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Why people of the same age show differences in age-related functional decline and whether biological aging can be slowed down through lifestyle changes and therapeutics are active research topics. Molecular tools that predict biological age based on DNA methylation markers, known as epigenetic clocks, are facilitating these efforts. In this issue, Kresovich et al. (Am J Epidemiol. 2021;190(6):984–993) investigated a cohort of non-Hispanic White women, demonstrating positive relationships between adiposity measures and the ticking rate of epigenetic clocks in blood. This commentary emphasizes that integrating molecular and genetic epidemiology approaches is crucial to dissecting the complex relationship between obesity and epigenetic aging. The early-life period is explored as a unique opportunity to gain novel insights into links between developmental processes and aging in later life. Last, the landscape of the next frontier in aging research is described in light of the imperative for transdisciplinary approaches to outline a shared vision and public health implementation dilemmas.

The association between cigarette smoking and inflammation is well known. However, the biological mechanisms behind the association are not fully understood, particularly the role of DNA methylation, which is known to be affected by smoking. Using 2-step epigenetic Mendelian randomization, we investigated the role of DNA methylation in the association between cigarette smoking and inflammation. In 822 African Americans from the Genetic Epidemiology Network of Arteriopathy, phase 2 (Jackson, Mississippi; 2000-2005), study population, we examined the association of cigarette smoking with DNA methylation using single nucleotide polymorphisms identified in previous genome-wide association studies of cigarette smoking. We then investigated the association of DNA methylation with levels of inflammatory markers using cis-methylation quantitative trait loci single nucleotide polymorphisms. We found that current smoking status was associated with the DNA methylation levels (M values) of cg03636183 in the coagulation factor II (thrombin) receptor-like 3 gene (F2RL3) (M = -0.64, 95% confidence interval (CI): -0.84, -0.45) and of cg19859270 in the G protein-coupled receptor 15 gene (GPR15) (M = -0.21, 95% CI: -0.27, -0.15). The DNA methylation levels of cg03636183 in F2RL3 were associated with interleukin-18 concentration (-0.11 pg/mL, 95% CI: -0.19, -0.04). These combined negative effects suggest that cigarette smoking increases interleukin-18 levels through the decrease in DNA methylation levels of cg03636183 in F2RL3.

The Department of Epidemiology at Johns Hopkins School of Hygiene and Public Health was founded in 1919, with Wade Hampton Frost as inaugural chair. In our Centennial Year, we review how our research and educational programs have changed. Early years focused on doctoral education in epidemiology and some limited undergraduate training for practice. Foundational work on concepts and methods linked to the infectious diseases of the day made major contributions to study designs and analytical methodologies, largely still in use. With the epidemiologic transition from infectious to chronic disease, new methods were developed. The Department of Chronic Diseases merged with the Department of Epidemiology in 1970, under the leadership of Abraham Lilienfeld. Leon Gordis became chair in 1975, and multiple educational tracks were developed. Genetic epidemiology began in 1979, followed by advances in infectious disease epidemiology spurred by the human immunodeficiency virus/acquired immune deficiency syndrome epidemic. Collaborations with the Department of Medicine led to development of the Welch Center for Prevention, Epidemiology, and Clinical Research in 1989. Between 1994 and 2008, the department experienced rapid growth in faculty and students. A new methods curriculum was instituted for upper-level epidemiologic training in 2006. Today’s research projects are increasingly collaborative, taking advantage of new technologies and methods of data collection, responding to “big data” analysis challenges. In our second century, the department continues to address issues of disease etiology and epidemiologic practice.

The field of genetic epidemiology is relatively young and brings together genetics, epidemiology, and biostatistics to identify and implement the best study designs and statistical analyses for identifying genes controlling risk for complex and heterogeneous diseases (i.e., those where genes and environmental risk factors both contribute to etiology). The field has moved quickly over the past 40 years partly because the technology of genotyping and sequencing has forced it to adapt while adhering to the fundamental principles of genetics. In the last two decades, the available tools for genetic epidemiology have expanded from a genetic focus (considering 1 gene at a time) to a genomic focus (considering the entire genome), and now they must further expand to integrate information from other “-omics” (e.g., epigenomics, transcriptomics as measured by RNA expression) at both the individual and the population levels. Additionally, we can now also evaluate gene and environment interactions across populations to better understand exposure and the heterogeneity in disease risk. The future challenges facing genetic epidemiology are considerable both in scale and techniques, but the importance of the field will not diminish because by design it ties scientific goals with public health applications.

In an attempt to reproduce the results of an inconclusive 1927 report by the British Medical Research Council on the hereditary versus social origins of rheumatic fever, Read, Ciocco, and Taussig, from Johns Hopkins University, with the support of Frost, conducted a case-control study in 1935 and 1936. Their study, which appeared in the American Journal of Hygiene in 1938, was outstanding for its clear and tidy rationale for separating hereditary from environmental causes. The authors compared the prevalence of rheumatic fever among the relatives of 33 children admitted for "incident" rheumatic fever and 33 control children admitted in a tuberculosis clinic for reasons other than rheumatic fever. Both rheumatic fever (cases) and tuberculosis (controls) were diseases of the poor. All family members of both cases and controls, including uncles, aunts, and grandparents, were eligible for interview and physical examination. The results were compatible with the presence of an "inherited predisposition" to rheumatic fever because the disease was more prevalent among the uncles, aunts, and grandparents of case patients than among those of control patients. Methodologically, the paper by Read, Ciocco, and Taussig is an important but almost completely forgotten milestone in the evolution of case-control studies and of genetic epidemiology.

Evidence from genetic association studies is accumulating rapidly. Field synopses have recently arisen as an unbiased way of systematically synthesizing this evidence. We performed a systematic review and appraisal of published field synopses in genetic epidemiology and assessed their main findings and methodological characteristics. We identified 61 eligible field synopses, published between January 1, 2007, and October 31, 2013, on 52 outcomes reporting 734 significant associations at the P < 0.05 level. The median odds ratio for these associations was 1.25 (interquartile range, 1.15-1.43). Egger's test was the most common method (n = 30 synopses) of assessing publication bias. Only 12 synopses (20%) used the Venice criteria to evaluate the epidemiologic credibility of their findings (n = 449 variants). Eleven synopses (18%) were accompanied by an online database that has been regularly updated. These synopses received more citations (P = 0.01) and needed a larger research team (P = 0.02) than synopses without an online database. Overall, field synopses are becoming a valuable tool for the identification of common genetic variants, especially when researchers follow relevant methodological guidelines. Our work provides a summary of the current status of the field synopses published to date and may help interested readers efficiently identify the online resources containing the relevant genetic evidence.

Congenital deficiency of the proprotein convertase subtilisine/kexin type 1 gene (PCSK1), which encodes proprotein convertase 1/3, causes a severe multihormonal disorder marked by early-onset obesity. The single nucleotide polymorphisms (SNPs) rs6232 and rs6234-rs6235 in PCSK1 have been associated with obesity. However, case-control studies carried out in populations of different ethnicities have only partly replicated this association. Moreover, these SNPs have only weakly been associated with body mass index (weight (kg)/height (m)(2)) at a genome-wide level of significance. To investigate this discrepancy, we conducted a systematic search for studies published before December 2013 and extracted relevant data. Pooled estimates were calculated for overall and subgroup analyses. This meta-analysis confirmed the association of PCSK1 SNPs with obesity and provides the first evidence that the association between PCSK1 rs6232 and obesity is stronger for childhood obesity than for adult obesity. Moreover, we identified weak associations with body mass index and significantly stronger associations with waist circumference for rs6234-rs6235. No difference was found in the association with different obesity grades, and no association of PCSK1 rs6234-rs6235 with obesity was identified in Asian populations. This systematic Human Genome Epidemiology (HuGE) review showed convincingly that the SNPs rs6232, rs6234, and rs6235 in PCSK1 are associated with obesity in Caucasians.

Genome-wide association studies (GWAS) and candidate gene analyses have led to the discovery of several dozen genetic polymorphisms associated with breast cancer susceptibility, many of which are considered well-established risk factors for the disease. Despite attempts to replicate these same variant-disease associations in African Americans, the evaluable populations are often too small to produce precise or consistent results. We estimated the associations between 83 previously identified single nucleotide polymorphisms (SNPs) and breast cancer among Carolina Breast Cancer Study (1993–2001) participants using maximum likelihood, Bayesian, and hierarchical methods. The selected SNPs were previous GWAS hits (n = 22), near-hits (n = 19), otherwise well-established risk loci (n = 5), or located in the same genes as selected variants (n = 37). We successfully replicated 18 GWAS-identified SNPs in whites (n = 2,352) and 10 in African Americans (n = 1,447). SNPs in the fibroblast growth factor receptor 2 gene (FGFR2) and the TOC high mobility group box family member 3 gene (TOX3) were strongly associated with breast cancer in both races. SNPs in the mitochondrial ribosomal protein S30 gene (MRPS30), mitogen-activated protein kinase kinase kinase 1 gene (MAP3K1), zinc finger, MIZ-type containing 1 gene (ZMIZ1), and H19, imprinted maternally expressed transcript gene (H19) were associated with breast cancer in whites, and SNPs in the estrogen receptor 1 gene (ESR1) and H19 gene were associated with breast cancer in African Americans. We provide precise and well-informed race-stratified odds ratios for key breast cancer–related SNPs. Our results demonstrate the utility of Bayesian methods in genetic epidemiology and provide support for their application in small, etiologically driven investigations.

The analytic approaches used in nutritional epidemiology for dietary pattern analyses share common characteristics with those of genetic epidemiology. In this issue of the Journal, Gorst-Rasmussen et al. (Am J Epidemiol. 2011;173(10):1097–1104) discuss one such approach. Application of methods used in genetic pattern analyses to nutritional epidemiology could prove valuable but raises important issues that need to be considered because dietary and genetic studies often address different types of questions in analyzing interrelated variables. These different aims require statistical methods that assume different characteristics of the underlying patterns. The authors briefly describe such differences to facilitate interpretation and applications of previous and future pattern studies. diet; myocardial infarction; statistics

Genetic epidemiology represents a hybrid of epidemiologic designs and statistical models that explicitly consider both genetic and environmental risk factors for disease. It is a relatively new field in public health; the term was first coined only 35 years ago. In this short time, the field has been through a major evolution, changing from a field driven by theory, without the technology for genetic measurement or computational capacity to apply much of the designs and methods developed, to a field driven by rapidly expanding technology in genomic measurement and computational analyses while epidemiologic theory struggles to keep up. In this commentary, we describe 4 different eras of genetic epidemiology, spanning this evolution from theory to technology, what we have learned, what we have added to the broader field of public health, and what remains to be done.

Growing knowledge about gene-disease associations will lead to new opportunities for genetic testing. Many experts predict that genetic testing will become increasingly important as a guide to prevention, clinical management, and drug treatment based on genetic susceptibilities. As part of a Human Genetic Epidemiology workshop convened by the Centers for Disease Control and Prevention, a group of experts evaluated the evidence needed when considering the appropriate use of new genetic tests. Because new tests are likely to vary in their predictive value, their potential to direct prevention or treatment efforts, and their personal and social consequences, the task of determining appropriate use will require careful consideration of a variety of factors, including the analytic validity, clinical validity, clinical utility, and ethical, legal, and social implications of the test. Standardized formats are needed to summarize what is known and not known about new genetic tests with respect to each of these features. Following criteria for the objective assessment of test properties, reports should be structured to enable policy makers, clinicians, and the public to identify the available evidence, so that uncertainties can be taken into account when considering test use and planning future research.

The authors summarize their experience in the conduct of meta-analysis of individual participants' data (MIPD) with time-to-event analyses in the field of genetic epidemiology. The MIPD offers many advantages compared with a meta-analysis of the published literature. These include standardization of case definitions, outcomes, and covariates; inclusion of updated information; the ability to fully test the assumptions of time-to-event models; better control of confounding; standardization of analyses of genetic loci that are in linkage disequilibrium; evaluation of alternative genetic models and multiple genes; consistent treatment of subpopulations; assessment of sampling bias; and the establishment of an international collaboration with the capability to prospectively update the meta-analyses and synthesize new information on multiple genetic loci and outcomes. The disadvantages of a MIPD compared with a meta-analysis of the published literature are that a much greater commitment of time and resources is required to collect primary data and to coordinate a large collaborative project. An MIPD may collect additional, unpublished data, but it is possible that not all published data may be contributed at the individual level. For questions that justify the required intensive effort, the MIPD method is a useful tool to help clarify the role of candidate genes in complex human diseases.

Over the past decade, leading epidemiologists have noted the importance of social factors in studying and understanding the distribution and determinants of disease in human populations; but to what extent are epidemiologic studies integrating genetic information and other biologic variables with information about individual-level risk factors and group-level or societal factors related to the broader residential, behavioral, or cultural context? There remains a need to consider ways to integrate genetic information with social and contextual information in epidemiologic studies, partly to combat the overemphasis on the importance of genetic factors as determinants of disease in human populations. Even in genome-wide association studies of coronary heart disease and other common complex diseases, only a small proportion of heritability is explained by the genetic variants identified to date. It is possible that familial clustering due to genetic factors has been overestimated and that important environmental or social influences (acting alone or in combination with genetic variants) have been overlooked. The accompanying article by Bressler et al. (Am J Epidemiol. 2010;171(1):14-23) highlights some of these important issues.

With case-parent triads, one can estimate genotype relative risks by measuring the apparent overtransmission of susceptibility genotypes from parents to affected offspring. Results obtained using such designs, properly analyzed, resist both bias due to population structure and bias due to self-selection. Most diseases are not purely genetic, and environmental cofactors can also be important. In this paper, the authors describe how a polytomous logistic regression method previously developed for studying genetic effects on a quantitative trait can be used with case-parent data to study multiplicative gene-by-environment interaction. The idea is that if the joint effect of exposure and genotype on risk is submultiplicative or supermultiplicative, then, conditional on the parental genotypes, inheritance of a susceptibility genotype by affected offspring will appear to have been influenced by the offspring's exposure level. The authors' approach tolerates exposure-complicated genetic population structure, and simulations suggest power and Type I error rates comparable to those of competitors. With this approach, one can estimate the usual interaction parameters under a much less stringent assumption than gene-environment independence in the source population. Incompletely genotyped triads can contribute through an expectation-maximization algorithm. To illustrate, the authors consider polymorphisms in detoxification pathway genes and maternal smoking in relation to the birth defect oral cleft.

Epigenetic modification can mediate environmental influences on gene expression and can modulate the disease risk associated with genetic variation. Epigenetic analysis therefore holds substantial promise for identifying mechanisms through which genetic and environmental factors jointly contribute to disease risk. The spatial and temporal variance in epigenetic profile is of particular relevance for developmental epidemiology and the study of aging, including the variable age at onset for many common diseases. This review serves as a general introduction to the topic by describing epigenetic mechanisms, with a focus on DNA methylation; genetic and environmental factors that influence DNA methylation; epigenetic influences on development, aging, and disease; and current methodology for measuring epigenetic profile. Methodological considerations for epidemiologic studies that seek to include epigenetic analysis are also discussed.

Genetic epidemiology is a rapidly evolving field, and the number of published reports is increasing geometrically (1). Many “significant” genetic associations are proposed, but several of them may represent false-positive results (2–5). As we have moved into large-scale testing of associations (6–8), understanding biases in statistically significant results is becoming increasingly useful. Evaluation of observational associations has traditionally rested on P values and hypothesis-testing using nominal levels of statistical significance. This widespread practice may bias the accumulated evidence on single associations or in large fields of research (9). Various types of biases have been described. In publication bias, studies with nominally statistically significant (“positive”) results are more likely to be published than other (“negative”) findings (10–13). The latter findings may be published after a delay (time-lag bias) (14) or may remain unpublished. Selective analysis and outcome reporting bias also lead to overrepresentation of nominally statistically significant outcomes and findings and concealment of non-statistically significant results within the same study (15–17). Finally, some statistically significant results may be fabricated (fake data), hopefully uncommonly (18–20). All of these biases converge towards increasing the relative proportion of nominally statistically significant findings in a body of evidence as compared with what would be expected in the absence of bias. Several tests have been proposed for assessing the presence of publication bias, but they have limitations (21–24). They are designed to be applied to data from single meta-analyses of studies that all pertain to the same research question. Inferences derived from single meta-analyses are limited by the fact that usually few studies are available; thus, these tests are underpowered (25, 26). Recently, Ioannidis and Trikalinos (27) proposed an exploratory test that examines whether there is an excess of significant results in a whole domain of research. Identification of an excess of significant findings with this test can result either from significance-chasing biases or from excessive heterogeneity (diversity of effects) between studies on the same research question. Between-study heterogeneity could be due either to genuine diversity or to biases. This test can be applied not only to single meta-analyses but also to whole domains of research across collections of many meta-analyses. Application in meta-analyses of clinical trials shows an overrepresentation of statistically significant treatment effects in some randomized clinical research, such as research on the effectiveness of diverse neuroleptic treatments (27). Some data suggest a possible excess of significant findings in genetic epidemiology, such as the Chinese-language literature on genetic associations or genetic associations in myocardial infarction (28, 29). Empirical surveys also corroborate the preference for reporting “positive” results in genetic epidemiology (30), and various investigators have reported evidence for publication bias in selected associations (31, 32). However, solid evidence requires the examination of large domains of genetic research, encompassing many hundreds of studies. Such large-scale evaluations may also help us evaluate whether excesses of statistically significant findings are due mostly to bias or to genuine heterogeneity. Here, we applied this exploratory test to cumulative data contained in the AlzGene database (33). Alzheimer's disease is a prolific field in genetic association studies. With over 1,000 individual publications included to date, AlzGene offers the unique opportunity to study a large domain of observational research for a potential excess of statistically significant findings and to try to understand the reasons for such an excess.

A natural randomization process, sometimes called Mendelian randomization, occurs at conception to determine a person's genotype. By combining information from genotype-disease and genotype-phenotype studies, it is possible to use this Mendelian randomization to derive an estimate of the association between phenotype (risk factor) and disease that is free of the confounding and reverse causation typical of classical epidemiology. When one is synthesizing evidence, studies evaluating genotype-phenotype associations, studies evaluating genotype-disease associations, and studies evaluating both are encountered, and methods should be used that allow for this structure. Plotting the log odds ratio of genotype-disease against the mean genotype-phenotype difference may help investigators detect departures from the assumptions underlying Mendelian randomization. Testing for differences between studies reporting on only the genotype-phenotype or genotype-disease association and those reporting on both associations may help in detecting reporting bias. This integrated approach to the meta-analysis of genotype-phenotype and genotype-disease studies is illustrated here using the example of the methylenetetrahydrofolate reductase (MTHFR) gene, homocysteine level, and coronary heart disease. An integrated meta-analytical approach may increase the precision of this estimate and provide information on the assumptions underlying Mendelian randomization. Serious biases may arise if the assumptions behind the analysis based on Mendelian randomization are not met.

Previous research has demonstrated isolated effects of host genetic factors on the progression of human immunodeficiency virus type 1 (HIV-1) infection. In this paper, the authors present a novel use of multivariable methods for estimating the prevented fraction of acquired immunodeficiency syndrome (AIDS) cases attributable to six restriction genes after accounting for their epidemiologic interactions. The methods presented will never yield a prevented fraction above 1. The study population consisted of a well-characterized cohort of 525 US men with HIV-1 seroconversion documented during follow-up (1984-1996). On the basis of a regression tree approach using a Cox proportional hazards model for times to clinical AIDS, the combinations of genes associated with the greatest protection, relative to the lack of a protective genotype, consisted of: 1) C-C chemokine receptor 5 (CCR5)-Delta 32 and C-C chemokine receptor 2 (CCR2)-64I (relative hazard = 0.44); 2) interleukin 10 (IL10)-+/+ in combination with CCR5-Delta 32 or CCR2-64I (relative hazard = 0.45); and 3) IL10-+/+ in combination with stromal-derived factor (SDF1)-3 'A and CCR5 promoter P1/approximately P1 (relative hazard = 0.37). Overall, 30% of potential AIDS cases were prevented by the observed combinations of restriction genes (95% confidence interval: 7, 47). However, the combined effect was confined to the first 4 years following HIV-1 seroconversion. Additional research is needed to identify AIDS restriction genes with stronger and long-lasting protection to better characterize the genetic epidemiology of HIV-1.

In January 2001, the Centers for Disease Control and Prevention and the National Institutes of Health convened an expert panel to develop recommendations for evaluating and synthesizing data from epidemiologic studies of the human genome. Experts in medicine, genetics, epidemiology, statistics, laboratory sciences, prevention effectiveness, and the social sciences discussed examples drawn from cancer, cardiovascular disease, human immunodeficiency virus infection, and other areas. Participants discussed issues for evaluating and synthesizing data from epidemiologic and genetic test studies (table 1) relevant to three areas: 1) prevalence of gene variants and gene-disease associations, 2) geneenvironment and gene-gene interactions, and 3) evaluation of genetic tests. The workshop recommendations in areas 1 and 3 are included in this issue of the Journal (1, 2). The recommendations of area 2 (gene-environment interaction) are in progress (D. J. Hunter, Channing Laboratory, unpublished manuscript). I summarize the meeting’s background and highlight the importance of the panel’s recommendations. Many scientists believe that advances in human genetics and the Human Genome Project will play a central role in the practice of medicine and public health in the 21st century by predicting and preventing disease and promoting health (3). However, to ensure a systematic translation of genetic research into clinical practice, ongoing epidemiologic data are needed, in addition to studies of gene function and biologic pathways, to quantify the impact of gene variants on the risk of various diseases and to identify and quantify the impact of modifiable risk factors that interact with gene variants (4). So far, most studies in this area come from family-based studies or highly selected groups. Results from population-based studies will help medical and public health professionals better target medical, behavioral, and environmental interventions. A systematic application of epidemiologic methods and approaches to the human genome—HuGE (4)—represents the continuum from gene discovery (traditional domain of genetic epidemiology) to risk characterization (domain of molecular epidemiology) and evaluation of genetic tests and services (applied epidemiology and health services research). As a multidisciplinary field, epidemiology has begun to address issues related to post-gene discovery with increasing emphasis on characterization of gene effects and genetic tests in populations (what do you do with a gene after you find one?). The continuum of studies can be divided into the three areas that are the topics of the workshop papers: 1) assessing the population prevalence of gene variants and evaluating genotype-disease associations; 2) assessing the impact of gene-environment and gene-gene interaction on disease risk; and 3) evaluating the usefulness and impact of genetic tests in populations. Because of the numerous genes that are discovered on a regular basis, an epidemiologic approach is needed for all three study domains. An analysis of the published epidemiologic literature on human genes for 2001 reveals that, of the 2,042 published articles, most reported on only the population prevalence of gene variants or simple gene-disease associations (82.0 percent), while 14.5 percent integrated the study of interactions (gene-gene and gene-environment) and only 3.5 percent dealt with evaluation of genetic tests (5). Epidemiologic studies of geneenvironment interaction and genetic tests are bound to increase as more genes are discovered, characterized, and used to develop diagnostic and predictive tests.

The study of the genetic epidemiology of airways obstruction has suggested that not only is COPD the prototype of a multifactorial disorder in which many factors contribute to the risk of its occurrence, but also that its definitive characteristic--impaired pulmonary function--may be a multi-disease factor which acts as a key component or cofactor in a number of other disorders: a common route to multiple and diverse pathological manifestations. Further evidence is needed to confirm or refute this hypothesis both of multiple possible causes and, especially, of multiple potential effects of airways obstruction. In view of the implications of such causes and effects, we look to continuing research to obtain and analyze a variety of appropriate data to evaluate further the validity of the hypothesis presented.