Measuring departures from Hardy-Weinberg: a Markov chain Monte Carlo method for estimating the inbreeding coefficient
Measuring departures from Hardy-Weinberg: a Markov chain Monte Carlo method for estimating the inbreeding coefficient
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DOI：
10.1046/j.1365-2540.1998.00360.x
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发表时间：
1998-06-01
期刊：
影响因子：
3.8
通讯作者：
Balding, DJ
中科院分区：
文献类型：
--
作者：
Ayres, KL;Balding, DJ
遗传学中许多成熟的统计方法都是在计算能力受到严重限制的环境中发展起来的。模拟方法学的最新进展现在使科学家能够使用桌面工作站来获得现代灵活的统计方法。我们说明了潜在的优势，现在可以考虑的问题，评估偏离哈代-温伯格（HW）平衡。已经建立了几个假设检验的HW，以及各种点估计方法的参数f，它测量偏离HW的近亲繁殖模型。我们提出了一个计算，贝叶斯方法评估偏离HW，它有一些重要的优势，现有的方法。该方法结合了滋扰参数的不确定性的影响-等位基因频率-以及f（这是滋扰参数的函数）的边界约束。结果自然地呈现在视觉上，利用现代计算机环境的图形功能，允许直接的解释。也许最重要的是，该方法是建立在一个灵活的，基于可能性的建模框架，它可以纳入近亲繁殖模型，如果合适的话，但也允许模型的假设进行调查，并在必要时，放宽。在适当的条件下，信息可以在基因座之间共享，也可能在种群之间共享，从而导致更精确的估计。该方法的优点说明了应用程序的模拟数据和数据分析的替代方法在最近的文献。
Many well-established statistical methods in genetics were developed in a climate of severe constraints on computational power. Recent advances in simulation methodology now bring modern, flexible statistical methods within the reach of scientists having access to a desktop workstation. We illustrate the potential advantages now available by considering the problem of assessing departures from Hardy-Weinberg (HW) equilibrium. Several hypothesis tests of HW have been established, as well as a variety of point estimation methods for the parameter f, which measures departures from HW under the inbreeding model. We propose a computational, Bayesian method for assessing departures from HW, which has a number of important advantages over existing approaches. The method incorporates the effects of uncertainty about the nuisance parameters - the allele frequencies - as well as the boundary constraints on f (which are functions of the nuisance parameters). Results are naturally presented visually, exploiting the graphics capabilities of modern computer environments to allow straightforward interpretation. Perhaps most importantly, the method is founded on a flexible, likelihood-based modelling framework, which can incorporate the inbreeding model if appropriate, but also allows the assumptions of the model to be investigated and, if necessary, relaxed. Under appropriate conditions, information can be shared across loci and, possibly, across populations, leading to more precise estimation. The advantages of the method are illustrated by application both to simulated data and to data analysed by alternative methods in the recent literature.