Clin Exp Pediatr.
2021 May;64(5):208-222. 10.3345/cep.2020.00633.
Understanding the genetics of systemic lupus erythematosus using Bayesian statistics and gene network analysis
- Nam SW
1 - Lee KS2
- Yang JW3
- Ko Y4
- Eisenhut M5
- Lee KH6,7,8
- Shin JI6,7,8
- Kronbichler A9
- Affiliations
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- 1Department of Rheumatology, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, Wonju, Korea
- 2Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
- 3Department of Nephrology, Yonsei University Wonju College of Medicine, Wonju, Korea
- 4Division of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin-si, Gyeonggi-do, Republic of Korea
- 5Department of Pediatrics, Luton & Dunstable University Hospital NHS Foundation Trust, Luton, United Kingdom
- 6Department of Pediatrics, Yonsei University College of Medicine, Seoul, Republic of Korea
- 7Division of Pediatric Nephrology, Severance Children’s Hospital, Seoul, Korea
- 8Institute of Kidney Disease Research, Yonsei University College of Medicine, Seoul, Republic of Korea
- 9Department of Internal Medicine IV (Nephrology and Hypertension), Medical University Innsbruck, Innsbruck, Austria
- KMID: 2515441
- DOI: http://doi.org/10.3345/cep.2020.00633
Abstract
- The publication of genetic epidemiology meta-analyses has increased rapidly, but it has been suggested that many of the statistically significant results are false positive. In addition, most such meta-analyses have been redundant, duplicate, and erroneous, leading to research waste. In addition, since most claimed candidate gene associations were false-positives, correctly interpreting the published results is important. In this review, we emphasize the importance of interpreting the results of genetic epidemiology meta-analyses using Bayesian statistics and gene network analysis, which could be applied in other diseases.
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