Application of Maximum Likelihood Methods to Population Genetic Data for the Estimation of Individual Fertilities
Application of Maximum Likelihood Methods to Population Genetic Data for the Estimation of Individual Fertilities
复制标题
最大似然法在群体遗传数据中的应用以估计个体生育力
DOI：
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发表时间：
1989
期刊：
影响因子：
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通讯作者：
B. Lindsay
中科院分区：
文献类型：
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作者：
K. Roeder;Bernie Devlin;B. Lindsay
SUMMARY The fitness of plants or animals within a population is largely determined by the number of offspring they produce. In natural populations lacking familial structure either one or both parents are often unknown.为了解决这个问题，可以对父母和后代进行一组遗传标记的基因分型。似然模型被提出来估计人口中男性或男性和女性父母的生育力，其中生育力被广泛定义为由某个个体作为父亲或母亲的人口中后代的一部分。针对三种情况开发了模型：母亲已知，男性生育能力因母本而异； the mother is known and the male fertilities are the same for all maternal parents;父母双方均未知，并且对父母对的生育力进行估计。 It is established that a unique maximum likelihood solution exists under conditions that are commonly met.对于不满足这些条件的情况，提出了一种方法来确定可唯一估计的参数的一组独立线性组合的估计。 Two algorithms are examined which can be used to find the parameter estimates.开发了估计量的方差、生育参数约束的似然比检验以及拟合优度检验。最后，给出了一个可行的例子。
SUMMARY The fitness of plants or animals within a population is largely determined by the number of offspring they produce. In natural populations lacking familial structure either one or both parents are often unknown. To circumvent this problem, parents and offspring can be genotyped for a set of genetic markers. Likelihood models are proposed to estimate the fertility of either male or male and female parents in a population where fertility is defined broadly as a fraction of progeny in the population fathered or mothered by some individual. Models are developed for three cases: the mother is known and the male fertilities differ depending on the maternal parent; the mother is known and the male fertilities are the same for all maternal parents; neither parent is known and fertilities are estimated for parent pairs. It is established that a unique maximum likelihood solution exists under conditions that are commonly met. For situations in which these conditions are not met, a method is presented to determine estimates of a set of independent linear combinations of the parameters that can be uniquely estimated. Two algorithms are examined which can be used to find the parameter estimates. The variance of the estimator, likelihood ratio tests for constraints on the fertility parameters, and a goodness-of-fit test are developed. Finally, a worked example is presented.