Analysis of genetic interrelationship among HLA-associated diseases.

We have developed a method to study the genetic relationship between any two HLA-associated diseases. We have considered the following hypotheses: (1) both diseases are caused by a common allele; (2) different alleles at the same locus predispose to the two diseases; (3) one disease is predisposed by two alleles, one of which can also lead to the second disease; and (4) different HLA-linked loci are involved in the etiology of each disease. For each hypothesis, we have derived the expected HLA haplotype-sharing distribution in sib pairs who are affected with two diseases. The comparison of the expectations indicate that, in many cases, the alternate hypotheses can be distinguished, if the sample size is appropriately large. The knowledge of the mode of inheritance of each disease is not usually necessary; however, it can greatly increase the power of the test. Analyses of data on pairwise combinations of rheumatoid arthritis (RA), autoimmune thyroid disease (ATD), and insulin-dependent (type I) diabetes mellitus (IDDM) suggest that (a) IDDM is predisposed by two HLA-linked alleles, one of which also predisposes to ATD, (b) one of the IDDM alleles also confers susceptibility to RA, and (c) although the HLA-linked susceptibilities to RA and ATD appear to be primarily due to distinct alleles, the ATD allele may also have a minor role in predisposition to RA.     

Genetic interrelationship between insulin-dependent diabetes mellitus, the autoimmune thyroid diseases, and rheumatoid arthritis.

To investigate the possible coinheritance of autoimmune diseases that are associated with the same HLA antigen, we studied 70 families in which at least two siblings had either type I diabetes mellitus (IDDM), autoimmune thyroid disease (ATD), rheumatoid arthritis (RA), or a combination of these diseases. HLA-A, B, and C typing was performed on all affected sibs in one generation or more. First, we estimated by sib-pair analysis the disease allele frequency (pD) and the mode of inheritance for each disease. According to the method of ascertainment entered into the analysis, the pD for ATD ranged from .120 to .180, for an additive (dominant) mode of inheritance. For RA, the pD ranged from .254 to .341, also for additive inheritance, although recessive inheritance could not be excluded. For IDDM, the pD ranged from .336 to .337 for recessive inheritance; additive inheritance was rejected. Second, we examined the distribution of shared parental haplotypes in pairs of siblings that were discordant for their autoimmune diseases. The results suggested that the same haplotype may predispose to both IDDM and ATD, or IDDM and RA, but not to both RA and ATD. Analysis of pedigrees supported this hypothesis. In 16 families typed for HLA-DR also, the haplotype predisposing to both IDDM and ATD was assigned from pedigree information to DR3 (44%), DR4 (39%), or DR5, DR6, or DR7 (5.5% each). In some families, these haplotypes segregated over several generations with ATD only (either clinical or subclinical), suggesting that in such families, ATD was a marker for a susceptibility to IDDM. In several families, an IDDM haplotype segregated with RA but not with ATD. This suggests that ATD- and RA-associated susceptibilities to IDDM may be biologically different and thus independently increase the risk of IDDM.     

Quantifying Missing Heritability at Known GWAS Loci

Recent work has shown that much of the missing heritability of complex traits can be resolved by estimates of heritability explained by all genotyped SNPs. However, it is currently unknown how much heritability is missing due to poor tagging or additional causal variants at known GWAS loci. Here, we use variance components to quantify the heritability explained by all SNPs at known GWAS loci in nine diseases from WTCCC1 and WTCCC2. After accounting for expectation, we observed all SNPs at known GWAS loci to explain more heritability than GWAS-associated SNPs on average (). For some diseases, this increase was individually significant: for Multiple Sclerosis (MS) () and for Crohn''s Disease (CD) (); all analyses of autoimmune diseases excluded the well-studied MHC region. Additionally, we found that GWAS loci from other related traits also explained significant heritability. The union of all autoimmune disease loci explained more MS heritability than known MS SNPs () and more CD heritability than known CD SNPs (), with an analogous increase for all autoimmune diseases analyzed. We also observed significant increases in an analysis of Rheumatoid Arthritis (RA) samples typed on ImmunoChip, with more heritability from all SNPs at GWAS loci () and more heritability from all autoimmune disease loci () compared to known RA SNPs (including those identified in this cohort). Our methods adjust for LD between SNPs, which can bias standard estimates of heritability from SNPs even if all causal variants are typed. By comparing adjusted estimates, we hypothesize that the genome-wide distribution of causal variants is enriched for low-frequency alleles, but that causal variants at known GWAS loci are skewed towards common alleles. These findings have important ramifications for fine-mapping study design and our understanding of complex disease architecture.     

Overlapping genetic susceptibility variants between three autoimmune disorders: rheumatoid arthritis, type 1 diabetes and coeliac disease

Introduction

Genome wide association studies, replicated by numerous well powered validation studies, have revealed a large number of loci likely to play a role in susceptibility to many multifactorial diseases. It is now well established that some of these loci are shared between diseases with similar aetiology. For example, a number of autoimmune diseases have been associated with variants in the PTPN22, TNFAIP3 and CTLA4 genes. Here we have attempted to define overlapping genetic variants between rheumatoid arthritis (RA), type 1 diabetes (T1D) and coeliac disease (CeD).

Methods

We selected eight SNPs previously identified as being associated with CeD and six T1D-associated SNPs for validation in a sample of 3,962 RA patients and 3,531 controls. Genotyping was performed using the Sequenom MassArray platform and comparison of genotype and allele frequencies between cases and controls was undertaken. A trend test P-value < 0.004 was regarded as significant.

Results

We found statistically significant evidence for association of the TAGAP locus with RA (P = 5.0 × 10^-4). A marker at one other locus, C1QTNF6, previously associated with T1D, showed nominal association with RA in the current study but did not remain statistically significant at the corrected threshold.

Conclusions

In exploring the overlap between T1D, CeD and RA, there is strong evidence that variation within the TAGAP gene is associated with all three autoimmune diseases. Interestingly a number of loci appear to be specific to one of the three diseases currently studied suggesting that they may play a role in determining the particular autoimmune phenotype at presentation.     

Pathway Analysis of GWAS Provides New Insights into Genetic Susceptibility to 3 Inflammatory Diseases

Although the introduction of genome-wide association studies (GWAS) have greatly increased the number of genes associated with common diseases, only a small proportion of the predicted genetic contribution has so far been elucidated. Studying the cumulative variation of polymorphisms in multiple genes acting in functional pathways may provide a complementary approach to the more common single SNP association approach in understanding genetic determinants of common disease. We developed a novel pathway-based method to assess the combined contribution of multiple genetic variants acting within canonical biological pathways and applied it to data from 14,000 UK individuals with 7 common diseases. We tested inflammatory pathways for association with Crohn''s disease (CD), rheumatoid arthritis (RA) and type 1 diabetes (T1D) with 4 non-inflammatory diseases as controls. Using a variable selection algorithm, we identified variants responsible for the pathway association and evaluated their use for disease prediction using a 10 fold cross-validation framework in order to calculate out-of-sample area under the Receiver Operating Curve (AUC). The generalisability of these predictive models was tested on an independent birth cohort from Northern Finland. Multiple canonical inflammatory pathways showed highly significant associations (p 10⁻³–10⁻²⁰) with CD, T1D and RA. Variable selection identified on average a set of 205 SNPs (149 genes) for T1D, 350 SNPs (189 genes) for RA and 493 SNPs (277 genes) for CD. The pattern of polymorphisms at these SNPS were found to be highly predictive of T1D (91% AUC) and RA (85% AUC), and weakly predictive of CD (60% AUC). The predictive ability of the T1D model (without any parameter refitting) had good predictive ability (79% AUC) in the Finnish cohort. Our analysis suggests that genetic contribution to common inflammatory diseases operates through multiple genes interacting in functional pathways.     

Pervasive sharing of genetic effects in autoimmune disease

Genome-wide association (GWA) studies have identified numerous, replicable, genetic associations between common single nucleotide polymorphisms (SNPs) and risk of common autoimmune and inflammatory (immune-mediated) diseases, some of which are shared between two diseases. Along with epidemiological and clinical evidence, this suggests that some genetic risk factors may be shared across diseases-as is the case with alleles in the Major Histocompatibility Locus. In this work we evaluate the extent of this sharing for 107 immune disease-risk SNPs in seven diseases: celiac disease, Crohn's disease, multiple sclerosis, psoriasis, rheumatoid arthritis, systemic lupus erythematosus, and type 1 diabetes. We have developed a novel statistic for Cross Phenotype Meta-Analysis (CPMA) which detects association of a SNP to multiple, but not necessarily all, phenotypes. With it, we find evidence that 47/107 (44%) immune-mediated disease risk SNPs are associated to multiple-but not all-immune-mediated diseases (SNP-wise P(CPMA)<0.01). We also show that distinct groups of interacting proteins are encoded near SNPs which predispose to the same subsets of diseases; we propose these as the mechanistic basis of shared disease risk. We are thus able to leverage genetic data across diseases to construct biological hypotheses about the underlying mechanism of pathogenesis.     

Detecting shared pathways linked to rheumatoid arthritis with other autoimmune diseases in a in silico analysis

Pathway-based analysis approach has exploded in use during the last several years. It is successful in recognizing additional biological insight of disease and finding groupings of risk genes that represent disease developing processes. Therefore, shared pathways, with pleiotropic effects, are important for understanding similar pathogenesis and indicating the common genetic origin of certain diseases. Here, we present a pathway analysis to reveal the potential disease associations between RA and three potential RA-related autoimmune diseases: psoriasis, diabetes mellitus, type 1 (T1D) and systemic lupus erythematosus (SLE). First, a comprehensive knowledge mining of public databases is performed to discover risk genes associated with RA, T1D, SLE and psoriasis; then by enrichment test of these genes, disease-related risk pathways are detected to recognize the pathways common for RA and three other diseases. Finally, the underlying disease associations are evaluated with the association rules mining method. In total, we identify multiple RA risk pathways with significant pleiotropic effects, the most unsurprising of which are the immunology related pathways. Meanwhile for the first time we highlight the involvement of the viral myocarditis pathway related to cardiovascular disease (CVD) in autoimmune diseases such as RA, psoriasis, T1D and SLE. Further Association rule mining results validate the strong association between RA and T1D and RA and SLE. It is clear that pleiotropy is a common property of pathways associated with disease traits. We provide novel pathway associations among RA and three autoimmune diseases. These results ascertain that there are shared genetic risk profiles that predispose individuals to autoimmune diseases.     

Selecting SNPs for association studies based on population frequencies: a novel interactive tool and its application to polygenic diseases

Common complex polygenic diseases as autoimmune diseases have not been completely understood on a molecular level. While many genes are known to be involved in the pathways responsible for the phenotype, explicit causes for the susceptibility of the disease remain to be elucidated. The susceptibility to disease is thought to be the result of genetic epistatic interactions between common polymorphic genes. This polymorphism is mostly caused by single nucleotide polymorphisms (SNPs). Human subpopulations are known to differ in the susceptibility to the diseases and generally in the distribution of single nucleotide polymorphisms. The here presented approach retrieves SNPs with the most divergent frequencies for selected human subpopulations to help defining properties for the experimental verification of SNPs within defined regions. A web-accessible program implementing this approach was evaluated for multiple sclerosis (MS), a common human polygenic disease. A link to a summary of data from "The SNP Consortium" (TSC) with sex-dependencies of SNPs is available. Associations of SNPs to genes, genetic markers and chromosomal loci are retrieved from the Ensembl project. This tool is recommended to be used in conjunction with microarray analyses or marker association studies that link genes or chromosomal loci to particular diseases.     

Analysis of families in the multiple autoimmune disease genetics consortium (MADGC) collection: the PTPN22 620W allele associates with multiple autoimmune phenotypes

Autoimmune disorders constitute a diverse group of phenotypes with overlapping features and a tendency toward familial aggregation. It is likely that common underlying genes are involved in these disorders. Until very recently, no specific alleles--aside from a few common human leukocyte antigen class II genes--had been identified that clearly associate with multiple different autoimmune diseases. In this study, we describe a unique collection of 265 multiplex families assembled by the Multiple Autoimmune Disease Genetics Consortium (MADGC). At least two of nine "core" autoimmune diseases are present in each of these families. These core diseases include rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), type 1 diabetes (T1D), multiple sclerosis (MS), autoimmune thyroid disease (Hashimoto thyroiditis or Graves disease), juvenile RA, inflammatory bowel disease (Crohn disease or ulcerative colitis), psoriasis, and primary Sjogren syndrome. We report that a recently described functional single-nucleotide polymorphism (rs2476601, encoding R620W) in the intracellular tyrosine phosphatase (PTPN22) confers risk of four separate autoimmune phenotypes in these families: T1D, RA, SLE, and Hashimoto thyroiditis. MS did not show association with the PTPN22 risk allele. These findings suggest a common underlying etiologic pathway for some, but not all, autoimmune disorders, and they suggest that MS may have a pathogenesis that is distinct from RA, SLE, and T1D. DNA and clinical data for the MADGC families are available to the scientific community; these data will provide a valuable resource for the dissection of the complex genetic factors that underlie the various autoimmune phenotypes.     

The Immunogenetic Architecture of Autoimmune Disease

The development of most autoimmune diseases includes a strong heritable component. This genetic contribution to disease ranges from simple Mendelian inheritance of causative alleles to the complex interactions of multiple weak loci influencing risk. The genetic variants responsible for disease are being discovered through a range of strategies from linkage studies to genome-wide association studies. Despite the rapid advances in genetic analysis, substantial components of the heritable risk remain unexplained, either owing to the contribution of an as-yet unidentified, “hidden,” component of risk, or through the underappreciated effects of known risk loci. Surprisingly, despite the variation in genetic control, a great deal of conservation appears in the biological processes influenced by risk alleles, with several key immunological pathways being modified in autoimmune diseases covering a broad spectrum of clinical manifestations. The primary translational potential of this knowledge is in the rational design of new therapeutics to exploit the role of these key pathways in influencing disease. With significant further advances in understanding the genetic risk factors and their biological mechanisms, the possibility of genetically tailored (or “personalized”) therapy may be realized.Autoimmune diseases affect a significant proportion of the population, with >4% of the European population suffering from one or more of these disorders (Vyse and Todd 1996; Cooper et al. 2009; Eaton et al. 2010). Although all autoimmune diseases share similarities in the basic immunological mechanisms, in other aspects, such as clinical manifestation and age of onset, individual diseases vary widely. A few rare autoimmune diseases with Mendelian inheritance patterns within families occur including APS-1 (autoimmune polyendocrine syndrome type 1), IPEX (immunodysregulation, polyendocrinopathy, and enteropathy X-linked) syndrome, and ALPS (autoimmune lymphoproliferative syndrome). Most autoimmune diseases are, however, multifactorial in nature, with susceptibility controlled by multiple genetic and environmental factors.The genetic component of more common autoimmune diseases can be calculated in several different manners, including the sibling recurrence risk (λ_s) and the twin concordance rate. The sibling recurrence risk is the ratio of the lifetime risk in siblings of patients to the lifetime population risk, whereas the twin concordance rate measures the proportion of the siblings of affected twins that are also affected. Most common autoimmune diseases, such as multiple sclerosis (MS), type 1 diabetes (T1D), rheumatoid arthritis (RA), and inflammatory bowel disease (IBD) are characterized by a sibling recurrence risk between 6 and 20 (Vyse and Todd 1996), and concordance rates of 25%–50% in monozygotic twins and 2%–12% in dizygotic twins (Cooper et al. 1999). A substantial proportion of relatives may also have subclinical evidence of autoimmunity without developing clinically overt disease. For example, 19% of healthy siblings of MS patients show antibody production in the cerebrospinal fluid, compared to 4% of unrelated healthy controls (Haghighi et al. 2000), whereas 4% of healthy first-degree relatives display lesions that are indistinguishable from those seen in patients and are not seen in unrelated healthy controls (De Stefano et al. 2006). Furthermore, comorbidity with the development of several autoimmune diseases in the same patient and clustering of several autoimmune diseases within families above what is expected by chance appear common (Cooper et al. 2009; Zhernakova et al. 2009). Together these data show a strong genetic component to autoimmune disease development.     

A systematic method for mapping multiple loci: an application to construct a genetic network for rheumatoid arthritis

The advent of high-throughput single nucleotide polymorphisms (SNPs) omics technologies has brought tremendous genetic data. Systematic evaluation of the genome-wide SNPs is expected to provide breakthroughs in the understanding of complex diseases. In this study, we developed a new systematic method for mapping multiple loci and applied the proposed method to construct a genetic network for rheumatoid arthritis (RA) via analysis of 746 multiplex families genotyped with more than five thousands of genome-wide SNPs. We successfully identified 41 significant SNPs relevant to RA, 25 associated genes and a number of important SNP-SNP interactions (SNP patterns). Many findings (loci, genes and interactions) have experimental support from previous studies while novel findings may define unknown genetic pathways for this complex disease. Finally, we constructed a genetic network by integrating the results from this analysis with the rapidly accumulated knowledge in biomedical domains, which gave us a more detailed insight onto the RA etiology. The results suggest that the proposed systematic method is powerful when applied to genome-wide association studies. Integrating the analysis of high-throughput SNP data with knowledge-based SNP functional annotation offers a promising way to reversely engineer the underlying genetic networks for complex human diseases.     

Mining functional gene modules linked with rheumatoid arthritis using a SNP-SNP network

The identification of functional gene modules that are derived from integration of information from different types of networks is a powerful strategy for interpreting the etiology of complex diseases such as rheumatoid arthritis (RA). Genetic variants are known to increase the risk of developing RA. Here, a novel method, the construction of a genetic network, was used to mine functional gene modules linked with RA. A polymorphism interaction analy-sis (PIA) algorithm was used to obtain cooperating single nucleotide polymorphisms (SNPs) that contribute to RA disease. The acquired SNP pairs were used to construct a SNP-SNP network. Sub-networks defined by hub SNPs were then extracted and turned into gene modules by mapping SNPs to genes using dbSNP database. We per-formed Gene Ontology (GO) analysis on each gene module, and some GO terms enriched in the gene modules can be used to investigate clustered gene function for better understanding RA pathogenesis. This method was applied to the Genetic Analysis Workshop 15 (GAW 15) RA dataset. The results show that genes involved in func-tional gene modules, such as CD160 (rs744877) and RUNX1 (rs2051179), are especially relevant to RA, which is supported by previous reports. Furthermore, the 43 SNPs involved in the identified gene modules were found to be the best classifiers when used as variables for sample classification.     

Multiplex Genotyping of Cytokine Gene SNPs Using Fluorescence Bead Array

Single nucleotide polymorphisms (SNPs) of genes that affect cytokine production and function are known to influence the susceptibility and progression of immune-related conditions such as infection, autoimmune diseases, transplantation, and cancer. We established a multiplex genotyping method to analyze the SNPs of cytokine genes by combining the multiplex PCR and bead array platform. Thirteen cytokine gene regions, including 20 SNPs, were amplified, and allele-specific primer extension was performed in a single tube. High-quality allele-specific primers were selected for signals greater than 1000 median fluorescence intensity (MFI) for positive alleles, and less than 500 MFI for negative alleles. To select and improve the extension primers, modifications for the reverse direction, length or refractory were performed. 24 primers in the forward or reverse direction step and 12 primers in length or refractory modifications were selected and showed high concordance with results by nucleotide sequencing. Among the 13 candidate cytokine genes, the SNPs of 12 cytokine genes, including IL-1α, IL-1R, IL-1RA, IL-1β, IL-2, IL-4, IL-4Rα, IL-6, IL-10, IL-12, TGF-β1, and TNF-α, were successfully defined with the selected allele-specific primers in healthy Korean subjects. Our genotyping system provides a fast and accurate detection for SNPs of multiple cytokine genes to investigate their association with immune-related diseases and transplantation outcomes.     

Single-nucleotide polymorphisms and haplotype LD analysis of the 29-kb IGF2 region on chromosome 11p15.5 in the Korean population

OBJECTIVE: We investigated sequence variations of the 29-kb insulin-like growth factor 2 (IGF2) region in human chromosome region 11p15.5 in the Korean population. This region consists of IGF2, insulin-like growth factor 2 antisense (IGF2AS), and the insulin gene, all important candidate genes for various diseases, including cancer, obesity, diabetes, and coronary disease. While single nucleotide polymorphisms (SNPs) have been identified for this region and used in association studies, ethnic differences in genetic variation at this site have not been addressed. To date, SNPs for the entire 29-kb region in the Korean population have not been reported. METHODS: We surveyed a population of 108 Koreans for SNPs in the 29-kb IGF2 region. RESULTS: We identified 62 SNPs, consisting of 6 SNPs in the promoter region, 17 in the untranslated region, 19 in introns, and 20 in the intergenic region. We also analyzed linkage disequilibrium (LD) patterns and haplotypes using 36 high-frequency (> 5%)SNPs and found a well-defined LD block spanning about 13 kb that includes 8 kb of the IGF2AS gene, with two hot-spot regions flanking the LD block. CONCLUSION: These SNPs may be useful as genetic markers in disease association studies in the Korean population.     

PTPN22 genetic variation: evidence for multiple variants associated with rheumatoid arthritis

The minor allele of the R620W missense single-nucleotide polymorphism (SNP) (rs2476601) in the hematopoietic-specific protein tyrosine phosphatase gene, PTPN22, has been associated with multiple autoimmune diseases, including rheumatoid arthritis (RA). These genetic data, combined with biochemical evidence that this SNP affects PTPN22 function, suggest that this phosphatase is a key regulator of autoimmunity. To determine whether other genetic variants in PTPN22 contribute to the development of RA, we sequenced the coding regions of this gene in 48 white North American patients with RA and identified 15 previously unreported SNPs, including 2 coding SNPs in the catalytic domain. We then genotyped 37 SNPs in or near PTPN22 in 475 patients with RA and 475 individually matched controls (sample set 1) and selected a subset of markers for replication in an additional 661 patients with RA and 1,322 individually matched controls (sample set 2). Analyses of these results predict 10 common (frequency >1%) PTPN22 haplotypes in white North Americans. The sole haplotype found to carry the previously identified W620 risk allele was strongly associated with disease in both sample sets, whereas another haplotype, identical at all other SNPs but carrying the R620 allele, showed no association. R620W, however, does not fully explain the association between PTPN22 and RA, since significant differences between cases and controls persisted in both sample sets after the haplotype data were stratified by R620W. Additional analyses identified two SNPs on a single common haplotype that are associated with RA independent of R620W, suggesting that R620W and at least one additional variant in the PTPN22 gene region influence RA susceptibility.     

Discovering variation of secondary metabolite diversity and its relationship with disease resistance in Cornus florida L.

Understanding intraspecific relationships between genetic and functional diversity is a major goal in the field of evolutionary biology and is important for conserving biodiversity. Linking intraspecific molecular patterns of plants to ecological pressures and trait variation remains difficult due to environment‐driven plasticity. Next‐generation sequencing, untargeted liquid chromatography–mass spectrometry (LC‐MS) profiling, and interdisciplinary approaches integrating population genomics, metabolomics, and community ecology permit novel strategies to tackle this problem. We analyzed six natural populations of the disease‐threatened Cornus florida L. from distinct ecological regions using genotype‐by‐sequencing markers and LC‐MS‐based untargeted metabolite profiling. We tested the hypothesis that higher genetic diversity in C. florida yielded higher chemical diversity and less disease susceptibility (screening hypothesis), and we also determined whether genetically similar subpopulations were similar in chemical composition. Most importantly, we identified metabolites that were associated with candidate loci or were predictive biomarkers of healthy or diseased plants after controlling for environment. Subpopulation clustering patterns based on genetic or chemical distances were largely congruent. While differences in genetic diversity were small among subpopulations, we did observe notable similarities in patterns between subpopulation averages of rarefied‐allelic and chemical richness. More specifically, we found that the most abundant compound of a correlated group of putative terpenoid glycosides and derivatives was correlated with tree health when considering chemodiversity. Random forest biomarker and genomewide association tests suggested that this putative iridoid glucoside and other closely associated chemical features were correlated to SNPs under selection.     

Novel associations for hypothyroidism include known autoimmune risk loci

Hypothyroidism is the most common thyroid disorder, affecting about 5% of the general population. Here we present the current largest genome-wide association study of hypothyroidism, in 3,736 cases and 35,546 controls. Hypothyroidism was assessed via web-based questionnaires. We identify five genome-wide significant associations, three of which are well known to be involved in a large spectrum of autoimmune diseases: rs6679677 near PTPN22, rs3184504 in SH2B3, and rs2517532 in the HLA class I region (p-values 2.8·10(-13), 2.6·10(-12), and 1.3·10(-8), respectively). We also report associations with rs4915077 near VAV3 (p-value 7.5·10(-10)) and rs925489 near FOXE1 (p value 2.4·10(-19)). VAV3 is involved in immune function, and FOXE1 and PTPN22 have previously been associated with hypothyroidism. Although the HLA class I region and SH2B3 have previously been linked with a number of autoimmune diseases, this is the first report of their association with thyroid disease. The VAV3 association is also novel. We also show suggestive evidence of association for hypothyroidism with a SNP in the HLA class II region (independent of the other HLA association) as well as SNPs in CAPZB, PDE8B, and CTLA4. CAPZB and PDE8B have been linked to TSH levels and CTLA4 to a variety of autoimmune diseases. These results suggest heterogeneity in the genetic etiology of hypothyroidism, implicating genes involved in both autoimmune disorders and thyroid function. Using a genetic risk profile score based on the top association from each of the five genome-wide significant regions in our study, the relative risk between the highest and lowest deciles of genetic risk is 2.0.     

Domain altering SNPs in the human proteome and their impact on signaling pathways

Single nucleotide polymorphisms (SNPs) constitute an important mode of genetic variations observed in the human genome. A small fraction of SNPs, about four thousand out of the ten million, has been associated with genetic disorders and complex diseases. The present study focuses on SNPs that fall on protein domains, 3D structures that facilitate connectivity of proteins in cell signaling and metabolic pathways. We scanned the human proteome using the PROSITE web tool and identified proteins with SNP containing domains. We showed that SNPs that fall on protein domains are highly statistically enriched among SNPs linked to hereditary disorders and complex diseases. Proteins whose domains are dramatically altered by the presence of an SNP are even more likely to be present among proteins linked to hereditary disorders. Proteins with domain-altering SNPs comprise highly connected nodes in cellular pathways such as the focal adhesion, the axon guidance pathway and the autoimmune disease pathways. Statistical enrichment of domain/motif signatures in interacting protein pairs indicates extensive loss of connectivity of cell signaling pathways due to domain-altering SNPs, potentially leading to hereditary disorders.     

Sex differences in autoimmune diseases

Women are more susceptible to a variety of autoimmune diseases including systemic lupus erythematosus (SLE), multiple sclerosis (MS), primary biliary cirrhosis, rheumatoid arthritis and Hashimoto's thyroiditis. This increased susceptibility in females compared to males is also present in animal models of autoimmune diseases such as spontaneous SLE in (NZBxNZW)F1 and NZM.2328 mice, experimental autoimmune encephalomyelitis (EAE) in SJL mice, thyroiditis, Sjogren's syndrome in MRL/Mp-lpr/lpr mice and diabetes in non-obese diabetic mice. Indeed, being female confers a greater risk of developing these diseases than any single genetic or environmental risk factor discovered to date. Understanding how the state of being female so profoundly affects autoimmune disease susceptibility would accomplish two major goals. First, it would lead to an insight into the major pathways of disease pathogenesis and, secondly, it would likely lead to novel treatments which would disrupt such pathways.     

Weighted Interaction SNP Hub (WISH) network method for building genetic networks for complex diseases and traits using whole genome genotype data

Background

High-throughput genotype (HTG) data has been used primarily in genome-wide association (GWA) studies; however, GWA results explain only a limited part of the complete genetic variation of traits. In systems genetics, network approaches have been shown to be able to identify pathways and their underlying causal genes to unravel the biological and genetic background of complex diseases and traits, e.g., the Weighted Gene Co-expression Network Analysis (WGCNA) method based on microarray gene expression data. The main objective of this study was to develop a scale-free weighted genetic interaction network method using whole genome HTG data in order to detect biologically relevant pathways and potential genetic biomarkers for complex diseases and traits.

Results

We developed the Weighted Interaction SNP Hub (WISH) network method that uses HTG data to detect genome-wide interactions between single nucleotide polymorphism (SNPs) and its relationship with complex traits. Data dimensionality reduction was achieved by selecting SNPs based on its: 1) degree of genome-wide significance and 2) degree of genetic variation in a population. Network construction was based on pairwise Pearson's correlation between SNP genotypes or the epistatic interaction effect between SNP pairs. To identify modules the Topological Overlap Measure (TOM) was calculated, reflecting the degree of overlap in shared neighbours between SNP pairs. Modules, clusters of highly interconnected SNPs, were defined using a tree-cutting algorithm on the SNP dendrogram created from the dissimilarity TOM (1-TOM). Modules were selected for functional annotation based on their association with the trait of interest, defined by the Genome-wide Module Association Test (GMAT). We successfully tested the established WISH network method using simulated and real SNP interaction data and GWA study results for carcass weight in a pig resource population; this resulted in detecting modules and key functional and biological pathways related to carcass weight.

Conclusions

We developed the WISH network method which is a novel 'systems genetics' approach to study genetic networks underlying complex trait variation. The WISH network method reduces data dimensionality and statistical complexity in associating genotypes with phenotypes in GWA studies and enables researchers to identify biologically relevant pathways and potential genetic biomarkers for any complex trait of interest.