North African Influences and Potential Bias in Case-Control Association Studies in the Spanish Population

Abstract

BackgroundDespite the limited genetic heterogeneity of Spanish populations, substantial evidences support that historical African influences have not affected them uniformly. Accounting for such population differences might be essential to reduce spurious results in association studies of genetic factors with disease. Using ancestry informative markers (AIMs), we aimed to measure the African influences in Spanish populations and to explore whether these might introduce statistical bias in population-based association studies.Methodology/Principal FindingsWe genotyped 93 AIMs in Spanish (from the Canary Islands and the Iberian Peninsula) and Northwest Africans, and conducted population and individual-based clustering analyses along with reference data from the HapMap, HGDP-CEPH, and other sources. We found significant differences for the Northwest African influence among Spanish populations from as low as ≈5% in Spanish from the Iberian Peninsula to as much as ≈17% in Canary Islanders, whereas the sub-Saharan African influence was negligible. Strikingly, the Northwest African ancestry showed a wide inter-individual variation in Canary Islanders ranging from 0% to 96%, reflecting the violent way the Islands were conquered and colonized by the Spanish in the XV century. As a consequence, a comparison of allele frequencies between Spanish samples from the Iberian Peninsula and the Canary Islands evidenced an excess of markers with significant differences. However, the inflation of p-values for the differences was adequately controlled by correcting for genetic ancestry estimates derived from a reduced number of AIMs.Conclusions/SignificanceAlthough the African influences estimated might be biased due to marker ascertainment, these results confirm that Northwest African genetic footprints are recognizable nowadays in the Spanish populations, particularly in Canary Islanders, and that the uneven African influences existing in these populations might increase the risk for false positives in association studies. Adjusting for population stratification assessed with a few dozen AIMs would be sufficient to control this effect.

Assessing the Validity of Asthma Associations for Eight Candidate Genes and Age at Diagnosis Effects

“…These EuroAIMs allowed to correct for major differences in Spanish populations due to the North African genetic influences observed in this population, with a mean value of 5–9% for mainland populations and 16–20% for Canary Islanders [28]. A principal component analysis (PCA) based on these genetic markers was used to derive the ancestry estimates in cases and controls as scores of the first principal component (PC1), by means of EIGENSOFT [29].…”

“…A principal component analysis (PCA) based on these genetic markers was used to derive the ancestry estimates in cases and controls as scores of the first principal component (PC1), by means of EIGENSOFT [29]. A full list of EuroAIMs used and the genotyping procedures have been detailed elsewhere [24], [28].…”

Polymorphisms in DNA-Repair Genes in a Cohort of Prostate Cancer Patients from Different Areas in Spain: Heterogeneity between Populations as a Confounding Factor in Association Studies

“…In that sense, it is known the presence of several significant axes of stratification, most prominently in a northern-south-eastern trend, but also along an east-west axis, among the genotype distribution of European population [2]. In the case of Spain, although populations inhabiting the Iberian Peninsula show a substantial genetic homogeneity [3], there are findings suggesting that Northwest African influences existing among the Spanish populations and these differences might increase the risk for false positives in genetic epidemiology studies [4].…”

IL-1 receptor–associated kinase 3 gene (IRAK3) variants associate with asthma in a replication study in the Spanish population

“…Additionally, 83 European ancestry informative markers (EuroAIMs; see Table E3 in this article's Online Repository at www.jacionline.org) were genotyped and used for principal component analysis with EIGENSOFT to derive the scores for the first principal component (PC1). 4 Association of individual tSNPs was tested with the Cochran-Armitage trend test, and then a logistic regression analysis was used to calculate odds ratios (ORs) with 95% CIs and to adjust for population stratification by using the PC1 scores. Additionally, 15 untyped SNPs of the gene were imputed and tested for association.…”