Statistical method for mapping QTLs for complex traits based on two backcross populations

Most important agronomic and quality traits of crops are quantitative in nature. The genetic variations in such traits are usually controlled by sets of genes called quantitative trait loci (QTLs), and the interactions between QTLs and the environment. It is crucial to understand the genetic archite...

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Bibliographic Details
Published in:Chinese science bulletin 2012-07, Vol.57 (21), p.2645-2654
Main Authors: Zhu, ZhiHong, Hayart, Yousaf, Yang, Jian, Cao, LiYong, Lou, XiangYang, Xu, HaiMing
Format: Article
Language:English
Subjects:
backcrossing
Chemistry/Food Science
Earth Sciences
Engineering
experimental design
genes
haploidy
Humanities and Social Sciences
inbreeding
Life Sciences
Monte Carlo method
multidisciplinary
Physics
plant breeding
quantitative trait loci
Science
Science (multidisciplinary)
上位性效应
品质性状
回交群体
基因定位
基础
数量性状位点
相互作用
统计方法
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Summary:Most important agronomic and quality traits of crops are quantitative in nature. The genetic variations in such traits are usually controlled by sets of genes called quantitative trait loci (QTLs), and the interactions between QTLs and the environment. It is crucial to understand the genetic architecture of complex traits to design efficient strategies for plant breeding. In the present study, a new experimental design and the corresponding statistical method are presented for QTL mapping. The proposed mapping population is composed of double backcross populations derived from backcrossing both homozygous parents to DH (double haploid) or RI (recombinant inbreeding) lines separately. Such an immortal mapping population allows for across-environment replications, and can be used to estimate dominance effects, epistatic effects, and QTL-environment interactions, remedying the drawbacks of a single backcross population. In this method, the mixed linear model approach is used to estimate the positions of QTLs and their various effects including the QTL additive, dominance, and epistatic effects, and QTL-environment interaction effects (QE). Monte Carlo simulations were conducted to investigate the performance of the proposed method and to assess the accuracy and efficiency of its estimations. The results showed that the proposed method could estimate the positions and the genetic effects of QTLs with high efficiency.
ISSN:1001-6538
1861-9541
DOI:10.1007/s11434-012-5279-8