Dissecting complex disease: the quest for the Philosopher's Stone?

Distinct genetic liability profiles define clinically relevant patient strata across common diseases

Stratified medicine holds great promise to tailor treatment to the needs of individual patients. While genetics holds great potential to aid patient stratification, it remains a major challenge to operationalize complex genetic risk factor profiles to deconstruct clinical heterogeneity. Contemporary approaches to this problem rely on polygenic risk scores (PRS), which provide only limited clinical utility and lack a clear biological foundation. To overcome these limitations, we develop the CASTom-iGEx approach to stratify individuals based on the aggregated impact of their genetic risk factor profiles on tissue specific gene expression levels. The paradigmatic application of this approach to coronary artery disease or schizophrenia patient cohorts identified diverse strata or biotypes. These biotypes are characterized by distinct endophenotype profiles as well as clinical parameters and are fundamentally distinct from PRS based groupings. In stark contrast to the latter, the CASTom-iGEx strategy discovers biologically meaningful and clinically actionable patient subgroups, where complex genetic liabilities are not randomly distributed across individuals but rather converge onto distinct disease relevant biological processes. These results support the notion of different patient biotypes characterized by partially distinct pathomechanisms. Thus, the universally applicable approach presented here has the potential to constitute an important component of future personalized medicine paradigms.

Precision Medicine for Whom? Public Health Outputs from "Genomics England" and "All of Us" to Make Up for Upstream and Downstream Exclusion

This paper problematizes the precision medicine approach embraced by the All of Us Research Program (US) and by Genomics England (UK) in terms of benefits distribution, by arguing that current "diversity and inclusion" efforts do not prevent exclusiveness, unless the framing and scope of the projects are revisited in public health terms. Grounded on document analysis and fieldwork interviews, this paper analyzes efforts to address potential patterns of exclusion upstream (from participating in precision medicine research) and downstream (from benefitting from precision medicine outputs). It argues that efforts for inclusion upstream are not corresponded downstream, and this unbalance jeopardizes the equitable capacities of the projects. It concludes that enhanced focus on socio-environmental determinants of health and aligned public health interventions as precision medicine outputs would be to the benefit of all and especially of those who are most at risk of (upstream as well as downstream) exclusion.

Modeling the enigma of complex disease etiology

BACKGROUND

Complex diseases often present as a diagnosis riddle, further complicated by the combination of multiple phenotypes and diseases as features of other diseases. With the aim of enhancing the determination of key etiological factors, we developed and tested a complex disease model that encompasses diverse factors that in combination result in complex diseases. This model was developed to address the challenges of classifying complex diseases given the evolving nature of understanding of disease and interaction and contributions of genetic, environmental, and social factors.

METHODS

Here we present a new approach for modeling complex diseases that integrates the multiple contributing genetic, epigenetic, environmental, host and social pathogenic effects causing disease. The model was developed to provide a guide for capturing diverse mechanisms of complex diseases. Assessment of disease drivers for asthma, diabetes and fetal alcohol syndrome tested the model.

RESULTS

We provide a detailed rationale for a model representing the classification of complex disease using three test conditions of asthma, diabetes and fetal alcohol syndrome. Model assessment resulted in the reassessment of the three complex disease classifications and identified driving factors, thus improving the model. The model is robust and flexible to capture new information as the understanding of complex disease improves.

CONCLUSIONS

The Human Disease Ontology's Complex Disease model offers a mechanism for defining more accurate disease classification as a tool for more precise clinical diagnosis. This broader representation of complex disease, therefore, has implications for clinicians and researchers who are tasked with creating evidence-based and consensus-based recommendations and for public health tracking of complex disease. The new model facilitates the comparison of etiological factors between complex, common and rare diseases and is available at the Human Disease Ontology website.

Cigarette smoking and prostate cancer: A systematic review and meta-analysis of prospective cohort studies

INTRODUCTION

Cigarette smoking is a well-known cancer-causing behavior and a leading cause of death from cancer. However, according to previously published research and meta-analyses, cigarette smoking has a significant inverse association with prostate cancer incidence. Therefore, this study aims to investigate this association based on updated evidence by conducting a systematic review and meta-analysis.

METHODS

A search for relevant articles was performed in PubMed and Scopus databases to obtain the pooled relative risk (RR) and the corresponding 95% confidence intervals (CIs) for the risk of prostate cancer incidence among smokers compared to non-smokers. Our search was limited to prospective cohort studies.

RESULTS

A total of 17 cohort studies were included in the systematic review. Fifteen studies were included in the meta-analysis and showed that cigarette smoking has an inverse association with prostate cancer incidence with a relative risk of 0.84 (95% CI: 0.78-0.91). From all cohorts included in this systematic review, five studies examined the association between current smokers and the risk of death from prostate cancer. Therefore, a meta-analysis of these cohort studies was performed and showed that current smokers had a 42% higher risk of death from prostate cancer when compared to non-smokers with a relative risk of 1.42 (95% CI: 1.20-1.68).

CONCLUSIONS

Data from observational studies suggest that cigarette smoking has an inverse association with prostate cancer incidence. However, smokers have an increased risk of death from prostate cancer. Important to realize that this lower risk for smokers might be attributed to low prostate cancer screening uptake among smokers, misclassification bias, or selection bias from the included original studies. In summary, our results indicate that the incidence of prostate cancer is lower among smokers. Nevertheless, smokers who develop the disease have a significantly worse prognosis.

Genomics knowledge and attitudes among European public health professionals: Results of a cross-sectional survey

Background

The international public health (PH) community is debating the opportunity to incorporate genomic technologies into PH practice. A survey was conducted to assess attitudes of the European Public Health Association (EUPHA) members towards their role in the implementation of public health genomics (PHG), and their knowledge and attitudes towards genetic testing and the delivery of genetic services.

Methods

EUPHA members were invited via monthly newsletter and e-mail to take part in an online survey from February 2017 to January 2018. A descriptive analysis of knowledge and attitudes was conducted, along with a univariate and multivariate analysis of their determinants.

Results

Five hundred and two people completed the questionnaire, 17.9% were involved in PHG activities. Only 28.9% correctly identified all medical conditions for which there is (or not) evidence for implementing genetic testing; over 60% thought that investing in genomics may divert economic resources from social and environmental determinants of health. The majority agreed that PH professionals may play different roles in incorporating genomics into their activities. Better knowledge was associated with positive attitudes towards the use of genetic testing and the delivery of genetic services in PH (OR = 1.48; 95% CI 1.01–2.18).

Conclusions

Our study revealed quite positive attitudes, but also a need to increase awareness on genomics among European PH professionals. Those directly involved in PHG activities tend to have a more positive attitude and better knowledge; however, gaps are also evident in this group, suggesting the need to harmonize practice and encourage greater exchange of knowledge among professionals.

Cutaneous Papillomaviruses and Non-melanoma Skin Cancer: Causal Agents or Innocent Bystanders?

There is still controversy in the scientific field about whether certain types of cutaneous human papillomaviruses (HPVs) are causally involved in the development of non-melanoma skin cancer (NMSC). Deciphering the etiological role of cutaneous HPVs requires - besides tissue culture systems - appropriate preclinical models to match the obtained results with clinical data from affected patients. Clear scientific evidence about the etiology and underlying mechanisms involved in NMSC development is fundamental to provide reasonable arguments for public health institutions to classify at least certain cutaneous HPVs as group 1 carcinogens. This in turn would have implications on fundraising institutions and health care decision makers to force - similarly as for anogenital cancer - the implementation of a broad vaccination program against "high-risk" cutaneous HPVs to prevent NMSC as the most frequent cancer worldwide. Precise knowledge of the multi-step progression from normal cells to cancer is a prerequisite to understand the functional and clinical impact of cofactors that affect the individual outcome and the personalized treatment of a disease. This overview summarizes not only recent arguments that favor the acceptance of a viral etiology in NMSC development but also reflects aspects of causality in medicine, the use of empirically meaningful model systems and strategies for prevention.

Synergism and Antagonism of Proximate Mechanisms Enable and Constrain the Response to Simultaneous Selection on Body Size and Development Time: An Empirical Test Using Experimental Evolution

Natural selection acts on multiple traits simultaneously. How mechanisms underlying such traits enable or constrain their response to simultaneous selection is poorly understood. We show how antagonism and synergism among three traits at the developmental level enable or constrain evolutionary change in response to simultaneous selection on two focal traits at the phenotypic level. After 10 generations of 25% simultaneous directional selection on all four combinations of body size and development time in Manduca sexta (Sphingidae), the changes in the three developmental traits predict 93% of the response of development time and 100% of the response of body size. When the two focal traits were under synergistic selection, the response to simultaneous selection was enabled by juvenile hormone and ecdysteroids and constrained by growth rate. When the two focal traits were under antagonistic selection, the response to selection was due primarily to change in growth rate and constrained by the two hormonal traits. The approach used here reduces the complexity of the developmental and endocrine mechanisms to three proxy traits. This generates explicit predictions for the evolutionary response to selection that are based on biologically informed mechanisms. This approach has broad applicability to a diverse range of taxa, including algae, plants, amphibians, mammals, and insects.

Non-coding genomic regions possessing enhancer and silencer potential are associated with healthy aging and exceptional survival

We have completed a genome-wide linkage scan for healthy aging using data collected from a family study, followed by fine-mapping by association in a separate population, the first such attempt reported. The family cohort consisted of parents of age 90 or above and their children ranging in age from 50 to 80. As a quantitative measure of healthy aging, we used a frailty index, called FI34, based on 34 health and function variables. The linkage scan found a single significant linkage peak on chromosome 12. Using an independent cohort of unrelated nonagenarians, we carried out a fine-scale association mapping of the region suggestive of linkage and identified three sites associated with healthy aging. These healthy-aging sites (HASs) are located in intergenic regions at 12q13-14. HAS-1 has been previously associated with multiple diseases, and an enhancer was recently mapped and experimentally validated within the site. HAS-2 is a previously uncharacterized site possessing genomic features suggestive of enhancer activity. HAS-3 contains features associated with Polycomb repression. The HASs also contain variants associated with exceptional longevity, based on a separate analysis. Our results provide insight into functional genomic networks involving non-coding regulatory elements that are involved in healthy aging and longevity.

Autism biomarkers: challenges, pitfalls and possibilities

Network perspectives, in their emphasis on components and their interactions, might afford the best approach to the complexities of the ASD realm. Categorical approaches are unlikely to be fruitful as one should not expect to find a single or even predominant underlying cause of autism behavior across individuals. It is possible that the complex, highly interactive, heterogeneous and individualistic nature of the autism realm is intractable in terms of identifying clinically useful biomarker tests. It is hopeful from an emergenic perspective that small corrective changes in a single component of a deleterious network/configuration might have large beneficial consequences on developmental trajectories and in later treatment. It is suggested that the relationship between ASD and intellectual disability might be fundamentally different in single-gene versus nonsyndromic ASD. It is strongly stated that available biomarker "tests" for autism/ASD will do more harm than good. Finally, the serotonin-melatonin-oxidative stress-placental intersection might be an especially fruitful area of biological investigation.

Is uncertainty in complex disease epidemiology resolvable?

The imposed limitations on what we can know about nature have been long recognized. Yet in the field of epidemiology a futile search for lifestyle-related risk factors for common chronic diseases continues unabated. This has led to the production of a growing body of evidence about potential lifestyle risk factors that tend to be marginal, contradictory, irreproducible, or hard to interpret. While epidemiologists are calling for a more refined methodology, I argue that our limitation in studying complex diseases is insurmountable. This is because the study of lifestyle-related small risks requires accurate measurement of multiple behaviors-exposures over a long period of time. It is also because in complex systems such as population's health, the effect of rich interactions between its parts cannot be predicted based on traditional causal models of epidemiology. Within complex systems, understanding the interactions between system components can be more important than the contribution of each to disease risk.

Drug-disease association and drug-repositioning predictions in complex diseases using causal inference-probabilistic matrix factorization

The high incidence of complex diseases has become a worldwide threat to human health. Multiple targets and pathways are perturbed during the pathological process of complex diseases. Systematic investigation of complex relationship between drugs and diseases is necessary for new association discovery and drug repurposing. For this purpose, three causal networks were constructed herein for cardiovascular diseases, diabetes mellitus, and neoplasms, respectively. A causal inference-probabilistic matrix factorization (CI-PMF) approach was proposed to predict and classify drug-disease associations, and further used for drug-repositioning predictions. First, multilevel systematic relations between drugs and diseases were integrated from heterogeneous databases to construct causal networks connecting drug-target-pathway-gene-disease. Then, the association scores between drugs and diseases were assessed by evaluating a drug's effects on multiple targets and pathways. Furthermore, PMF models were learned based on known interactions, and associations were then classified into three types by trained models. Finally, therapeutic associations were predicted based upon the ranking of association scores and predicted association types. In terms of drug-disease association prediction, modified causal inference included in CI-PMF outperformed existing causal inference with a higher AUC (area under receiver operating characteristic curve) score and greater precision. Moreover, CI-PMF performed better than single modified causal inference in predicting therapeutic drug-disease associations. In the top 30% of predicted associations, 58.6% (136/232), 50.8% (31/61), and 39.8% (140/352) hit known therapeutic associations, while precisions obtained by the latter were only 10.2% (231/2264), 8.8% (36/411), and 9.7% (189/1948). Clinical verifications were further conducted for the top 100 newly predicted therapeutic associations. As a result, 21, 12, and 32 associations have been studied and many treatment effects of drugs on diseases were investigated for cardiovascular diseases, diabetes mellitus, and neoplasms, respectively. Related chains in causal networks were extracted for these 65 clinical-verified associations, and we further illustrated the therapeutic role of etodolac in breast cancer by inferred chains. Overall, CI-PMF is a useful approach for associating drugs with complex diseases and provides potential values for drug repositioning.

Prevalence of musculoskeletal symptoms in hospital nurse technicians and licensed practical nurses: associations with demographic factors

Objective

: This cross-sectional study aimed at analyzing: 1. the main musculoskeletal symptoms (MSS) presented by hospital nursing workers and; 2. personal, occupational, and health factors related to MSS among them.

Method

: Two questionnaires were filled in by 245 nurse technicians (NTs) and licensed practical nurses (LPNs) (response rate 95%) associated with direct patient care sectors from a hospital. These questionnaires were: the standardized version of the Nordic Musculoskeletal Questionnaire (NMQ) and one including questions on 15 demographic independent variables potentially related to outcomes from the NMQ. Univariate analyses and binary logistic regression analyses were performed to identify which variables would explain the occurrence of MSS in different body regions.

Results:

The low back (57%), shoulder (52%), and neck (48%) were identified as the most affected regions. The logistic regression analysis showed that low back symptoms in the last 12 months were significantly associated with LPN activities (OR=2.36; CI=1.24-4.5) and previous sick leave due to MSS (OR=5.97; CI=1.2-29.1). Smoking was significantly associated with symptoms in the low back (OR=2.77; CI=1.13-6.8) and thoracic spine (OR=2.37; CI=1.04-5.40). Physical exercise showed a protective effect on the cervical spine (OR=0.42; CI=0.23-0.77). Previous sick leave was significantly associated with pain in the knees (OR=4.24; CI=1.33-13.5) and in the upper limbs (OR=5.36; CI=1.07-26.7).

Conclusions:

The nursing workers who were evaluated presented a high prevalence of MSS. Previous history of sick leave was strongly associated with the presence of symptoms in various body regions. These results indicate the need for preventive programs in the hospital environment in order to control more severe MSS in nursing professionals.

Systems biology as a conceptual framework for research in family medicine; use in predicting response to influenza vaccination

AIM

To introduce systems biology as a conceptual framework for research in family medicine, based on empirical data from a case study on the prediction of influenza vaccination outcomes. This concept is primarily oriented towards planning preventive interventions and includes systematic data recording, a multi-step research protocol and predictive modelling.

BACKGROUND

Factors known to affect responses to influenza vaccination include older age, past exposure to influenza viruses, and chronic diseases; however, constructing useful prediction models remains a challenge, because of the need to identify health parameters that are appropriate for general use in modelling patients' responses.

METHODS

The sample consisted of 93 patients aged 50-89 years (median 69), with multiple medical conditions, who were vaccinated against influenza. Literature searches identified potentially predictive health-related parameters, including age, gender, diagnoses of the main chronic ageing diseases, anthropometric measures, and haematological and biochemical tests. By applying data mining algorithms, patterns were identified in the data set. Candidate health parameters, selected in this way, were then combined with information on past influenza virus exposure to build the prediction model using logistic regression.

FINDINGS

A highly significant prediction model was obtained, indicating that by using a systems biology approach it is possible to answer unresolved complex medical uncertainties. Adopting this systems biology approach can be expected to be useful in identifying the most appropriate target groups for other preventive programmes.

Pain, nicotine, and smoking: research findings and mechanistic considerations

Tobacco addiction and chronic pain represent 2 highly prevalent and comorbid conditions that engender substantial burdens upon individuals and systems. Interrelations between pain and smoking have been of clinical and empirical interest for decades, and research in this area has increased dramatically over the past 5 years. We conceptualize the interaction of pain and smoking as a prototypical example of the biopsychosocial model. Accordingly, we extrapolated from behavioral, cognitive, affective, biomedical, and social perspectives to propose causal mechanisms that may contribute to the observed comorbidity between these 2 conditions. The extant literature was 1st dichotomized into investigations of either effects of smoking on pain or effects of pain on smoking. We then integrated these findings to present a reciprocal model of pain and smoking that is hypothesized to interact in the manner of a positive feedback loop, resulting in greater pain and increased smoking. Finally, we proposed directions for future research and discussed clinical implications for smokers with comorbid pain disorders. We observed modest evidence that smoking may be a risk factor in the multifactorial etiology of some chronically painful conditions and that pain may come to serve as a potent motivator of smoking. We also found that whereas animal studies yielded consistent support for direct pain-inhibitory effects of nicotine and tobacco, results from human studies were much less consistent. Future research in the emerging area of pain and smoking has the potential to inform theoretical and clinical applications with respect to tobacco smoking, chronic pain, and their comorbid presentation. (PsycINFO Database Record (c) 2011 APA, all rights reserved).

Beyond base pairs to bedside: a population perspective on how genomics can improve health

A decade after the sequencing of the human genome, the National Human Genome Research Institute announced a strategic plan for genomic medicine. It calls for evaluating the structure and biology of genomes, understanding the biology of disease, advancing the science of medicine, and improving the effectiveness of health care. Fulfilling the promise of genomics urgently requires a population perspective to complement the bench-to-bedside model of translation. A population approach should assess the contribution of genomics to health in the context of social and environmental determinants of disease; evaluate genomic applications that may improve health care; design strategies for integrating genomics into practice; address ethical, legal, and social issues; and measure the population health impact of new technologies.

Genetic effects at pleiotropic loci are context-dependent with consequences for the maintenance of genetic variation in populations

Context-dependent genetic effects, including genotype-by-environment and genotype-by-sex interactions, are a potential mechanism by which genetic variation of complex traits is maintained in populations. Pleiotropic genetic effects are also thought to play an important role in evolution, reflecting functional and developmental relationships among traits. We examine context-dependent genetic effects at pleiotropic loci associated with normal variation in multiple metabolic syndrome (MetS) components (obesity, dyslipidemia, and diabetes-related traits). MetS prevalence is increasing in Western societies and, while environmental in origin, presents substantial variation in individual response. We identify 23 pleiotropic MetS quantitative trait loci (QTL) in an F₁₆ advanced intercross between the LG/J and SM/J inbred mouse strains (Wustl:LG,SM-G16; n = 1002). Half of each family was fed a high-fat diet and half fed a low-fat diet; and additive, dominance, and parent-of-origin imprinting genotypic effects were examined in animals partitioned into sex, diet, and sex-by-diet cohorts. We examine the context-dependency of the underlying additive, dominance, and imprinting genetic effects of the traits associated with these pleiotropic QTL. Further, we examine sequence polymorphisms (SNPs) between LG/J and SM/J as well as differential expression of positional candidate genes in these regions. We show that genetic associations are different in different sex, diet, and sex-by-diet settings. We also show that over- or underdominance and ecological cross-over interactions for single phenotypes may not be common, however multidimensional synthetic phenotypes at loci with pleiotropic effects can produce situations that favor the maintenance of genetic variation in populations. Our findings have important implications for evolution and the notion of personalized medicine.

We look at gene-by-diet and gene-by-sex interactions underlying natural variation in multiple metabolic traits mapping to the same regions of the genome in a mouse model. We find that the underlying genetic architecture of these traits is different in different sex and diet contexts. We further use expression data and whole-genome polymorphism data to identify compelling candidates for experimental follow-up. We use these results to examine theoretical evolutionary predictions about how variation in populations can be maintained. There has been much discussion of late on how to use evolutionary theory to inform medical genomics. Mouse models may be especially appropriate for bridging the divide between evolutionary and biomedical research, because they allow the study of the effects of natural alleles on normal variation and because human-mouse homology is well defined. Our study is unique in examining quantitative trait loci from both evolutionary and biomedical perspectives, and we highlight the complex connections of the traits comprising the metabolic syndrome and the evolutionary implications of their underlying genetic architecture. This is important for understanding disease etiology and is relevant to personalized medicine.

Is life law-like?

Genes are generally assumed to be primary biological causes of biological phenotypes and their evolution. In just over a century, a research agenda that has built on Mendel's experiments and on Darwin's theory of natural selection as a law of nature has had unprecedented scientific success in isolating and characterizing many aspects of genetic causation. We revel in these successes, and yet the story is not quite so simple. The complex cooperative nature of genetic architecture and its evolution include teasingly tractable components, but much remains elusive. The proliferation of data generated in our "omics" age raises the question of whether we even have (or need) a unified theory or "law" of life, or even clear standards of inference by which to answer the question. If not, this not only has implications for the widely promulgated belief that we will soon be able to predict phenotypes like disease risk from genes, but also speaks to the limitations in the underlying science itself. Much of life seems to be characterized by ad hoc, ephemeral, contextual probabilism without proper underlying distributions. To the extent that this is true, causal effects are not asymptotically predictable, and new ways of understanding life may be required.

Current priorities for public health practice in addressing the role of human genomics in improving population health

In spite of accelerating human genome discoveries in a wide variety of diseases of public health significance, the promise of personalized health care and disease prevention based on genomics has lagged behind. In a time of limited resources, public health agencies must continue to focus on implementing programs that can improve health and prevent disease now. Nevertheless, public health has an important and assertive leadership role in addressing the promise and pitfalls of human genomics for population health. Such efforts are needed not only to implement what is known in genomics to improve health but also to reduce potential harm and create the infrastructure needed to derive health benefits in the future.

Apolipoprotein E genotypes, circulating C-reactive protein and angiographic coronary artery disease: the Ludwigshafen Risk and Cardiovascular Health Study

C-reactive protein (CRP) has been implicated in the development of atherosclerosis. The genetic polymorphism of apolipoprotein (apo) E is associated with the concentration of CRP. We analyzed the association between the apo E genotype, CRP and angiographic coronary artery disease (CAD). The concentration of CRP was similar in patients with stable CAD and in controls, but increased in patients presenting with acute coronary syndromes. In models adjusting for the main confounding variables, the alleles ɛ4 and ɛ2 were associated with decreased and increased concentrations of CRP, respectively, compared to the wild-type allele ɛ3. In spite of this, the ɛ2 allele was associated with a lower prevalence of angiographic CAD, while the slight over-representation of the ɛ4 allele was statistically not significant. We conclude that the apo E genotype is associated with circulating CRP. A causal role of CRP in the development of CAD would be supported if genotypes that raise CRP in the long-term were themselves associated with CAD. As we found the opposite, we suggest that the association between CRP and cardiovascular events reflects confounding and reverse causation.

Breast cancer genomics based on biobanks

Attempts to find genes contribution to complex diseases, such as cancer, require new study designs which incorporate an efficient use of population resources and modern genotyping technologies. We describe here two approaches, used by us for the study of breast cancer, both of which take the use of biobanks. One uses a cancer registry as a source of case information, which is then linked to a biobank on blood DNA. The biobank provides also samples from matched controls. After genotyping, clinical data are retrieved from hospital records, and the results can be presented for genotype-specific cancer risks, or similarly for genotype-specific clinical and survival parameters. The second approach uses registered data on cancer in families or among twins. On defined groups of patients, paraffin tissue is collected by contacting the pathology departments of the hospitals where the patients were diagnosed. Tumor and healthy tissue is prepared and used for mutation, the loss of heterozygosity, or copy number analysis. We believe that in the era of whole-genome genotyping technologies, the importance of well-characterized sample sets cannot be overemphasized. Samples rather than technologies limit the rate of gene discovery in complex diseases.

Prediction of influenza vaccination outcome by neural networks and logistic regression

The major challenge in influenza vaccination is to predict vaccine efficacy. The purpose of this study was to design a model to enable successful prediction of the outcome of influenza vaccination based on real historical medical data. A non-linear neural network approach was used, and its performance compared to logistic regression. The three neural network algorithms were tested: multilayer perceptron, radial basis and probabilistic in conjunction with parameter optimization and regularization techniques in order to create an influenza vaccination model that could be used for prediction purposes in the medical practice of primary health care physicians, where the vaccine is usually dispensed. The selection of input variables was based on a model of the vaccine strain which has frequently been changed and on which a poor influenza vaccine response is expected. The performance of models was measured by the average hit rate of negative and positive vaccine outcome. In order to test the generalization ability of the models, a 10-fold cross-validation procedure revealed that the model obtained by multilayer perceptron produced the highest average hit rate among neural network algorithms, and also outperformed the logistic regression model with regard to sensitivity and specificity. Sensitivity analysis was performed on the best model and the importance of input variables was discussed. Further research should focus on improving the performance of the model by combining neural networks with other intelligent methods in this field.

Association of repeatedly measured intermediate risk factors for complex diseases with high dimensional SNP data

Background

The causes of complex diseases are difficult to grasp since many different factors play a role in their onset. To find a common genetic background, many of the existing studies divide their population into controls and cases; a classification that is likely to cause heterogeneity within the two groups. Rather than dividing the study population into cases and controls, it is better to identify the phenotype of a complex disease by a set of intermediate risk factors. But these risk factors often vary over time and are therefore repeatedly measured.

Results

We introduce a method to associate multiple repeatedly measured intermediate risk factors with a high dimensional set of single nucleotide polymorphisms (SNPs). Via a two-step approach, we summarized the time courses of each individual and, secondly apply these to penalized nonlinear canonical correlation analysis to obtain sparse results.

Conclusions

Application of this method to two datasets which study the genetic background of cardiovascular diseases, show that compared to progression over time, mainly the constant levels in time are associated with sets of SNPs.

The association between smoking and low back pain: a meta-analysis

OBJECTIVE

To assess the association between smoking and low back pain with meta-analysis.

METHODS

We conducted a systematic search of the MEDLINE and EMBASE databases until February 2009. Eighty-one studies were reviewed and 40 (27 cross-sectional and 13 cohort) studies were included in the meta-analyses.

RESULTS

In cross-sectional studies, current smoking was associated with increased prevalence of low back pain in the past month (pooled odds ratio [OR] 1.30, 95% confidence interval [CI], 1.16-1.45), low back pain in the past 12 months (OR 1.33, 95% CI, 1.26-1.41), seeking care for low back pain (OR 1.49, 95% CI, 1.38-1.60), chronic low back pain (OR 1.79, 95% CI, 1.27-2.50) and disabling low back pain (OR 2.14, 95% CI, 1.11-4.13). Former smokers had a higher prevalence of low back pain compared with never smokers, but a lower prevalence of low back pain than current smokers. In cohort studies, both former (OR 1.32, 95% CI, 0.99-1.77) and current (OR 1.31, 95% CI, 1.11-1.55) smokers had an increased incidence of low back pain compared with never smokers. The association between current smoking and the incidence of low back pain was stronger in adolescents (OR 1.82, 95% CI, 1.42-2.33) than in adults (OR 1.16, 95% CI, 1.02-1.32).

CONCLUSIONS

Our findings indicate that both current and former smokers have a higher prevalence and incidence of low back pain than never smokers, but the association is fairly modest. The association between current smoking and the incidence of low back pain is stronger in adolescents than in adults.

Colloquium papers: Numbering the hairs on our heads: the shared challenge and promise of phenomics

Evolution and medicine share a dependence on the genotype-phenotype map. Although genotypes exist and are inherited in a discrete space convenient for many sorts of analyses, the causation of key phenomena such as natural selection and disease takes place in a continuous phenotype space whose relationship to the genotype space is only dimly grasped. Direct study of genotypes with minimal reference to phenotypes is clearly insufficient to elucidate these phenomena. Phenomics, the comprehensive study of phenotypes, is therefore essential to understanding biology. For all of the advances in knowledge that a genomic approach to biology has brought, awareness is growing that many phenotypes are highly polygenic and susceptible to genetic interactions. Prime examples are common human diseases. Phenomic thinking is starting to take hold and yield results that reveal why it is so critical. The dimensionality of phenotypic data are often extremely high, suggesting that attempts to characterize phenotypes with a few key measurements are unlikely to be completely successful. However, once phenotypic data are obtained, causation can turn out to be unexpectedly simple. Phenotypic data can be informative about the past history of selection and unexpectedly predictive of long-term evolution. Comprehensive efforts to increase the throughput and range of phenotyping are an urgent priority.

The novel "genomic pathway approach" to complex diseases: a reason for (over-)optimism?

BACKGROUND

Examination of the genetic structure of complex diseases by a "genomic pathway approach"--which applies stepwise model selection to sets of more than 1000 polymorphisms in studies of several hundred subjects--has recently been proposed. Models constructed through extensive selection procedures may yield misleading test statistics and measures of predictive performance; we aimed to quantify the extent of such problems inherent to stepwise regression on the genomic pathway scale.

METHODS

We performed permutation analyses and data-splitting approaches using one of the datasets examined in the paper that originally suggested this approach (n = 536; 1195 SNPs in 22 genes) (Lesnick et al. PLoS Genet. 2007;3:e98).

RESULTS

The P values for the genetic effects produced by standard testing severely overestimated the significance, resulting in our example in a standard P value of 3.5 x 10(-69) and a permutation P of 0.003 (95% confidence interval = 0.001 to 0.009). Furthermore, the apparent validity as measured by the area under the receiver operating characteristic curve in 90% training datasets (0.935 [interquartile range = 0.918-0.951]) was extremely overoptimistic when compared with the validity estimated from the excluded 10% validation subsets (0.564 [0.518-0.614]). This validated area under the receiver operating characteristic curve was lower than for models predicting case/control status solely from age and sex while excluding any genetic effects (median difference = -0.040 [95% confidence interval = -0.049 to -0.031]).

CONCLUSIONS

The application of stepwise model selection on the genomic pathway scale--at least in the simple form currently put forward--is prone to yield highly misleading results. We provide pointers to some promising alternatives.

Considerations for designing a prototype genetic test for use in translational research

BACKGROUND

Translational research is needed to explore how people will respond to personal genetic susceptibility information related to common health conditions. Maximizing the rigor of this research will require that genetic test results be returned to study participants. Currently, there is no established method that guides the selection of genetic variants to be used in research with these objectives.

METHODS AND RESULTS

To address this question, we designed a process to identify gene variants and health conditions to be included in a prototype genetic test for use in a larger research effort, the Multiplex Initiative. The intention of this exploration was to facilitate research that generates individual genetic test results that are returned to study participants. Inclusion criteria were developed as part of a transdisciplinary and iterative process that considered the weight of evidential support for genetic association with common health conditions, the appropriateness of use in human subjects research, and the recommendations of expert peer reviewers.

CONCLUSIONS

The selection process was designed to identify gene variants for the limited purpose of translational research and, therefore, should not be seen as producing a valid clinical test. However, this example of an applied selection process may provide guidance for researchers who are designing studies to evaluate the implications of genetic susceptibility testing through the return of personalized genetic information. As the rate of genomic discoveries increases, such research will be essential in steering the translation of this information towards the greatest public health benefit.

Translational disease interpretation with molecular networks

The integration of medical information into gene and protein networks could lead to a better understanding of complex diseases.

Molecular networks are being used to reconcile genotypes and phenotypes by integrating medical information. In this context, networks will be instrumental for the interpretation of disease at the personalized medicine level.

Strategies and issues in the detection of pathway enrichment in genome-wide association studies

A fundamental question in human genetics is the degree to which the polygenic character of complex traits derives from polymorphism in genes with similar or with dissimilar functions. The many genome-wide association studies now being performed offer an opportunity to investigate this, and although early attempts are emerging, new tools and modeling strategies still need to be developed and deployed. Towards this goal, we implemented a new algorithm to facilitate the transition from genetic marker lists (principally those generated by PLINK) to pathway analyses of representational gene sets in either threshold or threshold-free downstream applications (e.g. DAVID, GSEA-P, and Ingenuity Pathway Analysis). This was applied to several large genome-wide association studies covering diverse human traits that included type 2 diabetes, Crohn's disease, and plasma lipid levels. Validation of this approach was obtained for plasma HDL levels, where functional categories related to lipid metabolism emerged as the most significant in two independent studies. From analyses of these samples, we highlight and address numerous issues related to this strategy, including appropriate gene based correction statistics, the utility of imputed versus non-imputed marker sets, and the apparent enrichment of pathways due solely to the positional clustering of functionally related genes. The latter in particular emphasizes the importance of studies that directly tie genetic variation to functional characteristics of specific genes. The software freely provided that we have called ProxyGeneLD may resolve an important bottleneck in pathway-based analyses of genome-wide association data. This has allowed us to identify at least one replicable case of pathway enrichment but also to highlight functional gene clustering as a potentially serious problem that may lead to spurious pathway findings if not corrected.

What are genes "for" or where are traits "from"? What is the question?

For at least a century it has been known that multiple factors play a role in the development of complex traits, and yet the notion that there are genes "for" such traits, which traces back to Mendel, is still widespread. In this paper, we illustrate how the Mendelian model has tacitly encouraged the idea that we can explain complexity by reducing it to enumerable genes. By this approach many genes associated with simple as well as complex traits have been identified. But the genetic architecture of biological traits, or how they are made, remains largely unknown. In essence, this reflects the tension between reductionism as the current "modus operandi" of science, and the emerging knowledge of the nature of complex traits. Recent interest in systems biology as a unifying approach indicates a reawakened acceptance of the complexity of complex traits, though the temptation is to replace "gene for" thinking by comparably reductionistic "network for" concepts. Both approaches implicitly mix concepts of variants and invariants in genetics. Even the basic question is unclear: what does one need to know to "understand" the genetic basis of complex traits? New operational ideas about how to deal with biological complexity are needed.

Size matters: just how big is BIG?: Quantifying realistic sample size requirements for human genome epidemiology

BACKGROUND

Despite earlier doubts, a string of recent successes indicates that if sample sizes are large enough, it is possible-both in theory and in practice-to identify and replicate genetic associations with common complex diseases. But human genome epidemiology is expensive and, from a strategic perspective, it is still unclear what 'large enough' really means. This question has critical implications for governments, funding agencies, bioscientists and the tax-paying public. Difficult strategic decisions with imposing price tags and important opportunity costs must be taken.

METHODS

Conventional power calculations for case-control studies disregard many basic elements of analytic complexity-e.g. errors in clinical assessment, and the impact of unmeasured aetiological determinants-and can seriously underestimate true sample size requirements. This article describes, and applies, a rigorous simulation-based approach to power calculation that deals more comprehensively with analytic complexity and has been implemented on the web as ESPRESSO: (www.p3gobservatory.org/powercalculator.htm).

RESULTS

Using this approach, the article explores the realistic power profile of stand-alone and nested case-control studies in a variety of settings and provides a robust quantitative foundation for determining the required sample size both of individual biobanks and of large disease-based consortia. Despite universal acknowledgment of the importance of large sample sizes, our results suggest that contemporary initiatives are still, at best, at the lower end of the range of desirable sample size. Insufficient power remains particularly problematic for studies exploring gene-gene or gene-environment interactions. Discussion Sample size calculation must be both accurate and realistic, and we must continue to strengthen national and international cooperation in the design, conduct, harmonization and integration of studies in human genome epidemiology.

EU policies in personalized medicine-related technologies

Against the background of a number of first drug–diagnostic co-products developed and introduced into the European market, European decision-makers feel impelled to react and position themselves in the field of personalized medicine. Their reactions cover a broad range, from the analysis of knowledge requirements for market approval to the need for translational activities and the possible contribution of pharmacogenetics to public health. This article summarizes the current positions of European institutions, based on literature review and expert consultation for three items associated with personalized medicine: biobanks, genetic diagnostics and drug–diagnostic co-products, and provides an outlook on requirements for an effective future European policy on personalized medicine.

Point-counterpoint. The triumph of the null hypothesis: epidemiology in an age of change

The recent confusion concerning the relation between hormone replacement therapy and cardiovascular disease has stirred a new wave of debate about the value and future of epidemiology. Opponents of epidemiology suggest an ever-diminishing role in an age of small risks and complex diseases, yet proponents are not in consensus about how to adapt their discipline to the challenges associated with ageing societies and changing disease patterns. While epidemiology is likely to be increasingly called upon to make sense of the risks involved with these changes, wading into this era with a mindset and tools that were derived from epidemiology's 'golden era' of tackling major risk factors, has created more confusion than understanding. Increasingly, we need to downsize epidemiology to what is testable, measurable, and relevant, based on robust methodology and public health rationale. Applying an evolutionary perspective, that views health problems of modernity as a manifestation of the mismatch between our ancient genes and hi-tech lifestyles, can provide guidance for a 21st century research agenda.

Tilting at quixotic trait loci (QTL): an evolutionary perspective on genetic causation

Recent years have seen great advances in generating and analyzing data to identify the genetic architecture of biological traits. Human disease has understandably received intense research focus, and the genes responsible for most Mendelian diseases have successfully been identified. However, the same advances have shown a consistent if less satisfying pattern, in which complex traits are affected by variation in large numbers of genes, most of which have individually minor or statistically elusive effects, leaving the bulk of genetic etiology unaccounted for. This pattern applies to diverse and unrelated traits, not just disease, in basically all species, and is consistent with evolutionary expectations, raising challenging questions about the best way to approach and understand biological complexity.

C-reactive protein genotypes associated with circulating C-reactive protein but not with angiographic coronary artery disease: the LURIC study

AIMS

Circulating C-reactive protein is associated with future cardiovascular events. The causal role of C-reactive protein in the development of atherosclerosis remains controversial.

METHODS AND RESULTS

We analysed the association between three genetic polymorphisms (PM) (-717C>T, rs2794521; +1059G>C, rs1800947; +1444C>T, rs1130864) at the C-reactive protein locus and related haplotypes with both circulating C-reactive protein and angiographic coronary artery disease (CAD). The concentration of C-reactive protein was similar in patients with stable CAD and in controls, but increased in patients presenting with acute coronary syndromes. In models adjusting for the main confounding variables, the minor alleles of the +1059G>C (rs1800947) and the +1444C>T PM (rs1130864) were associated with decreased and increased concentrations of C-reactive protein, respectively. Haplotypes 1 and 4 decreased, and haplotype 2 increased C-reactive protein, whereas haplotype 3 had no appreciable effect. None of the genetic variants affecting circulating C-reactive protein was consistently associated with the prevalence of angiographic CAD.

CONCLUSION

A causal role of C-reactive protein in the development of CAD would require that genetic PM resulting in long-term modulation of the concentration of C-reactive protein be themselves associated with CAD. We were not able to detect such a relationship, which can be attributed to either a very small genetic effect size or the relationship between C-reactive protein and cardiovascular events may reflect confounding and reverse causation.

Familial risks for common diseases: etiologic clues and guidance to gene identification

Familial clustering of a disease is a direct indicator of a possible heritable cause, provided that environmental sharing can be excluded. If the familial clustering is lacking, the likelihood of a heritable influence is also small. In the era of genome scans, the consideration of data on heritability should be important in the assessment of the likely success of the genome scan. The availability of a Multigeneration Register in Sweden provides a reliable access to families throughout the last century. This Register has been extensively used to study a number of different diseases through linkage to the Hospital Discharge Register. In the present article we review the obtained and some unpublished results for nine main disease classes. For each of these, familial risks are given for four disease subtypes. As measures of familial clustering we use risks between siblings, twins and spouses. Disease correlation between spouses suggests environmental sharing and a higher correlation between siblings and particularly twins shows heritable effects. We will also comment on the established susceptibility genes and the risks conferred by them. The data suggest high heritabilities for chronic obstructive pulmonary disease, asthma, noninfective enteritis and colitis, cerebral palsy and endocrine and metabolic diseases. Among the performed first-generation genome scans on various diseases, the success appears to be related to the a priori heritability estimates. To our knowledge this is a first attempt to summarize familial risks for a large number of diseases using data from a single population on which reasonable uniform diagnostic criteria have been applied.

Banking expectations: the promises and problems of biobanks

In the last decade, governments, medical charities, pharmaceutical companies and disease advocacy organizations have spent considerable time and money developing biobanks to aid drug discovery and the investigation of disease. This article identifies and assesses the various expectations that have driven the investment in different types of biobanks. It suggests that they have been the focus of unrealistic promises about producing a ‘biobank revolution’ that will transform biomedicine and healthcare. We need more modest expectations about what can be achieved, and need to tackle certain conceptual and methodological challenges for biobanks to fulfill their potential.

Will genomics widen or help heal the schism between medicine and public health?

We discuss the "schism" between medicine and public health in light of advances in genomics and the expected evolution of health care toward personalized treatment and prevention. Undoubtedly, genomics could deepen the divide between the two worlds, but it also represents an important and perhaps unique opportunity for healing the schism, given the volume of new scientific discoveries and their potential applications in all areas of health and disease. We argue that the integration of genomics into health care and disease prevention requires a strong medicine-public health partnership in the context of a population approach to a translational research agenda that includes four overlapping areas: (1) a joint focus on prevention-a traditional public health concern but now a promise of genomics in the realm of individualized primary prevention and early detection, (2) a population perspective, which requires a large amount of population-level data to validate gene discoveries for clinical applications, (3) commitment to evidence-based knowledge integration with thousands of potential genomic applications in practice, and (4) emphasis on health services research to evaluate outcomes, costs, and benefits in the real world. A strong medicine-public health partnership in the genomics era is needed for the translation of all scientific discoveries for the benefit of population health.