National Key Laboratory of Crop Genetic Improvement and National Centre of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, 430070, China; National Key Laboratory of Wheat and Maize Crop Science, College of Life Sciences, Henan Agricultural University, Zhengzhou, 450002, China.
National Key Laboratory of Crop Genetic Improvement and National Centre of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, 430070, China.
J Genet Genomics. 2019 Jul 20;46(7):343-352. doi: 10.1016/j.jgg.2019.06.005. Epub 2019 Jul 23.
Precise mapping of quantitative trait loci (QTLs) is critical for assessing genetic effects and identifying candidate genes for quantitative traits. Interval and composite interval mappings have been the methods of choice for several decades, which have provided tools for identifying genomic regions harboring causal genes for quantitative traits. Historically, the concept was developed on the basis of sparse marker maps where genotypes of loci within intervals could not be observed. Currently, genomes of many organisms have been saturated with markers due to the new sequencing technologies. Genotyping by sequencing usually generates hundreds of thousands of single nucleotide polymorphisms (SNPs), which often include the causal polymorphisms. The concept of interval no longer exists, prompting the necessity of a norm change in QTL mapping technology to make use of the high-volume genomic data. Here we developed a statistical method and a software package to map QTLs by binning markers into haplotype blocks, called bins. The new method detects associations of bins with quantitative traits. It borrows the mixed model methodology with a polygenic control from genome-wide association studies (GWAS) and can handle all kinds of experimental populations under the linear mixed model (LMM) framework. We tested the method using both simulated data and data from populations of rice. The results showed that this method has higher power than the current methods. An R package named binQTL is available from GitHub.
精确地定位数量性状基因座（QTL）对于评估遗传效应和鉴定数量性状的候选基因至关重要。几十年来，区间和复合区间作图一直是首选方法，为鉴定含有数量性状因果基因的基因组区域提供了工具。从历史上看，这一概念是基于稀疏标记图谱发展起来的，在这些图谱中，无法观察到区间内基因座的基因型。目前，由于新的测序技术，许多生物体的基因组已经被标记饱和。测序的基因分型通常会产生数十万的单核苷酸多态性（SNP），其中通常包括因果多态性。区间的概念不再存在，这促使 QTL 作图技术需要进行规范变更，以利用大容量的基因组数据。在这里，我们开发了一种统计方法和一个软件包，通过将标记分组到单倍型块中（称为 bin）来绘制 QTL。这种新方法检测了 bin 与数量性状的关联。它借鉴了全基因组关联研究（GWAS）中的混合模型方法和多基因控制，可以在线性混合模型（LMM）框架下处理各种实验群体。我们使用模拟数据和水稻群体的数据测试了该方法。结果表明，该方法比当前方法具有更高的功效。一个名为 binQTL 的 R 包可以从 GitHub 获得。
