Effect of Exercise on Blood Pressure and Body Mass Index in At-Risk Populations

Higher blood pressure and basal metabolic rate (BMI) are health problems in the United States (U.S.), but particularly in high risk minority populations, in part because of limited access to adequate resources to help themselves become healthier. This Honors Paper aimed to examine the effect of an exercise intervention on blood pressure and BMI in high risk minority populations. The honors project is a part of the Finding A Better You (FABU) project by College of Health Profession faculty Dr. Murrock, Dr. MacCracken, and Dr. Juvancic-Heltzel. The FABU project assessed at risk individuals (lower income older adults) in Summit County and determined the outcomes of intervention classes about proper exercise and nutrition. Using a non-experimental design and convenience sampling, this honors project was guided by Bandura’s Social Cognition Theory, which describes behavior change in group settings. The project initially aimed to generate preliminary findings about whether or not exercise affects blood pressure and BMI in at risk populations. However, the coronavirus disrupted the delivery of the intervention and resulted in revising the research question to examine the effect of exercise classes over 12 weeks on blood pressure and BMI in a sample of minority adults

Synthesis and crystal structure of a neodymium borosilicate, Nd3BSi2O10

A lanthanide borosilicate, trineodymium borosilicate or Nd3BSi2O10, was synthesized using a flux method with LiCl, and its structure was determined from X-ray powder diffraction (XRD) and electron probe microanalysis (EPMA). The structure is composed of layers with [SiO4]4− and [BSiO6]5− anions alternating along the c axis linked by Nd3+ cations between them

CRUMBLER: A tool for the prediction of ancestry in cattle.

In many beef and some dairy production systems, crossbreeding is used to take advantage of breed complementarity and heterosis. Admixed animals are frequently identified by their coat color and body conformation phenotypes, however, without pedigree information it is not possible to identify the expected breed composition of an admixed animal and in the presence of selection, the actual composition may differ from expectation. As the roles of DNA and genotype data become more pervasive in animal agriculture, a systematic method for estimating the breed composition (the proportions of an animal's genome originating from ancestral pure breeds) has utility for a variety of downstream analyses including the estimation of genomic breeding values for crossbred animals, the estimation of quantitative trait locus effects, and heterosis and heterosis retention in advanced generation composite animals. Currently, there is no automated or semi-automated ancestry estimation platform for cattle and the objective of this study was to evaluate the utility of extant public software for ancestry estimation and determine the effects of reference population size and composition and number of utilized single nucleotide polymorphism loci on ancestry estimation. We also sought to develop an analysis pipeline that would simplify this process for members of the livestock genomics research community. We developed and tested a tool, "CRUMBLER", to estimate the global ancestry of cattle using ADMIXTURE and SNPweights based on a defined reference panel. CRUMBLER, was developed and evaluated in cattle, but is a species agnostic pipeline that facilitates the streamlined estimation of breed composition for individuals with potentially complex ancestries using publicly available global ancestry software and a specified reference population SNP dataset. We developed the reference panel from a large cattle genotype data set and breed association pedigree information using iterative analyses to identify purebred individuals that were representative of each breed. We also evaluated the numbers of markers necessary for breed composition estimation and simulated genotypes for advanced generation composite animals to evaluate the precision of the developed tool. The developed CRUMBLER pipeline extracts a specified subset of genotypes that is common to all current commercially available genotyping platforms, processes these into the file formats required for the analysis software, and predicts admixture proportions using the specified reference population allele frequencies

Analysis of polygenic selection in purebred and crossbred pig genomes using generation proxy selection mapping

Abstract Background Artificial selection on quantitative traits using breeding values and selection indices in commercial livestock breeding populations causes changes in allele frequency over time at hundreds or thousands of causal loci and the surrounding genomic regions. In population genetics, this type of selection is called polygenic selection. Researchers and managers of pig breeding programs are motivated to understand the genetic basis of phenotypic diversity across genetic lines, breeds, and populations using selection mapping analyses. Here, we applied generation proxy selection mapping (GPSM), a genome-wide association analysis of single nucleotide polymorphism (SNP) genotypes (38,294–46,458 markers) of birth date, in four pig populations (15,457, 15,772, 16,595 and 8447 pigs per population) to identify loci responding to artificial selection over a period of five to ten years. Gene-drop simulation analyses were conducted to provide context for the GPSM results. Selected loci within and across each population of pigs were compared in the context of swine breeding objectives. Results The GPSM identified 49 to 854 loci as under selection (Q-values less than 0.10) across 15 subsets of pigs based on combinations of populations. The number of significant associations increased when data were pooled across populations. In addition, several significant associations were identified in more than one population. These results indicate concurrent selection objectives, similar genetic architectures, and shared causal variants responding to selection across these pig populations. Negligible error rates (less than or equal to 0.02%) of false-positive associations were found when testing GPSM on gene-drop simulated genotypes, suggesting that GPSM distinguishes selection from random genetic drift in actual pig populations. Conclusions This work confirms the efficacy and the negligible error rates of the GPSM method in detecting selected loci in commercial pig populations. Our results suggest shared selection objectives and genetic architectures across swine populations. The identified polygenic selection highlights loci that are important to swine production

A multi-breed reference panel and additional rare variants maximize imputation accuracy in cattle

International audienceAbstractBackgroundDuring the last decade, the use of common-variant array-based single nucleotide polymorphism (SNP) genotyping in the beef and dairy industries has produced an astounding amount of medium-to-low density genomic data. Although low-density assays work well in the context of genomic prediction, they are less useful for detecting and mapping causal variants and the effects of rare variants are not captured. The objective of this project was to maximize the accuracies of genotype imputation from medium- and low-density assays to the marker set obtained by combining two high-density research assays (~ 850,000 SNPs), the Illumina BovineHD and the GGP-F250 assays, which contains a large proportion of rare and potentially functional variants and for which the assay design is described here. This 850 K SNP set is useful for both imputation to sequence-level genotypes and direct downstream analysis.ResultsWe found that a large multi-breed composite imputation reference panel that includes 36,131 samples with either BovineHD and/or GGP-F250 genotypes significantly increased imputation accuracy compared with a within-breed reference panel, particularly at variants with low minor allele frequencies. Individual animal imputation accuracies were maximized when more genetically similar animals were represented in the composite reference panel, particularly with complete 850 K genotypes. The addition of rare variants from the GGP-F250 assay to our composite reference panel significantly increased the imputation accuracy of rare variants that are exclusively present on the BovineHD assay. In addition, we show that an assay marker density of 50 K SNPs balances cost and accuracy for imputation to 850 K.ConclusionsUsing high-density genotypes on all available individuals in a multi-breed reference panel maximized imputation accuracy for tested cattle populations. Admixed animals or those from breeds with a limited representation in the composite reference panel were still imputed at high accuracy, which is expected to further increase as the reference panel expands. We anticipate that the addition of rare variants from the GGP-F250 assay will increase the accuracy of imputation to sequence level

Additional file 1 of Analysis of polygenic selection in purebred and crossbred pig genomes using generation proxy selection mapping

Additional file 1: Table S1: Proportion of variation in AGE explained by SNPs for each purebred subset using five replications of randomly simulated genotype data

Additional file 3 of Analysis of polygenic selection in purebred and crossbred pig genomes using generation proxy selection mapping

Additional file 3: Table S3: Population, chromosome, SNP effect, Q-value, gene identifier, associated human traits, and associated pig traits for SNPs associated with AGE from GPSM analyses

Additional file 2 of Analysis of polygenic selection in purebred and crossbred pig genomes using generation proxy selection mapping

Additional file 2: Table S2: Number of SNPs significantly associated with AGE for each subset using five replicates of randomly simulated genotype data