Genetic analyses of diverse populations improves discovery for complex traits.

Genome-wide association studies (GWAS) have laid the foundation for investigations into the biology of complex traits, drug development and clinical guidelines. However, the majority of discovery efforts are based on data from populations of European ancestry1-3. In light of the differential genetic architecture that is known to exist between populations, bias in representation can exacerbate existing disease and healthcare disparities. Critical variants may be missed if they have a low frequency or are completely absent in European populations, especially as the field shifts its attention towards rare variants, which are more likely to be population-specific4-10. Additionally, effect sizes and their derived risk prediction scores derived in one population may not accurately extrapolate to other populations11,12. Here we demonstrate the value of diverse, multi-ethnic participants in large-scale genomic studies. The Population Architecture using Genomics and Epidemiology (PAGE) study conducted a GWAS of 26 clinical and behavioural phenotypes in 49,839 non-European individuals. Using strategies tailored for analysis of multi-ethnic and admixed populations, we describe a framework for analysing diverse populations, identify 27 novel loci and 38 secondary signals at known loci, as well as replicate 1,444 GWAS catalogue associations across these traits. Our data show evidence of effect-size heterogeneity across ancestries for published GWAS associations, substantial benefits for fine-mapping using diverse cohorts and insights into clinical implications. In the United States-where minority populations have a disproportionately higher burden of chronic conditions13-the lack of representation of diverse populations in genetic research will result in inequitable access to precision medicine for those with the highest burden of disease. We strongly advocate for continued, large genome-wide efforts in diverse populations to maximize genetic discovery and reduce health disparities.

A phenome-wide association study (PheWAS) in the Population Architecture using Genomics and Epidemiology (PAGE) study reveals potential pleiotropy in African Americans.

We performed a hypothesis-generating phenome-wide association study (PheWAS) to identify and characterize cross-phenotype associations, where one SNP is associated with two or more phenotypes, between thousands of genetic variants assayed on the Metabochip and hundreds of phenotypes in 5,897 African Americans as part of the Population Architecture using Genomics and Epidemiology (PAGE) I study. The PAGE I study was a National Human Genome Research Institute-funded collaboration of four study sites accessing diverse epidemiologic studies genotyped on the Metabochip, a custom genotyping chip that has dense coverage of regions in the genome previously associated with cardio-metabolic traits and outcomes in mostly European-descent populations. Here we focus on identifying novel phenome-genome relationships, where SNPs are associated with more than one phenotype. To do this, we performed a PheWAS, testing each SNP on the Metabochip for an association with up to 273 phenotypes in the participating PAGE I study sites. We identified 133 putative pleiotropic variants, defined as SNPs associated at an empirically derived p-value threshold of p<0.01 in two or more PAGE study sites for two or more phenotype classes. We further annotated these PheWAS-identified variants using publicly available functional data and local genetic ancestry. Amongst our novel findings is SPARC rs4958487, associated with increased glucose levels and hypertension. SPARC has been implicated in the pathogenesis of diabetes and is also known to have a potential role in fibrosis, a common consequence of multiple conditions including hypertension. The SPARC example and others highlight the potential that PheWAS approaches have in improving our understanding of complex disease architecture by identifying novel relationships between genetic variants and an array of common human phenotypes.

Reproducibility of neuroimaging analyses across operating systems.

Neuroimaging pipelines are known to generate different results depending on the computing platform where they are compiled and executed. We quantify these differences for brain tissue classification, fMRI analysis, and cortical thickness (CT) extraction, using three of the main neuroimaging packages (FSL, Freesurfer and CIVET) and different versions of GNU/Linux. We also identify some causes of these differences using library and system call interception. We find that these packages use mathematical functions based on single-precision floating-point arithmetic whose implementations in operating systems continue to evolve. While these differences have little or no impact on simple analysis pipelines such as brain extraction and cortical tissue classification, their accumulation creates important differences in longer pipelines such as subcortical tissue classification, fMRI analysis, and cortical thickness extraction. With FSL, most Dice coefficients between subcortical classifications obtained on different operating systems remain above 0.9, but values as low as 0.59 are observed. Independent component analyses (ICA) of fMRI data differ between operating systems in one third of the tested subjects, due to differences in motion correction. With Freesurfer and CIVET, in some brain regions we find an effect of build or operating system on cortical thickness. A first step to correct these reproducibility issues would be to use more precise representations of floating-point numbers in the critical sections of the pipelines. The numerical stability of pipelines should also be reviewed.

Association of cancer susceptibility variants with risk of multiple primary cancers: The population architecture using genomics and epidemiology study.

BACKGROUND: Multiple primary cancers account for approximately 16% of all incident cancers in the United States. Although genome-wide association studies (GWAS) have identified many common genetic variants associated with various cancer sites, no study has examined the association of these genetic variants with risk of multiple primary cancers (MPC). METHODS: As part of the National Human Genome Research Institute (NHGRI) Population Architecture using Genomics and Epidemiology (PAGE) study, we used data from the Multiethnic Cohort (MEC) and Women's Health Initiative (WHI). Incident MPC (IMPC) cases (n = 1,385) were defined as participants diagnosed with more than one incident cancer after cohort entry. Participants diagnosed with only one incident cancer after cohort entry with follow-up equal to or longer than IMPC cases served as controls (single-index cancer controls; n = 9,626). Fixed-effects meta-analyses of unconditional logistic regression analyses were used to evaluate the associations between 188 cancer risk variants and IMPC risk. To account for multiple comparisons, we used the false-positive report probability (FPRP) to determine statistical significance. RESULTS: A nicotine dependence-associated and lung cancer variant, CHRNA3 rs578776 [OR, 1.16; 95% confidence interval (CI), 1.05-1.26; P = 0.004], and two breast cancer variants, EMBP1 rs11249433 and TOX3 rs3803662 (OR, 1.16; 95% CI, 1.04-1.28; P = 0.005 and OR, 1.13; 95% CI, 1.03-1.23; P = 0.006), were significantly associated with risk of IMPC. The associations for rs578776 and rs11249433 remained (P < 0.05) after removing subjects who had lung or breast cancers, respectively (P ≤ 0.046). These associations did not show significant heterogeneity by smoking status (Pheterogeneity ≥ 0.53). CONCLUSIONS: Our study has identified rs578776 and rs11249433 as risk variants for IMPC. IMPACT: These findings may help to identify genetic regions associated with IMPC risk.

Evidence of heterogeneity by race/ethnicity in genetic determinants of QT interval.

BACKGROUND: QT interval (QT) prolongation is an established risk factor for ventricular tachyarrhythmia and sudden cardiac death. Previous genome-wide association studies in populations of the European descent have identified multiple genetic loci that influence QT, but few have examined these loci in ethnically diverse populations. METHODS: Here, we examine the direction, magnitude, and precision of effect sizes for 21 previously reported SNPs from 12 QT loci, in populations of European (n = 16,398), African (n = 5,437), American Indian (n = 5,032), Hispanic (n = 1,143), and Asian (n = 932) descent as part of the Population Architecture using Genomics and Epidemiology (PAGE) study. Estimates obtained from linear regression models stratified by race/ethnicity were combined using inverse-variance weighted meta-analysis. Heterogeneity was evaluated using Cochran's Q test. RESULTS: Of 21 SNPs, 7 showed consistent direction of effect across all 5 populations, and an additional 9 had estimated effects that were consistent across 4 populations. Despite consistent direction of effect, 9 of 16 SNPs had evidence (P < 0.05) of heterogeneity by race/ethnicity. For these 9 SNPs, linkage disequilibrium plots often indicated substantial variation in linkage disequilibrium patterns among the various racial/ethnic groups, as well as possible allelic heterogeneity. CONCLUSIONS: These results emphasize the importance of analyzing racial/ethnic groups separately in genetic studies. Furthermore, they underscore the possible utility of trans-ethnic studies to pinpoint underlying casual variants influencing heritable traits such as QT.

Pleiotropic associations of risk variants identified for other cancers with lung cancer risk: the PAGE and TRICL consortia.

BACKGROUND: Genome-wide association studies have identified hundreds of genetic variants associated with specific cancers. A few of these risk regions have been associated with more than one cancer site; however, a systematic evaluation of the associations between risk variants for other cancers and lung cancer risk has yet to be performed. METHODS: We included 18023 patients with lung cancer and 60543 control subjects from two consortia, Population Architecture using Genomics and Epidemiology (PAGE) and Transdisciplinary Research in Cancer of the Lung (TRICL). We examined 165 single-nucleotide polymorphisms (SNPs) that were previously associated with at least one of 16 non-lung cancer sites. Study-specific logistic regression results underwent meta-analysis, and associations were also examined by race/ethnicity, histological cell type, sex, and smoking status. A Bonferroni-corrected P value of 2.5×10(-5) was used to assign statistical significance. RESULTS: The breast cancer SNP LSP1 rs3817198 was associated with an increased risk of lung cancer (odds ratio [OR] = 1.10; 95% confidence interval [CI] = 1.05 to 1.14; P = 2.8×10(-6)). This association was strongest for women with adenocarcinoma (P = 1.2×10(-4)) and not statistically significant in men (P = .14) with this cell type (P het by sex = .10). Two glioma risk variants, TERT rs2853676 and CDKN2BAS1 rs4977756, which are located in regions previously associated with lung cancer, were associated with increased risk of adenocarcinoma (OR = 1.16; 95% CI = 1.10 to 1.22; P = 1.1×10(-8)) and squamous cell carcinoma (OR = 1.13; CI = 1.07 to 1.19; P = 2.5×10(-5)), respectively. CONCLUSIONS: Our findings demonstrate a novel pleiotropic association between the breast cancer LSP1 risk region marked by variant rs3817198 and lung cancer risk.

Multiancestral analysis of inflammation-related genetic variants and C-reactive protein in the population architecture using genomics and epidemiology study.

BACKGROUND: C-reactive protein (CRP) is a biomarker of inflammation. Genome-wide association studies (GWAS) have identified single-nucleotide polymorphisms (SNPs) associated with CRP concentrations and inflammation-related traits such as cardiovascular disease, type 2 diabetes mellitus, and obesity. We aimed to replicate previous CRP-SNP associations, assess whether these associations generalize to additional race/ethnicity groups, and evaluate inflammation-related SNPs for a potentially pleiotropic association with CRP. METHODS AND RESULTS: We selected and analyzed 16 CRP-associated and 250 inflammation-related GWAS SNPs among 40 473 African American, American Indian, Asian/Pacific Islander, European American, and Hispanic participants from 7 studies collaborating in the Population Architecture using Genomics and Epidemiology (PAGE) study. Fixed-effect meta-analyses combined study-specific race/ethnicity-stratified linear regression estimates to evaluate the association between each SNP and high-sensitivity CRP. Overall, 18 SNPs in 8 loci were significantly associated with CRP (Bonferroni-corrected P<3.1×10(-3) for replication, P<2.0×10(-4) for pleiotropy): Seven of these were specific to European Americans, while 9 additionally generalized to African Americans (1), Hispanics (5), or both (3); 1 SNP was seen only in African Americans and Hispanics. Two SNPs in the CELSR2/PSRC1/SORT1 locus showed a potentially novel association with CRP: rs599839 (P=2.0×10(-6)) and rs646776 (P=3.1×10(-5)). CONCLUSIONS: We replicated 16 SNP-CRP associations, 10 of which generalized to African Americans and/or Hispanics. We also identified potentially novel pleiotropic associations with CRP for two SNPs previously associated with coronary artery disease and/or low-density lipoprotein-cholesterol. These findings demonstrate the benefit of evaluating genotype-phenotype associations in multiple race/ethnicity groups and looking for pleiotropic relationships among SNPs previously associated with related phenotypes.

Pleiotropy of cancer susceptibility variants on the risk of non-Hodgkin lymphoma: the PAGE consortium.

BACKGROUND: Risk of non-Hodgkin lymphoma (NHL) is higher among individuals with a family history or a prior diagnosis of other cancers. Genome-wide association studies (GWAS) have suggested that some genetic susceptibility variants are associated with multiple complex traits (pleiotropy). OBJECTIVE: We investigated whether common risk variants identified in cancer GWAS may also increase the risk of developing NHL as the first primary cancer. METHODS: As part of the Population Architecture using Genomics and Epidemiology (PAGE) consortium, 113 cancer risk variants were analyzed in 1,441 NHL cases and 24,183 controls from three studies (BioVU, Multiethnic Cohort Study, Women's Health Initiative) for their association with the risk of overall NHL and common subtypes [diffuse large B-cell lymphoma (DLBCL), follicular lymphoma (FL), chronic lymphocytic leukemia or small lymphocytic lymphoma (CLL/SLL)] using an additive genetic model adjusted for age, sex and ethnicity. Study-specific results for each variant were meta-analyzed across studies. RESULTS: The analysis of NHL subtype-specific GWAS SNPs and overall NHL suggested a shared genetic susceptibility between FL and DLBCL, particularly involving variants in the major histocompatibility complex region (rs6457327 in 6p21.33: FL OR=1.29, p=0.013; DLBCL OR=1.23, p=0.013; NHL OR=1.22, p=5.9 × E-05). In the pleiotropy analysis, six risk variants for other cancers were associated with NHL risk, including variants for lung (rs401681 in TERT: OR per C allele=0.89, p=3.7 × E-03; rs4975616 in TERT: OR per A allele=0.90, p=0.01; rs3131379 in MSH5: OR per T allele=1.16, p=0.03), prostate (rs7679673 in TET2: OR per C allele=0.89, p=5.7 × E-03; rs10993994 in MSMB: OR per T allele=1.09, p=0.04), and breast (rs3817198 in LSP1: OR per C allele=1.12, p=0.01) cancers, but none of these associations remained significant after multiple test correction. CONCLUSION: This study does not support strong pleiotropic effects of non-NHL cancer risk variants in NHL etiology; however, larger studies are warranted.

Lack of associations of ten candidate coronary heart disease risk genetic variants and subclinical atherosclerosis in four US populations: the Population Architecture using Genomics and Epidemiology (PAGE) study.

BACKGROUND: A number of genetic variants have been discovered by recent genome-wide association studies for their associations with clinical coronary heart disease (CHD). However, it is unclear whether these variants are also associated with the development of CHD as measured by subclinical atherosclerosis phenotypes, ankle brachial index (ABI), carotid artery intima-media thickness (cIMT) and carotid plaque. METHODS: Ten CHD risk single nucleotide polymorphisms (SNPs) were genotyped in individuals of European American (EA), African American (AA), American Indian (AI), and Mexican American (MA) ancestry in the Population Architecture using Genomics and Epidemiology (PAGE) study. In each individual study, we performed linear or logistic regression to examine population-specific associations between SNPs and ABI, common and internal cIMT, and plaque. The results from individual studies were meta-analyzed using a fixed effect inverse variance weighted model. RESULTS: None of the ten SNPs was significantly associated with ABI and common or internal cIMT, after Bonferroni correction. In the sample of 13,337 EA, 3809 AA, and 5353 AI individuals with carotid plaque measurement, the GCKR SNP rs780094 was significantly associated with the presence of plaque in AI only (OR = 1.32, 95% confidence interval: 1.17, 1.49, P = 1.08 × 10(-5)), but not in the other populations (P = 0.90 in EA and P = 0.99 in AA). A 9p21 region SNP, rs1333049, was nominally associated with plaque in EA (OR = 1.07, P = 0.02) and in AI (OR = 1.10, P = 0.05). CONCLUSIONS: We identified a significant association between rs780094 and plaque in AI populations, which needs to be replicated in future studies. There was little evidence that the index CHD risk variants identified through genome-wide association studies in EA influence the development of CHD through subclinical atherosclerosis as assessed by cIMT and ABI across ancestries.

Association of functional polymorphism rs2231142 (Q141K) in the ABCG2 gene with serum uric acid and gout in 4 US populations: the PAGE Study.

A loss-of-function mutation (Q141K, rs2231142) in the ATP-binding cassette, subfamily G, member 2 gene (ABCG2) has been shown to be associated with serum uric acid levels and gout in Asians, Europeans, and European and African Americans; however, less is known about these associations in other populations. Rs2231142 was genotyped in 22,734 European Americans, 9,720 African Americans, 3,849 Mexican Americans, and 3,550 American Indians in the Population Architecture using Genomics and Epidemiology (PAGE) Study (2008-2012). Rs2231142 was significantly associated with serum uric acid levels (P = 2.37 × 10(-67), P = 3.98 × 10(-5), P = 6.97 × 10(-9), and P = 5.33 × 10(-4) in European Americans, African Americans, Mexican Americans, and American Indians, respectively) and gout (P = 2.83 × 10(-10), P = 0.01, and P = 0.01 in European Americans, African Americans, and Mexican Americans, respectively). Overall, the T allele was associated with a 0.24-mg/dL increase in serum uric acid level (P = 1.37 × 10(-80)) and a 1.75-fold increase in the odds of gout (P = 1.09 × 10(-12)). The association between rs2231142 and serum uric acid was significantly stronger in men, postmenopausal women, and hormone therapy users compared with their counterparts. The association with gout was also significantly stronger in men than in women. These results highlight a possible role of sex hormones in the regulation of ABCG2 urate transporter and its potential implications for the prevention, diagnosis, and treatment of hyperuricemia and gout.

Phenome-wide association study (PheWAS) for detection of pleiotropy within the Population Architecture using Genomics and Epidemiology (PAGE) Network.

Using a phenome-wide association study (PheWAS) approach, we comprehensively tested genetic variants for association with phenotypes available for 70,061 study participants in the Population Architecture using Genomics and Epidemiology (PAGE) network. Our aim was to better characterize the genetic architecture of complex traits and identify novel pleiotropic relationships. This PheWAS drew on five population-based studies representing four major racial/ethnic groups (European Americans (EA), African Americans (AA), Hispanics/Mexican-Americans, and Asian/Pacific Islanders) in PAGE, each site with measurements for multiple traits, associated laboratory measures, and intermediate biomarkers. A total of 83 single nucleotide polymorphisms (SNPs) identified by genome-wide association studies (GWAS) were genotyped across two or more PAGE study sites. Comprehensive tests of association, stratified by race/ethnicity, were performed, encompassing 4,706 phenotypes mapped to 105 phenotype-classes, and association results were compared across study sites. A total of 111 PheWAS results had significant associations for two or more PAGE study sites with consistent direction of effect with a significance threshold of p<0.01 for the same racial/ethnic group, SNP, and phenotype-class. Among results identified for SNPs previously associated with phenotypes such as lipid traits, type 2 diabetes, and body mass index, 52 replicated previously published genotype-phenotype associations, 26 represented phenotypes closely related to previously known genotype-phenotype associations, and 33 represented potentially novel genotype-phenotype associations with pleiotropic effects. The majority of the potentially novel results were for single PheWAS phenotype-classes, for example, for CDKN2A/B rs1333049 (previously associated with type 2 diabetes in EA) a PheWAS association was identified for hemoglobin levels in AA. Of note, however, GALNT2 rs2144300 (previously associated with high-density lipoprotein cholesterol levels in EA) had multiple potentially novel PheWAS associations, with hypertension related phenotypes in AA and with serum calcium levels and coronary artery disease phenotypes in EA. PheWAS identifies associations for hypothesis generation and exploration of the genetic architecture of complex traits.

The application of cloud computing to scientific workflows: a study of cost and performance.

The current model of transferring data from data centres to desktops for analysis will soon be rendered impractical by the accelerating growth in the volume of science datasets. Processing will instead often take place on high-performance servers co-located with data. Evaluations of how new technologies such as cloud computing would support such a new distributed computing model are urgently needed. Cloud computing is a new way of purchasing computing and storage resources on demand through virtualization technologies. We report here the results of investigations of the applicability of commercial cloud computing to scientific computing, with an emphasis on astronomy, including investigations of what types of applications can be run cheaply and efficiently on the cloud, and an example of an application well suited to the cloud: processing a large dataset to create a new science product.

HNF1B and endometrial cancer risk: results from the PAGE study.

We examined the association between HNF1B variants identified in a recent genome-wide association study and endometrial cancer in two large case-control studies nested in prospective cohorts: the Multiethnic Cohort Study (MEC) and the Women's Health Initiative (WHI) as part of the Population Architecture using Genomics and Epidemiology (PAGE) study. A total of 1,357 incident cases of invasive endometrial cancer and 7,609 controls were included in the analysis (MEC: 426 cases/3,854 controls; WHI: 931 cases/3,755 controls). The majority of women in the WHI were European American, while the MEC included sizable numbers of African Americans, Japanese and Latinos. We estimated the odds ratios (ORs) per allele and 95% confidence intervals (CIs) of each SNP using unconditional logistic regression adjusting for age, body mass index, and four principal components of ancestry informative markers. The combined ORs were estimated using fixed effect models. Rs4430796 and rs7501939 were associated with endometrial cancer risk in MEC and WHI with no heterogeneity observed across racial/ethnic groups (P ≥ 0.21) or between studies (P ≥ 0.70). The OR(per allele) was 0.82 (95% CI: 0.75, 0.89; P = 5.63 × 10(-6)) for rs4430796 (G allele) and 0.79 (95% CI: 0.73, 0.87; P = 3.77 × 10(-7)) for rs7501939 (A allele). The associations with the risk of Type I and Type II tumors were similar (P ≥ 0.19). Adjustment for additional endometrial cancer risk factors such as parity, oral contraceptive use, menopausal hormone use, and smoking status had little effect on the results. In conclusion, HNF1B SNPs are associated with risk of endometrial cancer and that the associated relative risks are similar for Type I and Type II tumors.

Association of genetic variants and incident coronary heart disease in multiethnic cohorts: the PAGE study.

BACKGROUND: Genome-wide association studies identified several single nucleotide polymorphisms (SNP) associated with prevalent coronary heart disease (CHD), but less is known of associations with incident CHD. The association of 13 published CHD SNPs was examined in 5 ancestry groups of 4 large US prospective cohorts. METHODS AND RESULTS: The analyses included incident coronary events over an average 9.1 to 15.7 follow-up person-years in up to 26 617 white individuals (6626 events), 8018 black individuals (914 events), 1903 Hispanic individuals (113 events), 3669 American Indian individuals (595 events), and 885 Asian/Pacific Islander individuals (66 events). We used Cox proportional hazards models (with additive mode of inheritance) adjusted for age, sex, and ancestry (as needed). Nine loci were statistically associated with incident CHD events in white participants: 9p21 (rs10757278; P=4.7 × 10(-41)), 16q23.1 (rs2549513; P=0.0004), 6p24.1 (rs499818; P=0.0002), 2q36.3 (rs2943634; P=6.7 × 10(-6)), MTHFD1L (rs6922269, P=5.1 × 10(-10)), APOE (rs429358; P=2.7×10(-18)), ZNF627 (rs4804611; P=5.0 × 10(-8)), CXCL12 (rs501120; P=1.4 × 10(-6)) and LPL (rs268; P=2.7 × 10(-17)). The 9p21 region showed significant between-study heterogeneity, with larger effects in individuals age 55 years or younger and in women. Inclusion of coronary revascularization procedures among the incident CHD events introduced heterogeneity. The SNPs were not associated with CHD in black participants, and associations varied in other US minorities. CONCLUSIONS: Prospective analyses of white participants replicated several reported cross-sectional CHD-SNP associations.

Ten years of software sustainability at the Infrared Processing and Analysis Center.

This paper presents a case study of an approach to sustainable software architecture that has been successfully applied over a period of 10 years to astronomy software services at the NASA Infrared Processing and Analysis Center (IPAC), Caltech (http://www.ipac.caltech.edu). The approach was developed in response to the need to build and maintain the NASA Infrared Science Archive (http://irsa.ipac.caltech.edu), NASA's archive node for infrared astronomy datasets. When the archive opened for business in 1999 serving only two datasets, it was understood that the holdings would grow rapidly in size and diversity, and consequently in the number of queries and volume of data download. It was also understood that platforms and browsers would be modernized, that user interfaces would need to be replaced and that new functionality outside of the scope of the original specifications would be needed. The changes in scientific functionality over time are largely driven by the archive user community, whose interests are represented by a formal user panel. The approach has been extended to support four more major astronomy archives, which today host data from more than 40 missions and projects, to support a complete modernization of a powerful and unique legacy astronomy application for co-adding survey data, and to support deployment of Montage, a powerful image mosaic engine for astronomy. The approach involves using a component-based architecture, designed from the outset to support sustainability, extensibility and portability. Although successful, the approach demands careful assessment of new and emerging technologies before adopting them, and attention to a disciplined approach to software engineering and maintenance. The paper concludes with a list of best practices for software sustainability that are based on 10 years of experience at IPAC.

RseqFlow: workflows for RNA-Seq data analysis.

SUMMARY: We have developed an RNA-Seq analysis workflow for single-ended Illumina reads, termed RseqFlow. This workflow includes a set of analytic functions, such as quality control for sequencing data, signal tracks of mapped reads, calculation of expression levels, identification of differentially expressed genes and coding SNPs calling. This workflow is formalized and managed by the Pegasus Workflow Management System, which maps the analysis modules onto available computational resources, automatically executes the steps in the appropriate order and supervises the whole running process. RseqFlow is available as a Virtual Machine with all the necessary software, which eliminates any complex configuration and installation steps. AVAILABILITY AND IMPLEMENTATION: http://genomics.isi.edu/rnaseq CONTACT: wangying@xmu.edu.cn; knowles@med.usc.edu; deelman@isi.edu; tingchen@usc.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Communication and data-intensive science in the beginning of the 21st century.

The advent of data-intensive science has sharpened our need for better communication within and between the fields of science and technology, to name a few. No one mind can encompass all that is necessary to be successful in controlling and analyzing the data deluge we are experiencing. Therefore, we must bring together diverse fields, communicate clearly, and build crossdisciplinary methods and tools to realize its potential. This article is a summary of the communication issues and challenges as discussed in the Data-Intensive Science (DIS) workshop in Seattle, September 19-20, 2010.

New tools and methods for direct programmatic access to the dbSNP relational database.

Genome-wide association studies often incorporate information from public biological databases in order to provide a biological reference for interpreting the results. The dbSNP database is an extensive source of information on single nucleotide polymorphisms (SNPs) for many different organisms, including humans. We have developed free software that will download and install a local MySQL implementation of the dbSNP relational database for a specified organism. We have also designed a system for classifying dbSNP tables in terms of common tasks we wish to accomplish using the database. For each task we have designed a small set of custom tables that facilitate task-related queries and provide entity-relationship diagrams for each task composed from the relevant dbSNP tables. In order to expose these concepts and methods to a wider audience we have developed web tools for querying the database and browsing documentation on the tables and columns to clarify the relevant relational structure. All web tools and software are freely available to the public at http://cgsmd.isi.edu/dbsnpq. Resources such as these for programmatically querying biological databases are essential for viably integrating biological information into genetic association experiments on a genome-wide scale.

SPOT: a web-based tool for using biological databases to prioritize SNPs after a genome-wide association study.

SPOT (http://spot.cgsmd.isi.edu), the SNP prioritization online tool, is a web site for integrating biological databases into the prioritization of single nucleotide polymorphisms (SNPs) for further study after a genome-wide association study (GWAS). Typically, the next step after a GWAS is to genotype the top signals in an independent replication sample. Investigators will often incorporate information from biological databases so that biologically relevant SNPs, such as those in genes related to the phenotype or with potentially non-neutral effects on gene expression such as a splice sites, are given higher priority. We recently introduced the genomic information network (GIN) method for systematically implementing this kind of strategy. The SPOT web site allows users to upload a list of SNPs and GWAS P-values and returns a prioritized list of SNPs using the GIN method. Users can specify candidate genes or genomic regions with custom levels of prioritization. The results can be downloaded or viewed in the browser where users can interactively explore the details of each SNP, including graphical representations of the GIN method. For investigators interested in incorporating biological databases into a post-GWAS SNP selection strategy, the SPOT web tool is an easily implemented and flexible solution.