Mapping Quantitative Trait Loci Using the Experimental Designs of Recombinant Inbred Populations

Kao, C. R.

doi:10.1534/genetics.106.056416

Cited by 10 publications

(11 citation statements)

References 46 publications

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“…Although the method presented here concerns the F 2:3 design, its principal may be applicable to any type of F x:y design and some similar design (Kao 2006), that is granddaughter design (Weller et al 1990;Bovenhuis and Weller 1994;Mackinnon and Weller 1995;Ron et al 2001), NCIII design (Cockerham and Zeng 1996), with minor modifications (Zhang and Xu 2004). For example, the new method is extended to an F x:y design.…”

Section: Discussionmentioning

confidence: 99%

“…If the heritability of a trait is low, one can take family mean as a unit of phenotypic measurement (Mather and Jinks 1982;Lynch and Walsh 1998;Zhang and Xu 2004). This is an F 2:3 design in plant genetics (Austin and Lee 1996;Cockerham and Zeng 1996;Fisch et al 1996;Jiang and Zeng 1997;Chapman et al 2003;Zhang and Xu 2004;Kao 2006) and daughter or granddaughter design in animal genetics (Weller et al 1990). These designs are frequently used in mapping QTL in both plant and animal kingdom.…”

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Mapping Binary Trait Loci in the F2:3 Design

Zhu

Huang

Zhang

2007

Journal of Heredity

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In the inheritance analysis of quantitative trait with low heritability, the precision is relatively low. In this situation, an F(2:3) design, which is genotyped in F(2) plants and phenotyped in the F(2:3) progeny, is applied to increase the precision in the detection of quantitative trait loci (QTL). This is because that residual variance on the basis of family-mean-based observations has been significantly decreased by increasing the number of F(2:3) progeny. Our previous results showed that the mixture distribution for the F(2:3) family of heterozygous F(2) plant can significantly increase the power of QTL detection relative to the classical F(2) design. In this article, we extended our previous method from continuous traits to binary traits in the F(2:3) design. The method here also takes full advantage of the mixture distribution. However, the method presented here differs from our previous method in 2 aspects. One is that the penetrance model is integrated with the liability model for mapping binary trait loci (BTL), and another is that the phenotypic data used in the analysis are the sum of phenotypic values of F(2:3) progeny derived from each F(2) plant rather than the average of F(2:3) progeny due to the fact that the distribution of the sum follows binomial distribution. In addition, the threshold in the liability model could also be estimated. Therefore, a new framework of mapping BTL on the basis of a single BTL model was set up and implemented via the Expectation-Maximization algorithm. Results of simulated studies showed that the proposed method provides accurate estimates for both the effects and the locations of BTL, with high statistical power even under the low heritability. With the new method, we are ready to map BTL, as we can do for quantitative traits under the F(2:3) design. The computer program performing the analysis of the simulated data is available to users for real data analysis.

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

Mapping Binary Trait Loci in the F2:3 Design

Zhu

Huang

Zhang

2007

Journal of Heredity

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“…The component structures allow us to investigate some QTL mapping properties. For example, the additive (dominance) variances are found to increase (decrease) in the RI or IRI population, showing that these populations may facilitate (hinder) the estimation of the additive (dominance) effects (Kao, 2006). Also, the possible confounding problems in QTL estimation may be identified from the covariances between genetic effects (Kao & Zeng, 2002;Kao, 2006).…”

Section: (I) Data Structurementioning

confidence: 99%

A study on the mapping of quantitative trait loci in advanced populations derived from two inbred lines

Kao

Zeng

2009

Genet. Res.

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In genetic and biological studies, the F2 population is one of the most popular and commonly used experimental populations mainly because it can be readily produced and its genome structure possesses several niceties that allow for productive investigation. These niceties include the equivalence between the proportion of recombinants and recombination rates, the capability of providing a complete set of three genotypes for every locus and an analytically attractive first-order Markovian property. Recently, there has been growing interest in using the progeny populations from F2 (advanced populations) because their genomes can be managed to meet specific purposes or can be used to enhance investigative studies. These advanced populations include recombinant inbred populations, advanced intercrossed populations, intermated recombinant inbred populations and immortalized F2 populations. Due to an increased number of meiosis cycles, the genomes of these advanced populations no longer possess the Markovian property and are relatively more complicated and different from the F2 genomes. Although issues related to quantitative trait locus (QTL) mapping using advanced populations have been well documented, still these advanced populations are often investigated in a manner similar to the way F2 populations are studied using a first-order Markovian assumption. Therefore, more efforts are needed to address the complexities of these advanced populations in more details. In this article, we attempt to tackle these issues by first modifying current methods developed under this Markovian assumption to propose an ad hoc method (the Markovian method) and explore its possible problems. We then consider the specific genome structures present in the advanced populations without invoking this assumption to propose a more adequate method (the non-Markovian method) for QTL mapping. Further, some QTL mapping properties related to the confounding problems that result from ignoring epistasis and to mapping closely linked QTL are derived and investigated across the different populations. Simulations show that the non-Markovian method outperforms the Markovian method, especially in the advanced populations subject to selfing. The results presented here may give some clues to the use of advanced populations for more powerful and precise QTL mapping.

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“…Quantitative trait loci (QTL) detection has been a key step to provide deeper insight into the genetic mechanism of quantitative traits in many areas of biological researches, including crops, evolution, ecology and genetical genomics studies etc. (Lander and Botstein 1989;Haley and Knott 1992;Jansen 1993;Zeng 1994;Kao et al 1999;Sen and Churchill 2001;Broman 2003;Kao 2006;Lee et al 2014;Wei and Xu 2016). In QTL detection, it is often found that some of the genomic regions are relatively enriched in QTLs as compared to other regions, and that QTLs responsible for correlated traits frequently co-localize in some specific genetic regions (Goffinet and Gerber 2000;Schadt et al 2003;Chardon et al 2004;West et al 2007;Breitling et al 2008;Wu et al 2008;Li et al 2010;Ali et al 2013;Basnet et al 2015).…”

Section: Introductionmentioning

confidence: 99%

A Statistical Procedure for Genome-wide Detection of QTL Hotspots Using Public Databases with Application to Rice

Kao

Yang

2018

Preprint

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ii Running head： QTL Hotspot Detection ABSTRACT Genome-wide detection of quantitative trait loci (QTL) hotspots underlying variation in many molecular and phenotypic traits has been a key step in various biological studies since the QTL hotspots are highly informative and can be linked to the genes for the quantitative traits. Several statistical methods have been proposed to detect QTL hotspots.These hotspot detection methods rely heavily on permutation tests performed on summarized QTL data or individual-level data (with genotypes and phenotypes) from the genetical genomics experiments. In this article, we propose a statistical procedure for QTL hotspot detection by using the summarized QTL (interval) data collected in public webaccessible databases. First, a simple statistical method based on the uniform distribution is derived to convert the QTL interval data into the expected QTL frequency (EQF) matrix.And then, to account for the correlation structure among traits, the QTLs for correlated traits are grouped together into the same categories to form a reduced EQF matrix.Furthermore, a permutation algorithm on the EQF elements or on the QTL intervals is developed to compute a sliding scale of EQF thresholds, ranging from strict to liberal, for assessing the significance of QTL hotspots. With grouping, much stricter thresholds can be obtained to avoid the detection of spurious hotspots. Real example analysis and simulation study are carried out to illustrate our procedure, evaluate the performances and compare with other methods. It shows that our procedure can control the genome-wide error rates at the target levels, provide appropriate thresholds for correlated data and is comparable to the methods using individual-level data in hotspot detection. Depending on the thresholds used, more than 100 hotspots are detected in GRAMENE rice database. We also perform a genome-wide comparative analysis of the detected hotspots and the known genes collected in the Rice Q-TARO database. The comparative analysis reveals that the hotspots and genes are conformable in the sense that they co-localize closely and are functionally related to relevant traits. Our statistical procedure can provide a framework for exploring the networks among QTL hotspots, genes and quantitative traits in biological studies. The R codes that produce both numerical and graphical outputs of QTL hotspot detection in the genome are available on the worldwide web http://www.stat.sinica.edu.tw/~chkao/. Table S1 Nine major traits and their component traits in Gramene Rice database (http://www.gramene.org/). Major Trait Component Trait Yield 100-grain weight, 100-seed weight, 1000-grain weight, 1000-seed weight, biomass yield, brown rice yield, filled grain number, filled grain percentage, flower number, grain number, grain yield, grain yield per panicle, grain yield per plant, harvest index, large vascular bundle number to spikelet number ratio, leaf area to spikelet number ratio, panicle number, panicle tiller ratio, panicle weight, seed number, seed s...

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Mapping Quantitative Trait Loci Using the Experimental Designs of Recombinant Inbred Populations

Cited by 10 publications

References 46 publications

Mapping Binary Trait Loci in the F2:3 Design

Mapping Binary Trait Loci in the F2:3 Design

A study on the mapping of quantitative trait loci in advanced populations derived from two inbred lines

A Statistical Procedure for Genome-wide Detection of QTL Hotspots Using Public Databases with Application to Rice

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