Efficient Estimation of Mutation Rates during Individual Development by Minimization of Chi-Square.

Autor: Ai SM; Laboratory for Conservation and Utilization of Bioresources, Yunnan University, Kunming, 650091, China; Department of Applied Mathematics, Yunnan Agricultural University, Kunming, 650201, China., Gao JJ; Laboratory for Conservation and Utilization of Bioresources, Yunnan University, Kunming, 650091, China., Liu SQ; Laboratory for Conservation and Utilization of Bioresources, Yunnan University, Kunming, 650091, China., Fu YX; Laboratory for Conservation and Utilization of Bioresources, Yunnan University, Kunming, 650091, China; Human Genetics Center and Division of Biostatistics, School of Public Health, The University of Texas Health Science Center, Houston, Texas, 77030, United States of America.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2015 Aug 12; Vol. 10 (8), pp. e0135398. Date of Electronic Publication: 2015 Aug 12 (Print Publication: 2015).
DOI: 10.1371/journal.pone.0135398
Abstrakt: Mutation primarily occurs when cells divide and it is highly desirable to have knowledge of the rate of mutations for each of the cell divisions during individual development. Recently, recessive lethal or nearly lethal mutations which were observed in a large mutation accumulation experiment using Drosophila melanogaster suggested that mutation rates vary significantly during the germline development of male Drosophila melanogaster. The analysis of the data was based on a combination of the maximum likelihood framework with numerical assistance from a newly developed coalescent algorithm. Although powerful, the likelihood based framework is computationally highly demanding which limited the scope of the inference. This paper presents a new estimation approach by minimizing chi-square statistics which is asymptotically consistent with the maximum likelihood method. When only at most one mutation in a family is considered the minimization of chi-square is simplified to a constrained weighted minimum least square method which can be solved easily by optimization theory. The new methods effectively eliminates the computational bottleneck of the likelihood. Reanalysis of the published Drosophila melanogaster mutation data results in similar estimates of mutation rates. The new method is also expected to be applicable to the analysis of mutation data generated by next-generation sequencing technology.
Databáze: MEDLINE
Externí odkaz: