Comparison of biometrical approaches for QTL detection in multiple segregating families

Liu, Wenxin; Reif, Jochen C.; Ranc, Nicolas; Porta, Giovanni Della; Würschum, Tobias

doi:10.1007/s00122-012-1889-4

Cited by 33 publications

(31 citation statements)

References 36 publications

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“…Although joint-population analysis in our present study only included two populations sharing one common parent, the result clearly showed that much more QTL were detected and the accuracy of QTL localization was increased, with 318/412 QTL and average marker intervals 12.5/9.6 in single-population/joint-population analysis. Obviously, multiple population analysis could extensively improve the result of QTL detection (Blanc et al 2006;Myles et al 2009;Li et al 2011;Steinhoff et al 2011;Liu et al 2012). bins 2.06, 6.06-6.07 and 8.03-8.04, for TL at bins 4.06-4.07, 6.00, 6.04-6.05 and 9.03, for EH at bins 5.00, 5.00-5.01, 6.07, 6.08 and 7.05, for TB at bins 2.04-2.05, 2.05-2.06, 3.06-3.07, 5.05 and 7.04-7.05, for LOV at bins 1.03-1.04, 2.08-2.09, 4.01, 5.02-5.03, 10.06-10.07 and for LL at bins 1.06-1.07, 1.09, 4.00-4.01, 5.01-5.02, 5.08-5.09, 7.03-7.04 and 8.00-8.02.…”

Section: Comparison Of Qtl Detected In the Two Connected Ril Populatimentioning

confidence: 99%

“…QTL detection for plant traits using connected RIL populations has not been reported in high-oil maize. Also, QTL detection in multiple segregating families has not been adopted, although previous research proved that such methods could increase the power of QTL detection and the accuracy of QTL localization, and provide more robust estimates of QTL effects across different genetic backgrounds (Blanc et al 2006;Myles et al 2009;Li et al 2011;Steinhoff et al 2011;Liu et al 2012).…”

Section: Introductionmentioning

confidence: 98%

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Integrative detection and verification of QTL for plant traits in two connected RIL populations of high-oil maize

Yang

Dong

et al. 2015

Euphytica

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Plant traits play an important role in determining plant density and final grain yield. In this study, two related RIL populations developed from two crosses between one high-oil maize inbred and two normal dent maize inbreds were evaluated for 13 plant traits under four environments. QTL were detected within population and in joint-population analysis, and compared with the result obtained in the two F 2:3 generations. Our main objective was to find identical QTL and key genetic regions valuable in further research. Totally, 318 single-population QTL, 142 pairs of digenic epistasis and 412 joint-population QTL were detected. Joint-population analysis could detect much more QTL and increase the accuracy of QTL localization. Consistent QTL across generations, environments and analysis methods were observed for five traits at four bins. QTL for plant height and ear height at bin 3.05, and for leaf area at bin 6.03-6.04 had the highest consistency across most situations. These QTL with high consistency were worthy to be put into marker-assisted selection in trait improvement and to construct near isogenic lines in further research. Maize breeding could be improved by integrating marker assisted selection and phenotypic selection.

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Section: Comparison Of Qtl Detected In the Two Connected Ril Populatimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Integrative detection and verification of QTL for plant traits in two connected RIL populations of high-oil maize

Yang

Dong

et al. 2015

Euphytica

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“…Recent family mapping results based on elite maize breeding material have shown that QTL for complex traits such as grain yield and grain moisture could be detected (Blanc et al 2006;Liu et al 2011Liu et al , 2012bSteinhoff et al 2011). Applying a cross-validation approach revealed a relative bias in the estimates of genotypic variance explained by the detected QTL of 10-60 % depending on the trait and the experiment.…”

Section: Predictive Power Of Detected Qtl and Validationmentioning

confidence: 99%

Mapping QTL for agronomic traits in breeding populations

2012

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Detection of quantitative trait loci (QTL) in breeding populations offers the advantage that these QTL are of direct relevance for the improvement of crops via knowledge-based breeding. As phenotypic data are routinely generated in breeding programs and the costs for genotyping are constantly decreasing, it is tempting to exploit this information to unravel the genetic architecture underlying important agronomic traits in crops. This review characterizes the germplasm from breeding populations available for QTL detection, provides a classification of the different QTL mapping approaches that are available, and highlights important considerations concerning study design and biometrical models suitable for QTL analysis.

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“…Different types of LDLA analyses have been proposed to account for the LD component. The simplest is to consider that parents carrying the same allele at a given marker are IBD (Yu et al 2008;Liu et al 2012) as done in association mapping. Haplotype-based approaches also have been proposed to group parental alleles and tested by simulations (for instance Jansen et al 2003;Bink et al 2012;Leroux et al 2014).…”

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confidence: 99%

Linkage Disequilibrium with Linkage Analysis of Multiline Crosses Reveals Different Multiallelic QTL for Hybrid Performance in the Flint and Dent Heterotic Groups of Maize

et al. 2014

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Multiparental designs combined with dense genotyping of parents have been proposed as a way to increase the diversity and resolution of quantitative trait loci (QTL) mapping studies, using methods combining linkage disequilibrium information with linkage analysis (LDLA). Two new nested association mapping designs adapted to European conditions were derived from the complementary dent and flint heterotic groups of maize (Zea mays L.). Ten biparental dent families (N = 841) and 11 biparental flint families (N = 811) were genotyped with 56,110 single nucleotide polymorphism markers and evaluated as test crosses with the central line of the reciprocal design for biomass yield, plant height, and precocity. Alleles at candidate QTL were defined as (i) parental alleles, (ii) haplotypic identity by descent, and (iii) single-marker groupings. Between five and 16 QTL were detected depending on the model, trait, and genetic group considered. In the flint design, a major QTL (R 2 = 27%) with pleiotropic effects was detected on chromosome 10, whereas other QTL displayed milder effects (R 2 , 10%). On average, the LDLA models detected more QTL but generally explained lower percentages of variance, consistent with the fact that most QTL display complex allelic series. Only 15% of the QTL were common to the two designs. A joint analysis of the two designs detected between 15 and 21 QTL for the five traits. Of these, between 27 for silking date and 41% for tasseling date were significant in both groups. Favorable allelic effects detected in both groups open perspectives for improving biomass production. MOST traits of agronomic interest present a continuous variation resulting from the sum of the effects of various quantitative trait loci (QTL). Mapping these QTL is a first step toward elucidating their molecular nature and offers important application perspectives for marker-assisted breeding. QTL mapping started in plants with segregating families derived from the cross of two inbred lines (Lander and Botstein 1989). However, such biparental designs address only a small portion of the diversity available (a maximum of two alleles can segregate at a given QTL) and the accuracy of QTL positions is usually poor. To overcome these limitations, Rebai and Goffinet (1993) and Charcosset et al. (1994) proposed models for joint QTL detection in several biparental families connected to each other by the use of common parental lines. When the number of parents is less than the number of families, connections can be taken into account to reduce the number of allelic effects to be estimated in the detection model. This increases power and accuracy of detection when QTL behave additively (see Blanc et al. 2006). However, such a model makes the assumption that each parental line carries a different allele, which limits its benefit when the number of parental lines is high relative to the number of families, a situation commonly encountered in breeding programs. Recent advances in sequencing and genotyping technologies make it possi...

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Comparison of biometrical approaches for QTL detection in multiple segregating families

Cited by 33 publications

References 36 publications

Integrative detection and verification of QTL for plant traits in two connected RIL populations of high-oil maize

Integrative detection and verification of QTL for plant traits in two connected RIL populations of high-oil maize

Mapping QTL for agronomic traits in breeding populations

Linkage Disequilibrium with Linkage Analysis of Multiline Crosses Reveals Different Multiallelic QTL for Hybrid Performance in the Flint and Dent Heterotic Groups of Maize

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