Africa is inhabited by populations that show high levels of genetic diversity compared to most other continental populations today and it is thought to be the ancestral home of modern humans. African populations have the largest number of population specific autosomal, X-chromosomal and mitochondrial DNA haplotypes with non-African populations having only a subset of the genetic diversity present in Africa [1]. Estimates of F_ST (the classic measure of population subdivision) from mitochondrial DNA are much higher in Africa than other populations, as summarized by Tishkoff et al [1]. In addition, analyses from studies based on autosomal SNPs, STRPs or Alu elements show higher F_ST values for African populations [2–4]. Recent studies of world populations based on large genomic data also reported significant population structure among the African groups [5, 6]. However, given the cultural and linguistic diversity of African populations (with over 2000 distinct ethnic groups and languages), these studies have typically included only a handful of African populations indicating that most African populations have not been studied. As previously noted, most existing genetic data on African populations have come from a few countries that are relatively economically developed and/or with key research or medical centers [1]. Availability of more genetic data from sub Saharan Africa will clearly be useful in our understanding of population structure, demographic history and the efforts to map disease-causing genes.

Several genetic epidemiologic studies mapping complex disease-causing genes have been designed to take advantage of the population genetic characteristics of contemporary African populations for fine mapping of informative genomic regions. These characteristics include lower linkage disequilibrium values [5–9] and smaller haplotype block sizes [10, 11]. On the other hand, African populations have more divergent patterns of LD and more complex pattern of population substructure or stratification [12–17]. Population stratification refers to differences in allele frequencies between cases and controls due to systematic differences in ancestry rather than association of genes with disease and it can have a major impact on the ability of genetic epidemiologic studies to detect valid associations between a putative risk allele and a disease or trait.

We investigated population structure or stratification in four ethnic groups in two countries in West Africa (Akan and Gaa-Adangbe from Ghana, Yoruba and Igbo from Nigeria) using data from 372 autosomal microsatellite loci [see Additional file 1] typed in 493 unrelated persons (986 chromosomes). Firstly, we used a clustering algorithm to infer population structure in the whole sample while ignoring ethnic group information and compare our findings to reported ethnic grouping. Next, we used analysis of molecular variance (AMOVA) models on the same data. Finally, we estimate FST and allele sharing distances between all population pairs.

Using data from 372 microsatellite loci typed in 493 unrelated persons from four major ethnic groups in Nigeria and Ghana, we sought for evidence of population structure using several methods. Our results did not show any significant population substructure and no ethnic group corresponded to inferred clusters. This finding has been reported by others [5]. Although Rosenberg et al observed significant population structure among six African groups (Bantu-Kenya, Mandenka, Yoruba, San, Mbuti Pygmy and Blaka Pygmy), they reported that inferred clusters for some of the African populations did not correspond to predefined groups, unlike groups from America, Oceania and Eurasia [5].

The within-population component of genetic variation accounts for most of the diversity in the sample. This is consistent with previous findings [5] showing that the within-population component of genetic variance among six African populations studied was 96.9%; we estimated an even higher value of 99.9% in this study. The higher value of the within-population variance in this study is likely due to the smaller geographic area from which the samples were derived. The maximum distance between any two sites in this study is less than 700 miles and there are no major natural barriers e.g., mountains, between the regions inhabited by the groups. In addition, these four ethnic groups have a long history of trade and other interactions and they all speak languages belonging to the Niger-Kordofanian group. As noted by Cavalli-Sforza et al [18] the genetic relationships observed in West Africa indicate that major migrations and admixtures occurred within the region in earlier times

It is important to point out that despite the small amount of genetic differentiation in the sample as a whole, it was possible to distinguish between the groups from each country using a hierarchical AMOVA model and a dendrogram algorithm. Thus, the absence of significant population structure between the four groups did not mean that the groups could not be distinguished from each other. Rather, the data in Table 4 show that enough differences exist to separate the two populations from Nigeria from those from Ghana.

From the disease-mapping point of view, population stratification is important in the analysis of association genetic data, especially when that data is being used to infer the contribution of genetics to a disease. The presence of undetected population structure can mimic association (leading to more false positives) or mimic lack of association (leading to false negatives) [19]. While there has been much debate about the impact of population stratification on association studies, there are limited data that quantify the magnitude of this effect. The largest study to quantify this effect analyzed data from 11 case-control and case-cohort association studies [20] and showed that there was no statistically significant evidence for stratification. However, most of the studies evaluated above used limited number of markers making it difficult to completely rule out moderate levels of stratification that could lead to the finding of false positive associations.

Typically, efforts are made to minimize the effect of stratification during study design and data analysis, including a careful selection of cases and controls (e.g., matching) and by conducting family-based association tests. However, for the size of study needed to detect typical genetic effects in common diseases, even modest levels of population structure within population groups cannot be safely ignored [19]. Given this, we have searched for evidence of population stratification in this genetic epidemiologic study, the first of its kind for T2DM in West Africa. Noting that the number of markers needed to assess stratification depends on the magnitude of genetic effects under study [19], we have used a large number of markers, rather than just a few dozen as in many studies. The number of markers we have used (372) can bring the conservative 95^th percentile upper bound on the level of stratification to within 10% of the true value [20].

In summary, there was little evidence for significant population substructure in the four major West African ethnic groups represented in the AADM study sample. Classification of individuals into clusters showed symmetry, with roughly the same proportion of each ethnic group assigned to each cluster(s). Ethnicity apparently did not introduce differential allele frequencies that may affect analysis and interpretation of linkage and association studies. These findings, although not entirely surprising given the geographical proximity of these groups, provide important insights into the genetic relationships between the ethnic groups studied and confirm previous results that showed close genetic relationship between most studied West African groups.