Association Tests of Multiple Phenotypes: ATeMP

Joint analysis of multiple phenotypes has gained growing attention in genome-wide association studies (GWASs), especially for the analysis of multiple intermediate phenotypes which measure the same underlying complex human disorder. One of the multivariate methods, MultiPhen (O’ Reilly et al. 2012), employs the proportional odds model to regress a genotype on multiple phenotypes, hence ignoring the phenotypic distributions. Despite the flexibilities of MultiPhen, the properties and performance of MultiPhen are not well understood, especially when the phenotypic distributions are non-normal. In fact, it is well known in the statistical literature that the estimation is attenuated when the explanatory variables contain measurement errors. In this study, we first established an equivalence relationship between MultiPhen and the generalized Kendall tau association test, shedding light on why MultiPhen can perform well for joint association analysis of multiple phenotypes. Through the equivalence, we show that MultiPhen may lose power when the phenotypes are non-normal. To maintain the power, we propose two solutions (ATeMP-rn and ATeMP-or) to improve MultiPhen, and demonstrate their effectiveness through extensive simulation studies and a real case study from the Guangzhou Twin Eye Study.     

Genome-Wide Pathway Association Studies of Multiple Correlated Quantitative Phenotypes Using Principle Component Analyses

Genome-wide pathway association studies provide novel insight into the biological mechanism underlying complex diseases. Current pathway association studies primarily focus on single important disease phenotype, which is sometimes insufficient to characterize the clinical manifestations of complex diseases. We present a multi-phenotypes pathway association study(MPPAS) approach using principle component analysis(PCA). In our approach, PCA is first applied to multiple correlated quantitative phenotypes for extracting a set of orthogonal phenotypic components. The extracted phenotypic components are then used for pathway association analysis instead of original quantitative phenotypes. Four statistics were proposed for PCA-based MPPAS in this study. Simulations using the real data from the HapMap project were conducted to evaluate the power and type I error rates of PCA-based MPPAS under various scenarios considering sample sizes, additive and interactive genetic effects. A real genome-wide association study data set of bone mineral density (BMD) at hip and spine were also analyzed by PCA-based MPPAS. Simulation studies illustrated the performance of PCA-based MPPAS for identifying the causal pathways underlying complex diseases. Genome-wide MPPAS of BMD detected associations between BMD and KENNY_CTNNB1_TARGETS_UP as well as LONGEVITYPATHWAY pathways in this study. We aim to provide a applicable MPPAS approach, which may help to gain deep understanding the potential biological mechanism of association results for complex diseases.     

水稻基因枪法多基因转化研究

为探索多基因转化系统,利用基因枪法进行水稻3个质粒的共转化研究,结果从25个转基因植株中获得3个三转化植株N12、K4-39和K1-1-66,成功地将分别位于不同质粒载体上的GUS,hpt和RTBV CP基因同时导入到水稻中。3个三转化植株中N12、K4-39正常可育。对N12的后代遗传分析表明,该转基因植株中3个载体所携外源基因均呈孟德尔式分离(3:1),3个外源基因分别整合到水稻的两条染色体上。GUS基因与hpt连锁,RTBV CP位于另一条染色体上。     

Agrobacterium-Mediated Multiple Gene Transformation in Rice Using a Single Vector

The homodimeric hemoglobin gene (VHb), the trans-zeatin synthetase gene (tzs), the modified 5-enolpyruvylshikimate-3-phosphate synthase gene (EPSPS), a selectable marker gene (hpt), and a reporter gene (gus), as linked expression cassettes, were stacked into the T-DNA region of a binary vector and introduced simultaneously into immature embryos of the rice (Oryza sativa L.) varieties Xiushui-11, Qiufeng,Youfeng, and Hanfeng by Agrobacterium tumefaciens. A total of 1 153 transgenic lines was obtained through selection for hygromycin B resistance. Approximately 90.2% of the transgenic lines harbored all the transgenes. Integration of multiple transgenes occurred at one to three genetic loci. Expression analysis revealed that the transgenes were coexpressed and inherited in a simple Mendelian fashion in transgenic plants and the frequency of coexpression was approximately 85%. On the basis of the cointegration and coexpression of the transgenes, most transgenic families were considered to be useful in a breeding program.     

Agrobacterium-Mediated Multiple Gene Transformation in Rice Using a Single Vector

The homodimeric hemoglobin gene (VHb), the trans-zeatin synthetase gene (tzs), the modified 5-enolpyruvylshikimate-3-phosphate synthase gene (EPSPS), a selectable marker gene (hpt), and a reporter gene (gus), as linked expression cassettes, were stacked into the T-DNA region of a binary vector and introduced simultaneously into immature embryos of the rice (Oryza sativa L.) varieties Xiushui-11, Qiufeng,Youfeng, and Hanfeng by Agrobacterium tumefaciens. A total of 1 153 transgenic lines was obtained through selection for hygromycin B resistance. Approximately 90.2% of the transgenic lines harbored all the transgenes. Integration of multiple transgenes occurred at one to three genetic loci. Expression analysis revealed that the transgenes were coexpressed and inherited in a simple Mendelian fashion in transgenic plants and the frequency of coexpression was approximately 85%. On the basis of the cointegration and coexpression of the transgenes, most transgenic families were considered to be useful in a breeding program.     

Drug Repositioning through Systematic Mining of Gene Coexpression Networks in Cancer

Gene coexpression network analysis is a powerful “data-driven” approach essential for understanding cancer biology and mechanisms of tumor development. Yet, despite the completion of thousands of studies on cancer gene expression, there have been few attempts to normalize and integrate co-expression data from scattered sources in a concise “meta-analysis” framework. We generated such a resource by exploring gene coexpression networks in 82 microarray datasets from 9 major human cancer types. The analysis was conducted using an elaborate weighted gene coexpression network (WGCNA) methodology and identified over 3,000 robust gene coexpression modules. The modules covered a range of known tumor features, such as proliferation, extracellular matrix remodeling, hypoxia, inflammation, angiogenesis, tumor differentiation programs, specific signaling pathways, genomic alterations, and biomarkers of individual tumor subtypes. To prioritize genes with respect to those tumor features, we ranked genes within each module by connectivity, leading to identification of module-specific functionally prominent hub genes. To showcase the utility of this network information, we positioned known cancer drug targets within the coexpression networks and predicted that Anakinra, an anti-rheumatoid therapeutic agent, may be promising for development in colorectal cancer. We offer a comprehensive, normalized and well documented collection of >3000 gene coexpression modules in a variety of cancers as a rich data resource to facilitate further progress in cancer research.     

应用酵母单杂交系统分离水稻蜡质基因5'调控区结合蛋白的基因

在水稻蜡质基因５’上游区中一段３１ｂｐ 核苷酸序列能与水稻未成熟种子核蛋白特异结合。为了克隆这一核蛋白基因,以此３１ ｂｐ 序列构建成“鱼饵”质粒,从水稻ｃＤＮＡ文库中筛选到１３ 个阳性克隆。根据这些阳性克隆中插入ｃＤＮＡ 片段的相互杂交结果,对这些克隆进行了分组。     

大肠杆菌多基因共表达策略

利用基因工程手段实现多个基因在同一宿主菌中共表达是大肠杆菌细胞发育调节研究和代谢途径改造的有效手段。介绍了单一转录单元的多基因共表达载体、多重转录单元的多基因共表达和单基因载体的构建原理、特点、优势及转化策略,并着重介绍了利用LIC衔接子实现基因在多基因载体上定位连接的原理和方法。     

Chronic Ethanol Exposure Produces Time- and Brain Region-Dependent Changes in Gene Coexpression Networks

Repeated ethanol exposure and withdrawal in mice increases voluntary drinking and represents an animal model of physical dependence. We examined time- and brain region-dependent changes in gene coexpression networks in amygdala (AMY), nucleus accumbens (NAC), prefrontal cortex (PFC), and liver after four weekly cycles of chronic intermittent ethanol (CIE) vapor exposure in C57BL/6J mice. Microarrays were used to compare gene expression profiles at 0-, 8-, and 120-hours following the last ethanol exposure. Each brain region exhibited a large number of differentially expressed genes (2,000-3,000) at the 0- and 8-hour time points, but fewer changes were detected at the 120-hour time point (400-600). Within each region, there was little gene overlap across time (~20%). All brain regions were significantly enriched with differentially expressed immune-related genes at the 8-hour time point. Weighted gene correlation network analysis identified modules that were highly enriched with differentially expressed genes at the 0- and 8-hour time points with virtually no enrichment at 120 hours. Modules enriched for both ethanol-responsive and cell-specific genes were identified in each brain region. These results indicate that chronic alcohol exposure causes global ‘rewiring‘ of coexpression systems involving glial and immune signaling as well as neuronal genes.     

水稻同工酶基因多样性及非随机组合现象的研究

通过对６８０份栽培稻、８８份中国纯合普通野生稻的同工酶基因多样性和非随机组合现象的研究发现：（１）籼稻的多样性大于粳稻,对中国而言,普通野生稻大于籼稻,籼稻大于粳稻。（２）东南亚品种的非随机组合值很低,分化较低。基因多样性（Ｈ）、基因型频率（Ｆ）很高,是一个多样性中心。（３）南亚品种多样性和东南亚差不多,非随机组合值偏低,分化较差。但Ａｕｓ稻多样性很小,非随机组合值较高,分化较彻底。（４）除中国云南以外的中国其它品种籼粳分化最彻底,多样性很小。中国云南粳稻多样最小,籼稻多样性近中,非随机组合值和中国的其它品种接近,从同工酶上看,中国云南不像是一个多样性中心。但发现和Ｅｓｔ－２座位相关的非随机组合值很低。（５）籼粳交后代材料非随机组合值低,多样性偏高,推测籼粳交在水稻籼粳分化过程中起着很大的作用,有降低分化程度,增加多样性的作用。（６）中国普通野生稻非随机组合值很小,多数座位间不显著。可见,野生稻的分化是很小的。（７）广亲和品种与Ｅｓｔ－２相关的非随机组合值很低,全部不显著,但其余的组合都较高。与中国云南品种的非随机组合值结果十分类似。     

Gene Function Prediction from Functional Association Networks Using Kernel Partial Least Squares Regression

With the growing availability of large-scale biological datasets, automated methods of extracting functionally meaningful information from this data are becoming increasingly important. Data relating to functional association between genes or proteins, such as co-expression or functional association, is often represented in terms of gene or protein networks. Several methods of predicting gene function from these networks have been proposed. However, evaluating the relative performance of these algorithms may not be trivial: concerns have been raised over biases in different benchmarking methods and datasets, particularly relating to non-independence of functional association data and test data. In this paper we propose a new network-based gene function prediction algorithm using a commute-time kernel and partial least squares regression (Compass). We compare Compass to GeneMANIA, a leading network-based prediction algorithm, using a number of different benchmarks, and find that Compass outperforms GeneMANIA on these benchmarks. We also explicitly explore problems associated with the non-independence of functional association data and test data. We find that a benchmark based on the Gene Ontology database, which, directly or indirectly, incorporates information from other databases, may considerably overestimate the performance of algorithms exploiting functional association data for prediction.     

GUESS-ing Polygenic Associations with Multiple Phenotypes Using a GPU-Based Evolutionary Stochastic Search Algorithm

Genome-wide association studies (GWAS) yielded significant advances in defining the genetic architecture of complex traits and disease. Still, a major hurdle of GWAS is narrowing down multiple genetic associations to a few causal variants for functional studies. This becomes critical in multi-phenotype GWAS where detection and interpretability of complex SNP(s)-trait(s) associations are complicated by complex Linkage Disequilibrium patterns between SNPs and correlation between traits. Here we propose a computationally efficient algorithm (GUESS) to explore complex genetic-association models and maximize genetic variant detection. We integrated our algorithm with a new Bayesian strategy for multi-phenotype analysis to identify the specific contribution of each SNP to different trait combinations and study genetic regulation of lipid metabolism in the Gutenberg Health Study (GHS). Despite the relatively small size of GHS (n = 3,175), when compared with the largest published meta-GWAS (n>100,000), GUESS recovered most of the major associations and was better at refining multi-trait associations than alternative methods. Amongst the new findings provided by GUESS, we revealed a strong association of SORT1 with TG-APOB and LIPC with TG-HDL phenotypic groups, which were overlooked in the larger meta-GWAS and not revealed by competing approaches, associations that we replicated in two independent cohorts. Moreover, we demonstrated the increased power of GUESS over alternative multi-phenotype approaches, both Bayesian and non-Bayesian, in a simulation study that mimics real-case scenarios. We showed that our parallel implementation based on Graphics Processing Units outperforms alternative multi-phenotype methods. Beyond multivariate modelling of multi-phenotypes, our Bayesian model employs a flexible hierarchical prior structure for genetic effects that adapts to any correlation structure of the predictors and increases the power to identify associated variants. This provides a powerful tool for the analysis of diverse genomic features, for instance including gene expression and exome sequencing data, where complex dependencies are present in the predictor space.     

水稻R基因同源序列与遗传学上已知抗病基因的共定位(英文)

植物抗病（R）基因结构上的高度保守性,为利用基于PCR的方法快速分离R基因同源序列提供了基础。采用这种方法,我们曾从水稻中分离到8个R基因候选同源序列（Rgenecandidates,RGCs）。为了研究RGCs与遗传学上已知的R基因的关系,对它们进行了限制性片段长度多态性（RFLP）分析和染色体定位。DNA杂交结果显示RGCs都属于多基因家族（Fig．1）。6个RGCs（Osh359－1、Osh359－2、Osh359－3、Osh359－5、Os8558－3、Os8558－14）在两个籼稻品种H359和Acc8558中检测出多态性,并定位在水稻染色体上（Fig．2）。它们分别检测出了1、1、4、1、2和1个座位,共10个座位,其中9个定位在第11号染色体的3个区域上,即RFLP标记G181和C82之间（由Osh359－2、Osh359－3、Osh359－5和Os8558－3检测的6个座位）,G1465与C50之间（Osh359－3检测出的一个座位）,和C496附近（由Osh359－1和Os8558－14检测出的两个紧密连锁的座位）另有一个由Os8558－3检测出的座位定位到第8号染色体上,位于L457和G1082B之间。这些染色体区域包含近一半的遗传学上已知的抗病基因,如Xa-3、Xa－10、Pi－a和xa－13。这一结果表明RGCs与已知的抗病基因位于相同的染色体区域。此外,RGCs定位的结果表明,它们在水稻基因组中呈簇状分布,表现出     

Identifying Cancer Specific Functionally Relevant miRNAs from Gene Expression and miRNA-to-Gene Networks Using Regularized Regression

Identifying microRNA signatures for the different types and subtypes of cancer can result in improved detection, characterization and understanding of cancer and move us towards more personalized treatment strategies. However, using microRNA''s differential expression (tumour versus normal) to determine these signatures may lead to inaccurate predictions and low interpretability because of the noisy nature of miRNA expression data. We present a method for the selection of biologically active microRNAs using gene expression data and microRNA-to-gene interaction network. Our method is based on a linear regression with an elastic net regularization. Our simulations show that, with our method, the active miRNAs can be detected with high accuracy and our approach is robust to high levels of noise and missing information. Furthermore, our results on real datasets for glioblastoma and prostate cancer are confirmed by microRNA expression measurements. Our method leads to the selection of potentially functionally important microRNAs. The associations of some of our identified miRNAs with cancer mechanisms are already confirmed in other studies (hypoxia related hsa-mir-210 and apoptosis-related hsa-mir-296-5p). We have also identified additional miRNAs that were not previously studied in the context of cancer but are coherently predicted as active by our method and may warrant further investigation. The code is available in Matlab and R and can be downloaded on http://www.cs.toronto.edu/goldenberg/Anna_Goldenberg/Current_Research.html.