AN INTRODUCTION TO MODEL SELECTION FOR QUANTITATIVE TRAIT LOCUS ANALYSIS IN POLYPLOIDS

Substantial gains have been made in locating regions of agricultural genomes associated with characteristics, diseases, and agroeconomic traits. These gains have relied heavily on the ability to estimate the association between DNA markers and regions of a genome (quantitative trait loci or QTL) related to a particular trait. The majority of these advances have focused on diploid species (two homologous chromosomes per set), even though many important agricultural crops are, in fact, polyploid (more than two homologous chromosomes per set). The purpose of our work is to initiate an algorithmic approach for model selection and QTL detection in polyploid species. This approach involves the enumeration of all possible chromosomal configurations (models) that may result in a gamete, model reduction based on estimation of marker dosage from progeny data, and lastly model selection. While simplified for initial explanation, our approach has demonstrated itself as being extendible to many breeding schemes and less restricted settings

ADJUSTING POPULATION ESTIMATES FOR GENOTYPING ERROR IN NON-INVASIVE DNA-BASED MARK-RECAPTURE EXPERIMENTS

DNA from non-invasive sources is increasingly being used as molecular tags for markrecapture population estimation. These sources, however, provide small quantities of often contaminated DNA, which can lead to genotyping errors that will bias the population estimate. We describe a novel approach, called Genotyping Uncertainty Added Variance Adjustment (GUAVA), to address this problem. GUAVA incorporates an explicit model of genotyping error to generate a distribution of complete-information capture histories that is used to estimate the population size. This approach both reduces the genotyping-error bias and incorporates the additional uncertainty due to genotyping error into the variance of the estimate. We demonstrate this approach via simulated mark-recapture data with a range of genetic information, population sizes, sample sizes, and genotyping error-rates. The bias, variance, and coverage of the GUAVA estimates are shown to be superior to those of other available methods used to analyze this type of data. Because GUAVA assumes each sample is genotyped only once per locus, it also has the potential to save a great deal of time and money collecting consensus molecular information

Workplace-Based Practicum: Enabling Expansive Practices

Effective pre-service teacher education integrates theoretical and practical knowledge. One means of integration is practicum in a school workplace. In a time of variable approaches to, and models of, practicum, we outline an innovative model of school immersion as part of a teacher preparation program. We apply Fuller and Unwin’s (2004) expansive and restrictive conceptual framework of workplace learning to a case study of an immersive practicum experience to discuss themes of participation, personal development and institutional arrangements in relation to school-based practicum. Enablers and constraints are identified for our immersion model of workplace-based practicum. Based on the data analysis a number of implications for structuring an expansive practicum learning experience are outlined

Oleogustus: The Unique Taste of Fat

Considerable mechanistic data indicate there may be a sixth basic taste: fat. However, evidence demonstrating that the sensation of non-esterified fatty acids (the proposed stimuli for “fat taste”) differs qualitatively from other tastes is lacking. Using perceptual mapping, we demonstrate that medium and long-chain non-esterified fatty acids have a taste sensation that is distinct from other basic tastes (sweet, sour, salty, and bitter). While some overlap was observed between these NEFA and umami taste, this overlap is likely due to unfamiliarity with umami sensations rather than true similarity. Shorter chain fatty acids stimulate a sensation similar to sour, but as chain length increases this sensation changes. Fat taste oral signaling, and the different signals caused by different alkyl chain lengths, may hold implications for food product development, clinical practice, and public health policy

DEVELOPING PREDICTION EQUATIONS FOR FAT FREE LEAN IN THE PRESENCE OF AN UNKNOWN AMOUNT OF PROPORTIONAL MEASUREMENT ERROR

Published prediction equations for fat-free lean mass are widely used by producers for carcass evaluation. These regression equations are commonly derived under the assumption that the predictors are measured without error. In practice, however, it is known that some predictors, such as backfat and loin muscle depth, are measured imperfectly with variance that is proportional to the mean. Failure to account for these measurement errors will cause bias in the estimated equation. In this paper, we describe an empirical Bayes approach, using technical replicates, to accurately estimate the regression relationship in the presence of proportional measurement error. We demonstrate, via simulation studies, that this Bayesian approach dramatically improves the accuracy of the estimated equation in comparison to the fit from Ordinary Least Squares regression

INTRODUCTION TO BAYESIAN QUANTITATIVE TRAIT LOCUS ANALYSIS FOR POLYPLOIDS

Quantitative Trait Locus (QTL) mapping in polyploids is complicated by the un-observable parental QTL con guration, especially the number of copies (dosage) of the QTL. Existing techniques estimate the parental QTL con guration using a profile likelihood approach and do not address the uncertainty in the estimates. In this paper, a Bayesian method is proposed to jointly model the parameters including the parental QTL configuration, QTL location, and QTL effects. Inference for parameters is obtained by integrating the posterior distribution of the parameters via a Markov chain Monte Carlo (MCMC) sampler, which is a hybrid of the Metropolis-Hastings, Gibbs, and reversible jump samplers. Here, because the size of the parameter space varies for different parental QTL dosages, the reversible jump is utilized in order to allow the sampler to move between parameter spaces with di erent dimensionalities. Additional advantage of this Bayesian technique resides in its flexibility to incorporate prior information and treat missing data augmented. As an example, our method is applied to alfalfa experimental data to identify QTL related to winter hardiness

MODELING DNA METHYLATION TILING ARRAY DATA

Epigenetics is the study of heritable changes in gene function that occur without a change in DNA sequence. It has quickly emerged as an essential area for understanding inheritance and variation that cannot be explained by the DNA sequence alone. Epigenetic modifications have the potential to regulate gene expression and may play a role in diseases such as cancer. DNA methylation is a type of epigenetic modification that occurs when a methyl chemical group attaches to a cytosine base on the DNA molecule. To better understand this epigenetic mechanism, DNA methylation profiles can be constructed by identifying all locations of DNA methylation in a genomic region (e.g. chromosome or whole-genome). Large-scale studies of DNA methylation are supported by microarray technology known as tiling arrays. These arrays provide high-density coverage of genomic regions through the unbiased, systematic selection of probes that are tiled across the regions. Statistical methods are employed to estimate each probe’s DNA methylation status. Previous studies indicate that DNA methylation patterns of some organisms differ by genomic element (e.g., gene, transposon), suggesting that genomic annotation information may be useful in statistical analysis. In this work, a novel statistical model is proposed, which takes advantage of genomic annotation information that to date has not been effectively utilized in statistical analysis. Specifically, a hidden Markov model, which incorporates genomic annotation, is introduced and investigated through a simulation study and analysis of an Arabidopsis thaliana DNA methylation tiling array experiment

A SIMULATION STUDY OF EXPONENTIAL SEMIV ARlO GRAM ESTIMATION

Incorporating the spatial structure of data from agricultural field experiments into inference procedures has become an important topic in recent years. As part of a larger project to determine whether or not reliable predictions and estimates can be obtained for sample sizes often encountered in traditional field experimentation, this paper focuses on the small sample estimation of the parameters of the exponential semivariogram model. Simulation studies were conducted for both expanding and fixed domains. The results indicate large sample to sample variation in sample and fitted semivariograms, neither of which may be close to the true model. Distributions of individual parameter estimators are skewed and highly variable. Empirical coverage levels for large sample confidence intervals for the parameters are well below the nominal level and, contrary to what would be expected, decrease as the sample size increases. The results cast doubt on the success of incorporating spatial structure into traditional field data analyses