Wide efforts have taken place with complex metabolic disorders to emulate the success that linkage analysis has had in explaining the nature of monogenic metabolic diseases such as MODY (maturity-onset diabetes of the young) and FH (familial hypercholesterolemia). New linkage methods are being specifically developed and tested for complex disorders since some of the basic assumptions of traditional linkage analysis used with Mendelian traits are not valid. The nature of complex diseases precludes the use of extended families under the hypothesis that the same disease allele acts in most affected individuals throughout a pedigree. Rather, a multitude of genes and of rare and common alleles creates an apparently chaotic pattern of heterogeneity within and between families. Therefore, very simple family structures, in many studies even isolated sibling pairs, form the basis of efforts to compare the inheritance of disease with that of the chromosomal regions under investigation. Also, assumptions about how individual loci contribute to the overall disease inheritance used for the models applied in linkage computation have to be kept to a minimum. The overall effect of this, together with the potentially weak influence of many loci, is a heavy toll on the statistical power to detect individual contributing genes. This may be the reason why very few scans so far have yielded disease loci that meet genome-wide significance criteria. The confirmation of original loci in secondary studies has proven, as predicted, to be very difficult. Nevertheless, the overall emerging picture is very encouraging: one of the genome scans in type 2 diabetes has been carried through to the positional cloning of the underlying genetic variant, namely, the calpain 10-associated polymorphism in type 2 diabetes. Several other loci have been detected repeatedly throughout studies in various human racial groups, such as the chromosome 1q and 20q diabetes loci, and have become the target of collaborative fine-mapping efforts. Modifications to present methodology are in development with the goal to increase statistical power: examples are the use of intermediate traits with potentially increased genetic homogeneity, the investigation of admixed populations, and the study of linkage disequilibrium over wide genomic regions.