LinkImpute: Fast and Accurate Genotype Imputation for Nonmodel Organisms.
LinkImpute: Fast and Accurate Genotype Imputation for Nonmodel Organisms.
复制标题
DOI：
10.1534/g3.115.021667
复制
发表时间：
2015-09-15
期刊：
影响因子：
--
通讯作者：
Myles S
中科院分区：
文献类型：
--
作者：
Money D;Gardner K;Migicovsky Z;Schwaninger H;Zhong GY;Myles S
从一组个体获得全基因组范围的基因数据是许多基因组研究的第一步，包括全基因组关联和基因组选择。所有的基因分型方法都存在不同程度的数据缺失，而基因分型可以用来填补缺失的数据，提高下游分析的能力。像人和牛这样的模式生物受益于高质量的参考基因组和参考基因型板，这有助于推断的准确性。然而，在非模式生物中，遗传和物理图谱往往要么质量很差，要么完全不存在，也没有可用的参考基因型板。因此，需要专门为基因组资源不发达和标记顺序不可靠或未知的非模式生物设计的归属方法。本文介绍的LinkImpute是一个基于k近邻基因定位方法的无序标记软件LD-kNNi。它不需要物理或遗传图谱，而且它的设计是为了处理杂合子物种的非相基因数据。它利用了这样一个事实，即对推断有用的标记通常不是物理上接近缺失的基因，而是分布在整个基因组中。利用苹果、葡萄和玉米等不同杂合种质的测序分型数据，我们比较了LD-kNNi和几种基因型推定方法，结果表明，LD-kNNi方法速度快，精度与现有的最好的方法相当，在等位基因频率估计方面的偏差最小。
Obtaining genome-wide genotype data from a set of individuals is the first step in many genomic studies, including genome-wide association and genomic selection. All genotyping methods suffer from some level of missing data, and genotype imputation can be used to fill in the missing data and improve the power of downstream analyses. Model organisms like human and cattle benefit from high-quality reference genomes and panels of reference genotypes that aid in imputation accuracy. In nonmodel organisms, however, genetic and physical maps often are either of poor quality or are completely absent, and there are no panels of reference genotypes available. There is therefore a need for imputation methods designed specifically for nonmodel organisms in which genomic resources are poorly developed and marker order is unreliable or unknown. Here we introduce LinkImpute, a software package based on a k-nearest neighbor genotype imputation method, LD-kNNi, which is designed for unordered markers. No physical or genetic maps are required, and it is designed to work on unphased genotype data from heterozygous species. It exploits the fact that markers useful for imputation often are not physically close to the missing genotype but rather distributed throughout the genome. Using genotyping-by-sequencing data from diverse and heterozygous accessions of apples, grapes, and maize, we compare LD-kNNi with several genotype imputation methods and show that LD-kNNi is fast, comparable in accuracy to the best-existing methods, and exhibits the least bias in allele frequency estimates.