Asymptotic estimation theory of multipoint linkage analysis under perfect marker information
完全标记信息下多点连锁分析的渐近估计理论AI 解读 ↓
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完全标记信息下多点连锁分析的渐近估计理论
- 研究问题
- 在具有完全遗传标记信息时，如何估计染色体上的疾病易感位点，并比较遗传模型、评分函数和加权方案的渐近表现？
- 方法与思路
- 作者以观测表型为条件统一处理多类连锁分析，研究位点估计的复合泊松过程极限，并引入渐近斜率噪声比。
- 材料陈述的贡献
- 摘要称估计收敛速率为家系数的倒数，并定义渐近最优评分与权重；传统平方根理论失效，最大似然也非渐近最优。
- 阅读关注点
- 阅读时应核对完全标记信息与正则条件、极限过程及最优性的判据，并查明弱遗传模型和异质性讨论的适用范围。
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- 依据稿件版本未注明的OpenAlex索引摘要；完整条件和性能判据未展开，部分估计符号显示异常，须查原PDF确认记号。
原文摘要
We consider estimation of a disease susceptibility locus $\tau$ at a chromosome. With perfect marker data available, the estimator $\htau_N$ of $\tau$ based on $N$-pedigrees has a rate of convergence $N^{-1}$ under mild regularity conditions. The limiting distribution is the arg max of a certain compound Poisson process. Our approach is conditional on observed phenotypes, and therefore treats parametric and nonparametric linkage, as well as quantitative trait loci methods within a unified framework. A constant appearing in the asymptotics, the so-called asymptotic slope-to-noise ratio, is introduced as a performance measure for a given genetic model, score function and weighting scheme. This enables us to define asymptotically optimal score functions and weighting schemes. Interestingly, traditional $N^{-1/2}$ theory breaks down, in that, for instance, the ML-estimator is not asymptotically optimal. Further, the asymptotic estimation theory automatically takes uncertainty of $\tau$ into account, which is otherwise handled by means of multiple testing and Bonferroni-type corrections. Other potential applications of our approach that we discuss are general sampling criteria for planning of linkage studies, appropriate grid size of marker maps, robustness w.r.t. choice of map function (dropping assumption of no interference) and quantification of information loss due to heterogeneity (with linked or unlinked trait loci). We also discuss relations to pointwise performance criteria and pay special attention to weak genetic models, so-called local specificity models.