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We respond to criticisms of Mendelian randomization (MR) by Mukamal, Stampfer and Rimm (MSR). MSR consider that MR is receiving too much attention and should be renamed. We explain how MR links to Mendel’s laws, the origin of the name and our lack of concern regarding nomenclature. We address MSR’s substantive points regarding MR of alcohol and cardiovascular disease, an issue on which they dispute the MR findings. We demonstrate that their strictures with respect to population stratification, confounding, weak instrument bias, pleiotropy and confounding have been addressed, and summarise how the field has advanced in relation to the issues they raise. We agree with MSR that “the hard problem of conducting high-quality, reproducible epidemiology” should be addressed by epidemiologists. However we see more evidence of confrontation of this issue within MR, as opposed to conventional observational epidemiology, within which the same methods that have demonstrably failed in the past are simply rolled out into new areas, leaving their previous failures unexamined.

Contains fulltext : 182194.pdf (Publisher’s version ) (Open Access) The Rotterdam Study is a prospective cohort study ongoing since 1990 in the city of Rotterdam in The Netherlands. The study targets cardiovascular, endocrine, hepatic, neurological, ophthalmic, psychiatric, dermatological, otolaryngological, locomotor, and respiratory diseases. As of 2008, 14,926 subjects aged 45 years or over comprise the Rotterdam Study cohort. Since 2016, the cohort is being expanded by persons aged 40 years and over. The findings of the Rotterdam Study have been presented in over 1500 research articles and reports (see www.erasmus-epidemiology.nl/rotterdamstudy ). This article gives the rationale of the study and its design. It also presents a summary of the major findings and an update of the objectives and methods.

Myopia is a complex inherited ocular trait resulting from an interplay of genes and environmental factors, most of which are currently unknown. In two independent population-based cohorts consisting of 5,256 and 3,938 individuals from European descent, we tested for biological interaction between genetic predisposition and level of education on the risk of myopia. A genetic risk score was calculated based on 26 myopia-associated single nucleotide polymorphisms recently discovered by the Consortium for Refractive Error and Myopia. Educational level was obtained by questionnaire and categorized into primary, intermediate, and higher education. Refractive error was measured during a standardized ophthalmological examination. Biological interaction was assessed by calculation of the synergy index. Individuals at high genetic risk in combination with university-level education had a remarkably high risk of myopia (OR 51.3; 95 % CI 18.5–142.6), while those at high genetic risk with only primary schooling were at a much lower increased risk of myopia (OR 7.2, 95 % CI 3.1–17.0). The combined effect of genetic predisposition and education on the risk of myopia was far higher than the sum of these two effects (synergy index 4.2, 95 % CI 1.9–9.5). This epidemiological study provides evidence of a gene-environment interaction in which an individual’s genetic risk of myopia is significantly affected by his or her educational level. Electronic supplementary material The online version of this article (doi:10.1007/s10654-013-9856-1) contains supplementary material, which is available to authorized users.

Distinguishing true from false positive findings is a major challenge in human genetic epidemiology. Several strategies have been devised to facilitate this, including the positive predictive value (PPV) and a set of epidemiological criteria, known as the "Venice" criteria. The PPV measures the probability of a true association, given a statistically significant finding, while the Venice criteria grade the credibility based on the amount of evidence, consistency of replication and protection from bias. A vast majority of journals use significance thresholds to identify the true positive findings. We studied the effect of p value thresholds on the PPV and used the PPV and Venice criteria to define usable thresholds of statistical significance. Theoretical and empirical analyses of data published on AlzGene show that at a nominal p value threshold of 0.05 most "positive" findings will turn out to be false if the prior probability of association is below 0.10 even if the statistical power of the study is higher than 0.80. However, in underpowered studies (0.25) with a low prior probability of 1 × 10(-3), a p value of 1 × 10(-5) yields a high PPV (>96 %). Here we have shown that the p value threshold of 1 × 10(-5) gives a very strong evidence of association in almost all studies. However, in the case of a very high prior probability of association (0.50) a p value threshold of 0.05 may be sufficient, while for studies with very low prior probability of association (1 × 10(-4); genome-wide association studies for instance) 1 × 10(-7) may serve as a useful threshold to declare significance.

A recent report by Alzheimer Disease International showed that the number of persons suffering from Alzheimer’s disease will quadruple by 2050 (http://www.alz.co.uk/research/world-report-2011). This increase is predicted to occur globally and will likely pose a great societal burden, both in terms of financial costs as well as suffering for patients and care-givers [1]. Thus, there is a strong incentive to develop effective preventive and therapeutic strategies. In order to develop such strategies, full knowledge of the pathophysiology of Alzheimer’s disease is an essential first step. Similarly, identification of people at high risk of Alzheimer’s disease is equally important. Fully understanding the disease pathology and early detection methods would facilitate implementation of targeted interventions in those people, who would benefit from it most.

In the past 70 years, epidemiology has gone through asubstantial development of both methodology and moresophisticated analyses with determinant- and disease-specific elements. Despite the discussion earlier in thisjournal as to whether the duality of epidemiology’sembrace of both research methods and content-based topicsis a positive development [1, 2], it shows at least thatepidemiology as a discipline is able to re-invent itself andto adapt to the rapid scientific evolution which currentlytakes place. Whereas the methodological ‘toolkit’ keeps itssolid basis in cohort- and case-control designs, determi-nant-oriented genetic epidemiology and pharmacoepi-demiology, and disease-oriented cardiovascular andinfectious diseases epidemiology are examples of branch-ing within one discipline. While the start of epidemiologyhad its main focus on hypothesis-testing concerning theassociation between determinants and disease as well asrisk estimation, the importance of hypothesis-generatingstudies is increasing. A nice example is the technique ofgenome-wide association studies which led to an explosionof discoveries in genetic epidemiology, also by a newmodel of collaborative science [3]. However, genes remainwhile drugs come and disappear again from human society

Recent genome-wide association (GWA) studies identified several common variants for obesity: rs9939609 in FTO, rs7566605 near INSIG2 and both rs17782313 and rs17700633 near the MC4R gene. This study aimed to assess the influence of these polymorphisms on development of adiposity in European– (EA) and African–American (AA) youth in two ongoing longitudinal studies including 986 and 606 participants with age ranges of 10–25.8 and 4.0–23.9 years, respectively. Individual growth curve modeling was conducted separately in the two studies. We tested the effect of the SNPs on levels and increase with age (i.e., slope) of weight, body mass index (BMI), waist circumference and skinfolds from childhood to adulthood, and potential moderation by ethnicity or gender. Beta coefficients computed in the two studies were pooled using meta-analysis. Rs9939609 was associated with logtransformed levels of BMI (β = 0.021, P = 0.01), weight (β = 0.019, P = 0.04) and waist circumference (β = 0.012, P = 0.04). Rs17782313 was associated with triceps (β = 0.05, P = 0.02). Significant interactions of rs17700633 with gender were observed on subscapular-, suprailiac- and sum of skinfolds, with significant associations limited to males (P

Investigators in modern molecular/genetic epidemiology studies commonly analyze data on a vast number of candidate genetic markers. In such situations, rather than conventional estimation of effects (odds ratios), more accurate estimation methods are needed. The author proposes consideration of empirical Bayes and semi-Bayes methods, which yield 'adjustments for multiple estimations' by shrinking conventional effect estimates towards the overall average effect.

In this last issue of 2008 I take the opportunity to report some observations on recent trends in articles published in the EJE. In the first decade after Antonio Sanna founded the Journal in 1985 the emphasis was clearly on infectious diseases. Over half of the papers were in this category, and the methods papers generally also were on infectious diseases. This gradually changed after Claude Hannoun took over as editor-in-chief in 1995. He published a growing proportion of articles on chronic diseases, in particular cardiovascular disease. In recent years, the Journal has published increasingly papers on epidemiologic and statistical methods, on the objectives and study design of new epidemiologic studies, on genetic epidemiology, and on developmental epidemiology. Here I will review some of the highlights of the articles published in recent years, including those on infectious diseases. Recent methods articles have addressed issues of measurement of determinants and outcome, exposuremeasurement error and information bias [1–10]. We have published some work on the design and evaluation of questionnaires, in particular on assessment of physical activity. We also have published important articles on causal modelling, on confounding, and on effect-modification and interaction. In our section devoted to new studies the majority op articles has been on new birth cohort studies and other studies addressing early-life causes of adult diseases, and on the biobanks organized as part of those efforts [11–17]. In genetic epidemiology we have published some metaanalyses of candidate gene studies on various diseases, and in the near future we will undoubtedly publish the results of the major genome-wide association studies that are currently underway in many epidemiologic research groups [18–22]. In addition, we have published commentaries on the value, of lack thereof, of predictive genetic testing for complex diseases and more in general on the promises of translational research in genomics. We have published a substantial number of studies in developmental epidemiology, addressing fetal and other early life determinants of adult diseases [23–34]. This involved in particular studies of low birth weight and cardiovascular disease and diabetes, in different ethnic populations, and under different nutritional conditions. Increasingly, hemodynamic and endocrinological studies have addressed the hypothesis of early life causes of adult diseases. We have also published on the new studies in this field and on the epidemiologic resources that are now being build-up for future developmental epidemiologic studies. The Journal publishes still a substantial number of articles on infectious diseases, about ten per cent of all published papers [35–55]. The largest number of articles in recent years has been on HIV/AIDS and on hepatitis, and quite often on their interrelations. We have also published work on new methods in infectious disease epidemiology, and on their applications to diseases like tuberculosis, malaria and meningitis. We will continue to emphasize the publication of important epidemiologic studies on infectious diseases in the Journal. The EJE has become more selective over the years. In the first decade of its existence about half of the manuscripts that came to the office were published. In recent years this has gone down to about 15%, reflecting both a substantially larger number of manuscripts received and a stricter acceptance policy. In this the editors have been strongly supported by the work for the Journal of many A. Hofman (&) Department of Epidemiology, Erasmus Medical Center, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands e-mail: a.hofman@erasmusmc.nl

Objective To investigate the relationship between the apolipoprotein E (APOE) gene and the risk of mortality in normal weight, overweight and obese individuals. Methods and Results In a population-based study of 7,983 individuals aged 55 years and older, we compared the risks of all-cause and coronary heart disease (CHD) mortality by APOE genotype, both overall and in subgroups defined by body mass index (BMI). We found significant evidence for interaction between APOE and BMI in relation to total cholesterol (p = 0.04) and HDL cholesterol (p < 0.001). Overall, APOE*2 carriers showed a decreased risk of all-cause mortality. Analyses within BMI strata showed a beneficial effect of APOE*2 only in normal weight persons (adjusted hazard ratio (HR) 0.7[95% CI 0.5-0.9]). APOE*2 was not associated with a lower risk of all-cause mortality in overweight or obese persons. The effect of APOE*2 in normal weight individuals tended to be due to the risk of CHD mortality (adjusted HR 0.5 [95% CI 0.2-1.2]). Conclusion The APOE*2 allele confers a lower risk of all-cause mortality only to normal weight individuals.

This study was designed to conduct genomic analysis in two steps, such as the overall relative synonymous codon usage (RSCU) analysis of the five virus species in the orthomyxoviridae family, and more intensive pattern analysis of the four subtypes of influenza A virus (H1N1, H2N2, H3N2, and H5N1) which were isolated from human population. All the subtypes were categorized by their isolated regions, including Asia, Europe, and Africa, and most of the synonymous codon usage patterns were analyzed by correspondence analysis (CA). As a result, influenza A virus showed the lowest synonymous codon usage bias among the virus species of the orthomyxoviridae family, and influenza B and influenza C virus were followed, while suggesting that influenza A virus might have an advantage in transmitting across the species barrier due to their low codon usage bias. The ENC values of the host-specific HA and NA genes represented their different HA and NA types very well, and this reveals that each influenza A virus subtype uses different codon usage patterns as well as the amino acid compositions. In NP, PA and PB2 genes, most of the virus subtypes showed similar RSCU patterns except for H5N1 and H3N2 (A/HK/1774/1999) subtypes which were suspected to be transmitted across the species barrier, from avian and porcine species to human beings, respectively. This distinguishable synonymous codon usage patterns in non-human origin viruses might be useful in determining the origin of influenza A viruses in genomic levels as well as the serological tests. In this study, all the process, including extracting sequences from GenBank flat file and calculating codon usage values, was conducted by Java codes, and these bioinformatics-related methods may be useful in predicting the evolutionary patterns of pandemic viruses.

The importance of genetic factors for the susceptibility to disease has been widely recognized in the last years. Genes have been identified for monogenic diseases and the challenge lying ahead is the identification of genetic components of importance and the environments in which they are expressed for complex diseases, that is, multiple genetic factors act and interact with each other or environmental factors to add to the complexity. This paper gives a brief overview of some genetic epidemiological approaches, concepts and recent methodological developments related to the study of risk factors for cardiovascular disease in twin and family studies.

Genetic epidemiology is a young but rapidly developing discipline. Although its early years were largely dedicated to family-based research in monogenic disorders, now genetic-epidemiologic research increasingly focuses on complex, multifactorial disorders. Along with the development of the human-genome map and advances in molecular technology grows the importance of genetic-epidemiologic applications. Large-scale population-based studies, requiring close integration of genetic and epidemiologic research, determine future research in the field. In this paper, we review the basic principles underlying genetic-epidemiologic research, such as molecular genetics and familial aggregation of disease, as well as the typical study approaches of genome screening and candidate-gene studies.

Over the past 20 years, the interest of the scientific community was increasingly placed in the field of genetic epidemiology and molecular genetics of blood pressure control. This paper explores references related to essential hypertension, gene and genetic epidemiology indexed in the MedLine health science database during the period 1980-2001. A systematic literature search was performed using selected keywords, such as 'genetic', 'genome' or a combination of words. We considered the study heading and evaluated the time profile of published articles. A total number of 3116 publications was collected and analyzed. Allelic distribution for the most studied polymorphisms of the renin-angiotensin system in different world populations was reviewed and reported together with a detection of their frequency in Italy: essential hypertensive patients (n = 90), healthy unrelated subjects (n = 300). Molecular variants at angiotensinogen (M and T), angiotensin II type 1 receptor (A and C) and angiotensin-converting enzyme (D and I) genes were analyzed by amplified fragment length polymorphism. A significant association was detected by chi2 analysis for angiotensinogen and angiotensin II-type I receptor allele distribution in hypertensive patients, in accordance with previous reports. Genetic data and methods are contributing more and more to epidemiological studies of complex diseases, and their application is influenced by information availability and Genome Project results.

Genome-wide association studies (GWAS) have been successful in identifying loci associated with a wide range of complex human traits and diseases. Up to now, the majority of GWAS have focused on European populations. However, the inclusion of other ethnic groups as well as admixed populations in GWAS studies is rapidly rising following the pressing need to extrapolate findings to non-European populations and to increase statistical power. In this paper, we describe the methodological steps surrounding genetic data generation, quality control, study design and analytical procedures needed to run GWAS in the multiethnic and highly admixed Generation R Study, a large prospective birth cohort in Rotterdam, the Netherlands. Furthermore, we highlight a number of practical considerations and alternatives pertinent to the quality control and analysis of admixed GWAS data. Electronic supplementary material The online version of this article (doi:10.1007/s10654-015-9998-4) contains supplementary material, which is available to authorized users.

In the field of genetic epidemiology, where primary studies are often underpowered to detect small genetic effects and where conflicting results and non-replication of initial findings are often encountered [1], the importance of meta-analysis has been widely recognised, as demonstrated by the steep increase in the number of published genetic meta-analyses over time [2]. What has not received sufficient attention is the fact that the results of a

Balkan endemic nephropathy (BEN) is of great clinical importance in the restricted areas of Bulgaria, Rumania, Croatia, Serbia, Bosnia and Herzegovina. So far, studies on the etiological factors for BEN have not discovered any single environmental causative agent of this puzzling disease. These data reject the possibility of a purely environmental causation of BEN. The pattern of BEN transmission in the risk families is not typical for single gene disorders. Extensive epidemiological and genetic studies disclose characteristics of multifactorial (polygenic) inheritance of BEN. The evidences of 'familial tendency', variation of the risk for BEN depending on the number of sick parents and the degree of relatedness; the development of BEN in individuals from at-risk families who were born in non-endemic areas; the data that disease is not found in the gypsy population and the expressions of 3q25 cytogenetic marker suggest that the genetic factors play an important role as causative factors in BEN development. The possible impact of environmental triggers on individuals genetically predisposed to BEN could be supposed by the following data: the cytogenetic results of the increased frequency of folate sensitive Fra sites, spontaneous or radiation-induced aberrations in several bands in BEN patients, the data from the detailed analysis of breaks in BEN patients and controls that generate structural chromosome aberrations; the occurrence of BEN in immigrants. Genetical epidemiological approaches to etiology and prevention of BEN are proposed. The predisposing genes for BEN could be genes localized in a region between 3q25-3q26; transforming growth factor-β (TGF-β), genetic heterogeneity of xenobiotic-metabolizing enzymes; defects in the host's immune system. The predisposing genes for BEN patients with urinary tract tumors could be germline mutations in tumor suppressor genes and acquired somatic mutations in oncogenes.

Despite considerable progress in unravelling the genetic basis of dyslipidemias, most findings are based on families with extreme phenotypes. We studied lipid levels in an extended pedigree ascertained irrespective of phenotype from the population of a recent genetic isolate in the Netherlands. Heritabilities of plasma lipid measures were examined; this analysis also included estimates of the proportion of variance attributable to ApoE genotype. The association between inbreeding and lipids was also considered, as a substantial fraction of the population had known inbreeding. A total of 868 individuals from this pedigree, containing more than 60,000 people over 15 generations, were investigated in this study. Laboratory analysis of these subjects included the determination of fasting plasma lipids. ApoE epsilon2/3/4 status was ascertained using TaqMan assays. Heritabilities for plasma lipids were estimated with adjustments for multiple covariates using SOLAR. Heritabilities for total cholesterol (TC), high-density lipoprotein cholesterol (HDL), low-density lipoprotein cholesterol (LDL), triglycerides (TG), TC/HDL ratio, and TG/HDL ratio were found to be 0.35, 0.56, 0.30, 0.24, 0.49, and 0.39, respectively. The addition of ApoE genotype in the model significantly decreased these estimates (Deltah(2) = -0.030, -0.004, -0.054, and -0.006 for TC, HDL, LDL, and TG). In a further analysis, TC and LDL were positively associated with the extent of inbreeding (p (trend) = 0.02 and p (trend) = 0.05, respectively). These data provide estimates of lipid heritability unbiased due to selection and suggest that this population represents a good opportunity to localize novel genes influencing plasma lipid levels.

Ding et al. [1] recently published a paper on GEIRA, a gene-environment and gene–gene interaction research application. The authors developed a program in SAS and R to calculate interactions on an additive and multiplicative scale. We want to compliment the authors on providing such a useful tool, which may accommodate more frequent calculation of interaction on an additive scale. However, there is one flaw in the program, which the authors only partly acknowledge, that we would like to address. Measures of interaction on an additive scale, such as the proportion attributable to interaction (AP), cannot be validly calculated if any of the odds ratios of the independent and joint effects is smaller than 1, i.e. preventive [2]. The authors state in the discussion section that: “[] the OR associated with the genetic factor might still be less than 1 among subjects unexposed to the environmental factor. In this case, the interpretation for interaction should be cautious”. We think that ‘caution’ is not enough; in this case, the calculations should just not be performed. Fortunately, there is an easy solution to get valid estimates for measures of interaction on an additive scale when having preventive exposures by appropriately recoding one or both of the exposures. We have described this in detail elsewhere for general measures of additive interaction [2]. Tests for specific forms of antagonism may, however, require different forms of recoding [3]. The reason why using odds ratios smaller than 1 gives wrong results in the measures of interaction on an additive scale is because an odds ratio is restricted between 0 and 1 for a preventive factor while it can go from 1 to infinity for a risk factor. For example, an odds ratio of 0.60 means a relative risk reduction of 40%, whereas the inverse (1/0.60 = 1.67) means a relative increase in risk of 67%. If 0.60 is used in calculating a measure of additive interaction this leads to different results than if 1.67 is used in calculating the interaction. Moreover, with preventive exposures, measures such as the relative excess risk due to interaction or the attributable proportion can give results that are in the opposite direction of other additive interaction measures such as the synergy index [2]. A simple solution for the problem is recoding the variables in such a way that the stratum with the lowest risk when both factors are considered jointly becomes the reference category. The result of this recoding is that for one or both factors the absence of the exposure (rather than the presence) becomes the risk factor for the outcome. If factors are recoded one at a time, rather than jointly as we suggest, this can again result in invalid measures of interaction on an additive scale [2]. The authors of GEIRA might consider incorporating a recoding scheme in their program to prevent mistakes from being made when calculating measures of interaction on an additive scale with odds ratios smaller than 1. Alternatively, users of GEIRA could carry out this recoding themselves and apply GEIRA subsequently. Caution is always needed in interpreting the interaction after recoding as the exposure is changed to its opposite.

The Rotterdam Study is a prospective cohort study ongoing since 1990 in the city of Rotterdam in The Netherlands. The study targets cardiovascular, endocrine, hepatic, neurological, ophthalmic, psychiatric, dermatological, otolaryngological, locomotor, and respiratory diseases. As of 2008, 14,926 subjects aged 45 years or over comprise the Rotterdam Study cohort. The findings of the Rotterdam Study have been presented in over 1200 research articles and reports (see www.erasmus-epidemiology.nl/rotterdamstudy ). This article gives the rationale of the study and its design. It also presents a summary of the major findings and an update of the objectives and methods.

The progress of epidemiology depends on advances in epidemiologic methods and in research findings. For both, new technologies are highly important. It is, for example, difficult to understand the strong growth in epidemiologic data-analysis without the emergence of the computer, or of molecular and genetic epidemiology without major advances in molecular technology. The purpose of this editorial is to invite articles on the new technologies that are likely to play a crucial role in the further development of epidemiology and of epidemiologic research. For the practice of epidemiology and its methods, we already see the first glimpses of introduction of modern information technology [1–9]. We have witnessed the application of new technology in the remarkable explosion of genome-wide association studies (GWAs) of various diseases [10–27] and traits [28–39]. We also see the first results of new imaging studies and the emergence of the exceedingly important field of population imaging [26, 40–47]. The new array technologies used in GWAs have led to interesting changes in the way epidemiologic research is conceived and performed. The “hypothesis free” approach in the GWAs, i.e. approach without use of prior knowledge, has been considerably more productive than the candidate gene approach. This is sobering for those of us who have an optimistic view on what we know, and has led to the boutade that “most recent findings are false” [48]. The GWAs have also led to large-scale collaborations in epidemiology in a form and quantity that we had not witnessed before, although it remains to be seen whether this is a lasting phenomenon [10, 49]. The new imaging techniques that are currently applied in population studies are likely to be the beginning of an avalanche in epidemiologic studies of many diseases. These imaging techniques enable epidemiologists to study disease at an earlier stage than when a clinical diagnosis can be made, allow for objective assessment of the disease or trait, and make repeated assessment possible. These features are likely to be very advantageous, in particular for etiologic epidemiologic research [50–78]. The European Journal of Epidemiology publishes quite frequently descriptions of new studies and presents also occasionally updates of these studies’ objectives and design [3, 40, 41, 79–89]. We invite in particular new studies that make use of new technologies and we will also certainly consider manuscripts on the technical details of these technologies that are applied in epidemiologic research [90–92].