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

Genetics ◽  
1999 ◽  
Vol 151 (4) ◽  
pp. 1605-1619 ◽  
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/.

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 Power of quantitative trait locus mapping for polygenic binary traits using generalized and regression interval mapping in multi-family half-sib designs

2000 ◽  
Vol 76 (3) ◽  
pp. 305-317 ◽  
Author(s):  
HAJA N. KADARMIDEEN ◽  
LUC L. G. JANSS ◽  
JACK C. M. DEKKERS
Keyword(s):  
Quantitative Trait ◽  
Binary Data ◽  
Fixed Effects ◽  
Statistical Power ◽  
Interval Mapping ◽  
Parameter Estimates ◽  
Data Sets ◽  
Binary Traits ◽  
Trait Locus ◽  
The Impact

A generalized interval mapping (GIM) method to map quantitative trait loci (QTL) for binary polygenic traits in a multi-family half-sib design is developed based on threshold theory and implemented using a Newton–Raphson algorithm. Statistical power and bias of QTL mapping for binary traits by GIM is compared with linear regression interval mapping (RIM) using simulation. Data on 20 paternal half-sib families were simulated with two genetic markers that bracketed an additive QTL. Data simulated and analysed were: (1) data on the underlying normally distributed liability (NDL) scale, (2) binary data created by truncating NDL data based on three thresholds yielding data sets with three different incidences, and (3) NDL data with polygenic and QTL effects reduced by a proportion equal to the ratio of the heritabilities on the binary versus NDL scale (reduced-NDL). Binary data were simulated with and without systematic environmental (herd) effects in an unbalanced design. GIM and RIM gave similar power to detect the QTL and similar estimates of QTL location, effects and variances. Presence of fixed effects caused differences in bias between RIM and GIM, where GIM showed smaller bias which was affected less by incidence. The original NDL data had higher power and lower bias in QTL parameter estimates than binary and reduced-NDL data. RIM for reduced-NDL and binary data gave similar power and estimates of QTL parameters, indicating that the impact of the binary nature of data on QTL analysis is equivalent to its impact on heritability.

scholarly journals Integration and Modularity of Quantitative Trait Locus Effects on Geometric Shape in the Mouse Mandible

Genetics ◽  
2004 ◽  
Vol 166 (4) ◽  
pp. 1909-1921
Author(s):  
Christian Peter Klingenberg ◽  
Larry J Leamy ◽  
James M Cheverud
Keyword(s):  
Quantitative Trait ◽  
Parametric Bootstrap ◽  
Interval Mapping ◽  
Procrustes Analysis ◽  
Bootstrap Test ◽  
Mandible Shape ◽  
Trait Locus ◽  
F2 Generation ◽  
Analysis Interval ◽  
Qtl Effects

Abstract The mouse mandible has long served as a model system for complex morphological structures. Here we use new methodology based on geometric morphometrics to test the hypothesis that the mandible consists of two main modules, the alveolar region and the ascending ramus, and that this modularity is reflected in the effects of quantitative trait loci (QTL). The shape of each mandible was analyzed by the positions of 16 morphological landmarks and these data were analyzed using Procrustes analysis. Interval mapping in the F2 generation from intercrosses of the LG/J and SM/J strains revealed 33 QTL affecting mandible shape. The QTL effects corresponded to a variety of shape changes, but ordination or a parametric bootstrap test of clustering did not reveal any distinct groups of QTL that would affect primarily one module or the other. The correlations of landmark positions between the two modules tended to be lower than the correlations between arbitrary subsets of landmarks, indicating that the modules were relatively independent of each other and confirming the hypothesized location of the boundary between them. While these results are in agreement with the hypothesis of modularity, they also underscore that modularity is a question of the relative degrees to which QTL contribute to different traits, rather than a question of discrete sets of QTL contributing to discrete sets of traits.

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 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.

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 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.

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

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.

scholarly journals Genomic Locus Modulating IOP in the BXD RI Mouse Strains

2017 ◽  
Author(s):  
Rebecca King ◽  
Ying Li ◽  
Jiaxing Wang ◽  
Felix L. Struebing ◽  
Eldon E. Geisert
Keyword(s):  
Candidate Genes ◽  
Quantitative Trait ◽  
Significant Quantitative Trait Locus ◽  
Interval Mapping ◽  
Mouse Strains ◽  
Genomic Locus ◽  
Quantitative Trait Analysis ◽  
Significant Locus ◽  
Trait Locus ◽  
Different Strains

AbstractPurposeIntraocular pressure (IOP) is the primary risk factor for developing glaucoma. The present study examines genomic contribution to the normal regulation of IOP in the mouse.MethodsThe BXD recombinant inbred (RI) strain set was used to identify genomic loci modulating IOP. We measured the IOP from 532 eyes from 33 different strains. The IOP data will be subjected to conventional quantitative trait analysis using simple and composite interval mapping along with epistatic interactions to define genomic loci modulating normal IOP.ResultsThe analysis defined one significant quantitative trait locus (QTL) on Chr.8 (100 to 106 Mb). The significant locus was further examined to define candidate genes that modulate normal IOP. There are only two good candidate genes within the 6 Mb over the peak, Cdh8 (Cadherin 8) and Cdh11 (Cadherin 11). Expression analysis on gene expression and immunohistochemistry indicate that Cdh11 is the best candidate for modulating the normal levels of IOP.ConclusionsWe have examined the genomic regulation of IOP in the BXD RI strain set and found one significant QTL on Chr. 8. Within this QTL that are two potential candidates for modulating IOP with the most likely gene being Cdh11.

scholarly journals Simultaneous Mining of Linkage and Linkage Disequilibrium to Fine Map Quantitative Trait Loci in Outbred Half-Sib Pedigrees: Revisiting the Location of a Quantitative Trait Locus With Major Effect on Milk Production on Bovine Chromosome 14

Genetics ◽  
2002 ◽  
Vol 161 (1) ◽  
pp. 275-287 ◽  
Author(s):  
Frédéric Farnir ◽  
Bernard Grisart ◽  
Wouter Coppieters ◽  
Juliette Riquet ◽  
Paulette Berzi ◽  
...  
Keyword(s):  
Linkage Disequilibrium ◽  
Quantitative Trait ◽  
Milk Fat ◽  
Haplotype Analysis ◽  
Major Effect ◽  
Mapping Method ◽  
Substitution Effects ◽  
Marker Interval ◽  
Chromosome 14 ◽  
Trait Locus

Abstract A maximum-likelihood QTL mapping method that simultaneously exploits linkage and linkage disequilibrium and that is applicable in outbred half-sib pedigrees is described. The method is applied to fine map a QTL with major effect on milk fat content in a 3-cM marker interval on proximal BTA14. This proximal location is confirmed by applying a haplotype-based association method referred to as recombinant ancestral haplotype analysis. The origin of the discrepancy between the QTL position derived in this work and that of a previous analysis is examined and shown to be due to the existence of distinct marker haplotypes associated with QTL alleles having large substitution effects.

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