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

María Pino-Yanes,Carlos Flores,Jesús Villar,Almudena Corrales,Santiago Basaldúa,Alexis Hernández,Luisa Guerra,Dennis O'Rourke

doi:10.1371/journal.pone.0018389

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.

Highlights

Populations inhabiting the Iberian Peninsula have been influenced by the same major human prehistoric migrations that have affected the rest of European populations, regardless of the extent to which Neolithic expansions from the Near East have influenced their genetic makeup [1,2,3,4,5]
The potential ancestry informativeness of EuroAIMs for African and Spanish populations was initially evidenced by comparing genome-wide data from reference samples from the Human Genome Diversity Panel (HGDP) [32]
We have illustrated that a few dozen EuroAIMs ascertained to distinguish the NNW-SSE axis of genetic differentiation in Europe could be utilized to distinguish Northwest Africans from European populations

Summary

Introduction

Populations inhabiting the Iberian Peninsula have been influenced by the same major human prehistoric migrations that have affected the rest of European populations, regardless of the extent to which Neolithic expansions from the Near East have influenced their genetic makeup [1,2,3,4,5]. Several lines of evidence support the existence of identifiable unequal African influences in populations from Spain, both from the Iberian Peninsula and the Canary Islands. Despite 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

Objectives

Methods

Results

Conclusion