Box-Cox transformation for QTL mapping
Genetica, ISSN: 0016-6707, Vol: 128, Issue: 1-3, Page: 133-143
2006
- 19Citations
- 46Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations19
- Citation Indexes19
- 19
- CrossRef17
- Captures46
- Readers46
- 46
Article Description
The maximum likelihood method of QTL mapping assumes that the phenotypic values of a quantitative trait follow a normal distribution. If the assumption is violated, some forms of transformation should be taken to make the assumption approximately true. The Box-Cox transformation is a general transformation method which can be applied to many different types of data. The flexibility of the Box-Cox transformation is due to a variable, called transformation factor, appearing in the Box-Cox formula. We developed a maximum likelihood method that treats the transformation factor as an unknown parameter, which is estimated from the data simultaneously along with the QTL parameters. The method makes an objective choice of data transformation and thus can be applied to QTL analysis for many different types of data. Simulation studies show that (1) Box-Cox transformation can substantially increase the power of QTL detection; (2) Box-Cox transformation can replace some specialized transformation methods that are commonly used in QTL mapping; and (3) applying the Box-Cox transformation to data already normally distributed does not harm the result. © 2006 Springer.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=33749641003&origin=inward; http://dx.doi.org/10.1007/s10709-005-5577-z; http://www.ncbi.nlm.nih.gov/pubmed/17028946; http://link.springer.com/10.1007/s10709-005-5577-z; http://www.springerlink.com/index/10.1007/s10709-005-5577-z; http://www.springerlink.com/index/pdf/10.1007/s10709-005-5577-z; https://dx.doi.org/10.1007/s10709-005-5577-z; https://link.springer.com/article/10.1007/s10709-005-5577-z
Springer Science and Business Media LLC
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know