Multi-ethnic studies in complex traits

• Published in: Human molecular genetics 2011-10, Vol.20 (2), p.R206-R213
• Main Authors: JINGYUAN FU, FESTEN, Eleonora A. M, WIJMENGA, Cisca
• Format: Article
• Language: English
• Subjects: Biological and medical sciences; Continental Population Groups - ethnology; Continental Population Groups - genetics; Disease - ethnology; Disease - genetics; Fundamental and applied biological sciences. Psychology; Genetics of eukaryotes. Biological and molecular evolution; Genome-Wide Association Study; Humans; Molecular and cellular biology; Quantitative Trait Loci; Reviews
• ISSN: 0964-6906; 1460-2083
• DOI: 10.1093/hmg/ddr386

The successes of genome-wide association (GWA) studies have mainly come from studies performed in populations of European descent. Since complex traits are characterized by marked genetic heterogeneity, the findings so far may provide an incomplete picture of the genetic architecture of complex traits. However, the recent GWA studies performed on East Asian populations now allow us to globally assess the heterogeneity of association signals between populations of European ancestry and East Asians, and the possible obstacles for multi-ethnic GWA studies. We focused on four different traits that represent a broad range of complex phenotypes, which have been studied in both Europeans and East Asians: type 2 diabetes, systemic lupus erythematosus, ulcerative colitis and height. For each trait, we observed that most of the risk loci identified in East Asians were shared with Europeans. However, we also observed that a significant part of the association signals at these shared loci seems to be independent between populations. This suggests that disease aetiology is common between populations, but that risk variants are often population specific. These variants could be truly population specific and result from natural selection, genetic drift and recent mutations, or they could be spurious, caused by the limitations of the method of analysis employed in the GWA studies. We therefore propose a three-stage framework for multi-ethnic GWA analyses, starting with the commonly used single-nucleotide polymorphism-based analysis, and followed by a gene-based approach and a pathway-based analysis, which will take into account the heterogeneity of association between populations at different levels.