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- Summary:
- A reproducibility crisis is ongoing in scientific
research, where many studies may be difficult or impossible to replicate
and thereby validate, especially when the study involves a very large
sample size. Now researchers have developed a statistical tool that can
accurately estimate the replicability of a study, thus eliminating the
need to duplicate the work and effectively mitigating the
reproducibility crisis. _____________________________________________________
The new tool enhances the replicability of large genomic datasets, as ScienceDaily and Pennsylvania State University reports.
Researchers have developed a new statistical tool that can accurately estimate the replicability of a study, thus eliminating the need to duplicate the work. The method will be particularly useful for large genome-wide association studies.
Photo: National Cancer Institute, Unsplash
A reproducibility crisis is ongoing in scientific research, where many studies may be difficult or impossible to replicate and thereby validate, especially when the study involves a very large sample size. For example, to evaluate the validity of a high-throughput genetic study's findings scientists must be able to replicate the study and achieve the same results. Now researchers at Penn State and the University of Minnesota have developed a statistical tool that can accurately estimate the replicability of a study, thus eliminating the need to duplicate the work and effectively mitigating the reproducibility crisis.
The team used its new method, which they describe in a paper publishing today (March 30) in Nature Communications, to confirm the findings of a 2019 study on the genetic factors that contribute to smoking and drinking addiction but noted that it also can be applied to other genome-wide association studies -- or studies that investigate the genetic underpinnings for diseases...
Liu noted that the method can be applied to genome-wide association studies focused on a wide variety of traits. "I think in the next decade or so, an essential focus of biology will be to interpret and make sense of those genome-wide association study discoveries and whether we can translate some of them into medications to facilitate personalized medicine," he said. "We are excited to be able to offer this statistical approach as a service to the research community."
Other authors on the paper include graduate students Daniel McGuire, Yu Jiang, J. Dylan Weissenkampen, Scott Eckert, and Lina Yang; Postdoctoral Scholar Fang Chen; and Associate Professor of Public Health Sciences and Statistics Arthur Berg, all at Penn State. Mengzhen Liu and Scott Vrieze at the University of Minnesota also are authors on the paper.
Journal Reference:
Daniel McGuire, Yu Jiang, Mengzhen Liu, J. Dylan Weissenkampen, Scott
Eckert, Lina Yang, Fang Chen, Arthur Berg, Scott Vrieze, Bibo Jiang,
Qunhua Li, Dajiang J. Liu. Model-based assessment of replicability for genome-wide association meta-analysis. Nature Communications, 2021; 12 (1)
DOI: 10.1038/s41467-021-21226-z
Source: ScienceDaily and Pennsylvania State University
