4.2 Article

Analysis of the biases in the estimation of deleterious mutation parameters from natural populations at mutation-selection balance

期刊

GENETICS RESEARCH
卷 88, 期 3, 页码 177-189

出版社

HINDAWI LTD
DOI: 10.1017/S0016672307008506

关键词

-

向作者/读者索取更多资源

Indirect estimates of the genomic rate of deleterious mutations (lambda), their average homozygous effect (s) and their degree of dominance (h) can be obtained from genetic parameters of natural populations, assuming that the frequencies of the loci controlling a given fitness trait are at mutation-selection equilibrium. In 1996, H.-W. Deng and M. Lynch developed a general methodology for obtaining these estimates from inbreeding/outbreeding experiments. The prediction of the sign and magnitude of the biases incurred by these estimators is essential for a correct interpretation of the empirical results. However, the assessment of these biases has been tested so far under a rather limited model of the distribution of dominance effects. In this paper, the application of this method to outbred populations is evaluated, focusing on the level of variation in h values (sigma(2)(h)) and the magnitude of the negative correlation (r(s,h)) between s and h. It is shown that the method produces upwardly biased estimates of lambda and downwardly biased estimates of the average s in the reference situation where r(s,h) = 0, particularly for large values of sigma(2)(h), and biases of different sign depending on the magnitude of the correlation. A modification of the method, substituting the estimates of the average h for alternative ones, allows estimates to be obtained with little or no bias for the case Of r(s,h) = 0, but is otherwise biased. Information on r(s,h) and sigma(2)(h), gathered from mutation-accumulation experiments, suggests that sigma(2)(h) may be rather large and r(s,h) is usually negative but not higher than about -0.2, although the data are scarce and noisy, and should be used with caution.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.2
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据