8.1Introduction

Essentially all human traits, including susceptibility to disease, are governed by genetics and inßuenced to varying degrees by environmental exposures. Indeed, a new era has emerged during the past decade in our basic understanding of human genomes and our technical capacity to detect and characterize genetic variation. The application of modern genetic and genomic tools is revealing revolutionary insights into disease mechanisms for a multitude of complex diseases. Importantly, the comprehensive and unbiased nature of genome-wide screens removes the severe limitations imposed by employing more traditional hypothesis-driven candidate gene studies as the only approach to genetic discovery. Using genome-wide association (GWA) study approaches, tremendous progress has recently been made in identifying dozens of genetic loci that confer risk to autoimmune phenotypes, including systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), multiple sclerosis (MS), and CrohnÕs disease. These successes provide unequivocal evidence that modern genetic studies using state-of-the-art tools can lead to novel and important insights into disease pathogenesis.

In contrast, the genetic factors associated with SS are virtually unexplored. Large-scale studies in SS are in the very earliest stages of planning and implementation. Despite the current lack of comprehensive genetic studies in SS, collective evidence from mouse models, family studies, candidate gene studies, and genetic Þndings in

related diseases (e.g., SLE and RA) support an underlying etiology with complex genetic architecture. In this chapter, we review the existing evidence to support genetic contributions to SS, summarize the evidence for the few candidate genes studied to date, and brießy describe potential environmental inßuences that may interact with genetic risk loci to promote development of disease. We also discuss emerging genomic and proteomic data that provide useful insight into the underlying signaling pathways that are dysregulated in SS.

8.2Evidence Supporting a Genetic Component in SS

Twin studies and family-based studies are often used as a Þrst step toward estimating the relative inßuence of genetic versus environmental inßuences on disease. Studies demonstrating increased concordance rates of disease among monozygotic twins and familial aggregation showing that a phenotype clusters in families at rates above the population prevalence are often taken as evidence to support a genetic etiology. Such reports in SS are extremely limited. A few case reports of twins with SS have been published, but reliable twin concordance rates have not been estimated [1Ð4]. ScoÞeld et al. reported a case of monozygotic twins with SjšgrenÕs syndrome who both had increased serum levels of anti-60-kDa Ro/SS-A autoantibodies [4]. A set of adolescent dizygotic twins, both with shared diagnosis of primary SS (pSS), were described in

2005 by Houghton et al. [3]. Interestingly, one of the two sisters presented with pulmonary symptoms, uncommon in pediatric pSS. Using SLE and RA as closely related traits to estimate the potential genetic contribution to SS, if we may use SLE and RA to make a crude guess, then twin concordance could be expected to be between those of RA (15%) and SLE (25%), with a female sibling or fraternal twin rate of 2Ð4% and estimated odds of female sibling concordance (λ(lambda)s) between 8 and 20.

Several families consisting of multiple individuals with SS have been described [5Ð10]. SS patient family histories commonly include relatives with other autoimmune diseases (30Ð35%) and most often include SS (12%), autoimmune thyroid disease (AITD, 14%), RA (14%), and SLE (5Ð10%) [8, 11]. Multiple sclerosis has also been shown to cluster with SS in families [12]. In a large family study of SLE, 8 out of 60 members were found to share a diagnosis of SLE. Among individuals with SLE, all shared positive antinuclear autoantibodies (ANAs), six shared pleuritis and malar rash, Þve reported photosensitivity, and four shared nephritis. Of the 51 relatives who contributed samples and for which results were obtained, 29% had autoantibodies and 18% had autoimmune disease including 1 with SS [10]. These studies provide supportive evidence that underlying disease mechanisms among related autoimmune diseases are likely to be shared. This is also intuitively expected considering that so many immunologic, immunogenetic, and clinical features of SS are found in nearly every other autoimmune rheumatic disease.

Additional support for a genetic component to SS has been obtained from mouse models. Approximately 20 models have been reported and are reviewed in detail elsewhere [13]. At present, no individual mouse model fully represents the majority of key disease manifestations of human disease. However, most models available develop sialadenitis and/or dacryoadenitis, so these models do constitute valuable tools for evaluating initiation of disease, various components of the overall SS phenotype such as autoantibody production, and the effects of immune manipulation. While genetic mapping in mouse models of SS is

far from complete, several genes have been identiÞed. For example, the NFS/sld mouse develops sialadenitis that is characterized by inßammatory lesions containing CD3+ and CD4+ cells with few CD8+ and B cells. The mice carry an autosomal recessive gene, sld, and autoimmunity appears to be driven by reactivity against the cytoskeletal protein α(alpha)-fodrin [14, 15]. However, no anti-Ro/SS-A or anti-La/SS-B is detected in the NFS/sld model [16]. Another model, the aly/aly mouse, carries an autosomal recessive alymphoplasia (aly) mutation mapped to a gene that codes for an NF-κ(kappa)B-inducing kinase [17]. CD4+ T cells inÞltrate the lacrimal and salivary glands at 3 months, but no autoantibodies against nuclear elements or salivary gland have been identiÞed [18]. Another model includes the Id3 knockout in which T-cell receptor-mediated thymic selection at the time of T-cell development is disrupted. Mice deÞcient in Id3 develop anti-Ro/SS-A and anti-La/SS-B antibodies, dry eyes and mouth, and experience lymphocyte inÞltration in lacrimal and salivary glands [19]. Recovery of salivary function and improvements of histopathology were observed following treatment of a CD20 monoclonal antibody that depleted B lymphocytes [20]. Perhaps the Id3 knockout model will prove to be a useful animal model to guide immunotherapy development in pSS patients.

As with the majority of SS mouse models, dissection of the genetic loci that drives lymphocytic inÞltration, aberrant cytokine production, development of autoantibodies, and glandular dysfunction will provide important tools for understanding complex pathogenic mechanisms in human disease. Likewise, identiÞcation of causal genes in humans will be of extraordinary beneÞt for fully informing the development of mouse models that more accurately represents human disease.

8.3Technological Influences on Gene Discovery

Traditional scientiÞc inquiry is hypothesisdriven. From a genetics perspective, the kinds of studies considered standard just a few years

ago included genotyping of one or a few variants within a given candidate gene for a few hundred subjects. To provide perspective on how increased technical capacity for genotyping has led to the remarkable success of discovery-based approaches in disease gene mapping, we summarize key features of the human genome and describe important advances in our understanding of variation.

The human genome contains approximately 3 billion base pairs of genetic sequence. Approximately 21,000 genes are encoded, the majority of which give rise to multiple messenger RNA (mRNA) transcripts and protein isoforms through processes such as alternative splicing or post-translational modiÞcations. Naturally occurring genetic variation in the human genome is abundant, dispersed throughout the genome, and includes single-nucleotide polymorphisms (SNPs), variable number short tandem repeats (VNTRs), insertion/deletions (indels), and gene copy number variation (CNV). Across all classes of variation type, approximately 90% of sites that are polymorphic between any two copies of the human genome are SNPs [21].

Within the human genome, approximately 90% of heterozygosity is attributable to some 10Ð 15 million variants (primarily SNPs) considered to be common as deÞned by population allele frequencies >1%. The remaining 10% are considered rare or even absent (i.e., monomorphic) in certain populations [22, 23]. The large contribution of common variation to human heterogeneity is the result of the shared ancestry and population history of human populations with important implications for disease gene mapping. The majority of genetic variation arose before the dramatic expansion and dispersal of the African population 10,000Ð40,000 years ago and is shared by all humans.

This shared ancestry of humans explains the extensive correlation among nearby chromosomal variants, termed linkage disequilibrium (LD) or haplotypes. Studies have shown that throughout much of the genome, a high degree of ancestral LD is observed (termed Òhaplotype blocksÓ), with only 3Ð5 common haplotypes accounting for90% of the genetic variation observed at any

given region [21, 24]. Disease variants that were deleterious during evolution (such as mutations that cause early-onset severe diseases) are typically rare, due to selection. Conversely, disease variants that act after reproduction may have been neutral or subject to balancing selection (e.g., sickle cell and malaria). In such cases, the genetic variation underlying disease may be common.

An important implication of the patterns of variation in the genome for association studies is that a relatively small number of variants can often be informative surrogates for nearby variants and essentially ÒtagÓ the common variation within a large interval. Thus, not all variation needs to be genotyped in order to detect a genetic association effect. Indeed, genotyping of about 1% ( 1 million) of all SNPs can ÒcaptureÓ the great majority of the association effects originating from the 10 million variants present in the human genome. Genotyping technologies have been built around selecting the most informative subset of SNPs based on these patterns of LD for screening the genome. On the other hand, not every genetic association detected with the screening SNPs in a genome scan means the true disease causal variant has been genotyped. A screening SNP may simply be in LD with the nearby true causal variant and serving as an informative marker. Additional genotyping (ÒÞne mappingÓ) and sequencing are nearly always required to more precisely determine the true causal variant detected in association studies.

A rapid and unprecedented swell of new genetic discoveries has been fostered by the development of affordable, high-throughput genotyping technology. Microarray-based platforms that provide technical capacity for genome-wide association (GWA) studies interrogating between 300,000 and 500,000 SNPs have been exceptionally fruitful for prostate cancer, breast cancer, and autoimmune diseases such as T1D, RA, and SLE [25Ð30]. For example, we now recognize over 30 genes/loci in which robust genetic association with SLE is established [31]. In CrohnÕs disease, association with more than 30 genes/loci has also been identiÞed [32]. Ongoing studies are expected to continue to reveal numerous additional genes that contribute

Fig. 8.1 Components of genomic studies amenable to high-throughput technologies. Genetic studies using genome-wide association approaches to characterize variation in DNA can test >1 million variants using microarrays. Gene expression studies also employ microarray technology to measure levels of mRNA transcripts for essentially every gene, deÞned as the transcriptome. Proteomic studies use large-scale methods such as mass spectrometry to characterize structure and function of the entire set of proteins

to SLE and other autoimmune diseases related to SS.

More recently, further increases in throughput for genotyping technology allow simultaneous interrogation of over 1 million genetic variants in a single experiment and often within an economic realm of feasibility to include several hundreds, if not thousands, of subjects. Advances in genotyping technologies that allow high throughput, unbiased interrogation of essentially all genes are complemented by parallel technologies that interrogate RNA transcripts or proteins at the genome-wide level using costeffective approaches (Fig. 8.1). These tremendously more comprehensive, discovery-based approaches allow detection of novel genes, transcripts, and proteins that are associated with disease but have never before been hypothesized to have a role in disease.

8.4Lessons from SLE and Other Autoimmune Diseases

Numerous genetic loci that are associated with more than one autoimmune disease have been identiÞed in recent years, conÞrming a basis for shared etiology. Associations of certain human leukocyte antigen (HLA) loci, such as HLA-DR variants (alleles), have been extensively reported in SS, SLE, RA, and others [33]. The list of non-HLA genes implicated in multiple autoimmune diseases also continues to grow. Examples include association of CTLA-4 with AITD; type 1

diabetes (T1D); celiac disease; WegenerÕs granulomatosis; SLE; vitiligo; AddisonÕs disease, and RA [34Ð41]; PD-1 with RA, T1D, and SLE [42]; and PTPN22 with SLE, RA, and T1D; GravesÕs disease and HashimotoÕs thyroiditis [43Ð48]; CTLA-4 (cytotoxic T-lymphocyte antigen 4), PTPN22 (protein tyrosine phosphatase, non-receptor type 22), and PD-1 (programmed death 1) are all expressed in lymphocytes and play important regulatory roles during immune responses. Interestingly, IRF5 (interferon regulatory factor 5) and STAT4 (signal transducer and activator of transcription 4) are genes strongly associated with SLE and implicated in multiple autoimmune diseases including recent data suggesting association with pSS [49]. Both IRF5 and STAT4 regulate innate immune responses involving interferon pathway signaling. Murine studies are also consistent with models of multigenic inheritance, and numerous susceptibility loci have been identiÞed that are shared across different autoimmune mouse models for SS as well as for other autoimmune diseases [50].

Of the diseases most commonly thought to overlap with SS, SLE has received the most attention in comparison studies and provides a model of the expectations for genetic studies in SS. Genetic studies in SLE are perhaps the most advanced and successful in terms of overall progress toward understanding etiology, largely due to recent GWA studies [31, 51]. Before 2007, there were nine established SLE susceptibility genes identiÞed through candidate gene studies or family-based linkage approaches. Among the more than 30 genetic associations now recognized in SLE, 3 major biological themes are conÞrmed by the speciÞc genes involved: immune complex processing, Toll-like receptor (TLR) function and type-I interferon (IFN) signaling, and signal transduction in lymphocytes (reviewed in Harley et al. [51] and Moser et al. [31]). Multiple genes from each of these three pathways are associated with SLE. Genes involved in immune complex processing include Fc receptors (e.g., FcGR2A, FcGR3B, FcGR3A), complement components (e.g., C4A, C4B, C2, C1q), and others (e.g., CRP, ITGAM). Genes involved in TLR/IFN pathways include IRF5,