Score Statistics for Mapping Quantitative Trait Loci

Chang, Myron; Wu, Rongling; Wu, Samuel S.; Casella, George

doi:10.2202/1544-6115.1386

Cited by 28 publications

(29 citation statements)

References 37 publications

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“…Under some regular conditions, the score and LRT statistics are asymptotically equivalent in large sample [35]. But, an interesting characteristic of the score statistic is that it can be approximated by a sum of independent random components.…”

Section: Methodsmentioning

confidence: 99%

Multiple trait multiple interval mapping of quantitative trait loci from inbred line crosses

2012

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BackgroundAlthough many experiments have measurements on multiple traits, most studies performed the analysis of mapping of quantitative trait loci (QTL) for each trait separately using single trait analysis. Single trait analysis does not take advantage of possible genetic and environmental correlations between traits. In this paper, we propose a novel statistical method for multiple trait multiple interval mapping (MTMIM) of QTL for inbred line crosses. We also develop a novel score-based method for estimating genome-wide significance level of putative QTL effects suitable for the MTMIM model. The MTMIM method is implemented in the freely available and widely used Windows QTL Cartographer software.ResultsThroughout the paper, we provide compelling empirical evidences that: (1) the score-based threshold maintains proper type I error rate and tends to keep false discovery rate within an acceptable level; (2) the MTMIM method can deliver better parameter estimates and power than single trait multiple interval mapping method; (3) an analysis of Drosophila dataset illustrates how the MTMIM method can better extract information from datasets with measurements in multiple traits.ConclusionsThe MTMIM method represents a convenient statistical framework to test hypotheses of pleiotropic QTL versus closely linked nonpleiotropic QTL, QTL by environment interaction, and to estimate the total genotypic variance-covariance matrix between traits and to decompose it in terms of QTL-specific variance-covariance matrices, therefore, providing more details on the genetic architecture of complex traits.

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Section: Methodsmentioning

confidence: 99%

Multiple trait multiple interval mapping of quantitative trait loci from inbred line crosses

2012

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“…To test whether there is a variant that affects offspring phenotypes, we can formulate the following hypotheses:

\begin{matrix} {normalH}_{0} : a = 0, d = 0, h = 0, u_{1} = 0, v_{1} = 0, v_{2} = 0, u_{2} = 0 \\ {normalH}_{1} : At least one of these equalities does not hold, \end{matrix}

under each of which the likelihood values and their log‐likelihood ratio can be calculated. To determine the significance of the test, we calculate the critical threshold by permutation tests or score statistics (Chang et al ., ).…”

Section: Statistical Estimationmentioning

confidence: 97%

“…( 4) under each of which the likelihood values and their log-likelihood ratio can be calculated. To determine the significance of the test, we calculate the critical threshold by permutation tests or score statistics (Chang et al, 2009).…”

Section: Hypothesis Testsmentioning

confidence: 99%

A unified DNA sequence and non‐DNA sequence mapping model of complex traits

et al. 2019

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Summary Increasing evidence shows that quantitative inheritance is based on both DNA sequence and non‐DNA sequence variants. However, how to simultaneously detect these variants from a mapping study has been unexplored, hampering our effort to illustrate the detailed genetic architecture of complex traits. We address this issue by developing a unified model of quantitative trait locus (QTL) mapping based on an open‐pollinated design composed of randomly sampling maternal plants from a natural population and their half‐sib seeds. This design forms a two‐level hierarchical platform for a joint linkage‐linkage disequilibrium analysis of population structure. The EM algorithm was implemented to estimate and test DNA sequence‐based effects and non‐DNA sequence‐based effects of QTLs. We applied this model to analyze genetic mapping data from the OP design of a gymnosperm coniferous species, Torreya grandis, identifying 25 significant DNA sequence and non‐DNA sequence QTLs for seedling height and diameter growth in different years. Results from computer simulation show that the unified model has good statistical properties and is powerful for QTL detection. Our model enables the tests of how a complex trait is affected differently by DNA‐based effects and non‐DNA sequence‐based transgenerational effects, thus allowing a more comprehensive picture of genetic architecture to be charted and quantified.

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“…By calculating and plotting the log-likelihood ratio of the null hypothesis and alternative hypothesis throughout the genome, we can detect the genomic distribution of significant QTLs. The critical threshold is determined from permutation tests or score statistics (Chang et al, 2009).…”

Section: Estimation and Testsmentioning

confidence: 99%

A dissection model for mapping complex traits

et al. 2019

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Summary Many quantitative traits are composites of other traits that contribute differentially to genetic variation. Quantitative trait locus (QTL) mapping of these composite traits can benefit by incorporating the mechanistic process of how their formation is mediated by the underlying components. We propose a dissection model by which to map these interconnected components traits under a joint likelihood setting. The model can test how a composite trait is determined by pleiotropic QTLs for its component traits or jointly by different sets of QTLs each responsible for a different component. The model can visualize the pattern of time‐varying genetic effects for individual components and their impacts on composite traits. The dissection model was used to map two composite traits, stemwood volume growth decomposed into its stem height, stem diameter and stem form components for Euramerican poplar adult trees, and total lateral root length constituted by its average lateral root length and lateral root number components for Euphrates poplar seedlings. We found the pattern of how QTLs for different components contribute to phenotypic variation in composite traits. The detailed understanding of the genetic machineries of composite traits will not only help in the design of molecular breeding in plants and animals, but also shed light on the evolutionary processes of quantitative traits under natural selection.

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Score Statistics for Mapping Quantitative Trait Loci

Cited by 28 publications

References 37 publications

Multiple trait multiple interval mapping of quantitative trait loci from inbred line crosses

Multiple trait multiple interval mapping of quantitative trait loci from inbred line crosses

A unified DNA sequence and non‐DNA sequence mapping model of complex traits

A dissection model for mapping complex traits

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