Quantitative Trait Locus Mapping Based on Resampling in a Vast Maize Testcross Experiment and Its Relevance to Quantitative Genetics for Complex Traits

Schön, C. C.; Utz, H. Friedrich; Groh, S.; Truberg, Bernd; Openshaw, Steve; Melchinger, Albrecht E.

doi:10.1534/genetics.167.1.485

Cited by 242 publications

(186 citation statements)

References 25 publications

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“…The poor precision of QTL effect estimation is also reflected in the large range of estimated p G ÀES and p G ÀTS values (Figure 4). Our results corroborate previous findings (Schön et al, 2004;Liu et al, 2013) as we observed an average relative bias of 38%. This is higher than what was observed in a previous study using the same data set where a cross-validation was used assuming the QTL detected with the full data set as fixed (Würschum et al, 2011a).…”

Section: Predictive Power and Its Biassupporting

confidence: 93%

“…Recent work has shown this parameter to be overestimated with a relative bias between 10 and 60% depending on the population and the complexity of the trait (Utz et al, 2000;Schön et al, 2004;Liu et al, 2013). This reduction of p G ÀTS as compared with p G ÀES indicates a large upward bias in predictors of the proportion of explained genotypic variance, that is, QTL effects inferred from the ES.…”

Section: Predictive Power and Its Biasmentioning

confidence: 99%

“…However, for marker-assisted selection to be superior to classical phenotypic selection based on field evaluation, the following criteria must be met: (i) the QTL positions must be estimated accurately to minimize the number of recombinations between the QTL and the markers used for selection, (ii) QTL effects must be estimated with high precision and (iii) the genotypic variance explained by the detected QTL must be high and estimated without bias. For linkage mapping, it has been shown through simulation studies (Beavis 1998) and experimental data analysis (Utz et al, 2000;Schön et al, 2004), that both the QTL effects and the proportion of explained genotypic variance are often overestimated. This results in a wrong, oftentimes excessively optimistic assessment of the prospects of marker-assisted selection.…”

Section: Introductionmentioning

confidence: 99%

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Cross-validation in association mapping and its relevance for the estimation of QTL parameters of complex traits

Würschum

Kraft²

2013

Heredity

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Association mapping has become a widely applied genomic approach to identify quantitative trait loci (QTL) and dissect the genetic architecture of complex traits. However, approaches to assess the quality of the obtained QTL results are lacking. We therefore evaluated the potential of cross-validation in association mapping based on a large sugar beet data set. Our results show that the proportion of the population that should be used as estimation and validation sets, respectively, depends on the size of the mapping population. Generally, a fivefold cross-validation, that is, 20% of the lines as independent validation set, appears appropriate for commonly used population sizes. The predictive power for the proportion of genotypic variance explained by QTL was overestimated by on average 38% indicating a strong bias in the estimated QTL effects. The crossvalidated predictive power ranged between 4 and 50%, which are more realistic estimates of this parameter for complex traits. In addition, QTL frequency distributions can be used to assess the precision of QTL position estimates and the robustness of the detected QTL. In summary, cross-validation can be a valuable tool to assess the quality of QTL parameters in association mapping.

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Section: Predictive Power and Its Biassupporting

confidence: 93%

Section: Predictive Power and Its Biasmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Cross-validation in association mapping and its relevance for the estimation of QTL parameters of complex traits

Würschum

Kraft²

2013

Heredity

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“…Different conclusions regarding the number, magnitude, and distribution of QTL for complex traits might be drawn if larger populations were evaluated (29,30). Comparing LD mapping of 305 lines versus 522 lines demonstrates the beneficial effect of larger population size in this study (Table S6).…”

Section: Discussionmentioning

confidence: 79%

Joint linkage–linkage disequilibrium mapping is a powerful approach to detecting quantitative trait loci underlying drought tolerance in maize

Zhang

Shah

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

198

129

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This paper describes two joint linkage-linkage disequilibrium (LD) mapping approaches: parallel mapping (independent linkage and LD analysis) and integrated mapping (datasets analyzed in combination). These approaches were achieved using 2,052 single nucleotide polymorphism (SNP) markers, including 659 SNPs developed from drought-response candidate genes, screened across three recombinant inbred line (RIL) populations and 305 diverse inbred lines, with anthesis-silking interval (ASI), an important trait for maize drought tolerance, as the target trait. Mapping efficiency was improved significantly due to increased population size and allele diversity and balanced allele frequencies. Integrated mapping identified 18 additional quantitative trait loci (QTL) not detected by parallel mapping. The use of haplotypes improved mapping efficiency, with the sum of phenotypic variation explained (PVE) increasing from 5.4% to 23.3% for single SNPbased analysis. Integrated mapping with haplotype further improved the mapping efficiency, and the most significant QTL had a PVE of up to 34.7%. Normal allele frequencies for 113 of 277 (40.8%) SNPs with minor allele frequency (<5%) in 305 lines were recovered in three RIL populations, three of which were significantly associated with ASI. The candidate genes identified by two significant haplotype loci included one for a SET domain protein involved in the control of flowering time and the other encoding aldo/keto reductase associated with detoxification pathways that contribute to cellular damage due to environmental stress. Joint linkage-LD mapping is a powerful approach for detecting QTL underlying complex traits, including drought tolerance.anthesis-silking interval | drought resistance | haplotype loci | integrated quantitative trait locus mapping

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“…Genetic variation for complex traits, such as yield potential in elite maize populations, is controlled by many genetic factors, each with relatively small effects (Schö n et al, 2004;Holland, 2007). Therefore, the usage of QTL mapping and marker-assisted selection approaches for yield potential in maize is questionable (Holland, 2004).…”

Section: Introductionmentioning

confidence: 99%

Direct mapping of density response in a population of B73 × Mo17 recombinant inbred lines of maize (Zea Mays L.)

et al. 2009

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Maize yield per unit area has dramatically increased over time as have plant population densities, but the genetic basis for plant response to density is unknown as is its stability over environments. To elucidate the genetic basis of plant response to density in maize, we mapped QTL for plant density-related traits in a population of 186 recombinant inbred lines (RILs) derived from the cross of inbred lines B73 and Mo17. All RILs were evaluated for growth, development, and yield traits at moderate (50 000 plants per hectare) and high (100 000 plants per hectare) plant densities. The results show that genetic control of the traits evaluated is multigenic in their response to density. Five of the seven loci significant for final height showed statistical evidence for epistatic interactions. Other traits such as days to anthesis, anthesis-to-silking interval, barrenness, ears per plant, and yield per plant all showed statistical evidence for an epistatic interaction. Locus by density interactions are of critical importance for anthesis-to-silking interval, barrenness, and ears per plant. A second independent experiment to examine the stability of QTL for barrenness in a new environment clearly showed that the multilocus QTL were stable across environments in their differential response to density. In this verification experiment, the four-locus QTL was used to choose lines with the four unfavorable alleles and compare them with the lines with four favorable alleles and the effect was confirmed.

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Quantitative Trait Locus Mapping Based on Resampling in a Vast Maize Testcross Experiment and Its Relevance to Quantitative Genetics for Complex Traits

Cited by 242 publications

References 25 publications

Cross-validation in association mapping and its relevance for the estimation of QTL parameters of complex traits

Cross-validation in association mapping and its relevance for the estimation of QTL parameters of complex traits

Joint linkage–linkage disequilibrium mapping is a powerful approach to detecting quantitative trait loci underlying drought tolerance in maize

Direct mapping of density response in a population of B73 × Mo17 recombinant inbred lines of maize (Zea Mays L.)

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