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  • 1
    Publication Date: 2014-01-22
    Description: The formation of phenotypic traits, such as biomass production, tumor volume and viral abundance, undergoes a complex process in which interactions between genes and developmental stimuli take place at each level of biological organization from cells to organisms. Traditional studies emphasize the impact of genes by directly linking DNA-based markers with static phenotypic values. Functional mapping, derived to detect genes that control developmental processes using growth equations, has proven powerful for addressing questions about the roles of genes in development. By treating phenotypic formation as a cohesive system using differential equations, a different approach—systems mapping—dissects the system into interconnected elements and then map genes that determine a web of interactions among these elements, facilitating our understanding of the genetic machineries for phenotypic development. Here, we argue that genetic mapping can play a more important role in studying the genotype–phenotype relationship by filling the gaps in the biochemical and regulatory process from DNA to end-point phenotype. We describe a new framework, named network mapping, to study the genetic architecture of complex traits by integrating the regulatory networks that cause a high-order phenotype. Network mapping makes use of a system of differential equations to quantify the rule by which transcriptional, proteomic and metabolomic components interact with each other to organize into a functional whole. The synthesis of functional mapping, systems mapping and network mapping provides a novel avenue to decipher a comprehensive picture of the genetic landscape of complex phenotypes that underlie economically and biomedically important traits.
    Print ISSN: 1467-5463
    Electronic ISSN: 1477-4054
    Topics: Biology , Computer Science
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  • 2
    Publication Date: 2014-03-18
    Description: Epigenetic modifications may play an important role in the formation and progression of complex diseases through the regulation of gene expression. The systematic identification of epigenetic variants that contribute to human diseases can be made possible using genome-wide association studies (GWAS), although epigenetic effects are currently not included in commonly used case–control designs for GWAS. Here, we show that epigenetic modifications can be integrated into a case–control setting by dissolving the overall genetic effect into its different components, additive, dominant and epigenetic. We describe a general procedure for testing and estimating the significance of each component based on a conventional chi-squared test approach. Simulation studies were performed to investigate the power and false-positive rate of this procedure, providing recommendations for its practical use. The integration of epigenetic variants into GWAS can potentially improve our understanding of how genetic, environmental and stochastic factors interact with epialleles to construct the genetic architecture of complex diseases.
    Print ISSN: 1467-5463
    Electronic ISSN: 1477-4054
    Topics: Biology , Computer Science
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  • 3
    Publication Date: 2013-11-10
    Description: Despite our expanding knowledge about the biochemistry of gene regulation involved in host–pathogen interactions, a quantitative understanding of this process at a transcriptional level is still limited. We devise and assess a computational framework that can address this question. This framework is founded on a mixture model-based likelihood, equipped with functionality to cluster genes per dynamic and functional changes of gene expression within an interconnected system composed of the host and pathogen. If genes from the host and pathogen are clustered in the same group due to a similar pattern of dynamic profiles, they are likely to be reciprocally co-evolving. If genes from the two organisms are clustered in different groups, this means that they experience strong host–pathogen interactions. The framework can test the rates of change for individual gene clusters during pathogenic infection and quantify their impacts on host–pathogen interactions. The framework was validated by a pathological study of poplar leaves infected by fungal Marssonina brunnea in which co-evolving and interactive genes that determine poplar–fungus interactions are identified. The new framework should find its wide application to studying host–pathogen interactions for any other interconnected systems.
    Print ISSN: 1467-5463
    Electronic ISSN: 1477-4054
    Topics: Biology , Computer Science
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  • 4
    Publication Date: 2015-09-12
    Description: Genome-wide association studies (GWAS) have identified several common loci contributing to non-obstructive azoospermia (NOA). However, a substantial fraction of NOA heritability remains undefined, especially those low-frequency [defined here as having a minor allele frequency (MAF) between 0.5 and 5%] and rare (MAF below 0.5%) variants. Here, we performed a 3-stage exome-wide association study in Han Chinese men to evaluate the role of low-frequency or rare germline variants in NOA development. The discovery stage included 962 NOA cases and 1348 healthy male controls genotyped by exome chips and was followed by a 2-stage replication with an additional 2168 cases and 5248 controls. We identified three low-frequency variants located at 6p22.2 (rs2298090 in HIST1H1E encoding p.Lys152Arg: OR = 0.30, P = 2.40 x 10 –16 ) and 6p21.33 (rs200847762 in FKBPL encoding p.Pro137Leu: OR = 0.11, P = 3.77 x 10 –16 ; rs11754464 in MSH5 : OR = 1.78, P = 3.71 x 10 –7 ) associated with NOA risk after Bonferroni correction. In summary, we report an instance of newly identified signals for NOA risk in genes previously undetected through GWAS on 6p22.2–6p21.33 in a Chinese population and highlight the role of low-frequency variants with a large effect in the process of spermatogenesis.
    Print ISSN: 0964-6906
    Electronic ISSN: 1460-2083
    Topics: Biology , Medicine
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  • 5
    Publication Date: 2015-10-07
    Description: Haloarcula hispanica is one of members of the Halobacteriaceae, which displays particularly low restriction activity and is therefore important as one of the most tractable haloarchaea for archaeal genetic research. Although the Har. hispanica S-layer protein has been reported glycosylated, the S-layer glycoprotein and its glycosylation have not been investigated yet. In this study, the S-layer proteins of Har. hispanica were extracted and characterized. The S-layer was found containing two different glycoproteins which shared highly similar amino acid sequences. The genes coding for these two S-layer glycoproteins were found next to each other in the genome. Moreover, the N- and O-linked glycans were released from these two S-layer glycoproteins for structural determination. Based on the mass spectrometry and nuclear magnetic resonance, the N -glycan was determined as a branched trisaccharide containing a 225 Da residue corresponded to a 2-amino-6-sulfo-2, 6-dideoxy-quinovose, which was the first time that a naturally occurring form of sulfoquinovosamine was identified. Besides, the O -glycan was characterized as a Glcα-1,4-Gal disaccharide by mass spectrometry combined with monosaccharide composition analysis and glycosidase treatment. The determination of the N - and O -glycan structure will be helpful for studying the diverse protein glycosylation pathways in archaea utilizing H. hispanica as a new model.
    Print ISSN: 0959-6658
    Electronic ISSN: 1460-2423
    Topics: Biology , Medicine
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  • 6
    Publication Date: 2014-01-22
    Description: Traditional approaches for genetic mapping are to simply associate the genotypes of a quantitative trait locus (QTL) with the phenotypic variation of a complex trait. A more mechanistic strategy has emerged to dissect the trait phenotype into its structural components and map specific QTLs that control the mechanistic and structural formation of a complex trait. We describe and assess such a strategy, called structural mapping, by integrating the internal structural basis of trait formation into a QTL mapping framework. Electrical impedance spectroscopy (EIS) has been instrumental for describing the structural components of a phenotypic trait and their interactions. By building robust mathematical models on circuit EIS data and embedding these models within a mixture model-based likelihood for QTL mapping, structural mapping implements the EM algorithm to obtain maximum likelihood estimates of QTL genotype-specific EIS parameters. The uniqueness of structural mapping is to make it possible to test a number of hypotheses about the pattern of the genetic control of structural components. We validated structural mapping by analyzing an EIS data collected for QTL mapping of frost hardiness in a controlled cross of jujube trees. The statistical properties of parameter estimates were examined by simulation studies. Structural mapping can be a powerful alternative for genetic mapping of complex traits by taking account into the biological and physical mechanisms underlying their formation.
    Print ISSN: 1467-5463
    Electronic ISSN: 1477-4054
    Topics: Biology , Computer Science
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  • 7
    Publication Date: 2015-03-19
    Description: Dynamic changes of gene expression reflect an intrinsic mechanism of how an organism responds to developmental and environmental signals. With the increasing availability of expression data across a time–space scale by RNA-seq, the classification of genes as per their biological function using RNA-seq data has become one of the most significant challenges in contemporary biology. Here we develop a clustering mixture model to discover distinct groups of genes expressed during a period of organ development. By integrating the density function of multivariate Poisson distribution, the model accommodates the discrete property of read counts characteristic of RNA-seq data. The temporal dependence of gene expression is modeled by the first-order autoregressive process. The model is implemented with the Expectation-Maximization algorithm and model selection to determine the optimal number of gene clusters and obtain the estimates of Poisson parameters that describe the pattern of time-dependent expression of genes from each cluster. The model has been demonstrated by analyzing a real data from an experiment aimed to link the pattern of gene expression to catkin development in white poplar. The usefulness of the model has been validated through computer simulation. The model provides a valuable tool for clustering RNA-seq data, facilitating our global view of expression dynamics and understanding of gene regulation mechanisms.
    Print ISSN: 1467-5463
    Electronic ISSN: 1477-4054
    Topics: Biology , Computer Science
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  • 8
    Publication Date: 2012-10-10
    Description: Diverse bacteria contain DNA with sulfur incorporated stereo-specifically into their DNA backbone at specific sequences (phosphorothioation). We found that in vitro oxidation of phosphorothioate (PT) DNA by hydrogen peroxide (H 2 O 2 ) or peracetic acid has two possible outcomes: DNA backbone cleavage or sulfur removal resulting in restoration of normal DNA backbone. The physiological relevance of this redox reaction was investigated by challenging PT DNA hosting Salmonella enterica cells using H 2 O 2 . DNA phosphorothioation was found to correlate with increasing resistance to the growth inhibition by H 2 O 2 . Resistance to H 2 O 2 was abolished when each of the three dnd genes, required for phosphorothioation, was inactivated. In vivo , PT DNA is more resistant to the double-strand break damage caused by H 2 O 2 than PT-free DNA. Furthermore, sulfur on the modified DNA was consumed and the DNA was converted to PT-free state when the bacteria were incubated with H 2 O 2 . These findings are consistent with a hypothesis that phosphorothioation modification endows DNA with reducing chemical property, which protects the hosting bacteria against peroxide, explaining why this modification is maintained by diverse bacteria.
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
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  • 9
    Publication Date: 2014-07-19
    Description: With the availability of gene expression data by RNA-seq, powerful statistical approaches for grouping similar gene expression profiles across different environments have become increasingly important. We describe and assess a computational model for clustering genes into distinct groups based on the pattern of gene expression in response to changing environment. The model capitalizes on the Poisson distribution to capture the count property of RNA-seq data. A two-stage hierarchical expectation–maximization (EM) algorithm is implemented to estimate an optimal number of groups and mean expression amounts of each group across two environments. A procedure is formulated to test whether and how a given group shows a plastic response to environmental changes. The impact of gene–environment interactions on the phenotypic plasticity of the organism can also be visualized and characterized. The model was used to analyse an RNA-seq dataset measured from two cell lines of breast cancer that respond differently to an anti-cancer drug, from which genes associated with the resistance and sensitivity of the cell lines are identified. We performed simulation studies to validate the statistical behaviour of the model. The model provides a useful tool for clustering gene expression data by RNA-seq, facilitating our understanding of gene functions and networks.
    Print ISSN: 1467-5463
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    Topics: Biology , Computer Science
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  • 10
    Publication Date: 2012-12-20
    Description: In this article, we describe a long-non-coding RNA (lncRNA) and disease association database (LncRNADisease), which is publicly accessible at http://cmbi.bjmu.edu.cn/lncrnadisease . In recent years, a large number of lncRNAs have been identified and increasing evidence shows that lncRNAs play critical roles in various biological processes. Therefore, the dysfunctions of lncRNAs are associated with a wide range of diseases. It thus becomes important to understand lncRNAs’ roles in diseases and to identify candidate lncRNAs for disease diagnosis, treatment and prognosis. For this purpose, a high-quality lncRNA–disease association database would be extremely beneficial. Here, we describe the LncRNADisease database that collected and curated approximately 480 entries of experimentally supported lncRNA–disease associations, including 166 diseases. LncRNADisease also curated 478 entries of lncRNA interacting partners at various molecular levels, including protein, RNA, miRNA and DNA. Moreover, we annotated lncRNA–disease associations with genomic information, sequences, references and species. We normalized the disease name and the type of lncRNA dysfunction and provided a detailed description for each entry. Finally, we developed a bioinformatic method to predict novel lncRNA–disease associations and integrated the method and the predicted associated diseases of 1564 human lncRNAs into the database.
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
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