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Genome-wide discovery of monotone mutations using logistic regression analysis of evolutionary experiment with Drosophila
Journal of the Korean Data & Information Science Society 2019;30:503-13
Published online March 31, 2019;
© 2019 Korean Data and Information Science Society.

Minjung Kwak1

1Department of Statistics, Yeungnam University
Correspondence to: Associate professor, Department of Statistics, Yeungnam University, Kyeongsan 38541, Korea. E-mail:
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2016R1D1A1B03931050).
Received December 27, 2018; Revised January 11, 2019; Accepted January 11, 2019.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
The genomic basis of adaptation to novel environments is a fundamental prob- lem in evolutionary biology that has gained additional importance in the light of the recent discussion about global change. Here, we combined experimental evolution in Drosophila melanogaster with the genome-wide next-generation sequencing of DNA pools to identify alleles that are favorable in a different laboratory environment and traced their trajectories during the adaptive process. We applied logistic regression analysis to detect alleles that showed changes over time using the maximum likelihood method. After applying Bonferroni correction to adjust for multiple comparisons we found 12,166 significant genetic markers using a logistic regression approach whereas the commonly used Cochran-Mantel-Haenszel test identified 2254 significant genetic markers. Our results provide a useful tool for testing the time-dependency of allele frequencies in genome-wide evolutionary experiment data.
Keywords : Drosophila, evolutionary experiment, high-throughput sequencing, genome- wide test, logistic regression.