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Search Results: 1 - 10 of 46616 matches for " Zhao-Bang Zeng "
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Statistical Methods for Mapping Multiple QTL
Wei Zou,Zhao-Bang Zeng
International Journal of Plant Genomics , 2008, DOI: 10.1155/2008/286561
Abstract: Since Lander and Botstein proposed the interval mapping method for QTL mapping data analysis in 1989, tremendous progress has been made in the last many years to advance new and powerful statistical methods for QTL analysis. Recent research progress has been focused on statistical methods and issues for mapping multiple QTL together. In this article, we review this progress. We focus the discussion on the statistical methods for mapping multiple QTL by maximum likelihood and Bayesian methods and also on determining appropriate thresholds for the analysis.
From Classical Genetics to Quantitative Genetics to Systems Biology: Modeling Epistasis
David L. Aylor,Zhao-Bang Zeng
PLOS Genetics , 2008, DOI: 10.1371/journal.pgen.1000029
Abstract: Gene expression data has been used in lieu of phenotype in both classical and quantitative genetic settings. These two disciplines have separate approaches to measuring and interpreting epistasis, which is the interaction between alleles at different loci. We propose a framework for estimating and interpreting epistasis from a classical experiment that combines the strengths of each approach. A regression analysis step accommodates the quantitative nature of expression measurements by estimating the effect of gene deletions plus any interaction. Effects are selected by significance such that a reduced model describes each expression trait. We show how the resulting models correspond to specific hierarchical relationships between two regulator genes and a target gene. These relationships are the basic units of genetic pathways and genomic system diagrams. Our approach can be extended to analyze data from a variety of experiments, multiple loci, and multiple environments.
Models and partition of variance for quantitative trait loci with epistasis and linkage disequilibrium
Tao Wang, Zhao-Bang Zeng
BMC Genetics , 2006, DOI: 10.1186/1471-2156-7-9
Abstract: Starting from the classical description of Cockerham genetic model, we first represent the model in a multiple regression setting by using indicator variables to describe the segregation of QTL alleles. In this setting, the definition of additive, dominance and epistatic effects of QTL and the basis for the partition of genetic variance are elaborated. We then build the connection between this general genetic model and a few specialized models (a haploid model, a diploid F2 model and a general two-allele model), and derive the genetic effects and partition of genetic variance for multiple QTL with epistasis and linkage disequilibrium for these specialized models.In this paper, we study extensively the composition and property of the genetic model parameters, such as genetic effects and partition of genetic variance, when both epistasis and linkage disequilibrium are considered. This is the first time that both epistasis and linkage disequilibrium are considered in modeling multiple QTL. This analysis would help us to understand the structure of genetic parameters and relationship of various genetic quantities, such as allelic frequencies and linkage disequilibrium, on the definition of genetic effects, and will also help us to understand and properly interpret estimates of the genetic effects and variance components in a QTL mapping experiment.Modeling quantitative trait loci (QTL) started with Yule [1,2] and Pearson [3] (see [4,5] for the early history of quantitative genetics). However, it was Fisher [6] who laid the firm foundation for quantitative genetics. Fisher defined gene effects (additive, dominance and epistatic effects) based on the partition of genetic variance. He partitioned the genetic variance into a portion due to additive effects (averaged allelic substitution effects), a portion due to dominance effects (allelic interactions), and a portion due to epistatic effects (non-allelic interactions) of genes. He then studied the correlation between relat
Contribution of genetic effects to genetic variance components with epistasis and linkage disequilibrium
Tao Wang, Zhao-Bang Zeng
BMC Genetics , 2009, DOI: 10.1186/1471-2156-10-52
Abstract: In this paper, we further explore the differences and links between the F∞ and Cockerham models. First, we show that the Cockerham type models are allelic based models with a special modification to correct a confounding problem. Several important moment functions, which are useful for partition of variance components in Cockerham models, are also derived. Next, we discuss properties of the F∞ models in partition of genotypic variances. Its difference from that of the Cockerham models is addressed. Finally, for a two-locus biallelic QTL model with epistasis and LD between the loci, we present detailed formulas for calculation of the genetic variance components in terms of the additive, dominant and epistatic effects in an F∞ model. A new way of linking the Cockerham and F∞ model parameters through their coding variables of genotypes is also proposed, which is especially useful when reduced F∞ models are applied.The Cockerham type models are allele-based models with a focus on partition of genotypic variances into various genetic variance components, which are contributed by allelic effects and their interactions. By contrast, the F∞ regression models are genotype-based models focusing on modeling and testing of within-locus genotypic effects and locus-by-locus genotypic interactions. When there is no need to distinguish the paternal and maternal allelic effects, these two types of models are transferable. Transformation between an F∞ model's parameters and its corresponding Cockerham model's parameters can be established through a relationship between their coding variables of genotypes. Genetic variance components in terms of the additive, dominance and epistatic genetic effects in an F∞ model can then be calculated by translating formulas derived for the Cockerham models.Genetic models provide a basis for analyzing genetic properties in study populations. For quantitative traits, one type of models that has long been used in experimental designed populations for a
eQTL Viewer: visualizing how sequence variation affects genome-wide transcription
Wei Zou, David L Aylor, Zhao-Bang Zeng
BMC Bioinformatics , 2007, DOI: 10.1186/1471-2105-8-7
Abstract: We have developed a web-based tool, called eQTL Viewer, to visualize the relationships between the expression trait genes and the candidate genes in the eQTL regions using Scalable Vector Graphics. The plot generated by eQTL Viewer has the capacity to display mapping results with high resolutions at a variety of scales, and superimpose biological annotations onto the mapping results dynamically.Our tool provides an efficient and intuitive way for biologists to explore transcriptional regulation patterns, and to generate hypotheses on the genetic basis of transcriptional regulations.Transcriptional control is a crucial step in organ development and cellular responses to environmental changes. Recent studies have demonstrated that mRNA expression levels vary in both natural and experimental populations [1]. Expression Quantitative Trait Locus (eQTL) mapping seeks to explain such variations by identifying the relationships between the transcript abundance and specific genomic markers [2]. When the transcript abundance is treated as a continuous trait for the purpose of mapping, it is termed an expression trait (eTrait).eQTL mapping differs from the classical QTL mapping in two important ways, which allow us to ask questions that we cannot address by the traditional approach. First, there is a one-to-one relationship between an eTrait and a gene with its expression profile assayed in the mapping population. The biological information associated with an eTrait gene can be used to suggest potential causal genes for an eQTL. This is important as an eQTL usually spans a large genomic region that contains many genes. For example, eQTL analysis has been used to infer cis- and trans-acting regulatory regions in yeast [3-5], mice and rat [6-8]. If an eQTL is mapped to a genomic region where the eTrait gene is located, it may suggest the cis-regulatory mechanism for the eQTL, i.e., certain sequence variations around the gene region of the eTrait may directly influence the transc
Multiple trait multiple interval mapping of quantitative trait loci from inbred line crosses
Costa E Silva Luciano,Wang Shengchu,Zeng Zhao-Bang
BMC Genetics , 2012, DOI: 10.1186/1471-2156
Abstract: Background Although many experiments have measurements on multiple traits, most studies performed the analysis of mapping of quantitative trait loci (QTL) for each trait separately using single trait analysis. Single trait analysis does not take advantage of possible genetic and environmental correlations between traits. In this paper, we propose a novel statistical method for multiple trait multiple interval mapping (MTMIM) of QTL for inbred line crosses. We also develop a novel score-based method for estimating genome-wide significance level of putative QTL effects suitable for the MTMIM model. The MTMIM method is implemented in the freely available and widely used Windows QTL Cartographer software. Results Throughout the paper, we provide compelling empirical evidences that: (1) the score-based threshold maintains proper type I error rate and tends to keep false discovery rate within an acceptable level; (2) the MTMIM method can deliver better parameter estimates and power than single trait multiple interval mapping method; (3) an analysis of Drosophila dataset illustrates how the MTMIM method can better extract information from datasets with measurements in multiple traits. Conclusions The MTMIM method represents a convenient statistical framework to test hypotheses of pleiotropic QTL versus closely linked nonpleiotropic QTL, QTL by environment interaction, and to estimate the total genotypic variance-covariance matrix between traits and to decompose it in terms of QTL-specific variance-covariance matrices, therefore, providing more details on the genetic architecture of complex traits.
Mapping QTL for Grain Yield under Moisture Stress Environments in Rice (Oryza sativa L.)
Supriyo CHAKRABORTY,Sheng-Chu WANG,Zhao-Bang ZENG
Notulae Scientia Biologicae , 2011,
Abstract: Polygenes (QTLs) for grain yield were mapped on rice chromosomes under two moisture stress environments by multiple interval mapping (MIM) method in a double haploid (DH) population derived from a cross between a deep-rooted japonica and a shallow-rooted indica genotype. In environment 1 (E1), the MIM detected a total of six QTLs for grain yield on chromosomes-two QTLs on chromosome 1 and four QTLs on chromosome 5 along with one additive x additive epistasis. But in environment 2 (E2), the MIM detected five QTLs for grain yield on two chromosomes-three QTLs on chromosome 1 and two QTLs on chromosome 7. One common QTL on chromosome 1 flanked by the markers RG109-ME1014 was detected in both the environments, although the other detected QTLs differed between environments. The magnitude of QTL effect, percent genetic variance and percent phenotypic variance explained by each QTL was also estimated in both environments. The common QTL explained about 26.05 and 13.93% of genetic variance in E1 and E2, respectively. Estimated broad sense heritability for grain yield was 48.01 in E1 and 25.27% in E2.
Genes related to apoptosis predict necrosis of the liver as a phenotype observed in rats exposed to a compendium of hepatotoxicants
Lingkang Huang, Alexandra N Heinloth, Zhao-Bang Zeng, Richard S Paules, Pierre R Bushel
BMC Genomics , 2008, DOI: 10.1186/1471-2164-9-288
Abstract: Rats were treated individually with one of seven known hepatotoxicants and were analyzed for gene expression by microarray. Liver samples were grouped by the level of necrosis exhibited in the tissue. Analysis of significantly differentially expressed genes between adjacent necrosis levels revealed that inflammation follows programmed cell death in response to the agents. Using a Random Forest classifier with feature selection, 21 informative genes were identified which achieved 90%, 80% and 60% prediction accuracies of necrosis against independent test data derived from the livers of rats exposed to acetaminophen, carbon tetrachloride, and allyl alcohol, respectively. Pathway and gene network analyses of the genes in the signature revealed several gene interactions suggestive of apoptosis as a process possibly involved in the manifestation of necrosis of the liver from exposure to the hepatotoxicants. Cytotoxic effects of TNF-α, as well as transcriptional regulation by JUN and TP53, and apoptosis-related genes possibly lead to necrosis.The data analysis, gene selection and prediction approaches permitted grouping of the classes of rat liver samples exhibiting necrosis to improve the accuracy of predicting the level of necrosis as a phenotypic end-point observed from the exposure. The strategy, along with pathway analysis and gene network reconstruction, led to the identification of 1) expression profiles of genes as a signature of necrosis and 2) perturbed regulatory processes that exhibited biological relevance to the manifestation of necrosis from exposure of rat livers to the compendium of hepatotoxicants.Hepatotoxicity is one of the most commonly observed adverse effects in response to many environmental and toxic exposures and is of major concern in the drug development industry [1]. The liver's response to insults depends on the properties of the stressor, the dose received and if the exposure is acute or chronic. Examples of injury or damage are fatty liver,
Metabolomics Reveals Amino Acids Contribute to Variation in Response to Simvastatin Treatment
Miles Trupp, Hongjie Zhu, William R. Wikoff, Rebecca A. Baillie, Zhao-Bang Zeng, Peter D. Karp, Oliver Fiehn, Ronald M. Krauss, Rima Kaddurah-Daouk
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0038386
Abstract: Statins are widely prescribed for reducing LDL-cholesterol (C) and risk for cardiovascular disease (CVD), but there is considerable variation in therapeutic response. We used a gas chromatography-time-of-flight mass-spectrometry-based metabolomics platform to evaluate global effects of simvastatin on intermediary metabolism. Analyses were conducted in 148 participants in the Cholesterol and Pharmacogenetics study who were profiled pre and six weeks post treatment with 40 mg/day simvastatin: 100 randomly selected from the full range of the LDL-C response distribution and 24 each from the top and bottom 10% of this distribution (“good” and “poor” responders, respectively). The metabolic signature of drug exposure in the full range of responders included essential amino acids, lauric acid (p<0.0055, q<0.055), and alpha-tocopherol (p<0.0003, q<0.017). Using the HumanCyc database and pathway enrichment analysis, we observed that the metabolites of drug exposure were enriched for the pathway class amino acid degradation (p<0.0032). Metabolites whose change correlated with LDL-C lowering response to simvastatin in the full range responders included cystine, urea cycle intermediates, and the dibasic amino acids ornithine, citrulline and lysine. These dibasic amino acids share plasma membrane transporters with arginine, the rate-limiting substrate for nitric oxide synthase (NOS), a critical mediator of cardiovascular health. Baseline metabolic profiles of the good and poor responders were analyzed by orthogonal partial least square discriminant analysis so as to determine the metabolites that best separated the two response groups and could be predictive of LDL-C response. Among these were xanthine, 2-hydroxyvaleric acid, succinic acid, stearic acid, and fructose. Together, the findings from this study indicate that clusters of metabolites involved in multiple pathways not directly connected with cholesterol metabolism may play a role in modulating the response to simvastatin treatment. Trial Registration ClinicalTrials.gov NCT00451828
Enteric Microbiome Metabolites Correlate with Response to Simvastatin Treatment
Rima Kaddurah-Daouk, Rebecca A. Baillie, Hongjie Zhu, Zhao-Bang Zeng, Michelle M. Wiest, Uyen Thao Nguyen, Katie Wojnoonski, Steven M. Watkins, Miles Trupp, Ronald M. Krauss
PLOS ONE , 2011, DOI: 10.1371/journal.pone.0025482
Abstract: Although statins are widely prescribed medications, there remains considerable variability in therapeutic response. Genetics can explain only part of this variability. Metabolomics is a global biochemical approach that provides powerful tools for mapping pathways implicated in disease and in response to treatment. Metabolomics captures net interactions between genome, microbiome and the environment. In this study, we used a targeted GC-MS metabolomics platform to measure a panel of metabolites within cholesterol synthesis, dietary sterol absorption, and bile acid formation to determine metabolite signatures that may predict variation in statin LDL-C lowering efficacy. Measurements were performed in two subsets of the total study population in the Cholesterol and Pharmacogenetics (CAP) study: Full Range of Response (FR), and Good and Poor Responders (GPR) were 100 individuals randomly selected from across the entire range of LDL-C responses in CAP. GPR were 48 individuals, 24 each from the top and bottom 10% of the LDL-C response distribution matched for body mass index, race, and gender. We identified three secondary, bacterial-derived bile acids that contribute to predicting the magnitude of statin-induced LDL-C lowering in good responders. Bile acids and statins share transporters in the liver and intestine; we observed that increased plasma concentration of simvastatin positively correlates with higher levels of several secondary bile acids. Genetic analysis of these subjects identified associations between levels of seven bile acids and a single nucleotide polymorphism (SNP), rs4149056, in the gene encoding the organic anion transporter SLCO1B1. These findings, along with recently published results that the gut microbiome plays an important role in cardiovascular disease, indicate that interactions between genome, gut microbiome and environmental influences should be considered in the study and management of cardiovascular disease. Metabolic profiles could provide valuable information about treatment outcomes and could contribute to a more personalized approach to therapy.
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