Candidate Gene Identification for Systemic Lupus Erythematosus Using Network Centrality Measures and Gene Ontology

Systemic lupus erythematosus (SLE) commonly accredited as “the great imitator” is a highly complex disease involving multiple gene susceptibility with non-specific symptoms. Many experimental and computational approaches have been used to investigate the disease related candidate genes. But the limited knowledge of gene function and disease correlation and also lack of complete functional details about the majority of genes in susceptible locus, encumbrances the identification of SLE related candidate genes. In this paper, we have studied the human immunome network (undirected) using various graph theoretical centrality measures integrated with the gene ontology terms to predict the new candidate genes. As a result, we have identified 8 candidate genes, which may act as potential targets for SLE disease. We have also carried out the same analysis by replacing the human immunome network with human immunome signaling network (directed) and as an outcome we have obtained 5 candidate genes as potential targets for SLE disease. From the comparison study, we have found these two approaches are complementary in nature.     

Genome-Wide Analysis of Copy Number Variation Identifies Candidate Gene Loci Associated with the Progression of Non-Alcoholic Fatty Liver Disease

Between 10 and 25% of individuals with non-alcoholic fatty liver disease (NAFLD) develop hepatic fibrosis leading to cirrhosis and hepatocellular carcinoma (HCC). To investigate the molecular basis of disease progression, we performed a genome-wide analysis of copy number variation (CNV) in a total of 49 patients with NAFLD [10 simple steatosis and 39 non-alcoholic steatohepatitis (NASH)] and 49 matched controls using high-density comparative genomic hybridization (CGH) microarrays. A total of 11 CNVs were found to be unique to individuals with simple steatosis, whilst 22 were common between simple steatosis and NASH, and 224 were unique to NASH. We postulated that these CNVs could be involved in the pathogenesis of NAFLD progression. After stringent filtering, we identified four rare and/or novel CNVs that may influence the pathogenesis of NASH. Two of these CNVs, located at 13q12.11 and 12q13.2 respectively, harbour the exportin 4 (XPO4) and phosphodiesterase 1B (PDE1B) genes which are already known to be involved in the etiology of liver cirrhosis and HCC. Cross-comparison of the genes located at these four CNV loci with genes already known to be associated with NAFLD yielded a set of genes associated with shared biological processes including cell death, the key process involved in ‘second hit’ hepatic injury. To our knowledge, this pilot study is the first to provide CNV information of potential relevance to the NAFLD spectrum. These data could prove invaluable in predicting patients at risk of developing NAFLD and more importantly, those who will subsequently progress to NASH.     

两个位点主基因控制的质量—数量性状的遗传分析

应用极大似然法和EM算法提出了关于两个位点主基因控制的质量．数量性状的遗传分析方法,参照质量性状两位点互作在F_2代的分离比率建立了7种遗传假设及其似然比测验的程序,讨论了应用这一方法时应注意的几个问题．     

Genome-Wide Association in Tomato Reveals 44 Candidate Loci for Fruit Metabolic Traits

Genome-wide association studies have been successful in identifying genes involved in polygenic traits and are valuable for crop improvement. Tomato (Solanum lycopersicum) is a major crop and is highly appreciated worldwide for its health value. We used a core collection of 163 tomato accessions composed of S. lycopersicum, S. lycopersicum var cerasiforme, and Solanum pimpinellifolium to map loci controlling variation in fruit metabolites. Fruits were phenotyped for a broad range of metabolites, including amino acids, sugars, and ascorbate. In parallel, the accessions were genotyped with 5,995 single-nucleotide polymorphism markers spread over the whole genome. Genome-wide association analysis was conducted on a large set of metabolic traits that were stable over 2 years using a multilocus mixed model as a general method for mapping complex traits in structured populations and applied to tomato. We detected a total of 44 loci that were significantly associated with a total of 19 traits, including sucrose, ascorbate, malate, and citrate levels. These results not only provide a list of candidate loci to be functionally validated but also a powerful analytical approach for finding genetic variants that can be directly used for crop improvement and deciphering the genetic architecture of complex traits.In crops, linkage mapping has proved invaluable for detecting quantitative trait loci (QTLs) for traits of interest and to unravel their underlying genetic architecture. This approach is based on the analysis of the segregation of polymorphism between the parental lines and their progeny. However, one of the limitations of this approach is the reduced number of recombination events that occur per generation (for review, see Korte and Farlow, 2013). This leads to extended linkage blocks that reduce the accuracy of the linkage mapping. An alternative to linkage-based mapping studies is to perform linkage disequilibrium (LD) mapping in a population of theoretically unrelated individuals. The ancestral polymorphism segregating through this population (or panel) is far more informative compared with the polymorphism of the parental lines of the linkage mapping population (Mauricio, 2001). LD mapping, also known as genome-wide association (GWA), relies on the natural patterns of LD in the population investigated. The aim of GWA is to reveal trait-associated loci by taking advantage of the level of LD. Depending on the decay of LD, the mapping resolution can be narrowed from a large genomic portion where the level of LD is relatively high to a single marker when the LD level is very low.Following domestication, crops are prone to (1) increased levels of LD, (2) population structure (remote common ancestry of large groups of individuals), and (3) cryptic relatedness (the presence of close relatives in a sample of unrelated individuals; Riedelsheimer et al., 2012). Population structure and cryptic relatedness may lead to false-positive association in GWA studies (Astle and Balding, 2009), but their effect is now relatively well accounted for in mixed linear models (for review, see Sillanpää, 2011; Listgarten et al., 2012). The problem of high LD in GWA scans also must be taken into account: Segura et al. (2012) investigated this difficulty by proposing a multilocus mixed model (MLMM) that handles the confounding effect of background loci that may be present throughout the genome due to LD. This approach revealed multiple loci in LD and associated with sodium concentration in leaves in Arabidopsis (Arabidopsis thaliana), while previous methods failed to identify this complex pattern (Segura et al., 2012).In parallel, the development of cost-effective high-throughput sequencing technologies has identified increasingly dense variant loci necessary to conduct GWA scans, especially in model species such as rice (Oryza sativa) for agronomic traits (Huang et al., 2010) or maize (Zea mays) for drought tolerance (Lu et al., 2010; for review, see Soto-Cerda and Cloutier, 2012). However, GWA is not restricted to model species and is becoming increasingly widespread in nonmodel ones such as sunflower (Helianthus annuus; Mandel et al., 2013) and tomato (Solanum lycopersicum; Xu et al., 2013), where numerous associations have been successfully identified for traits related to plant architecture (branching in the case of sunflower) and fruit quality (e.g. fresh weight in tomato).Tomato is a crop of particular interest, as the fruit are an important source of fiber and nutrients in the human diet and a model for the study of fruit development (Giovannoni, 2001). Over the last two decades, numerous QTLs have been identified for traits such as fresh weight using linkage approaches (Frary et al., 2000; Zhang et al., 2012; Chakrabarti et al., 2013) but also for other fruit-related traits such as fruit ascorbic acid levels (Stevens et al., 2007), sensory and instrumental quality traits (Causse et al., 2002), sugar and organic acids (Fulton et al., 2002), and metabolic components (Schauer et al., 2008). Large tomato germplasm collections have been characterized at the molecular level using simple sequence repeat (Ranc et al., 2008) and single-nucleotide polymorphism (SNP) markers (Blanca et al., 2012; Shirasawa et al., 2013), giving insights into population structure, tomato evolutionary history, and the genetic architecture of traits of agronomic interest. These screens of nucleotide diversity were made possible (for review, see Bauchet and Causse, 2012) in the last couple of years due to the release of the tomato genome sequence (Tomato Genome Consortium, 2012) and derived genomic tools such as a high-density SNP genotyping array (Sim et al., 2012). The combination of large germplasm collections, high-throughput genomic tools, and traits of economic interest provide a framework to apply genome-wide association study (GWAS) in this species. In tomato, previous association studies have been limited to a targeted region (e.g. chromosome 2; Ranc et al., 2012), used low-density genome-wide-distributed SNP markers (Xu et al., 2013), or investigated a limited number of agronomic traits with low precision on the association panel (Shirasawa et al., 2013).Using tomato as a model, we aimed to investigate the genetic architecture of traits related to fruit metabolic composition at high resolution. To reach this objective, we carried out an investigation into LD patterns at the genome-wide scale and a GWA scan using the MLMM approach. We present results on the genetic architecture of fruit metabolic composition for metabolites such as organic acids, amino acids, sugars, and ascorbate in tomato.     

Fine Mapping and Candidate Gene Analysis of the Leaf-Color Gene ygl-1 in Maize

A novel yellow-green leaf mutant yellow-green leaf-1 (ygl-1) was isolated in self-pollinated progenies from the cross of maize inbred lines Ye478 and Yuanwu02. The mutant spontaneously showed yellow-green character throughout the lifespan. Meanwhile, the mutant reduced contents of chlorophyll and Car, arrested chloroplast development and lowered the capacity of photosynthesis compared with the wild-type Lx7226. Genetic analysis revealed that the mutant phenotype was controlled by a recessive nuclear gene. The ygl-1 locus was initially mapped to an interval of about 0.86 Mb in bin 1.01 on the short arm of chromosome 1 using 231 yellow-green leaf individuals of an F₂ segregating population from ygl-1/Lx7226. Utilizing four new polymorphic SSR markers, the ygl-1 locus was narrowed down to a region of about 48 kb using 2930 and 2247 individuals of F₂ and F₃ mapping populations, respectively. Among the three predicted genes annotated within this 48 kb region, GRMZM2G007441, which was predicted to encode a cpSRP43 protein, had a 1-bp nucleotide deletion in the coding region of ygl-1 resulting in a frame shift mutation. Semi-quantitative RT-PCR analysis revealed that YGL-1 was constitutively expressed in all tested tissues and its expression level was not significantly affected in the ygl-1 mutant from early to mature stages, while light intensity regulated its expression both in the ygl-1 mutant and wild type seedlings. Furthermore, the mRNA levels of some genes involved in chloroplast development were affected in the six-week old ygl-1 plants. These findings suggested that YGL-1 plays an important role in chloroplast development of maize.     

GroupRank: Rank Candidate Genes in PPI Network by Differentially Expressed Gene Groups

Many cell activities are organized as a network, and genes are clustered into co-expressed groups if they have the same or closely related biological function or they are co-regulated. In this study, based on an assumption that a strong candidate disease gene is more likely close to gene groups in which all members coordinately differentially express than individual genes with differential expression, we developed a novel disease gene prioritization method GroupRank by integrating gene co-expression and differential expression information generated from microarray data as well as PPI network. A candidate gene is ranked high using GroupRank if it is differentially expressed in disease and control or is close to differentially co-expressed groups in PPI network. We tested our method on data sets of lung, kidney, leukemia and breast cancer. The results revealed GroupRank could efficiently prioritize disease genes with significantly improved AUC value in comparison to the previous method with no consideration of co-exprssed gene groups in PPI network. Moreover, the functional analyses of the major contributing gene group in gene prioritization of kidney cancer verified that our algorithm GroupRank not only ranks disease genes efficiently but also could help us identify and understand possible mechanisms in important physiological and pathological processes of disease.     

Global Analysis of the Human Pathophenotypic Similarity Gene Network Merges Disease Module Components

The molecular complexity of genetic diseases requires novel approaches to break it down into coherent biological modules. For this purpose, many disease network models have been created and analyzed. We highlight two of them, “the human diseases networks” (HDN) and “the orphan disease networks” (ODN). However, in these models, each single node represents one disease or an ambiguous group of diseases. In these cases, the notion of diseases as unique entities reduces the usefulness of network-based methods. We hypothesize that using the clinical features (pathophenotypes) to define pathophenotypic connections between disease-causing genes improve our understanding of the molecular events originated by genetic disturbances. For this, we have built a pathophenotypic similarity gene network (PSGN) and compared it with the unipartite projections (based on gene-to-gene edges) similar to those used in previous network models (HDN and ODN). Unlike these disease network models, the PSGN uses semantic similarities. This pathophenotypic similarity has been calculated by comparing pathophenotypic annotations of genes (human abnormalities of HPO terms) in the “Human Phenotype Ontology”. The resulting network contains 1075 genes (nodes) and 26197 significant pathophenotypic similarities (edges). A global analysis of this network reveals: unnoticed pairs of genes showing significant pathophenotypic similarity, a biological meaningful re-arrangement of the pathological relationships between genes, correlations of biochemical interactions with higher similarity scores and functional biases in metabolic and essential genes toward the pathophenotypic specificity and the pleiotropy, respectively. Additionally, pathophenotypic similarities and metabolic interactions of genes associated with maple syrup urine disease (MSUD) have been used to merge into a coherent pathological module.Our results indicate that pathophenotypes contribute to identify underlying co-dependencies among disease-causing genes that are useful to describe disease modularity.     

Linkage Relationships of Seventeen Human Gene Loci as determined by Man-Mouse Somatic Cell Hybrids

Electrophoretic studies of the enzymes produced by sixty-four independently derived hybrid clones have made possible the detection of autosomal linkages on human chromosomes.     

ncRNA候选基因sptl的克隆与初步分析

在用suc2信号肽捕获系统对小鼠胚胎cDNA文库筛选的过程中,反复获得一个相同的强阳性克隆,命名为sptl.对该克隆的序列分析表明插入序列由697 bp组成,6个开放阅读框中共有37个启始密码子(ATG)和80个终止密码子(TGA、TAG、TAA);没有较大的有意义开放读框存在.经BLAST分析,结果显示该序列定位于小鼠第17号染色体长臂,没有发现同源基因.Northern blot和RT-PCR分析表明,该序列仅表达于小鼠卵巢组织,全长约4.5～5.0kb.酵母转化和序列截短实验提示,该序列能够介导蔗糖转换酶向细胞外的分泌.因此,推测sptl很有可能是一个新的非编码RNA的一部分,参与蛋白质的分泌过程.     

Dynamic QTL Analysis and Candidate Gene Mapping for Waterlogging Tolerance at Maize Seedling Stage

Soil waterlogging is one of the major abiotic stresses adversely affecting maize growth and yield. To identify dynamic expression of genes or quantitative trait loci (QTL), QTL associated with plant height, root length, root dry weight, shoot dry weight and total dry weight were identified via conditional analysis in a mixed linear model and inclusive composite interval mapping method at three respective periods under waterlogging and control conditions. A total of 13, 19 and 23 QTL were detected at stages 3D|0D (the period during 0–3 d of waterlogging), 6D|3D and 9D|6D, respectively. The effects of each QTL were moderate and distributed over nine chromosomes, singly explaining 4.14–18.88% of the phenotypic variation. Six QTL (ph6-1, rl1-2, sdw4-1, sdw7-1, tdw4-1 and tdw7-1) were identified at two consistent stages of seedling development, which could reflect a continuous expression of genes; the remaining QTL were detected at only one stage. Thus, expression of most QTL was influenced by the developmental status. In order to provide additional evidence regarding the role of corresponding genes in waterlogging tolerance, mapping of Expressed Sequence Tags markers and microRNAs were conducted. Seven candidate genes were observed to co-localize with the identified QTL on chromosomes 1, 4, 6, 7 and 9, and may be important candidate genes for waterlogging tolerance. These results are a good starting point for understanding the genetic basis for selectively expressing of QTL in different stress periods and the common genetic control mechanism of the co-localized traits.     

Human Rod Monochromacy: Linkage Analysis and Mapping of a Cone Photoreceptor Expressed Candidate Gene on Chromosome 2q11

We have performed linkage analysis in eight families with rod monochromacy, an autosomal recessively inherited condition with complete color blindness. Significant linkage was found with markers located at the pericentromeric region of chromosome 2. A maximum lod score of 5.36 was obtained for marker D2S2333 at θ = 0.00. Mapping of meiotic breakpoints localized the disease gene between markers D2S2187 and D2S2229. Homozygosity for a number of subsequent markers indicating identity by descent was found in two families and provides evidence for a further refinement of the locus proximal to D2S373. This defines an interval of ≈3 cM covering theACHM2locus for rod monochromacy. Radiation hybrid mapping of theCNGA3gene encoding the α-subunit of the cGMP gated cation channel in human cone photoreceptors resulted in a maximum lod score of 16.1 with marker D2S2311 combined with a calculated physical distance of 6.19cR_10,000. Screening of the CEPH YAC library and subsequent STS mapping indicated the physical order cen–D2S2222–D2S2175–(D2S2187/D2S2311)–qtel ofmarkers on 2q11 and showed that theCNGA3gene maps most closely to D2S2187 and D2S2311. These data indicate that theCNGA3gene maps within the critical interval of theACHM2locus for rod monochromacy and thus is a candidate gene for this disease.     

Association of Genetic Loci with Sleep Apnea in European Americans and African-Americans: The Candidate Gene Association Resource (CARe)

Although obstructive sleep apnea (OSA) is known to have a strong familial basis, no genetic polymorphisms influencing apnea risk have been identified in cross-cohort analyses. We utilized the National Heart, Lung, and Blood Institute (NHLBI) Candidate Gene Association Resource (CARe) to identify sleep apnea susceptibility loci. Using a panel of 46,449 polymorphisms from roughly 2,100 candidate genes on a customized Illumina iSelect chip, we tested for association with the apnea hypopnea index (AHI) as well as moderate to severe OSA (AHI≥15) in 3,551 participants of the Cleveland Family Study and two cohorts participating in the Sleep Heart Health Study.Among 647 African-Americans, rs11126184 in the pleckstrin (PLEK) gene was associated with OSA while rs7030789 in the lysophosphatidic acid receptor 1 (LPAR1) gene was associated with AHI using a chip-wide significance threshold of p-value<2×10⁻⁶. Among 2,904 individuals of European ancestry, rs1409986 in the prostaglandin E2 receptor (PTGER3) gene was significantly associated with OSA. Consistency of effects between rs7030789 and rs1409986 in LPAR1 and PTGER3 and apnea phenotypes were observed in independent clinic-based cohorts.Novel genetic loci for apnea phenotypes were identified through the use of customized gene chips and meta-analyses of cohort data with replication in clinic-based samples. The identified SNPs all lie in genes associated with inflammation suggesting inflammation may play a role in OSA pathogenesis.     

Large-Scale Analysis of Network Bistability for Human Cancers

Protein–protein interaction and gene regulatory networks are likely to be locked in a state corresponding to a disease by the behavior of one or more bistable circuits exhibiting switch-like behavior. Sets of genes could be over-expressed or repressed when anomalies due to disease appear, and the circuits responsible for this over- or under-expression might persist for as long as the disease state continues. This paper shows how a large-scale analysis of network bistability for various human cancers can identify genes that can potentially serve as drug targets or diagnosis biomarkers.     

Schizophrenia Gene Networks and Pathways and Their Applications for Novel Candidate Gene Selection

Background

Schizophrenia (SZ) is a heritable, complex mental disorder. We have seen limited success in finding causal genes for schizophrenia from numerous conventional studies. Protein interaction network and pathway-based analysis may provide us an alternative and effective approach to investigating the molecular mechanisms of schizophrenia.

Methodology/Principal Findings

We selected a list of schizophrenia candidate genes (SZGenes) using a multi-dimensional evidence-based approach. The global network properties of proteins encoded by these SZGenes were explored in the context of the human protein interactome while local network properties were investigated by comparing SZ-specific and cancer-specific networks that were extracted from the human interactome. Relative to cancer genes, we observed that SZGenes tend to have an intermediate degree and an intermediate efficiency on a perturbation spreading throughout the human interactome. This suggested that schizophrenia might have different pathological mechanisms from cancer even though both are complex diseases. We conducted pathway analysis using Ingenuity System and constructed the first schizophrenia molecular network (SMN) based on protein interaction networks, pathways and literature survey. We identified 24 pathways overrepresented in SZGenes and examined their interactions and crosstalk. We observed that these pathways were related to neurodevelopment, immune system, and retinoic X receptor (RXR). Our examination of SMN revealed that schizophrenia is a dynamic process caused by dysregulation of the multiple pathways. Finally, we applied the network/pathway approach to identify novel candidate genes, some of which could be verified by experiments.

Conclusions/Significance

This study provides the first comprehensive review of the network and pathway characteristics of schizophrenia candidate genes. Our preliminary results suggest that this systems biology approach might prove promising for selection of candidate genes for complex diseases. Our findings have important implications for the molecular mechanisms for schizophrenia and, potentially, other psychiatric disorders.     

ncRNA候选基因spt1的克隆与初步分析

在用suc2信号肽捕获系统对小鼠胚胎cDNA文库筛选的过程中,反复获得一个相同的强阳性克隆,命名为spt1。对该克隆的序列分析表明：插入序列由697bp组成,6个开放阅读框中共有37个启始密码子(ATG)和80个终止密码子(TGA、TAG、TAA);没有较大的有意义开放读框存在。经BLAST分析,结果显示该序列定位于小鼠第17号染色体长臂,没有发现同源基因。NortherTl blot和RT-PCR分析表明,该序列仅表达于小鼠卵巢组织,全长约4．5～5．0kb。酵母转化和序列截短实验提示,该序列能够介导蔗糖转换酶向细胞外的分泌。因此,推测spt1很有可能是一个新的非编码RNA的一部分,参与蛋白质的分泌过程。     

应用基因芯片技术研究原发性腹膜后脂肪肉瘤组织特异性候选基因

目的:原发性腹膜后脂肪肉瘤是一种少见的恶性肿瘤,发病率大约占全部恶性肿瘤的1％,但其却是腹膜后最常见的软组织肿瘤.通过研究原发性腹膜后脂肪肉瘤和正常脂肪组织间的基因表达差异,可以寻找到与原发性腹膜后脂肪肉瘤发生、发展相关的候选基因并为其防治研究提供线索.方法:应用基因芯片技术检测2例原发性腹膜后脂肪肉瘤和相应的正常脂肪组织全基因组序列,分析其基因表达差异.结果:两组间的差异表达基因3828个,其中表达上调有1837个基因,表达下调有1991个基因.其中涉及细胞增殖、黏附、凋亡以及信号通路等多种生物学过程.结论:通过基因表达谱芯片筛选出与原发性腹膜后脂肪肉瘤发生和进展密切相关的基因为其基础研究及临床早期诊断、预防和治疗提供潜在的分子标记和靶基因.