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Practical Considerations for Dividing Data into Subsets Prior to PPL Analysis

机译：在PPL分析之前将数据划分为子集的实际注意事项

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摘要

Objective: The PPL, a class of statistics for complex trait genetic mapping in humans, utilizes Bayesian sequential updating to accumulate evidence for or against linkage across potentially heterogeneous data (sub)sets. Here, we systematically explore the relative efficacy of alternative subsetting approaches for purposes of PPL calculation. Methods: We simulated genotypes for three pedigree sets (sib pairs; 2-3 generations; 4 generations or more) based on families from an ongoing study. For each pedigree set, 100 replicates were generated under different levels of heterogeneity (1000 under 'no linkage'). Within each replicate, updating was performed across subsets defined randomly (RAND2, RAND4), by true (TRUE) linkage status, with a realistic (REAL) classification, by individual pedigree (PED), or without any subsetting (NONE). Results: Under 'linkage', REAL yields larger PPLs compared to NONE, RAND2, RAND4, or PED. Under 'no linkage', RAND2, RAND4 and PED yield PPLs close to NONE. Conclusions: We have examined the impact of different subsetting strategies on the sampling behavior of the PPL. Our results underscore the utility of finding variables that can help delineate more homogeneous data subsets and demonstrate that, once such variables are found, sequential updating can be highly beneficial in the presence of appreciable heterogeneity at a linked locus, without inflation at an unlinked locus. [PUBLICATION ABSTRACT]
机译：目的：PPL是人类复杂性状基因作图的一类统计数据，它利用贝叶斯顺序更新来收集支持或反对跨潜在异质数据（子）集的关联的证据。在这里，我们系统地探讨了用于PPL计算的替代子集方法的相对功效。方法：我们根据一项正在进行的研究，根据三个家族谱系（同胞对； 2-3代； 4代或以上）模拟了基因型。对于每个谱系集，在不同的异质性水平下生成了100个重复（“无关联”下有1000个）。在每个重复中，更新是通过真实（TRUE）链接状态，真实（REAL）分类，单个谱系（PED）或没有任何子集（NONE）跨随机定义的子集（RAND2，RAND4）执行的。结果：在“链接”下，与NONE，RAND2，RAND4或PED相比，REAL产生更大的PPL。在“无关联”下，RAND2，RAND4和PED产生的PPL接近无。结论：我们已经研究了不同子集策略对PPL采样行为的影响。我们的结果强调了发现变量的实用性，该变量可以帮助描绘更均一的数据子集，并证明，一旦找到此类变量，在链接位点存在明显异质性而未链接位点没有膨胀的情况下，顺序更新将非常有益。 [出版物摘要]

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