Epstein Michael P, Lin Xihong, Boehnke Michael
Department of Biostatistics, University of Michigan, 1420 Washington Heights, Ann Arbor, MI 48109-2029, USA.
Am J Hum Genet. 2002 Apr;70(4):886-95. doi: 10.1086/339517. Epub 2002 Mar 5.
Ascertainment-adjusted parameter estimates from a genetic analysis are typically assumed to reflect the parameter values in the original population from which the ascertained data were collected. Burton et al. (2000) recently showed that, given unmodeled parameter heterogeneity, the standard ascertainment adjustment leads to biased parameter estimates of the population-based values. This finding has important implications in complex genetic studies, because of the potential existence of unmodeled genetic parameter heterogeneity. The authors further stated the important point that, given unmodeled heterogeneity, the ascertainment-adjusted parameter estimates reflect the true parameter values in the ascertained subpopulation. They illustrated these statements with two examples. By revisiting these examples, we demonstrate that if the ascertainment scheme and the nature of the data can be correctly modeled, then an ascertainment-adjusted analysis returns population-based parameter estimates. We further demonstrate that if the ascertainment scheme and data cannot be modeled properly, then the resulting ascertainment-adjusted analysis produces parameter estimates that generally do not reflect the true values in either the original population or the ascertained subpopulation.
遗传分析中经确认调整后的参数估计通常被假定为反映了收集到确认数据的原始人群中的参数值。伯顿等人（2000年）最近表明，在存在未建模参数异质性的情况下，标准的确认调整会导致基于人群值的参数估计出现偏差。这一发现对于复杂的遗传研究具有重要意义，因为可能存在未建模的遗传参数异质性。作者进一步指出了一个重要观点，即在存在未建模异质性的情况下，经确认调整后的参数估计反映了确认亚群中的真实参数值。他们用两个例子说明了这些观点。通过重新审视这些例子，我们证明，如果能够正确地对确认方案和数据性质进行建模，那么经确认调整后的分析会得出基于人群的参数估计。我们还证明，如果不能正确地对确认方案和数据进行建模，那么由此产生的经确认调整后的分析所产生的参数估计通常既不能反映原始人群中的真实值，也不能反映确认亚群中的真实值。
