In this issue of Twin Research, we describe different facets of a European Community funded effort, GenomEUtwin, which capitalises on eight of the world's largest and best characterised twin registers and a multi-national population cohort, MORGAM. This international study, reaching beyond the geographical borders of Europe, is based on linkage and association strategies designed to identify genetic contributors to health and disease using integrated expertise of participating groups in genetics, epidemiology and biostatistics. By merging information from numerous epidemiological and genetic databases, GenomEUtwin will create an intellectual and technical infrastructure for future genetic epidemiological studies aiming to define genetic and life style risks for common human diseases.

With the rapid advances in molecular biology, the near completion of the human genome, the development of appropriate statistical genetic methods and the availability of the necessary computing power, the identification of quantitative trait loci has now become a realistic prospect for quantitative geneticists. We briefly describe the theoretical biometrical foundations underlying quantitative genetics. These theoretical underpinnings are translated into mathematical equations that allow the assessment of the contribution of observed (using DNA samples) and unobserved (using known genetic relationships) genetic variation to population variance in quantitative traits. Several statistical models for quantitative genetic analyses are described, such as models for the classical twin design, multivariate and longitudinal genetic analyses, extended twin analyses, and linkage and association analyses. For each, we show how the theoretical biometrical model can be translated into algebraic equations that may be used to generate scripts for statistical genetic software packages, such as Mx, Lisrel, SOLAR, or MERLIN. For using the former program a web-library (available from http://www.psy.vu.nl/mxbib) has been developed of freely available scripts that can be used to conduct all genetic analyses described in this paper.

Genetic linkage analysis for complex diseases offers a major challenge to geneticists. In these complex diseases multiple genetic loci are responsible for the disease and they may vary in the size of their contribution; the effect of any single one of them is likely to be small. In many situations, like in extensive twin registries, trait values have been recorded for a large number of individuals, and preliminary studies have revealed summary measures for those traits, like mean, variance and components of variance, including heritability. Given the small effect size, a random sample of twins will require a prohibitively large sample size. It is well known that selective sampling is far more efficient in terms of genotyping effort. In this paper we derive easy expressions for the information contributed by sib pairs for the detection of linkage to a quantitative trait locus (QTL). We consider random samples as well as samples of sib pairs selected on the basis of their trait values. These expressions can be rapidly computed and do not involve simulation. We extend our results for quantitative traits to dichotomous traits using the concept of a liability threshold model. We present tables with required sample sizes for height, insulin levels and migraine, three of the traits studied in the GenomEUtwin project.

Database infrastructure has become a critical component for competitive life sciences research and discovery. The explosion of data requires that the data are properly loaded, accessed, managed, queried, analyzed, and shared with others. The key purpose of the population-based twin cohorts housed at different institutions in Europe is to gather an extremely large quantity of information from their twin populations, and share it. Longitudinal research over a long period of time, hopefully generations, demands completely new methods and systems to handle the gathering of information and storing. These cohorts bring to the fore problems concerning the need for a standardization of research data and a computer and storage strategy. In the following we describe the preliminary strategy being implemented in the Database Core of GenomEUtwin.

Multiallelic short tandem repeat polymorphisms, or microsatellites, are useful markers in genome wide scans to identify chromosomal regions containing genes underlying disease loci. The biallelic single nucleotide polymorphism (SNP) can be used to fine map previously identified large candidate regions or to test functional candidate genes by association analysis. In the GenomEUtwin project the population based impact of susceptibility genes for six multifactorial traits will be studied. A genome wide panel of informative human microsatellite markers will be analyzed by fluorescent capillary electrophoresis in well characterized twin and population samples. Contrary to microsatellites, selection of the most informative panels of SNPs is hampered by imperfect data on the allele frequencies and population distribution of SNPs markers in the databases. Therefore, selection of SNPs requires a substantial amount of bioinformatics, and, the SNPs need to be validated experimentally in the relevant populations prior to genotyping large sample sets. In the GenomEUtwin project, large scale genotyping of SNPs will be performed using the SNPstreamUHT and MassARRAY genotyping systems that are based on the primer extension reaction principle combined with fluorescent and mass spectrometric detection, respectively. Production of the genotyping data will be a joint effort by GenomEUtwin partners at the University of Helsinki, the National Public Health Institute in Helsinki, Finland and Uppsala University, Sweden. All genotyping data will be stored in a common database established specifically for the GenomEUtwin project, from where it can be accessed by the twin research centres that provided the samples for genotyping.

Amajor component of variation in body height is due to genetic differences, but environmental factors have a substantial contributory effect. In this study we aimed to analyse whether the genetic architecture of body height varies between affluent western societies. We analysed twin data from eight countries comprising 30,111 complete twin pairs by using the univariate genetic model of the Mx statistical package. Body height and zygosity were self-reported in seven populations and measured directly in one population. We found that there was substantial variation in mean body height between countries; body height was least in Italy (177 cm in men and 163 cm in women) and greatest in the Netherlands (184 cm and 171 cm, respectively). In men there was no corresponding variation in heritability of body height, heritability estimates ranging from 0.87 to 0.93 in populations under an additive genes/unique environment (AE) model. Among women the heritability estimates were generally lower than among men with greater variation between countries, ranging from 0.68 to 0.84 when an additive genes/shared environment/unique environment (ACE) model was used. In four populations where an AE model fit equally well or better, heritability ranged from 0.89 to 0.93. This difference between the sexes was mainly due to the effect of the shared environmental component of variance, which appears to be more important among women than among men in our study populations. Our results indicate that, in general, there are only minor differences in the genetic architecture of height between affluent Caucasian populations, especially among men.

Body mass index (BMI), a simple anthropometric measure, is the most frequently used measure of adiposity and has been instrumental in documenting the worldwide increase in the prevalence of obesity witnessed during the last decades. Although this increase in overweight and obesity is thought to be mainly due to environmental changes, i.e., sedentary lifestyles and high caloric diets, consistent evidence from twin studies demonstrates high heritability and the importance of genetic differences for normal variation in BMI. We analysed self-reported data on BMI from approximately 37,000 complete twin pairs (including opposite sex pairs) aged 20–29 and 30–39 from eight different twin registries participating in the GenomEUtwin project. Quantitative genetic analyses were conducted and sex differences were explored. Variation in BMI was greater for women than for men, and in both sexes was primarily explained by additive genetic variance in all countries. Sex differences in the variance components were consistently significant. Results from analyses of opposite sex pairs also showed evidence of sex-specific genetic effects suggesting there may be some differences between men and women in the genetic factors that influence variation in BMI. These results encourage the continued search for genes of importance to the body composition and the development of obesity. Furthermore, they suggest that strategies to identify predisposing genes may benefit from taking into account potential sex specific effects.

Migraine is a common neurovascular brain disorder that is manifested in recurrent episodes of disabling headache. The aim of the present study was to compare the prevalence and heritability of migraine across six of the countries that participate in GenomEUtwin project including a total number of 29,717 twin pairs. Migraine was assessed by questionnaires that differed between most countries. It was most prevalent in Danish and Dutch females (32% and 34%, respectively), whereas the lowest prevalence was found in the younger and older Finnish cohorts (13% and 10%, respectively). The estimated genetic variance (heritability) was significant and the same between sexes in all countries. Heritability ranged from 34% to 57%, with lowest estimates in Australia, and highest estimates in the older cohort of Finland, the Netherlands, and Denmark. There was some indication that part of the genetic variance was non-additive, but this was significant in Sweden only. In addition to genetic factors, environmental effects that are non-shared between members of a twin pair contributed to the liability of migraine. After migraine definitions are homogenized among the participating countries, the GenomEUtwin project will provide a powerful resource to identify the genes involved in migraine.

Despite the decline in coronary heart disease in many European countries, the disease remains an enormous public health problem. Although we know a great deal about environmental risk factors for coronary heart disease, a heritable component was recognized a long time ago. The earliest and best known examples of how our genetic constitution may determine cardiovascular risk relate to lipoprotein(a), familial hypercholesterolaemia and apolipoprotein E. In the past 20 years a fair number of polymorphisms assessed singly have shown strong associations with the disease but most are subject to poor repeatability. Twins constitute a compelling natural experiment to establish the genetic contribution to coronary heart disease and its risk factors. GenomEUtwin, a recently funded Framework 5 Programme of the European Community, affords the opportunity of comparing the heritability of risk factors in different European Twin Registries. As an illustration we present the heritabilities of systolic and diastolic blood pressure, based on data from over 4000 twin pairs from six different European countries and Australia. Heritabilities for systolic blood pressure are between 52 and 66% and for diastolic blood pressure between 44 and 66%. There is no evidence of sex differences in heritability estimates and very little to no evidence for a significant contribution of shared family environment. A non-twin based prospective case/cohort study of coronary heart disease and stroke (MORGAM) will allow hypotheses relating to cardiovascular disease, generated in the twin cohorts, to be tested prospectively in adult populations. Twin studies have also contributed to our understanding of the life course hypothesis, and GenomEUtwin has the potential to add to this.

Stroke is one of the leading causes of severe disability and death in the world. In the present article we outline possibilities and limitations for future stroke research within the GenomEUtwin. The combined sample of twins born before 1958 from Denmark, Finland, and Sweden, and available for follow-up into the second millennium for non-fatal and fatal stroke events through national inpatient and death registers exceeds 70,000 twin pairs. This sample size will enable the study of genetic influences on stroke and major stroke subtypes. Large samples of twins in GenomEUtwin have been followed up repeatedly through interviews and questionnaires concerning a variety of exposures and potential risk factors for stroke. We briefly outline how this information can be combined with the health register information for epidemiologic and genetic epidemiologic studies of stroke. We also present the number of twin pairs concordant and discordant for stroke in Denmark, Finland and Sweden, and time lags between events for twins concordant for stroke. This information illustrates that the number of affected sib pairs for linkage studies is relatively limited, but the sample sizes are promising for association studies.

Previous twin studies have indicated that approximately 25% of the variation in life span can be attributed to genetic factors and recent studies have also suggested a moderate clustering of extreme longevity within families. Here we discuss various definitions of extreme longevity and some analytical approaches with special attention to the challenges due to censored data. Lexis diagrams are provided for the Danish, Dutch, Finnish, Italian, Norwegian, and Swedish Twin registries hereby outlining possibilities for longevity studies within GenomEUtwin. We extend previous analyses of lifespan for the Danish 1870–1900 twin cohorts to include the new 1901–1910 cohorts, which are consistent with the previous findings. The size of the twin cohorts in GenomEUtwin and the existence of population-based, nationwide health and death registers make epidemiological studies of longevity very powerful. The combined GenomEUtwin sample will also allow detailed age-specific heritability analyses of lifespan. Finally, it will provide a resource for identifying unusual sibships (i.e., dizygotic twin pairs) where both survived to extreme ages, as a basis for discovering genetic variants of importance for extreme survival.

The post-genomic era is witnessing a proliferation of large-scale and population based genetic and genomic research projects. Many countries have or are establishing research biobanks and, as with GenomEUtwin, there is great interest in building multinational projects that link genotypic and phenotypic information from different centers. Clearly, the conduct of these projects raises multiple ethical issues, and the knowledge generated will continually recast the ethical, legal and social implications (ELSI) of such research. Maximising the scientific profit from this work while minimizing the risks to the participants requires full integration of ethics components into the structure and functioning of these projects. GenomEUtwin is organized around five intellectual cores, including an Ethics Core which operates across the entire project. This paper describes the role of the Ethics Core and presents an overview of the guidelines on which the principles followed in GenomEUtwin are based. We outline the major ethical concerns of our project and highlight complexities arising from diverse national legislations. Finally, the role of empirically based ethics research is discussed for understanding the ethical, legal, social and economic implications of human genetics and genomics research.