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一种用于多倍体的成对相关性估计器。

A pairwise relatedness estimator for polyploids.

作者信息

Huang Kang, Ritland Kermit, Guo Songtao, Shattuck Milena, Li Baoguo

机构信息

Key Laboratory of Resource Biology and Biotechnology in Western China of Ministry of Education, and College of Life Sciences, Northwest University, Xi'an, ShaanXi, 710069, China.

出版信息

Mol Ecol Resour. 2014 Jul;14(4):734-44. doi: 10.1111/1755-0998.12217. Epub 2014 Jan 27.

DOI:10.1111/1755-0998.12217
PMID:24460904
Abstract

Studies in genetics and ecology often require estimates of relatedness coefficients based on genetic marker data. Many diploid estimators have been developed using either method-of-moments or maximum-likelihood estimates. However, there are no relatedness estimators for polyploids. The development of a moment estimator for polyploids with polysomic inheritance, which simultaneously incorporates the two-gene relatedness coefficient and various 'higher-order' coefficients, is described here. The performance of the estimator is compared to other estimators under a variety of conditions. When using a small number of loci, the estimator is biased because of an increase in ill-conditioned matrices. However, the estimator becomes asymptotically unbiased with large numbers of loci. The ambiguity of polyploid heterozygotes (when balanced heterozygotes cannot be distinguished from unbalanced heterozygotes) is also considered; as with low numbers of loci, genotype ambiguity leads to bias. A software, PolyRelatedness, implementing this method and supporting a maximum ploidy of 8 is provided.

摘要

遗传学和生态学研究通常需要根据遗传标记数据估计亲缘系数。许多二倍体估计方法是利用矩估计法或最大似然估计法开发的。然而,目前还没有针对多倍体的亲缘关系估计方法。本文描述了一种用于具有多体遗传的多倍体的矩估计方法,该方法同时纳入了双基因亲缘系数和各种“高阶”系数。在各种条件下,将该估计方法的性能与其他估计方法进行了比较。当使用少量位点时,由于病态矩阵的增加,该估计方法存在偏差。然而,随着位点数量的增加,该估计方法渐近无偏。还考虑了多倍体杂合子的模糊性(即平衡杂合子与不平衡杂合子无法区分时);与少量位点的情况一样,基因型模糊性会导致偏差。提供了一个名为PolyRelatedness的软件,该软件实现了此方法,并支持最高为8的倍性。

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A pairwise relatedness estimator for polyploids.一种用于多倍体的成对相关性估计器。
Mol Ecol Resour. 2014 Jul;14(4):734-44. doi: 10.1111/1755-0998.12217. Epub 2014 Jan 27.
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