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author:("seladin, MF")
1.  MHC associations with clinical and autoantibody manifestations in European SLE 
Genes and immunity  2014;15(4):210-217.
Systemic lupus erythematosus (SLE) is a clinically heterogeneous disease affecting multiple organ systems and characterized by autoantibody formation to nuclear components. Although genetic variation within the major histocompatibility complex (MHC) is associated with SLE, its role in the development of clinical manifestations and autoantibody production is not well defined. We conducted a meta-analysis of four independent European SLE case collections for associations between SLE sub-phenotypes and MHC single-nucleotide polymorphism genotypes, human leukocyte antigen (HLA) alleles and variant HLA amino acids. Of the 11 American College of Rheumatology criteria and 7 autoantibody sub-phenotypes examined, anti-Ro/SSA and anti-La/SSB antibody subsets exhibited the highest number and most statistically significant associations. HLA-DRB1*03:01 was significantly associated with both sub-phenotypes. We found evidence of associations independent of MHC class II variants in the anti-Ro subset alone. Conditional analyses showed that anti-Ro and anti-La subsets are independently associated with HLA-DRB1*0301, and that the HLA-DRB1*03:01 association with SLE is largely but not completely driven by the association of this allele with these sub-phenotypes. Our results provide strong evidence for a multilevel risk model for HLA-DRB1*03:01 in SLE, where the association with anti-Ro and anti-La antibody-positive SLE is much stronger than SLE without these autoantibodies.
PMCID: PMC4102853  PMID: 24598797
Sub-phenotype analysis; MHC; meta-analysis; genetics; systemic lupus erythematosus; Europeans
2.  Pathway-based analysis of primary biliary cirrhosis genome-wide association studies 
Genes and immunity  2013;14(3):179-186.
Genome-wide association studies (GWAS) have successfully identified several loci associated with primary biliary cirrhosis (PBC) risk. Pathway analysis complements conventional GWAS analysis. We applied the recently developed linear combination test for pathways to datasets drawn from independent PBC GWAS in Italian and Canadian subjects. Of the Kyoto Encyclopedia of Genes and Genomes and BioCarta pathways tested, 25 pathways in the Italian dataset (449 cases, 940 controls) and 26 pathways in the Canadian dataset (530 cases, 398 controls) were associated with PBC susceptibility (P < 0.05). After correcting for multiple comparisons, only the eight most significant pathways in the Italian dataset had FDR < 0.25 with tumor necrosis factor/stress-related signaling emerging as the top pathway (P = 7.38 × 10−4, FDR = 0.18). Two pathways, phosphatidylinositol signaling and hedgehog signaling, were replicated in both datasets (P < 0.05), and subjected to two additional complementary pathway tests. Both pathway signals remained significant in the Italian dataset on modified gene set enrichment analysis (P < 0.05). In both GWAS, variants nominally associated with PBC were significantly overrepresented in the phosphatidylinositol pathway (Fisher exact P < 0.05). These results point to established and novel pathway-level associations with inherited predisposition to PBC that on further independent replication and functional validation, may provide fresh insights into PBC etiology.
PMCID: PMC3780793  PMID: 23392275
linear combination test; phosphatidylinositol signaling; hedgehog signaling; autoimmune disease
3.  Supervised machine learning and logistic regression identifies novel epistatic risk factors with PTPN22 for rheumatoid arthritis 
Genes and immunity  2010;11(3):199-208.
Investigating genetic interactions (epistasis) has proven difficult despite the recent advances of both laboratory methods and statistical developments. With no ‘best’ statistical approach available, combining several analytical methods may be optimal for detecting epistatic interactions. Using a multi-stage analysis that incorporated supervised machine learning and methods of association testing, we investigated epistatic interactions with a well-established genetic factor (PTPN22 1858T) in a complex autoimmune disease (rheumatoid arthritis (RA)). Our analysis consisted of four principal stages: Stage I (data reduction)—identifying candidate chromosomal regions in 292 affected sibling pairs, by predicting PTPN22 concordance using multipoint identity-by-descent probabilities and a supervised machine learning algorithm (Random Forests); Stage II (extension analysis)—testing detailed genetic data within candidate chromosomal regions for epistasis with PTPN22 1858T in 677 cases and 750 controls using logistic regression; Stage III (replication analysis)—confirmation of epistatic interactions in 947 cases and 1756 controls; Stage IV (combined analysis)—a pooled analysis including all 1624 RA cases and 2506 control subjects for final estimates of effect size. A total of seven replicating epistatic interactions were identified. SNP variants within CDH13, MYO3A, CEP72 and near WFDC1 showed significant evidence for interaction with PTPN22, affecting susceptibility to RA.
PMCID: PMC3118040  PMID: 20090771
epistasis; rheumatoid arthritis; PTPN22; Random Forests
4.  A candidate gene study of CLEC16A does not provide evidence of association with risk for anti-CCP-positive rheumatoid arthritis 
Genes and immunity  2010;11(6):504-508.
CLEC16A, a putative immunoreceptor, was recently established as a susceptibility locus for type I diabetes and multiple sclerosis. Subsequently, associations between CLEC16A and rheumatoid arthritis (RA), Addison’s disease and Crohn’s disease have been reported. A large comprehensive and independent investigation of CLEC16A variation in RA was pursued. This study tested 251 CLEC16A single-nucleotide polymorphisms in 2542 RA cases (85% anti-cyclic citrullinated peptide (anti-CCP) positive) and 2210 controls (N = 4752). All individuals were of European ancestry, as determined by ancestry informative genetic markers. No evidence for significant association between CLEC16A variation and RA was observed. This is the first study to fully characterize common genetic variation in CLEC16A including assessment of haplotypes and gender-specific effects. The previously reported association between RA and rs6498169 was not replicated. Results show that CLEC16A does not have a prominent function in susceptibility to anti-CCP-positive RA.
PMCID: PMC2992879  PMID: 20220768
rheumatoid arthritis; anti-CCP antibodies; autoimmunity; CLEC16A; KIAA0350
5.  Association of PDCD1 genetic variation with risk and clinical manifestations of systemic lupus erythematosus in a multiethnic cohort 
Genes and immunity  2007;8(4):279-287.
We evaluated the roles of five single-nucleotide polymorphisms (SNPs) within PDCD1, and haplotypes defined by these SNPs, for the development of systemic lupus erythematosus (SLE) and specific sub-phenotypes (nephritis, antiphospholipid antibody positive, arthritis and double-stranded DNA positive) within a multiethnic US cohort of 1036 patients. Family based analyses were performed using 844 simplex families from four ethnic groups (Caucasian, Asian, Hispanic and African American). Subjects were genotyped for five ‘tag’ SNPs (selected from 15) to provide complete genetic information in all main ethnic groups. We employed transmission disequilibrium testing to assess risk for SLE by allele or haplotype, and multiple logistic regression analysis of SLE cases to examine associations with specific sub-phenotypes. In family based analyses, a haplotype containing the PD1.3A allele was significantly associated with SLE susceptibility among Caucasian families (P = 0.01). Among Hispanic families, two novel SNPs were associated with SLE risk (P = 0.005 and 0.01). In multivariate logistic regression analyses, five haplotypes were associated with specific sub-phenotypes among the different ethnic groups. These results suggest that PDCD1 genetic variation influences the risk and expression of SLE and that these associations vary according to ethnic background.
PMCID: PMC2925679  PMID: 17344889
systemic lupus erythematosus; PDCD1; family-based methods; haplotypes

Results 1-5 (5)