Finding genes influencing susceptibility to complex diseases in the post-genome era.

Abstract

During the last decade, hundreds of genes that harbor mutations causing simple Mendelian disorders have been identified using a combination of linkage analysis and positional cloning techniques. Traditional approaches to gene mapping have been largely unsuccessful in mapping genes influencing so-called 'complex' genetic diseases, however, because of low power and other factors. Complex genetic diseases do not display simple Mendelian patterns of inheritance, although genes do have an influence and close relatives of probands consequently have an increased risk. These disorders are thought to be due to the combined effects of variation at multiple interacting genes and the environment. Complex diseases have a significant impact on human health because of their high population incidence (unlike simple Mendelian disorders, which tend to be rare). New techniques are being developed aimed specifically at mapping genes conferring susceptibility to complex diseases. A project aimed at mapping genes influencing susceptibility to a complex disease may be undertaken in several stages: establishing a genetic basis for the disease in one or more populations; measuring the distribution of gene effects; studying statistical power using models; carrying out marker-based mapping studies using linkage or association. Quantitative genetic models can be used to estimate the heritability of a complex (polygenic) disease, as well as to predict the distribution of gene effects and to test whether one or more quantitative trait loci (QTLs) exist. Such models can be used to predict the power of different mapping approaches, but are often unrealistic and therefore provide only approximate predictions. Linkage analyses, association studies and family-based association tests are all hindered by low power and other specific problems. Association studies tend to be more powerful but can generate spurious associations due to population admixture. Alternative strategies for association mapping include the use of recent founder populations or unique isolated populations that are genetically homogeneous, and the use of unlinked markers (so-called genomic controls) to assign different regions of the genome of an admixed individual to particular source populations. Linkage disequilibrium observed in a sample of unrelated affected and normal individuals can also be used to fine-map a disease susceptibility locus in a candidate region. New Bayesian strategies make use of an annotated human genome sequence to further refine the position of a candidate disease susceptibility locus.