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本文引用的文献

1
Estimation of inbreeding and kinship coefficients via latent identity-by-descent states.基于潜在的亲缘关系状态估计近亲系数和亲缘系数。
Bioinformatics. 2024 Feb 1;40(2). doi: 10.1093/bioinformatics/btae082.
2
Fast and accurate relatedness estimation from high-throughput sequencing data in the presence of inbreeding.存在近亲交配时,从高通量测序数据中快速准确地估计亲缘关系。
Gigascience. 2019 May 1;8(5). doi: 10.1093/gigascience/giz034.
3
Runs of homozygosity: windows into population history and trait architecture.纯合子区域:揭示群体历史和性状结构的窗口。
Nat Rev Genet. 2018 Apr;19(4):220-234. doi: 10.1038/nrg.2017.109. Epub 2018 Jan 15.
4
ESTIMATING RELATEDNESS USING GENETIC MARKERS.使用遗传标记估计亲缘关系
Evolution. 1989 Mar;43(2):258-275. doi: 10.1111/j.1558-5646.1989.tb04226.x.
5
A Unified Characterization of Population Structure and Relatedness.群体结构与亲缘关系的统一表征
Genetics. 2017 Aug;206(4):2085-2103. doi: 10.1534/genetics.116.198424. Epub 2017 May 26.
6
Estimating Seven Coefficients of Pairwise Relatedness Using Population-Genomic Data.利用群体基因组数据估计成对亲缘关系的七个系数
Genetics. 2017 May;206(1):105-118. doi: 10.1534/genetics.116.190660. Epub 2017 Mar 24.
7
Model-free Estimation of Recent Genetic Relatedness.近期遗传相关性的无模型估计
Am J Hum Genet. 2016 Jan 7;98(1):127-48. doi: 10.1016/j.ajhg.2015.11.022.
8
Pedigrees or markers: Which are better in estimating relatedness and inbreeding coefficient?系谱还是标记:在估计亲缘关系和近亲繁殖系数方面哪个更好?
Theor Popul Biol. 2016 Feb;107:4-13. doi: 10.1016/j.tpb.2015.08.006. Epub 2015 Sep 3.
9
Non-identifiability of identity coefficients at biallelic loci.双等位基因位点上身份系数的不可识别性。
Theor Popul Biol. 2014 Mar;92:22-9. doi: 10.1016/j.tpb.2013.11.001. Epub 2013 Nov 21.
10
Improving the accuracy and efficiency of identity-by-descent detection in population data.提高群体数据中基于关联的身份检测的准确性和效率。
Genetics. 2013 Jun;194(2):459-71. doi: 10.1534/genetics.113.150029. Epub 2013 Mar 27.

EMIBD9:从标记基因型估计9个压缩的同源染色体片段(IBD)系数、近亲繁殖系数和亲缘关系。

EMIBD9: Estimating 9 condensed IBD coefficients, inbreeding and relatedness from marker genotypes.

作者信息

Wang Jinliang

机构信息

Institute of Zoology, Zoological Society of London, London, NW1 4RY, United Kingdom.

出版信息

Heredity (Edinb). 2025 Apr;134(3-4):155-161. doi: 10.1038/s41437-024-00739-5. Epub 2025 Mar 27.

DOI:10.1038/s41437-024-00739-5
PMID:40148499
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11976962/
Abstract

EMIBD9 is a computer programme implementing two likelihood methods for estimating the 9 condensed IBD coefficients, Δ = {Δ, Δ, …, Δ}, between a pair of individuals from their genotype data. Inbreeding coefficients of and relatedness (or kinship coefficient) between individuals are then calculated from the estimated Δ. One method is designed to apply to a small sample or a sample containing a high proportion of close relatives where allele frequencies and their powers or products are poorly estimated by assuming a large sample of non-inbred and unrelated individuals. It adopts an expectation maximisation (EM) algorithm to estimate both Δ and allele frequencies jointly and iteratively. The other method is designed to apply to a large sample of individuals containing few close relatives. It is fast because it, like all previous estimators, estimates Δ only and does not make iterative updates of allele frequencies by accounting for the inferred relatedness through the EM algorithm. EMIBD9 has both methods implemented for multiple computer platforms (Windows, Mac and Linux), and the Windows version has a GUI that facilitates data input and results visualisation. The GUI can also be used to simulate genotype data which are used to investigate factors affecting relatedness estimation accuracy, to optimise the experimental design of a relatedness study, and to compare the performance of different relatedness estimators.

摘要

EMIBD9是一个计算机程序,它实现了两种似然方法,用于根据一对个体的基因型数据估计9个压缩的同宗系数Δ = {Δ₁, Δ₂, …, Δ₉}。然后根据估计出的Δ计算个体之间的近交系数和亲缘关系(或亲属系数)。一种方法设计用于小样本或包含高比例近亲的样本,在假设大量非近亲且无亲缘关系个体的情况下,等位基因频率及其幂次或乘积估计不佳。它采用期望最大化(EM)算法来联合迭代估计Δ和等位基因频率。另一种方法设计用于包含少量近亲的大量个体样本。它速度快,因为与之前所有估计器一样,它仅估计Δ,且不通过EM算法考虑推断出的亲缘关系来对等位基因频率进行迭代更新。EMIBD9在多个计算机平台(Windows、Mac和Linux)上实现了这两种方法,并且Windows版本有一个图形用户界面(GUI),便于数据输入和结果可视化。该GUI还可用于模拟基因型数据,这些数据用于研究影响亲缘关系估计准确性的因素、优化亲缘关系研究的实验设计以及比较不同亲缘关系估计器的性能。