文章的标题是这个帖子的主题,是因为这篇文章勾引的

1 A simple method for calculating the statistical power for detecting a QTL located in a marker interval

文章摘要部分特别提到:This method allows us to answer one of the fundamental questions in designing a
QTL mapping experiment: What is the minimum marker density required to detect a QTL explaining a certain heritable proportion of the phenotypic variance (denoted by h2) with a power g under a Type I error a in an F2 or other mating designs with a sample size n? Computing the statistical power only requires the ability to evaluate a noncentral F-distribution function and the inverse function of this distribution.

A_simple_method_for_calculating_the_statistical.pdf

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2 Statistical methods for QTL mapping in cereals

2002 REV Statistical methods for QTL mapping in cereals.pdf

3 Believe it or not, QTLs are accurate!

Believe it or not, QTLs are accurate.pdf

4 To clone or not to clone plant QTLs: present and future challenges

2005 REV To clone or not to clone plant QTLs.pdf

5 Epistasis: too often neglected in complex trait studies?

2004 REV Epistasis too often neglected in.pdf

6 FINDING THE MOLECULAR BASIS OF QUANTITATIVE TRAITS: SUCCESSES AND PITFALLS(这篇是2001年的NATURE REVIEWS  GENETICS).那时的现状:However,identifying the molecular bases of QTL remains a challenge

2001 REV FINDING THE MOLECULAR BASIS of qtl.pdf

7 The role of QTLs in the breeding of high-yielding rice(这里总结了水稻中已经克隆了的QTL,6,7对比看可能会有点意思)

The role of QTLs in the breeding of high-yielding rice.pdf

QTL和环境互做的分析

Assessing the importance of genotype £ environment interaction for root traits in rice using a mapping population II: conventional QTL analysis(这篇文章的方法:双因素方差分析法（Two-way analysis of variance）.把QTL基因型当成一个因素,环境当成另外一个因素.逐个QTL分析)

2006 RIL root traits in rice using a mapping population II conventional QTL analysis.pdf

Identification of Quantitative Trait Loci in Rice for Yield, Yield Components, and Agronomic Traits across Years and Locations(这篇文章的方法为:additive main eff ects and multiplicative interaction,AMMI).

2006 RIL root traits in rice using a mapping population II conventional QTL analysis.pdf

该方法的优点:(AMMI) model is useful in understanding both main effects and G × E interaction in multi-location variety trials The AMMI model combines ANOVA for genotype and environment main eff ects with PCA of the G × E interaction into a single model with additive and multiplicative parameters. It has proven useful for understanding complex G × E interaction .Results can be graphed in a very informative biplot that shows both main and interaction eff ects for both genotypes and environments.