Person: McGeachie, Michael
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McGeachie
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Michael
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McGeachie, Michael
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Publication Expression Quantitative Trait Loci Information Improves Predictive Modeling of Disease Relevance of Non-Coding Genetic Variation(Public Library of Science, 2015) Croteau-Chonka, Damien; Rogers, Angela J.; Raj, Towfique; McGeachie, Michael; Qiu, Weiliang; Ziniti, John P.; Stubbs, Benjamin J.; Liang, Liming; Martinez, Fernando D.; Strunk, Robert C.; Lemanske, Robert F.; Liu, Andrew H.; Stranger, Barbara E.; Carey, Vincent; Raby, BenjaminDisease-associated loci identified through genome-wide association studies (GWAS) frequently localize to non-coding sequence. We and others have demonstrated strong enrichment of such single nucleotide polymorphisms (SNPs) for expression quantitative trait loci (eQTLs), supporting an important role for regulatory genetic variation in complex disease pathogenesis. Herein we describe our initial efforts to develop a predictive model of disease-associated variants leveraging eQTL information. We first catalogued cis-acting eQTLs (SNPs within 100kb of target gene transcripts) by meta-analyzing four studies of three blood-derived tissues (n = 586). At a false discovery rate < 5%, we mapped eQTLs for 6,535 genes; these were enriched for disease-associated genes (P < 10−04), particularly those related to immune diseases and metabolic traits. Based on eQTL information and other variant annotations (distance from target gene transcript, minor allele frequency, and chromatin state), we created multivariate logistic regression models to predict SNP membership in reported GWAS. The complete model revealed independent contributions of specific annotations as strong predictors, including evidence for an eQTL (odds ratio (OR) = 1.2–2.0, P < 10−11) and the chromatin states of active promoters, different classes of strong or weak enhancers, or transcriptionally active regions (OR = 1.5–2.3, P < 10−11). This complete prediction model including eQTL association information ultimately allowed for better discrimination of SNPs with higher probabilities of GWAS membership (6.3–10.0%, compared to 3.5% for a random SNP) than the other two models excluding eQTL information. This eQTL-based prediction model of disease relevance can help systematically prioritize non-coding GWAS SNPs for further functional characterization.Publication CGBayesNets: Conditional Gaussian Bayesian Network Learning and Inference with Mixed Discrete and Continuous Data(Public Library of Science, 2014) McGeachie, Michael; Chang, Hsun-Hsien; Weiss, ScottBayesian Networks (BN) have been a popular predictive modeling formalism in bioinformatics, but their application in modern genomics has been slowed by an inability to cleanly handle domains with mixed discrete and continuous variables. Existing free BN software packages either discretize continuous variables, which can lead to information loss, or do not include inference routines, which makes prediction with the BN impossible. We present CGBayesNets, a BN package focused around prediction of a clinical phenotype from mixed discrete and continuous variables, which fills these gaps. CGBayesNets implements Bayesian likelihood and inference algorithms for the conditional Gaussian Bayesian network (CGBNs) formalism, one appropriate for predicting an outcome of interest from, e.g., multimodal genomic data. We provide four different network learning algorithms, each making a different tradeoff between computational cost and network likelihood. CGBayesNets provides a full suite of functions for model exploration and verification, including cross validation, bootstrapping, and AUC manipulation. We highlight several results obtained previously with CGBayesNets, including predictive models of wood properties from tree genomics, leukemia subtype classification from mixed genomic data, and robust prediction of intensive care unit mortality outcomes from metabolomic profiles. We also provide detailed example analysis on public metabolomic and gene expression datasets. CGBayesNets is implemented in MATLAB and available as MATLAB source code, under an Open Source license and anonymous download at http://www.cgbayesnets.com.Publication A polymorphism in HLA-G modifies statin benefit in asthma(2014) Naidoo, Devesh; Wu, Ann; Brilliant, Murray H; Denny, Joshua; Ingram, Christie; Kitchner, Terrie E; Linneman, James G; McGeachie, Michael; Roden, Dan M; Shaffer, Christian M; Shah, Anushi; Weeke, Peter; Weiss, Scott; Xu, Hua; Medina, Marisa WSeveral reports have shown that statin treatment benefits patients with asthma, however inconsistent effects have been observed. The mir-152 family (148a, 148b and 152) has been implicated in asthma. These microRNAs suppress HLA-G expression, and rs1063320, a common SNP in the HLA-G 3’UTR which is associated with asthma risk, modulates miRNA binding. We report that statins up-regulate mir-148b and 152, and affect HLA-G expression in an rs1063320 dependent fashion. In addition, we found that individuals who carried the G minor allele of rs1063320 had reduced asthma related exacerbations (emergency department visits, hospitalizations or oral steroid use) compared to non-carriers (p=0.03) in statin users ascertained in the Personalized Medicine Research Project at the Marshfield Clinic (n=421). These findings support the hypothesis that rs1063320 modifies the effect of statin benefit in asthma, and thus may contribute to variation in statin efficacy for the management of this disease.Publication CMTR1 is associated with increased asthma exacerbations in patients taking inhaled corticosteroids(John Wiley and Sons Inc., 2015) Dahlin, Amber; Denny, Joshua; Roden, Dan M.; Brilliant, Murray H.; Ingram, Christie; Kitchner, Terrie E.; Linneman, James G.; Shaffer, Christian M.; Weeke, Peter; Xu, Hua; Kubo, Michiaki; Tamari, Mayumi; Clemmer, George L.; Ziniti, John; McGeachie, Michael; Tantisira, Kelan; Weiss, Scott; Wu, AnnAbstract Inhaled corticosteroids (ICS) are the most effective controller medications for asthma, and variability in ICS response is associated with genetic variation. Despite ICS treatment, some patients with poor asthma control experience severe asthma exacerbations, defined as a hospitalization or emergency room visit. We hypothesized that some individuals may be at increased risk of asthma exacerbations, despite ICS use, due to genetic factors. A GWAS of 237,726 common, independent markers was conducted in 806 Caucasian asthmatic patients from two population‐based biobanks: BioVU, at Vanderbilt University Medical Center (VUMC) in Tennessee (369 patients), and Personalized Medicine Research Project (PMRP) at the Marshfield Clinic in Wisconsin (437 patients). Using a case–control study design, the association of each SNP locus with the outcome of asthma exacerbations (defined as asthma‐related emergency department visits or hospitalizations concurrent with oral corticosteroid use), was evaluated for each population by logistic regression analysis, adjusting for age, gender and the first four principal components. A meta‐analysis of the results was conducted. Validation of expression of selected candidate genes was determined by evaluating an independent microarray expression data set. Our study identified six novel SNPs associated with differential risk of asthma exacerbations (P < 10−05). The top GWAS result, rs2395672 in CMTR1, was associated with an increased risk of exacerbations in both populations (OR = 1.07, 95% CI 1.03–1.11; joint P = 2.3 × 10−06). Two SNPs (rs2395672 and rs279728) were associated with increased risk of exacerbations, while the remaining four SNPs (rs4271056, rs6467778, rs2691529, and rs9303988) were associated with decreased risk. Three SNPs (rs2395672, rs6467778, and rs2691529) were present in three genes: CMTR1, TRIM24 and MAGI2. The CMTR1 mRNA transcript was significantly differentially expressed in nasal lavage samples from asthmatics during acute exacerbations, suggesting potential involvement of this gene in the development of this phenotype. We show that genetic variability may contribute to asthma exacerbations in patients taking ICS. Furthermore, our studies implicate CMTR1 as a novel candidate gene with potential roles in the pathogenesis of asthma exacerbations.Publication Patterns of Growth and Decline in Lung Function in Persistent Childhood Asthma(New England Journal of Medicine (NEJM/MMS), 2016) McGeachie, Michael; Yates, Katherine; Zhou, Xiaobo; Guo, Feng; Sternberg, Alice L.; Van Natta, Mark L.; Wise, Robert A.; Szefler, Stanley J.; Sharma, Sunita; Kho, Alvin; Cho, Michael; Croteau-Chonka, Damien; Castaldi, Peter; Jain, Gaurav; Sanyal, Amartya; Zhan, Ye; Lajoie, Bryan R.; Dekker, Job; Stamatoyannopoulos, John; Covar, Ronina A.; Zeiger, Robert S.; Adkinson, N. Franklin; Williams, Paul; Kelly, H. William; Grasemann, Hartmut; Vonk, Judith M.; Koppelman, Gerard H.; Postma, Dirkje S.; Raby, Benjamin; Houston, Isaac; Lu, Quan; Fuhlbrigge, Anne; Tantisira, Kelan; Silverman, Edwin; Tonascia, James; Weiss, Scott; Strunk, Robert C.BACKGROUND: Tracking longitudinal measurements of growth and decline in lung function in patients with persistent childhood asthma may reveal links between asthma and subsequent chronic airflow obstruction. METHODS: We classified children with asthma according to four characteristic patterns of lung-function growth and decline on the basis of graphs showing forced expiratory volume in 1 second (FEV1), representing spirometric measurements performed from childhood into adulthood. Risk factors associated with abnormal patterns were also examined. To define normal values, we used FEV1 values from participants in the National Health and Nutrition Examination Survey who did not have asthma. RESULTS: Of the 684 study participants, 170 (25%) had a normal pattern of lung-function growth without early decline, and 514 (75%) had abnormal patterns: 176 (26%) had reduced growth and an early decline, 160 (23%) had reduced growth only, and 178 (26%) had normal growth and an early decline. Lower baseline values for FEV1, smaller bronchodilator response, airway hyperresponsiveness at baseline, and male sex were associated with reduced growth (P<0.001 for all comparisons). At the last spirometric measurement (mean [±SD] age, 26.0±1.8 years), 73 participants (11%) met Global Initiative for Chronic Obstructive Lung Disease spirometric criteria for lung-function impairment that was consistent with chronic obstructive pulmonary disease (COPD); these participants were more likely to have a reduced pattern of growth than a normal pattern (18% vs. 3%, P<0.001). CONCLUSIONS: Childhood impairment of lung function and male sex were the most significant predictors of abnormal longitudinal patterns of lung-function growth and decline. Children with persistent asthma and reduced growth of lung function are at increased risk for fixed airflow obstruction and possibly COPD in early adulthood. (Funded by the Parker B. Francis Foundation and others; ClinicalTrials.gov number, NCT00000575.)Publication Longitudinal Prediction of the Infant Gut Microbiome with Dynamic Bayesian Networks(Nature Publishing Group, 2016) McGeachie, Michael; Sordillo, Joanne; Gibson, Travis; Weinstock, George M.; Liu, Yang-Yu; Gold, Diane; Weiss, Scott; Litonjua, AugustoSequencing of the 16S rRNA gene allows comprehensive assessment of bacterial community composition from human body sites. Previously published and publicly accessible data on 58 preterm infants in the Neonatal Intensive Care Unit who underwent frequent stool collection was used. We constructed Dynamic Bayesian Networks from the data and analyzed predictive performance and network characteristics. We constructed a DBN model of the infant gut microbial ecosystem, which explicitly captured specific relationships and general trends in the data: increasing amounts of Clostridia, residual amounts of Bacilli, and increasing amounts of Gammaproteobacteria that then give way to Clostridia. Prediction performance of DBNs with fewer edges were overall more accurate, although less so on harder-to-predict subjects (p = 0.045). DBNs provided quantitative likelihood estimates for rare abruptions events. Iterative prediction was less accurate (p < 0.001), but showed remarkable insensitivity to initial conditions and predicted convergence to a mix of Clostridia, Gammaproteobacteria, and Bacilli. DBNs were able to identify important relationships between microbiome taxa and predict future changes in microbiome composition from measured or synthetic initial conditions. DBNs also provided likelihood estimates for sudden, dramatic shifts in microbiome composition, which may be useful in guiding further analysis of those samples.Publication The metabolomics of asthma control: a promising link between genetics and disease(John Wiley & Sons, Ltd, 2015) McGeachie, Michael; Dahlin, Amber; Qiu, Weiliang; Croteau-Chonka, Damien; Savage, Jessica; Wu, Ann; Wan, Emily; Sordillo, Joanne; Al-Garawi, Amal; Martinez, Fernando D; Strunk, Robert C; Lemanske, Robert F; Liu, Andrew H; Raby, Benjamin; Weiss, Scott; Clish, Clary B; Lasky-Su, JessicaShort-acting β agonists (e.g., albuterol) are the most commonly used medications for asthma, a disease that affects over 300 million people in the world. Metabolomic profiling of asthmatics taking β agonists presents a new and promising resource for identifying the molecular determinants of asthma control. The objective is to identify novel genetic and biochemical predictors of asthma control using an integrative “omics” approach. We generated lipidomic data by liquid chromatography tandem mass spectrometry (LC-MS), using plasma samples from 20 individuals with asthma. The outcome of interest was a binary indicator of asthma control defined by the use of albuterol inhalers in the preceding week. We integrated metabolomic data with genome-wide genotype, gene expression, and methylation data of this cohort to identify genomic and molecular indicators of asthma control. A Conditional Gaussian Bayesian Network (CGBN) was generated using the strongest predictors from each of these analyses. Integrative and metabolic pathway over-representation analyses (ORA) identified enrichment of known biological pathways within the strongest molecular determinants. Of the 64 metabolites measured, 32 had known identities. The CGBN model based on four SNPs (rs9522789, rs7147228, rs2701423, rs759582) and two metabolites—monoHETE_0863 and sphingosine-1-phosphate (S1P) could predict asthma control with an AUC of 95%. Integrative ORA identified 17 significantly enriched pathways related to cellular immune response, interferon signaling, and cytokine-related signaling, for which arachidonic acid, PGE2 and S1P, in addition to six genes (CHN1, PRKCE, GNA12, OASL, OAS1, and IFIT3) appeared to drive the pathway results. Of these predictors, S1P, GNA12, and PRKCE were enriched in the results from integrative and metabolic ORAs. Through an integrative analysis of metabolomic, genomic, and methylation data from a small cohort of asthmatics, we implicate altered metabolic pathways, related to sphingolipid metabolism, in asthma control. These results provide insight into the pathophysiology of asthma control.Publication Whole genome prediction and heritability of childhood asthma phenotypes(John Wiley and Sons Inc., 2016) McGeachie, Michael; Clemmer, George L.; Croteau‐Chonka, Damien C.; Castaldi, Peter; Cho, Michael; Sordillo, Joanne; Lasky‐Su, Jessica A.; Raby, Benjamin; Tantisira, Kelan; Weiss, ScottAbstract Introduction: While whole genome prediction (WGP) methods have recently demonstrated successes in the prediction of complex genetic diseases, they have not yet been applied to asthma and related phenotypes. Longitudinal patterns of lung function differ between asthmatics, but these phenotypes have not been assessed for heritability or predictive ability. Herein, we assess the heritability and genetic predictability of asthma‐related phenotypes. Methods: We applied several WGP methods to a well‐phenotyped cohort of 832 children with mild‐to‐moderate asthma from CAMP. We assessed narrow‐sense heritability and predictability for airway hyperresponsiveness, serum immunoglobulin E, blood eosinophil count, pre‐ and post‐bronchodilator forced expiratory volume in 1 sec (FEV1), bronchodilator response, steroid responsiveness, and longitudinal patterns of lung function (normal growth, reduced growth, early decline, and their combinations). Prediction accuracy was evaluated using a training/testing set split of the cohort. Results: We found that longitudinal lung function phenotypes demonstrated significant narrow‐sense heritability (reduced growth, 95%; normal growth with early decline, 55%). These same phenotypes also showed significant polygenic prediction (areas under the curve [AUCs] 56% to 62%). Including additional demographic covariates in the models increased prediction 4–8%, with reduced growth increasing from 62% to 66% AUC. We found that prediction with a genomic relatedness matrix was improved by filtering available SNPs based on chromatin evidence, and this result extended across cohorts. Conclusions: Longitudinal reduced lung function growth displayed extremely high heritability. All phenotypes with significant heritability showed significant polygenic prediction. Using SNP‐prioritization increased prediction across cohorts. WGP methods show promise in predicting asthma‐related heritable traits.Publication Joint GWAS Analysis: Comparing similar GWAS at different genomic resolutions identifies novel pathway associations with six complex diseases(Elsevier BV, 2014) McGeachie, Michael; Clemmer, George L.; Lasky-Su, Jessica; Dahlin, Amber; Raby, Benjamin; Weiss, ScottWe show here that combining two existing genome wide association studies (GWAS) yields additional biologically relevant information, beyond that obtained by either GWAS separately. We propose Joint GWAS Analysis, a method that compares a pair of GWAS for similarity among the top SNP associations, top genes identified, gene functional clusters, and top biological pathways. We show that Joint GWAS Analysis identifies additional enriched biological pathways that would be missed by traditional Single-GWAS analysis. Furthermore, we examine the similarities of six complex genetic disorders at the SNP-level, gene-level, gene-cluster-level, and pathway-level. We make concrete hypotheses regarding novel pathway associations for several complex disorders considered, based on the results of Joint GWAS Analysis. Together, these results demonstrate that common complex disorders share substantially more genomic architecture than has been previously realized and that the meta-analysis of GWAS needs not be limited to GWAS of the same phenotype to be informative.Publication Genome-wide expression profiles identify potential targets for gene-environment interactions in asthma severity(Elsevier BV, 2015) Sordillo, Joanne; Kelly, Roxanne; Bunyavanich, Supinda; McGeachie, Michael; Qiu, Weiliang; Croteau-Chonka, Damien; Soto-Quiros, Manuel; Avila, Lydiana; Celedón, Juan C.; Brehm, John M.; Weiss, Scott; Gold, Diane; Litonjua, Augusto A.Background: Gene by environment interaction (G × E) studies utilizing GWAS data are often underpowered after adjustment for multiple comparisons. Differential gene expression, in response to the exposure of interest, may capture the most biologically relevant genes at the genome-wide level. Methods: We used differential genome-wide expression profiles from the Home Allergens and Asthma Birth cohort in response to Der f 1 allergen (sensitized vs. non-sensitized) to inform a G × E study of dust mite exposure and asthma severity. Polymorphisms in differentially expressed genes were identified in GWAS data from CAMP, a clinical trial in childhood asthmatics. Home dust mite allergen (< or ≥ 10µg/g dust) was assessed at baseline, and (≥ 1) severe asthma exacerbation (emergency room (ER) visit or hospitalization for asthma in the first trial year) served as the disease severity outcome. The Genetics of Asthma in Costa Rica (GACRS) study, and a Puerto Rico/Connecticut asthma cohortwere used for replication. Results: IL-9, IL-5 and PRG2 expression was up-regulated in Der f 1 stimulated PBMCs from dust mite sensitized individuals (adj. p value <0.04). IL-9 polymorphisms (rs11741137, rs2069885, rs1859430) showed evidence for interaction with dust mite in CAMP (p=0.02 to 0.03), with replication in GACRS (p=0.04). Subjects with the dominant genotype for these IL-9 polymorphisms were more likely to report a severe asthma exacerbation if exposed to elevated dust mite. Conclusions: Genome-wide differential gene expression in response to dust mite allergen identified IL-9, a biologically plausible gene target that may interact with environmental dust mite to increase severe asthma exacerbations in children.