QTL analysis in plants; where are we now?

Kearsey, M. J.; FARQUHAR, A G L

doi:10.1046/j.1365-2540.1998.00500.x

Cited by 412 publications

(236 citation statements)

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“…A positive impact of such LD is the low marker density required to adequately cover the genome. Conversely, QTL positioning has low resolution such that the marker could be as much as 10-30 cM (centi-Morgans) from the causal allele (Kearsey and Farquhar 1998). …”

Section: Introductionmentioning

confidence: 99%

Genome-wide association study for oat (Avena sativa L.) beta-glucan concentration using germplasm of worldwide origin

et al. 2012

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Detection of quantitative trait loci (QTL) controlling complex traits followed by selection has become a common approach for selection in crop plants. The QTL are most often identified by linkage mapping using experimental F2, backcross, advanced inbred, or doubled haploid families. An alternative approach for QTL detection are genome-wide association studies (GWAS) that use pre-existing lines such as those found in breeding programs. We explored the implementation of GWAS in oat (Avena sativa L.) to identify QTL affecting β-glucan concentration, a soluble dietary fiber with several human health benefits when consumed as a whole grain. A total of 431 lines of worldwide origin were tested over 2 years and genotyped using Diversity Array Technology (DArT) markers. A mixed model approach was used where both population structure fixed effects and pair-wise kinship random effects were included. Various mixed models that differed with respect to population structure and kinship were tested for their ability to control for false positives. As expected, given the level of population structure previously described in oat, population structure did not play a large role in controlling for false positives. Three independent markers were significantly associated with β-glucan concentration. Significant marker sequences were compared with rice and one of the three showed sequence homology to genes localized on rice chromosome seven adjacent to the CslFgene family, known to have β-glucan synthase function. Results indicate that GWAS in oat can be a successful option for QTL detection, more so with future development of higher-density markers. Abstract Detection of quantitative trait loci (QTL) controlling complex traits followed by selection has become a common approach for selection in crop plants. The QTL are most often identified by linkage mapping using experimental F 2 , backcross, advanced inbred, or doubled haploid families. An alternative approach for QTL detection are genome-wide association studies (GWAS) that use pre-existing lines such as those found in breeding programs. We explored the implementation of GWAS in oat (Avena sativa L.) to identify QTL affecting b-glucan concentration, a soluble dietary fiber with several human health benefits when consumed as a whole grain. A total of 431 lines of worldwide origin were tested over 2 years and genotyped using Diversity Array Technology (DArT) markers. A mixed model approach was used where both population structure fixed effects and pair-wise kinship random effects were included. Various mixed models that differed with respect to population structure and kinship were tested for their ability to control for false positives. As expected, given the level of population structure previously described in oat, population structure did not play a large role in controlling for false positives. Three independent markers were significantly associated with b-glucan concentration. Significant marker sequences were compared with rice and one of the three showed sequence hom...

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Section: Introductionmentioning

confidence: 99%

Genome-wide association study for oat (Avena sativa L.) beta-glucan concentration using germplasm of worldwide origin

et al. 2012

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“…It can be noted that the average number of QTL per experiment (4.6) is close and from 15.9 to 9.9 cM on chromosome 10. On average for these loci, use of meta-analysis decreases the size of to that of 4 estimated by Kearsey and Farquhar (1998) over a very wide range of plant species and traits. All confidence intervals by a factor of 1.8 and therefore increases the precision of QTL mapping, which faciliregions of the map, except 4S and 7S, contain QTL involved in the variation of flowering time, although the tates the identification of relevant candidate genes.…”

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“…Almost 80 genes involved sides studies addressing flowering time for its direct interest for maize adaptation to temperate climates (Ragot in the timing of flowering are cloned and described for this species. Genetic, molecular, and physiological analet al 1995), this trait is also frequently scored as a component of yield (Mechin et al 2001), drought stress (Veldyses led to the elaboration of a model of the genetic interactions between these genes (Koornneef et al 1998;boom and Lee 1996), or pest resistance (Bohn et al 2000). A large body of QTL information is therefore Blazquez 2000).…”

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confidence: 99%

Genetic Architecture of Flowering Time in Maize As Inferred From Quantitative Trait Loci Meta-analysis and Synteny Conservation With the Rice Genome

et al. 2004

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Genetic architecture of flowering time in maize was addressed by synthesizing a total of 313 quantitative trait loci (QTL) available for this trait. These were analyzed first with an overview statistic that highlighted regions of key importance and then with a meta-analysis method that yielded a synthetic genetic model with 62 consensus QTL. Six of these displayed a major effect. Meta-analysis led in this case to a twofold increase in the precision in QTL position estimation, when compared to the most precise initial QTL position within the corresponding region. The 62 consensus QTL were compared first to the positions of the few flowering-time candidate genes that have been mapped in maize. We then projected rice candidate genes onto the maize genome using a synteny conservation approach based on comparative mapping between the maize genetic map and japonica rice physical map. This yielded 19 associations between maize QTL and genes involved in flowering time in rice and in Arabidopsis. Results suggest that the combination of meta-analysis within a species of interest and synteny-based projections from a related model plant can be an efficient strategy for identifying new candidate genes for trait variation.

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“…In practice, it would be expected that when markers are not fully informative, information from multiple markers along the chromosome can be used for inferring IBD probabilities using hidden Markov models and this improves the effects of reduced marker heterozygosity. In a literature review [16] regarding QTL analysis in plants, it is shown that more than 50% of all the QTL surveyed gave evidence of a dominance and overdominance mode of gene action as the most plausible models for the dominant QTL. These results, however, are likely due to the significance threshold imposed to declare that a QTL is real, which will cause very large bias in the dominance effects [16].…”

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confidence: 99%

“…In a literature review [16] regarding QTL analysis in plants, it is shown that more than 50% of all the QTL surveyed gave evidence of a dominance and overdominance mode of gene action as the most plausible models for the dominant QTL. These results, however, are likely due to the significance threshold imposed to declare that a QTL is real, which will cause very large bias in the dominance effects [16]. So the interpretation of a significant dominance, either a complete or an overdominant model, should be considered with caution.…”

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confidence: 99%

Further insights of the variance component method for detecting QTL in livestock and aquacultural species: relaxing the assumption of additive effects

Martínez¹

2008

Genet. Sel. Evol.

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-Complex traits may show some degree of dominance at the gene level that may influence the statistical power of simple models, i.e. assuming only additive effects to detect quantitative trait loci (QTL) using the variance component method. Little has been published on this topic even in species where relatively large family sizes can be obtained, such as poultry, pigs, and aquacultural species. This is important, when the idea is to select regions likely to be harbouring dominant QTL or in marker assisted selection. In this work, we investigated the empirical power and accuracy to both detect and localise dominant QTL with or without incorporating dominance effects explicitly in the model of analysis. For this purpose, populations with variable family sizes and constant population size and different values for dominance variance were simulated. The results show that when using only additive effects there was little loss in power to detect QTL and estimates of position, using or not using dominance, were empirically unbiased. Further, there was little gain in accuracy of positioning the QTL with most scenarios except when simulating an overdominant QTL.QTL / additive effect / dominance / power / REML

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QTL analysis in plants; where are we now?

Cited by 412 publications

References 50 publications

Genome-wide association study for oat (Avena sativa L.) beta-glucan concentration using germplasm of worldwide origin

Genome-wide association study for oat (Avena sativa L.) beta-glucan concentration using germplasm of worldwide origin

Genetic Architecture of Flowering Time in Maize As Inferred From Quantitative Trait Loci Meta-analysis and Synteny Conservation With the Rice Genome

Further insights of the variance component method for detecting QTL in livestock and aquacultural species: relaxing the assumption of additive effects

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