A graph theoretic approach to testing associations between disparate sources of functional genomic data

The last few years have seen the advent of high-throughput technologies to analyze various properties of the transcriptome and proteome of several organisms. The congruency of these different data sources, or lack thereof, can shed light on the mechanisms that govern cellular function. A central challenge for bioinformatics research is to develop a unified framework for combining the multiple sources of functional genomics information and testing associations between them, thus obtaining a robust and integrated view of the underlying biology. We present a graph theoretic approach to test the significance of the association between multiple disparate sources of functional genomics data by proposing two statistical tests, namely edge permutation and node label permutation tests. We demonstrate the use of the proposed tests by finding significant association between a Gene Ontology-derived predictome and data obtained from mRNA expression and phenotypic experiments for Saccharomyces cerevisiae. Moreover, we employ the graph theoretic framework to recast a surprising discrepancy presented in Giaever et al. (2002) between gene expression and knockout phenotype, using expression data from a different set of experiments

Coverage and error models of protein-protein interaction data by directed graph analysis

Directed graph and multinomial error models were used to assess and characterize the error statistics in all published large-scale datasets for Saccharomyces cerevisia

Human rhinovirus infection causes different DNA methylation changes in nasal epithelial cells from healthy and asthmatic subjects

BACKGROUND: Mechanisms underlying the development of virus-induced asthma exacerbations remain unclear. To investigate if epigenetic mechanisms could be involved in virus-induced asthma exacerbations, we undertook DNA methylation profiling in asthmatic and healthy nasal epithelial cells (NECs) during Human Rhinovirus (HRV) infection in vitro. METHODS: Global and loci-specific methylation profiles were determined via Alu element and Infinium Human Methylation 450 K microarray, respectively. Principal components analysis identified the genomic loci influenced the most by disease-status and infection. Real-time PCR and pyrosequencing were used to confirm gene expression and DNA methylation, respectively. RESULTS: HRV infection significantly increased global DNA methylation in cells from asthmatic subjects only (43.6% to 44.1%, p = 0.04). Microarray analysis revealed 389 differentially methylated loci either based on disease status, or caused by virus infection. There were disease-associated DNA methylation patterns that were not affected by HRV infection as well as HRV-induced DNA methylation changes that were unique to each group. A common methylation locus stood out in response to HRV infection in both groups, where the small nucleolar RNA, H/ACA box 12 (SNORA12) is located. Further analysis indicated that a relationship existed between SNORA12 DNA methylation and gene expression in response to HRV infection. CONCLUSIONS: We describe for the first time that Human rhinovirus infection causes DNA methylation changes in airway epithelial cells that differ between asthmatic and healthy subjects. These epigenetic differences may possibly explain the mechanism by which respiratory viruses cause asthma exacerbations

The Coincidence Between Increasing Age, Immunosuppression, and the Incidence of Patients With Glioblastoma

Background: Glioblastoma (GBM) is the most aggressive primary brain tumor in adults and is associated with a median overall survival (mOS) of 16–21 months. Our previous work found a negative association between advanced aging and the survival benefit after treatment with immunotherapy in an experimental brain tumor model. Given the recent phase III clinical success of immunotherapy in patients with many types of cancer, but not for patients with GBM, we hypothesize that aging enhances immunosuppression in the brain and contributes to the lack of efficacy for immunotherapy to improve mOS in patients with malignant glioma. Herein, we compare epidemiological data for the incidence and mortality of patients with central nervous system (CNS) cancers, in addition to immune-related gene expression in the normal human brain, as well as peripheral blood immunological changes across the adult lifespan.Methods: Data were extracted from the National Cancer Institute’s surveillance, epidemiology, and end results (SEER)-, the Broad Institute’s Genotype Tissue Expression project (GTEx)-, and the University of California San Francisco’s 10k Immunomes-databases and analyzed for associations with aging.Results: The proportion of elderly individuals, defined as ≥65 years of age, has predominantly increased for more than 100 years in the United States. Over time, the rise in elderly United States citizens has correlated with an increased incidence and mortality rate associated with primary brain and other CNS cancer. With advanced aging, human mRNA expression for factors associated with immunoregulation including immunosuppressive indoleamine 2,3 dioxygenase 1 (IDO) and programmed death-ligand 1 (PD-L1), as well as the dendritic cell surface marker, CD11c, increase in the brain of normal human subjects, coincident with increased circulating immunosuppressive Tregs and decreased cytolytic CD8+ T cells in the peripheral blood. Strikingly, these changes are maximally pronounced in the 60–69 year old group; consistent with the median age of a diagnosis for GBM.Conclusion: These data demonstrate a significant association between normal human aging and increased immunosuppression in the circulation and CNS; particularly late in life. Our data raise several hypotheses including that, aging: (i) progressively suppresses normal immunosurveillance and thereby contributes to GBM cell initiation and/or outgrowth; (ii) decreases immunotherapeutic efficacy against malignant glioma

The Localized Scleroderma Quality Of Life Instrument (LOSQI): A Disease-Specific Survey Using Anchoring Vignettes

The main goal of this project was to develop and provide validity evidence for a disease- specific quality of life survey to be used with pediatric localized scleroderma (LS) patients. This new survey, called the Localized Scleroderma Quality of Life Instrument (LoSQI), incorporated unique features associated with the disease, not captured by current surveys. As a secondary goal, the feasibility and usefulness of anchoring vignettes with pediatric patients were examined. The project included three phases; content domain development and item generation, a pilot study, and a field test. Validity evidence was gathered from multiple sources including test content, internal structure, and in relation to other variables. Overall, there was initial support for use of the LoSQI with pediatric LS patients. Patients indicated general understanding and readability of the items, and there was qualitative evidence for content validity. Exploratory factor analysis suggested the utility of reporting a total score along with two subscale scores, (1) Pain and Physical Functioning and (2) Body Image and Social Support. Reliability of both the subscale and total scores was acceptable. There was less evidence for use of anchoring vignettes in this context, as there was a high frequency of ties in rankings, which limited the utility of statistical models. Despite limitations from a small sample size and skewed response distributions, the pilot study and the field test provided promising initial evidence that the LoSQI can be used to capture HRQoL in LS patients ages 10-20 years. Future studies should examine responsiveness of the scores to change and optimal capture of HRQoL in patients <10 years of age

Variants in the fetal genome near pro-inflammatory cytokine genes on 2q13 associate with gestational duration

The duration of pregnancy is influenced by fetal and maternal genetic and non-genetic factors. Here we report a fetal genome-wide association meta-analysis of gestational duration, and early preterm, preterm, and postterm birth in 84,689 infants. One locus on chromosome 2q13 is associated with gestational duration; the association is replicated in 9,291 additional infants (combined P= 3.96 x 10(-14)). Analysis of 15,588 mother-child pairs shows that the association is driven by fetal rather than maternal genotype. Functional experiments show that the lead SNP, rs7594852, alters the binding of the HIC1 transcriptional repressor. Genes at the locus include several interleukin 1 family members with roles in pro-inflammatory pathways that are central to the process of parturition. Further understanding of the underlying mechanisms will be of great public health importance, since giving birth either before or after the window of term gestation is associated with increased morbidity and mortality.Peer reviewe

Genome-wide association study of offspring birth weight in 86 577 women identifies five novel loci and highlights maternal genetic effects that are independent of fetal genetics

Funding Information: Researchers were funded by investment from the European Regional Development Fund (ERDF) and the European Social Fund (ESF) Convergence Programme for Cornwall and the Isles of Scilly [J.T.]; European Research Council (ERC) [grant: SZ-245 50371-GLUCOSEGENES-FP7-IDEAS-ERC to T.M.F., A.R.W.], [ERC Consolidator Grant, ERC-2014-CoG-648916 to V.W.V.J.], [P.R.N.]; University of Bergen, KG Jebsen and Helse Vest [P.R.N.]; Wellcome Trust Senior Investigator Awards [A.T.H. (WT098395), M.I.M. (WT098381)]; National Institute for Health Research (NIHR) Senior Investigator Award (NF-SI-0611–10219); Sir Henry Dale Fellowship (Wellcome Trust and Royal Society grant: WT104150) [R.M.F., R.N.B.]; 4-year studentship (Grant Code: WT083431MF) [R.C.R]; the European Research Council under the European Union’s Seventh Framework Programme (FP/2007– 2013)/ERC Grant Agreement (grant number 669545; Develop Obese) [D.A.L.]; US National Institute of Health (grant: R01 DK10324) [D.A.L, C.L.R]; Wellcome Trust GWAS grant (WT088806) [D.A.L] and NIHR Senior Investigator Award (NF-SI-0611–10196) [D.A.L]; Wellcome Trust Institutional Strategic Support Award (WT097835MF) [M.A.T.]; The Diabetes Research and Wellness Foundation Non-Clinical Fellowship [J.T.]; Australian National Health and Medical Research Council Early Career Fellowship (APP1104818) [N.M.W.]; Daniel B. Burke Endowed Chair for Diabetes Research [S.F.A.G.]; UK Medical Research Council Unit grants MC_UU_12013_5 [R.C.R, L.P, S.R, C.L.R, D.M.E., D.A.L.] and MC_UU_12013_4 [D.M.E.]; Medical Research Council (grant: MR/M005070/1) [M.N.W., S.E.J.]; Australian Research Council Future Fellowship (FT130101709) [D.M.E] and (FT110100548) [S.E.M.]; NIHR Oxford Biomedical Research Centre (BRC); Oak Foundation Fellowship and Novo Nordisk Foundation (12955) [B.F.]; FRQS research scholar and Clinical Scientist Award by the Canadian Diabetes Association and the Maud Menten Award from the Institute of Genetics– Canadian Institute of Health Research (CIHR) [MFH]; CIHR— Frederick Banting and Charles Best Canada Graduate Scholarships [C.A.]; FRQS [L.B.]; Netherlands Organization for Health Research and Development (ZonMw–VIDI 016.136.361) [V.W.V.J.]; National Institute on Aging (R01AG29451) [J.M.M.]; 2010–2011 PRIN funds of the University of Ferrara—Holder: Prof. Guido Barbujani, Supervisor: Prof. Chiara Scapoli—and in part sponsored by the European Foundation for the Study of Diabetes (EFSD) Albert Renold Travel Fellowships for Young Scientists, ‘5 per mille’ contribution assigned to the University of Ferrara, income tax return year 2009 and the ENGAGE Exchange and Mobility Program for ENGAGE training funds, ENGAGE project, grant agreement HEALTH-F4–2007-201413 [L.M.]; ESRC (RES-060–23-0011) [C.L.R.]; National Institute of Health Research ([S.D., M.I.M.], Senior Investigator Award (NF-SI-0611–10196) [D.A.L]); Australian NHMRC Fellowships Scheme (619667) [G.W.M]. For study-specific funding, please see Supplementary Material. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. Funding to pay the Open Access publication charges for this article was provided by the Charity Open Access Fund (COAF). Funding Information: We are extremely grateful to the participants and families who contributed to all of the studies and the teams of investigators involved in each one. These include interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists and nurses. This research has been conducted using the UK Biobank Resource (Application numbers 7036 and 12703). For additional study-specific acknowledgements, please see Supplementary Material. Conflict of Interest statement. D.A.L. has received support from Roche Diagnostics and Medtronic for biomarker research unrelated to the work presented here. Funding Researchers were funded by investment from the European Regional Development Fund (ERDF) and the European Social Fund (ESF) Convergence Programme for Cornwall and the Isles of Scilly [J.T.]; European Research Council (ERC) [grant: SZ-245 50371-GLUCOSEGENES-FP7-IDEAS-ERC to T.M.F., A.R.W.], [ERC Consolidator Grant, ERC-2014-CoG-648916 to V.W.V.J.], [P.R.N.]; University of Bergen, KG Jebsen and Helse Vest [P.R.N.]; Wellcome Trust Senior Investigator Awards [A.T.H. (WT098395), M.I.M. (WT098381)]; National Institute for Health Research (NIHR) Senior Investigator Award (NF-SI-0611-10219); Sir Henry Dale Fellowship (Wellcome Trust and Royal Society grant: WT104150) [R.M.F., R.N.B.]; 4-year studentship (Grant Code: WT083431MF) [R.C.R]; the European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013)/ERC Grant Agreement (grant number 669545; Develop Obese) [D.A.L.]; US National Institute of Health (grant: R01 DK10324) [D.A.L, C.L.R]; Wellcome Trust GWAS grant (WT088806) [D.A.L] and NIHR Senior Investigator Award (NF-SI-0611-10196) [D.A.L]; Wellcome Trust Institutional Strategic Support Award (WT097835MF) [M.A.T.]; The Diabetes Research and Wellness Foundation Non-Clinical Fellowship [J.T.]; Australian National Health and Medical Research Council Early Career Fellowship (APP1104818) [N.M.W.]; Daniel B. Burke Endowed Chair for Diabetes Research [S.F.A.G.]; UK Medical Research Council Unit grants MC_UU_12013_5 [R.C.R, L.P, S.R, C.L.R, D.M.E., D.A.L.] and MC_UU_12013_4 [D.M.E.]; Medical Research Council (grant: MR/M005070/1) [M.N.W., S.E.J.]; Australian Research Council Future Fellowship (FT130101709) [D.M.E] and (FT110100548) [S.E.M.]; NIHR Oxford Biomedical Research Centre (BRC); Oak Foundation Fellowship and Novo Nordisk Foundation (12955) [B.F.]; FRQS research scholar and Clinical Scientist Award by the Canadian Diabetes Association and the Maud Menten Award from the Institute of Genetics-Canadian Institute of Health Research (CIHR) [MFH]; CIHR-Frederick Banting and Charles Best Canada Graduate Scholarships [C.A.]; FRQS [L.B.]; Netherlands Organization for Health Research and Development (ZonMw-VIDI 016.136.361) [V.W.V.J.]; National Institute on Aging (R01AG29451) [J.M.M.]; 2010-2011 PRIN funds of the University of Ferrara-Holder: Prof. Guido Barbujani, Supervisor: Prof. Chiara Scapoli-and in part sponsored by the European Foundation for the Study of Diabetes (EFSD) Albert Renold Travel Fellowships for Young Scientists, '5 per mille' contribution assigned to the University of Ferrara, income tax return year 2009 and the ENGAGE Exchange and Mobility Program for ENGAGE training funds, ENGAGE project, grant agreement HEALTH-F4-2007-201413 [L.M.]; ESRC (RES-060-23-0011) [C.L.R.]; National Institute of Health Research ([S.D., M.I.M.], Senior Investigator Award (NFSI-0611-10196) [D.A.L]); Australian NHMRC Fellowships Scheme (619667) [G.W.M]. For study-specific funding, please see Supplementary Material. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. Funding to pay the Open Access publication charges for this article was provided by the Charity Open Access Fund (COAF). Publisher Copyright: © The Author(s) 2018.Genome-wide association studies of birth weight have focused on fetal genetics, whereas relatively little is known about the role of maternal genetic variation. We aimed to identify maternal genetic variants associated with birth weight that could highlight potentially relevant maternal determinants of fetal growth. We meta-analysed data on up to 8.7 million SNPs in up to 86 577 women of European descent from the Early Growth Genetics (EGG) Consortium and the UK Biobank. We used structural equation modelling (SEM) and analyses of mother-child pairs to quantify the separate maternal and fetal genetic effects. Maternal SNPs at 10 loci (MTNR1B, HMGA2, SH2B3, KCNAB1, L3MBTL3, GCK, EBF1, TCF7L2, ACTL9, CYP3A7) were associated with offspring birth weight at P<5 x 10(-8). In SEM analyses, at least 7 of the 10 associations were consistent with effects of the maternal genotype acting via the intrauterine environment, rather than via effects of shared alleles with the fetus. Variants, or correlated proxies, at many of the loci had been previously associated with adult traits, including fasting glucose (MTNR1B, GCK and TCF7L2) and sex hormone levels (CYP3A7), and one (EBF1) with gestational duration. The identified associations indicate that genetic effects on maternal glucose, cytochrome P450 activity and gestational duration, and potentially on maternal blood pressure and immune function, are relevant for fetal growth. Further characterization of these associations in mechanistic and causal analyses will enhance understanding of the potentially modifiable maternal determinants of fetal growth, with the goal of reducing the morbidity and mortality associated with low and high birth weights.Peer reviewe

Genome-wide associations for birth weight and correlations with adult disease

Birth weight (BW) is influenced by both foetal and maternal factors and in observational studies is reproducibly associated with future risk of adult metabolic diseases including type 2 diabetes (T2D) and cardiovascular disease1. These lifecourse associations have often been attributed to the impact of an adverse early life environment. We performed a multi-ancestry genome-wide association study (GWAS) meta-analysis of BW in 153,781 individuals, identifying 60 loci where foetal genotype was associated with BW (P <5x10-8). Overall, ˜15% of variance in BW could be captured by assays of foetal genetic variation. Using genetic association alone, we found strong inverse genetic correlations between BW and systolic blood pressure (rg-0.22, P =5.5x10-13), T2D (rg-0.27, P =1.1x10-6) and coronary artery disease (rg-0.30, P =6.5x10-9) and, in large cohort data sets, demonstrated that genetic factors were the major contributor to the negative covariance between BW and future cardiometabolic risk. Pathway analyses indicated that the protein products of genes within BW-associated regions were enriched for diverse processes including insulin signalling, glucose homeostasis, glycogen biosynthesis and chromatin remodelling. There was also enrichment of associations with BW in known imprinted regions (P =1.9x10-4). We have demonstrated that lifecourse associations between early growth phenotypes and adult cardiometabolic disease are in part the result of shared genetic effects and have highlighted some of the pathways through which these causal genetic effects are mediated