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.

Motivation: The sequencing of the human genome has made it possible to identify an informative set of >1 million single nucleotide polymorphisms (SNPs) across the genome that can be used to carry out genome-wide association studies (GWASs). The availability of massive amounts of GWAS data has necessitated the development of new biostatistical methods for quality control, imputation and analysis issues including multiple testing. This work has been successful and has enabled the discovery of new associations that have been replicated in multiple studies. However, it is now recognized that most SNPs discovered via GWAS have small effects on disease susceptibility and thus may not be suitable for improving health care through genetic testing. One likely explanation for the mixed results of GWAS is that the current biostatistical analysis paradigm is by design agnostic or unbiased in that it ignores all prior knowledge about disease pathobiology. Further, the linear modeling framework that is employed in GWAS often considers only one SNP at a time thus ignoring their genomic and environmental context. There is now a shift away from the biostatistical approach toward a more holistic approach that recognizes the complexity of the genotype–phenotype relationship that is characterized by significant heterogeneity and gene–gene and gene–environment interaction. We argue here that bioinformatics has an important role to play in addressing the complexity of the underlying genetic basis of common human diseases. The goal of this review is to identify and discuss those GWAS challenges that will require computational methods. Contact:jason.h.moore@dartmouth.edu

Essential hypertension is a common disease that has complex multifactorial etiology. For this reason, it is not surprising that studies of the effects of single genes on hypertension have often failed to replicate the original findings. We propose, as a working hypothesis, that the failure to replicate some single locus results is because the impact of single alleles on the risk of hypertension is dependent on genetic variations at other loci (i.e. gene-gene interactions) and on environmental factors (i.e. gene-environment interactions). Thus, studies that do not consider the appropriate genetic and/or environmental contexts may not identify important susceptibility loci. The identification and characterization of such gene-gene and gene-environment interactions have been limited by lack of powerful statistical methods and/or a lack of large enough sample sizes. Here, we review the general problem of identifying gene-gene interactions and describe several traditional and several newer methods that are being used to assess complex genetic interactions in essential hypertension.