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The current development of densely spaced collections of single nucleotide polymorphisms (SNPs) will lead to genomewide association studies for a wide range of diseases in many different populations. Determinations of the appropriate number of SNPs to genotype involve a balancing of power and cost. Several variables are important in these determinations. We show that there are different combinations of sample size and marker density that can be expected to achieve the same power. Within certain bounds, investigators can choose between designs with more subjects and fewer markers or those with more markers and fewer subjects. Which designs are more cost-effective depends on the cost of phenotyping versus the cost of genotyping. We show that, under the assumption of a set cost for genotyping, one can calculate a "threshold cost" for phenotyping; when phenotyping costs per subject are less than this threshold, designs with more subjects will be more cost-effective than designs with more markers. This framework for determining a cost-effective study will aid in the planning of studies, especially if there are choices to be made with respect to phenotyping methods or study populations. [ABSTRACT FROM AUTHOR]

Genomewide association studies may offer the best promise for genetic mapping of complex traits. Such studies in outbred populations require very densely spaced single-nucleotide polymorphisms. In recently founded population isolates, however, extensive linkage disequilibrium (LD) may make these studies feasible with currently available sets of short tandem repeat markers, spaced at intervals as large as a few centimorgans. We report the results of a genomewide association study of severe bipolar disorder (BP-I), using patients from the isolated population of the central valley of Costa Rica. We observed LD with BP-I on several chromosomes; the most striking results were in proximal 8p, a region that has previously shown linkage to schizophrenia. This region could be important for severe psychiatric disorders, rather than for a specific phenotype. [ABSTRACT FROM AUTHOR]