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

Therefore, we were astonished and disappointed to encoun­ ter Buchanan’s summary dismissal of genetic epidemiology as a misguided and hopeless quest for the philosopher’s stone. Delivered scarcely 3 years after completion of the Human Genome Project, this judgement is clearly premature. Population-based epidemiological research that makes the most of newly available information and techniques is just beginning. We should expect this research to take time—years, if not decades—to appropriately conduct, analyse, report, and synthesize. Although we agree that public health programmes should continue to promote a healthy diet, adequate physical activity, and smoking cessation, it makes no sense to assert that ‘the preponderance of cases of complex chronic disease are owing to exogenous experience rather than endogenous genetic suscep­ tibility’. 10 Obviously, ‘people are not born with complex, late-onset disease’; on the other hand, people who continue to eat too much, spend too much time on the couch and smoke (despite vigorous public health education campaigns) do not always develop heart disease. Clearly, there is much more to learn about gene–environment interactions underlying these diseases and to use this knowledge in intervention efforts. As we have argued elsewhere, 14 the public health sig­ nificance of genomic research on common complex diseases with strong environmental determinants lies not in finding new genetic ‘causes’ of these diseases but in helping us to better recognize and modify interacting environmental risk factors. Each investigation that increases our understanding of gene–environment interaction, etiological heterogeneity, pathogenesis, and natural history of common diseases adds to a knowledge base for estimating risks and guiding

/pdf/genomics-epidemiology-and-common-complex-diseases-let-s-not-37ky46wv5r.pdf

Genomics, epidemiology, and common complex diseases: let's not throw out the baby with the bathwater!

Trends in postneonatal mortality (PNM) rates in the United States were analyzed for the period 1962 through 1978 using National Center for Health Statistics birth and death certificate data. The PNM rates declined from 5.5 to 3.6 per 1,000 live births for whites and from 15.6 to 7.6 per 1,000 live births for blacks. Most of the decline in PNM rates could be accounted for by a drop in mortality from infectious diseases. A dramatic increase occurred in the reported rates of unexplained sudden infant death (SID), which emerged as the leading reported cause of PNM. The second leading cause of PNM was birth defects among whites and infectious diseases among blacks. Gaps in PNM continued to exist between whites and blacks, and between metropolitan and nonmetropolitan areas. These gaps suggest that further improvement in PNM may be possible by improving access to health care. The massive increase in the rates of SID, although partially explained by coding or reporting phenomena, warrants active pursuit for a better pathophysiologic and etiologic delineation of the entity. ( JAMA 1984;252:367-372)

Trends in Postneonatal Mortality in the United States 1962 Through 1978

Data on 6,210 full-term sibships (gestational age greater than 36 weeks) reconstructed from birth certificates registered in Maryland between 1980 and 1984 were used to study sibship aggregation and recurrence risk of intrauterine growth retardation (IUGR) while considering maternal and infant covariates. In this study, IUGR was defined as being below the 10th percentile for race-, sex-, and gestational-age-specific birth weight based on all births registered in Maryland during 1984. Maternal factors (race, age at delivery, marital status, amount of prenatal care, concurrent illnesses, and complications during pregnancy) and infant factors (congenital malformations and first born child or not) were used as covariates in a modified logistic regression model which allowed correlation among sibs. First-time mothers who were under age 30 years, unmarried, received too little prenatal care, or had any reported pregnancy complications were at high risk of delivering an infant with IUGR. Even given the effects of these risk factors, however, there was a significant correlation in risk among full sibs (r = .237 +/- .028), which, combined with information on risk factors, can be used to estimate sibling recurrence risk for IUGR.

Predicting intrauterine growth retardation in sibships while considering maternal and infant covariates

https://globalheartjournal.com/articles/10.1016/j.gheart.2019.02.003/galley/449/download/

Muin J. Khoury

Papers

Genomics, epidemiology, and common complex diseases: let's not throw out the baby with the bathwater!

Trends in Postneonatal Mortality in the United States 1962 Through 1978

Predicting intrauterine growth retardation in sibships while considering maternal and infant covariates

Predictive Analytics: Helping Guide the Implementation Research Agenda at the National Heart, Lung, and Blood Institute

Concepts and terms in genetic epidemiology: some similarities to infectious disease epidemiology.