Understanding the genetics of systemic lupus erythematosus using Bayesian statistics and gene network analysis
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..
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:64 |
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| Enthalten in: |
Clinical and Experimental Pediatrics - 64(2021), 5, Seite 208-222 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Seoung Wan Nam [VerfasserIn] |
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| Links: |
doi.org [kostenfrei] |
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| Themen: |
Bayesian false-discovery probability |
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| doi: |
10.3345/cep.2020.00633 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
DOAJ055633455 |
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| 520 | |a 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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