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Autoimmune Diseases
Volume 2014 (2014), Article ID 203435, 11 pages
Review Article

Genes Associated with SLE Are Targets of Recent Positive Selection

1Department of Medicine, Medical University of South Carolina, Charleston, SC 29425, USA
2Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC 29425, USA
3Department of Public Health Sciences, Wake Forest School of Medicine and Center for Public Health Genomics, Winston-Salem, NC 27157, USA

Received 23 September 2013; Accepted 12 November 2013; Published 23 January 2014

Academic Editor: Juan-Manuel Anaya

Copyright © 2014 Paula S. Ramos et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The reasons for the ethnic disparities in the prevalence of systemic lupus erythematosus (SLE) and the relative high frequency of SLE risk alleles in the population are not fully understood. Population genetic factors such as natural selection alter allele frequencies over generations and may help explain the persistence of such common risk variants in the population and the differential risk of SLE. In order to better understand the genetic basis of SLE that might be due to natural selection, a total of 74 genomic regions with compelling evidence for association with SLE were tested for evidence of recent positive selection in the HapMap and HGDP populations, using population differentiation, allele frequency, and haplotype-based tests. Consistent signs of positive selection across different studies and statistical methods were observed at several SLE-associated loci, including PTPN22, TNFSF4, TET3-DGUOK, TNIP1, UHRF1BP1, BLK, and ITGAM genes. This study is the first to evaluate and report that several SLE-associated regions show signs of positive natural selection. These results provide corroborating evidence in support of recent positive selection as one mechanism underlying the elevated population frequency of SLE risk loci and supports future research that integrates signals of natural selection to help identify functional SLE risk alleles.