Genes and mechanisms involved in common complex diseases, such as the autoimmune disorders that affect approximately 5% of the population, remain obscure. Here we identify polymorphisms of the cytotoxic T lymphocyte antigen 4 gene (CTLA4)--which encodes a vital negative regulatory molecule of the immune system--as candidates for primary determinants of risk of the common autoimmune disorders Graves' disease, autoimmune hypothyroidism and type 1 diabetes. In humans, disease susceptibility was mapped to a non-coding 6.1 kb 3' region of CTLA4, the common allelic variation of which was correlated with lower messenger RNA levels of the soluble alternative splice form of CTLA4. In the mouse model of type 1 diabetes, susceptibility was also associated with variation in CTLA-4 gene splicing with reduced production of a splice form encoding a molecule lacking the CD80/CD86 ligand-binding domain. Genetic mapping of variants conferring a small disease risk can identify pathways in complex disorders, as exemplified by our discovery of inherited, quantitative alterations of CTLA4 contributing to autoimmune tissue destruction.

Following the identification of several disease-associated polymorphisms by whole genome association analysis, interest is now focussing on the detection of effects that, due to their interaction with other genetic (or environmental) factors, may not be identified by using standard single-locus tests. In addition to increasing power to detect association, there is also a hope detecting interactions between loci will allow us to elucidate the biological and biochemical pathways underpinning disease. Here I provide a critical survey of the current methodological approaches (and related software packages) used to detect interactions between genetic loci that contribute to human genetic disease. I also discuss the difficulties in determining the biologcal relevance of statistical interactions.The search for genetic factors that influence common, complex traits, and the characterisation of the effects of those factors is both a goal and a challenge for modern geneticists. In the last couple of years, the field has been revolutionised by the success of genome-wide association (GWA) studies 1 2 3 4 5 . Most such studies have used a singlelocus analysis strategy, whereby each variant is tested individually for association with some phenotype. However, an oft-cited reason for the lack of success in genetic studies of complex disease 6 7 is the existence of interactions between loci. If a genetic factor operates primarily through a complex mechanism involving multiple other genes, and possibly environmental factors, the fear is that the effect will be missed if one examines it in isolation, without allowing for its potential interactions with these other (unknown) factors. For this reason, several methods and software packages 8 9 10 11 12 13 14 15 have been developed to consider statistical interactions between loci, when analysing data from genetic association studies. Although, in some cases, the motivation for such analyses is to increase the power to detect effects 16 , in other cases the motivation has been to detect statistical interactions between loci that are informtive about the biological and biochemical pathways underpinning the disease 7 . We return to this complex issue of biological interpretation of statistical interaction later in the article.The purpose of this Review article is to provide a survey of the current methodological approaches and related software packages that are currently used to detect interactions between genetic loci that contribute to human genetic disease. Although the focus is on human genetics, many of the concepts and approaches are strongly related to methods used in animal and plant genetics. I begin by describing what is meant by (statistical) interaction, and setting up definitions and notation for following sections. I then explain how one may test for interaction between two (or more) known genetic factors, and how one may address the slightly different question of testing for association with a single factor, while at the same time allowing for interaction with othe...

High blood pressure is a highly heritable and modifiable risk factor for cardiovascular disease. We report the largest genetic association study of blood pressure traits (systolic, diastolic, pulse pressure) to date in over one million people of European ancestry. We identify 535 novel blood pressure loci that not only offer new biological insights into blood pressure regulation but also reveal shared genetic architecture between blood pressure and lifestyle exposures. Our findings identify new biological pathways for blood pressure regulation with potential for improved cardiovascular disease prevention in the future.

Epistasis, the interaction between genes, is a topic of current interest in molecular and quantitative genetics. A large amount of research has been devoted to the detection and investigation of epistatic interactions. However, there has been much confusion in the literature over definitions and interpretations of epistasis. In this review, we provide a historical background to the study of epistatic interaction effects and point out the differences between a number of commonly used definitions of epistasis. A brief survey of some methods for detecting epistasis in humans is given. We note that the degree to which statistical tests of epistasis can elucidate underlying biological interactions may be more limited than previously assumed.