Ayres K L, Balding D J
Department of Applied Statistics, University of Reading, PO Box 240, Earley Gate, Reading RG6 6FN, U.K.
Heredity (Edinb). 1998 Jun;80 ( Pt 6):769-77. doi: 10.1046/j.1365-2540.1998.00360.x.
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 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 he 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.
遗传学中许多成熟的统计方法是在计算能力受到严格限制的环境下发展起来的。模拟方法的最新进展使现代灵活的统计方法能够为使用台式工作站的科学家所用。我们通过考虑评估偏离哈迪-温伯格（HW）平衡的问题来说明目前可用的潜在优势。已经建立了几种HW的假设检验，以及在近亲繁殖模型下测量偏离HW的参数的各种点估计方法。我们提出了一种用于评估偏离HW的计算贝叶斯方法，该方法相对于现有方法具有许多重要优势。该方法纳入了关于干扰参数（等位基因频率）的不确定性影响，以及对f的边界约束（f是干扰参数的函数）。利用现代计算机环境的图形功能直观地呈现结果，以便直接解释。也许最重要的是，该方法基于灵活的、基于似然的建模框架，在适当的情况下可以纳入近亲繁殖模型，但也允许对模型的假设进行研究，并在必要时放宽。在适当的条件下，可以跨基因座甚至跨群体共享信息，从而实现更精确的估计。通过将该方法应用于模拟数据以及近期文献中用其他方法分析的数据，说明了该方法的优势。
