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Genome-wide association studies (GWAS) have provided a large set of genetic loci influencing the risk for many common diseases. Association studies typically analyze one specific trait in single populations in an isolated fashion without taking into account the potential phenotypic and genetic correlation between traits. However, GWA data can be efficiently used to identify overlapping loci with analogous or contrasting effects on different diseases.
Here, we describe a new approach to systematically prioritize and interpret available GWA data. We focus on the analysis of joint and disjoint genetic determinants across diseases. Using network analysis, we show that variant-based approaches are superior to locus-based analyses. In addition, we provide a prioritization of disease loci based on network properties and discuss the roles of hub loci across several diseases. We demonstrate that, in general, agonistic associations appear to reflect current disease classifications, and present the potential use of effect sizes in refining and revising these agonistic signals. We further identify potential branching points in disease etiologies based on antagonistic variants and describe plausible small-scale models of the underlying molecular switches.
The observation that a surprisingly high fraction (>15%) of the SNPs considered in our study are associated both agonistically and antagonistically with related as well as unrelated disorders indicates that the molecular mechanisms influencing causes and progress of human diseases are in part interrelated. Genetic overlaps between two diseases also suggest the importance of the affected entities in the specific pathogenic pathways and should be investigated further.
Keywords:Genome-wide association study; Genetic overlap; Shared variant network; Disease comorbidity
In the past years enormous progress has been made in the identification of complex trait susceptibility loci. The application of genome-wide association studies (GWAS) created a still growing set of genetic markers associated with increased risk for a multitude of different diseases [1]. In contrast to few single loci exerting large effects on some phenotypes – mostly immune-related traits – the majority of traits was only associated with loci displaying small effects of odds ratios ranging from 1.1 to 1.5 [2]. Meta-analyses of several GWA studies further extended the set of known disease-related associations with even lower-effect variants. Despite the impressive progress in the field, for most traits only a small proportion of the total heritability is yet explained by known risk variants [3]. A notable exception is type 1 diabetes (T1D) where validated risk loci explain a large proportion of the total heritability [4]. In contrast, for most traits a considerably larger number of variants was reported to be associated, but typically these explain less than 50% of the total heritability [5].
Intriguingly, although published individual GWAS are usually carried out for one trait at a time, a significant overlap in the associations of several complex diseases becomes apparent [6]. Besides effects on a specific phenotype, loci and single SNPs thus may also exert pleiotropic effects by contributing to a variety of traits. While it is not surprising that susceptibility genes for closely related traits should be shared, multi-functionality of a gene in phenotype presentation, i.e. pleiotropy, sensu stricto refers to seemingly unrelated and distinct traits [7]. Loci or variants affecting several traits might have small effects on each specific trait, but may be of major biological interest while indicating shared or branching etiological mechanisms. In principle, the influence of such loci can be agonistic or antagonistic, i.e. involve concurrent similar or opposite effects of the same variant for different traits. So far, few studies attempted to study such loci in a systemic fashion and rather focused on shared risk variants in closely related traits like autoimmune diseases [8-10], heart diseases [11] or cancer [12].
In order to identify shared or branching pathways of related as well as diverse (i.e. medically and phenotypically distinct) diseases, we performed a systematic comparative analysis of genetic commonalities and differences across traditionally defined traits using the available repository of GWAS results. In the context of network medicine [13], we utilized an approach based on the diseasome concept [14] and investigated high-significance associations beyond conventional single-marker analysis in a hypothesis-free and comprehensive way. In former studies we found differing approaches of gene and locus assignment to association markers which partially led to controversial results (e.g. [15]). We therefore developed a more sophisticated locus assignment method and evaluate its reliability by utilizing the information contained directly in the reported markers. For this variant-based approach we manually curated a high-quality data set to construct a network extending the knowledge on genetic overlaps between diseases as provided by GWA studies.
Results and discussion
Considerable discrepancies across GWAS through differing genotyping platforms, varying sample sizes and diverging measures of statistical significance demand accurate data selection. Therefore, to sustain the genuine variant-linked information provided by GWAS, we combined several steps of data curation and filtering. To provide a comprehensive base for the analysis of potentially multi-functional loci and variants, respectively, we compiled two network representations of the information made available by GWA studies: the locus-based "shared locus network" (SLN, Figure 1B) and the variant-based "shared variant network" (SVN, Figure 1C). To be able to cluster diseases by their "genetic relatedness", we additionally created a disease-centric projection of the SVN (Additional file 1: Figure S1).
Additional file 1. Figure S1. Disease-centric projection of the SVN. The SVN (see Figure 1C) is transformed in a network consisting of diseases only. Here, two traits are connected if they are associated with the same variant. The colors of the disease nodes correspond to disease classes according to the MeSH ontology, multi-colored nodes indicate an association with different disease classes. The node size reflects the number of traits a disease has shared associations with. The direction of the shared variants is indicated by the edge color reflecting the corresponding allelic information: gray indicates agonistic variant(s), red corresponds to antagonistic variant(s), and blue mark both agonistic and antagonistic signals in the two corresponding traits.
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Additional file 2. Table S1. List of all disease-variant associations contained in the SVN. Contained is the high-quality data set which was used for the construction of the SVN, ordered by the rs-number of the tagging SNP. The first column contains this rs-number of the tagging SNP, the second column lists the disease associations and the third column gives the PubMed ID of the GWAS publication the association was reported in. In the fourth column the (gene or intergenic) locus of the tagging SNP can be found. The sixth column gives the SNP and the risk allele reported in the GWAS. If the rs-numbers of the tagging SNP (column 1) diverges from the rs-number listed here, the association was assigned via LD. For these cases, in column seven the corresponding allele of the tagging SNP is given, followed by the P-value and the odds ratio reported with the SNP (i.e. the reported SNP in column six). Blue row-coloring identifies non-HLA located antagonistic SNPs, while rows containing agonistic SNPs are not colored. Rows in green list antagonistic SNPs in the HLA region (not considered in the manuscript). Tagging SNPs which we included in our rationale are marked in bold red font.
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Additional file 3. Table S2. CPMA P-values for autoimmune-linked SNPs and their corresponding loci in the SVN. Listed are all SNPs contained in Supplementary Table 1 for which association data could be obtained from [8]. The second column gives the LD-based loci of the SNPs as used in the SVN. The third column contains the CPMA P-Values.
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Figure 1. Illustration of the different disease networks based on genome-wide association data.A: The bipartite graph constructed from all association data. The two disjoint node sets are diseases (n = 111) and loci (n = 734; 508 gene loci and 226 intergenic loci), connected to each other by an edge if a variant (n = 1,120) within the respective locus is associated with the corresponding trait. B: The SLN (shared locus network) consisting of 84 traits and 157 loci, retrieved by removing isolated traits and loci that are associated with a single trait only. C: The SVN (shared variant network) that corresponds to a variant-based representation of the data. Here, a trait and a locus are linked if the locus contains a variant comprising associations with this and at least one other trait. The network consists of 175 SNPs located in 94 loci that are associated with 55 diseases (see also Additional file 2: Table S1). The colors of the disease nodes correspond to disease classes according to the MeSH ontology, multi-colored nodes indicate an association with different disease classes; loci are depicted as transparent, diamond-shaped nodes. The node size reflects the number of loci a disease is associated with. In C, the edge color reflects the allelic information: gray indicates agonistic variant(s), red corresponds to antagonistic variant(s), and blue mark both agonistic and antagonistic signals.
We defined the associated loci over the variant-based linkage disequilibrium (LD) measure r2 and, accordingly, expected the SLN and the SVN (Figure 1B and C) to be of similar shape. However, when visually comparing the networks, significant differences in size and structure became apparent. Therefore, we performed further analyses to compare established property measures of the networks in detail to investigate potential reasons for this divergence.
Shared locus vs. shared association analysis
Despite the size difference, the SLN shows greater network heterogeneity (SLN = 1.30, SVN = 1.17) and lower centralization (SLN = 0.175, SVN = 0.205) and density (SLN = 0.014, SVN = 0.021) values than the SVN. Furthermore, the intersection between the SVN and the SLN lacks not only 5% of the nodes but also 10% of the edges of the SVN. These numbers imply that the process of translating LD data into locus information is at least partly inconsistent. Analysis of the structure of the assigned LD blocks showed two error sources in shared locus analysis (illustrated in Figure 2). First, variants in two independent LD blocks are assigned the same locus but are not in LD (assignment error I). Thus, shared loci are found that are not reflected in the variant based data. Second, if two SNPs are in strong LD but the individual long range LD patterns of the SNPs diverge (e.g. the LD block of one SNP covers a greater area at the given r2-threshold), an assignment error II occurs. In this case the two SNPs are assigned to different loci (in the example above, this is due to the different sizes of their LD blocks which may contain distinct gene sets) and their LD connection is lost. These observations suggest that (i), the SLN contains loci which overlap between traits but the associated markers are not in strong LD, (ii), there are several traits which are connected to the SLN via a single, potentially misleading link (as not mirrored in the variant-based data), and, (iii), even a LD-based locus assignment is unable to identify all shared associations (n = 25 of unidentified loci, based on the assignment error II). Due to this limited sensitivity and specificity in detecting LD-based correlations between the reported markers on locus scale, we used the smaller but more accurate variant-based SVN for further analyses. Moreover, the risk allele data in the SVN allows for the inclusion of the direction of the effects, i.e. agonistic and antagonistic, on different traits. Diseases that share antagonistic or agonistic, respectively, associated variants are listed in Tables 1 and 2. Another advantage is that the SVN can be compared to other variant-based approaches assessing the genetic overlap between traits. Recently, a statistic to identify SNPs with effects across phenotypes (the cross-phenotype meta-analysis statistic, CPMA) was proposed by Cotsapas et al. [8]. It compares the distribution of association P-values of a SNP across seven GWAS on distinct autoimmune diseases to the exponential(1)-distribution representing the expected decay rate of association P-values. As in our approach we use pre-filtered associations, this method cannot readily be employed on our data. However, using the data provided by Cotsapas and colleagues on autoimmune loci in the SVN, we retrieved CPMA P-values on 30 SNPs (∼ 17%) corresponding to 28 loci (∼ 30%) in our data. The CPMA classified all SNPs as significantly effective across diseases (P < 0.05, see Additional file 2: Table S2). Thus, we were able to validate nearly one third of the loci contained in the SVN by an independent approach, which underlines the suitability of the network.
Figure 2. LD based locus assignment and its error sources. At the example of chromosome 8q21.11, LD-based locus assignment is given for 6 exemplary SNPs (blue box). LD information is given by a color scale displaying the LD-measure r2 with red depicting strong LD, blue low LD and white no LD. Example SNPs in LD are connected with black dashed lines. In the gray boxes, the two error sources of automated locus assignment are given. An assignment error I occurs if two variants not in LD, i.e. in two independent LD blocks, are located in the same gene, intergenic region or gene desert and thus are assigned the same locus. Here, this is the case for the variants rs-A/rs-B and rs-E/rs-F, respectively. The consequence of this type of error is a shared association on the locus level not mirrored on the variant level. An assignment error II is introduced if two variants are in LD but diverge in their assigned locus. Here, this is the case for rs-C and rs-D. Due to such abnormalities in the LD data the link between both variants is lost if only the locus level is considered.
Table 1. Antagonistically linked traits
Table 2. Agonistically linked traits
Topology of the SVN
Its degree distribution attributes the SVN a scale-free network, i.e. it approximates a power-law (P(k) ∼ k−γ; γ = 1.32; R2 = 0.69) (Additional file 4: Figure S2). Interestingly, also when considering the two node types separately, disease nodes (γ = 0.97; R2 = 0.71) as well as locus nodes (γ = 2.98; R2 = 0.93) show scale-free degree distributions (Additional file 4: Figure S2). The scale-free property classifies the network (and its two sets of node types, respectively) as structured, i.e. non-random [13]. It has to be considered that the limited size of the SVN leads to inaccuracies in distribution fitting and thus reduces the explanatory value of this observation. However, as clinically related diseases (i.e. diseases which present similar symptoms) should present a higher genetic overlap than unrelated disorders, this finding meets expectations.
Additional file 4. Figure S2. Network properties of the SVN. A: The log-log-plot of the degree distribution of the SVN follows a power-law (γ = 1.32; R2 = 0.69) and therefore attributes the SVN to be scale-free and, thus, non-random. B: The modular structure of the SVN was confirmed by the topological coefficient which follows a power-law distribution on a log-log-scale. When considering the two node types separately, in both cases a scale-free topology can be identified: C: disease nodes (γ = 0.97; R2 = 0.71) and D: locus nodes (γ = 2.98; R2 = 0.93).
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The variant-based SVN also shows no artificial character with regards to its topology. Both locus and disease node sets comprise hubs, here defined as nodes with a degree >3, which form the central elements in the network. As in each GWAS multiple markers are associated with a single disease, one would expect hubs to be constituted mostly of disease nodes. In line with that, 74% of the hubs in our network are disease nodes. The remaining 26% are loci hubs (seven gene loci and three intergenic loci). Several of these loci have been previously identified as influencing susceptibility to multiple diseases like the HLA region on chromosome 6 [16], a cancer locus at chromosome 8q24 [12], and a coronary artery disease locus at chromosome 9p21 [11]. Further hub loci are PTPN22, a known player across several autoimmunity disorders [17], and IL23R, which has been shown to direct inflammatory processes [18]. In addition, we observed hubs which have not yet been described as predisposing to a whole group of diseases, such as TNPO3 which appears to predispose to various autoimmune diseases like systemic lupus erythematosus, systemic scleroderma, and rheumatoid arthritis [19-21], or TNFSF15, which shows associations with several inflammatory diseases [22-25]. As expected, in the majority of cases the traits linked to one hub can be assigned to the same disease group and, further, diseases which are not obviously related to other disorders linked to the respective hub are mostly associated with antagonistic signals. For instance, in a four-gene locus at chromosome 17q12 (GSDML/IKZF3/ORMDL3/ZPBP2, see Additional file 4: Table S1), four autoimmune diseases are associated with the same risk allele that in turn has opposite effects on asthma [20,25-27]. Thus, our results indicate that loci associated with several diseases have an effect specific to a certain disease group rather than effects on unrelated diseases, and that, if there is an effect on an unrelated disease, it can often be distinguished by the direction of the effect.
Disease clustering mirrors trait relatedness
To identify shared and branching mechanisms we split the SNP association data into agonistic and antagonistic variants. Since in most cases there is no solid and comprehensive basis of experimental data that would allow for a more sensitive classification, we suggest that the best available indication of distinct effects of a variant on two diseases is the signal itself being different. Therefore, we define a SNP to be agonistic if all disorders are associated with the same risk allele of the SNP. Conversely, we consider a SNP antagonistic if the associated risk alleles differ between diseases. Accordingly, in the analysis of genetic overlaps as a measure of trait similarity only agonistic variants were included.
As similar diseases are more likely to share associations than diseases in distinct classes, we expected the SVN to be organized in a modular fashion. This was confirmed by the decrease of the degree distribution of the topological coefficient with the number of links per node (Additional file 4: Figure S2). To retrieve these modules, we applied a hierarchical clustering approach. The SVN contains two node types (loci and traits). As we wanted to directly assess variant-based disease relatedness, we used its disease centric projection for hierarchical correlation clustering. For data normalization, we calculated the Pearson correlation coefficient (PCC) for all pairs of diseases based on their genetic agonistic overlap with all other diseases. The clustering returned 15 disease clusters (Figure 3) and six diseases which show no or only weak correlation with any other disease. With the exception of the heterogeneous cluster 5 (hypertriglyceridemia, ovarian neoplasms, lymphoid leukemia, atopic dermatitis), the clusters mostly contain related diseases. However, many clusters also contain traits unrelated to the other phenotypes like schizophrenia in the autoimmune cluster 2. This indicates that clinical disease classifications appear to be reflected on the genetic level in general. Notably, several small clusters contain diseases which are either linked through common environmental risk factors – like smoking for lung neoplasms, peripheral vascular diseases, and chronic obstructive pulmonary disease – or present high frequencies of comorbidity, e.g. type 2 diabetes (T2D) and obesity. To get an insight into the overall extent of reported comorbidities of the diseases within the 15 clusters, we used publicly available resources [28,29] and literature mining. The within-cluster fraction of disease co-occurrence ranged from 75% to 100% (μ = 95.89%, σ = 8.66%) which provides empirical evidence of the interrelation of diseases clustered together by genetic information. Such clusters containing diseases that present high ratios of comorbidity may be potential artifacts due to "contaminated" disease cohorts including a substantial number of comorbid cases. The unbiased search for the relation of a trait marker to a disease phenotype as performed in GWAS does not distinguish between markers for a primary or related secondary (comorbid) disease. Our results suggest that this aspect may have been underestimated in some studies, albeit the presence of independently shared etiological mechanisms can naturally not be ruled out in general.
Figure 3. Clustering of diseases with respect to genetic signals. We applied complete-linkage hierarchical clustering to identify groups of traits which show homogeneous patterns of genetic overlap to other disorders. We calculated for each pair of diseases the Pearson correlation of the patterns of overlap to the other diseases. The correlation values are ranging from −1 (white) indicating complete negative correlation to +1 (black) reflecting a perfect positive correlation. As the minimal value of the correlation coefficient was > −0.1, we collapsed the range of negative correlation. In red numbers, the 15 disease clusters are denoted. The Euclidian distance threshold was chosen as the maximal distance at which the six diseases showing no or only weak correlation with any other disease (disease names in gray) remain non-clustered.
Overall, the outcome of the clustering poses the question of the extent of the influence of phenotype classification and population stratification on GWAS results. Frequent comorbidities (also of seemingly unrelated diseases such as obesity and cancer [30]), diagnostic difficulties in highly related diseases like Crohn's disease (CD) and ulcerative colitis (UC) [31], and structural (genetic) differences in population subgroups are known to complicate GWAS and impact their outcomes [32]. With growing sample sizes in case–control studies, the potential of false positives produced by such phenomena also increases. As a response, manifold control procedures to handle these and other confounding factors have been developed which are widely used and well appreciated [32]. The heterogeneity of the clusters we retrieved once more highlights the need for the development and application of such methods.
Odds ratio as potential indicator of primary effects
In the context of agonistic association overlap between related diseases, we used the odds ratios (ORs) reported with the SNPs to investigate their impact on the respective traits. In general, the highest ORs are reported for associations of autoimmune diseases to the HLA locus on chromosome 6. Associations with traits where few gene variants with strong effects are reported, e.g. rs6107516 in the prion protein PRNP associated with Creutzfeldt-Jakob disease (OR = 38.5) or rs2071348 in the hemoglobin gene cluster at 11p15.4 associated with beta-thalassemia (OR = 4.33), are exceptions from the majority of associations displaying small ORs [33,34]. Based on the effect size of variants associated with more than one trait and the same risk allele, we identified three patterns.
First, we identified variants which are likely to present general agonistic risk factors for a group of related diseases or syndromes such as rs13015714 in an interleukine receptor gene cluster at chromosome 2q12.1. This SNP is associated with celiac disease (CeD) and Crohn's disease (CD) with equal ORs (OR = 1.19) [24,35]. In cases of frequent comorbidities, though, comparable ORs have limited informative value. The SNP rs9939609 in the FTO gene for instance is associated with T2D and obesity with nearly equal ORs (OR = 1.34 and OR = 1.32) and thus appears to link two coequal traits of the metabolic syndrome (we refer to the definition of the International Diabetes Foundation, 2006) [36,37]. However, for SNPs in the FTO gene it has been shown that adjustment for body mass index results in the loss of significance (OR∼ 1.0) of the association with T2D [38].
Second, SNPs appeared in several cases to be primarily associated with one disease, which in turn represents a risk factor for another associated trait. For instance, rs2200733 on chromosome 4q25 is linked to atrial fibrillation with a higher OR (OR = 1.72) than to stroke (OR = 1.26) [39,40]. Another example is rs964184 which is located proximal to the apolipoprotein gene cluster on chromosome 11q23 which is associated with hypertriglyceridemia with a markedly higher OR (OR = 3.28) than to coronary disease (OR = 1.13) [41,42]. The lower effects of the markers on the hypothesized "secondary sequels" may be explained by the fact that these are caused by the primary diseases, but with less than 100% penetrance.
Third, we speculate that the OR might allow conclusions with respect to the evaluation of an association in cases where similar traits are linked to the same SNP with diverging effect sizes. For instance, CD and ulcerative colitis (UC) share multiple risk loci. The two diseases are strongly related in their etiology and pathology. Thus, a clinical distinction of both diseases is difficult if based only on few criteria and might lead to inaccurate case ascertainment leading to mixed associations [31]. However, for several SNPs such as rs11209026, which is located in the IL23R gene, we found notably higher effects on CD (OR = 3.84) than on UC (OR = 1.74) [25,43]. Conversely, rs3024505 which lies proximal to the IL10 gene shows a greater effect on UC (OR = 1.46) than on CD (OR = 1.12) [24,44]. Interestingly, it has been shown that IL23 is selectively upregulated in CD while levels in UC patients are normal and IL10 expression appears to be higher in UC as compared to CD [24,45]. Thus, the OR might – similar to the above examples – allow for identifying potentially misleading associations in closely related diseases which may result from diagnostic errors.
Identification of branching etiologies
We searched for evidence that antagonistic signals represent genetic indicators of branching points in the etiologies of two diseases or disease groups. For the assessment of potentially multifunctional variants we therefore focused on markers with inverse effects. We identified 44 such variants, which represent almost 4% of the original association data analyzed and about 25% of the SNPs associated with more than one disease. Of those 44 variants, about one fifth (n = 9) are located in the HLA region. SNP-markers in that region are known to differ in their ability to capture the classical HLA-alleles [46] and therefore were not considered further for the present analysis.
For cases where the function of the harboring genes is known, we were able to identify conclusive models. For instance, rs2736100 in the telomerase reverse transcriptase (TERT) gene was reported to exert antagonistic effects in idiopatic pulmonary fibrosis (IPF) and testicular germ cell tumor (TGCT) and two other cancer traits [47-53]. Whereas telomerase activity is generally upregulated in tumors sustaining proliferation and potentiating mutagenesis and transformation of cancer cells [54], in IPF limited cell division due to decreased telomerase activity is thought to contribute to the phenomenon of high percentages of apoptotic cells in fibroblasts [55]. Consistent with that observation, disturbed telomerase activity in TGCT is believed to form a distinct mechanism of cancerogenesis in this tumor type [53]. This distinction from other cancer traits is believed to be based on the fact that testicular germ cells are the only adult cell type with high telomerase expression [56]. Another example is the telomerase RNA component TERC, which is essential for TERT functioning. Opposite alleles of SNP rs10936599 are associated with CeD and colorectal cancer (CRC) [35,57]. Jones et al. showed that rs2293607, a variant tagged by rs10936599, alone is sufficient to modulate TERC expression [58]. While in CRC this leads to TERC overexpression and longer telomeres, the opposite might apply to CeD, which exhibits telomere reduction and genomic instability [58,59]. The observation that both constituents of the telomerase complex contain independent antagonistic variants is an intriguing finding. It suggests parallel, autonomous evolution of two functionally interacting loci gone to fixation at a trade-off between early cell senescence or increased apoptosis rates (as in IPF and CeD) and oncogenesis.
A further example is rs1393350 in the tyrosinase (TYR) gene where the opposite alleles are linked to vitiligo and melanoma [60,61], potentially mirroring the inverse correlation observed for the two traits. The phenomenon is based on the presentation of TYR (self-) antigens on the cell surface of melanocytes. It is hypothesized that in vitiligo the immune system is hypersensitive towards TYR antigens, which are overexpressed in melanoma cells [62]. A possible explanation may be that opposite alleles differentially influence the antigenicity of the TYR protein, thereby conferring protection from melanoma but susceptibility to vitiligo through immune surveillance and vice versa.
In cases of functionally less or uncharacterized genes and their involvement in the associated diseases, our approach can still be used to suggest potential trait-specific effects. Antagonistic effects of rs12720356 (localized in the TYK2 gene) in CD and psoriasis, for instance, might point towards different patterns of cytokine signaling in these two diseases [24,63,64]. Likewise, rs12727642 and rs35675666, both located in the PARK7 gene and inversely associated with CeD and UC, could indicate differential effects of oxidative stress on each trait [25,35].
Variant-based analysis of joint and disjoint genetic features
In this study, we identified overlapping genetic associations and their corresponding loci with analogous or contrasting effects on different diseases. We addressed the methodological challenges of the identification of the functional entities affected by GWAS-detected variants.
Associations formally implicate genomic regions which are captured via tagging SNPs representing haplotype blocks. By using the population-specific LD-based haplotype data provided by the HapMap project [65,66] or, more recently, the 1000 genomes project [67], SNP arrays are constructed aiming at a high coverage of the total genome variation, but without considering biologically functional aspects. The advantage of GWAS as a method is its unbiased approach to identify genomic regions compromised in a disease; a major drawback is that the association of markers without knowledge of the causal variants and their effects does not allow for a straightforward biological interpretation.
As we show, the reliability of an automated assignment of LD-based loci to the trait-associated variants is strongly context-dependent. Especially in cases of high gene density or, conversely, in intergenic regions/gene deserts, resolving GWAS signals is not possible without further knowledge. Simplifications such as more basic locus assignment approaches which neglect the LD structure of the genome (e.g. classifying a SNP as affecting only the most proximal gene) may seem more intuitive, might facilitate analyses and could be useful to identify causal disease-gene associations. These correct associations of genes which are detected through significant enrichment of a harbored tagging variant in a patient cohort may not be discovered when incorporating LD data in cases where the LD block of the respective variant spans across several genes. However, such approaches disregard a basic principle defining the current GWAS paradigm, namely the use of LD information in the design of genotyping arrays to achieve the genome-wide coverage of common SNPs. Hence, it can be problematic to project the variant-based GWAS data on genes or loci. Accordingly, we decided to use variant-based methods and concentrated on strong gene candidates identified via the gene function of single-gene loci whenever suggesting potential biological effects of the considered variants.
In the analysis of genetic overlaps we followed the hypothesis that the effects of variants shared across several diseases correspond to the reported risk alleles. If the risk allele is the same in all associated diseases, we assume the effect to be the same, i.e. that there is a common underlying etiology. For closely related diseases a positive correlation is not surprising, e.g. a GWAS on psoriatic arthritis (PSA) will also detect agonistic variants such as rs33980500 that are also associated with psoriasis (PS) [68,69]. Indeed, the vast majority of agonistic variants in our data set links groups of related diseases and thus may mark interesting target regions for therapeutic interventions. However, we also found a few agonistic signals connecting apparently unrelated diseases, e.g. rs6010620 which exerts susceptibility for both glioma and atopic dermatitis (AD) [50,51,70,71]. If our hypothesis is correct, an endophenotype influencing both diseases may be present which has yet to be identified. For antagonistic SNPs, on the other hand, we describe plausible mechanisms that may render variants protective against one trait and predisposing to another, labeling the affected genes/loci as pleiotropic. If pleiotropic effects are as frequent as evolutionary modelers postulate [2,72] and this effects can be identified by analyses based on GWAS, this might have great implications for the development and use of therapeutics because it would enable avoidance of potential side effects when targeting such loci. Already, there are more than 50 genotype/drug interactions known for which therapeutic dosing recommendations are available [73].
Our results present new starting points for studying the genetics of complex diseases. The observation that more than 15% of the SNPs considered in our study are associated both agonistically and antagonistically with related as well as unrelated disorders indicates that the molecular mechanisms influencing causes and progress of human diseases are in part interrelated. Genetic overlaps between two diseases also suggest the importance of the affected entities in the specific pathogenic pathways and should be investigated further. These may be secondary, such as genes involved in inflammatory responses related to T2D as well as cancer [30,38]. The findings presented also demonstrate the need to clarify the relation of any phenotype linked to an associated marker. For directly interrelated diseases such as PS and PSA often PS patients without present arthritis or arthritis in the past are used as additional control group. Associations are then interpreted as PSA-specific if not as strongly associated with PS [74,75]. Comparable procedures may proof useful in frequently co-occurring diseases genetically linked by agonistic variants. Nevertheless, the complex genetics of multifactorial diseases asks for a better understanding of the functions underlying common disorders. An improved characterization of the endophenotype, such as metabolite or protein concentrations, may enhance our understanding of identical pathomechanisms that link agonistic genetic loci to clinically distinct traits. Pleiotropic effects, on the other hand, that are harbored in the same locus may trigger different mechanisms interfering with the genetic or environmental background. The detailed examination of antagonistically associated loci may thus lead to first insight into the mechanism of the various types of pleiotropy in human diseases.
Association selection and curation
We obtained the core list of candidate sentinel SNPs from 'A Catalog of Published Genome-Wide Association Studies' [1] accessed on June 30, 2011 ( http://www.genome.gov/gwastudies webcite). Additional associations where retrieved from HuGE Navigator [76] and automated Text Mining [77]. New (i.e. not contained in the GWAS Catalog) association markers were manually tested on compliance with the criteria for inclusion in the GWAS Catalog before insertion in the candidate list. Associations with copy number variants (CNVs) as well as with pending SNPs were removed. For consistency, SNP identifiers were mapped to the RefSNP numbers of the same dbSNP release (build 131, http://www.ncbi.nlm.nih.gov/snp webcite). For the same reason we semi-automatically translated trait descriptions to the official terms given in the Medical Subject Headings (MeSH). In this process, associations with quantitative and non-disease traits were eliminated. Finally, we lowered the association P-value threshold from 10−5 (as in the GWAS Catalog criteria) to 10−7 as to reduce the potential of artificial associations. The workflow of the methods of our approach is sketched in Figure 4.
Figure 4. Data prioritization and analysis workflow. We established a semi-automated curation pipeline which automatically gathers and annotates GWA data obtained from three sources (locus assignment included). Last step of the preprocessing was the manual inspection of risk alleles and odds ratios. With this data set at hand, we construct a locus-based (SLN) and a variant-based (SVN) network representation of the data. For quality reasons, we then limited analyses to the SVN and investigated the contained variants and their effects further.
Construction of GWAS networks
For the construction of the locus-based data representation, we defined an associated locus as the whole genomic region captured by SNPs in strong LD, r2 ≥ 0.8, with the marker originally reported in a GWAS contained in our data set. The locus is then characterized as all genes located within this genomic region (referred to as "gene locus") (Figure 2). If the region contains no genes, the locus is assigned to its chromosomal location (referred to as "intergenic locus"). LD data and gene information were obtained with the SNAP tool [78]. After locus assignment, our final data set consisted of 111 different traits linked via 1,120 SNPs to 508 gene loci and 226 intergenic loci.
Based on this list we constructed a bipartite graph consisting of two disjoint sets of nodes (Figure 1A) representing the complete association data. The first node set corresponds to the traits, whereas the other set comprises the associated loci. Two nodes are connected by an edge if a variant within the respective locus is associated with the corresponding trait. By removal of isolated traits, i.e. traits which share no associated locus with another trait (n = 27) (Figure 1A), and cutting out loci which are associated with only one trait (n = 577), we retrieved the SLN (Figure 1B).
To obtain a variant-based representation of the data, we repeated the network generation on marker scale by utilizing the set of variants associated with more than one distinct trait. For this, we used the LD data to mutually assign the associated traits of sentinel SNPs in pairwise LD if not already present. In other words, each variant is, in addition to its own associated traits, assigned the traits associated with all correlated SNPs. This set consists of 175 SNPs located in 94 loci and associated with 55 diseases (Additional file 2: Table S1). In the resulting bipartite SVN, a trait and a locus are linked if the locus contains a variant which comprises associations with this and at least one other trait. Here, the allele information was included in the graph visualization by coloring of the edges (Figure 1C). Both the SLN and the SVN are provided as machine-readable files, see Additional files 5 and 6.
Additional file 5. Graph data of the SLN in yEd graphml format. View with yEd ( http://www.yworks.com/en/products_yed_about.html webcite). Using yEd, the file can be converted to GML format which is readable by Cytoscape ( http://www.cytoscape.org/ webcite).
Format: GRAPHML Size: 660KB Download file
Additional file 6. Graph data of the SVN in yEd graphml format. View with yEd ( http://www.yworks.com/en/products_yed_about.html webcite). Using yEd, the file can be converted to GML format which is readable by Cytoscape ( http://www.cytoscape.org/ webcite).
Format: GRAPHML Size: 407KB Download file
Network analysis
The network concepts which we used to compare the properties of the SLN and the SVN are defined as given in [79]:
where aij = 1 if nodes i and j are connected and 0 otherwise. mean(k) denotes the mean connectivity, which for a node i is defined as .
To automatically distinguish the two node sets contained in the SVN, we used directed edges. Direction is always from disease (source) to locus (target). The distinct node degree distributions thus are identical to the indegree distribution and the outdegree distribution. The topological coefficient as a measure of modularity [80]Ti of a node i is defined as:
where Ni is the number of neighbors of i and S(i, j) is the number of shared neighbors of nodes i and j (undefined if i and j do not share a neighbor) plus one if j is a neighbor of i.
Power-law functions of the form y = eaxb were fitted using least squares fitting where the coefficients are defined as and . As goodness-of-fit measure, we give the coefficient of determination
for the linear transformation of the power-law functions, i.e. ln y = a + b ln x.
Determination of agonistic and antagonistic effects
For all variants associated with more than one trait, we manually extracted the risk alleles (OR > 1, independently of major or minor allele status) and odds ratios from the reporting studies. The alleles were mapped to the forward DNA strand according to dbSNP 131. The same procedure was applied to markers which were indirectly associated with a trait over LD. If for all traits the same associated risk allele (and corresponding allele, respectively) was reported, the SNP was classified as agonistic. If the risk alleles of a SNP were opposed in the associated diseases, the variant was classified as antagonistic.
Genetic clustering
We applied complete-linkage hierarchical clustering to identify groups of traits genetically overlapping with respect to agonistic signals. Normalization was performed using the linear PCC defined as where the input are the vectors of the variant-based agonistic overlap of two distinct diseases X and Y to all other diseases. Thus, disorders which are clustered together show a homogeneous association overlap pattern to all other diseases, while diseases which are not clearly assigned to a cluster present a more heterogeneous pattern relatively unique in the SNP data. For cluster definition, we used a Euclidian distance threshold of 1.71. This threshold was determined as the maximal distance at which the six traits not correlating with other diseases (Figure 3) remain non-clustered.
Calculation of the CPMA statistic for autoimmune loci
We downloaded the dataset S1 from [8] and extracted the information on autoimmune-linked SNPs contained in the SVN. We used the Z-scores given in the file to compute two-sided P-values for all seven GWAS. Using the CPMA code provided on http://www.cotsapaslab.info/index.php/software/cpma/ webcite we calculated the CPMA P-values as described in [8].
Competing interests
The authors have nothing to declare.
Authors' contributions
Conceived and designed the study, curated and analyzed the data, wrote the manuscript: MA, MLH, HB, SW. Curated the data, wrote and critically revised the manuscript: ER. Delivered and interpreted data: BW. Contributed to manuscript writing: AF, MK, AP, HWM. Interpreted the data: JW. Curated and interpreted data: MR. Conceived the study: TI, VS. All authors read and approved the final manuscript.
This work was supported by the Helmholtz Alliance on Systems Biology (project CoReNe and the Joint Technology Platforms); the German Federal Ministry of Education and Research as part of the National Genome Research Network [NGFN 01GS 0429, NGFN 01GS 0818]; the German Federal Ministry of Education and Research in its MedSys initiative (project SysMBo) [FKZ: 0315494A]; the TUM Graduate School for Information Science in Health; the Christiane Kühne Center for Allergy Research and Education ( http://www.ck-care.ch/); the Schleswig-Holstein Excellence Cluster "Inflammation at Interfaces"; and a Heisenberg professorship of the DFG [WE 2678/4-1 to S.W.]. The funders had no role in study design, data collection, interpretation and analysis, decision to publish, or preparation of the manuscript.
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As mentioned in the previous article, having just gone through the most expensive election in U.S. history, $6 billion spent, what action can we expect Congress to take on this issue in the coming session? According to an Associated Press poll released in 2012, 83 percent of Americans believe that there should be at least some limits on the political expenditures corporations and unions can make. However, there is little consensus on what should be done, and can be done constitutionally, to reign in the amount of money spent on elections in this country.
A week after the November election, Represent.Us, an organization which includes members of the Occupy Wall Street Movement, the head of the DC Tea Party and former lobbyist Jack Abramoff, began gathering signatures on a petition calling for an overhaul of the campaign finance system. The measure, titled the American Anti-Corruption Act, was drafted by former Federal Election Commission (FEC) Chairman Trevor Potter and would require public disclosure of all campaign contributions and limit contributions across the board. The group plans to ask lawmakers to sponsor the bill once the petition reaches one million signatures, which they hope to accomplish by late 2013.
With the anniversary of Citizens United coming up later this month, a movement to pass a constitutional amendment to overturn the decision being driven by Common Cause, one of the more high-profile campaign finance reform groups, has been receiving national attention recently. During the 2012 presidential campaign, President Obama expressed support for a constitutional amendment, but that route to altering the campaign finance landscape seems unlikely. Amending the constitution is no easy feat; it requires a two-thirds approval from Congress and then three-fourths of the states. However, as President Obama noted, "even if the amendment process falls short, it can shine a spotlight of the super-PAC phenomenon and help apply pressure for change."
In the 112th Congress, there were several legislative efforts put forth to reform the campaign finance system. A bill introduced by Representative David Price (D-NC) would have provided a 5-to-1 matching fund for the first $250 of contributions by individuals to House, Senate and presidential candidates. Representative John Sarbanes (D-MD) introduced the Grassroots Democracy Act, a public financing measure that would have given every citizen a $50 refundable tax credit to donate to candidates, and would match every $1 of private donations with $5 if that campaign rejects PAC money, and with $10 if the campaign only accepts small donations. For the past several sessions of Congress, Representative Chris Van Hollen (D-MD) has introduced legislation, the DISCLOSE Act, that would require a disclosure report to be filed with the FEC within 24 hours of any spending and the filing of a new report every time another $10,000 or more is spent. The DISCLOSE Act passed the House in 2010, but then failed in the Senate. Most of the other legislative efforts got even less traction.
In the 113th Congress, Representative Van Hollen wasted no time in re-introducing his DISCLOSE bill, doing so on the first day of the new session. While the transparency goals of the DISCLOSE Act have received bipartisan support from a few Republicans in the Senate, Minority Leader Mitch McConnell (R-KY) denounced it as an assault on free speech, and its fate remains uncertain. With the second anniversary of the Citizens United decision coming up on January 21, many of the advocacy groups mentioned above are planning events to mark the occasion and bring attention to the cause of campaign finance reform, but it remains to be seen if Congress will take action on this issue, especially amid the looming debt ceiling, sequester and array of other issues that require Members' attention.
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HOTLINE - week of September 6, 2011
CWS launches appeal for Hurricane Irene recovery; Roma family finds the door to a better life; Reducing disaster's risks despite climate change; Change is coming soon!
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CWS launches appeal for Hurricane Irene recovery
The mammoth storm may be over but Hurricane Irene's effects are felt from North Carolina through New England. Irene dumped 5 to 15 inches of rain over a 36-hour period August 27-28, causing massive flooding and killing some 40 people. Hundreds of thousands of homes were damaged and many roads were washed away, particularly in rural areas.
Roma family finds the door to a better life
Duda Giltena was brought up the same way as other Roma children – believing their lives were determined by fate. She then married Ismet, whose path had been similar. "We never went to school: we didn't know how to read, write or count," she says.
Reducing disaster's risks despite climate change
"Climate change is affecting communities already dealing with problems like hunger, malnutrition, poverty and lack of access to clean drinking water," says CWS Indonesia Director Michael Koeniger. "The key is adapting to a changing climate."
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PAWTUCKET – Plate discipline and strike zone recognition can be found on the first page of the hitter's manual. Knowing the pitches one can drive goes hand-in-hand with refusing to chase the ones that dance outside the strike zone, tempting as they may be.
One day Yale head baseball coach John Stuper passed along this nugget to Ryan Lavarnway, words of wisdom mentioned to him by Gene Tenace, a former teammate of Stuper's with the St. Louis Cardinals in the early 80s.
The ironic part? Stuper was a pitcher who failed to hit more than .140 in his four major-league seasons. Knowing full well Lavarnway takes to advice like kids jumping in pools on a hot summer day, Stuper believed Tenace's message stood zero chance of becoming some forgotten footnote.
"Tenace used to say, 'They (pitchers) throw it in that little bitty box because you're ahead of them 2-0. You're not fouling it off, you're hitting it hard somewhere,'" Stuper shared earlier this week.
Several years later, the swing Lavarnway has honed through countless repetitions in the batting cage has placed him on the cusp of becoming a major leaguer. The otherworldly stats the catcher/DH for the Pawtucket Red Sox has compiled since June 13, the day he was promoted from Double-A Portland, has lifted him into rarified company among Red Sox farmhands.
It wouldn't be a stretch to anoint Lavarnway the best pure power prospect that's been drafted and developed under Theo Epstein's watch, a claim backed by three straight campaigns of 20 or more home runs. Regarding the 2011 season, it's become only a matter of when, not if, the 23-year-old clears 30 homers.
Yet in order to understand how Lavarnway has turned the berm in left-center field at McCoy Stadium into a souvenir haven and the parking lot situated beyond left field into the open range, where car windshields quake in fear, one must understand this: Lavarnway simply did not turn on the power facet of his game overnight.
Improvement came from countless hours of hitting in the indoor cage during the wintertime. Don't discount the tee work and soft toss, two baseball remedies designed to smooth out the approach while at the same time sharpening hand-eye coordination. Lavarnway would also reveal his devotion by lugging a 10-pound weight vest around the New Haven campus.
In no way was Lavarnway going to get cheated out of getting stronger and better.
"He's a worker beyond words. I don't think I've ever had anybody go after it like him," Stuper said, mentioning in the same sentence "the strength coach had to kick him out of the weight room numerous times for being there too much. He was very single-minded. He was going to be a great college player and he's going to become a great big leaguer."
Stuper said Lavarnway started to better understand the nuances of the strike zone heading into his sophomore year at Yale, when his home run total jumped from six in 43 games as a freshman to 14, also in 43 games played. He also rode the lift in his newfangled honed-in approach to an unheard of 186-point lift in his batting average, from .281 as a first-year Bulldog to a NCAA-leading .467. (Lavarnway "slumped" to .398 as a junior in 2008, the same year the Red Sox selected him in the sixth round.)
"As a freshman, yeah, he would bite at breaking balls out of the zone. After that, you couldn't get him to bite," said Stuper, who remains in contact with Lavarnway via text message. "During his sophomore and junior years, it reached the point that it got scary to throw him batting practice indoors. He was hitting the ball so hard."
Said Lavarnway, "Every pitch and every swing became a part of me."
PawSox hitting coach Chili Davis is a believer that young hitters can spend all the time in the weight room they want, but the power aspect won't start surfacing until deciphering the strike zone. Make the pitcher pitch from a position of weakness, not strength.
Thanks to Lavarnway's ability to eliminate the guessing game, he's allowed his natural swing to supply the power. His measurements – 6-foot-4, 225 pounds – conjure up a certain image, like somebody grunting and snarling while competing in the World's Strongest Man competition. Couple his hulking attributes with keen awareness and there's your reason why Lavarnway has avoided getting off to a slow start in his PawSox career.
After 33 games at the Triple-A level, Lavarnway is batting .376 with 12 doubles, 11 homers and 31 RBI, numbers that have made him a scary presence in the heart of manager Arnie Beyeler's lineup. In the "you decide what's more impressive" category, the California native has 15 multi-hit games – six coming in his last eight starts – while going hitless just seven times.
Adjustment period required? Hardly.
"I didn't envision a call-up this quickly, but I'm definitely pleased and happy with the progress I'm making," said a clearly humble Lavarnway, who has swatted 25 homers in 88 games between Portland and Pawtucket. "I felt ready to be (in Class AAA) and feel I can excel at this level."
"He's making Triple-A look like college right now," said Stuper. "Guys at that level are around the plate more with better stuff, but he's been able to thrive."
Davis has seen enough in the month-plus he's worked with Lavarnway to draw the following conclusion: he has all the mechanics necessary to scold the ball all over the stadium and beyond the fences. Even if the swing goes slightly awry, the player's approach can't completely hold him back from driving the ball someplace.
"With the swing he has and as strong as he is, the mentality is that he's not afraid to get beat," Davis said. "Even if he does get beat, he has enough power to maybe not hit it out of the ballpark, but to still drive the ball hard and over the infielder's head.
"His swing is connected to his hands and lower body, which enables him to create a real good swing path," Davis added. "He's always trying to get the barrel of the bat to the ball, no matter where it is. It's an aggressive swing, but it's a controlled swing also."
It's a swing that is still present regardless if he's walloping home runs or sending screaming shots to the left-field corner. By not deviating from what works, Lavarnway has gone out and treated International League pitchers like a piñata.
"I texted him (Wednesday morning) and said, 'You must be seeing the ball really, really well,'" relayed Stuper about his most recent check-in with his former player. "Apparently he is."
Lavarnway's increased power totals hardly represent the lone fluctuation his numbers have undergone since turning pro. He increased his walk numbers (70 in 2010 compared to 50 in 2009) while lowering his strikeouts over the same period (104 in 2010, down from 113 the previous year). Even if he cools off slightly, Lavarnway stands to finish the season above .300 – his batting mark stood at .318 when he woke up Thursday morning – for the first time dating back to his college days.
Again, this comes from understanding what works and what doesn't all while staying within the boundaries of the strike zone.
"The guy has a plan when he goes to the plate," said Beyeler, who also managed Lavarnway in Portland last season. "Whatever it is – I don't know because it changes as he watches pitchers, but he's not afraid to take a bad swing."
Added Stuper, "Ryan's plate discipline is his best attribute. Great strike zone recognition."
Regardless if you talk to someone from Lavarnway's past or present, they all hum the same tune: it's only a matter of time before he becomes a major league contributor. Certainly the Red Sox are in no rush to call up Lavarnway (presently he's not included on the 40-man roster), though it would have been intriguing to see if Boston would have given serious thought to promoting him had the Jason Varitek/Jarrod Saltalamacchia catching combo continued to provide little offense, as was the case through the season's first six weeks.
Whenever that day happens, Stuper will beam with pride. The Yale coach is adamant that Lavarnway is baseball's answer to the American Dream – that hard work plus acknowledgment of that "little bitty box" can bear fruit with the utmost rewards.
"This kid is so special, just so special," said Stuper. " It's going to be hard to not find a spot in a big-league lineup with his bat."
[1] http://www.pawtuckettimes.com/sites/default/files/0721PAWSOX LAVARNWAY DUGOUT.jpg
[2] mailto:email@example.com


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Union Pacific Transportation Associate Interview Questions
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7 Candidate Interview Reviews Back to all InterviewsSort: Popular Date Difficulty
Transportation Associate Interview
I applied through a staffing agency. The process took 2+ months. I interviewed at Union Pacific (Saint Charles, IL) in July 2011.
invited to hiring session with approx 10 others. It took most of the day, ended with no talk of a call back
Interview Questions
- what does safety mean to you? Answer Question
- where do you see yourself in 5 years Answer Question
Other Interview Reviews for Union Pacific
Helpful (1)
Transportation Associate InterviewNo OfferPositive ExperienceEasy Interview
The process took 1 day. I interviewed at Union Pacific (Elmhurst, IL) in February 2011.
The hiring session consisted of a presentation about the position and then a test battery. The test battery consisted of a business acumen (intelligence) test, a 5 minute test that no one can complete and a very long personality test. The intelligence test is not that difficult, mostly common sense. The 5 minute test is just there to see how well you work under pressure and how quickly and accurately you think. The personality test is long and very thorough. After that you break for lunch and proceed to 1:1 interviews. The interview questions are safety focused and evaluate how you handled learning new information and strategies that you used to overcome challenges. Provide examples of leadership, ability to adapt, willingness to relocate and examples of safety. I recently got an email stating that I passed the test battery but have not yet found out if I will proceed to the next step which is to fly out to Omaha, Nebraska for the second round of interviews.
Interview Questions
- Describe one instance where you had to use your knowledge of safety to prevent an accident? Answer Question
Helpful (1)
Transportation Associate InterviewNo OfferPositive ExperienceAverage Interview
I applied through an employee referral. The process took 1 day. I interviewed at Union Pacific in February 2011.
The interview itself was held at the Union Pacific's Barns Yard complex in Portland, Oregon. The day started off with a roll call and then an hour long brief about the company, what to expect for each of the three management tracks, and then an overview of benefits. It was pretty impressive, as long as you are willing to travel anywhere west of the Mississippi and put in your time. Following the presentation, there was a four part test. The UP website lays out what will be included in the test pretty well. The only criticism is that the practice test provided gives a very small taste of the questions that are in each section. Testing takes about three hours with breaks. The interview itself was a 1:1 but I have heard from recent candidates that they went through a panel interview for this first round. It lasts about 25 minutes and includes standard questions about your qualifications, how they apply to the position, and why you want to be a part of the Union Pacific Railroad. Overall, it a relatively relaxed interview process. The HR manager for the Portland Service Group is easy to speak with so just come well prepared. This is the first of essentially a three round interview process. Following the posting of your test results in about a week, you will be invited to panel interview in Roseville, California if you qualify on the test and receive a strong recommendation from the interviewer. The company pays for this trip so it is perk. It takes 2-4 weeks to receive the invitation call from one of the managers and the Regional Human Resources Director. Between your initial interview and the Roseville interview, there should be a phone interview as well. I just completed this first interview process so hopefully more information will follow.
Interview Questions
- Describe a time when you had to coordinate with others in order to accomplish a task? Answer Question
- Why do you want to work for the Union Pacific Railroad? Answer Question
- Think back to a time when you had to prioritize duties at work and how did you do it? Answer Question
Helpful (1)
Transportation Associate InterviewNo OfferNeutral ExperienceAverage Interview
I applied online. The process took 1 day. I interviewed at Union Pacific in February 2011.
I applied for the opening on the compnay website and was invited to a hiring session. I completed an operations management exam, personality assessment and a principles/ethics survey. The larger exam was timed and we had 1 hour to complete it. The other part of the examination was timed at 5 minutes and was impossible to complete. It's goal is to see how you perform under pressure. The questions were relatively easy and only required a bit of concentration. The testing and persononality/ethics portion took approximately 3 hours total. After the testing was completed we were all interviewed one at a time. The interview itself was very typical. It consisted of mainly behavioral questions. They are VERY concerned about safety and some of the interview questions will revolve around that. They will also ask questions about your leadership background. The recruiter made it a point to make everyone understand the adversities we would face if hired by Union Pacific. She made it clear we would be working holidays, long hours and wouldn't have much time for family life. Starting pay is about $50,000 annually with the eligibility for a pay raise at 6 months, 1 year and when you reach a permanent location. She also made it clear that we would be relocating very frequently with very little notice. You also may be relocated to very rural areas of the United States. She also made it clear that management is a salaried position and is not a part of the union. But with the compensation you will receive it is not a big deal. You have to wait two weeks for your test results. If you succeed in your interview you will be flown to Omaha, Nebraska for a panel interview. I passed the exam, but am still waiting on word for a second interview. If offered a second interview I will not accept it because I have already been hired by another company. Overall, I believe the job would be both challenging and fulfilling. But I do not recommend it to anyone trying to raise a family. I do not want my family uprooted every couple years, nor do I want to be working every holiday and weekend. Union Pacific is a great company that will reward you with high compensation and great benefits. But the trade-off is something you should heavily consider.
Interview Questions
- Give me an example of when you observed coworkers violating safety protocol. How did you handle it? Answer Question
- Tell me about a time when you were almost successfully completed with a complex task and had to start over at the last minute. What did you do? Answer Question
Transportation Associate InterviewNo OfferPositive ExperienceAverage Interview
I applied online. The process took 1 day. I interviewed at Union Pacific in February 2011.
I applied for the Operations Management Training Program online in late December of 2010. In January I got a call that I was selected for a Hiring Session/Interview. The website gave me a list of cities these would be in and I selected the city closest to me. In the latter half of February 2011, I attended the hiring session which consisted of a small presentation about the company. There were some people attending who were already employees of UP who were there to take the Management Skills Test. We then spent three hours taking the test battery. After the tests were completed we adjourned for lunch and then came back at set times for a 1:1 interview with the HR Representative. The interview was very safety minded. Questions were asked about my history managing safety programs, etc. They then talk about how difficult the program is and how hard it can be on people.
Interview Questions
- Tell me about a time you started something new, whether it be a job, class, etc. and how did you learn the skill and how quickly did it take? Answer Question
Transportation Associate InterviewNo OfferPositive ExperienceAverage Interview
I applied online. The process took 1 day. I interviewed at Union Pacific in February 2011.
Very comfortable setting, the interviewer was a fill in for another person. Consisted of Introduction of Company, Job overview, and three test (of which examples are online), and one on one interview
Interview Questions
- some standard personality questions Answer Question
Helpful (1)
Transportation Associate InterviewAccepted OfferPositive ExperienceEasy Interview
I applied online. The process took 2 days. I interviewed at Union Pacific in May 2007.
You apply online. You go to a local hotel and take a battery of skills, personality and interview tests. 2nd interview is with a panel of Directors, VP's and GMs. A location city if offered to you.
Interview Questions
- How does your experience relate to this job 1 Answer
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Healthy Food Happy You TV Show
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On every episode we have a nutritionist or a vegan chef as a guest who is going to prepare a healthy/delicious vegan recipe, and then talk about the benefits of a whole foods plant-based diet.
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Hi, My heater isn't working properly, it comes on briefly when I've been using the car most of the day then goes off again, any ideas? oh I've tried it on A/C and on eco.
Just to add the front seat heaters are both working.
Started by Dawnl, Jan 05 2011 10:00 AM
4 replies to this topic
Posted 05 January 2011 - 10:00 AM
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Posted 07 January 2011 - 04:50 PM
Just to say I topped up the coolant and the heaters working now, cool or should I say hot?
Posted 08 January 2011 - 05:45 PM
Hi dawnl
nice sense of humour
nice sense of humour
Posted 08 January 2011 - 08:57 PM
Hi dawnl
nice sense of humour
Yea, I've got one at the mo, but lets see what the garage come up with on tuesday, maybe I'll be cool again!!!!!
Posted 31 January 2011 - 09:42 AM
Yea, I've got one at the mo, but lets see what the garage come up with on tuesday, maybe I'll be cool again!!!!!
Didn't go to the garage, the coolant levels fine now and heater working a treat,
Maybe I was having a female vs car moment! (I can say that being female!!)
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Arneson and Neely are the first NSU volleyball players to make the all-academic team since Yelena Enwere in 2009. The Lady Demons have not had two athletes make the team since Flavia Bella and Isabela Duarte made the third team in 2005.
The NSU volleyball team (20-13 overall, 12-6 SLC) enjoyed a record-breaking season in which it recorded its first 20-win season since 1975 and the most Southland wins in program history. The Lady Demons finished tied for third place in the conference standings, another program record.
"I am very proud of these two young ladies," said NSU co-head coach Hugh Hernesman. "They, as well as numerous other players in our program, represent our program's and department's core value of academic achievement.
"Both Mackenzie and Keelie were vital contributors to our program's on-court success this past season but, more importantly, they also represent the importance we place on our academics here at Northwestern State. In studying the list of nominees, it was a very competitive group with a large number of qualified student-athletes, so they should be especially proud of being selected."
Arneson is a hospitality, management and tourism major with a 3.61 grade-point average. The Tomball, Texas native earned First Team All-Southland Conference honors after finishing second in the conference in digs at 5.00 per set.
Arneson broke the NSU single-season digs record after amassing 618 digs to surpass her own record of 539.
Neely is majoring in health and exercise sciences and has a cumulative grade-point average of 3.92. The sophomore outside hitter registered 319 kills, 72 blocks and 35 aces.
This is Neely's first Southland Conference accolade.


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Taken last night on my walk home along Jackson Street looking Southwest.
Taken last night on my walk home along Jackson Street looking Southwest.
Here's a nice pic from PJPink's Jackson Ward collection. Last summer, "Talk of the Town" was just that as things spun out of control on Broad Street.
Jackson Ward's very own larger than life milk bottle is the "Pic of the Day" over at RVANews. Click the Pic to see the original
Under certain conditions, the late afternoon winter sun lights up the CNB tower like an aztec temple. For a few seconds to a few minutes it shines like gold. You've got to have your camera ready. It disappears quickly and you may not see it again till next winter. Here's one I took last January. […]
I didn't make my planned photo expedition this morning, so here a peak at last year's trip. Happy Holiday and Happy New Year. I hope it's a good one.
Taken on a chilly Friday Afternoon with a sky full of ice crystals. First published on year ago today. I looks like it could have been taken this morning.
Fritz' kitchen, like his whole apartment is a piece of living art. He has a storage system that only he understands, but for the most part he knows where everything is, even if they are all stored in identical looking coffee cans. First published Dec. 14, 2010
These butteries and flowers brighten up this tree planter next to Ettamae's on 2nd Street. See the full picture after the break.
Where am I?


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ST. PETERSBURG, Russia (MarketWatch) — World leaders gathering for the Group of 20 summit remained divided over whether to back punitive military strikes on the Syrian regime, reflecting the domestic political concerns many of their leaders are facing.
President Barack Obama hopes to use the two-day G-20 gathering in St. Petersburg to shape an international consensus behind his call for a military assault aimed at deterring Syrian leader Bashar al-Assad from deploying chemical weapons in the future. But just as Congress is divided over authorizing a strike, the global community seems equally uncertain about what action to take.
Italy's leader cautioned that a strike might lead to wider conflict. The head of the European Council ruled out a military solution. Germany is pushing to act through the International Criminal Court. Russia remains protective of a Syrian regime that is its longtime ally and weapons client.
And from Rome, Pope Francis urged leaders of the G-20 nations, the world's largest economies, to avoid the "futile pursuit" of military action.
The Next 24: Big Jobs Report Hits
Early Friday, markets will get the last jobs report before the Fed's mid-September meeting, and a good number means the central bank could start its much-anticipated stimulus reduction this month. Traders also will watch for Syria news from the G20 meeting, plus earnings from Infoblox and Cooper Cos.
France is one of the few Western allies supporting a possible strike. President Francois Hollande hopes to get European leaders to coalesce around a summit statement Friday denouncing the Assad regime over chemical weapons.
"It's really important that the Europeans present at the G-20 are on the same page in condemning the use of chemical arms and condemning the regime that used them," Hollande said.
For Obama, the summit represents a fateful diplomatic moment. Should he fall short of assembling broad international backing and strike Assad with limited support, it could prove to be a setback for a president who won the Nobel Peace Prize in part for his commitment to multilateral action.
To win international support, U.S. officials will be arguing their case on Syria before a summit focused on global economic issues, said Ben Rhodes, a deputy national security adviser.
"We believe it's important for people to raise their voices on behalf of international norms that countries around the world have signed onto for many years," Rhodes said.
Italian Prime Minister Enrico Letta said some world leaders doubted Obama would be able to limit military engagement to his stated aim of carrying out punitive strikes against the Assad regime for what the U.S. says is its use of chemical weapons in a massacre outside Damascus on Aug. 21.
"There are some who interpret this as the start of something of which we don't know the end," Letta said. "These are obviously two very different scenarios, and I'm sure that's part of what's blocking things and creating so much difficulty."
Seib & Wessel: Syria a Wedge for U.S.'s Allies
Aaron David Miller from the Woodrow Wilson Center tells WSJ's Jerry Seib how he thinks the issue of military action against Syria could impact the relationship between the U.S. and its allies.
Obama's toughest obstacle is the summit host, Russian President Vladimir Putin. Frustrated by Russia's lack of cooperation on Syria, as well as Moscow's decision to grant former NSA contractor Edward Snowden temporary asylum, Obama canceled a meeting with Putin that had been scheduled in advance of the G-20.
The two men traded pleasantries at an arrival ceremony Thursday, shaking hands and smiling before cameras for a few seconds. It was a warmer display than the one at a summit in Northern Ireland in June, when Obama's attempt to break the ice fell flat.


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