Guangwei Li
Yanru Cui
Yiming Yu
Qifa Zhang
Shizhong Xu
aNational Key Laboratory of Crop Genetic Improvement and National Centre of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, 430070, China
bDepartment of Botany and Plant Sciences, University of California Riverside, Riverside, CA, 92521, USA
cNational Key Laboratory of Wheat and Maize Crop Science, College of Life Sciences, Henan Agricultural University, Zhengzhou, 450002, China
dCollege of Agronomy, Hebei Agricultural University, Baoding, 071001, China
More InformationCorresponding author: E-mail address: qifazh@mail.hzau.edu.cn (Qifa Zhang);E-mail address: shizhong.xu@ucr.edu (Shizhong Xu)
Received Date: 2018-12-31
Accepted Date:2019-06-21
Rev Recd Date:2019-06-16
Available Online: 2019-07-23 Publish Date:2019-07-20
Abstract
Abstract
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.Keywords: Genome-wide association studies,
Linear mixed model,
Polygene,
Proximal contamination,
QTL mapping
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