Bone mineral density (BMD) is the most important predictor of fracture risk. We performed the largest meta-analysis to date on lumbar spine and femoral neck BMD, including 17 genome-wide association studies and 32,961 individuals of European and East Asian ancestry. We tested the top-associated BMD markers for replication in 50,933 independent subjects and for risk of low-trauma fracture in 31,016 cases and 102,444 controls. We identified 56 loci (32 novel)associated with BMD atgenome-wide significant level (P<5×10−8). Several of these factors cluster within the RANK-RANKL-OPG, mesenchymal-stem-cell differentiation, endochondral ossification and the Wnt signalling pathways. However, we also discovered loci containing genes not known to play a role in bone biology. Fourteen BMD loci were also associated with fracture risk (P<5×10−4, Bonferroni corrected), of which six reached P<5×10−8 including: 18p11.21 (C18orf19), 7q21.3 (SLC25A13), 11q13.2 (LRP5), 4q22.1 (MEPE), 2p16.2 (SPTBN1) and 10q21.1 (DKK1). These findings shed light on the genetic architecture and pathophysiological mechanisms underlying BMD variation and fracture susceptibility.
Early genome-wide association (GWA) studies on Parkinson’s disease (PD) have not been able to yield conclusive, replicable signals of association, perhaps due to limited sample size. We aimed to investigate whether association signals derived from the meta-analysis of the first two GWA investigations might be replicable in different populations. We examined six single-nucleotide polymorphisms (SNPs) (rs1000291, rs1865997, rs2241743, rs2282048, rs2313982, and rs3018626) that had reached nominal significance with at least two of three different strategies proposed in a previous analysis of the original GWA studies. Investigators from the “Genetic Epidemiology of Parkinson’s Disease” (GEOPD) consortium were invited to join in this study. Ten teams contributed replication data from 3,458 PD cases and 3,719 controls. The data from the two previously published GWAs (599 PD cases, 592 controls and 443 sibling pairs) were considered as well. All data were synthesized using both fixed and random effects models. The summary allelic odds ratios were ranging from 0.97 to 1.09 by random effects, when all data were included. The summary estimates of the replication data sets (excluding the original GWA data) were very close to 1.00 (range 0.98–1.09) and none of the effects were nominally statistically significant. The replication data sets had significantly different results than the GWA data. Our data do not support evidence that any of these six SNPs reflect susceptibility markers for PD. Much stronger signals of statistical significance in GWA platforms are needed to have substantial chances of replication. Specifically in PD genetics, this would require much larger GWA studies and perhaps novel analytical techniques.
Parkinson’s disease; meta-analysis; genome-wide association
Knowledge integration includes knowledge management, synthesis, and translation processes. It aims to maximize the use of collected scientific information and accelerate translation of discoveries into individual and population health benefits. Accumulated evidence in cancer epidemiology constitutes a large share of the 2.7 million articles on cancer in PubMed. We examine the landscape of knowledge integration in cancer epidemiology. Past approaches have mostly used retrospective efforts of knowledge management and traditional systematic reviews and meta-analyses. Systematic searches identify 2,332 meta-analyses, about half of which are on genetics and epigenetics. Meta-analyses represent 1:89-1:1162 of published articles in various cancer subfields. Recently, there are more collaborative meta-analyses with individual-level data, including those with prospective collection of measurements [e.g., genotypes in genome-wide association studies (GWAS)]; this may help increase the reliability of inferences in the field. However, most meta-analyses are still done retrospectively with published information. There is also a flurry of candidate gene meta-analyses with spuriously prevalent "positive" results. Prospective design of large research agendas, registration of datasets, and public availability of data and analyses may improve our ability to identify knowledge gaps, maximize and accelerate translational progress or—at a minimum—recognize dead ends in a more timely fashion.
Recent systematic reviews and empirical evaluations of the cognitive sciences literature suggest that publication and other reporting biases are prevalent across diverse domains of cognitive science. This review summarizes the various forms of publication and reporting biases and other questionable research practices, and overviews the available methods for probing into their existence. We discuss the available empirical evidence for the presence of such biases across the neuroimaging, animal, other pre-clinical, psychological, clinical trials, and genetics literature in the cognitive sciences. We also highlight emerging solutions (from study design to data analyses and reporting) to prevent bias and improve the fidelity in the field of cognitive science research.
publication bias; reporting bias; cognitive sciences; neuroscience; bias
High-profile studies have provided conflicting results regarding the involvement of the Omi/HtrA2 gene in Parkinson’s disease (PD) susceptibility. Therefore, we performed a large-scale analysis of the association of common Omi/HtrA2 variants in the Genetic Epidemiology of Parkinson’s disease (GEO-PD) consortium.
GEO-PD sites provided clinical and genetic data including affection status, gender, ethnicity, age at study, age at examination (all subjects); age at onset and family history of PD (patients). Genotyping was performed for the five most informative SNPs spanning the Omi/HtrA2 gene in approximately 2–3 kb intervals (rs10779958, rs2231250, rs72470544, rs1183739, rs2241028). Fixed as well as random effect models were used to provide summary risk estimates of Omi/HtrA2 variants.
The 20 GEO-PD sites provided data for 6378 cases and 8880 controls. No overall significant associations for the five Omi/HtrA2 SNPs and PD were observed using either fixed effect or random effect models. The summary odds ratios ranged between 0.98 and 1.08 and the estimates of between-study heterogeneity were not large (non-significant Q statistics for all 5 SNPs; I2 estimates 0–28%). Trends for association were seen for participants of Scandinavian descent for rs2241028 (OR 1.41, p = 0.04) and for rs1183739 for age at examination (cut-off 65 years; OR 1.17, p = 0.02), but these would not be significant after adjusting for multiple comparisons and their Bayes factors were only modest.
This largest association study performed to define the role of any gene in the pathogenesis of Parkinson’s disease revealed no overall strong association of Omi/HtrA2 variants with PD in populations worldwide.
Omi; HtrA2; Genetics; Parkinson’s disease; PARK13
The aim was to evaluate papers retracted due to falsification in high-impact journals.
Study Design and Setting
We selected articles retracted due to allegations of falsification in January 1, 1980 to March 1, 2006 from journals with impact factor >10 and >30,000 annual citations. We evaluated characteristics of these papers and misconduct-involved authors and assessed whether they correlated with time to retraction. We also compared retracted articles vs. matched nonretracted articles in the same journals.
Fourteen eligible journals had 63 eligible retracted articles. Median time from publication to retraction was 28 months; it was 79 months for articles where a senior researcher was implicated in the misconduct vs. 22 months when junior researchers were implicated (log-rank P < 0.001). For the 25 implicated authors, the median time from the first publication of a fraudulent paper to the first retraction was 34 months, again with a clear difference according to researcher rank (log-rank P = 0.001). Retracted articles didn’t differ from matched nonretracted papers in citations received within 12 months, number of authors, country, funding, or field, but were twofold more likely to have multinational authorship (P = 0.049).
Retractions due to falsification can take a long time, especially when senior researchers are implicated. Fraudulent articles are not obviously distinguishable from nonfraudulent ones.
Fraud; Falsification; Retraction; Impact; Journals; Senior investigators
To understand the translational trajectory of genomic tests in cancer screening, diagnosis, prognosis, and treatment, we reviewed tests that have been assessed by recommendation and guideline developers.
For each test, we marked translational milestones by determining when the genomic association with cancer was first discovered and studied in patients, and when a health application for a specified clinical use was successfully demonstrated and approved or cleared by the US Food and Drug Administration. To identify recommendations and guidelines, we reviewed the websites of cancer, genomic, and general guideline developers and professional organizations. We searched the in vitro diagnostics database of the US Food and Drug Administration for information, and we searched PubMed for translational milestones. Milestones were examined against type of recommendation, Food and Drug Administration approval or clearance, disease rarity, and test purpose.
Of the 45 tests we identified, 9 received strong recommendations for their usage in clinical settings, 14 received positive but moderate recommendations, and 22 were not currently recommended. For 18 tests, two or more different sources had issued recommendations, with 67% concordance. Only five tests had Food and Drug Administration approval, and an additional five had clearance. The median time from discovery to recommendation statement was 14.7 years.
In general, there were no associations found between translational trajectory and recommendation category.
cancer; genomics; genetic testing; recommendations and guidelines; translational research
During the last two decades, epidemiology has undergone a rapid evolution toward collaborative research. The proliferation of multi-institutional, interdisciplinary consortia has acquired particular prominence in cancer research. Herein, we describe the characteristics of a network of 49 established cancer epidemiology consortia (CEC) currently supported by the Epidemiology and Genomics Research Program (EGRP) at the National Cancer Institute (NCI). This collection represents the largest disease-based research network for collaborative cancer research established in population sciences. We describe the funding trends, geographic distribution and areas of research focus. The CEC have been partially supported by 201 grants and yielded 3876 publications between 1995 and 2011. We describe this output in terms of interdisciplinary collaboration and translational evolution. We discuss challenges and future opportunities in the establishment and conduct of large-scale team science within the framework of CEC, review future prospects for this approach to large scale, interdisciplinary cancer research and describe a model for the evolution of an integrated Network of Cancer Consortia optimally suited to address and support 21st century epidemiology.
consortium; epidemiology; cancer; interdisciplinary research; translation
A nutrient-wide approach may be useful comprehensively to test and validate associations between nutrients (derived from foods and supplements) and blood pressure (BP) in an unbiased manner.
Methods and Results
Data from 4,680 participants ages 40–59 in the cross-sectional International Study of Macro/Micro-nutrients and Blood Pressure (INTERMAP) were stratified randomly into training and testing sets. NHANES cross-sectional cohorts of 1999–2000 to 2005–2006 were used for external validation. We performed multiple linear regression analyses associating each of 82 nutrients and 3 urine electrolytes with systolic and diastolic BP in the INTERMAP training set. Significant findings were validated in the INTERMAP testing set and further in the NHANES cohorts (False Discovery Rate <5% in training, p<0.05 for internal and external validation). Among the validated nutrients, alcohol and urinary sodium-to-potassium ratio were directly associated with systolic BP, and dietary phosphorus, magnesium, iron, thiamin, folacin, and riboflavin were inversely associated with systolic BP. In addition, dietary folacin, and riboflavin were inversely associated with diastolic BP. The absolute effect sizes in the validation data (NHANES) ranged from 0.97 mmHg lower systolic BP (phosphorus) to 0.39 mmHg lower systolic BP (thiamin) per 1SD difference in nutrient variable. Inclusion of nutrient intake from supplements in addition to foods gave similar results for some nutrients, though it attenuated the associations of folacin, thiamin and riboflavin intake with BP.
We identified significant inverse associations between B vitamins and BP, relationships hitherto poorly investigated. Our analyses represent a systematic unbiased approach to the evaluation and validation of nutrient-BP associations.
lood pressure; diet; epidemiology; nutrition
Many clinical trials examine a composite outcome of admission to hospital and death, or infer a relationship between hospital admission and survival benefit. This assumes concordance of the outcomes “hospital admission” and “death.” However, whether the effects of a treatment on hospital admissions and readmissions correlate to its effect on serious outcomes such as death is unknown. We aimed to assess the correlation and concordance of effects of medical interventions on admission rates and mortality.
We searched the Cochrane Database of Systematic Reviews from its inception to January 2012 (issue 1, 2012) for systematic reviews of treatment comparisons that included meta-analyses for both admission and mortality outcomes. For each meta-analysis, we synthesized treatment effects on admissions and death, from respective randomized trials reporting those outcomes, using random-effects models. We then measured the concordance of directions of effect sizes and the correlation of summary estimates for the 2 outcomes.
We identified 61 meta-analyses including 398 trials reporting mortality and 182 trials reporting admission rates; 125 trials reported both outcomes. In 27.9% of comparisons, the point estimates of treatment effects for the 2 outcomes were in opposite directions; in 8.2% of trials, the 95% confidence intervals did not overlap. We found no significant correlation between effect sizes for admission and death (Pearson r = 0.07, p = 0.6). Our results were similar when we limited our analysis to trials reporting both outcomes.
In this metaepidemiological study, admission and mortality outcomes did not correlate, and discordances occurred in about one-third of the treatment comparisons included in our analyses. Both outcomes convey useful information and should be reported separately, but extrapolating the benefits of admission to survival is unreliable and should be avoided.
MicroRNA (miR) expression may have prognostic value for many types of cancers. However, the miR literature comprises many small studies. We systematically reviewed and synthesized the evidence.
Using MEDLINE (last update December 2010), we identified English language studies that examined associations between miRs and cancer prognosis using tumor specimens for more than 10 patients during classifier development. We included studies that assessed a major clinical outcome (nodal disease, disease progression, response to therapy, metastasis, recurrence, or overall survival) in an agnostic fashion using either polymerase chain reaction or hybridized oligonucleotide microarrays.
Forty-six articles presenting results on 43 studies pertaining to 20 different types of malignancy were eligible for inclusion in this review. The median study size was 65 patients (interquartile range [IQR] = 34–129), the median number of miRs assayed was 328 (IQR = 250–470), and overall survival or recurrence were the most commonly measured outcomes (30 and 19 studies, respectively). External validation was performed in 21 studies, 20 of which reported at least one nominally statistically significant result for a miR classifier. The median hazard ratio for poor outcome in externally validated studies was 2.52 (IQR = 2.26–5.40). For all classifier miRs in studies that evaluated overall survival across diverse malignancies, the miRs most frequently associated with poor outcome after accounting for differences in miR assessment due to platform type were let-7 (decreased expression in patients with cancer) and miR 21 (increased expression).
MiR classifiers show promising prognostic associations with major cancer outcomes and specific miRs are consistently identified across diverse studies and platforms. These types of classifiers require careful external validation in large groups of cancer patients that have adequate protection from bias. –
We studied the entire agenda of randomized clinical trials in pulmonary hyper-tension (PH) using sociological methods. We explored the geometry of the PH network to interpret the evidence on multiple competing treatments for the same indication.
We searched MEDLINE, Embase and Cochrane Library Databases for published studies. We queried clinicaltrials.gov and WHO International Clinical Trials Registry platform for non-published studies.
We found 75 randomized trials (41 published [n = 4136 participants] and 34 registered unpublished [planned n = 3470 participants]). Of the published randomized studies, all used placebo as the comparator arm except for two nonindustry-sponsored comparisons between phosphodiestearase-5 (PDE-5) inhibitors and endothelin receptor antagonists (ERA), and one study comparing two different regimens of treprostinil. Similarly, only five unpublished/ongoing trials used an active PH treatment as comparator (PDE-5 inhibitors versus ERA (n = 3), different doses of sildenafil (n = 1) and two formulations of epoprostenol (n = 1). Of the 75 trials, 47 were sponsored by the manufacturer of the tested active product(s), and only two trials were sponsored by two companies comparing their products.
The relative merits of different treatment options are not directly known, as there are very few head-to-head comparisons. A limited number of ongoing studies are using active FDA-approved PH-treatments for comparison. This lack of information can be overcome by carefully designing comparative effectiveness trials.
Pulmonary hypertension; Treatment
Multiple interventions have been tested in acute respiratory distress syndrome (ARDS). We examined the entire agenda of published randomized controlled trials (RCTs) in ARDS that reported on mortality and of respective meta-analyses.
We searched PubMed, the Cochrane Library and Web of Knowledge until July 2013. We included RCTs in ARDS published in English. We excluded trials of newborns and children; and those on short-term interventions, ARDS prevention or post-traumatic lung injury. We also reviewed all meta-analyses of RCTs in this field that addressed mortality. Treatment modalities were grouped in five categories: mechanical ventilation strategies and respiratory care, enteral or parenteral therapies, inhaled / intratracheal medications, nutritional support and hemodynamic monitoring.
We identified 159 published RCTs of which 93 had overall mortality reported (n= 20,671 patients) - 44 trials (14,426 patients) reported mortality as a primary outcome. A statistically significant survival benefit was observed in 8 trials (7 interventions) and two trials reported an adverse effect on survival. Among RTCs with >50 deaths in at least 1 treatment arm (n=21), 2 showed a statistically significant mortality benefit of the intervention (lower tidal volumes and prone positioning), 1 showed a statistically significant mortality benefit only in adjusted analyses (cisatracurium) and 1 (high-frequency oscillatory ventilation) showed a significant detrimental effect. Across 29 meta-analyses, the most consistent evidence was seen for low tidal volumes and prone positioning in severe ARDS.
There is limited supportive evidence that specific interventions can decrease mortality in ARDS. While low tidal volumes and prone positioning in severe ARDS seem effective, most sporadic findings of interventions suggesting reduced mortality are not corroborated consistently in large-scale evidence including meta-analyses.
Acute respiratory distress syndrome; treatment; survival; mortality
The study of genetic influences on drug response and efficacy (‘pharmacogenetics’) has existed for over 50 years. Yet, we still lack a complete picture of how genetic variation, both common and rare, affects each individual's responses to medications. Exome sequencing is a promising alternative method for pharmacogenetic discovery as it provides information on both common and rare variation in large numbers of individuals. Using exome data from 2203 AA and 4300 Caucasian individuals through the NHLBI Exome Sequencing Project, we conducted a survey of coding variation within 12 Cytochrome P450 (CYP) genes that are collectively responsible for catalyzing nearly 75% of all known Phase I drug oxidation reactions. In addition to identifying many polymorphisms with known pharmacogenetic effects, we discovered over 730 novel nonsynonymous alleles across the 12 CYP genes of interest. These alleles include many with diverse functional effects such as premature stop codons, aberrant splicesites and mutations at conserved active site residues. Our analysis considering both novel, predicted functional alleles as well as known, actionable CYP alleles reveals that rare, deleterious variation contributes markedly to the overall burden of pharmacogenetic alleles within the populations considered, and that the contribution of rare variation to this burden is over three times greater in AA individuals as compared with Caucasians. While most of these impactful alleles are individually rare, 7.6–11.7% of individuals interrogated in the study carry at least one newly described potentially deleterious alleles in a major drug-metabolizing CYP.
We conducted a meta analysis of Parkinson’s disease genome-wide association studies using a common set of 7,893,274 variants across 13,708 cases and 95,282 controls. Twenty-six loci were identified as genome-wide significant; these and six additional previously reported loci were then tested in an independent set of 5,353 cases and 5,551 controls. Of the 32 tested SNPs, 24 replicated, including 6 novel loci. Conditional analyses within loci show four loci including GBA, GAK/DGKQ, SNCA, and HLA contain a secondary independent risk variant. In total we identified and replicated 28 independent risk variants for Parkinson disease across 24 loci. While the effect of each individual locus is small, a risk profile analysis revealed a substantial cummulative risk in a comparison highest versus lowest quintiles of genetic risk (OR=3.31, 95% CI: 2.55, 4.30; p-value = 2×10−16). We also show 6 risk loci associated with proximal gene expression or DNA methylation.
Industry involvement has been associated with more favourable cost-effectiveness ratios in cost-effectiveness analyses, but the mechanisms for this association are unclear. We evaluated whether the assumed accuracy of the Papanicolaou (Pap) test was correlated with the features of cost-effectiveness analysis studies.
We searched PubMed (last updated April 2010) for cost-effectiveness analysis studies in which at least one strategy involved the Pap test for cervical cancer. We assessed the baseline assumed diagnostic sensitivity and specificity of the Pap test in each study and the association of these values with three levels of manufacturer involvement in the study.
Among 88 analyzed cost-effectiveness analysis studies, the assumed sensitivity of the Pap test was lower in studies with manufacturer-affiliated authors, manufacturer funding or manufacturer-related competing interests versus studies without (mean sensitivity 60% v. 70%, p < 0.001). The assumed specificity of the Pap test was lower in cost-effectiveness analyses involving new screening tests (mean 93% v. 96%, p = 0.016). The assumed specificity did not differ between trials with manufacturer involvement versus those without (mean 95% v. 95%, p = 0.755).
The results of cost-effectiveness analyses may be affected by a downgrading of the assumed diagnostic accuracy of the standard Pap test against which newer tests or interventions are compared. New technology then seems to have more favourable results against a straw-man comparator.
The allocation of research resources should favor conditions responsible for the greatest disease burden. This is particularly important in pediatric populations, which have been underrepresented in clinical research. Our aim was to measure the association between the focus of pediatric clinical trials and burden of disease and to identify neglected clinical domains.
We performed a cross-sectional study of clinical trials by using trial records in ClinicalTrials.gov. All trials started in 2006 or after and studying patient-level interventions in pediatric populations were included. Age-specific measures of disease burden were obtained for 21 separate conditions for high-, middle-, and low-income countries. We measured the correlation between number of pediatric clinical trials and disease burden for each condition.
Neuropsychiatric conditions and infectious diseases were the most studied conditions globally in terms of number of trials (874 and 847 trials, respectively), while intentional injuries (5 trials) and maternal conditions (4 trials) were the least studied. Clinical trials were only moderately correlated with global disease burden (r = 0.58, P = .006). Correlations were also moderate within each of the country income levels, but lowest in low-income countries (r = .47, P = .03). Globally, the conditions most understudied relative to disease burden were injuries (–260 trials for unintentional injuries and –160 trials for intentional injuries), nutritional deficiencies (–175 trials), and respiratory infections (–171 trials).
Pediatric clinical trial activity is only moderately associated with pediatric burden of disease, and least associated in low-income countries. The mismatch between clinical trials and disease burden identifies key clinical areas for focus and investment.
clinical trials; burden of disease; pediatric research
For most associations of common polymorphisms with common diseases, the genetic model of inheritance is unknown. We extended and applied a Bayesian meta-analysis approach to data from 19 studies on 17 replicated associations for type 2 diabetes. For 13 polymorphisms, the data fit very well to an additive model, for 4 polymorphisms the data were consistent with either an additive or dominant model, and for 2 polymorphisms with an additive or recessive model of inheritance for the diabetes risk allele. Results were robust to using different priors and after excluding data where index polymorphisms had been examined indirectly through proxy markers. The Bayesian meta-analysis model yielded point estimates for the genetic effects that are very similar to those previously reported based on fixed or random effects models, but uncertainty about several of the effects was substantially larger. We also examined the extent of between-study heterogeneity in the genetic model and found generally small values of the between-study deviation for the genetic model parameter. Heterosis could not be excluded in 4 SNPs. Information on the genetic model of robustly replicated GWA-derived association signals may be useful for predictive modeling, and for designing biological and functional experiments.
The publicly available online database MelGene provides a comprehensive, regularly updated, collection of data from genetic association studies in cutaneous melanoma (CM), including random-effects meta-analysis results of all eligible polymorphisms. The updated database version includes data from 192 publications with information on 1114 significantly associated polymorphisms across 280 genes, along with new front-end and back-end capabilities. Various types of relationships between data are calculated and visualized as networks. We constructed 13 different networks containing the polymorphisms and the genes included in MelGene. We explored the derived network representations under the following questions: (i) are there nodes that deserve consideration regarding their network connectivity characteristics? (ii) What is the relation of either the genome-wide or nominally significant CM polymorphisms/genes with the ones highlighted by the network representation? We show that our network approach using the MelGene data reveals connections between statistically significant genes/ polymorphisms and other genes/polymorphisms acting as ‘hubs’ in the reconstructed networks. To the best of our knowledge, this is the first database containing data from a comprehensive field synopsis and systematic meta-analyses of genetic polymorphisms in CM that provides user-friendly tools for in-depth molecular network visualization and exploration. The proposed network connections highlight potentially new loci requiring further investigation of their relation to melanoma risk.
Database URL: http://www.melgene.org.
The advent of genome-wide association studies has allowed considerable progress in the identification and robust replication of common gene variants that confer susceptibility to common diseases and other phenotypes of interest. These genetic effect sizes are almost invariably moderate-to-small in magnitude and single studies, even if large, are underpowered to detect them with confidence. Meta-analysis of many genome-wide association studies improves the power to detect more associations, and to investigate the consistency or heterogeneity of these associations across diverse datasets and study populations. In this review, we discuss the key methodological issues in the set-up, information gathering and processing, and analysis of meta-analyses of genome-wide association datasets. We illustrate, as an example, the application of meta-analysis methods in the elucidation of common genetic variants associated with type 2 diabetes. Finally, we discuss the prospects and caveats for future application of meta-analysis methods in the genome-wide setting.
meta-analysis; genome-wide association studies; replication; heterogeneity; methods
Variants within the leucine-rich repeat kinase 2 gene are recognized as the most frequent genetic cause of Parkinson’s disease. Leucine-rich repeat kinase 2 variation related to susceptibility to disease displays many features that reflect the nature of complex late-onset sporadic disorders, such as Parkinson’s disease. The Genetic Epidemiology of Parkinson’s disease consortium recently performed the largest genetic association study for variants in the leucine-rich repeat kinase 2 gene across 23 different sites in 15 countries. Herein we detail the allele frequencies for the novel risk factors (p.A419V and p.M1646T) and the protective haplotype (p.N551K-R1398H-K1423K) reported in the original publication. Simple population allele frequencies can not only provide an insight into the clinical relevance of specific variants but also help genetically define patient groups. Establishing individual patient-based genomic susceptibility profiles incorporating both risk and protective factors will determine future diagnostic and treatment strategies.
Parkinson disease; LRRK2; genetics; association study
In 2012, the National Cancer Institute (NCI) engaged the scientific community to provide a vision for cancer epidemiology in the 21st century. Eight overarching thematic recommendations, with proposed corresponding actions for consideration by funding agencies, professional societies, and the research community emerged from the collective intellectual discourse. The themes are (i) extending the reach of epidemiology beyond discovery and etiologic research to include multilevel analysis, intervention evaluation, implementation, and outcomes research; (ii) transforming the practice of epidemiology by moving towards more access and sharing of protocols, data, metadata, and specimens to foster collaboration, to ensure reproducibility and replication, and accelerate translation; (iii) expanding cohort studies to collect exposure, clinical and other information across the life course and examining multiple health-related endpoints; (iv) developing and validating reliable methods and technologies to quantify exposures and outcomes on a massive scale, and to assess concomitantly the role of multiple factors in complex diseases; (v) integrating “big data” science into the practice of epidemiology; (vi) expanding knowledge integration to drive research, policy and practice; (vii) transforming training of 21st century epidemiologists to address interdisciplinary and translational research; and (viii) optimizing the use of resources and infrastructure for epidemiologic studies. These recommendations can transform cancer epidemiology and the field of epidemiology in general, by enhancing transparency, interdisciplinary collaboration, and strategic applications of new technologies. They should lay a strong scientific foundation for accelerated translation of scientific discoveries into individual and population health benefits.
big data; clinical trials; cohort studies; epidemiology; genomics; medicine; public health; technologies; training; translational research
Remarkable progress has been made in the last decade in new methods for biological measurements using sophisticated technologies that go beyond the established genome, proteome, and gene expression platforms. These methods and technologies create opportunities to enhance cancer epidemiologic studies. In this article, we describe several emerging technologies and evaluate their potential in epidemiologic studies. We review the background, assays, methods, and challenges, and offer examples of the use of mitochondrial DNA and copy number assessments, epigenomic profiling (including methylation, histone modification, microRNAs (miRNAs), and chromatin condensation), metabolite profiling (metabolomics), and telomere measurements. We map the volume of literature referring to each one of these measurement tools and the extent to which efforts have been made at knowledge integration (e.g. systematic reviews and meta-analyses). We also clarify strengths and weaknesses of the existing platforms and the range of type of samples that can be tested with each of them. These measurement tools can be used in identifying at-risk populations and providing novel markers of survival and treatment response. Rigorous analytical and validation standards, transparent availability of massive data, and integration in large-scale evidence are essential in fulfilling the potential of these technologies.
Epigenetics; methylation; mitochondria; risk assessment; telomerase