Biomedical subjects

Hongyu Zhao

Publications and source records attributed to Hongyu Zhao.

At least 37 records · Page 2Linked to original sources

PubMed2006

Mapping quantitative trait loci in noninbred mosquito crosses.

The identification of genes that affect quantitative traits has been of great interest to geneticists for many decades, and many statistical methods have been developed to map quantitative trait loci (QTL). Most QTL mapping studies in experimental organisms use purely inbred lines, where the two homologous chromosomes in each individual are identical. As a result, many existing QTL mapping methods developed for experimental organisms are applicable only to genetic crosses between inbred lines. However, it may be difficult to obtain inbred lines for certain organisms, e.g., mosquitoes. Although statistical methods for QTL mapping in outbred populations, e.g., humans, can be applied for such crosses, these methods may not fully take advantage of the uniqueness of these crosses. For example, we can generally assume that the two grandparental lines are homozygous at the QTL of interest, but such information is not be utilized through methods developed for outbred populations. In addition, mating types and phases can be relatively easy to establish through the analysis of adjacent markers due to the large number of offspring that can be collected, substantially simplifying the computational need. In this article, motivated by a mosquito intercross experiment involving two selected lines that are not genetically homozygous across the genome, we develop statistical methods for QTL mapping for genetic crosses involving noninbred lines. In our procedure, we first infer parental mating types and use likelihood-based methods to infer phases in each parent on the basis of genotypes of offspring and one parent. A hidden Markov model is then employed to estimate the number of high-risk alleles at marker positions and putative QTL positions between markers in each offspring, and QTL mapping is finally conducted through the inferred QTL configuration across all offspring in all crosses. The performance of the proposed methods is assessed through simulation studies, and the usefulness of this method is demonstrated through its application to a mosquito data set.

Alleles↗

PubMed2006

Effects of a novel tylophorine analog on collagen-induced arthritis through inhibition of the innate immune response.

OBJECTIVE: To test the effects of a novel tylophorine analog, DCB 3503, on the prevention and treatment of collagen-induced arthritis (CIA) and to elucidate its underlying mechanisms. METHODS: DBA/1J mice were immunized with type II collagen, and in some cases, lipopolysaccharide (LPS) was used to boost the development of arthritis. DCB 3503 was injected intraperitoneally before or after the onset of CIA. Mice were monitored to assess the effects of DCB 3503 on the clinical severity of the disease, and pathologic changes in the joints were examined histologically. Levels of tumor necrosis factor alpha (TNFalpha) and interleukin-1beta (IL-1beta) in serum and joint tissues were measured by enzyme-linked immunosorbent assay and by cytometric bead array analysis. The effect of DCB 3503 on LPS-induced proinflammatory cytokines from bone marrow-derived dendritic cells was determined by flow cytometry. RESULTS: DCB 3503 significantly suppressed the development and progression of CIA. Moreover, DCB 3503 completely blocked the LPS-triggered acceleration of joint inflammation and destruction. Consistent with its effects in vivo, DCB 3503 significantly suppressed the synthesis of proinflammatory cytokines in inflamed joints as well as cytokine synthesis by macrophages examined ex vivo. Treatment also reduced the levels of inflammatory cytokines (IL-6, IL-12, TNFalpha, and monocyte chemotactic protein 1) produced by bone marrow-derived dendritic cells in vitro. However, DCB 3503 showed no direct effects on T cell proliferation and B cell antibody response. CONCLUSION: Because of its ability to specifically suppress innate immune responses, DCB 3503 may be a novel therapeutic agent for inflammatory arthritis in humans.

Alkaloids↗

PubMed2006

Macrophage migration inhibitory factor promoter polymorphisms and the clinical expression of scleroderma.

OBJECTIVE: To investigate the potential association between functional polymorphisms in the gene for the innate mediator, macrophage migration inhibitory factor (MIF), and the clinical expression of systemic sclerosis (SSc). METHODS: Genomic DNA samples and clinical data were collected from the Scleroderma Family Registry and DNA Repository at the University of Texas Health Science Center at Houston. A total of 740 subjects were studied; 203 of them had diffuse cutaneous SSc (dcSSc), 283 had limited cutaneous SSc (lcSSc), and the remaining 254 healthy subjects served as controls. Association analyses were performed on the whole data set and on patient and sex subsets. Significant relationships were determined between clinical variables and MIF polymorphisms for each disease subtype in the studied groups. RESULTS: The frequency of the -173*C MIF allele, which was previously reported to be associated with high production of MIF, was lower in the lcSSc group (12.6%) than in the dcSSc (19.2%) or control (18.5%) groups (P = 0.010 and P = 0.011, respectively). Haplotype analysis for 2 closely linked polymorphisms in the MIF promoter showed that in white subjects with lcSSc or dcSSc, the lcSSc population had a significantly lower representation of the high-expression MIF haplotype defined by -173*C and -794 with 7 CATT repeats (C7) (P = 0.015, odds ratio 1.94 [95% confidence interval 1.14-3.32]). Fibroblasts encoding the C7 MIF haplotype were observed to produce more MIF upon in vitro stimulation than those with a non-C7 haplotype. CONCLUSION: Functional promoter polymorphisms in the MIF gene affect the clinical presentation of SSc. The proinflammatory haplotype defined by C7 is underrepresented in patients with lcSSc.

Female↗

PubMed2006

Haplotype analysis in the presence of informatively missing genotype data.

It is common to have missing genotypes in practical genetic studies, but the exact underlying missing data mechanism is generally unknown to the investigators. Although some statistical methods can handle missing data, they usually assume that genotypes are missing at random, that is, at a given marker, different genotypes and different alleles are missing with the same probability. These include those methods on haplotype frequency estimation and haplotype association analysis. However, it is likely that this simple assumption does not hold in practice, yet few studies to date have examined the magnitude of the effects when this simplifying assumption is violated. In this study, we demonstrate that the violation of this assumption may lead to serious bias in haplotype frequency estimates, and haplotype association analysis based on this assumption can induce both false-positive and false-negative evidence of association. To address this limitation in the current methods, we propose a general missing data model to characterize missing data patterns across a set of two or more markers simultaneously. We prove that haplotype frequencies and missing data probabilities are identifiable if and only if there is linkage disequilibrium between these markers under our general missing data model. Simulation studies on the analysis of haplotypes consisting of two single nucleotide polymorphisms illustrate that our proposed model can reduce the bias both for haplotype frequency estimates and association analysis due to incorrect assumption on the missing data mechanism. Finally, we illustrate the utilities of our method through its application to a real data set.

Algorithms↗

PubMed2006

A comparison of several methods for haplotype frequency estimation and haplotype reconstruction for tightly linked markers from general pedigrees.

Haplotype inference for tightly linked markers from general pedigrees remains a challenging problem. Only a few methods are available to efficiently and accurately estimate haplotype frequencies and reconstruct haplotypes for a large number of tightly linked markers from general pedigrees in the presence of missing data, and their performance has not been carefully and extensively evaluated. In this paper, we compare four published methods for haplotype reconstruction and frequency estimation for tightly linked markers from general pedigrees, including HAPLORE, GENEHUNTER, PedPhase, and MERLIN. We review these methods and discuss the differences between them in terms of the models and computational strategies employed. We assess their performance based on simulations using pedigrees and haplotypes on tightly linked single nucleotide polymorphisms from real studies. We investigate the effect of several factors, including the missing rate, the departure from Hardy-Weinberg Equilibrium, and the sample size, on the accuracy for haplotype inference. We also compare these methods with a widely used method for haplotype inference from unrelated individuals, PHASE, by treating individuals within a pedigree as unrelated samples. This comparison allows us to investigate the relative efficiency in haplotype inference using pedigree data. Our results indicate that incorporation of pedigree information can improve the precision for haplotype frequency estimation and the accuracy for haplotype reconstruction. Among four haplotyping methods capable of analyzing general pedigrees, HAPLORE and MERLIN have comparable performance and outperform the other two methods in almost all situations.

Computer Simulation↗

PubMed2006

The meiosis-specific zip4 protein regulates crossover distribution by promoting synaptonemal complex formation together with zip2.

We have characterized Zip4 (a.k.a. Spo22), a meiosis-specific protein essential for chromosome synapsis in budding yeast. In the absence of Zip4, the synaptonemal complex protein Zip1 fails to polymerize along chromosomes. Zip2 and Zip3 are previously characterized components of the synapsis initiation complex. Zip4 forms a functional unit with Zip2 that is distinct from Zip3. Zip2 and Zip4 are mutually dependent for their chromosomal localization; in polycomplexes, the pattern of Zip2/Zip4 localization is distinct from that of Zip3. Crossing-over is decreased in the zip4 mutant (as in zip1, zip2, and zip3); the remaining crossovers are largely dependent on a parallel pathway utilizing Mms4. zip4 displays a novel phenotype: negative crossover interference, meaning that crossovers tend to cluster. This clustering depends on Zip1. Our results suggest an interaction between crossover pathways such that a protein (Zip1) acting in one pathway influences the distribution of crossovers promoted by a parallel (Mms4-dependent) pathway.

Chromosomal Proteins, Non-Histone↗

PubMed2006

Acute drug-induced vascular injury in beagle dogs: pathology and correlating genomic expression.

Acute vascular injury that leads to vascular inflammation is a common finding in the preclinical toxicity testing of drugs in rats and dogs. However, the relevance of this finding for risk to humans is unclear. Concern about the safety of these drugs is heightened by the current lack of noninvasive clinical methods to predict the onset of vascular damage in animals or humans. Determining the relevance of this poorly understood preclinical outcome for humans requires a better understanding of the molecular mechanisms of injury in addition to the development of sensitive and specific leading biomarkers for the clinical diagnosis of acute vascular damage. Most molecular research on this toxicity has been performed in rats, but recent development of canine gene expression microarrays makes transcriptomic studies now possible in the dog. In this study, we investigated the molecular mechanisms of drug-induced vascular injury in dogs using gene arrays. After treating Beagles with toxic doses of CI-947, an adenosine receptor agonist, we profiled gene expression in the coronary arteries and correlated those changes with histopathology at 16 and 24 hours after dosing. The results demonstrated that pathobiological processes such as stimulation of the innate immune response, increased extracellular matrix turnover and oxidative stress were active at times of very early injury.

Acute Disease↗

PubMed2006

Parametric and nonparametric FDR estimation revisited.

Nonparametric and parametric approaches have been proposed to estimate false discovery rate under the independent hypothesis testing assumption. The parametric approach has been shown to have better performance than the nonparametric approaches. In this article, we study the nonparametric approaches and quantify the underlying relations between parametric and nonparametric approaches. Our study reveals the conservative nature of the nonparametric approaches, and establishes the connections between the empirical Bayes method and p-value-based nonparametric methods. Based on our results, we advocate using the parametric approach, or directly modeling the test statistics using the empirical Bayes method.

Bayes Theorem↗

PubMed2006

A non-parametric approach to population structure inference using multilocus genotypes.

Inference of population structure from genetic markers is helpful in diverse situations, such as association and evolutionary studies. In this paper, we describe a two-stage strategy in inferring population structure using multilocus genotype data. In the first stage, we use dimension reduction methods such as singular value decomposition to reduce the dimension of the data, and in the second stage, we use clustering methods on the reduced data to identify population structure. The strategy has the ability to identify population structure and assign each individual to its corresponding subpopulation. The strategy does not depend on any population genetics assumptions (such as Hardy-Weinberg equilibrium and linkage equilibrium between loci within populations) and can be used with any genotype data. When applied to real and simulated data, the strategy is found to have similar or better performance compared with STRUCTURE, the most popular method in current use. Therefore, the proposed strategy provides a useful alternative to analyse population data.

Cluster Analysis↗

PubMed2006

MALDI-MS data analysis for disease biomarker discovery.

In this chapter, we address the issue of matrix-assisted laser desorption/ionization mass spectrometry (MS) data analysis for disease biomarker discovery. We first give a general framework of MS data analysis, then focus on several key steps. After that, we show some application examples using an ovarian sera cancer dataset. Finally, we discuss the limitations of current approaches and possible future research directions.

Animals↗

PubMed2006

Growth hormone secretagogue receptor antagonists as anti-obesity therapies? Still an open question.

Ghrelin woas recently de-orphaned as an endogenous ligand of growth hormone secretagogue receptor (GHS-R), and is implicated as a short-term meal initiator and a long-term energy balance regulator. Administration of ghrelin causes increases in food intake and body weight in both rodents and humans. Inhibiting its actions with GHS-R anti-sense oligonucleotides, anti-ghrelin antibodies, and peptide antagonists leads to decreased food intake and weight loss in rodents. Despite the much-publicized promise of providing a novel approach for anti-obesity treatment, limited progress has been made in developing small-molecule GHS-R antagonists and no such compound has been advanced to clinical trials. This review will summarize the recent progress in small-molecule GHS-R antagonists and offer some insight into this area of research based on the experience at Abbott Laboratories.

Animals↗

PubMed2005

A Bayesian genome screening of maximum number of drinks as an alcoholism phenotype with the new Haseman-Elston method.

Common human disorders, such as alcoholism, may be the result of interactions of many genes as well as environmental risk factors. Therefore, it is important to incorporate gene x gene and gene x environment interactions in complex disease gene mapping. In this study, we applied a robust Bayesian genome screening method that can incorporate interaction effects to map genes underlying alcoholism through its application to the data of the Collaborative Studies on Genetics of Alcoholism provided by Genetic Analysis Workshop 14. Our Bayesian genome screening method uses the regression-based stochastic variable selection, coupled with the new Haseman-Elston method to identify markers linked to phenotypes of interest. Compared to traditional linkage methods based on single-gene disease models, our method allows for multilocus disease models for simultaneous screening including both main and interaction (epistatic) effects. It is conceptually simple and computationally efficient through the use of Gibbs sampler. We conducted genome-wide analysis and comparison between scans based on microsatellites and single-nucleotide polymorphisms. A total of 328 microsatellites and 11,560 single-nucleotide polymorphisms (by Affymetrix) on 22 autosomal chromosomes and sex chromosome were used.

Alcoholism↗

PubMed2005

Whole-genome association studies on alcoholism comparing different phenotypes using single-nucleotide polymorphisms and microsatellites.

Alcoholism is a complex disease. As with other common diseases, genetic variants underlying alcoholism have been illusive, possibly due to the small effect from each individual susceptible variant, gene x environment and gene x gene interactions and complications in phenotype definition. We conducted association tests, the family-based association tests (FBAT) and the backward haplotype transmission association (BHTA), on the Collaborative Study of the Genetics of Alcoholism (COGA) data provided by Genetic Analysis Workshop (GAW) 14. Efron's local false discovery rate method was applied to control the proportion of false discoveries. For FBAT, we compared the results based on different types of genetic markers (single-nucleotide polymorphisms (SNPs) versus microsatellites) and different phenotype definitions (clinical diagnoses versus electrophysiological phenotypes). Significant association results were found only between SNPs and clinical diagnoses. In contrast, significant results were found only between microsatellites and electrophysiological phenotypes. In addition, we obtained the association results for SNPs and microsatellites using COGA diagnosis as phenotype based on BHTA. In this case, the results for SNPs and microsatellites are more consistent. Compared to FBAT, more significant markers are detected with BHTA.

Alcoholism↗

PubMed2005

Comparison of single-nucleotide polymorphisms and microsatellites in inference of population structure.

Single-nucleotide polymorphisms (SNPs) are a class of attractive genetic markers for population genetic studies and for identifying genetic variations underlying complex traits. However, the usefulness and efficiency of SNPs in comparison to microsatellites in different scientific contexts, e.g., population structure inference or association analysis, still must be systematically evaluated through large empirical studies. In this article, we use the Collaborative Studies on Genetics of Alcoholism (COGA) data from Genetic Analysis Workshop 14 (GAW14) to compare the performance of microsatellites and SNPs in the whole human genome in the context of population structure inference. A total of 328 microsatellites and 15,840 SNPs are used to infer population structure in 236 unrelated individuals. We find that, on average, the informativeness of random microsatellites is four to twelve times that of random SNPs for various population comparisons, which is consistent with previous studies. Our results also indicate that for the combined set of microsatellites and SNPs, SNPs constitute the majority among the most informative markers and the use of these SNPs leads to better inference of population structure than the use of microsatellites. We also find that the inclusion of less informative markers may add noise and worsen the results.

Genetic Loci↗

PubMed2005

Whole-genome linkage analysis in mapping alcoholism genes using single-nucleotide polymorphisms and microsatellites.

There is currently a great interest in using single-nucleotide polymorphisms (SNPs) in genetic linkage and association studies because of the abundance of SNPs as well as the availability of high-throughput genotyping technologies. In this study, we compared the performance of whole-genome scans using SNPs with microsatellites on 143 pedigrees from the Collaborative Studies on Genetics of Alcoholism provided by Genetic Analysis Workshop 14. A total of 315 microsatellites and 10,081 SNPs from Affymetrix on 22 autosomal chromosomes were used in our analyses. We found that the results from the two scans had good overall concordance. One region on chromosome 2 and two regions on chromosome 7 showed significant linkage signals (i.e., NPL >or= 2) for alcoholism from both the SNP and microsatellite scans. The different results observed between the two scans may be explained by the difference observed in information content between the SNPs and the microsatellites.

Alcoholism↗

PubMed2005

Whole-genome association analysis to identify markers associated with recombination rates using single-nucleotide polymorphisms and microsatellites.

Recombination during meiosis is one of the most important biological processes, and the level of recombination rates for a given individual is under genetic control. In this study, we conducted genome-wide association studies to identify chromosomal regions associated with recombination rates. We analyzed genotype data collected on the pedigrees in the Collaborative Study on the Genetics on Alcoholism data provided by Genetic Analysis Workshop 14. A total of 315 microsatellites and 10,081 single-nucleotide polymorphisms from Affymetrix on 22 autosomal chromosomes were used in our association analysis. Genome-wide gender-specific recombination counts for family founders were inferred first and association analysis was performed using multiple linear regressions. We used the positive false discovery rate (pFDR) to account for multiple comparisons in the two genome-wide scans. Eight regions showed some evidence of association with recombination counts based on the single-nucleotide polymorphism analysis after adjusting for multiple comparisons. However, no region was found to be significant using microsatellites.

Female↗

PubMed2005

Detecting and aligning peaks in mass spectrometry data with applications to MALDI.

In this paper, we address the peak detection and alignment problem in the analysis of mass spectrometry data. To deal with the peak redundancy problem existing in the MALDI data acquired in the reflectron mode, we propose to use the amplitude modulation technique in peak detection. The alignment of two peak sets is formulated as a non-rigid registration problem and is solved using a robust point matching (RPM) approach. To align multiple peak sets, we first use a super set method to find a common peak set among all peak sets as a standard and then align all peak sets to the standard using the robust point matching approach in a sequential manner (i.e. We align only one peak set to the standard each time, thus reducing the multiple peak set alignment problem to a simpler two peak set alignment problem). Experimental results from a study of ovarian cancer data set show that the quantitative cross-correlation coefficients among technical replicates are increased after peak alignment. Additional comparisons also demonstrate that our method has a similar performance as the hierarchical clustering method, although the implementations of these methods are different.

Female↗

PubMed2005

Characterization of a likelihood based method and effects of markers informativeness in evaluation of admixture and population group assignment.

BACKGROUND: Detection and evaluation of population stratification are crucial issues in the conduct of genetic association studies. Statistical approaches useful for understanding these issues have been proposed; these methods rely on information gained from genotyping sets of markers that reflect population ancestry. Before using these methods, a set of markers informative for differentiating population genetic substructure (PGS) is necessary. We have previously evaluated the performance of a Bayesian clustering method implemented in the software STRUCTURE in detecting PGS with a particular informative marker set. In this study, we implemented a likelihood based method (LBM) in evaluating the informativeness of the same selected marker panel, with respect to assessing potential for stratification in samples of European Americans (EAs) and African Americans (AAs), that are known to be admixed. LBM calculates the probability of a set of genotypes based on observations in a reference population with known specific allele frequencies for each marker, assuming Hardy Weinberg equilibrium (HWE) for each marker and linkage equilibrium among markers. RESULTS: In EAs, the assignment accuracy by LBM exceeded 99% using the most efficient marker FY, and reached perfect assignment accuracy using the 10 most efficient markers excluding FY. In AAs, the assignment accuracy reached 96.4% using FY, and >95% when using at least the 9 most efficient markers. The comparison of the observed and reference allele frequencies (which were derived from previous publications and public databases) shows that allele frequencies observed in EAs matched the reference group more accurately than allele frequencies observed in AAs. As a result, the LBM performed better in EAs than AAs, as might be expected given the dependence of LBMs on prior knowledge of allele frequencies. Performance was not dependent on sample size. CONCLUSION: The performance of the LBM depends on the efficiency and number of markers, and depends greatly on how representative the available reference allele frequencies are for those of the population being assigned. This method is of value when the parental population is known and relevant allele frequencies are available.

Black or African American↗