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Supervised machine learning and logistic regression identifies novel epistatic risk factors with PTPN22 for rheumatoid arthritis

Genes & Immunity volume 11, pages 199–208 (2010)Cite this article

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Abstract

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.

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Acknowledgements

We are very grateful for the contributions made by the study participants and we thank Ben Goldstein for analytical discussions. PKG is supported by grant from the National Institute of Health (NO1-AR-2-2263) and the Eileen Ludwig Greenland Center for Rheumatoid Arthritis (RO1 AR44422). LAC is supported by the National Institute of Health (K24 AR02175). These studies were carried out in part at the General Clinical Research Center, Moffitt Hospital, University of California at San Francisco, with funds provided by the National Center for Research Resources, US Public Health Service (5 MO1 RR-00079). The UCSF Rheumatoid Arthritis Genetics Project collection was supported by the US Public Health Service (P60 AR 20684). The SERA cohort is supported by the National Institute of Health (R01 AR051394).

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Authors and Affiliations

  1. Division of Epidemiology, School of Public Health, University of California, Berkeley, CA, USA

    F B S Briggs, P P Ramsay, E Madden & L F Barcellos

  2. Department of Epidemiology, Colorado School of Public Health, University of Colorado, Denver, Aurora, CO, USA,

    J M Norris

  3. Integrated Department in Immunology, University of Colorado School of Medicine, Aurora, CO, USA

    V M Holers

  4. Department of Internal Medicine and Omaha VA Medical Center, University of Nebraska Medical Center, Omaha, NE, USA

    T R Mikuls

  5. Department of Medicine, Jyväskylä Central Hospital, Jyväskylä, Finland

    T Sokka

  6. Rowe Program in Molecular Medicine and Human Genetics, University of California, Davis, CA, USA

    M F Seldin

  7. Feinstein Institute for Medical Research, North Shore Long Island Jewish Health System, Manhasset, NY, USA

    P K Gregersen

  8. Department of Medicine, Rosalind Russell Medical Research Center for Arthritis, University of California, San Francisco, CA, USA

    L A Criswell

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  1. F B S Briggs
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Briggs, F., Ramsay, P., Madden, E. et al. Supervised machine learning and logistic regression identifies novel epistatic risk factors with PTPN22 for rheumatoid arthritis. Genes Immun 11, 199–208 (2010). https://doi.org/10.1038/gene.2009.110

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