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

The integration of genomics into paediatric and perinatal epidemiology: guidelines for submitting human genome epidemiology (HuGE) reviews.

To assess and advance training of twenty-first century cancer epidemiologists, the National Cancer Institute (NCI) sought to obtain a snapshot of the cancer epidemiology training landscape by conducting a survey across academic institutions and cancer centers, focusing on four key training areas driving current cancer epidemiology research (“drivers”): (1) collaboration, (2) novel methods/technologies, (3) multilevel analysis, and (4) knowledge integration. Complementary to the survey, we conducted a portfolio analysis of active NCI-funded training grants. In the present report, we provide our findings from this effort and contribute to the on-going conversation regarding the training of next-generation cancer epidemiologists. Analyses and insights gained from conversations with leaders/educators across 24 academic institutions/cancer centers and the portfolio analysis of training grants echoed contemporaneous conversation that cancer epidemiology training must adapt to meet the needs of the changing research environment. Currently, with the exception of novel methods/technologies, cancer epidemiology trainees receive the majority of their training in collaboration, multilevel approaches, and knowledge integration/translation either informally, ad hoc, or not at all; exposure to these identified drivers varied considerably by institution, mentor, and other external as well as internal factors.

Training the Twenty-First Century Cancer Epidemiologist

The long-term goal of the landmark US Precision Medicine Initiative, now termed the All of Us Research Program, is to generate new knowledge based on research on individual variation in lifestyle, environment, and biological makeup that would lead to transformative personalization and improvements in health in all Americans. Using research data gathered over many years from one million or more participants, the All of Us Research Program holds great promise in health promotion, and personalized precision detection, prevention, treatment, and control of a wide variety of diseases including cardiovascular diseases. This promise in precision health is important to all Americans, but for cardiovascular diseases, it is particularly so in African Americans who shoulder the largest burden of cardiovascular mortality and morbidity in the United States. It is also important in Hispanic Americans and other underserved racial and ethnic minority populations in whom the prevalence of ideal cardiovascular health metrics remains very low. This chapter addresses the emerging concepts in precision medicine and precision public health for cardiovascular health. We begin with describing concepts of how race, ethnicity, and genetic ancestry are used in biomedical research today. We then address the huge challenge of Eurocentric biases that result from the lack of diversity in genomics research, and the real potential for current genomics tools and applications to exacerbate health inequities in underrepresented racial and ethnic minority populations. We conclude by addressing emerging concepts in trust and patient engagement in biomedical research and the role they play in African American, Hispanic, and other underserved racial and ethnic populations in the United States.

Emerging Precision Medicine Concepts and Cardiovascular Health in African Americans and Hispanics

To meet the complex needs of a growing number of cancer survivors, it is essential to accelerate the translation of survivorship research into evidence-based interventions and, as appropriate, recommendations for care that may be implemented in a wide variety of settings. Current progress in translating research into care is stymied, with results of many studies un- or underutilized. To better understand this problem and identify strategies to encourage the translation of survivorship research findings into practice, four agencies (American Cancer Society, Centers for Disease Control and Prevention, LIVE STRONG Foundation, National Cancer Institute) hosted a meeting in June, 2012, titled: “Biennial Cancer Survivorship Research Conference: Translating Science to Care.” Meeting participants concluded that accelerating science into care will require a coordinated, collaborative effort by individuals from diverse settings, including researchers and clinicians, survivors and families, public health professionals, and policy makers. This commentary describes an approach stemming from that meeting to facilitate translating research into care by changing the process of conducting research—improving communication, collaboration, evaluation, and feedback through true and ongoing partnerships. We apply the T0-T4 translational process model to survivorship research and provide illustrations of its use. The resultant framework is intended to orient stakeholders to the role of their work in the translational process and facilitate the transdisciplinary collaboration needed to translate basic discoveries into best practices regarding clinical care, self-care/management, and community programs for cancer survivors. Finally, we discuss barriers to implementing translational survivorship science identified at the meeting, along with future directions to accelerate this process.

Muin J. Khoury

Papers

The integration of genomics into paediatric and perinatal epidemiology: guidelines for submitting human genome epidemiology (HuGE) reviews.

Training the Twenty-First Century Cancer Epidemiologist

Emerging Precision Medicine Concepts and Cardiovascular Health in African Americans and Hispanics

an action Plan for t ranslating Cancer Survivorship r esearch Into Care

Lam et al. Respond to “Driving for Further Evolution”