Research guidelines in the era of large-scale collaborations: an analysis of Genome-wide Association Study Consortia

Association between Gastric Cancer and 12 Autoimmune Diseases: A Mendelian Randomization Study

BACKGROUND

Whether the positive associations of gastric cancer (GC) with autoimmune diseases are causal has always been controversial. This study aims to estimate the causal relationship between GC and 12 autoimmune diseases by means of Mendelian randomization (MR) analysis.

METHODS

After rigorous evaluation, potential candidate single nucleotide polymorphisms (SNPs) for GC and 12 autoimmune diseases were extracted from genome-wide association study (GWAS) datasets. We performed the MR analyses using the inverse variance weighted (IVW) method as the primary approach to the analysis. Three sensitivity analysis methods were added to assess the robustness of the results. In addition, heterogeneity was measured using Cochran's Q-value, and horizontal pleiotropy was assessed using MR-Egger regression and leave-one-out analysis.

RESULTS

The IVW result, which is the main method of analysis, shows no evidence of a causal association between GC and any autoimmune disease. The results of IVW analysis show the relationship between rheumatoid arthritis (p = 0.1389), systemic lupus erythematosus (p = 0.1122), Crohn's disease (p = 0.1509), multiple sclerosis (p = 0.3944), primary sclerosing cholangitis (p = 0.9022), primary biliary cirrhosis (p = 0.7776), type 1 diabetes (p = 0.9595), ulcerative colitis (p = 0.5470), eczema (p = 0.3378), asthma (p = 0.7436), celiac disease (p = 0.4032), and psoriasis (p = 0.7622) and GC susceptibility. The same result was obtained with the weighted median and the MR-egger (p > 0.05).

CONCLUSION

Our study did not find a genetic causal relationship between susceptibility to these autoimmune diseases and GC, which suggests that unmeasured confounders (e.g., inflammatory processes) or shared genetic architecture may be responsible for the reported epidemiologic associations. Further studies of ancestral diversity are warranted to validate such causal associations.

Reproducibility of prediction models in health services research

The field of health services research studies the health care system by examining outcomes relevant to patients and clinicians but also health economists and policy makers. Such outcomes often include health care spending, and utilization of care services. Building accurate prediction models using reproducible research practices for health services research is important for evidence-based decision making. Several systematic reviews have summarized prediction models for outcomes relevant to health services research, but these systematic reviews do not present a thorough assessment of reproducibility and research quality of the prediction modelling studies. In the present commentary, we discuss how recent advances in prediction modelling in other medical fields can be applied to health services research. We also describe the current status of prediction modelling in health services research, and we summarize available methodological guidance for the development, update, external validation and systematic appraisal of prediction models.

Identifying novel associations in GWAS by hierarchical Bayesian latent variable detection of differentially misclassified phenotypes

Background

Heterogeneity in the definition and measurement of complex diseases in Genome-Wide Association Studies (GWAS) may lead to misdiagnoses and misclassification errors that can significantly impact discovery of disease loci. While well appreciated, almost all analyses of GWAS data consider reported disease phenotype values as is without accounting for potential misclassification.

Results

Here, we introduce Phenotype Latent variable Extraction of disease misdiagnosis (PheLEx), a GWAS analysis framework that learns and corrects misclassified phenotypes using structured genotype associations within a dataset. PheLEx consists of a hierarchical Bayesian latent variable model, where inference of differential misclassification is accomplished using filtered genotypes while implementing a full mixed model to account for population structure and genetic relatedness in study populations. Through simulations, we show that the PheLEx framework dramatically improves recovery of the correct disease state when considering realistic allele effect sizes compared to existing methodologies designed for Bayesian recovery of disease phenotypes. We also demonstrate the potential of PheLEx for extracting new potential loci from existing GWAS data by analyzing bipolar disorder and epilepsy phenotypes available from the UK Biobank. From the PheLEx analysis of these data, we identified new candidate disease loci not previously reported for these datasets that have value for supplemental hypothesis generation.

Conclusion

PheLEx shows promise in reanalyzing GWAS datasets to provide supplemental candidate loci that are ignored by traditional GWAS analysis methodologies.

Screening for gene-environment (G×E) interaction using omics data from exposed individuals: an application to gene-arsenic interaction

Identifying gene-environment interactions is a central challenge in the quest to understand susceptibility to complex, multi-factorial diseases. Developing an understanding of how inter-individual variability in inherited genetic variation alters the effects of environmental exposures will enhance our knowledge of disease mechanisms and improve our ability to predict disease and target interventions to high-risk sub-populations. Limited progress has been made identifying gene-environment interactions in the epidemiological setting using existing statistical approaches for genome-wide searches for interaction. In this paper, we describe a novel two-step approach using omics data to conduct genome-wide searches for gene-environment interactions. Using existing genome-wide SNP data from a large Bangladeshi cohort study specifically designed to assess the effect of arsenic exposure on health, we evaluated gene-arsenic interactions by first conducting genome-wide searches for SNPs that modify the effect of arsenic on molecular phenotypes (gene expression and DNA methylation features). Using this set of SNPs showing evidence of interaction with arsenic in relation to molecular phenotypes, we then tested SNP-arsenic interactions in relation to skin lesions, a hallmark characteristic of arsenic toxicity. With the emergence of additional omics data in the epidemiologic setting, our approach may have the potential to boost power for genome-wide interaction research, enabling the identification of interactions that will enhance our understanding of disease etiology and our ability to develop interventions targeted at susceptible sub-populations.

Screening for gene-environment (G×E) interaction using omics data from exposed individuals: an application to gene-arsenic interaction

Identifying gene-environment interactions is a central challenge in the quest to understand susceptibility to complex, multi-factorial diseases. Developing an understanding of how inter-individual variability in inherited genetic variation alters the effects of environmental exposures will enhance our knowledge of disease mechanisms and improve our ability to predict disease and target interventions to high-risk sub-populations. Limited progress has been made identifying gene-environment interactions in the epidemiological setting using existing statistical approaches for genome-wide searches for interaction. In this paper, we describe a novel two-step approach using omics data to conduct genome-wide searches for gene-environment interactions. Using existing genome-wide SNP data from a large Bangladeshi cohort study specifically designed to assess the effect of arsenic exposure on health, we evaluated gene-arsenic interactions by first conducting genome-wide searches for SNPs that modify the effect of arsenic on molecular phenotypes (gene expression and DNA methylation features). Using this set of SNPs showing evidence of interaction with arsenic in relation to molecular phenotypes, we then tested SNP-arsenic interactions in relation to skin lesions, a hallmark characteristic of arsenic toxicity. With the emergence of additional omics data in the epidemiologic setting, our approach may have the potential to boost power for genome-wide interaction research, enabling the identification of interactions that will enhance our understanding of disease etiology and our ability to develop interventions targeted at susceptible sub-populations.

Scanning the horizon: towards transparent and reproducible neuroimaging research

Functional neuroimaging techniques have transformed our ability to probe the neurobiological basis of behaviour and are increasingly being applied by the wider neuroscience community. However, concerns have recently been raised that the conclusions that are drawn from some human neuroimaging studies are either spurious or not generalizable. Problems such as low statistical power, flexibility in data analysis, software errors and a lack of direct replication apply to many fields, but perhaps particularly to functional MRI. Here, we discuss these problems, outline current and suggested best practices, and describe how we think the field should evolve to produce the most meaningful and reliable answers to neuroscientific questions.

Trouble in Paradise: Problems in Academic Research Co-authoring

Scholars and policy-makers have expressed concerns about the crediting of coauthors in research publications. Most such problems fall into one of two categories, excluding deserving contributors or including undeserving ones. But our research shows that there is no consensus on "deserving" or on what type of contribution suffices for co-authorship award. Our study uses qualitative data, including interviews with 60 US academic science or engineering researchers in 14 disciplines in a set of geographically distributed research-intensive universities. We also employ data from 161 website posts provided by 93 study participants, again US academic scientists. We examine a variety of factors related to perceived unwarranted exclusion from co-author credit and unwarranted inclusion, providing an empirically-informed conceptual model to explain co-author crediting outcomes. Determinants of outcomes include characteristics of disciplines and fields, institutional work culture, power dynamics and team-specific norms and decision processes.

A human rights approach to an international code of conduct for genomic and clinical data sharing

Fostering data sharing is a scientific and ethical imperative. Health gains can be achieved more comprehensively and quickly by combining large, information-rich datasets from across conventionally siloed disciplines and geographic areas. While collaboration for data sharing is increasingly embraced by policymakers and the international biomedical community, we lack a common ethical and legal framework to connect regulators, funders, consortia, and research projects so as to facilitate genomic and clinical data linkage, global science collaboration, and responsible research conduct. Governance tools can be used to responsibly steer the sharing of data for proper stewardship of research discovery, genomics research resources, and their clinical applications. In this article, we propose that an international code of conduct be designed to enable global genomic and clinical data sharing for biomedical research. To give this proposed code universal application and accountability, however, we propose to position it within a human rights framework. This proposition is not without precedent: international treaties have long recognized that everyone has a right to the benefits of scientific progress and its applications, and a right to the protection of the moral and material interests resulting from scientific productions. It is time to apply these twin rights to internationally collaborative genomic and clinical data sharing.

The Ethical, Legal, and Social Implications Program of the National Human Genome Research Institute: reflections on an ongoing experiment

For more than 20 years, the Ethical, Legal, and Social Implications (ELSI) Program of the National Human Genome Research Institute has supported empirical and conceptual research to anticipate and address the ethical, legal, and social implications of genomics. As a component of the agency that funds much of the underlying science, the program has always been an experiment. The ever-expanding number of issues the program addresses and the relatively low level of commitment on the part of other funding agencies to support such research make setting priorities especially challenging. Program-supported studies have had a significant impact on the conduct of genomics research, the implementation of genomic medicine, and broader public policies. The program's influence is likely to grow as ELSI research, genomics research, and policy development activities become increasingly integrated. Achieving the benefits of increased integration while preserving the autonomy, objectivity, and intellectual independence of ELSI investigators presents ongoing challenges and new opportunities.

Collaborative cancer epidemiology in the 21st century: the model of cancer consortia

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 3,876 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.

What is next after the genes for autoimmunity?

Clinical pathologies draw us to envisage disease as either an independent entity or a diverse set of traits governed by common physiopathological mechanisms, prompted by environmental assaults throughout life. Autoimmune diseases are not an exception, given they represent a diverse collection of diseases in terms of their demographic profile and primary clinical manifestations. Although they are pleiotropic outcomes of non-specific disease genes underlying similar immunogenetic mechanisms, research generally focuses on a single disease. Drastic technologic advances are leading research to organize clinical genomic multidisciplinary approaches to decipher the nature of human biological systems. Once the currently costly omic-based technologies become universally accessible, the way will be paved for a cleaner picture to risk quantification, prevention, prognosis and diagnosis, allowing us to clearly define better phenotypes always ensuring the integrity of the individuals studied. However, making accurate predictions for most autoimmune diseases is an ambitious challenge, since the understanding of these pathologies is far from complete. Herein, some pitfalls and challenges of the genetics of autoimmune diseases are reviewed, and an approximation to the future of research in this field is presented.