Genetics, Vol. 162, 1487-1500, November 2002, Copyright © 2002

Estimation of Deleterious Genomic Mutation Parameters in Natural Populations by Accounting for Variable Mutation Effects Across Loci

Hong-Wen Denga,b, Guimin Gaoa, and Jin-Long Lic
a Osteoporosis Research Center and Department of Biological Sciences, Creighton University, Omaha, Nebraska 68131,
b Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, ChangSha, Hunan 410081, People's Republic of China
c Center for Medical Informatics, Yale University School of Medicine, New Haven, Connecticut 06520-8009

Corresponding author: Hong-Wen Deng, Creighton University, 601 N. 30th St., STE. 6787, Omaha, NE 68131., deng{at}creighton.edu (E-mail)

Communicating editor: Z-B. ZENG

The genomes of all organisms are subject to continuous bombardment of deleterious genomic mutations (DGM). Our ability to accurately estimate various parameters of DGM has profound significance in population and evolutionary genetics. The Deng-Lynch method can estimate the parameters of DGM in natural selfing and outcrossing populations. This method assumes constant fitness effects of DGM and hence is biased under variable fitness effects of DGM. Here, we develop a statistical method to estimate DGM parameters by considering variable mutation effects across loci. Under variable mutation effects, the mean fitness and genetic variance for fitness of parental and progeny generations across selfing/outcrossing in outcrossing/selfing populations and the covariance between mean fitness of parents and that of their progeny are functions of DGM parameters: the genomic mutation rate U, average homozygous effect , average dominance coefficient , and covariance of selection and dominance coefficients cov(h, s). The DGM parameters can be estimated by the algorithms we developed herein, which may yield improved estimation of DGM parameters over the Deng-Lynch method as demonstrated by our simulation studies. Importantly, this method is the first one to characterize cov(h, s) for DGM.





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