Suppr超能文献

在不同人群设计中从低密度到高密度单核苷酸多态性(SNP)面板对缺失基因型进行填充

Imputation of missing genotypes from low- to high-density SNP panel in different population designs.

作者信息

He S, Wang S, Fu W, Ding X, Zhang Q

机构信息

Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture of China, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China; Department of Cytogenetics and Genome Analysis, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, 06466, Germany.

出版信息

Anim Genet. 2015 Feb;46(1):1-7. doi: 10.1111/age.12236. Epub 2014 Nov 28.

Abstract

Imputation of missing genotypes, in particular from low density to high density, is an important issue in genomic selection and genome-wide association studies. Given the marker densities, the most important factors affecting imputation accuracy are the size of the reference population and the relationship between individuals in the reference (genotyped with high-density panel) and study (genotyped with low-density panel) populations. In this study, we investigated the imputation accuracies when the reference population (genotyped with Illumina BovineSNP50 SNP panel) contained sires, halfsibs, or both sires and halfsibs of the individuals in the study population (genotyped with Illumina BovineLD SNP panel) using three imputation programs (fimpute v2.2, findhap v2, and beagle v3.3.2). Two criteria, correlation between true and imputed genotypes and missing rate after imputation, were used to evaluate the performance of the three programs in different scenarios. Our results showed that fimpute performed the best in all cases, with correlations from 0.921 to 0.978 when imputing from sires to their daughters or between halfsibs. In general, the accuracies of imputing between halfsibs or from sires to their daughters were higher than were those imputing between non-halfsibs or from sires to non-daughters. Including both sires and halfsibs in the reference population did not improve the imputation performance in comparison with when only including halfsibs in the reference population for all the three programs.

摘要

缺失基因型的填充,尤其是从低密度到高密度的填充,是基因组选择和全基因组关联研究中的一个重要问题。考虑到标记密度,影响填充准确性的最重要因素是参考群体的大小以及参考群体(用高密度芯片进行基因分型)和研究群体(用低密度芯片进行基因分型)中个体之间的关系。在本研究中,我们使用三个填充程序(fimpute v2.2、findhap v2和beagle v3.3.2),研究了参考群体(用Illumina BovineSNP50 SNP芯片进行基因分型)包含研究群体(用Illumina BovineLD SNP芯片进行基因分型)中个体的父系、半同胞或父系和半同胞时的填充准确性。使用两个标准,即真实基因型与填充基因型之间的相关性以及填充后的缺失率,来评估这三个程序在不同情况下的性能。我们 的结果表明,fimpute在所有情况下表现最佳,当从父系向其女儿或在半同胞之间进行填充时,相关性在0.921至0.978之间。一般来说,在半同胞之间或从父系向其女儿进行填充的准确性高于在非半同胞之间或从父系向非女儿进行填充的准确性。对于所有这三个程序,与参考群体中仅包含半同胞相比,在参考群体中同时包含父系和半同胞并没有提高填充性能。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验