scholarly journals Novel Resampling Improves Statistical Power for Multiple-Trait QTL Mapping

2017 ◽  
Vol 7 (3) ◽  
pp. 813-822 ◽  
Author(s):  
Riyan Cheng ◽  
R. W. Doerge ◽  
Justin Borevitz
Keyword(s):  
Qtl Mapping ◽  
Statistical Power ◽  
Multiple Trait

scholarly journals Bayesian mixture structural equation modelling in multiple-trait QTL mapping

2010 ◽  
Vol 92 (3) ◽  
pp. 239-250 ◽  
Author(s):  
XIAOJUAN MI ◽  
KENT ESKRIDGE ◽  
DONG WANG ◽  
P. STEPHEN BAENZIGER ◽  
B. TODD CAMPBELL ◽  
...  
Keyword(s):  
Qtl Mapping ◽  
Structural Equation ◽  
Statistical Power ◽  
Bayes Factor ◽  
Causal Structure ◽  
Mapping Method ◽  
Parameter Estimates ◽  
Detection Accuracy ◽  
Multiple Traits ◽  
Multiple Trait

SummaryQuantitative trait loci (QTLs) mapping often results in data on a number of traits that have well-established causal relationships. Many multi-trait QTL mapping methods that account for correlation among the multiple traits have been developed to improve the statistical power and the precision of QTL parameter estimation. However, none of these methods are capable of incorporating the causal structure among the traits. Consequently, genetic functions of the QTL may not be fully understood. In this paper, we developed a Bayesian multiple QTL mapping method for causally related traits using a mixture structural equation model (SEM), which allows researchers to decompose QTL effects into direct, indirect and total effects. Parameters are estimated based on their marginal posterior distribution. The posterior distributions of parameters are estimated using Markov Chain Monte Carlo methods such as the Gibbs sampler and the Metropolis–Hasting algorithm. The number of QTLs affecting traits is determined by the Bayes factor. The performance of the proposed method is evaluated by simulation study and applied to data from a wheat experiment. Compared with single trait Bayesian analysis, our proposed method not only improved the statistical power of QTL detection, accuracy and precision of parameter estimates but also provided important insight into how genes regulate traits directly and indirectly by fitting a more biologically sensible model.

scholarly journals QTL Mapping in Outbred Tetraploid (and Diploid) Diallel Populations

Genetics ◽  
2021 ◽  
Author(s):  
Rodrigo R Amadeu ◽  
Patricio R Munoz ◽  
Chaozhi Zheng ◽  
Jeffrey B Endelman
Keyword(s):  
Qtl Mapping ◽  
Statistical Power ◽  
Deviance Information Criterion ◽  
Diploid Species ◽  
Additive Model ◽  
Chromosome 1 ◽  
Additive Effects ◽  
Significance Level ◽  
Qtl Discovery ◽  
Half Diallel

Abstract Over the last decade, multiparental populations have become a mainstay of genetics research in diploid species. Our goal was to extend this paradigm to autotetraploids by developing software for quantitative trait locus (QTL) mapping in connected F1 populations derived from a set of shared parents. For QTL discovery, phenotypes are regressed on the dosage of parental haplotypes to estimate additive effects. Statistical properties of the model were explored by simulating half-diallel diploid and tetraploid populations with different population sizes and numbers of parents. Across scenarios, the number of progeny per parental haplotype (pph) largely determined the statistical power for QTL detection and accuracy of the estimated haplotype effects. Multi-allelic QTL with heritability 0.2 were detected with 90% probability at 25 pph and genome-wide significance level 0.05, and the additive haplotype effects were estimated with over 90% accuracy. Following QTL discovery, the software enables a comparison of models with multiple QTL and non-additive effects. To illustrate, we analyzed potato tuber shape in a half-diallel population with 3 tetraploid parents. A well-known QTL on chromosome 10 was detected, for which the inclusion of digenic dominance lowered the Deviance Information Criterion (DIC) by 17 points compared to the additive model. The final model also contained a minor QTL on chromosome 1, but higher order dominance and epistatic effects were excluded based on the DIC. In terms of practical impacts, the software is already being used to select offspring based on the effect and dosage of particular haplotypes in breeding programs.

scholarly journals PCA-Based Multiple-Trait GWAS Analysis: A Powerful Model for Exploring Pleiotropy

Animals ◽  
2018 ◽  
Vol 8 (12) ◽  
pp. 239 ◽  
Author(s):  
Wengang Zhang ◽  
Xue Gao ◽  
Xinping Shi ◽  
Bo Zhu ◽  
Zezhao Wang ◽  
...  
Keyword(s):  
Quantitative Trait ◽  
Statistical Power ◽  
Muscle Development ◽  
Association Studies ◽  
Simulated Data ◽  
Principal Component ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Multiple Trait ◽  
Gwas Analysis

Principal component analysis (PCA) is a potential approach that can be applied in multiple-trait genome-wide association studies (GWAS) to explore pleiotropy, as well as increase the power of quantitative trait loci (QTL) detection. In this study, the relationship of test single nucleotide polymorphisms (SNPs) was determined between single-trait GWAS and PCA-based GWAS. We found that the estimated pleiotropic quantitative trait nucleotides (QTNs) β * ^ were in most cases larger than the single-trait model estimations ( β 1 ^ and β 2 ^ ). Analysis using the simulated data showed that PCA-based multiple-trait GWAS has improved statistical power for detecting QTL compared to single-trait GWAS. For the minor allele frequency (MAF), when the MAF of QTNs was greater than 0.2, the PCA-based model had a significant advantage in detecting the pleiotropic QTNs, but when its MAF was reduced from 0.2 to 0, the advantage began to disappear. In addition, as the linkage disequilibrium (LD) of the pleiotropic QTNs decreased, its detection ability declined in the co-localization effect model. Furthermore, on the real data of 1141 Simmental cattle, we applied the PCA model to the multiple-trait GWAS analysis and identified a QTL that was consistent with a candidate gene, MCHR2, which was associated with presoma muscle development in cattle. In summary, PCA-based multiple-trait GWAS is an efficient model for exploring pleiotropic QTNs in quantitative traits.

scholarly journals The full-sib intercross line (FSIL): a QTL mapping design for outcrossing species

1999 ◽  
Vol 73 (1) ◽  
pp. 61-73 ◽  
Author(s):  
J. Z. SONG ◽  
M. SOLLER ◽  
A. GENIZI
Keyword(s):  
Qtl Mapping ◽  
Positional Cloning ◽  
Statistical Power ◽  
Mapping Population ◽  
Dna Pooling ◽  
Mapping Accuracy ◽  
Total Size ◽  
Outcrossing Species ◽  
Line Cross ◽  
Selective Dna Pooling

A full-sib intercross line (FSIL) is constructed in an outcrossing species by mating two parents and intercrossing their progeny to form a large intercross line. For given statistical power, a FSIL design requires only slightly more individuals than an F2 design derived from inbred line cross, but 6- to 10-fold fewer than a half-sib or full-sib design. Due to population-wide linkage disequilibrium, a FSIL is amenable to analysis by selective DNA pooling. In addition, a FSIL is maintained by continued intercrossing so that DNA samples and phenotypic information are accumulated across generations. Continued intercrossing also leads to map expansion and thus to increased mapping accuracy in the later generations. A FSIL can thus provide a bridge to positional cloning of quantitative trait loci (QTL) and marker-assisted selection in outcrossers; and is particularly effective in exploiting the QTL mapping potential of crosses between selection lines or phenotypically differentiated populations that differ in frequency, but are not at fixation, for alternative QTL alleles. In the course of the power analyses, it is shown that for F2 and FSIL designs, power is a function of Nd2 alone, where N is the total size of the mapping population and d is the standardized gene effect; while for half-sib and full-sib populations, power is a function of Nd2 and of the number of families included in the mapping population. This provides a convenient means of estimating power for a wide variety of mapping designs.

scholarly journals Multiple-trait QTL mapping and genomic prediction for wool traits in sheep

2017 ◽  
Vol 49 (1) ◽  
Author(s):  
Sunduimijid Bolormaa ◽  
Andrew A. Swan ◽  
Daniel J. Brown ◽  
Sue Hatcher ◽  
Nasir Moghaddar ◽  
...  
Keyword(s):  
Qtl Mapping ◽  
Genomic Prediction ◽  
Multiple Trait

Mapping Quantitative Trait Loci in F2 Incorporating Phenotypes of F3 Progeny

Genetics ◽  
2004 ◽  
Vol 166 (4) ◽  
pp. 1981-1993 ◽  
Author(s):  
Yuan-Ming Zhang ◽  
Shizhong Xu
Keyword(s):  
Qtl Mapping ◽  
Statistical Method ◽  
Mixture Model ◽  
Statistical Power ◽  
Plant Traits ◽  
Simulated Data ◽  
Real Data ◽  
Fundamental Principle ◽  
Laboratory Animals ◽  
Daughter Design

AbstractIn plants and laboratory animals, QTL mapping is commonly performed using F2 or BC individuals derived from the cross of two inbred lines. Typical QTL mapping statistics assume that each F2 individual is genotyped for the markers and phenotyped for the trait. For plant traits with low heritability, it has been suggested to use the average phenotypic values of F3 progeny derived from selfing F2 plants in place of the F2 phenotype itself. All F3 progeny derived from the same F2 plant belong to the same F2:3 family, denoted by F2:3. If the size of each F2:3 family (the number of F3 progeny) is sufficiently large, the average value of the family will represent the genotypic value of the F2 plant, and thus the power of QTL mapping may be significantly increased. The strategy of using F2 marker genotypes and F3 average phenotypes for QTL mapping in plants is quite similar to the daughter design of QTL mapping in dairy cattle. We study the fundamental principle of the plant version of the daughter design and develop a new statistical method to map QTL under this F2:3 strategy. We also propose to combine both the F2 phenotypes and the F2:3 average phenotypes to further increase the power of QTL mapping. The statistical method developed in this study differs from published ones in that the new method fully takes advantage of the mixture distribution for F2:3 families of heterozygous F2 plants. Incorporation of this new information has significantly increased the statistical power of QTL detection relative to the classical F2 design, even if only a single F3 progeny is collected from each F2:3 family. The mixture model is developed on the basis of a single-QTL model and implemented via the EM algorithm. Substantial computer simulation was conducted to demonstrate the improved efficiency of the mixture model. Extension of the mixture model to multiple QTL analysis is developed using a Bayesian approach. The computer program performing the Bayesian analysis of the simulated data is available to users for real data analysis.

scholarly journals QTL Mapping in Outbred Tetraploid (and Diploid) Diallel Populations

2020 ◽  
Author(s):  
Rodrigo R. Amadeu ◽  
Patricio R. Munoz ◽  
Chaozhi Zheng ◽  
Jeffrey B. Endelman
Keyword(s):  
Qtl Mapping ◽  
Statistical Power ◽  
False Positive Rate ◽  
Diploid Species ◽  
Companion Paper ◽  
Bayesian Regression ◽  
Comprehensive Treatment ◽  
Parental Genotype ◽  
Trait Locus ◽  
Half Diallel

AbstractOver the last decade, multiparental populations have become a mainstay of genetics research in diploid species. Our goal was to extend this paradigm to autotetraploids by creating computational tools for the analysis of connected F1 populations derived from a set of shared parents. In a companion paper, software to reconstruct F1 progeny in terms of parental haplotypes was described. For this study, we developed software for quantitative trait locus (QTL) mapping via Bayesian regression of phenotypes on the parental genotype probabilities. Statistical properties of the QTL model were explored by analyzing simulated half-diallel diploid and tetraploid populations with different population sizes, genome sizes, and numbers of parents. As expected, the LOD threshold needed to control the false positive rate increased with genome size, ploidy, and parents. Across the different scenarios, the number of progeny per parental haplotype (pph) largely determined the statistical power for QTL detection and the accuracy of the estimated haplotype effects. A QTL with heritability 0.1 was detected with 90% probability at 60 pph, while only 40 pph were needed to estimate the haplotypes with 90% accuracy. Our methodology includes a comprehensive treatment of dominance for multi-allelic QTL, which was illustrated by analyzing potato tuber shape in a 3 × 3 half-diallel population. A well-known QTL on chromosome 10 was detected, and the best-fit model included both additive and dominance effects. In terms of practical impacts on breeding, the software is already being used to select offspring based on the effect and dosage of particular haplotypes.

scholarly journals Statistical power of QTL mapping methods applied to bacteria counts

2001 ◽  
Vol 78 (3) ◽  
pp. 303-316 ◽  
Author(s):  
P. TILQUIN ◽  
W. COPPIETERS ◽  
J. M. ELSEN ◽  
F. LANTIER ◽  
C. MORENO ◽  
...  
Keyword(s):  
Qtl Mapping ◽  
Statistical Power ◽  
Parametric Method ◽  
Mapping Method ◽  
Point Of View ◽  
Parametric Methods ◽  
Statistical Point ◽  
The Right ◽  
Non Parametric ◽  
Normally Distributed

Most QTL mapping methods assume that phenotypes follow a normal distribution, but many phenotypes of interest are not normally distributed, e.g. bacteria counts (or colony-forming units, CFU). Such data are extremely skewed to the right and can present a high amount of zero values, which are ties from a statistical point of view. Our objective is therefore to assess the efficiency of four QTL mapping methods applied to bacteria counts: (1) least-squares (LS) analysis, (2) maximum-likelihood (ML) analysis, (3) non-parametric (NP) mapping and (4) nested ANOVA (AN). A transformation based on quantiles is used to mimic observed distributions of bacteria counts. Single positions (1 marker, 1 QTL) as well as chromosome scans (11 markers, 1 QTL) are simulated. When compared with the analysis of a normally distributed phenotype, the analysis of raw bacteria counts leads to a strong decrease in power for parametric methods, but no decrease is observed for NP. However, when a mathematical transformation (MT) is applied to bacteria counts prior to analysis, parametric methods have the same power as NP. Furthermore, parametric methods, when coupled with MT, outperform NP when bacteria counts have a very high proportion of zeros (70·8%). Our results show that the loss of power is mainly explained by the asymmetry of the phenotypic distribution, for parametric methods, and by the existence of ties, for the non-parametric method. Therefore, mapping of QTL for bacterial diseases, as well as for other diseases assessed by a counting process, should focus on the occurrence of ties in phenotypes before choosing the appropriate QTL mapping method.

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