50 research outputs found

    Data and Programs HDY-11-OR0427R

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    This folder contains many files related to the manuscript number HDY-11-OR0427R. In the Simulation folder, there are two datasets (binary and binomial) and two SAS programs used to analyze these data. In the Wheat folder, there is a dataset collected from a wheat QTL mapping experiment (Dou et al. 2009) and a SAS program that analyzes this dataset. The wheat data can also be downloaded from the original publication's journal website (Supplemental Material)

    Parameters of three genes of the Dallas Heart Study estimated separately using the BhGLM method.

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    1<p>The numbers after for the six fixed effects are the standard errors.</p>2<p>The theoretical <i>p</i>-value () for each gene was calculated using a threshold of 3.84 for the test statistic.</p>3<p>The empirical <i>p</i>-value (permutation) was calculated using a threshold drawn from the permutation study.</p

    Parameters of three genes of the Dallas Heart Study estimated separately using the ARR method proposed in this study.

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    1<p>The numbers after for the six fixed effects are the standard errors.</p>2<p>The theoretical <i>p</i>-value () for each gene was calculated using a threshold of 3.84 for the test statistic.</p>3<p>Theoretical <i>p</i>-value () for each gene was calculated using a threshold of 2.71 for the test statistic.</p>4<p>The empirical <i>p</i>-value (permutation) was calculated using a threshold drawn from the permutation study.</p

    Parameters of three genes of the Dallas Heart Study estimated jointly using the BhGLM method.

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    <p>Parameters of three genes of the Dallas Heart Study estimated jointly using the BhGLM method.</p

    Power comparison between ARR and BhGLM at significance level of 0.05.

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    <p>The top panel (A) gives the powers of the adaptive ridge regression (ARR) and the Bayesian hierarchical generalized linear model (BhGLM) evaluated at the threshold of 3.84. The panel in the middle (B) shows the powers of ARR and BhGLM evaluated at the threshold 2.71 for ARR and 3.84 for BhGLM. The bottom panel (C) shows the powers of ARR and BhGLM using thresholds of 3.45 and 9.78, respectively, to control the 0.05 Type I error rate.</p

    Significance level for each marker in <i>ANGPTL3</i>, <i>ANGPTL4</i> and <i>ANGPTL5</i> generated from the joint analyses.

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    <p><i>P</i> value is shown on the –log<sub>10</sub> scale. The top panels show the result of the adaptive ridge regression (ARR) analysis and the bottom panels show the results of the Bayesian hierarchical generalized linear model (BhGLM) analysis. The red dots represent variants with <i>p</i>-values smaller than 0.05, i.e., .</p

    Estimated QTL effects from the main effect model for grain weight.

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    <p><i><sup>a</sup></i>add: additive effect; dom: dominance effect. Total number of effects is 38, only 30 effects with a <i>p</i>-value ≀0.01 are listed in this table.</p><p><i><sup>b</sup></i>The estimated marker effect is denoted by and the standard deviation is denoted by .</p><p><i><sup>c</sup></i><i>P</i>-value is obtained via <i>t</i>-test.</p><p><i><sup>d</sup></i>Phenotypic variation explained.</p

    An Infinitesimal Model for Quantitative Trait Genomic Value Prediction

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    <div><p>We developed a marker based infinitesimal model for quantitative trait analysis. In contrast to the classical infinitesimal model, we now have new information about the segregation of every individual locus of the entire genome. Under this new model, we propose that the genetic effect of an individual locus is a function of the genome location (a continuous quantity). The overall genetic value of an individual is the weighted integral of the genetic effect function along the genome. Numerical integration is performed to find the integral, which requires partitioning the entire genome into a finite number of bins. Each bin may contain many markers. The integral is approximated by the weighted sum of all the bin effects. We now turn the problem of marker analysis into bin analysis so that the model dimension has decreased from a virtual infinity to a finite number of bins. This new approach can efficiently handle virtually unlimited number of markers without marker selection. The marker based infinitesimal model requires high linkage disequilibrium of all markers within a bin. For populations with low or no linkage disequilibrium, we develop an adaptive infinitesimal model. Both the original and the adaptive models are tested using simulated data as well as beef cattle data. The simulated data analysis shows that there is always an optimal number of bins at which the predictability of the bin model is much greater than the original marker analysis. Result of the beef cattle data analysis indicates that the bin model can increase the predictability from 10% (multiple marker analysis) to 33% (multiple bin analysis). The marker based infinitesimal model paves a way towards the solution of genetic mapping and genomic selection using the whole genome sequence data.</p> </div

    LOD scores of individual markers and bins of the carcass trait of beef cattle.

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    <p>(a) The top panel shows the LOD scores of individual marker analysis (simple regression analysis for each marker). (b) The panel at the bottom shows the LOD scores of the bins obtained from the adaptive infinitesimal model analysis with a bin size of 5.9 (the optimal bin size). The number of bins under this optimal size was 3186.</p

    Estimated QTL effects from the main effect model for yield per plant.

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    <p><i><sup>a</sup></i>add: additive effect; dom: dominance effect. Total number of effects is 4, all with a <i>p</i>-value ≀0.01.</p><p><i><sup>b</sup></i>The estimated marker effect is denoted by and the standard deviation is denoted by .</p><p><i><sup>c</sup></i><i>P</i>-value is obtained via <i>t</i>-test.</p><p><i><sup>d</sup></i>Phenotypic variation explained.</p
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