A fast and reliable computational method for estimating population genetic parameters.
نویسنده
چکیده
The estimation of ancestral and current effective population sizes in expanding populations is a fundamental problem in population genetics. Recently it has become possible to scan entire genomes of several individuals within a population. These genomic data sets can be used to estimate basic population parameters such as the effective population size and population growth rate. Full-data-likelihood methods potentially offer a powerful statistical framework for inferring population genetic parameters. However, for large data sets, computationally intensive methods based upon full-likelihood estimates may encounter difficulties. First, the computational method may be prohibitively slow or difficult to implement for large data. Second, estimation bias may markedly affect the accuracy and reliability of parameter estimates, as suggested from past work on coalescent methods. To address these problems, a fast and computationally efficient least-squares method for estimating population parameters from genomic data is presented here. Instead of modeling genomic data using a full likelihood, this new approach uses an analogous function, in which the full data are replaced with a vector of summary statistics. Furthermore, these least-squares estimators may show significantly less estimation bias for growth rate and genetic diversity than a corresponding maximum-likelihood estimator for the same coalescent process. The least-squares statistics also scale up to genome-sized data sets with many nucleotides and loci. These results demonstrate that least-squares statistics will likely prove useful for nonlinear parameter estimation when the underlying population genomic processes have complex evolutionary dynamics involving interactions between mutation, selection, demography, and recombination.
منابع مشابه
Application of endurance time method in damage assessment of concrete moment frames
Nonlinear Time History (NTH) analysis is currently the most reliable method for estimating structural behavior. Considerable computational demand and complexity of this method may cause difficulty for its routine practical application. Based on the Methodology of Endurance Time (ET) method, it can estimate the nonlinear response of structures with a much lower computational cost. In this resear...
متن کاملDetermination of Optimal SVM Parameters by Using GA/PSO
The use of support vector machine (SVM) for function approximation has increased over the past few years. Unfortunately, the practical use of SVM is limited because the quality of SVM models heavily depends on a proper setting of SVM hyper-parameters and SVM kernel parameters. Therefore, it is necessary to develop an automated, reliable, and relatively fast approach to determine the values of t...
متن کاملAirfoil Shape Optimization with Adaptive Mutation Genetic Algorithm
An efficient method for scattering Genetic Algorithm (GA) individuals in the design space is proposed to accelerate airfoil shape optimization. The method used here is based on the variation of the mutation rate for each gene of the chromosomes by taking feedback from the current population. An adaptive method for airfoil shape parameterization is also applied and its impact on the optimum desi...
متن کاملEstimating genetic parameters of agronomic and uality traits in a diallel cross of sugarbeet
متن کامل
Estimating Genetic Parameters of Body Weight Traits in Kourdi Sheep
For estimating genetic parameters for body weight traits in Kourdi sheep data were collected from 1996 to 2013 in Kourdi Breeding Station in Northern Khorasan province of Iran. Studied traits were birth weight (BW), weaning weight (WW), six-month weight (6MW), nine-month weight (9MW) and yearling weight (YW). The fixed effects in the model were lambing year, sex, type of birth and age of dam. (...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Genetics
دوره 179 2 شماره
صفحات -
تاریخ انتشار 2008