PMCC PMCC

Search tips
Search criteria

Advanced
Results 1-6 (6)
 

Clipboard (0)
None

Select a Filter Below

Journals
Year of Publication
Document Types
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.
doi:10.1038/gene.2014.6
PMCID: PMC4102853  PMID: 24598797
Sub-phenotype analysis; MHC; meta-analysis; genetics; systemic lupus erythematosus; Europeans
4.  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.
doi:10.1038/gene.2009.110
PMCID: PMC3118040  PMID: 20090771
epistasis; rheumatoid arthritis; PTPN22; Random Forests
5.  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.
doi:10.1038/gene.2010.7
PMCID: PMC2992879  PMID: 20220768
rheumatoid arthritis; anti-CCP antibodies; autoimmunity; CLEC16A; KIAA0350
6.  Rheumatoid arthritis: a view of the current genetic landscape 
Genes and immunity  2008;10(2):101-111.
The field of genetics and autoimmune diseases is undergoing a rapid and unprecedented expansion with new genetic findings being reported at an astounding pace. It is now clear that multiple genes contribute to each of the major autoimmune disorders, with significant genetic overlaps among them. Rheumatoid arthritis (RA) is no exception to this, and emerging data are beginning to reveal the outlines of new diagnostic subgroups, complex overlapping relationships with other autoimmune disorders and potential new targets for therapy. This review describes the evolving genetic landscape of RA, with the full knowledge that our current view is far from complete. However, with the first round of genome-wide association scans now completed, it is reasonable to begin to take stock of the direction in which the major common genetic risk factors are leading us.
doi:10.1038/gene.2008.77
PMCID: PMC2730780  PMID: 18987647
Rheumatoid arthritis; polymorphism; genome-wide association

Results 1-6 (6)