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 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.

Quantifying the Relative Contribution of the Heterozygous Class to QTL Detection Power

2011 ◽  
Vol 10 (1) ◽  
Author(s):  
Benjamin McClosky ◽  
Xiwen Ma ◽  
Steven D. Tanksley
Keyword(s):  
Statistical Power ◽  
Additive Model ◽  
Additive Effects ◽  
Full Model ◽  
Qtl Detection ◽  
Relative Contribution ◽  
Software Packages ◽  
Rigorous Study ◽  
The Cost ◽  
Standard Software

Basic statistical theory implies that genotypic class cardinalities play a strong role in determining power to detect QTL, but the classes do not contribute equal information to the model. For example, while it is generally accepted that homozygotes contribute more to the detection of additive effects, heterozygotes are necessary to detect dominance effects. The literature on QTL detection often mentions the importance of genotypic class sizes in passing (Belknap (1998); Belknap et al. (1996); Jin et al. (2004); Kliebenstein (2007); Kao (2006); Martinez et al. (2002)), but no rigorous study of their relative values appears to exist. The purpose of this paper is to quantify the relative contribution of the heterozygous class. Researchers can use these results in evaluating the tradeoff between gain in statistical power and the cost of developing populations with specified genotypic class sizes. In addition, we arrive at the surprising conclusion that a misspecified additive model often outperforms a full model that incorporates dominance. This result is significant because standard software packages normally use the full model by default.

scholarly journals A test for multigroup comparison using partial least squares path modeling

2019 ◽  
Vol 29 (3) ◽  
pp. 464-477 ◽  
Author(s):  
Michael Klesel ◽  
Florian Schuberth ◽  
Jörg Henseler ◽  
Bjoern Niehaves
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Statistical Power ◽  
Euclidean Distance ◽  
Geodesic Distance ◽  
Group Differences ◽  
Path Modeling ◽  
Content Type ◽  
Significance Level ◽  
Squared Euclidean Distance

Purpose People seem to function according to different models, which implies that in business and social sciences, heterogeneity is a rule rather than an exception. Researchers can investigate such heterogeneity through multigroup analysis (MGA). In the context of partial least squares path modeling (PLS-PM), MGA is currently applied to perform multiple comparisons of parameters across groups. However, this approach has significant drawbacks: first, the whole model is not considered when comparing groups, and second, the family-wise error rate is higher than the predefined significance level when the groups are indeed homogenous, leading to incorrect conclusions. Against this background, the purpose of this paper is to present and validate new MGA tests, which are applicable in the context of PLS-PM, and to compare their efficacy to existing approaches. Design/methodology/approach The authors propose two tests that adopt the squared Euclidean distance and the geodesic distance to compare the model-implied indicator correlation matrix across groups. The authors employ permutation to obtain the corresponding reference distribution to draw statistical inference about group differences. A Monte Carlo simulation provides insights into the sensitivity and specificity of both permutation tests and their performance, in comparison to existing approaches. Findings Both proposed tests provide a considerable degree of statistical power. However, the test based on the geodesic distance outperforms the test based on the squared Euclidean distance in this regard. Moreover, both proposed tests lead to rejection rates close to the predefined significance level in the case of no group differences. Hence, our proposed tests are more reliable than an uncontrolled repeated comparison approach. Research limitations/implications Current guidelines on MGA in the context of PLS-PM should be extended by applying the proposed tests in an early phase of the analysis. Beyond our initial insights, more research is required to assess the performance of the proposed tests in different situations. Originality/value This paper contributes to the existing PLS-PM literature by proposing two new tests to assess multigroup differences. For the first time, this allows researchers to statistically compare a whole model across groups by applying a single statistical test.

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 The contribution of dominance to the understanding of quantitative genetic variation

2011 ◽  
Vol 93 (2) ◽  
pp. 139-154 ◽  
Author(s):  
ROBIN WELLMANN ◽  
JÖRN BENNEWITZ
Keyword(s):  
Qtl Mapping ◽  
Inbreeding Depression ◽  
Quantitative Trait ◽  
Breeding Value ◽  
Additive Effects ◽  
Additive Variance ◽  
Dominance Variance ◽  
True Value ◽  
Value Estimation ◽  
Trait Locus

SummaryKnowledge of the genetic architecture of a quantitative trait is useful to adjust methods for the prediction of genomic breeding values and to discover the extent to which common assumptions in quantitative trait locus (QTL) mapping experiments and breeding value estimation are violated. It also affects our ability to predict the long-term response of selection. In this paper, we focus on additive and dominance effects of QTL. We derive formulae that can be used to estimate the number of QTLs that affect a quantitative trait and parameters of the distribution of their additive and dominance effects from variance components, inbreeding depression and results from QTL mapping experiments. It is shown that a lower bound for the number of QTLs depends on the ratio of squared inbreeding depression to dominance variance. That is, high inbreeding depression must be due to a sufficient number of QTLs because otherwise the dominance variance would exceed the true value. Moreover, the second moment of the dominance coefficient depends only on the ratio of dominance variance to additive variance and on the dependency between additive effects and dominance coefficients. This has implications on the relative frequency of overdominant alleles. It is also demonstrated how the expected number of large QTLs determines the shape of the distribution of additive effects. The formulae are applied to milk yield and productive life in Holstein cattle. Possible sources for a potential bias of the results are discussed.

scholarly journals The Use of Genetic Markers to Measure Genomic Response to Selection in Livestock

Genetics ◽  
2002 ◽  
Vol 162 (3) ◽  
pp. 1381-1388
Author(s):  
Luis Gomez-Raya ◽  
Hanne Gro Olsen ◽  
Frode Lingaas ◽  
Helge Klungland ◽  
Dag Inge Våge ◽  
...  
Keyword(s):  
Growth Performance ◽  
Genetic Markers ◽  
Artificial Selection ◽  
Statistical Power ◽  
Gene Effects ◽  
Significance Level ◽  
Genome Wide ◽  
A Genome ◽  
Sperm Typing ◽  
Genomic Response

Abstract A method to measure genomic response to natural and artificial selection by means of genetic markers in livestock is proposed. Genomic response through several levels of selection was measured using sequential testing for distorted segregation of alleles among selected and nonselected sons, single-sperm typing, and a test with records for growth performance. Statistical power at a significance level of 0.05 was >0.5 for a marker linked to a QTL with recombination fractions 0, 0.10, and 0.20 for detecting genomic responses for gene effects of 0.6, 0.7, and 1.0 phenotypic standard deviations, respectively. Genomic response to artificial selection in six commercial bull sire families comprising 285 half-sib sons selected for growth performance was measured using 282 genetic markers evenly distributed over the cattle genome. A genome-wide test using selected sons was significant (P < 0.001), indicating that selection induces changes in the genetic makeup of commercial cattle populations. Markers located in chromosomes 6, 10, and 16 identified regions in those chromosomes that are changing due to artificial selection as revealed by the association of records of performance with alleles at specific markers. Either natural selection or genetic drift may cause the observed genomic response for markers in chromosomes 1, 7, and 17.

scholarly journals Efficiency of generations for estimating marker-associated QTL effects by multiple regression.

Genetics ◽  
1993 ◽  
Vol 135 (1) ◽  
pp. 223-231
Author(s):  
J Moreno-Gonzalez
Keyword(s):  
North Carolina ◽  
Stepwise Regression ◽  
Additive Effects ◽  
Significance Level ◽  
Recombinant Inbreds ◽  
The North ◽  
Dh Lines ◽  
Genetic Standard Deviation ◽  
Better Than ◽  
Qtl Effects

Abstract Knowledge about the efficiency of generations for estimating marker-associated QTLs is needed for selection. The objective of this paper is to develop a theory to compare the efficiency of segregating generations and testcrosses from the cross of two inbred lines differing in value for a quantitative trait (P1 x P2) for estimating additive, dominance and heterotic effects of QTLs by stepwise regression. An equation that predicts the smallest gene effect in genetic standard deviation units that can be detected with 50% chance at a significance level as a function of the heritability (h2) and the recombination frequency (r) of markers was developed for the segregating generations and testcrosses. For estimating additive effects, the most efficient generation was the doubled-haploid (DH) lines; the most inefficient was the North Carolina Design III (NCD III), followed by selfed backcrosses (SB); the selfed families from F2 individual plants (F2:3 lines) are inferior to the recombinant inbreds (RI) for low r, but are better than RI for high h2 and r. Dominance effects are less efficiently estimated than additive effects. The NCD III is better than the SB and the F2:3 lines for detecting dominance effects. The RI and DH do not estimate dominance effects. The differential heterotic QTL effects of lines P1 and P2 when crossed with tester T can be estimated by evaluating testcrosses of individual F2 plants (F2T), recombinant inbreds (RIT) and double-haploid lines (DHT). The DHT is superior to the other generations. The F2T is better than the RIT for r > or = 0.20, but inferior for r < or = 0.1 or low heritability.

scholarly journals Cumulative areawise testing in wavelet analysis and its application to geophysical time series

2016 ◽  
Vol 23 (1) ◽  
pp. 45-57 ◽  
Author(s):  
Justin A. Schulte
Keyword(s):  
Wavelet Analysis ◽  
Statistical Power ◽  
Signal To Noise Ratio ◽  
Statistical Significance ◽  
Scientific Investigation ◽  
Statistical Significance Testing ◽  
Significance Level ◽  
Significance Levels ◽  
Geophysical Time Series ◽  
Selection Of

Abstract. Statistical significance testing in wavelet analysis was improved through the development of a cumulative areawise test. The test was developed to eliminate the selection of two significance levels that an existing geometric test requires for implementation. The selection of two significance levels was found to make the test sensitive to the chosen pointwise significance level, which may preclude further scientific investigation. A set of experiments determined that the cumulative areawise test has greater statistical power than the geometric test in most cases, especially when the signal-to-noise ratio is high. The number of false positives identified by the tests was found to be similar if the respective significance levels were set to 0.05.

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