Twin studies have been a valuable source of information about the genetic basis of complex traits. Large, worldwide registers of twin data have been established to maximize the potential of twin studies. These resources can be used to study the genetic epidemiology of complex traits and diseases, the interaction between genotype and environmental factors, and the causes of co-morbidity between traits and diseases. Twin studies offer unique opportunities for selected sampling in linkage and association studies of quantitative trait loci (QTLs). However, ascertainment bias, problems with phenotypic assessment, lack of follow-up, and environmental noise can complicate the interpretation of results. Recent advances in genetics, such as the completion of the human genome sequence and the development of powerful statistical tools, have opened new avenues for human genetics research. Twin studies can help disentangle the contributions of genes, the shared environment, and individual-specific environments to complex traits. Classical twin studies compare phenotypic similarities between monozygotic (MZ) and dizygotic (DZ) twins to estimate the extent of genetic variation. Multivariate analyses, structural equation modeling, and multivariate twin designs have enhanced the ability to study the causes of association and co-morbidity between traits. Twin registers, which collect data on twins and their families, are valuable resources for epidemiological and molecular-genetic studies. They provide large, unbiased samples that can verify the role of genetic variation in common traits. The European Community's GENOMEUTWIN project aims to integrate the efforts of leading genetic and epidemiological researchers in twin research, combining epidemiological and phenotypic data collection with genome-wide genotyping. Twin studies have shown that genetics significantly contributes to the variation seen in multifactorial traits and complex diseases, and they have also provided evidence that lifestyle risk factors may be influenced by genes.Twin studies have been a valuable source of information about the genetic basis of complex traits. Large, worldwide registers of twin data have been established to maximize the potential of twin studies. These resources can be used to study the genetic epidemiology of complex traits and diseases, the interaction between genotype and environmental factors, and the causes of co-morbidity between traits and diseases. Twin studies offer unique opportunities for selected sampling in linkage and association studies of quantitative trait loci (QTLs). However, ascertainment bias, problems with phenotypic assessment, lack of follow-up, and environmental noise can complicate the interpretation of results. Recent advances in genetics, such as the completion of the human genome sequence and the development of powerful statistical tools, have opened new avenues for human genetics research. Twin studies can help disentangle the contributions of genes, the shared environment, and individual-specific environments to complex traits. Classical twin studies compare phenotypic similarities between monozygotic (MZ) and dizygotic (DZ) twins to estimate the extent of genetic variation. Multivariate analyses, structural equation modeling, and multivariate twin designs have enhanced the ability to study the causes of association and co-morbidity between traits. Twin registers, which collect data on twins and their families, are valuable resources for epidemiological and molecular-genetic studies. They provide large, unbiased samples that can verify the role of genetic variation in common traits. The European Community's GENOMEUTWIN project aims to integrate the efforts of leading genetic and epidemiological researchers in twin research, combining epidemiological and phenotypic data collection with genome-wide genotyping. Twin studies have shown that genetics significantly contributes to the variation seen in multifactorial traits and complex diseases, and they have also provided evidence that lifestyle risk factors may be influenced by genes.