Understanding the genetics of systemic lupus erythematosus using bayesian statistics and gene network analysis

Seoung Wan Nam; Kwang Seob Lee; Jae Won Yang; Younhee Ko; Michael Eisenhut; Keum Hwa Lee; Jae Il Shin; Andreas Kronbichler

doi:10.3345/CEP.2020.00633

Understanding the genetics of systemic lupus erythematosus using bayesian statistics and gene network analysis

Seoung Wan Nam, Kwang Seob Lee, Jae Won Yang, Younhee Ko, Michael Eisenhut, Keum Hwa Lee, Jae Il Shin, Andreas Kronbichler

Department of Pediatrics

Research output: Contribution to journal › Review article › peer-review

2 Citations (Scopus)

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.

Original language	English
Pages (from-to)	208-222
Number of pages	15
Journal	Clinical and Experimental Pediatrics
Volume	64
Issue number	5
DOIs	https://doi.org/10.3345/CEP.2020.00633
Publication status	Published - 2021

Bibliographical note

Funding Information:
Acknowledgments This study was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2017R1D1A1B03032457) and Hankuk University of Foreign Studies Research Fund.

Publisher Copyright:
© 2021 by The Korean Pediatric Society.

All Science Journal Classification (ASJC) codes

Pediatrics, Perinatology, and Child Health
Pediatrics

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10.3345/CEP.2020.00633

Cite this

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title = "Understanding the genetics of systemic lupus erythematosus using bayesian statistics and gene network analysis",

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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doi = "10.3345/CEP.2020.00633",

language = "English",

volume = "64",

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AU - Nam, Seoung Wan

AU - Lee, Kwang Seob

AU - Yang, Jae Won

AU - Ko, Younhee

AU - Eisenhut, Michael

AU - Lee, Keum Hwa

AU - Shin, Jae Il

AU - Kronbichler, Andreas

N1 - Funding Information: Acknowledgments This study was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2017R1D1A1B03032457) and Hankuk University of Foreign Studies Research Fund. Publisher Copyright: © 2021 by The Korean Pediatric Society.

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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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Understanding the genetics of systemic lupus erythematosus using bayesian statistics and gene network analysis

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

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