Browsing by Author "Hoeschele, Ina"

Now showing 1 - 20 of 44
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    Adrenocortical Challenge Response and Genomic Analyses in Scottish Terriers With Increased Alkaline Phosphate Activity
    Zimmerman, Kurt L.; Panciera, David L.; Hoeschele, Ina; Monroe, William E.; Todd, S. Michelle; Werre, Stephen R.; LeRoith, Tanya; Fecteau, Kellie; Lake, Bathilda B. (Frontiers, 2018-10-09)
    Scottish terriers (ST) frequently have increased serum alkaline phosphatase (ALP) of the steroid isoform. Many of these also have high serum concentrations of adrenal sex steroids. The study’s objective was to determine the cause of increased sex steroids in ST with increased ALP. Adrenal gland suppression and stimulation were compared by low dose dexamethasone (LDDS), human chorionic gonadotropin (HCG) and adrenocorticotropic hormone (ACTH) response tests. Resting plasma pituitary hormones were measured. Steroidogenesis-related mRNA expression was evaluated in six ST with increased ALP, eight dogs of other breeds with pituitary-dependent hyperadrenocorticism (HAC), and seven normal dogs. The genome-wide association of single nucleotide polymorphisms (SNP) with ALP activity was evaluated in 168 ST. ALP (reference interval 8–70 U/L) was high in all ST (1,054 U/L) and HAC (985 U/L) dogs. All HAC dogs and 2/8 ST had increased cortisol post-ACTH administration. All ST and 2/7 Normal dogs had increased sex steroids post-ACTH. ST and Normal dogs had similar post-challenge adrenal steroid profiles following LDDS and HCG. Surprisingly, mRNA of hydroxysteroid 17-beta dehydrogenase 2 (HSD17B2) was lower in ST and Normal dogs than HAC. HSD17B2 facilities metabolism of sex steroids. A SNP region was identified on chromosome 5 in proximity to HSD17B2 that correlated with increased serum ALP. ST in this study with increased ALP had a normal pituitary-adrenal axis in relationship to glucocorticoids and luteinizing hormone.We speculate the identified SNP and HSD17B2 gene may have a role in the pathogenesis of elevated sex steroids and ALP in ST.
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    Age-related variations in the methylome associated with gene expression in human monocytes and T cells
    Reynolds, Lindsay M.; Taylor, Jackson R.; Ding, Jingzhong; Lohman, Kurt; Johnson, Craig; Siscovick, David; Burke, Gregory L.; Post, Wendy; Shea, Steven; Jacobs, David R. Jr.; Stunnenberg, Hendrik G.; Kritchevsky, Stephen B.; Hoeschele, Ina; McCall, Charles E.; Herrington, David M.; Tracy, Russell P.; Liu, Yongmei (Springer Nature, 2014-11)
    Age-related variations in DNA methylation have been reported; however, the functional relevance of these differentially methylated sites (age-dMS) are unclear. Here we report potentially functional age-dMS, defined as age-and cis-gene expression-associated methylation sites (age-eMS), identified by integrating genome-wide CpG methylation and gene expression profiles collected ex vivo from circulating T cells (227 CD4+ samples) and monocytes (1,264 CD14+ samples, age range: 55-94 years). None of the age-eMS detected in 227 T-cell samples are detectable in 1,264 monocyte samples, in contrast to the majority of age-dMS detected in T cells that replicated in monocytes. Age-eMS tend to be hypomethylated with older age, located in predicted enhancers and preferentially linked to expression of antigen processing and presentation genes. These results identify and characterize potentially functional age-related methylation in human T cells and monocytes, and provide novel insights into the role age-dMS may have in the aging process.
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    Assessment of Penalized Regression for Genome-wide Association  Studies
    Yi, Hui (Virginia Tech, 2014-08-27)
    The data from genome-wide association studies (GWAS) in humans are still predominantly analyzed using single marker association methods. As an alternative to Single Marker Analysis (SMA), all or subsets of markers can be tested simultaneously. This approach requires a form of Penalized Regression (PR) as the number of SNPs is much larger than the sample size. Here we review PR methods in the context of GWAS, extend them to perform penalty parameter and SNP selection by False Discovery Rate (FDR) control, and assess their performance (including penalties incorporating linkage disequilibrium) in comparison with SMA. PR methods were compared with SMA on realistically simulated GWAS data consisting of genotype data from single and multiple chromosomes and a continuous phenotype and on real data. Based on our comparisons our analytic FDR criterion may currently be the best approach to SNP selection using PR for GWAS. We found that PR with FDR control provides substantially more power than SMA with genome-wide type-I error control but somewhat less power than SMA with Benjamini-Hochberg FDR control. PR controlled the FDR conservatively while SMA-BH may not achieve FDR control in all situations. Differences among PR methods seem quite small when the focus is on variable selection with FDR control. Incorporating LD into PR by adapting penalties developed for covariates measured on graphs can improve power but also generate morel false positives or wider regions for follow-up. We recommend using the Elastic Net with a mixing weight for the Lasso penalty near 0.5 as the best method.
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    Bayesian estimation of genetic parameters for multivariate threshold and continuous phenotypes and molecular genetic data in simulated horse populations using Gibbs sampling
    Stock, Kathrin F.; Distl, Ottmar; Hoeschele, Ina (2007-05-09)
    Background Requirements for successful implementation of multivariate animal threshold models including phenotypic and genotypic information are not known yet. Here simulated horse data were used to investigate the properties of multivariate estimators of genetic parameters for categorical, continuous and molecular genetic data in the context of important radiological health traits using mixed linear-threshold animal models via Gibbs sampling. The simulated pedigree comprised 7 generations and 40000 animals per generation. Additive genetic values, residuals and fixed effects for one continuous trait and liabilities of four binary traits were simulated, resembling situations encountered in the Warmblood horse. Quantitative trait locus (QTL) effects and genetic marker information were simulated for one of the liabilities. Different scenarios with respect to recombination rate between genetic markers and QTL and polymorphism information content of genetic markers were studied. For each scenario ten replicates were sampled from the simulated population, and within each replicate six different datasets differing in number and distribution of animals with trait records and availability of genetic marker information were generated. (Co)Variance components were estimated using a Bayesian mixed linear-threshold animal model via Gibbs sampling. Residual variances were fixed to zero and a proper prior was used for the genetic covariance matrix. Results Effective sample sizes (ESS) and biases of genetic parameters differed significantly between datasets. Bias of heritability estimates was -6% to +6% for the continuous trait, -6% to +10% for the binary traits of moderate heritability, and -21% to +25% for the binary traits of low heritability. Additive genetic correlations were mostly underestimated between the continuous trait and binary traits of low heritability, under- or overestimated between the continuous trait and binary traits of moderate heritability, and overestimated between two binary traits. Use of trait information on two subsequent generations of animals increased ESS and reduced bias of parameter estimates more than mere increase of the number of informative animals from one generation. Consideration of genotype information as a fixed effect in the model resulted in overestimation of polygenic heritability of the QTL trait, but increased accuracy of estimated additive genetic correlations of the QTL trait. Conclusion Combined use of phenotype and genotype information on parents and offspring will help to identify agonistic and antagonistic genetic correlations between traits of interests, facilitating design of effective multiple trait selection schemes.
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    Bayesian QTL mapping using skewed Student-tdistributions
    von Rohr, Peter; Hoeschele, Ina (2002-01-15)
    In most QTL mapping studies, phenotypes are assumed to follow normal distributions. Deviations from this assumption may lead to detection of false positive QTL. To improve the robustness of Bayesian QTL mapping methods, the normal distribution for residuals is replaced with a skewed Student-t distribution. The latter distribution is able to account for both heavy tails and skewness, and both components are each controlled by a single parameter. The Bayesian QTL mapping method using a skewed Student-t distribution is evaluated with simulated data sets under five different scenarios of residual error distributions and QTL effects.
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    Blood monocyte transcriptome and epigenome analyses reveal loci associated with human atherosclerosis
    Liu, Yongmei; Reynolds, Lindsay M.; Ding, Jingzhong; Hou, Li; Lohman, Kurt; Young, Tracey; Cui, Wei; Huang, Zhiqing; Grenier, Carole; Wan, Ma; Stunnenberg, Hendrik G.; Siscovick, David; Hou, Lifang; Psaty, Bruce M.; Rich, Stephen S.; Rotter, Jerome I.; Kaufman, Joel D.; Burke, Gregory L.; Murphy, Susan F.; Jacobs, David R. Jr.; Post, Wendy; Hoeschele, Ina; Bell, Douglas A.; Herrington, David M.; Parks, John S.; Tracy, Russell P.; McCall, Charles E.; Stein, James H. (Springer Nature, 2017-08-30)
    Little is known regarding the epigenetic basis of atherosclerosis. Here we present the CD14+ blood monocyte transcriptome and epigenome signatures associated with human atherosclerosis. The transcriptome signature includes transcription coactivator, ARID5B, which is known to form a chromatin derepressor complex with a histone H3K9Me2-specific demethylase and promote adipogenesis and smooth muscle development. ARID5B CpG (cg25953130) methylation is inversely associated with both ARID5B expression and atherosclerosis, consistent with this CpG residing in an ARID5B enhancer region, based on chromatin capture and histone marks data. Mediation analysis supports assumptions that ARID5B expression mediates effects of cg25953130 methylation and several cardiovascular disease risk factors on atherosclerotic burden. In lipopolysaccharide-stimulated human THP1 monocytes, ARID5B knockdown reduced expression of genes involved in atherosclerosis-related inflammatory and lipid metabolism pathways, and inhibited cell migration and phagocytosis. These data suggest that ARID5B expression, possibly regulated by an epigenetically controlled enhancer, promotes atherosclerosis by dysregulating immunometabolism towards a chronic inflammatory phenotype.
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    Causal Gene Network Inference from Genetical Genomics Experiments via Structural Equation Modeling
    Liu, Bing (Virginia Tech, 2006-09-11)
    The goal of this research is to construct causal gene networks for genetical genomics experiments using expression Quantitative Trait Loci (eQTL) mapping and Structural Equation Modeling (SEM). Unlike Bayesian Networks, this approach is able to construct cyclic networks, while cyclic relationships are expected to be common in gene networks. Reconstruction of gene networks provides important knowledge about the molecular basis of complex human diseases and generally about living systems. In genetical genomics, a segregating population is expression profiled and DNA marker genotyped. An Encompassing Directed Network (EDN) of causal regulatory relationships among genes can be constructed with eQTL mapping and selection of candidate causal regulators. Several eQTL mapping approaches and local structural models were evaluated in their ability to construct an EDN. The edges in an EDN correspond to either direct or indirect causal relationships, and the EDN is likely to contain cycles or feedback loops. We implemented SEM with genetics algorithms to produce sub-models of the EDN containing fewer edges and being well supported by the data. The EDN construction and sparsification methods were tested on a yeast genetical genomics data set, as well as the simulated data. For the simulated networks, the SEM approach has an average detection power of around ninety percent, and an average false discovery rate of around ten percent.
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    Cell Cycle Model System for Advancing Cancer Biomarker Research
    Lazar, Iuliana M.; Hoeschele, Ina; de Morais, Juliana; Tenga, Milagros J. (Springer Nature, 2017-12-21)
    Progress in understanding the complexity of a devastating disease such as cancer has underscored the need for developing comprehensive panels of molecular markers for early disease detection and precision medicine applications. The present study was conducted to assess whether a cohesive biological context can be assigned to protein markers derived from public data mining, and whether mass spectrometry can be utilized to screen for the co-expression of functionally related biomarkers to be recommended for further exploration in clinical context. Cell cycle arrest/release experiments of MCF7/SKBR3 breast cancer and MCF10 non-tumorigenic cells were used as a surrogate to support the production of proteins relevant to aberrant cell proliferation. Information downloaded from the scientific public domain was queried with bioinformatics tools to generate an initial list of 1038 cancer-associated proteins. Mass spectrometric analysis of cell extracts identified 352 proteins that could be matched to the public list. Differential expression, enrichment, and protein-protein interaction analysis of the proteomic data revealed several functionally-related clusters of relevance to cancer. The results demonstrate that public data derived from independent experiments can be used to inform biological research and support the development of molecular assays for probing the characteristics of a disease.
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    Comparisons of Holstein, Brown Swiss, and Jersey cows for age at first calving, first calving interval, and true herd-life up to five years in seven regions of the United States
    Garcia-Peniche, Teresa Beatriz (Virginia Tech, 2004-12-10)
    The objectives of this research were to evaluate breed differences for heat-stress resistance using age at first calving and first calving interval, and to assess breed by region interactions for seven regions of the United States for survival-related traits up to five years of age in Brown Swiss, Holstein, and Jersey cows. Age at first calving and first calving interval were studied in farms with two breeds, with Holstein and Brown Swiss or Holstein and Jersey cows. The survival-related traits were analyzed in farms with one or two breeds. Seven regions within the United States were defined: Northeast, Northwest, Central north, Central, Central south, Southwest and Southeast. The fertility traits were also analyzed in seven individual states: Wisconsin, Ohio, Oregon, California, Arizona, Florida, and Texas. Brown Swiss were older than Holsteins at first calving (833 ± 2.4 d vs. 806 ± 2.0 d in regions, and 830 ± 3.1 d vs. 803 ± 2.4 d in states), but Holsteins and Brown Swiss did not differ for first calving interval. Jerseys were younger than Holsteins at first calving and had shorter first calving intervals (P < 0.01). In data from individual states, Holsteins housed with Brown Swiss were older at first calving than Holsteins housed with Jerseys (800 ± 2.7 d vs. 780 ± 2.5 d). Holsteins housed with Jerseys had slightly shorter first calving intervals than Holsteins housed with Brown Swiss, and the interaction of "type of Holstein: with season of the first calving was highly significant (P < 0.01). Region and season effects were smaller for Jerseys than for Holsteins, thus, Jerseys showed evidence of heat-stress resistance with respect to Holsteins. Management modified age at first calving in Holsteins, depending on the type of herd they were located in. Longer calving intervals might have been partly due to voluntary waiting period to breed the cows. The survival-related traits were evaluated up to five years of age. They consisted of stayability, number of completed lactations, days lived, herd-life, and total days in milk. For herds with one breed, the order for stayability to five years of age, from longer to shorter-lived breed was: Brown Swiss, Jersey and Holstein, but for the ratio of days in milk to herd-life the order was: Holstein, Jersey and Brown Swiss, and for the ratio of days in milk to days lived, it was: Jersey, and Holstein and Brown Swiss tied. This last ordering was the same for number of lactations completed by five years of age. The results for two-breed herds were similar since Brown Swiss and Jerseys had larger (Chi-square P < 0.01) probabilities of living past five years of age than Holsteins, and for days in milk and number of lactations completed, Jerseys had higher values than Holsteins (P < 0.01), but Holsteins and Brown Swiss tied in some analyses. Breed by region interaction was always significant. If all other conditions were assumed equal, Jerseys would give fastest returns by five years of age. The overall conclusion is that Jerseys performed better for the traits analyzed, all of them highly influenced by environmental conditions.
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    Correlation of predicted breeding values across environments in the presence of selection for direct and maternal breeding values
    Diaz-Martin, Clara (Virginia Tech, 1992-08-05)
    A simulation approach was used to determine the effects of multitrait selection on the correlations of sire direct and maternal predicted breeding values across environments. True and predicted direct and maternal breeding values (BV) of sires were simulated for sires evaluated independently in two different environments. Prediction error variances and covariances among direct and maternal BV within environments were required for the simulation. To obtain the necessary input parameters, a variety of MME coefficient matrices were created and inverted to inspect relationship among accuracies and correlations of prediction errors in sire evaluation models. An empirical prediction equation to predict the necessary prediction error covariances was obtained. Divergent, directional and random multitrait selection was then practiced using direct and maternal predicted BV as selection criteria. Samples of 40 sires were randomly obtained from each selected population. Observed correlations between direct and maternal predicted BV across environments were compared to expectations derived from univariate distribution theory. Selection definitely affected the expectations. However, the adjustment developed from univariate theory appeared to accommodate the effect of selection in these expectations.
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    Cross-species transcriptional analysis reveals conserved and host-specific neoplastic processes in mammalian glioma
    Connolly, Nina P.; Shetty, Amol C.; Stokum, Jesse A.; Hoeschele, Ina; Siegel, Marni B.; Miller, C. Ryan; Kim, Anthony J.; Ho, Cheng-Ying; Davila, Eduardo; Simard, J. Marc; Devine, Scott E.; Rossmeisl, John H. Jr.; Holland, Eric C.; Winkles, Jeffrey A.; Woodworth, Graeme F. (Springer Nature, 2018-01-19)
    Glioma is a unique neoplastic disease that develops exclusively in the central nervous system (CNS) and rarely metastasizes to other tissues. This feature strongly implicates the tumor-host CNS microenvironment in gliomagenesis and tumor progression. We investigated the differences and similarities in glioma biology as conveyed by transcriptomic patterns across four mammalian hosts: rats, mice, dogs, and humans. Given the inherent intra-tumoral molecular heterogeneity of human glioma, we focused this study on tumors with upregulation of the platelet-derived growth factor signaling axis, a common and early alteration in human gliomagenesis. The results reveal core neoplastic alterations in mammalian glioma, as well as unique contributions of the tumor host to neoplastic processes. Notable differences were observed in gene expression patterns as well as related biological pathways and cell populations known to mediate key elements of glioma biology, including angiogenesis, immune evasion, and brain invasion. These data provide new insights regarding mammalian models of human glioma, and how these insights and models relate to our current understanding of the human disease.
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    Data integration and visualization for systems biology data
    Cheng, Hui (Virginia Tech, 2010-10-27)
    Systems biology aims to understand cellular behavior in terms of the spatiotemporal interactions among cellular components, such as genes, proteins and metabolites. Comprehensive visualization tools for exploring multivariate data are needed to gain insight into the physiological processes reflected in these molecular profiles. Data fusion methods are required to integratively study high-throughput transcriptomics, metabolomics and proteomics data combined before systems biology can live up to its potential. In this work I explored mathematical and statistical methods and visualization tools to resolve the prominent issues in the nature of systems biology data fusion and to gain insight into these comprehensive data. In order to choose and apply multivariate methods, it is important to know the distribution of the experimental data. Chi square Q-Q plot and violin plot were applied to all M. truncatula data and V. vinifera data, and found most distributions are right-skewed (Chapter 2). The biplot display provides an effective tool for reducing the dimensionality of the systems biological data and displaying the molecules and time points jointly on the same plot. Biplot of M. truncatula data revealed the overall system behavior, including unidentified compounds of interest and the dynamics of the highly responsive molecules (Chapter 3). The phase spectrum computed from the Fast Fourier transform of the time course data has been found to play more important roles than amplitude in the signal reconstruction. Phase spectrum analyses on in silico data created with two artificial biochemical networks, the Claytor model and the AB2 model proved that phase spectrum is indeed an effective tool in system biological data fusion despite the data heterogeneity (Chapter 4). The difference between data integration and data fusion are further discussed. Biplot analysis of scaled data were applied to integrate transcriptome, metabolome and proteome data from the V. vinifera project. Phase spectrum combined with k-means clustering was used in integrative analyses of transcriptome and metabolome of the M. truncatula yeast elicitation data and of transcriptome, metabolome and proteome of V. vinifera salinity stress data. The phase spectrum analysis was compared with the biplot display as effective tools in data fusion (Chapter 5). The results suggest that phase spectrum may perform better than the biplot. This work was funded by the National Science Foundation Plant Genome Program, grant DBI-0109732, and by the Virginia Bioinformatics Institute.
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    Designing and modeling high-throughput phenotyping data in quantitative genetics
    Yu, Haipeng (Virginia Tech, 2020-04-09)
    Quantitative genetics aims to bridge the genome to phenome gap. The advent of high-throughput genotyping technologies has accelerated the progress of genome to phenome mapping, but a challenge remains in phenotyping. Various high-throughput phenotyping (HTP) platforms have been developed recently to obtain economically important phenotypes in an automated fashion with less human labor and reduced costs. However, the effective way of designing HTP has not been investigated thoroughly. In addition, high-dimensional HTP data bring up a big challenge for statistical analysis by increasing computational demands. A new strategy for modeling high-dimensional HTP data and elucidating the interrelationships among these phenotypes are needed. Previous studies used pedigree-based connectetdness statistics to study the design of phenotyping. The availability of genetic markers provides a new opportunity to evaluate connectedness based on genomic data, which can serve as a means to design HTP. This dissertation first discusses the utility of connectedness spanning in three studies. In the first study, I introduced genomic connectedness and compared it with traditional pedigree-based connectedness. The relationship between genomic connectedness and prediction accuracy based on cross-validation was investigated in the second study. The third study introduced a user-friendly connectedness R package, which provides a suite of functions to evaluate the extent of connectedness. In the last study, I proposed a new statistical approach to model high-dimensional HTP data by leveraging the combination of confirmatory factor analysis and Bayesian network. Collectively, the results from the first three studies suggested the potential usefulness of applying genomic connectedness to design HTP. The statistical approach I introduced in the last study provides a new avenue to model high-dimensional HTP data holistically to further help us understand the interrelationships among phenotypes derived from HTP.
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    Ecology of Root Nodule Bacterial Diversity: Implications for Soybean Growth
    Sharaf, Hazem (Virginia Tech, 2021-11-30)
    Diazotrophs supply legumes such as soybean (Glycine max L. Merr) with nitrogen (N) needed for protein synthesis through biological nitrogen fixation (BNF). Through BNF, these bacteria such as Bradyrhizobium that reside in soybean root nodules, convert atmospheric nitrogen (N2) into ammonia (NH3/ NH4), a form that is biologically available for use by the plants, in return for photosynthate carbon from the plant. Abiotic stresses such as drought disrupt BNF and subsequently affects soybean yield. In addition, increasing demand for soybean is leading to supplementing its growth with synthetic N fertilizer. However, fertilizer application is known for its detrimental effects on the environment causing waterways eutrophication contributing to global warming. On the other hand, diazotrophs can supply soybean with up to 90% of N need. As such, improving the understanding and exploiting the relationship between soybean and diazotrophs is key to promoting the sustainable growing of soybean. This dissertation here investigates three main questions. First, how the soybean-diazotrophs respond to changes in water such as rainfall and irrigation. Second, how changes in these bacterial diazotrophs are related to levels of BNF, and N-related soybean molecular markers. Finally, as my colleagues and I found non-diazotrophs in the nodules of some soybean plants, I was curious about the role they are playing inside the nodules in concert with the diazotrophs. The main hypotheses tested in this dissertation are that root nodule bacterial community (bacteriome) would (1) vary by plant type, (2) respond to changes in water, and (3) be related to BNF. To answer the research questions, I devised the dissertation as follows. In Chapter 2, my colleagues and I used nine commercial cultivars of soybean that vary in drought tolerance and agronomic traits. We show that soybean sometimes, but not always, harbor a consortium of non-nitrogen fixing bacteria belonging to Pseudomonadaceae and Enterobacteriaceae families. However, as expected, nodules diazotrophs rather than non-diazotrophs responded most to changes in soil water status. In chapter 3, I used a collection of 24 genotypes of soybean that vary in their ability to fix nitrogen. The results revealed that the bacteriome diazotroph alpha diversity metrics, phylogenetic richness and evenness, was correlated with changes in BNF. Moreover, few N-related molecular markers were associated with some of the bacteria. However, we have also observed a strong effect of the environment on the diazotroph driven process of BNF (i.e. 39%-75%). For chapter 4, we sequenced three of the Pseudomonas spp. strains that were subsequently recovered again from a diversity of soybean nodules in field trials. I found that one of the strains has the ability to adapt to the nodule's unique hypoxic conditions, supporting Bradyrhizobium nodulation and possibly nodule iron. The results include the draft assembly of the proposed Pseudomonas nodulensis sp. nov. as a novel species of nodule adapted bacteria belonging to the P. fluorescens complex. The results of this dissertation contribute to the basic knowledge needed to advance sustainable breeding and management of soybean. Nodule diazotrophs are sensitive to water status e.g. drought, and other experiments have shown that the nodule bacteriome is the driver of BNF. Thus, improving the understanding and exploiting the nodule bacteriome will support developing more resilient cultivars of soybean that are efficient in BNF, and tolerant of stress. Identifying and testing diazotrophs and atypical nodule bacteria will provide a platform for developing new inoculants and biofertilizers.
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    Estimation of additive, dominance and epistatic variance components using finite locus models implemented with a single-site Gibbs and a descent graph sampler
    Du, F. X.; Hoeschele, Ina (Cambridge University Press, 2000-10)
    In a previous contribution, we implemented a finite locus model (FLM) for estimating additive and dominance genetic variances via a Bayesian method and a single-site Gibbs sampler. We observed a dependency of dominance variance estimates on locus number in the analysis FLM. Here, we extended the FLM to include two-locus epistasis and implemented the analysis with two genotype samplers (Gibbs and descent graph) and three different priors for genetic effects (uniform and variable across loci, uniform and constant across loci, and normal). Phenotypic data were simulated for two pedigrees with 6300 and 12300 individuals in closed populations, using several different, non-additive genetic models. Replications of these data were analysed with FLMs differing in the number of loci. Simulation results indicate that the dependency of non-additive genetic variance estimates on locus number persisted in all implementation strategies we investigated. However, this dependency was considerably diminished with normal priors for genetic effects as compared with uniform priors (constant or variable across loci). Descent graph sampling of genotypes modestly improved variance components estimation compared with Gibbs sampling. Moreover, a larger pedigree produced considerably better variance components estimation, suggesting this dependency might originate from data insufficiency. As the FLM represents an appealing alternative to the infinitesimal model for genetic parameter estimation and for inclusion of polygenic background variation in QTL mapping analyses. further improvements are warranted and might be achieved via improvement of the sampler or treatment of the number of loci as an unknown.
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    Estimation of Variance Components in Finite Polygenic Models and Complex Pedigrees
    Lahti, Katharine Gage (Virginia Tech, 1998-06-05)
    Various models of the genetic architecture of quantitative traits have been considered to provide the basis for increased genetic progress. The finite polygenic model (FPM), which contains a finite number of unlinked polygenic loci, is proposed as an improvement to the infinitesimal model (IM) for estimating both additive and dominance variance for a wide range of genetic models. Analysis under an additive five-loci FPM by either a deterministic Maximum Likelihood (DML) or a Markov chain Monte Carlo (MCMC) Bayesian method (BGS) produced accurate estimates of narrow-sense heritability (0.48 to 0.50 with true values of h2 = 0.50) for phenotypic data from a five-generation, 6300-member pedigree simulated without selection under either an IM, FPMs containing five or forty loci with equal homozygote difference, or a FPM with eighteen loci of diminishing homozygote difference. However, reducing the analysis to a three- or four-loci FPM resulted in some biased estimates of heritability (0.53 to 0.55 across all genetic models for the 3-loci BGS analysis and 0.47 to 0.48 for the 40-loci FPM and the infinitesimal model for both the 3- and 4-loci DML analyses). The practice of cutting marriage and inbreeding loops utilized by the DML method expectedly produced overestimates of additive genetic variance (55.4 to 66.6 with a true value of sigma squared sub a = 50.0 across all four genetic models) for the same pedigree structure under selection, while the BGS method was mostly unaffected by selection, except for slight overestimates of additive variance (55.0 and 58.8) when analyzing the 40-loci FPM and the infinitesimal model, the two models with the largest numbers of loci. Changes to the BGS method to accommodate estimation of dominance variance by sampling genotypes at individual loci are explored. Analyzing the additive data sets with the BGS method, assuming a five-loci FPM including both additive and dominance effects, resulted in accurate estimates of additive genetic variance (50.8 to 52.2 for true sigma squared sub a = 50.0) and no significant dominance variance (3.7 to 3.9) being detected where none existed. The FPM has the potential to produce accurate estimates of dominance variance for large, complex pedigrees containing inbreeding, whereas the IM suffers severe limitations under inbreeding. Inclusion of dominance effects into the genetic evaluations of livestock, with the potential increase in accuracy of additive breeding values and added ability to exploit specific combining abilities, is the ultimate goal.
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    Exploitation of nonadditive variance through nonrandom mating
    DeStefano, Anita Louise (Virginia Tech, 1990-08-05)
    Mixed model equations to predict additive and nonadditive genetic values also predict specific combining abilities or combination effects among sire and dams or among sires and maternal grandsires (mgs). Current mating programs, utilizing nonadditive genetic variance only by avoiding mating between close relatives to prevent inbreeding depression, could be improved upon by use of predicted combination effects due to nonadditive variation beyond inbreeding. Simulation was employed to evaluate increase in progeny performance from nonrandom mating based on predicted combination effects among sires and mgs over random mating. Nonrandom mating strategies included mate allocation by linear programming, which is optimum, and two approximations, sequential selection based on progeny merit, and sequential selection based on deviation of progeny merit from mgs average. Genetic parameters were heritability equal to .05, .15, or .25 and ratio of dominance variance to phenotypic variance equal to .05, .10, or .15. These dominance ratios represent the range of recent estimates for yield and type traits. A total of 400 bulls were grouped by .99, .85, and .70 PTA reliability, with the first group being sires and mgs of the others. Using recurrence equations for combination effects, a matrix of true combination effects among the bulls was created. Reliabilites for estimated combination effects were computed for three types of bull populations; one with much information available (.41 to .79 ), one with little information ( .15 to .41 ) and one with an intermediate amount of information available (.15 to .79) and used to form matrices of estimated combination effects. Herds consisted of cows sired by .99 and .85 reliability bulls. Four mating groups of 123 cows, mated to 10 bulls from all bull groups, produced heifers to replace the herd. Herds were replicated 20 times for each type of bull population and each combination of heritability and dominance ratio. The three nonrandom mating strategies yielded means significantly different from random mating (p ≤ .05). When scaled by the standard deviation of milk yield, gains made by linear programming were 12.3 to 40.1 kg for low-reliability populations, 16.4 to 46.4 kg for intermediate reliability populations, and 31.0 to 80.3 kg for high reliability populations. Herds modified to utilize embryo transfer had less gain in progeny merit due to combination effects (20kg) with nonrandom mating compared to non-ET herds with identical heritability and dominance ratio, when donor cows were selected by estimated breeding value. Selection of donor cows based on combination effects yielded large gains (90.72kg) but such selection would only be justified in populations where nonadditive variance was more important than additive. A procedure for routinely approximating reliabilites of combination effects using information from three sources (information on parent subclasses, information on progeny subclasses, and records in subclass of interest) was presented.
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    Factors affecting the accuracy and stability of sire proofs from progeny test herds
    Meinert, Todd Richard (Virginia Tech, 1991)
    Change in Modified Contemporary Comparison proofs during first and second crop period was computed from up to eight proofs during both periods for AI and non-AI sampled Holstein bulls with repeatability of last evaluation≥.90. Effect of proof number within testing period on the bull's milk or fat evaluation was estimated with bull absorbed. AI and non-AI sampled bulls' proofs increased from initial first crop proof and then remained fairly constant during the remainder of first crop period. With inclusion of second crop daughters, proofs dropped significantly more for non-AI than AI sampled bulls. This drop increased for non-AI sampled bulls born after 1976, but was unchanged for AI sampled bulls. A measure of change was calculated using last second crop proof minus the second to last first crop proof. Expected standard deviation of change was calculated and used to stratify bulls into eight change classes. A larger proportion of non-AI sampled bulls have proofs that dropped than could be explained by chance alo e. Results indicated that non-AI sampled bulls were less stable than AI sampled bulls' proofs and that stability of non-AI sampled bulls has diminished over time. For one of the studs that had stability of their bulls' proofs examined, their young sire sampling program was investigated. Individual phenotypic and genetic records of first crop and non-first crop cows in 3449 herds participating in the AI stud's young sire sampling program from 1971 to 1987 were used to characterize the sampling program, to estimate genetic trend across and within the progeny test herds, and to compute within herd means and standard deviations of various traits (herd characteristics). Herd characteristics of progeny test herds were utilized in predicting within herd genetic trend d in predicting changes in proofs of bulls sampled by the stud. For bulls sampled by this stud, average herd characteristics and variability of herd characteristics across the contributing herds was calculated and used to predict the measure of proof change in the first study. Average herd-year characteristics and variability of herd-year characteristics explained 39% to 46% of the variation in milk. and fat proof changes. In general, variability of herd-year characteristics and average within herd-year standard deviation herd-year characteristic variables explained most of the changes in proofs. Genetic trend across the progeny test herds was large for milk (105 kg) and fat (3.1 kg) yield. Genetic trend computed from PTAs of sires of first crop cows increased 58 kg milk and 1.5 kg fat per year from 1971 to 1978 and 176 kg milk. and 5.5 kg fat per year from 1979-87. The genetic level of daughters of young sires born after 1983 was equivalent or exceeded the genetic level of cows from other sires in the herd. Results indicated that within herd genetic improvement will not be hurt and may actually be enhanced from participating in a young sire sampling program depending upon sire selection of cows not bred to young sires. Herd characteristics explained forty-five and fifty-one percent of the differences in within herd genetic trends for milk and fat yield, respectively. Average sire PTA of non-first crop daughters accounted for 80% and 67% of the explainable differences. Other herd characteristics indicated that herds with larger within herd standard deviation milk yields, larger number of young sires represented, younger cows, less average days open, and greater percentage of cows sired by AI sires made faster rates of genetic improvement.
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    The Genetic Basis of Phytate, Oligosaccharide Content, and Emergence in Soybean
    Glover, Natasha M. (Virginia Tech, 2011-06-27)
    Soybean [Glycine max (L.) Merr] is one of the U.S.'s most economically important crops due to the protein and oil content of seeds. The major storage form of phosphorus in soybean seeds is found in the form of phytate, but because of its negative nutritional and environmental impacts, seed phytate and raffinosaccharide content have been a recent focus of breeders and molecular geneticists. The soybean line CX1834 is a low phytate mutant known to have two low phytate QTLs on linkage groups (LGs) L and N. The first objective of this research was to determine the genetic basis of the low phytate trait in CX1834. By using the whole genome sequence, we identified two candidate multidrug resistance-associated (MRP) ABC transporter genes. Sequencing the genes from CX1834 and comparing them to the reference genome sequence revealed a single nucleotide polymorphism (SNP) in the MRP gene located on LG N (causing a stop codon), and a SNP mutation in the MRP gene located on LG L (causing an amino acid change from arginine to lysine). One major concern with low phytate soybeans is the low seedling emergence. The second objective was to undertake a population-wide study of emergence in the recombinant inbred population CX1834 x V99-3337, over two years and two locations. We found a positive correlation between phytate level and emergence, and that variation among year, location, genotypic class, year x genotypic class, and year x location interactions were significantly affecting emergence. V99-5089, in addition to being low phytate, has high sucrose and low raffinosaccharide content. This phenotype of V99-5089 has been previously determined to be due to a SNP mutation in its myo-inositol phosphate synthase (MIPS) gene located on LG B1. The third objective was to use the recombinant inbred population derived from CX1834 x V99-5089 to observe the combinations of all three mutations to see how the different alleles impact phytate and raffinosaccharide content. The individuals with all three mutations, as well as those with the two MRP mutations together had lower phytate than the other genotypic classes. However, these lines (all three mutations) had unexpectedly high stachyose.
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    Genomics of Climatic Adaptation in Populus Trichocarapa
    Zhang, Man (Virginia Tech, 2016-08-10)
    Temperate tree species exhibit seasonal growth cycling, and the timing of such transition varies with local climate. Under anthropogenic climate change, the local pattern of growth and dormancy in tree populations is expected to become uncoupled with shifting seasonal environmental signals, particularly temperature. Thus, an understanding of the genetic underpinnings of local adaptation is key to predicting the fate of tree populations in the future. In this thesis, we coupled sampling of range-wide natural accessions of P. trichocarpa with adaptive trait phenotyping and genome-wide genotyping to uncover relationships between genotype, phenotype, and environment. We detected strong correlations between adaptive phenotypes, climate, and geography, which suggested climatic selection driving adaptation of these populations to local environments. We subsequently combined genotype-phenotype association tests with sliding window analysis and identified regions strongly associated with these adaptive traits. We also compared adaptive markers identified in two independent GWAS on samples across latitude and altitude transects and found a set of associated variants shared across both transects. We further scanned the genome with three selection tests to identify regions showing evidence of recent positive and divergent selection. By comparing candidate selection regions across altitude and latitude, we detected a set of overlapping regions showing differentiation across gradients of the same climate variables. We validated the functional imortance of these selection regions by combining GWAS and showed that selection regions contain a strong signature of phenotypic associations. We also studied the distribution of deleterious allels across genome and natural populations, and found that deleterious alleles preferentially accumulate in regions of low recombination and hithihking regions. Finally, marginal populations contained more deleterious alleles compared with central populations, which is likely due to ineffective selection in small populations and recent bottlenecks associated with postglacial recolonization. These findings provide new insights into the genomic architecture underlying climatic adaptation and how selection drives adaptive evolution of tree species.