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author:("marcellus, LF")
2.  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
3.  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

Results 1-3 (3)