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A maximum-likelihood estimation of pairwise relatedness for autopolyploids

机译:多倍体成对相关性的最大似然估计

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摘要

Relatedness between individuals is central to ecological genetics. Multiple methods are available to quantify relatedness from molecular data, including method-of-moment and maximum-likelihood estimators. We describe a maximum-likelihood estimator for autopolyploids, and quantify its statistical performance under a range of biologically relevant conditions. The statistical performances of five additional polyploid estimators of relatedness were also quantified under identical conditions. When comparing truncated estimators, the maximum-likelihood estimator exhibited lower root mean square error under some conditions and was more biased for non-relatives, especially when the number of alleles per loci was low. However, even under these conditions, this bias was reduced to be statistically insignificant with more robust genetic sampling. We also considered ambiguity in polyploid heterozygote genotyping and developed a weighting methodology for candidate genotypes. The statistical performances of three polyploid estimators under both ideal and actual conditions (including inbreeding and double reduction) were compared. The software package POLYRELATEDNESS is available to perform this estimation and supports a maximum ploidy of eight.
机译:人与人之间的亲密关系是生态遗传学的核心。有多种方法可用于从分子数据中量化相关性,包括矩量法和最大似然估计器。我们描述了一个最大的似然估计为同倍体,并量化其在一系列生物学相关条件下的统计性能。在相同条件下,还对另外五个相关性多倍体估计量的统计性能进行了量化。当比较截断的估计量时,最大似然估计值在某些情况下显示出较低的均方根误差,并且对于非亲属的偏倚更大,尤其是当每个基因座的等位基因数量较低时。但是,即使在这些条件下,通过更强大的基因采样,这种偏倚也已减少到统计学上可忽略的程度。我们还考虑了多倍体杂合子基因分型的歧义性,并为候选基因型开发了加权方法。比较了三种多倍体估计量在理想和实际条件下(包括近交和双倍减法)的统计性能。软件包POLYRELATEDNESS可用于执行此估计,并支持最大八倍体。

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