Browsing by Subject "Pathway analysis"
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Item A genome-wide search for pleiotropy in more than 100,000 harmonized longitudinal cognitive domain scores(BMC, 2023-06-22) Kang, Moonil; Ang, Ting Fang Alvin; Devine, Sherral A.; Sherva, Richard; Mukherjee, Shubhabrata; Trittschuh, Emily H.; Gibbons, Laura E.; Scollard, Phoebe; Lee, Michael; Choi, Seo-Eun; Klinedinst, Brandon; Nakano, Connie; Dumitrescu, Logan C.; Durant, Alaina; Hohman, Timothy J.; Cuccaro, Michael L.; Saykin, Andrew J.; Kukull, Walter A.; Bennett, David A.; Wang, Li-San; Mayeux, Richard P.; Haines, Jonathan L.; Pericak-Vance, Margaret A.; Schellenberg, Gerard D.; Crane, Paul K.; Au, Rhoda; Lunetta, Kathryn L.; Mez, Jesse B.; Farrer, Lindsay A.; Radiology and Imaging Sciences, School of MedicineBackground: More than 75 common variant loci account for only a portion of the heritability for Alzheimer's disease (AD). A more complete understanding of the genetic basis of AD can be deduced by exploring associations with AD-related endophenotypes. Methods: We conducted genome-wide scans for cognitive domain performance using harmonized and co-calibrated scores derived by confirmatory factor analyses for executive function, language, and memory. We analyzed 103,796 longitudinal observations from 23,066 members of community-based (FHS, ACT, and ROSMAP) and clinic-based (ADRCs and ADNI) cohorts using generalized linear mixed models including terms for SNP, age, SNP × age interaction, sex, education, and five ancestry principal components. Significance was determined based on a joint test of the SNP's main effect and interaction with age. Results across datasets were combined using inverse-variance meta-analysis. Genome-wide tests of pleiotropy for each domain pair as the outcome were performed using PLACO software. Results: Individual domain and pleiotropy analyses revealed genome-wide significant (GWS) associations with five established loci for AD and AD-related disorders (BIN1, CR1, GRN, MS4A6A, and APOE) and eight novel loci. ULK2 was associated with executive function in the community-based cohorts (rs157405, P = 2.19 × 10-9). GWS associations for language were identified with CDK14 in the clinic-based cohorts (rs705353, P = 1.73 × 10-8) and LINC02712 in the total sample (rs145012974, P = 3.66 × 10-8). GRN (rs5848, P = 4.21 × 10-8) and PURG (rs117523305, P = 1.73 × 10-8) were associated with memory in the total and community-based cohorts, respectively. GWS pleiotropy was observed for language and memory with LOC107984373 (rs73005629, P = 3.12 × 10-8) in the clinic-based cohorts, and with NCALD (rs56162098, P = 1.23 × 10-9) and PTPRD (rs145989094, P = 8.34 × 10-9) in the community-based cohorts. GWS pleiotropy was also found for executive function and memory with OSGIN1 (rs12447050, P = 4.09 × 10-8) and PTPRD (rs145989094, P = 3.85 × 10-8) in the community-based cohorts. Functional studies have previously linked AD to ULK2, NCALD, and PTPRD. Conclusion: Our results provide some insight into biological pathways underlying processes leading to domain-specific cognitive impairment and AD, as well as a conduit toward a syndrome-specific precision medicine approach to AD. Increasing the number of participants with harmonized cognitive domain scores will enhance the discovery of additional genetic factors of cognitive decline leading to AD and related dementias.Item Circular RNA detection identifies circPSEN1 alterations in brain specific to autosomal dominant Alzheimer's disease(BMC, 2022-03-04) Chen, Hsiang‑Han; Eteleeb, Abdallah; Wang, Ciyang; Fernandez, Maria Victoria; Budde, John P.; Bergmann, Kristy; Norton, Joanne; Wang, Fengxian; Ebl, Curtis; Morris, John C.; Perrin, Richard J.; Bateman, Randall J.; McDade, Eric; Xiong, Chengjie; Goate, Alison; Farlow, Martin; Chhatwal, Jasmeer; Schofield, Peter R.; Chui, Helena; Harari, Oscar; Cruchaga, Carlos; Ibanez, Laura; Dominantly Inherited Alzheimer Network; Neurology, School of MedicineBackground: Autosomal-dominant Alzheimer's disease (ADAD) is caused by pathogenic mutations in APP, PSEN1, and PSEN2, which usually lead to an early age at onset (< 65). Circular RNAs are a family of non-coding RNAs highly expressed in the nervous system and especially in synapses. We aimed to investigate differences in brain gene expression of linear and circular transcripts from the three ADAD genes in controls, sporadic AD, and ADAD. Methods: We obtained and sequenced RNA from brain cortex using standard protocols. Linear counts were obtained using the TOPMed pipeline; circular counts, using python package DCC. After stringent quality control (QC), we obtained the counts for PSEN1, PSEN2 and APP genes. Only circPSEN1 passed QC. We used DESeq2 to compare the counts across groups, correcting for biological and technical variables. Finally, we performed in-silico functional analyses using the Circular RNA interactome website and DIANA mirPath software. Results: Our results show significant differences in gene counts of circPSEN1 in ADAD individuals, when compared to sporadic AD and controls (ADAD = 21, AD = 253, Controls = 23-ADADvsCO: log2FC = 0.794, p = 1.63 × 10-04, ADADvsAD: log2FC = 0.602, p = 8.22 × 10-04). The high gene counts are contributed by two circPSEN1 species (hsa_circ_0008521 and hsa_circ_0003848). No significant differences were observed in linear PSEN1 gene expression between cases and controls, indicating that this finding is specific to the circular forms. In addition, the high circPSEN1 levels do not seem to be specific to PSEN1 mutation carriers; the counts are also elevated in APP and PSEN2 mutation carriers. In-silico functional analyses suggest that circPSEN1 is involved in several pathways such as axon guidance (p = 3.39 × 10-07), hippo signaling pathway (p = 7.38 × 10-07), lysine degradation (p = 2.48 × 10-05) or Wnt signaling pathway (p = 5.58 × 10-04) among other KEGG pathways. Additionally, circPSEN1 counts were able to discriminate ADAD from sporadic AD and controls with an AUC above 0.70. Conclusions: Our findings show the differential expression of circPSEN1 is increased in ADAD. Given the biological function previously ascribed to circular RNAs and the results of our in-silico analyses, we hypothesize that this finding might be related to neuroinflammatory events that lead or that are caused by the accumulation of amyloid-beta.Item Common biological networks underlie genetic risk for alcoholism in African- and European-American populations(Wiley, 2013) Kos, Mark Z.; Yan, Jia; Dick, Danielle M.; Agrawal, Arpana; Bucholz, Kathleen K.; Rice, John P.; Johnson, Eric O.; Schuckit, Marc; Kuperman, Sam; Kramer, John; Goate, Alison M.; Tischfield, Jay A.; Foroud, Tatiana; Nurnberger, John, Jr.; Hesselbrock, Victor; Porjesz, Bernice; Bierut, Laura J.; Edenberg, Howard J.; Almasy, Laura; Medical and Molecular Genetics, School of MedicineAlcohol dependence (AD) is a heritable substance addiction with adverse physical and psychological consequences, representing a major health and economic burden on societies worldwide. Genes thus far implicated via linkage, candidate gene and genome-wide association studies (GWAS) account for only a small fraction of its overall risk, with effects varying across ethnic groups. Here we investigate the genetic architecture of alcoholism and report on the extent to which common, genome-wide SNPs collectively account for risk of AD in two US populations, African-Americans (AAs) and European-Americans (EAs). Analyzing GWAS data for two independent case-control sample sets, we compute polymarker scores that are significantly associated with alcoholism (P = 1.64 × 10(-3) and 2.08 × 10(-4) for EAs and AAs, respectively), reflecting the small individual effects of thousands of variants derived from patterns of allelic architecture that are population specific. Simulations show that disease models based on rare and uncommon causal variants (MAF < 0.05) best fit the observed distribution of polymarker signals. When scoring bins were annotated for gene location and examined for constituent biological networks, gene enrichment is observed for several cellular processes and functions in both EA and AA populations, transcending their underlying allelic differences. Our results reveal key insights into the complex etiology of AD, raising the possibility of an important role for rare and uncommon variants, and identify polygenic mechanisms that encompass a spectrum of disease liability, with some, such as chloride transporters and glycine metabolism genes, displaying subtle, modifying effects that are likely to escape detection in most GWAS designs.Item Decitabine reactivated pathways in platinum resistant ovarian cancer(Impact Journals, 2014) Fang, Fang; Zuo, Qingyao; Pilrose, Jay; Wang, Yinu; Shen, Changyu; Li, Meng; Wulfridge, Phillip; Matei, Daniela; Nephew, Kenneth P.; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public HealthCombination therapy with decitabine, a DNMTi and carboplatin resensitized chemoresistant ovarian cancer (OC) to platinum inducing promising clinical activity. We investigated gene-expression profiles in tumor biopsies to identify decitabine-reactivated pathways associated with clinical response. Gene-expression profiling was performed using RNA from paired tumor biopsies before and 8 days after decitabine from 17 patients with platinum resistant OC. Bioinformatic analysis included unsupervised hierarchical-clustering, pathway and GSEA distinguishing profiles of "responders" (progression-free survival, PFS>6 months) and "non-responders" (PFS< 6 months). Functional validation of selected results was performed in OC cells/tumors. Pre-treatment tumors from responders expressed genes associated with enhanced glycosphingolipid biosynthesis, translational misregulation, decreased ABC transporter expression, TGF-β signaling, and numerous metabolic pathways. Analysis of post-treatment biopsies from responders revealed overexpression of genes associated with reduced Hedgehog pathway signaling, reduced DNA repair/replication, and cancer-associated metabolism. GO and GSEA analyses revealed upregulation of genes associated with glycosaminoglycan binding, cell-matrix adhesion, and cell-substrate adhesion. Computational findings were substantiated by experimental validation of expression of key genes involved in two critical pathways affected by decitabine (TGF-β and Hh). Gene-expression profiling identified specific pathways altered by decitabine and associated with platinum-resensitization and clinical benefit in OC. Our data could influence patient stratification for future studies using epigenetic therapies.Item Effects of Lithium Monotherapy for Bipolar Disorder on Gene Expression in Peripheral Lymphocytes(Karger Publishers, 2016-10) Anand, Amit; McClintick, Jeanette N.; Murrell, Jill; Karne, Harish; Nurnberger, John I.; Edenberg, Howard J.; Psychiatry, School of MedicineBackground This study investigated the effect of lithium monotherapy on peripheral lymphocyte gene expression in bipolar disorder (BD). Method Twenty-two medication-free bipolar subjects (11 hypomanic, 11 depressed) were started on lithium monotherapy. Closely matched healthy subjects (n = 15) were included as controls but did not receive treatment. Blood RNA samples were collected at baseline and after 2 and 8 weeks of treatment. RNA expression was measured using the Affymetrix GeneChip® Human Gene 1.0 ST Array followed by Ingenuity pathways analysis. The results for the contrast of weeks 2 and 8 were not significantly different and were combined. Results In BD subjects, 56 genes showed significant (false discovery rate <0.1) expression changes from baseline; the effect sizes and directions for all of these were similar at weeks 2 and 8. Among these were immune-related genes (IL5RA, MOK, IFI6, and RFX2), purinergic receptors (P2RY14, P2RY2, and ADORA3) and signal transduction-related genes (CAMK1 and PIK3R6). Pathway and upstream regulator analysis also revealed that lithium altered several immune- and signal transduction-related functions. Differentially expressed genes did not correlate with week 8 clinical response, but other genes involved in protein synthesis and degradation did. Conclusion Peripheral gene expression may serve as a biomarker of lithium effect.Item Genetic associations with childhood brain growth, defined in two longitudinal cohorts(Wiley, 2018-06) Szekely, Eszter; Schwantes-An, Tae-Hwi Linus; Justice, Cristina M.; Sabourin, Jeremy A.; Jansen, Philip R.; Muetzel, Ryan L.; Sharp, Wendy; Tiemeier, Henning; Sung, Heejong; White, Tonya J.; Wilson, Alexander F.; Shaw, Philip; Medical and Molecular Genetics, School of MedicineGenome-wide association studies (GWASs) are unraveling the genetics of adult brain neuroanatomy as measured by cross-sectional anatomic magnetic resonance imaging (aMRI). However, the genetic mechanisms that shape childhood brain development are, as yet, largely unexplored. In this study we identify common genetic variants associated with childhood brain development as defined by longitudinal aMRI. Genome-wide single nucleotide polymorphism (SNP) data were determined in two cohorts: one enriched for attention-deficit/hyperactivity disorder (ADHD) (LONG cohort: 458 participants; 119 with ADHD) and the other from a population-based cohort (Generation R: 257 participants). The growth of the brain's major regions (cerebral cortex, white matter, basal ganglia, and cerebellum) and one region of interest (the right lateral prefrontal cortex) were defined on all individuals from two aMRIs, and a GWAS and a pathway analysis were performed. In addition, association between polygenic risk for ADHD and brain growth was determined for the LONG cohort. For white matter growth, GWAS meta-analysis identified a genome-wide significant intergenic SNP (rs12386571, P = 9.09 × 10-9 ), near AKR1B10. This gene is part of the aldo-keto reductase superfamily and shows neural expression. No enrichment of neural pathways was detected and polygenic risk for ADHD was not associated with the brain growth phenotypes in the LONG cohort that was enriched for the diagnosis of ADHD. The study illustrates the use of a novel brain growth phenotype defined in vivo for further study.Item Genome-wide association study of rate of cognitive decline in Alzheimer’s Disease patients identifies novel genes and pathways(Wiley, 2020-08) Sherva, Richard; Gross, Alden; Mukherjee, Shubhabrata; Koesterer, Ryan; Amouyel, Philippe; Philippe, Celine; Dufouil, Carole; Bennett, David A.; Chibnik, Lori; Cruchaga, Carlos; del-Aguila, Jorge; Farrer, Lindsay A.; Mayeux, Richard; Munsie, Leanne; Winslow, Ashley; Ashley, Stephen; Saykin, Andrew J.; Kauwe, John S.K.; Crane, Paul K.; Green, Robert C.; Radiology and Imaging Sciences, School of MedicineIntroduction: Variability exists in the disease trajectories of Alzheimer's disease (AD) patients. We performed a genome-wide association study to examine rate of cognitive decline (ROD) in patients with AD. Methods: We tested for interactions between genetic variants and time since diagnosis to predict the ROD of a composite cognitive score in 3946 AD cases and performed pathway analysis on the top genes. Results: Suggestive associations (P < 1.0 × 10-6 ) were observed on chromosome 15 in DNA polymerase-γ (rs3176205, P = 1.11 × 10-7 ), chromosome 7 (rs60465337,P = 4.06 × 10-7 ) in contactin-associated protein-2, in RP11-384F7.1 on chromosome 3 (rs28853947, P = 5.93 × 10-7 ), family with sequence similarity 214 member-A on chromosome 15 (rs2899492, P = 5.94 × 10-7 ), and intergenic regions on chromosomes 16 (rs4949142, P = 4.02 × 10-7 ) and 4 (rs1304013, P = 7.73 × 10-7 ). Significant pathways involving neuronal development and function, apoptosis, memory, and inflammation were identified. Discussion: Pathways related to AD, intelligence, and neurological function determine AD progression, while previously identified AD risk variants, including the apolipoprotein (APOE) ε4 and ε2 variants, do not have a major impact.Item Genome-wide network-based pathway analysis of CSF t-tau/Aβ1-42 ratio in the ADNI cohort(Springer Nature, 2017-05-30) Cong, Wang; Meng, Xianglian; Li, Jin; Zhang, Qiushi; Chen, Feng; Liu, Wenjie; Wang, Ying; Cheng, Sipu; Yao, Xiaohui; Yan, Jingwen; Kim, Sungeun; Saykin, Andrew J.; Liang, Hong; Shen, Li; Alzheimer’s Disease Neuroimaging Initiative; Radiology and Imaging Sciences, School of MedicineBACKGROUND: The cerebrospinal fluid (CSF) levels of total tau (t-tau) and Aβ1-42 are potential early diagnostic markers for probable Alzheimer's disease (AD). The influence of genetic variation on these CSF biomarkers has been investigated in candidate or genome-wide association studies (GWAS). However, the investigation of statistically modest associations in GWAS in the context of biological networks is still an under-explored topic in AD studies. The main objective of this study is to gain further biological insights via the integration of statistical gene associations in AD with physical protein interaction networks. RESULTS: The CSF and genotyping data of 843 study subjects (199 CN, 85 SMC, 239 EMCI, 207 LMCI, 113 AD) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were analyzed. PLINK was used to perform GWAS on the t-tau/Aβ1-42 ratio using quality controlled genotype data, including 563,980 single nucleotide polymorphisms (SNPs), with age, sex and diagnosis as covariates. Gene-level p-values were obtained by VEGAS2. Genes with p-value ≤ 0.05 were mapped on to a protein-protein interaction (PPI) network (9,617 nodes, 39,240 edges, from the HPRD Database). We integrated a consensus model strategy into the iPINBPA network analysis framework, and named it as CM-iPINBPA. Four consensus modules (CMs) were discovered by CM-iPINBPA, and were functionally annotated using the pathway analysis tool Enrichr. The intersection of four CMs forms a common subnetwork of 29 genes, including those related to tau phosphorylation (GSK3B, SUMO1, AKAP5, CALM1 and DLG4), amyloid beta production (CASP8, PIK3R1, PPA1, PARP1, CSNK2A1, NGFR, and RHOA), and AD (BCL3, CFLAR, SMAD1, and HIF1A). CONCLUSIONS: This study coupled a consensus module (CM) strategy with the iPINBPA network analysis framework, and applied it to the GWAS of CSF t-tau/Aβ1-42 ratio in an AD study. The genome-wide network analysis yielded 4 enriched CMs that share not only genes related to tau phosphorylation or amyloid beta production but also multiple genes enriching several KEGG pathways such as Alzheimer's disease, colorectal cancer, gliomas, renal cell carcinoma, Huntington's disease, and others. This study demonstrated that integration of gene-level associations with CMs could yield statistically significant findings to offer valuable biological insights (e.g., functional interaction among the protein products of these genes) and suggest high confidence candidates for subsequent analyses.Item Identification of novel alternative splicing biomarkers for breast cancer with LC/MS/MS and RNA-Seq(BMC, 2020-12-03) Zhang, Fan; Deng, Chris K.; Wang, Mu; Deng, Bin; Barber, Robert; Huang, Gang; Biochemistry and Molecular Biology, School of MedicineBackground: Alternative splicing isoforms have been reported as a new and robust class of diagnostic biomarkers. Over 95% of human genes are estimated to be alternatively spliced as a powerful means of producing functionally diverse proteins from a single gene. The emergence of next-generation sequencing technologies, especially RNA-seq, provides novel insights into large-scale detection and analysis of alternative splicing at the transcriptional level. Advances in Proteomic Technologies such as liquid chromatography coupled tandem mass spectrometry (LC-MS/MS), have shown tremendous power for the parallel characterization of large amount of proteins in biological samples. Although poor correspondence has been generally found from previous qualitative comparative analysis between proteomics and microarray data, significantly higher degrees of correlation have been observed at the level of exon. Combining protein and RNA data by searching LC-MS/MS data against a customized protein database from RNA-Seq may produce a subset of alternatively spliced protein isoform candidates that have higher confidence. Results: We developed a bioinformatics workflow to discover alternative splicing biomarkers from LC-MS/MS using RNA-Seq. First, we retrieved high confident, novel alternative splicing biomarkers from the breast cancer RNA-Seq database. Then, we translated these sequences into in silico Isoform Junction Peptides, and created a customized alternative splicing database for MS searching. Lastly, we ran the Open Mass spectrometry Search Algorithm against the customized alternative splicing database with breast cancer plasma proteome. Twenty six alternative splicing biomarker peptides with one single intron event and one exon skipping event were identified. Further interpretation of biological pathways with our Integrated Pathway Analysis Database showed that these 26 peptides are associated with Cancer, Signaling, Metabolism, Regulation, Immune System and Hemostasis pathways, which are consistent with the 256 alternative splicing biomarkers from the RNA-Seq. Conclusions: This paper presents a bioinformatics workflow for using RNA-seq data to discover novel alternative splicing biomarkers from the breast cancer proteome. As a complement to synthetic alternative splicing database technique for alternative splicing identification, this method combines the advantages of two platforms: mass spectrometry and next generation sequencing and can help identify potentially highly sample-specific alternative splicing isoform biomarkers at early-stage of cancer.Item PAGER 2.0: an update to the pathway, annotated-list and gene-signature electronic repository for Human Network Biology(Oxford Academic, 2018-01-04) Yue, Zongliang; Zheng, Qi; Neylon, Michael T.; Yoo, Minjae; Shin, Jimin; Zhao, Zhiying; Tan, Aik Choon; Chen, Jake Yue; BioHealth Informatics, School of Informatics and ComputingIntegrative Gene-set, Network and Pathway Analysis (GNPA) is a powerful data analysis approach developed to help interpret high-throughput omics data. In PAGER 1.0, we demonstrated that researchers can gain unbiased and reproducible biological insights with the introduction of PAGs (Pathways, Annotated-lists and Gene-signatures) as the basic data representation elements. In PAGER 2.0, we improve the utility of integrative GNPA by significantly expanding the coverage of PAGs and PAG-to-PAG relationships in the database, defining a new metric to quantify PAG data qualities, and developing new software features to simplify online integrative GNPA. Specifically, we included 84 282 PAGs spanning 24 different data sources that cover human diseases, published gene-expression signatures, drug-gene, miRNA-gene interactions, pathways and tissue-specific gene expressions. We introduced a new normalized Cohesion Coefficient (nCoCo) score to assess the biological relevance of genes inside a PAG, and RP-score to rank genes and assign gene-specific weights inside a PAG. The companion web interface contains numerous features to help users query and navigate the database content. The database content can be freely downloaded and is compatible with third-party Gene Set Enrichment Analysis tools. We expect PAGER 2.0 to become a major resource in integrative GNPA. PAGER 2.0 is available at http://discovery.informatics.uab.edu/PAGER/.