Browsing by Author "Wang, Lei"
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Item Genetic Interactions Explain Variance in Cingulate Amyloid Burden: An AV-45 PET Genome-Wide Association and Interaction Study in the ADNI Cohort(Hindawi, 2015-09-03) Li, Jin; Zhang, Qiushi; Chen, Feng; Yan, Jingwen; Kim, Sungeun; Wang, Lei; Feng, Weixing; Saykin, Andrew J.; Liang, Hong; Shen, Li; Radiology and Imaging Sciences, School of MedicineAlzheimer's disease (AD) is the most common neurodegenerative disorder. Using discrete disease status as the phenotype and computing statistics at the single marker level may not be able to address the underlying biological interactions that contribute to disease mechanism and may contribute to the issue of "missing heritability." We performed a genome-wide association study (GWAS) and a genome-wide interaction study (GWIS) of an amyloid imaging phenotype, using the data from Alzheimer's Disease Neuroimaging Initiative. We investigated the genetic main effects and interaction effects on cingulate amyloid-beta (Aβ) load in an effort to better understand the genetic etiology of Aβ deposition that is a widely studied AD biomarker. PLINK was used in the single marker GWAS, and INTERSNP was used to perform the two-marker GWIS, focusing only on SNPs with p ≤ 0.01 for the GWAS analysis. Age, sex, and diagnosis were used as covariates in both analyses. Corrected p values using the Bonferroni method were reported. The GWAS analysis revealed significant hits within or proximal to APOE, APOC1, and TOMM40 genes, which were previously implicated in AD. The GWIS analysis yielded 8 novel SNP-SNP interaction findings that warrant replication and further investigation.Item Genome-wide association and interaction studies of CSF T-tau/Aβ42 ratio in ADNI cohort(Elsevier, 2017) Li, Jin; Zhang, Qiushi; Chen, Feng; Meng, Xianglian; Liu, Wenjie; Chen, Dandan; Yan, Jingwen; Kim, Sungeun; Wang, Lei; Feng, Weixing; Saykin, Andrew J.; Liang, Hong; Shen, Li; Department of Radiology and Imaging Sciences, IU School of MedicineThe pathogenic relevance in Alzheimer’s disease (AD) presents a decrease of cerebrospinal fluid (CSF) amyloid-ß42 (Aß42) burden and an increase in CSF total-tau (T-tau) levels. In this work, we performed genome-wide association study (GWAS) and genome-wide interaction study (GWIS) of T-tau/Aß42 ratio as an AD imaging quantitative trait (QT) on 843 subjects and 563,980 single nucleotide polymorphisms (SNPs) in ADNI cohort. We aim to identify not only SNPs with significant main effects but also SNPs with interaction effects to help explain “missing heritability”. Linear regression method was used to detect SNP-SNP interactions among SNPs with uncorrected p-value≤0.01 from the GWAS. Age, gender and diagnosis were considered as covariates in both studies. The GWAS results replicated the previously reported AD-related genes APOE, APOC1 and TOMM40, as well as identified 14 novel genes, which showed genome-wide statistical significance. GWIS revealed 7 pairs of SNPs meeting the cell-size criteria and with bonferroni-corrected p-value≤0.05. As we expect, these interaction pairs all had marginal main effects but explained a relatively high-level variance of T-tau/Aß42, demonstrating their potential association with AD pathology.Item Greater male than female variability in regional brain structure across the lifespan(Wiley, 2021) Wierenga, Lara M.; Doucet, Gaelle E.; Dima, Danai; Agartz, Ingrid; Aghajani, Moji; Akudjedu, Theophilus N.; Albajes‐Eizagirre, Anton; Alnæs, Dag; Alpert, Kathryn I.; Andreassen, Ole A.; Anticevic, Alan; Asherson, Philip; Banaschewski, Tobias; Bargallo, Nuria; Baumeister, Sarah; Baur‐Streubel, Ramona; Bertolino, Alessandro; Bonvino, Aurora; Boomsma, Dorret I.; Borgwardt, Stefan; Bourque, Josiane; Braber, Anouk; Brandeis, Daniel; Breier, Alan; Brodaty, Henry; Brouwer, Rachel M.; Buitelaar, Jan K.; Busatto, Geraldo F.; Calhoun, Vince D.; Canales‐Rodríguez, Erick J.; Cannon, Dara M.; Caseras, Xavier; Castellanos, Francisco X.; Chaim‐Avancini, Tiffany M.; Ching, Christopher R. K.; Clark, Vincent P.; Conrod, Patricia J.; Conzelmann, Annette; Crivello, Fabrice; Davey, Christopher G.; Dickie, Erin W.; Ehrlich, Stefan; Ent, Dennis; Fisher, Simon E.; Fouche, Jean‐Paul; Franke, Barbara; Fuentes‐Claramonte, Paola; Geus, Eco J. C.; Di Giorgio, Annabella; Glahn, David C.; Gotlib, Ian H.; Grabe, Hans J.; Gruber, Oliver; Gruner, Patricia; Gur, Raquel E.; Gur, Ruben C.; Gurholt, Tiril P.; Haan, Lieuwe; Haatveit, Beathe; Harrison, Ben J.; Hartman, Catharina A.; Hatton, Sean N.; Heslenfeld, Dirk J.; Heuvel, Odile A.; Hickie, Ian B.; Hoekstra, Pieter J.; Hohmann, Sarah; Holmes, Avram J.; Hoogman, Martine; Hosten, Norbert; Howells, Fleur M.; Hulshoff Pol, Hilleke E.; Huyser, Chaim; Jahanshad, Neda; James, Anthony C.; Jiang, Jiyang; Jönsson, Erik G.; Joska, John A.; Kalnin, Andrew J.; Karolinska Schizophrenia Project (KaSP) Consortium; Klein, Marieke; Koenders, Laura; Kolskår, Knut K.; Krämer, Bernd; Kuntsi, Jonna; Lagopoulos, Jim; Lazaro, Luisa; Lebedeva, Irina S.; Lee, Phil H.; Lochner, Christine; Machielsen, Marise W. J.; Maingault, Sophie; Martin, Nicholas G.; Martínez‐Zalacaín, Ignacio; Mataix‐Cols, David; Mazoyer, Bernard; McDonald, Brenna C.; McDonald, Colm; McIntosh, Andrew M.; McMahon, Katie L.; McPhilemy, Genevieve; Meer, Dennis; Menchón, José M.; Naaijen, Jilly; Nyberg, Lars; Oosterlaan, Jaap; Paloyelis, Yannis; Pauli, Paul; Pergola, Giulio; Pomarol‐Clotet, Edith; Portella, Maria J.; Radua, Joaquim; Reif, Andreas; Richard, Geneviève; Roffman, Joshua L.; Rosa, Pedro G. P.; Sacchet, Matthew D.; Sachdev, Perminder S.; Salvador, Raymond; Sarró, Salvador; Satterthwaite, Theodore D.; Saykin, Andrew J.; Serpa, Mauricio H.; Sim, Kang; Simmons, Andrew; Smoller, Jordan W.; Sommer, Iris E.; Soriano‐Mas, Carles; Stein, Dan J.; Strike, Lachlan T.; Szeszko, Philip R.; Temmingh, Henk S.; Thomopoulos, Sophia I.; Tomyshev, Alexander S.; Trollor, Julian N.; Uhlmann, Anne; Veer, Ilya M.; Veltman, Dick J.; Voineskos, Aristotle; Völzke, Henry; Walter, Henrik; Wang, Lei; Wang, Yang; Weber, Bernd; Wen, Wei; West, John D.; Westlye, Lars T.; Whalley, Heather C.; Williams, Steven C. R.; Wittfeld, Katharina; Wolf, Daniel H.; Wright, Margaret J.; Yoncheva, Yuliya N.; Zanetti, Marcus V.; Ziegler, Georg C.; Zubicaray, Greig I.; Thompson, Paul M.; Crone, Eveline A.; Frangou, Sophia; Tamnes, Christian K.; Psychiatry, School of MedicineFor many traits, males show greater variability than females, with possible implications for understanding sex differences in health and disease. Here, the ENIGMA (Enhancing Neuro Imaging Genetics through Meta-Analysis) Consortium presents the largest-ever mega-analysis of sex differences in variability of brain structure, based on international data spanning nine decades of life. Subcortical volumes, cortical surface area and cortical thickness were assessed in MRI data of 16,683 healthy individuals 1-90 years old (47% females). We observed significant patterns of greater male than female between-subject variance for all subcortical volumetric measures, all cortical surface area measures, and 60% of cortical thickness measures. This pattern was stable across the lifespan for 50% of the subcortical structures, 70% of the regional area measures, and nearly all regions for thickness. Our findings that these sex differences are present in childhood implicate early life genetic or gene-environment interaction mechanisms. The findings highlight the importance of individual differences within the sexes, that may underpin sex-specific vulnerability to disorders.Item Hippocampal Surface Mapping of Genetic Risk Factors in AD via Sparse Learning Models(Office of the Vice Chancellor for Research, 2012-04-13) Wan, Jing; Kim, Sungeun; Inlow, Mark; Nho, Kwangsik; Swaminathan, Shanker; Risacher, Shannon L.; Fang, Shiaofen; Weiner, Michael W.; Beg, M. Faisal; Wang, Lei; Saykin, Andrew J.; Shen, Li; ADNIGenetic mapping of hippocampal shape, an under-explored area, has strong potential as a neurodegeneration biomarker for AD and MCI. This study investigates the genetic effects of top candidate single nucleotide polymorphisms (SNPs) on hippocampal shape features as quantitative traits (QTs) in a large cohort. FS+LDDMM was used to segment hippocampal surfaces from MRI scans and shape features were extracted after surface registration. Elastic net (EN) and sparse canonical correlation analysis (SCCA) were proposed to examine SNP-QT associations, and compared with multiple regression (MR). Although similar in power, EN yielded substantially fewer predictors than MR. Detailed surface mapping of global and localized genetic effects were identified by MR and EN to reveal multi-SNP-single-QT relationships, and by SCCA to discover multi-SNP-multi-QT associations. Shape analysis identified stronger SNP-QT correlations than volume analysis. Sparse multivariate models have greater power to reveal complex SNP-QT relationships. Genetic analysis of quantitative shape features has considerable potential for enhancing mechanistic understanding of complex disorders like AD.Item How to Choose In Vitro Systems to Predict In Vivo Drug Clearance: A System Pharmacology Perspective(Hindawi Publishing Corporation, 2015) Wang, Lei; Chiang, ChienWei; Liang, Hong; Wu, Hengyi; Feng, Weixing; Quinney, Sara K.; Li, Jin; Li, Lang; Department of Obstetrics and Gynecology, IU School of MedicineThe use of in vitro metabolism data to predict human clearance has become more significant in the current prediction of large scale drug clearance for all the drugs. The relevant information (in vitro metabolism data and in vivo human clearance values) of thirty-five drugs that satisfied the entry criteria of probe drugs was collated from the literature. Then the performance of different in vitro systems including Escherichia coli system, yeast system, lymphoblastoid system and baculovirus system is compared after in vitro-in vivo extrapolation. Baculovirus system, which can provide most of the data, has almost equal accuracy as the other systems in predicting clearance. And in most cases, baculovirus system has the smaller CV in scaling factors. Therefore, the baculovirus system can be recognized as the suitable system for the large scale drug clearance prediction.Item Identification of rifampin-regulated functional modules and related microRNAs in human hepatocytes based on the protein interaction network(BioMed Central, 2016-08-22) Li, Jin; Wang, Ying; Dai, Xuefeng; Cong, Wang; Feng, Weixing; Xu, Chengzhen; Deng, Yulin; Wang, Yue; Skaar, Todd C.; Liang, Hong; Liu, Yunlong; Wang, Lei; Department of Medical and Molecular Genetics, IU School of MedicineBACKGROUND: In combination with gene expression profiles, the protein interaction network (PIN) constructs a dynamic network that includes multiple functional modules. Previous studies have demonstrated that rifampin can influence drug metabolism by regulating drug-metabolizing enzymes, transporters, and microRNAs (miRNAs). Rifampin induces gene expression, at least in part, by activating the pregnane X receptor (PXR), which induces gene expression; however, the impact of rifampin on global gene regulation has not been examined under the molecular network frameworks. METHODS: In this study, we extracted rifampin-induced significant differentially expressed genes (SDG) based on the gene expression profile. By integrating the SDG and human protein interaction network (HPIN), we constructed the rifampin-regulated protein interaction network (RrPIN). Based on gene expression measurements, we extracted a subnetwork that showed enriched changes in molecular activity. Using the Kyoto Encyclopedia of Genes and Genomes (KEGG), we identified the crucial rifampin-regulated biological pathways and associated genes. In addition, genes targeted by miRNAs that were significantly differentially expressed in the miRNA expression profile were extracted based on the miRNA-gene prediction tools. The miRNA-regulated PIN was further constructed using associated genes and miRNAs. For each miRNA, we further evaluated the potential impact by the gene interaction network using pathway analysis. RESULTS AND DISCCUSSION: We extracted the functional modules, which included 84 genes and 89 interactions, from the RrPIN, and identified 19 key rifampin-response genes that are associated with seven function pathways that include drug response and metabolism, and cancer pathways; many of the pathways were supported by previous studies. In addition, we identified that a set of 6 genes (CAV1, CREBBP, SMAD3, TRAF2, KBKG, and THBS1) functioning as gene hubs in the subnetworks that are regulated by rifampin. It is also suggested that 12 differentially expressed miRNAs were associated with 6 biological pathways. CONCLUSIONS: Our results suggest that rifampin contributes to changes in the expression of genes by regulating key molecules in the protein interaction networks. This study offers valuable insights into rifampin-induced biological mechanisms at the level of miRNAs, genes and proteins.Item Image Registration and Predictive Modeling: Learning the Metric on the Space of Diffeomorphisms(Springer, 2018-09) Mussabayeva, Ayagoz; Kroshnin, Alexey; Kurmukov, Anvar; Dodonova, Yulia; Shen, Li; Cong, Shan; Wang, Lei; Gutman, Boris A.; Computer Information and Graphics Technology, School of Engineering and TechnologyWe present a method for metric optimization in the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework, by treating the induced Riemannian metric on the space of diffeomorphisms as a kernel in a machine learning context. For simplicity, we choose the kernel Fischer Linear Discriminant Analysis (KLDA) as the framework. Optimizing the kernel parameters in an Expectation-Maximization framework, we define model fidelity via the hinge loss of the decision function. The resulting algorithm optimizes the parameters of the LDDMM norm-inducing differential operator as a solution to a group-wise registration and classification problem. In practice, this may lead to a biology-aware registration, focusing its attention on the predictive task at hand such as identifying the effects of disease. We first tested our algorithm on a synthetic dataset, showing that our parameter selection improves registration quality and classification accuracy. We then tested the algorithm on 3D subcortical shapes from the Schizophrenia cohort Schizconnect. Our Schizophrenia-Control predictive model showed significant improvement in ROC AUC compared to baseline parameters.Item An integrated investigation of lake storage and water level changes in the Paiku Co basin, central Himalayas(Elsevier, 2018) Lei, Yanbin; Yao, Tandong; Yang, Kun; Bird, Broxton W.; Tian, Lide; Zhang, Xiaowen; Wang, Weicai; Xiang, Yang; Dai, Yufeng; Lazhu; Zhou, Jing; Wang, Lei; Earth Sciences, School of ScienceSince the late 1990s, lakes in the southern Tibetan Plateau (TP) have shrunk considerably, which contrasts with the rapid expansion of lakes in the interior TP. Although these spatial trends have been well documented, the underlying hydroclimatic mechanisms are not well understood. Since 2013, we have carried out comprehensive water budget observations at Paiku Co, an alpine lake in the central Himalayas. In this study, we investigate water storage and lake level changes on seasonal to decadal time scales based on extensive in-situ measurements and satellite observations. Bathymetric surveys show that Paiku Co has a mean and maximum water depth of 41.1 m and 72.8 m, respectively, and its water storage was estimated to be 109.3 × 108 m3 in June 2016. On seasonal scale between 2013 and 2017, Paiku Co’s lake level decreased slowly between January and May, increased considerably between June and September, and then decreased rapidly between October and January. On decadal time scale, Paiku Co’s lake level decreased by 3.7 ± 0.3 m and water storage reduced by (10.2 ± 0.8) × 108 m3 between 1972 and 2015, accounting for 8.5% of the total water storage in 1972. This change is consistent with a trend towards drier conditions in the Himalaya region during the recent decades. In contrast, glacial lakes within Paiku Co’s basin expanded rapidly, indicating that, unlike Paiku Co, glacial meltwater was sufficient to compensate the effect of the reduced precipitation.Item Integrative network analysis of rifampinregulated miRNAs and their functions in human hepatocytes(IOS, 2015) Li, Jin; Wang, Ying; Wang, Lei; Liang, Hong; Feng, Weixing; Meng, Xianglian; Cong, Wang; Liu, Yunlong; Department of Medical & Molecular Genetics, IU School of MedicineRifampin is an important drug used in the treatment of tuberculosis, and it increases the drug metabolism in human hepatocytes. Previous studies have shown that rifampin can indirectly influence drug deposition through the regulation of molecular interactions of miRNA, PXR and other genes. The potential functions of miRNAs associated with rifampin- induced drug disposition are poorly understood. In this study, significantly differentially expressed miRNAs (SDEM) were extracted and used to predict the miRNA-regulated co-expression target genes (MCeTG). Additionally, a miRNA-regulated co-expressed protein interaction network (MCePIN) was constructed for SDEM by extending from the protein interaction network (PIN). The functioning of the miRNAs were analyzed using GO analysis and KEGG pathway enrichment analysis. A total of 20 miRNAs belonging to SDEM were identified, and 632 miRNA-regulated genes were predicted. The MCePIN was constructed by extending from PIN, and 10 miRNAs and 33 genes that are relevant to 7 functions, including response to wounding, wound healing, response to drug, defense response, inflammatory response, liver development and drug metabolism, were discerned. The results provided by this study offer valuable insights into the effect of rifampin on miRNAs, genes and protein levels.Item Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging(Springer Nature, 2017-09) Petrov, Dmitry; Gutman, Boris A.; Yu, Shih-Hua (Julie); van Erp, Theo G.M.; Turner, Jessica A.; Schmaal, Lianne; Veltman, Dick; Wang, Lei; Alpert, Kathryn; Isaev, Dmitry; Zavaliangos-Petropulu, Artemis; Ching, Christopher R.K.; Calhoun, Vince; Glahn, David; Satterthwaite, Theodore D.; Andreasen, Ole Andreas; Borgwardt, Stefan; Howells, Fleur; Groenewold, Nynke; Voineskos, Aristotle; Radua, Joaquim; Potkin, Steven G.; Crespo-Facorro, Benedicto; Tordesillas-Gutirrez, Diana; Shen, Li; Lebedeva, Irina; Spalletta, Gianfranco; Donohoe, Gary; Kochunov, Peter; Rosa, Pedro G.P.; James, Anthony; Dannlowski, Udo; Baune, Berhard T.; Aleman, Andre; Gotlib, Ian H.; Walter, Henrik; Walter, Martin; Soares, Jair C.; Ehrlich, Stefan; Gur, Ruben C.; Doan, N. Trung; Agartz, Ingrid; Westlye, Lars T.; Harrisberger, Fabienne; Richer-Rossler, Anita; Uhlmann, Anne; Stein, Dan J.; Dickie, Erin W.; Pomarol-Clotet, Edith; Fuentes-Claramonte, Paola; Canales-Rodriguez, Erick Jorge; Salvador, Raymond; Huang, Alexander J.; Roiz-Santianez, Roberto; Cong, Shan; Tomyshev, Alexander; Piras, Fabrizio; Vecchio, Daniela; Banaj, Nerisa; Ciullo, Valentina; Hong, Elliot; Busatto, Geraldo; Zanetti, Marcus V.; Serpa, Mauricio H.; Cervenka, Simon; Kelly, Sinead; Grotegerd, Dominik; Sacchet, Matthew D.; Veer, Illya M.; Li, Meng; Wu, Mon-Ju; Irungu, Benson; Walton, Esther; Thompson, Paul M.; Medicine, School of MedicineAs very large studies of complex neuroimaging phenotypes become more common, human quality assessment of MRI-derived data remains one of the last major bottlenecks. Few attempts have so far been made to address this issue with machine learning. In this work, we optimize predictive models of quality for meshes representing deep brain structure shapes. We use standard vertex-wise and global shape features computed homologously across 19 cohorts and over 7500 human-rated subjects, training kernelized Support Vector Machine and Gradient Boosted Decision Trees classifiers to detect meshes of failing quality. Our models generalize across datasets and diseases, reducing human workload by 30-70%, or equivalently hundreds of human rater hours for datasets of comparable size, with recall rates approaching inter-rater reliability.