Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.

http://ibgwww.colorado.edu/cdrom2009/ScriptsA/purcell/GWAS/boulder_09_plink_intro.pdf

PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses

Much biomedical research is observational. The reporting of such research is often inadequate, which hampers the assessment of its strengths and weaknesses and of a study's generalisability. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Initiative developed recommendations on what should be included in an accurate and complete report of an observational study. We defined the scope of the recommendations to cover three main study designs: cohort, case-control, and cross-sectional studies. We convened a 2-day workshop in September 2004, with methodologists, researchers, and journal editors to draft a checklist of items. This list was subsequently revised during several meetings of the coordinating group and in e-mail discussions with the larger group of STROBE contributors, taking into account empirical evidence and methodological considerations. The workshop and the subsequent iterative process of consultation and revision resulted in a checklist of 22 items (the STROBE Statement) that relate to the title, abstract, introduction, methods, results, and discussion sections of articles. 18 items are common to all three study designs and four are specific for cohort, case-control, or cross-sectional studies. A detailed Explanation and Elaboration document is published separately and is freely available on the Web sites of PLoS Medicine, Annals of Internal Medicine, and Epidemiology. We hope that the STROBE Statement will contribute to improving the quality of reporting of observational studies.

/pdf/the-strengthening-the-reporting-of-observational-studies-in-1a60tcw2sx.pdf

The Strengthening the Reporting of Observational Studies in Epidemiology [STROBE] statement: guidelines for reporting observational studies

Much biomedical research is observational. The reporting of such research is often inadequate, which hampers the assessment of its strengths and weaknesses and of a study’s generalizability. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Initiative developed recommendations on what should be included in an accurate and complete report of an observational study. We defined the scope of the recommendations to cover three main study designs: cohort, case-control and cross-sectional studies. We convened a two-day workshop, in September 2004, with methodologists, researchers and journal editors to draft a checklist of items. This list was subsequently revised during several meetings of the coordinating group and in e-mail discussions with the larger group of STROBE contributors, taking into account empirical evidence and methodological considerations. The workshop and the subsequent iterative process of consultation and revision resulted in a checklist of 22 items (the STROBE Statement) that relate to the title, abstract, introduction, methods, results and discussion sections of articles. Eighteen items are common to all three study designs and four are specific for cohort, case-control, or cross-sectional studies. A detailed Explanation and Elaboration document is published separately and is freely available on the web sites of PLoS Medicine, Annals of Internal Medicine and Epidemiology. We hope that the STROBE Statement will contribute to improving the quality of reporting of observational studies.

/pdf/the-strengthening-the-reporting-of-observational-studies-in-5fvhasotyl.pdf

The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

XI. STRATEGIES FOR IMPROVING DIABETES CARE D iabetes is a chronic illness that requires continuing medical care and patient self-management education to prevent acute complications and to reduce the risk of long-term complications. Diabetes care is complex and requires that many issues, beyond glycemic control, be addressed. A large body of evidence exists that supports a range of interventions to improve diabetes outcomes. These standards of care are intended to provide clinicians, patients, researchers, payors, and other interested individuals with the components of diabetes care, treatment goals, and tools to evaluate the quality of care. While individual preferences, comorbidities, and other patient factors may require modification of goals, targets that are desirable for most patients with diabetes are provided. These standards are not intended to preclude more extensive evaluation and management of the patient by other specialists as needed. For more detailed information, refer to Bode (Ed.): Medical Management of Type 1 Diabetes (1), Burant (Ed): Medical Management of Type 2 Diabetes (2), and Klingensmith (Ed): Intensive Diabetes Management (3). The recommendations included are diagnostic and therapeutic actions that are known or believed to favorably affect health outcomes of patients with diabetes. A grading system (Table 1), developed by the American Diabetes Association (ADA) and modeled after existing methods, was utilized to clarify and codify the evidence that forms the basis for the recommendations. The level of evidence that supports each recommendation is listed after each recommendation using the letters A, B, C, or E.

Standards of Medical Care in Diabetes

“ A foolish consistency is the hobgoblin of little minds, adored by little statesmen and philosophers and divines.”—Ralph Waldo Emerson 

A goal of genetic association studies is to identify disease-causing genes. True positive associations between genetic variants and disease traits have the

https://cebp.aacrjournals.org/content/cebp/16/5/864.full.pdf

Genetic Association Studies of Cancer: Where Do We Go from Here?

From genes to public health: are we ready for DNA-based population screening?

There is expanding consensus on the need to modernize the training of cancer epidemiologists to accommodate rapidly emerging technological advancements and the digital age, which are transforming the practice of cancer epidemiology. There is also a growing imperative to extend cancer epidemiology research that is etiological to that which is applied and has the potential to affect individual and public health. Medical schools and schools of public health are recognizing the need to develop such integrated programs; however, we lack the data to estimate how many current training programs are effectively equipping epidemiology students with the knowledge and tools to design, conduct, and analyze these increasingly complex studies. There is also a need to develop new mentoring approaches to account for the transdisciplinary team-science environment that now prevails. With increased dialogue among schools of public health, medical schools, and cancer centers, revised competencies and training programs at predoctoral, doctoral, and postdoctoral levels must be developed. Continuous collection of data on the impact and outcomes of such programs is also recommended.

The Next Generation of Large-Scale Epidemiologic Research: Implications for Training Cancer Epidemiologists

Even before the human genome was sequenced, Khoury and Dorman published an editorial in the American Journal of Epidemiology (AJE) calling for a population-based approach to health-related discoveries that stemmed from the Human Genome Project (1). The approach was termed “human genome epidemiology” (HuGE for short) and led to the formation of an informal collaboration (HuGENet) (2) to explore the systematic use of epidemiologic methods to investigate the role of genetic variation in health and disease. That inaugural editorial outlined the goals of human genome epidemiology, which ranged from estimating the population prevalence of gene variants to evaluating genetic tests and services. In 2000, the AJE proposed a schema for publishing HuGE reviews, which were envisioned as systematic summaries of population-based data on gene-disease associations for use by researchers, health officials, and policymakers (3). Each review would address a particular combination of 1 or more genetic variants and health outcomes, summarizing the available data on genotype prevalence, gene-disease associations, and gene-environment interactions and commenting on its implications for population health. The AJE offered to consider HuGE reviews for publication, and 10 more journals followed suit. From 2000 through 2013, the AJE published 65 HuGE reviews, and 58 more reviews appeared in other journals. Together, these HuGE reviews assessed the relationships of variants of 195 genes with specific outcomes, including inherited disorders (e.g., sickle-cell anemia), common diseases (e.g., coronary artery disease), several cancers, birth defects, and other conditions. The surge in genetic association studies during the last decade has been well documented in online databases, including the HuGE Navigator and the GWAS Catalog, which captures genome-wide association studies (GWAS) (4, 5). The number of genetic association studies published annually grew from 2,514 in 2001 to 10,940 in 2013 (http://www. hugenavigator.net). From the beginning, it was clear that many reported genetic associations with common diseases were spurious; furthermore, even consistently replicated associations had mostly small effects (6). Meta-analysis thus emerged as an important tool for assessing gene-disease associations, both for neutralizing reports of spurious associations and for revealing subtle associations. In 2009, HuGENet authors recommended reporting the results of primary genetic association studies in sufficient detail to allow their evaluation for quality and inclusion in systematic reviews (7, 8). Updated HuGE review guidelines recommended the use of meta-analysis to arrive at summary estimates of association, as well as a heuristic to help gauge their reliability (the “Venice criteria”) (9). In 2009, Minelli et al. (10) published a systematic review of meta-analyses of published genetic association studies. They found that general methodological problems—such as failure to document search strategy, account for publication bias, or test for heterogeneity—were common, although no more common than among meta-analyses in other fields. However, methodological considerations specific to genetics—such as Hardy-Weinberg equilibrium, genotype frequency, choice of genetic model, and population stratification—were often poorly addressed or completely ignored. The authors concluded with a set of practical recommendations for the conduct and reporting of such meta-analyses. Although meta-analyses of genetic associations account for only a small proportion of publications on gene-disease associations, they have proliferated rapidly from only 29 in 2001 to 1,606 in 2013. During that time, the genetic association research strategy shifted focus from candidate gene studies that evaluated 1 or a few polymorphisms to GWAS for the discoveryof new candidate genes; recent years have seen an increase in studies of rare variants in both common and

/pdf/editorial-updated-guidance-on-human-genome-epidemiology-huge-503jmwlfsf.pdf

Editorial: Updated Guidance on Human Genome Epidemiology (HuGE) Reviews and Meta-Analyses of Genetic Associations

In this issue of the Journal, Kumar gives an extensive historical account of the evidence-based medicine movement, and posits that modern medicine is rapidly moving from the current paradigm of “evidence-based medicine” towards the paradigm of “genomic medicine” in the 21st century (Kumar 2008). In this editorial, we elaborate on one angle of this transition, namely the necessity for genomic medicine to become more evidence-based. The premature introduction of technologies into healthcare settings could potentially overwhelm the health system financially, legally and ethically. In addition, the lack of coverage and reimbursement policies by governments and health insurers will lead to differential penetration of and access to technologies of unknown benefits, potentially exacerbating health disparities.

/pdf/why-should-genomic-medicine-become-more-evidence-based-1gsi3ro608.pdf

Muin J. Khoury

Papers

Genetic Association Studies of Cancer: Where Do We Go from Here?

From genes to public health: are we ready for DNA-based population screening?

The Next Generation of Large-Scale Epidemiologic Research: Implications for Training Cancer Epidemiologists

Editorial: Updated Guidance on Human Genome Epidemiology (HuGE) Reviews and Meta-Analyses of Genetic Associations

Why should genomic medicine become more evidence-based?