Evaluation of the phenotypic and genomic background of variability based on litter size of Large White pigs

Background The genetic background of trait variability has captured the interest of ecologists and animal breeders because the genes that control it could be involved in buffering various environmental effects. Phenotypic variability of a given trait can be assessed by studying the heterogeneity of the residual variance, and the quantitative trait loci (QTL) that are involved in the control of this variability are described as variance QTL (vQTL). This study focuses on litter size (total number born, TNB) and its variability in a Large White pig population. The variability of TNB was evaluated either using a simple method, i.e. analysis of the log-transformed variance of residuals (LnVar), or the more complex double hierarchical generalized linear model (DHGLM). We also performed a single-SNP (single nucleotide polymorphism) genome-wide association study (GWAS). To our knowledge, this is only the second study that reports vQTL for litter size in pigs and the first one that shows GWAS results when using two methods to evaluate variability of TNB: LnVar and DHGLM. Results Based on LnVar, three candidate vQTL regions were detected, on Sus scrofa chromosomes (SSC) 1, 7, and 18, which comprised 18 SNPs. Based on the DHGLM, three candidate vQTL regions were detected, i.e. two on SSC7 and one on SSC11, which comprised 32 SNPs. Only one candidate vQTL region overlapped between the two methods, on SSC7, which also contained the most significant SNP. Within this vQTL region, two candidate genes were identified, ADGRF1, which is involved in neurodevelopment of the brain, and ADGRF5, which is involved in the function of the respiratory system and in vascularization. The correlation between estimated breeding values based on the two methods was 0.86. Three-fold cross-validation indicated that DHGLM yielded EBV that were much more accurate and had better prediction of missing observations than LnVar. Conclusions The results indicated that the LnVar and DHGLM methods resulted in genetically different traits. Based on their validation, we recommend the use of DHGLM over the simpler method of log-transformed variance of residuals. These conclusions can be useful for future studies on the evaluation of the variability of any trait in any species.

Differential Item Functioning Among English Language Learners on a Large-Scale Mathematics Assessment

The current study research showed the nature and potential sources of the gaps in mathematics achievement between English language learners (ELLs) and non-English language learners (non-ELLs). The nature of achievement gap was examined using three DIF methodologies: including Mantel-Haenszel procedure, Rasch model, and Hierarchical Generalized Linear Model (HGLM). These were conducted at the item level in contrast to total test level. Results revealed that the three DIF approaches identified 10 common items. These 10 items demonstrated in favor of non-ELLs. Findings from this study will help educational researchers, administrators, and policymakers understand the nature of the achievement gap in mathematics at item level so that United States can be competitive in middle school mathematics education. This study also suggested that item writers and test developers should construct assessments where language is equally accessible for ELL students.

Exploring How Functional Traits Modulate Species Response to Topography in Baxian Mountain, North China

The associations between functional traits and species response to environments have aroused more and more ecologists’ interest and can provide insights into understanding and explaining how plants respond to the environment. Here, we applied a hierarchical generalized linear model to quantifying the role of functional traits in plants response to topography. Functional traits data, including specific leaf area, maximum height, seed mass and stem wood density, together with elevation, aspect and slope were used in the model. In our results, species response to elevation and aspect were modulated by maximum height and seed mass. Shorter-statured tree species had a more positive response than taller ones to an increase in elevation. Compared to light-seeded trees, heavy-seeded trees responded more positively to more southerly aspects where the soil was drier. In this study, the roles of maximum height and seed mass in determining species distribution along elevation and aspect gradients were highlighted respectively where plants are confronted with low-temperature and soil moisture deficit conditions. This work contributes to the understanding of how traits may be associated with species responses along mesoscale environmental gradients.

Multiple shifts in gene network interactions shape phenotypes of Drosophila melanogaster selected for long and short night sleep duration

All but the simplest phenotypes are believed to result from interactions between two or more genes forming complex networks of gene regulation. Sleep is a complex trait known to depend on the system of feedback loops of the circadian clock, and on many other genes; however, the main components regulating the phenotype and how they interact remain an unsolved puzzle. Genomic and transcriptomic data may well provide part of the answer, but a full account requires a suitable quantitative framework. Here we conducted an artificial selection experiment for sleep duration with RNA-seq data acquired each generation. The phenotypic results are robust across replicates and previous experiments, and the transcription data provides a high-resolution, time-course data set for the evolution of sleep-related gene expression. In addition to a Hierarchical Generalized Linear Model analysis of differential expression that accounts for experimental replicates we develop a flexible Gaussian Process model that estimates interactions between genes. 145 gene pairs are found to have interactions that are different from controls. Our method not only is considerably more specific than standard correlation metrics but also more sensitive, finding correlations not significant by other methods. Statistical predictions were compared to experimental data from public databases on gene interactions.

Multilevel Approach to Exploring Predictors of College Readiness in Reading and Mathematics

For high school graduates and non-graduates, this paper explored significant student and school level predictors of college readiness in reading and mathematics for 9,952 students from 52 schools in one of the largest school districts in the United States. This study employed a two-level Hierarchical Generalized Linear Model (HGLM) that included student level (level-1) and school level (level-2) predictors in order to predict three categories of college readiness formed in combination with high school graduates and non-graduates. The results presented the list of significant predictors and across-school variances for predicting college readiness in reading and mathematics. The results found several academic, behavioral, and demographic predictors at student and school levels producing significant effects on college readiness in reading and mathematics. The across-school variance components for predicting the probabilities of mastery in college readiness both in reading and mathematics are found significant.

Randomized Clinical Trial on Sodium Fluoride with Tricalcium Phosphate

This 24-mo randomized controlled trial was based on a double-blind parallel design, and it compared the effectiveness of 2 fluoride application protocols in arresting dentine caries in primary teeth. Three-year-old children with active dentine caries were recruited and randomly allocated to 2 treatment groups. Children in group A received a semiannual application of a 25% silver nitrate (AgNO3) solution followed by a commercially available varnish with 5% sodium fluoride (NaF) on the carious tooth surfaces. Children in group B received a semiannual application of a 25% AgNO3 solution followed by another commercially available varnish with 5% NaF containing functionalized tricalcium phosphate (fTCP). Carious tooth surfaces that were hard when probing were classified as arrested. Intention-to-treat analysis and a hierarchical generalized linear model were undertaken. A total of 408 children with 1,831 tooth surfaces with active dentine caries were recruited at baseline, and 356 children (87%) with 1,607 tooth surfaces (88%) were assessed after 24 mo. At the 24-mo evaluation, the mean (SD) number of arrested carious tooth surfaces per child were 1.8 (2.2) and 2.6 (3.3) for group A (without fTCP) and group B (with fTCP), respectively ( P = 0.003). The arrest rates at the tooth surface level were 42% for group A and 57% for group B ( P < 0.001). Results of the hierarchical generalized linear model indicated that protocol B (with fTCP) had a higher predicted probability (PP = 0.656) in arresting dentine caries than protocol A (without fTCP; PP = 0.500) when the carious lesions were on buccal/lingual surfaces, were on anterior teeth, had dental plaque coverage, and were in children from low-income families ( P = 0.046). In conclusion, protocol B, which applied a 25% AgNO3 solution followed by a commercially available 5% NaF varnish with fTCP semiannually, is more effective in arresting dentine caries in primary teeth as compared with protocol A, which applied a 25% AgNO3 solution followed by another commercially available 5% NaF varnish without fTCP semiannually (ClinicalTrials.gov NCT03423797).

Alternative learning strategies for spatio-temporal processes of complex animal behavior

The estimation of spatio-temporal dynamics of animal behavior processes is complicated by nonlinear interactions. Alternative learning methods such as machine learning, deep learning, and reinforcement learning have proven successful for approximating nonlinear system mechanisms for prediction and classification. These alternative learning frameworks can be linked to statistical models in a hierarchical framework to improve ecological inference and prediction in the presence of uncertainty. This dissertation provides three methodological extensions of alternative learning with statistical uncertainty quantification for modeling animal behavior dynamics at different scales. First, an efficient Bayesian Markov model is developed to provide inference on white-fronted geese behavior from individual accelerometer and location data while accounting for classification uncertainty. Second, nonlinear basis function expansions produced by a spatio-temporal echo state network are used as features in a hierarchical generalized linear model for predicting spatial patterns of mallard duck settling pattern counts. Lastly, Bayesian inverse reinforcement learning is developed to estimate the behavioral state costs for collective animal groups.