African Origins: The modern human originated in Africa and subsequently spread across the globe. However, the genetic relationships among the diverse populations on the African continent have been unclear. Tishkoff et al. (p. 1035; see the cover, published online 30 April) provide a detailed genetic analysis of most major groups of African populations. The findings suggest that Africans represent 14 ancestral populations. Populations tend to be of mixed ancestry which documents historical migrations. The data mainly support but sometimes challenge proposed relationships between groups of self-identified ethnicity previously hypothesized on the basis of linguistic studies. The authors also examined populations of African Americans and individuals of mixed ancestry from Cape Town, documenting the variation and origins of admixture within these groups.

Genome-wide association studies (GWAS) have evolved over the last ten years into a powerful tool for investigating the genetic architecture of human disease. In this work, we review the key concepts underlying GWAS, including the architecture of common diseases, the structure of common human genetic variation, technologies for capturing genetic information, study designs, and the statistical methods used for data analysis. We also look forward to the future beyond GWAS.

Motivation: Polymorphisms in human genes are being described in remarkable numbers. Determining which polymorphisms and which environmental factors are associated with common, complex diseases has become a daunting task. This is partly because the effect of any single genetic variation will likely be dependent on other genetic variations (gene–gene interaction or epistasis) and environmental factors (gene–environment interaction). Detecting and characterizing interactions among multiple factors is both a statistical and a computational challenge. To address this problem, we have developed a multifactor dimensionality reduction (MDR) method for collapsing high-dimensional genetic data into a single dimension thus permitting interactions to be detected in relatively small sample sizes. In this paper, we describe the MDR approach and an MDR software package. Results: We developed a program that integrates MDR with a cross-validation strategy for estimating the classification and prediction error of multifactor models. The software can be used to analyze interactions among 2–15 genetic and/or environmental factors. The dataset may contain up to 500 total variables and a maximum of 4000 study subjects. Availability: Information on obtaining the executable code, example data, example analysis, and documentation is available upon request. Contact: moore@phg.mc.vanderbilt.edu Supplementary information: All supplementary information can be found at http://phg.mc.vanderbilt.edu/Software/MDR. * To whom correspondence should be addressed.