scholarly journals Detection of QTL × environment interaction in maize by a least squares interval mapping method

Heredity ◽  
1997 ◽  
Vol 78 (2) ◽  
pp. 146-157 ◽  
Author(s):  
Mirella Sari-Gorla ◽  
Tadeusz Calinski ◽  
Zygmunt Kaczmarek ◽  
Pawel Krajewski
Keyword(s):  
Least Squares ◽  
Interval Mapping ◽  
Mapping Method ◽  
Environment Interaction

Detection of QTL×environment interaction in maize by a least squares interval mapping method

Heredity ◽  
1997 ◽  
Vol 78 (2) ◽  
pp. 146-157 ◽  
Author(s):  
MIRELLA SARI-GORLA ◽  
TADEUSZ CALINSKI ◽  
ZYGMUNT KACZMAREK ◽  
PAWEL KRAJEWSKI
Keyword(s):  
Least Squares ◽  
Interval Mapping ◽  
Mapping Method ◽  
Environment Interaction

scholarly journals Comparison of methods for regression interval mapping in QTL analysis with non-normal traits

1997 ◽  
Vol 69 (1) ◽  
pp. 69-74 ◽  
Author(s):  
AHMED REBAÏ
Keyword(s):  
Logistic Regression ◽  
Least Squares ◽  
Qtl Analysis ◽  
Estimation Procedure ◽  
Interval Mapping ◽  
Mapping Method ◽  
Comparison Of Methods ◽  
Trait Distribution ◽  
Departure From Normality ◽  
Normally Distributed

We compare the powers of three methods for the QTL analysis of non-normally distributed traits. We describe the nonparametric and the logistic regression approaches recently proposed in the literature and study the properties of the standard regression interval mapping method when the trait is not normally distributed. It is shown that the standard approach is robust against non-normality and behaves quite well for both continuous and discrete characters. The loss of power compared with the nonparametric or the logistic approach is generally minor. Moreover, the least squares estimation procedure of the regression interval mapping is not affected by departure from normality. The use of other approaches could be restricted to extreme cases where the trait distribution is very skewed.

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

Genetics ◽  
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.

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

Genetics ◽  
1998 ◽  
Vol 149 (3) ◽  
pp. 1547-1555 ◽  
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.

scholarly journals Identification of quantitative trait loci for leaf traits in rice

Genetika ◽  
2016 ◽  
Vol 48 (2) ◽  
pp. 643-652 ◽  
Author(s):  
Baoyan Jia ◽  
Xinhua Zhao ◽  
Yang Qin ◽  
Muhammad Irfan ◽  
Tae-Heon Kim ◽  
...  
Keyword(s):  
Recombinant Inbred Lines ◽  
Leaf Traits ◽  
Rice Cultivar ◽  
Interval Mapping ◽  
Mapping Method ◽  
Japonica Rice ◽  
Phenotypic Variance ◽  
Simple Sequence ◽  
Variance Explained ◽  
Map Distance

A recombinant inbred lines (RILs) population of 90 lines were developed from a subspecies cross between an indica type cultivar, ?Cheongcheong?, and a japonica rice cultivar, ?Nagdong? was evaluated for leaf traits in 2009. A genetic linkage map consisting of 154 simple sequence repeat (SSR) markers was constructed, covering 1973.6 cM of 12 chromosomes with an average map distance of 13.9 cM between markers. By composite interval mapping method a total of 19 QTLs were identified for the leaf traits on 5 chromosomes (Chr.1, Chr.3, Chr.6, Chr.8 and Chr.11). The percentage of phenotypic variance explained by each QTL varied from 8.1% to 29.4%. Five pleiotropic effects loci were identified on chromosomes 1,6.

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

2016 ◽  
Vol 14 (3) ◽  
pp. e07SC01 ◽  
Author(s):  
Junqiang Ding ◽  
Jinliang Ma ◽  
Jiafa Chen ◽  
Tangshun Ai ◽  
Zhimin Li ◽  
...  
Keyword(s):  
Quantitative Trait ◽  
Genetic Basis ◽  
Mixed Model ◽  
Short Communication ◽  
Genotypic Variation ◽  
Interval Mapping ◽  
Mapping Method ◽  
Additive Effects ◽  
Breeding Programs ◽  
Model Composite

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.

Mapping QTLs for 100-seed weight in an interspecific soybean cross of Williams 82 (Glycine max) and PI 366121 (Glycine soja)

2017 ◽  
Vol 68 (2) ◽  
pp. 148 ◽  
Author(s):  
Krishnanand P. Kulkarni ◽  
Sovetgul Asekova ◽  
Dong-Ho Lee ◽  
Kristin Bilyeu ◽  
Jong Tae Song ◽  
...  
Keyword(s):  
Seed Weight ◽  
Glycine Soja ◽  
Recombinant Inbred Lines ◽  
Molecular Genetic ◽  
Interval Mapping ◽  
Major Influence ◽  
Environment Interaction ◽  
Molecular Genetic Basis ◽  
The Individual ◽  
100 Seed Weight

Seed weight can be an important component for soybean quality and yield. The objective of the present study was to identify quantitative trait loci (QTLs) for 100-seed weight by using 169 recombinant inbred lines (RILs) derived from the cross Williams 82 × PI 366121. The parental lines and RILs were grown for four consecutive years (2012–15) in the field. The seeds were harvested after maturity, dried and used to measure 100-seed weight. Analysis of variance indicated significant differences among the RILs for 100-seed weight. The environment had significant effect on seed-weight expression as indicated by the genotype × environment interaction. QTL analysis employing inclusive composite interval mapping of additive QTLs implemented in QTL IciMapping (Version 4.1) identified nine QTLs (LOD >3) on chromosomes 1, 2, 6, 8, 13, 14, 17 and 20. The individual QTLs explained phenotypic variation in the range 6.1–12.4%. The QTLs were detected in one or two environments, indicating major influence of the growing environment on seed-weight expression. Four QTLs identified in this study, qSW-02_1, qSW-06_1, qSW-13_1 and qSW-14_1, were found to be new QTLs. The findings of the study may be helpful to reveal the molecular genetic basis of the seed-weight trait in soybean.

scholarly journals Gene–environment interactions in complex diseases: genetic models and methods for QTL mapping in multiple half-sib populations

2006 ◽  
Vol 88 (2) ◽  
pp. 119-131 ◽  
Author(s):  
HAJA N. KADARMIDEEN ◽  
YONGJUN LI ◽  
LUC L. G. JANSS
Keyword(s):  
Qtl Mapping ◽  
Scale Effects ◽  
High Standard ◽  
Interval Mapping ◽  
Mapping Method ◽  
Genetic Models ◽  
Gene Environment ◽  
Trait Locus ◽  
Outbred Populations ◽  
Binary Scale

An interval quantitative trait locus (QTL) mapping method for complex polygenic diseases (as binary traits) showing QTL by environment interactions (QEI) was developed for outbred populations on a within-family basis. The main objectives, within the above context, were to investigate selection of genetic models and to compare liability or generalized interval mapping (GIM) and linear regression interval mapping (RIM) methods. Two different genetic models were used: one with main QTL and QEI effects (QEI model) and the other with only a main QTL effect (QTL model). Over 30 types of binary disease data as well as six types of continuous data were simulated and analysed by RIM and GIM. Using table values for significance testing, results show that RIM had an increased false detection rate (FDR) for testing interactions which was attributable to scale effects on the binary scale. GIM did not suffer from a high FDR for testing interactions. The use of empirical thresholds, which effectively means higher thresholds for RIM for testing interactions, could repair this increased FDR for RIM, but such empirical thresholds would have to be derived for each case because the amount of FDR depends on the incidence on the binary scale. RIM still suffered from higher biases (15–100% over- or under-estimation of true values) and high standard errors in QTL variance and location estimates than GIM for QEI models. Hence GIM is recommended for disease QTL mapping with QEI. In the presence of QEI, the model including QEI has more power (20–80% increase) to detect the QTL when the average QTL effect is small (in a situation where the model with a main QTL only is not too powerful). Top-down model selection is proposed in which a full test for QEI is conducted first and then the model is subsequently simplified. Methods and results will be applicable to human, plant and animal QTL mapping experiments.

scholarly journals Interval mapping methods for detecting QTL affecting survival and time-to-event phenotypes

2005 ◽  
Vol 85 (2) ◽  
pp. 139-149 ◽  
Author(s):  
C. R. MORENO ◽  
J. M. ELSEN ◽  
P. LE ROY ◽  
V. DUCROCQ
Keyword(s):  
Normal Distribution ◽  
Censored Data ◽  
Survival Data ◽  
Cox Model ◽  
Gaussian Model ◽  
Weibull Model ◽  
Interval Mapping ◽  
Mapping Method ◽  
Distribution Data ◽  
Qtl Detection

Quantitative trait loci (QTL) are usually searched for using classical interval mapping methods which assume that the trait of interest follows a normal distribution. However, these methods cannot take into account features of most survival data such as a non-normal distribution and the presence of censored data. We propose two new QTL detection approaches which allow the consideration of censored data. One interval mapping method uses a Weibull model (W), which is popular in parametrical modelling of survival traits, and the other uses a Cox model (C), which avoids making any assumption on the trait distribution. Data were simulated following the structure of a published experiment. Using simulated data, we compare W, C and a classical interval mapping method using a Gaussian model on uncensored data (G) or on all data (G′=censored data analysed as though records were uncensored). An adequate mathematical transformation was used for all parametric methods (G, G′ and W). When data were not censored, the four methods gave similar results. However, when some data were censored, the power of QTL detection and accuracy of QTL location and of estimation of QTL effects for G decreased considerably with censoring, particularly when censoring was at a fixed date. This decrease with censoring was observed also with G′, but it was less severe. Censoring had a negligible effect on results obtained with the W and C methods.

scholarly journals An investigation of the power for separating closely linked QTL in experimental populations

2010 ◽  
Vol 92 (4) ◽  
pp. 283-294 ◽  
Author(s):  
CHEN-HUNG KAO ◽  
MIAO-HUI ZENG
Keyword(s):  
Power Analysis ◽  
Statistical Power ◽  
Random Mating ◽  
Interval Mapping ◽  
Mapping Method ◽  
Marker Interval ◽  
Correlation Structures ◽  
Experimental Populations ◽  
Trait Locus ◽  
Qtl Effects

SummaryHu & Xu (2008) developed a statistical method for computing the statistical power for detecting a quantitative trait locus (QTL) located in a marker interval. Their method is based on the regression interval mapping method and allows experimenters to effectively investigate the power for detecting a QTL in a population. This paper continues to work on the power analysis of separating multiple-linked QTLs. We propose simple formulae to calculate the power of separating closely linked QTLs located in marker intervals. The proposed formulae are simple functions of information numbers, variance inflation factors and genetic parameters of a statistical model in a population. Both regression and maximum likelihood interval mappings suitable for detecting QTL in the marker intervals are considered. In addition, the issue of separating linked QTLs in the progeny populations from an F2 subject to further self and/or random mating is also touched upon. One of the primary keys to our approach is to derive the genotypic distributions of three and four loci for evaluating the correlation structures between pairwise unobservable QTLs in the model across populations. The proposed formulae allow us to predict the power of separation when several factors, such as sample sizes, sizes and directions of QTL effects, distances between QTLs, interval sizes and relative QTL positions in the intervals, are considered together at a time in different experimental populations. Numerical justifications and Monte Carlo simulations were provided for confirmation and illustration.

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