Journal of Autoimmunity 25 (2005) 46e48 www.elsevier.com/locate/issn/08968411
Review
The genetics of systemic lupus erythematosus Marta E. Alarco´n-Riquelme* Department of Genetics and Pathology, Uppsala University, Dag Hammarskjo¨lds va¨g 20, 751 85, Uppsala, Sweden Received 24 May 2005; revised 25 May 2005; accepted 7 September 2005
Abstract For years the identification of candidate genes has been approached in various ways. The latest technology allows for whole genome SNP analysis in genetic association studies. These studies pose new challenges but also new possibilities. I hereby review what is known to date on lupus genetics and what challenges we are to overcome with the new available methods. Ó 2005 Elsevier Ltd. All rights reserved. Keywords: Genome scan; Systemic lupus erythematosus; Single nucleotide polymorphisms; Microsatellites; Association
It has recently been proposed that the genetic factors involving autoimmune diseases might be shared among these [1]. This is why the knowledge of the pathophysiological mechanisms behind one of the diseases will allow new hypotheses to be tested in other diseases. Susceptibility genes may be shared as recently evidenced by the identification of PDCD1 and PTPN22 as the genes involved in susceptibility for type 1 diabetes, rheumatoid arthritis and systemic lupus erythematosus [2e8]. SLE is a relatively rare disease with a prevalence of 0.05% in Caucasian populations [9]. It is thought to be more frequent in African-Americans, Chinese and Mexicans, however no proper epidemiological studies have been performed to date for these. The reason for this apparent bias appears to be the severity of the disease in these population groups given by possible correlation with lower socioeconomic factors and education, leading to an increase frequency in hospital visits, severe outcomes and damage development [10]. SLE is a complex disease, signifying that genetic and environmental factors are involved in disease development. Their interaction is not known, but recently findings suggesting a role for the EBV [11] have been put forward. In addition it is most likely that other factors such as nutrients, as well
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as environmental hazards such as smoking and pesticides may play a role. Well known is the fact that several drugs do induce lupus in susceptible individuals [12]. We may envision that the genes having a role in lupus do so at various levels of disease pathophysiology. For a thorough review on the subject please see [13]. The study of the genetic factors behind SLE has been approached in several ways in the past 10 years. These are reviewed here. 1. Multicase family-based genome scans and the microsatellite era With the identification of tandem repeat variation in the human genome, the possibility of studying the genetics of human diseases with linkage analysis similar to what had been done with monogenic diseases was open. Several groups, mainly in the US, started the collection of families having two or more affected with SLE, as has been done for other diseases [14e16]. In Europe only a group from Sweden have such a collection [17]. The only new gene identified through whole-genome scanning and linkage analysis was PDCD1 identified by the group from Sweden [4,18]. Linkage to the region where this gene is located, namely on 2q37.3 was identified in families of Swedish and Icelandic origin, independently. The combined LOD
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score reached 4.24 (at marker D2S125). The lack of adequate human genome sequence led to the analysis of PDCD1 as a candidate gene. The complete gene was sequenced, several polymorphisms discovered and association identified with an intronic polymorphism. It was later shown that this intronic polymorphism allelically abrogated binding by a transcriptional repressor, RUNX1, to the sequence, which was shown to be a regulatory sequence, most possibly a silencer. To date no novel genes have been identified for lupus using this strategy. In Table 1, a list of the candidate genes within the most prominent loci detected with linkage analysis is presented.
are those discovered by the production of knock-out mice or animals deficient for a given gene. The most common phenotype studied has been autoantibody production and glomerulonephritis. Several genes have been identified but few have been tested in the human. One clear example is PDCD1, a gene identified in the human and also in a KO model, strongly supporting the role of the gene in the disease [25]. A list of examples is shown in Table 2.
2. Animal models as helping tools in the identification of lupus genes
The simple comparison of two groups of individuals, one with a given phenotype and the other representing a population group without such phenotype is the most classic way for identification of genes for complex diseases. It is also the most powerful. Several genes have been identified by these means, which otherwise does have a number of drawbacks. First, the risk for false positive results is important. It has been recognized more and more that population stratification can play an important role in the generation of statistical errors, both type I and type II [26,27]. To date researchers have had quite a controversy regarding the replication of association studies. The use of a second population for study, or the acquisition of a small p value, small enough to resist multiple testing corrections are now required to have a gene nominated as a true gene for a disease. As complex diseases such as lupus are believed to be the result of multiple genes with varying effects and its prevalence is quite low compared to other diseases, these requirements appear to be too strict. Table 3 shows a list of genes that have been identified for lupus using association studies. This list shows genes that have been replicated in several studies. A hypothesis lay always behind the study of each of these genes, however today we have the means to study whole genome association studies.
Lupus is one of the few complex disorders that count with spontaneous animal models that resemble the human disease to a large extent. In particular, the development of glomerulonephritis is a main feature of these models. For several years, various groups have produced crosses of the most important strains of mice [19e21] and this has resulted, after many years, in a couple of new genes. Most recent is the discovery of the CD2 family of genes [22]. The CD2 cluster of genes is found in mouse chromosome 1 and lies in a region highly homologous to regions detected with genetic linkage analysis in humans. This cluster comprises a heterogeneous set of genes whose common denominator is the capacity to work as molecules that allow the interaction between various cell types. However the proteins codified by these genes are expressed in very diverse peripheral blood subpopulations. The analysis of the genetic region in the mouse showed that a wild-type haplotype was responsible for the disease phenotype, but one single gene could not be pointed out. Abnormalities in the Cr2 gene have also been described [23]. Another gene identified in crosses of susceptible inbred mouse strains was Ifi202, an interferon inducible gene [24]. This gene was identified within a mouse QTL and shown to have allelically differential expression from the normal counterpart. Studies have been ongoing to prove if these genes identified in the mouse do play a role in the disease process in the human. 3. Knocking out the disease from engineered mice Another approach that has been used and that has given numerous candidates to the list of genes involved in human lupus Table 1 Candidate genes in loci linked to lupus in microsatellite genome scans Region
LOD score
Candidate gene(s)
1q23 1q31 1q42 2q37 4p13 6p21 16q13 17q11
3.5 3.8 3.5 4.24 3.2 4.2 3.6 3.5
FcG receptors, CD2, CR2, FCRL3 CD45, HF1, Ro (60 kDa) PARP PDCD1 ? HLA, TNF, C4 ? Chemokine receptors
4. Genetic association studies as the most powerful tool for identification of genes
5. The whole genome at hand The development of new techniques for genotyping single nucleotide polymorphisms on a genome-wide basis has opened new possibilities for the study of complex diseases. As for today, the genotyping of hundreds of thousands of SNPs is possible. Most attractive is the use of this technology in genetic association studies while for family-based studies their usefulness is more controversial. How to deal with results from such amount of data? The main challenge resides in the analysis of the data. And how to correct for the multiple tests to be performed. Several researchers claim that multiple Table 2 Candidate genes for lupus identified through mouse studies Quantitative trait loci (QTLs)
Ifi202, CD2, Cr2
Engineered mice (knock-outs)
CD22, PDCD1, Ro-60, Cbl-b, Era, glutathione-S-transferase Fas (Lpr mice), Fas-ligand (gld mice), SHP-1 (motheaten)
Spontaneous recessive mutations
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Table 3 Candidate genes identified through genetic association studies (this includes only genes with very low p values or replicated in several studies) IL10 MCP-1 CTLA4 OPN PTPN22
FcGRIIA, IIB, IIIA and IIIB Mannose binding lectin (MBL) IRF5 ACE
IL6 DRB1, C4AQ0 TYK2 DNase I
testing has to be done. For 500,000 SNPs we need a p value of 10ÿ8 at least to correct and still obtain a p value of 0.05. To date only the HLA has provided such low p values. Replication is also a requirement. Replication is also a requirement. A second and at times a third population have to be tested. There are possibilities to solve the dilemma of multiple testing such as permutation tests and larger samples. In addition population substructure, the varied behavior of SNPs across the genome also pose problems today unknown for us. As to larger samples, for some diseases with a relatively low prevalence, such as SLE, the collection of large samples of relatively homogenous population groups seems to be a difficult problem. In addition, the cost of genotyping a large sample makes this requirement unsuitable even for the best financed. Are we to leave the task behind? I do not think so, I believe we can take each gene and study it in depth after getting a hint from a whole-genome association study. We still have many hypotheses to test; the whole genome scan genotyping is just the beginning.
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