Short communication: QTL mapping for ear tip-barrenness in maize

Barren tip on corn ear is an important agronomic trait in maize, which is highly associated with grain yield. Understanding the genetic basis of tip-barrenness may help to reduce the ear tip-barrenness in breeding programs. In this study, ear tip-barrenness was evaluated in two environments in a F2:3 population, and it showed significant genotypic variation for ear tip-barrenness in both environments. Using mixed-model composite interval mapping method, three additive effects quantitative trait loci (QTL) for ear tip-barrenness were mapped on chromosomes 2, 3 and 6, respectively. They explained 16.6% of the phenotypic variation, and no significant QTL × Environment interactions and digenic interactions were detected. The results indicated that additive effect was the main genetic basis for ear tip-barrenness in maize. This is the first report of QTL mapped for ear tip-barrenness in maize.

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Deciphering the Genetic Architecture of Plant Height in Soybean Using Two RIL Populations Sharing a Common M8206 Parent

Plants ◽

10.3390/plants8100373 ◽

2019 ◽

Vol 8 (10) ◽

pp. 373 ◽

Cited By ~ 1

Author(s):

Yongce Cao ◽

Shuguang Li ◽

Guoliang Chen ◽

Yanfeng Wang ◽

Javaid Akhter Bhat ◽

...

Keyword(s):

Plant Height ◽

Quantitative Trait ◽

Composite Interval Mapping ◽

Mixed Model ◽

Interval Mapping ◽

Interaction Effects ◽

Epistatic Effect ◽

Environment Interaction ◽

Additive Effects ◽

Yield And Quality

Plant height (PH) is an important agronomic trait that is closely related to soybean yield and quality. However, it is a complex quantitative trait governed by multiple genes and is influenced by environment. Unraveling the genetic mechanism involved in PH, and developing soybean cultivars with desirable PH is an imperative goal for soybean breeding. In this regard, the present study used high-density linkage maps of two related recombinant inbred line (RIL) populations viz., MT and ZM evaluated in three different environments to detect additive and epistatic effect quantitative trait loci (QTLs) as well as their interaction with environments for PH in Chinese summer planting soybean. A total of eight and 12 QTLs were detected by combining the composite interval mapping (CIM) and mixed-model based composite interval mapping (MCIM) methods in MT and ZM populations, respectively. Among these QTLs, nine QTLs viz., QPH-2, qPH-6-2MT, QPH-6, qPH-9-1ZM, qPH-10-1ZM, qPH-13-1ZM, qPH-16-1MT, QPH-17 and QPH-19 were consistently identified in multiple environments or populations, hence were regarded as stable QTLs. Furthermore, Out of these QTLs, three QTLs viz., qPH-4-2ZM, qPH-15-1MT and QPH-17 were novel. In particular, QPH-17 could detect in both populations, which was also considered as a stable and major QTL in Chinese summer planting soybean. Moreover, eleven QTLs revealed significant additive effects in both populations, and out of them only six showed additive by environment interaction effects, and the environment-independent QTLs showed higher additive effects. Finally, six digenic epistatic QTLs pairs were identified and only four additive effect QTLs viz., qPH-6-2MT, qPH-19-1MT/QPH-19, qPH-5-1ZM and qPH-17-1ZM showed epistatic effects. These results indicate that environment and epistatic interaction effects have significant influence in determining genetic basis of PH in soybean. These results would not only increase our understanding of the genetic control of plant height in summer planting soybean but also provide support for implementing marker assisted selection (MAS) in developing cultivars with ideal plant height as well as gene cloning to elucidate the mechanisms of plant height.

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Bayesian Mapping of Multiple Quantitative Trait Loci From Incomplete Inbred Line Cross Data

Genetics ◽

10.1093/genetics/148.3.1373 ◽

1998 ◽

Vol 148 (3) ◽

pp. 1373-1388

Author(s):

Mikko J Sillanpää ◽

Elja Arjas

Keyword(s):

Quantitative Trait Loci ◽

Quantitative Trait ◽

Composite Interval Mapping ◽

Backcross Progeny ◽

Random Variable ◽

Interval Mapping ◽

Mapping Method ◽

Structure Mapping ◽

Standard Interval ◽

Trait Loci

Abstract A novel fine structure mapping method for quantitative traits is presented. It is based on Bayesian modeling and inference, treating the number of quantitative trait loci (QTLs) as an unobserved random variable and using ideas similar to composite interval mapping to account for the effects of QTLs in other chromosomes. The method is introduced for inbred lines and it can be applied also in situations involving frequent missing genotypes. We propose that two new probabilistic measures be used to summarize the results from the statistical analysis: (1) the (posterior) QTL-intensity, for estimating the number of QTLs in a chromosome and for localizing them into some particular chromosomal regions, and (2) the location wise (posterior) distributions of the phenotypic effects of the QTLs. Both these measures will be viewed as functions of the putative QTL locus, over the marker range in the linkage group. The method is tested and compared with standard interval and composite interval mapping techniques by using simulated backcross progeny data. It is implemented as a software package. Its initial version is freely available for research purposes under the name Multimapper at URL http://www.rni.helsinki.fi/~mjs.

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A Rank-Based Nonparametric Method for Mapping Quantitative Trait Loci in Outbred Half-Sib Pedigrees: Application to Milk Production in a Granddaughter Design

Genetics ◽

10.1093/genetics/149.3.1547 ◽

1998 ◽

Vol 149 (3) ◽

pp. 1547-1555 ◽

Cited By ~ 2

Author(s):

Wouter Coppieters ◽

Alexandre Kvasz ◽

Frédéric Farnir ◽

Juan-Jose Arranz ◽

Bernard Grisart ◽

...

Keyword(s):

Quantitative Trait Loci ◽

Quantitative Trait ◽

Real Data ◽

Interval Mapping ◽

Mapping Method ◽

Residual Variance ◽

Data Set ◽

Wilcoxon Rank Sum Test ◽

Holstein Friesian ◽

Trait Loci

Abstract We describe the development of a multipoint nonparametric quantitative trait loci mapping method based on the Wilcoxon rank-sum test applicable to outbred half-sib pedigrees. The method has been evaluated on a simulated dataset and its efficiency compared with interval mapping by using regression. It was shown that the rank-based approach is slightly inferior to regression when the residual variance is homoscedastic normal; however, in three out of four other scenarios envisaged, i.e., residual variance heteroscedastic normal, homoscedastic skewed, and homoscedastic positively kurtosed, the latter outperforms the former one. Both methods were applied to a real data set analyzing the effect of bovine chromosome 6 on milk yield and composition by using a 125-cM map comprising 15 microsatellites and a granddaughter design counting 1158 Holstein-Friesian sires.

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Footprints in time: comparative quantitative trait loci mapping of the pitcher-plant mosquito, Wyeomyia smithii

Proceedings of The Royal Society B Biological Sciences ◽

10.1098/rspb.2012.1917 ◽

2012 ◽

Vol 279 (1747) ◽

pp. 4551-4558 ◽

Cited By ~ 17

Author(s):

William E. Bradshaw ◽

Kevin J. Emerson ◽

Julian M. Catchen ◽

William A. Cresko ◽

Christina M. Holzapfel

Keyword(s):

Quantitative Trait Loci ◽

Adaptive Evolution ◽

Quantitative Trait ◽

Genetic Basis ◽

Natural Populations ◽

Cumulative Effect ◽

Single Species ◽

Interval Mapping ◽

Photoperiodic Response ◽

Trait Loci

Identifying regions of the genome contributing to phenotypic evolution often involves genetic mapping of quantitative traits. The focus then turns to identifying regions of ‘major’ effect, overlooking the observation that traits of ecological or evolutionary relevance usually involve many genes whose individual effects are small but whose cumulative effect is large. Herein, we use the power of fully interfertile natural populations of a single species of mosquito to develop three quantitative trait loci (QTL) maps: one between two post-glacially diverged populations and two between a more ancient and a post-glacial population. All demonstrate that photoperiodic response is genetically a highly complex trait. Furthermore, we show that marker regressions identify apparently ‘non-significant’ regions of the genome not identified by composite interval mapping, that the perception of the genetic basis of adaptive evolution is crucially dependent upon genetic background and that the genetic basis for adaptive evolution of photoperiodic response is highly variable within contemporary populations as well as between anciently diverged populations.

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Neighbor QTL: an interval mapping method for quantitative trait loci underlying plant neighborhood effects

10.1101/2020.05.20.089474 ◽

2020 ◽

Author(s):

Yasuhiro Sato ◽

Kazuya Takeda ◽

Atsushi J. Nagano

Keyword(s):

Qtl Mapping ◽

Neighborhood Effects ◽

Quantitative Trait ◽

Recombinant Inbred Lines ◽

Insect Herbivory ◽

R Package ◽

Interval Mapping ◽

Mapping Method ◽

Neighbor Effects ◽

Genotype Probabilities

AbstractPhenotypes of sessile organisms, such as plants, rely not only on their own genotype but also on the genotypes of neighboring individuals. Previously, we incorporated such neighbor effects into a single-marker regression using the Ising model of ferromagnetism. However, little is known about how to incorporate neighbor effects in quantitative trait locus (QTL) mapping. In this study, we propose a new method for interval QTL mapping of neighbor effects, named “Neighbor QTL”. The algorithm of neighbor QTL involves the following: (i) obtaining conditional self-genotype probabilities with recombination fraction between flanking markers, (ii) calculating neighbor genotypic identity using the self-genotype probabilities, and (iii) estimating additive and dominance deviation for neighbor effects. Our simulation using F2 and backcross lines showed that the power to detect neighbor effects increased as the effective range became smaller. The neighbor QTL was applied to insect herbivory on Col × Kas recombinant inbred lines of Arabidopsis thaliana. Consistent with previous evidence, the pilot experiment detected a self QTL effect on the herbivory at GLABRA1 locus. We also observed a weak QTL on chromosome 4 regarding neighbor effects on the herbivory. The neighbor QTL method is available as an R package (https://cran.r-project.org/package=rNeighborQTL), providing a novel tool to investigate neighbor effects in QTL studies.

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Using the mixed model for interval mapping of quantitative trait loci in outbred line crosses

Genetics Research ◽

10.1017/s0016672301004931 ◽

2001 ◽

Vol 77 (2) ◽

pp. 199-207 ◽

Cited By ~ 8

Author(s):

Y. NAGAMINE ◽

C. S. HALEY

Keyword(s):

Animal Model ◽

Quantitative Trait Loci ◽

Qtl Analysis ◽

Quantitative Trait ◽

Fixed Effects ◽

Mixed Model ◽

Interval Mapping ◽

Estimation Methods ◽

Polygenic Effect ◽

Trait Loci

Interval mapping by simple regression is a powerful method for the detection of quantitative trait loci (QTLs) in line crosses such as F2 populations. Due to the ease of computation of the regression approach, relatively complex models with multiple fixed effects, interactions between QTLs or between QTLs and fixed effects can easily be accommodated. However, polygenic effects, which are not targeted in QTL analysis, cannot be treated as random effects in a least squares analysis. In a cross between true inbred lines this is of no consequence, as the polygenic effect contributes just to the residual variance. In a cross between outbred lines, however, if a trait has high polygenic heritability, the additive polygenic effect has a large influence on variation in the population. Here we extend the fixed model for the regression interval mapping method to a mixed model using an animal model. This makes it possible to use not only the observations from progeny (e.g. F2), but also those from the parents (F1) to evaluate QTLs and polygenic effects. We show how the animal model using parental observations can be applied to an outbred cross and so increase the power and accuracy of QTL analysis. Three estimation methods, i.e. regression and an animal model either with or without parental observations, are applied to simulated data. The animal model using parental observations is shown to have advantages in estimating QTL position and additive genotypic value, especially when the polygenic heritability is large and the number of progeny per parent is small.

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QTL detection of yield-related traits of cashew

Cropp Breeding and Applied Biotechnology ◽

10.1590/s1984-70332012000100008 ◽

2012 ◽

Vol 12 (1) ◽

pp. 60-66 ◽

Cited By ~ 5

Author(s):

José Jaime Vasconcelos Cavalcanti ◽

Francisco Herbeth Costa dos Santos ◽

Fanuel Pereira da Silva ◽

Cássia Renata Pinheiro

Keyword(s):

Quantitative Trait ◽

Marker Assisted Selection ◽

Interval Mapping ◽

Phenotypic Variance ◽

Qtl Detection ◽

Total Phenotypic Variance ◽

Breeding Programs ◽

Multiple Qtl ◽

Male Flowers ◽

Selection Of

The identification of quantitative trait loci (QTL) and marker-assisted selection with a view to breeding programs have aroused great interest, including for cashew improvement. This study identified QTL for yield-related traits: nut weight, male and hermaphrodite flowers. The traits were evaluated in 71 F1 genotypes of the cross CCP 1001 x CP 96. The methods of interval mapping and multiple QTL mapping were applied to identify QTL. Eleven QTL were detected: three for nut weight, four for male flowers and four for hermaphrodite flowers. The QTL accounted for 3.79 to 12.98 % of the total phenotypic variance and had phenotypic effects of -31.81 to 34.25 %. The potential for marker-assisted selection of the QTL hf-2f and hf-3m is great and the phenotypic effects and percentage of phenotypic variation higher than of the others.

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Genetic Basis of Climatic Adaptation in Scots Pine by Bayesian Quantitative Trait Locus Analysis

Genetics ◽

10.1093/genetics/156.3.1309 ◽

2000 ◽

Vol 156 (3) ◽

pp. 1309-1322 ◽

Cited By ~ 1

Author(s):

Päivi Hurme ◽

Mikko J Sillanpää ◽

Elja Arjas ◽

Tapani Repo ◽

Outi Savolainen

Keyword(s):

Quantitative Trait Locus ◽

Scots Pine ◽

Quantitative Trait ◽

Genetic Basis ◽

Natural Populations ◽

Backcross Progeny ◽

Mapping Method ◽

Frost Hardiness ◽

Bud Set ◽

Trait Locus

Abstract We examined the genetic basis of large adaptive differences in timing of bud set and frost hardiness between natural populations of Scots pine. As a mapping population, we considered an “open-pollinated backcross” progeny by collecting seeds of a single F1 tree (cross between trees from southern and northern Finland) growing in southern Finland. Due to the special features of the design (no marker information available on grandparents or the father), we applied a Bayesian quantitative trait locus (QTL) mapping method developed previously for outcrossed offspring. We found four potential QTL for timing of bud set and seven for frost hardiness. Bayesian analyses detected more QTL than ANOVA for frost hardiness, but the opposite was true for bud set. These QTL included alleles with rather large effects, and additionally smaller QTL were supported. The largest QTL for bud set date accounted for about a fourth of the mean difference between populations. Thus, natural selection during adaptation has resulted in selection of at least some alleles of rather large effect.

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Bayesian Mapping of Multiple Quantitative Trait Loci From Incomplete Outbred Offspring Data

Genetics ◽

10.1093/genetics/151.4.1605 ◽

1999 ◽

Vol 151 (4) ◽

pp. 1605-1619 ◽

Cited By ~ 15

Author(s):

Mikko J Sillanpää ◽

Elja Arjas

Keyword(s):

Quantitative Trait ◽

Simulated Data ◽

Random Variable ◽

Interval Mapping ◽

Marker Locus ◽

Mapping Method ◽

Monte Carlo Algorithm ◽

Data Sets ◽

Outcrossing Species ◽

Trait Locus

Abstract A general fine-scale Bayesian quantitative trait locus (QTL) mapping method for outcrossing species is presented. It is suitable for an analysis of complete and incomplete data from experimental designs of F2 families or backcrosses. The amount of genotyping of parents and grandparents is optional, as well as the assumption that the QTL alleles in the crossed lines are fixed. Grandparental origin indicators are used, but without forgetting the original genotype or allelic origin information. The method treats the number of QTL in the analyzed chromosome as a random variable and allows some QTL effects from other chromosomes to be taken into account in a composite interval mapping manner. A block-update of ordered genotypes (haplotypes) of the whole family is sampled once in each marker locus during every round of the Markov Chain Monte Carlo algorithm used in the numerical estimation. As a byproduct, the method gives the posterior distributions for linkage phases in the family and therefore it can also be used as a haplotyping algorithm. The Bayesian method is tested and compared with two frequentist methods using simulated data sets, considering two different parental crosses and three different levels of available parental information. The method is implemented as a software package and is freely available under the name Multimapper/outbred at URL http://www.rni.helsinki.fi/~mjs/.

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Precision mapping of quantitative trait loci.

Genetics ◽

10.1093/genetics/136.4.1457 ◽

1994 ◽

Vol 136 (4) ◽

pp. 1457-1468 ◽

Cited By ~ 80

Author(s):

Z B Zeng

Keyword(s):

Quantitative Trait Loci ◽

Qtl Mapping ◽

Quantitative Trait ◽

Interval Mapping ◽

Mapping Method ◽

Search Problem ◽

Test Statistic ◽

Advantages And Disadvantages ◽

A Genome ◽

Trait Loci

Abstract Adequate separation of effects of possible multiple linked quantitative trait loci (QTLs) on mapping QTLs is the key to increasing the precision of QTL mapping. A new method of QTL mapping is proposed and analyzed in this paper by combining interval mapping with multiple regression. The basis of the proposed method is an interval test in which the test statistic on a marker interval is made to be unaffected by QTLs located outside a defined interval. This is achieved by fitting other genetic markers in the statistical model as a control when performing interval mapping. Compared with the current QTL mapping method (i.e., the interval mapping method which uses a pair or two pairs of markers for mapping QTLs), this method has several advantages. (1) By confining the test to one region at a time, it reduces a multiple dimensional search problem (for multiple QTLs) to a one dimensional search problem. (2) By conditioning linked markers in the test, the sensitivity of the test statistic to the position of individual QTLs is increased, and the precision of QTL mapping can be improved. (3) By selectively and simultaneously using other markers in the analysis, the efficiency of QTL mapping can be also improved. The behavior of the test statistic under the null hypothesis and appropriate critical value of the test statistic for an overall test in a genome are discussed and analyzed. A simulation study of QTL mapping is also presented which illustrates the utility, properties, advantages and disadvantages of the method.

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