MCQTL: multi-allelic QTL mapping in multi-cross design

Jourjon, Marie‐Françoise; Jasson, Sylvain; Marcel, Jacques; Ngom, Baba; Mangin, Brigitte

doi:10.1093/bioinformatics/bth481

Cited by 110 publications

(89 citation statements)

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“…A second method for detecting QTL for each F1 population was performed using MCQTL v.5.2.4 (Jourjon et al 2005) with the linear marker regression model (Haley and Knott 1992). Either the additive allelic effect alone (additive model) or the additive and dominance allelic effects (dominance model), as described in Kawamura et al (2011), were used as the QTL model.…”

Section: Qtl Analysismentioning

confidence: 99%

“…Model parameters were estimated for each QTL (position, confidence region, and R 2 ). Finally, a QTL connected analysis for both two F1 populations together was performed with MCQTL v.5.2.4 (Jourjon et al 2005). The QTL model was a connected model assuming that the QTL allelic effects were identical within the two F1 populations for the common parent FRT51.…”

Section: Qtl Analysismentioning

confidence: 99%

“…Up to four different alleles may be segregating. For this reason, we used co-dominant markers (SSRs) to facilitate both the map construction and QTL detection (Van Ooijen 2006;Van Ooijen 2009;Jourjon et al 2005). This type of approach was proved to be successful in other plant species (Clement et al 2003;Souza et al 2013).…”

Section: Genetic Map Constructionmentioning

confidence: 99%

“…The alleles of different parents can be compared within a single model using MCQTL software (Jourjon et al 2005). Studies on maize (Blanc et al 2006;Blanc et al 2007) and on ryegrass (Pauly et al 2012) reported a higher number of QTLs detected with this connected model than in single-population analyses.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of three QTL detection models on biochemical, sensory, and yield characters in Coffea canephora

Mangin¹,

Lefebvre-Pautigny

Jasson³

et al. 2014

Tree Genetics & Genomes

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Coffea canephora is subject to enormous competitive challenges from other crops, especially for farmer sustainability and consumer requirements. Coffee breeding programs have to focus on specific traits linked to these two key targets, such as quality character, largely depending on the bean's biochemical composition and field yield. Two segregating populations A and B, from crosses between a hybrid (Congolese×Guinean) FRT58 parental clone and a Congolese FRT51 genotype and between two Congolese parents FRT67 and FRT51, respectively, were used to characterize the quantitative trait loci (QTL) involved in agronomic and biochemical traits. A consensus genetic map was established using 249 SSRs covering 1,201 cM. Three QTL detection models per population with MapQTL (model I) and MCQTL (model II) followed by a connected population approach with MCQTL (model III) were compared based on their efficiency, precision for QTL detection, and their genetic effect assessment (additive, dominance, and parental-favorable allele). The analysis detected a total of 143 QTLs, 60 of which were shared between the three models; 28 found with two models; and two, 13, and 40 specific from models I, II, and III, respectively. The last model III based on connected populations is much more efficient in detecting QTLs with low variance explained and led to the genetic characterization of favorable allele. Thanks to this comparison of three QTL detection models on our quantitative genetic study, we will give a new insight for coffee breeding programs dedicated to managing complex agronomic or qualitative traits.

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Section: Qtl Analysismentioning

confidence: 99%

Section: Qtl Analysismentioning

confidence: 99%

Section: Genetic Map Constructionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparison of three QTL detection models on biochemical, sensory, and yield characters in Coffea canephora

Mangin¹,

Lefebvre-Pautigny

Jasson³

et al. 2014

Tree Genetics & Genomes

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“…For example, composite interval mapping (CIM) (Zeng 1994), originally developed for biparental populations, has been used in QTL mapping for MAGIC populations to control genomic background. Other methods and programs of QTL mapping in MAGIC populations include MCQTL (Jourjon et al 2005), R/qtl (Broman et al 2003), R happy (Mott et al 2000), and R/mpMap (Huang and George 2011), most of which have an option to perform CIM. However, there is an intrinsic limitation in cofactor selection, which is more problematic in MAGIC populations than in biparental populations.…”

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confidence: 99%

A Random-Model Approach to QTL Mapping in Multiparent Advanced Generation Intercross (MAGIC) Populations

Wei

2015

Genetics

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Most standard QTL mapping procedures apply to populations derived from the cross of two parents. QTL detected from such biparental populations are rarely relevant to breeding programs because of the narrow genetic basis: only two alleles are involved per locus. To improve the generality and applicability of mapping results, QTL should be detected using populations initiated from multiple parents, such as the multiparent advanced generation intercross (MAGIC) populations. The greatest challenges of QTL mapping in MAGIC populations come from multiple founder alleles and control of the genetic background information. We developed a random-model methodology by treating the founder effects of each locus as random effects following a normal distribution with a locus-specific variance. We also fit a polygenic effect to the model to control the genetic background. To improve the statistical power for a scanned marker, we release the marker effect absorbed by the polygene back to the model. In contrast to the fixed-model approach, we estimate and test the variance of each locus and scan the entire genome one locus at a time using likelihood-ratio test statistics. Simulation studies showed that this method can increase statistical power and reduce type I error compared with composite interval mapping (CIM) and multiparent whole-genome average interval mapping (MPWGAIM). We demonstrated the method using a public Arabidopsis thaliana MAGIC population and a mouse MAGIC population.KEYWORDS best linear unbiased prediction; empirical Bayes; mixed model; polygene; restricted maximum likelihood; multiparental populations; Multiparent Advanced Generation Inter-Cross (MAGIC); MPP T HERE is an urgent need to develop and study multiparent advanced generation intercross (MAGIC) populations (Rakshit et al. 2012). Along with nested association mapping populations (Yu et al. 2008), the MAGIC population is called a second-generation mapping resource (Rakshit et al. 2012). Using MAGIC populations to perform QTL mapping was first proposed for mice by Threadgill et al. (2002). Such a population is called the Collaborative Cross (CC) population (Churchill et al. 2004;Collaborative Cross Consortium 2012). Simulation studies showed that an eight-parent CC population with 1000 progenies is capable of increasing mapping resolution to the sub-centimorgan range (Valdar et al. 2006). MAGIC populations in Drosophila melanogaster are called Drosophila Synthetic Population Resources (DSPR) (MacDonald and Long 2007; King et al. 2012a, et al.b). A review of MAGIC populations in crops can be found in Huang et al. (2015). The first plant MAGIC population was developed in Arabidopsis thaliana by Kover et al. (2009). The population will be described later. Subsequently, MAGIC populations have been developed in wheat (Huang et al. 2012;Mackay et al. 2014), rice (Bandillo et al. 2013), and other crop species (Gaur et al. 2012;Pascual et al. 2015;Sannemann et al. 2015). One key difference between MAGIC populations and other multiparent populations is ...

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Designing Marker‐Assisted Inbred Line Development Strategies Using Computer Simulation

Ginkel

2010

Plant Breeding Reviews

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MCQTL: multi-allelic QTL mapping in multi-cross design

Abstract: The program is available on request after signing a licence agreement; free of charge for academic and non-profit organizations at http://www.genoplante.org (Bioinformatics products).

Cited by 110 publications

References 13 publications

Comparison of three QTL detection models on biochemical, sensory, and yield characters in Coffea canephora

Comparison of three QTL detection models on biochemical, sensory, and yield characters in Coffea canephora

A Random-Model Approach to QTL Mapping in Multiparent Advanced Generation Intercross (MAGIC) Populations

Designing Marker‐Assisted Inbred Line Development Strategies Using Computer Simulation

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