QTLNetwork: mapping and visualizing genetic architecture of complex traits in experimental populations

Yang, Jian; Hu, Chengcheng; Hu, Han; Yu, Renbo; Xia, Zhen; Ye, Xiuzi; Zhu, Jun

doi:10.1093/bioinformatics/btm494

Cited by 390 publications

(248 citation statements)

References 12 publications

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“…The significance level of each trait in each of the three RIL mapping populations was obtained by permutation analyses using Map Manager QTX software (Manly et al 2001) (ESM Table S1). QTLNetwork v2.0 (Yang et al 2008) was used with default settings to search for epistatic effects among markers. The graphical representation of the map and identified QTL was created using MapChart software (Voorrips 2002).…”

Section: Linkage Map Constructionmentioning

confidence: 99%

QTL analysis in multiple sorghum populations facilitates the dissection of the genetic and physiological control of tillering

Alam

Mace²,

Oosterom

et al. 2014

Theor Appl Genet

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target environments. The aims of this study were to identify QTL for tillering and component traits associated with the S/D balance or PTT, to develop a framework model for the genetic control of tillering in sorghum. Four mapping populations were grown in a number of experiments in south east Queensland, Australia. The QTL analysis suggested that the contribution of traits associated with either the S/D balance or PTT to the genotypic differences in tillering differed among populations. Thirty-four tillering QTL were identified across the populations, of which 15 were novel to this study. Additionally, half of the tillering QTL co-located with QTL for component traits. A comparison of tillering QTL and candidate gene locations identified numerous coincident QTL and gene locations across populations, including the identification of common non-synonymous SNPs in the parental genotypes of two mapping populations in a sorghum homologue of MAX1, a gene involved in the control of tiller bud outgrowth through the production of strigolactones. Combined with a framework for crop physiological processes that underpin genotypic differences in tillering, the co-location of QTL for tillering and component traits and candidate genes allowed the development of a framework QTL model for the genetic control of tillering in sorghum.

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Section: Linkage Map Constructionmentioning

confidence: 99%

QTL analysis in multiple sorghum populations facilitates the dissection of the genetic and physiological control of tillering

Alam

Mace²,

Oosterom

et al. 2014

Theor Appl Genet

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show abstract

“…A critical value of the Likelihood Ratio statistic of 5.99 (w 2 0.05,2 ) or greater at the exact peak position of the composite QTL for two or more individual traits comprising the composite trait was interpreted as statistical support for pleiotropy (Jiang and Zeng, 1995). Finally, epistasis between all pair-wise combinations of markers was investigated for each trait using QTLNetwork 2.0 (http:// ibi.zju.edu.cn/software/qtlnetwork/) using the default settings (Yang et al, 2008). This analytic method controls for inflated Type I error rates resulting from multiple tests, and marker interactions with ao0.05 were considered significant.…”

Section: Linkage Map Construction and Qtl Analysismentioning

confidence: 99%

The genetic basis of speciation in the Giliopsis lineage of Ipomopsis (Polemoniaceae)

2013

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One of the most powerful drivers of speciation in plants is pollinator-mediated disruptive selection, which leads to the divergence of floral traits adapted to the morphology and behavior of different pollinators. Despite the widespread importance of this speciation mechanism, its genetic basis has been explored in only a few groups. Here, we characterize the genetic basis of pollinator-mediated divergence of two species in genus Ipomopsis, I. guttata and I. tenuifolia, using quantitative trait locus (QTL) analyses of floral traits and other variable phenotypes. We detected one to six QTLs per trait, with each QTL generally explaining small to modest amounts of the phenotypic variance of a backcross hybrid population. In contrast, flowering time and anthocyanin abundance (a metric of color variation) were controlled by a few QTLs of relatively large effect. QTLs were strongly clustered within linkage groups, with 26 of 37 QTLs localized to six marker-interval 'hotspots,' all of which harbored pleiotropic QTLs. In contrast to other studies that have examined the genetic basis of pollinator shifts, our results indicate that, in general, mutations of small to modest effect on phenotype were involved. Thus, the evolutionary transition between the distinct pollination modes of I. guttata and I. tenuifolia likely proceeded incrementally, rather than saltationally.

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“…QTL Cartographer (Wang et al 2005), QTLNetwork (Yang et al 2005), PlabQTL (Utz and Melchinger 1996) are limited in flexibility. The software does not account for experimental error effects (i.e.…”

Section: Introductionmentioning

confidence: 99%

Multi-environment QTL mixed models for drought stress adaptation in wheat

et al. 2008

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Many quantitative trait loci (QTL) detection methods ignore QTL-by-environment interaction (QEI) and are limited in accommodation of error and environment-specific variance. This paper outlines a mixed model approach using a recombinant inbred spring wheat population grown in six drought stress trials. Genotype estimates for yield, anthesis date and height were calculated using the best design and spatial effects model for each trial. Parsimonious factor analytic models best captured the variance-covariance structure, including genetic correlations, among environments. The 1RS.1BL rye chromosome translocation (from one parent) which decreased progeny yield by 13.8 g m -2 was explicitly included in the QTL model. Simple interval mapping (SIM) was used in a genome-wide scan for significant QTL, where QTL effects were fitted as fixed environmentspecific effects. All significant environment-specific QTL were subsequently included in a multi-QTL model and evaluated for main and QEI effects with non-significant QEI effects being dropped. QTL effects (either consistent or environment-specific) included eight yield, four anthesis, and six height QTL. One yield QTL co-located (or was linked) to an anthesis QTL, while another co-located with a height QTL. In the final multi-QTL model, only one QTL for yield (6 g m -2 ) was consistent across environments (no QEI), while the remaining QTL had significant QEI effects (average size per environment of 5.1 g m -2 ). Compared to single trial analyses, the described framework allowed explicit modelling and detection of QEI effects and incorporation of additional classification information about genotypes.

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QTLNetwork: mapping and visualizing genetic architecture of complex traits in experimental populations

Abstract: http://ibi.zju.edu.cn/software/qtlnetwork.

Cited by 390 publications

References 12 publications

QTL analysis in multiple sorghum populations facilitates the dissection of the genetic and physiological control of tillering

QTL analysis in multiple sorghum populations facilitates the dissection of the genetic and physiological control of tillering

The genetic basis of speciation in the Giliopsis lineage of Ipomopsis (Polemoniaceae)

Multi-environment QTL mixed models for drought stress adaptation in wheat

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