challenging clinical situations, such as the tension between chronic pain and addiction, are also illustrated.