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.

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Association of the T-cell regulatory gene CTLA4 with susceptibility to autoimmune disease

Problems of reporting genetic associations with complex outcomes.

To fully understand the allelic variation that underlies common diseases, complete genome sequencing for many individuals with and without disease is required. This is still not technically feasible. However, recently it has become possible to carry out partial surveys of the genome by genotyping large numbers of common SNPs in genome-wide association studies. Here, we outline the main factors - including models of the allelic architecture of common diseases, sample size, map density and sample-collection biases - that need to be taken into account in order to optimize the cost efficiency of identifying genuine disease-susceptibility loci.

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Genome-wide association studies: theoretical and practical concerns

Phenotypic variation among organisms is central to evolutionary adaptations underlying natural and artificial selection, and also determines individual susceptibility to common diseases. These types of complex traits pose special challenges for genetic analysis because of gene-gene and gene-environment interactions, genetic heterogeneity, low penetrance, and limited statistical power. Emerging genome resources and technologies are enabling systematic identification of genes underlying these complex traits. We propose standards for proof of gene discovery in complex traits and evaluate the nature of the genes identified to date. These proof-of-concept studies demonstrate the insights that can be expected from the accelerating pace of gene discovery in this field.

https://science.sciencemag.org/content/sci/298/5602/2345.full.pdf

Finding Genes That Underlie Complex Traits

To the editor: The increasing availability of polymorphism data has allowed more gene association studies to be carried out and the number of published genetic association studies is growing rapidly. Studies done secondarily to successful linkage studies over the last decade have also fueled the increase in published association studies. Although there are single-nucleotide polymorphism and human variation databases 1,2 , there is currently no public repository for genetic association data. It is difficult to query association data in a systematic manner or to integrate association data with other molecular databases. OMIM 3 , the main repository of genetic information for mendelian disorders, is largely text based and is of a historical narrative design, making it difficult to compare large sets of molecular data. Moreover, OMIM archives mature, high-quality data of high significance, the standard in rare mendelian disorders. Although this data is useful, OMIM does not routinely collect findings of lower significance or negative findings. The study of nonmendelian, common complex disorders is often a struggle to find disease relevance with lower significance values, and often conflicting evidence. Negative data are often not reported or are marginalized into obscure and less accessible scientific journals, resulting in a publication bias favoring positive genetic associations 4. Here, we describe the development of a genetic association database (GAD; http://geneticassociationdb.nih.gov) that aims to collect, standardize and archive genetic association study data and to make it easily accessible to the scientific community. There are no standards for designing, implementing, interpreting or reporting association studies (e.g., sample size, replication, significant P values), although guidelines have been suggested 4–7. The literature is filled with alternative, idiosyncratic and arbitrary gene names and gene symbols, as well as a continuum of phenotypic descriptions. Studies using arbitrary nomenclature continue to be published, making cross-comparison and meta-analysis difficult. One goal of GAD is to standardize molecular nomenclature in the archival process by including official HUGO gene symbols. After this assignment, each record is annotated with links to molecular databases (LocusLink, GeneCards, HapMap, etc.) and reference databases (PubMed, CDC), among others. Once they are standardized, integrating association data with other molecular databases, data mining tools, annotation and future sources of molecular data (e.g., gene interactions, quantitative trait loci) can be done systematically. Moreover, cross-comparison and meta-analysis of studies becomes more efficient. There are three main components of GAD: a web interface, Perl modules and the database, which uses the Oracle RDBMS. …

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The Genetic Association Database

It is increasingly apparent that the identification of true genetic associations in common multifactorial disease will require studies comprising thousands rather than the hundreds of individuals employed to date. Using 2,873 families, we were unable to confirm a recently published association of the interleukin 12B gene in 422 type I diabetic families. These results emphasize the need for large datasets, small P values and independent replication if results are to be reliable.

Patricia McKinney

Papers

Parameters for reliable results in genetic association studies in common disease.