In silico gene prioritization by integrating multiple data sources

Identifying disease genes is crucial to the understanding of disease pathogenesis, and to the improvement of disease diagnosis and treatment. In recent years, many researchers have proposed approaches to prioritize candidate genes by considering the relationship of candidate genes and existing known disease genes, reflected in other data sources. In this paper, we propose an expandable framework for gene prioritization that can integrate multiple heterogeneous data sources by taking advantage of a unified graphic representation. Gene-gene relationships and gene-disease relationships are then defined based on the overall topology of each network using a diffusion kernel measure. These relationship measures are in turn normalized to derive an overall measure across all networks, which is utilized to rank all candidate genes. Based on the informativeness of available data sources with respect to each specific disease, we also propose an adaptive threshold score to select a small subset of candidate genes for further validation studies. We performed large scale cross-validation analysis on 110 disease families using three data sources. Results have shown that our approach consistently outperforms other two state of the art programs. A case study using Parkinson disease (PD) has identified four candidate genes (UBB, SEPT5, GPR37 and TH) that ranked higher than our adaptive threshold, all of which are involved in the PD pathway. In particular, a very recent study has observed a deletion of TH in a patient with PD, which supports the importance of the TH gene in PD pathogenesis. A web tool has been implemented to assist scientists in their genetic studies.     

Compression and dilation of the membrane-cortex layer generates rapid changes in cell shape

Rapid changes in cellular morphology require a cell body that is highly flexible yet retains sufficient strength to maintain structural integrity. We present a mechanism that meets both of these requirements. We demonstrate that compression (folding) and subsequent dilation (unfolding) of the coupled plasma membrane–cortex layer generates rapid shape transformations in rounded cells. Two- and three-dimensional live-cell images showed that the cyclic process of membrane-cortex compression and dilation resulted in a traveling wave of cortical actin density. We also demonstrate that the membrane-cortex traveling wave led to amoeboid-like cell migration. The compression–dilation hypothesis offers a mechanism for large-scale cell shape transformations that is complementary to blebbing, where the plasma membrane detaches from the actin cortex and is initially unsupported when the bleb extends as a result of cytosolic pressure. Our findings provide insight into the mechanisms that drive the rapid morphological changes that occur in many physiological contexts, such as amoeboid migration and cytokinesis.     

Tracking Shallow Chemical Gradients by Actin-Driven Wandering of the Polarization Site

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Highlights? A wandering polarity site distributes growth in yeast cells responding to pheromone ? Wandering, but not polarity establishment, is actin dependent ? A mathematical model incorporating polarity and vesicle traffic exhibits wandering ? Blocking wandering impairs gradient tracking     

Linkage analysis of plasma dopamine ��-hydroxylase activity in families of patients with schizophrenia

Dopamine β-hydroxylase (DβH) catalyzes the conversion of dopamine to norepinephrine. DβH enters the plasma after vesicular release from sympathetic neurons and the adrenal medulla. Plasma DβH activity (pDβH) varies widely among individuals, and genetic inheritance regulates that variation. Linkage studies suggested strong linkage of pDβH to ABO on 9q34, and positive evidence for linkage to the complement fixation locus on 19p13.2-13.3. Subsequent association studies strongly supported DBH, which maps adjacent to ABO, as the locus regulating a large proportion of the heritable variation in pDβH. Prior studies have suggested that variation in pDβH, or genetic variants at DβH, associate with differences in expression of psychotic symptoms in patients with schizophrenia and other idiopathic or drug-induced brain disorders, suggesting that DBH might be a genetic modifier of psychotic symptoms. As a first step toward investigating that hypothesis, we performed linkage analysis on pDβH in patients with schizophrenia and their relatives. The results strongly confirm linkage of markers at DBH to pDβH under several models (maximum multipoint LOD score, 6.33), but find no evidence to support linkage anywhere on chromosome 19. Accounting for the contributions to the linkage signal of three SNPs at DBH, rs1611115, rs1611122, and rs6271 reduced but did not eliminate the linkage peak, whereas accounting for all SNPs near DBH eliminated the signal entirely. Analysis of markers genome-wide uncovered positive evidence for linkage between markers at chromosome 20p12 (multi-point LOD?=?3.1 at 27.2?cM). The present results provide the first direct evidence for linkage between DBH and pDβH, suggest that rs1611115, rs1611122, rs6271 and additional unidentified variants at or near DBH contribute to the genetic regulation of pDβH, and suggest that a locus near 20p12 also influences pDβH.     

Two-marker association tests yield new disease associations for coronary artery disease and hypertension

It has been postulated that multiple-marker methods may have added ability, over single-marker methods, to detect genetic variants associated with disease. The Wellcome Trust Case Control Consortium (WTCCC) provided the first successful large genome-wide association studies (GWAS) which included single-marker association analyses for seven common complex diseases. Of those signals detected, only one was associated with coronary artery disease (CAD), and none were identified for hypertension (HTN). Our objective was to find additional genetic associations and pathways for cardiovascular disease by examining the WTCCC data for variants associated with CAD and HTN using two-marker testing methods. We applied two-marker association testing to the WTCCC dataset, which includes ~2,000 affected individuals with each disorder, and a shared pool of ~3,000 controls, all genotyped using Affymetrix GeneChip 500 K arrays. For CAD, we detected single nucleotide polymorphisms (SNP) pairs in three genes showing genome-wide significance: HFE2, STK32B, and DIPC2. The most notable SNP pairs in a non-protein-coding region were at 9p21, a known major CAD-associated region. For HTN, we detected SNP pairs in five genes: GPR39, XRCC4, MYO6, ZFAT, and MACROD2. Four further associated SNP pair regions were at least 70 kb from any known gene. We have shown that novel, multiple-marker, statistical methods can be of use in finding variants in GWAS. We describe many new, associated variants for both CAD and HTN and describe their known genetic mechanisms.     

Identifying when weather influences life-history traits of grazing herbivores

1. There is increasing evidence that density-independent weather effects influence life-history traits and hence the dynamics of populations of animals. Here, we present a novel statistical approach to estimate when such influences are strongest. The method is demonstrated by analyses investigating the timing of the influence of weather on the birth weight of sheep and deer. 2. The statistical technique allowed for the pattern of temporal correlation in the weather data enabling the effects of weather in many fine-scale time intervals to be investigated simultaneously. Thus, while previous studies have typically considered weather averaged across a single broad time interval during pregnancy, our approach enabled examination simultaneously of the relationships with weekly and fortnightly averages throughout the whole of pregnancy. 3. We detected a positive effect of temperature on the birth weight of deer, which is strongest in late pregnancy (mid-March to mid-April), and a negative effect of rainfall on the birthweight of sheep, which is strongest during mid-pregnancy (late January to early February). The possible mechanisms underlying these weather-birth weight relationships are discussed. 4. This study enhances our insight into the pattern of the timing of influence of weather on early development. The method is of much more general application and could provide valuable insights in other areas of ecology in which sequences of intercorrelated explanatory variables have been collected in space or in time.     

Improving power in contrasting linkage-disequilibrium patterns between cases and controls

Genetic association studies offer an opportunity to find genetic variants underlying complex human diseases. The success of this approach depends on the linkage disequilibrium (LD) between markers and the disease variant(s) in a local region of the genome. Because, in the region with a disease mutation, the LD pattern among markers may differ between cases and controls, in some scenarios, it is useful to compare a measure of this LD, to map disease mutations. For example, using the composite correlation to characterize the LD among markers, Zaykin et al. recently suggested an "LD contrast" test and showed that it has high power under certain haplotype-driven disease models. Furthermore, it is likely that individual variants observed at different positions in a gene act jointly with each other to influence the phenotype, and the LD contrast test is also a useful method to detect such joint action. However, the LD among markers introduced by mutations and their joint action is usually confounded by background LD, which is measured at the population level, especially in a local region with disease mutations. Because the measures of LD that are usually used, such as the composite correlation, represent both effects, they may not be optimal for the purpose of detecting association when high background LD exists. Here, we describe a test that improves the LD contrast test by taking into account the background LD. Because the proposed test is developed in a regression framework, it is very flexible and can be extended to continuous traits and to incorporate covariates. Our simulation results demonstrate the validity and substantially higher power of the proposed method over current methods. Finally, we illustrate our new method by applying it to real data from the International Collaborative Study on Hypertension in Blacks.