Browsing by Author "Wang, Lei"

Now showing 1 - 10 of 26
  • Thumbnail Image
    Item
    A multistate transition model for statin‐induced myopathy and statin discontinuation
    (Wiley, 2021) Zhu, Yuxi; Chiang, Chien-Wei; Wang, Lei; Brock, Guy; Milks, M. Wesley; Cao, Weidan; Zhang, Pengyue; Zeng, Donglin; Donneyong, Macarius; Li, Lang; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    The overarching goal of this study was to simultaneously model the dynamic relationships among statin exposure, statin discontinuation, and potentially statin-related myopathic outcomes. We extracted data from the Indiana Network of Patient Care for 134,815 patients who received statin therapy between January 4, 2004, and December 31, 2008. All individuals began statin treatment, some discontinued statin use, and some experienced myopathy and/or rhabdomyolysis while taking the drug or after discontinuation. We developed a militate model to characterize 12 transition probabilities among six different states defined by use or discontinuation of statin and its associated myopathy or rhabdomyolysis. We found that discontinuation of statin therapy was common and frequently early, with 44.4% of patients discontinuing therapy after 1 month, and discontinuation is a strong indicator for statin-induced myopathy (risk ratio, 10.8; p < 0.05). Women more likely than men (p < 0.05) and patients aged 65 years and older had a higher risk than those aged younger than 65 years to discontinue statin use or experience myopathy. In conclusion, we introduce an innovative multistate model that allows clear depiction of the relationship between statin discontinuation and statin-induced myopathy. For the first time, we have successfully demonstrated and quantified the relative risk of myopathy between patients who continued and discontinued statin therapy. Age and sex were two strong risk factors for both statin discontinuation and incident myopathy.
  • Thumbnail Image
    Item
    Brain gray matter reduction and premature brain aging after breast cancer chemotherapy: a longitudinal multicenter data pooling analysis
    (Springer, 2023) de Ruiter, Michiel B.; Deardorff, Rachael L.; Blommaert, Jeroen; Chen, Bihong T.; Dumas, Julie A.; Schagen, Sanne B.; Sunaert, Stefan; Wang, Lei; Cimprich, Bernadine; Peltier, Scott; Dittus, Kim; Newhouse, Paul A.; Silverman, Daniel H.; Schroyen, Gwen; Deprez, Sabine; Saykin, Andrew J.; McDonald, Brenna C.; Radiology and Imaging Sciences, School of Medicine
    Brain gray matter (GM) reductions have been reported after breast cancer chemotherapy, typically in small and/or cross-sectional cohorts, most commonly using voxel-based morphometry (VBM). There has been little examination of approaches such as deformation-based morphometry (DBM), machine-learning-based brain aging metrics, or the relationship of clinical and demographic risk factors to GM reduction. This international data pooling study begins to address these questions. Participants included breast cancer patients treated with (CT+, n = 183) and without (CT-, n = 155) chemotherapy and noncancer controls (NC, n = 145), scanned pre- and post-chemotherapy or comparable intervals. VBM and DBM examined GM volume. Estimated brain aging was compared to chronological aging. Correlation analyses examined associations between VBM, DBM, and brain age, and between neuroimaging outcomes, baseline age, and time since chemotherapy completion. CT+ showed longitudinal GM volume reductions, primarily in frontal regions, with a broader spatial extent on DBM than VBM. CT- showed smaller clusters of GM reduction using both methods. Predicted brain aging was significantly greater in CT+ than NC, and older baseline age correlated with greater brain aging. Time since chemotherapy negatively correlated with brain aging and annual GM loss. This large-scale data pooling analysis confirmed findings of frontal lobe GM reduction after breast cancer chemotherapy. Milder changes were evident in patients not receiving chemotherapy. CT+ also demonstrated premature brain aging relative to NC, particularly at older age, but showed evidence for at least partial GM recovery over time. When validated in future studies, such knowledge could assist in weighing the risks and benefits of treatment strategies.
  • Thumbnail Image
    Item
    Correction: PhenoDEF: a corpus for annotating sentences with information of phenotype definitions in biomedical literature
    (BMC, 2022-07-20) Binkheder, Samar; Wu, Heng‑Yi; Quinney, Sara K.; Zhang, Shijun; Zitu, Md. Muntasir; Chiang, Chien‑Wei; Wang, Lei; Jones, Josette; Li, Lang; BioHealth Informatics, School of Informatics and Computing
    PhenoDEF: a corpus for annotating sentences with information of phenotype definitions in biomedical literature. Binkheder S, Wu HY, Quinney SK, Zhang S, Zitu MM, Chiang CW, Wang L, Jones J, Li L. J Biomed Semantics. 2022 Jun 11;13(1):17. doi: 10.1186/s13326-022-00272-6. PMID: 35690873
  • Thumbnail Image
    Item
    Cortical thickness across the lifespan: Data from 17,075 healthy individuals aged 3-90 years
    (Wiley, 2022-01) Frangou, Sophia; Modabbernia, Amirhossein; Williams, Steven C.R.; Papachristou, Efstathios; Doucet, Gaelle E.; Agartz, Ingrid; Aghajani, Moji; Akudjedu, Theophilus N.; Albajes-Eizagirre, Anton; Alnæs, Dag; Alpert, Kathryn I.; Andersson, Micael; Andreasen, Nancy C.; Andreassen, Ole A.; Asherson, Philip; Banaschewski, Tobias; Bargallo, Nuria; Baumeister, Sarah; Baur-Streubel, Ramona; Bertolino, Alessandro; Bonvino, Aurora; Boomsma, Dorret I.; Borgwardt, Stefan; Bourque, Josiane; Brandeis, Daniel; Breier, Alan; Brodaty, Henry; Brouwer, Rachel M.; Buitelaar, Jan K.; Busatto, Geraldo F.; Buckner, Randy L.; Calhoun, Vincent; Canales-Rodríguez, Erick J.; Cannon, Dara M.; Caseras, Xavier; Castellanos, Francisco X.; Cervenka, Simon; Chaim-Avancini, Tiffany M.; Ching, Christopher R.K.; Chubar, Victoria; Clark, Vincent P.; Conrod, Patricia; Conzelmann, Annette; Crespo-Facorro, Benedicto; Crivello, Fabrice; Crone, Eveline A.; Dale, Anders M.; Dannlowski, Udo; Davey, Christopher; de Geus, Eco J.C.; de Haan, Lieuwe; de Zubicaray, Greig I.; den Braber, Anouk; Dickie, Erin W.; Di Giorgio, Annabella; Doan, Nhat Trung; Dørum, Erlend S.; Ehrlich, Stefan; Erk, Susanne; Espeseth, Thomas; Fatouros-Bergman, Helena; Fisher, Simon E.; Fouche, Jean-Paul; Franke, Barbara; Frodl, Thomas; Fuentes-Claramonte, Paola; Glahn, David C.; Gotlib, Ian H.; Grabe, Hans-Jörgen; Grimm, Oliver; Groenewold, Nynke A.; Grotegerd, Dominik; Gruber, Oliver; Gruner, Patricia; Gur, Rachel E.; Gur, Ruben C.; Hahn, Tim; Harrison, Ben J.; Hartman, Catharine A.; Hatton, Sean N.; Heinz, Andreas; Heslenfeld, Dirk J.; Hibar, Derrek P.; Hickie, Ian B.; Ho, Beng-Choon; 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; Jernigan, Terry L.; Jiang, Jiyang; Jönsson, Erik G.; Joska, John A.; Kahn, Rene; Kalnin, Andrew; Kanai, Ryota; Klein, Marieke; Klyushnik, Tatyana P.; Koenders, Laura; Koops, Sanne; Krämer, Bernd; Kuntsi, Jonna; Lagopoulos, Jim; Lázaro, Luisa; Lebedeva, Irina; Lee, Won Hee; Lesch, Klaus-Peter; Lochner, Christine; Machielsen, Marise W.J.; Maingault, Sophie; Martin, Nicholas G.; Martínez-Zalacaín, Ignacio; Mataix-Cols, David; Mazoyer, Bernard; McDonald, Colm; McDonald, Brenna C.; McIntosh, Andrew M.; McMahon, Katie L.; McPhilemy, Genevieve; Meinert, Susanne; Menchón, José M.; Medland, Sarah E.; Meyer-Lindenberg, Andreas; Naaijen, Jilly; Najt, Pablo; Nakao, Tomohiro; Nordvik, Jan E.; Nyberg, Lars; Oosterlaan, Jaap; Ortiz-García de la Foz, Víctor; Paloyelis, Yannis; Pauli, Paul; Pergola, Giulio; Pomarol-Clotet, Edith; Portella, Maria J.; Potkin, Steven G.; Radua, Joaquim; Reif, Andreas; Rinker, Daniel A.; Roffman, Joshua L.; Rosa, Pedro G.P.; Sacchet, Matthew D.; Sachdev, Perminder S.; Salvador, Raymond; Sánchez-Juan, Pascual; Sarró, Salvador; Satterthwaite, Theodore D.; Saykin, Andrew J.; Serpa, Mauricio H.; Schmaal, Lianne; Schnell, Knut; Schumann, Gunter; Sim, Kang; Smoller, Jordan W.; Sommer, Iris; Soriano-Mas, Carles; Stein, Dan J.; Strike, Lachlan T.; Swagerman, Suzanne C.; Tamnes, Christian K.; Temmingh, Henk S.; Thomopoulos, Sophia I.; Tomyshev, Alexander S.; Tordesillas-Gutiérrez, Diana; Trollor, Julian N.; Turner, Jessica A.; Uhlmann, Anne; van den Heuvel, Odile A.; van den Meer, Dennis; van der Wee, Nic J.A.; van Haren, Neeltje E.M.; van't Ent, Dennis; van Erp, Theo G.M.; Veer, Ilya M.; Veltman, Dick J.; Voineskos, Aristotle; Völzke, Henry; Walter, Henrik; Walton, Esther; Wang, Lei; Wang, Yang; Wassink, Thomas H.; Weber, Bernd; Wen, Wei; West, John D.; Westlye, Lars T.; Whalley, Heather; Wierenga, Lara M.; Wittfeld, Katharina; Wolf, Daniel H.; Worker, Amanda; Wright, Margaret J.; Yang, Kun; Yoncheva, Yulyia; Zanetti, Marcus V.; Ziegler, Georg C.; Karolinska Schizophrenia Project (KaSP); Thompson, Paul M.; Dima, Danai; Radiology and Imaging Sciences, School of Medicine
    Delineating the association of age and cortical thickness in healthy individuals is critical given the association of cortical thickness with cognition and behavior. Previous research has shown that robust estimates of the association between age and brain morphometry require large-scale studies. In response, we used cross-sectional data from 17,075 individuals aged 3-90 years from the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to infer age-related changes in cortical thickness. We used fractional polynomial (FP) regression to quantify the association between age and cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma method. Interindividual variability was estimated using meta-analysis and one-way analysis of variance. For most regions, their highest cortical thickness value was observed in childhood. Age and cortical thickness showed a negative association; the slope was steeper up to the third decade of life and more gradual thereafter; notable exceptions to this general pattern were entorhinal, temporopolar, and anterior cingulate cortices. Interindividual variability was largest in temporal and frontal regions across the lifespan. Age and its FP combinations explained up to 59% variance in cortical thickness. These results may form the basis of further investigation on normative deviation in cortical thickness and its significance for behavioral and cognitive outcomes.
  • Thumbnail Image
    Item
    DSCN: Double-target selection guided by CRISPR screening and network
    (Public Library of Science, 2022-08-19) Liu, Enze; Wu, Xue; Wang, Lei; Huo, Yang; Wu, Huanmei; Li, Lang; Cheng, Lijun; Medicine, School of Medicine
    Cancer is a complex disease with usually multiple disease mechanisms. Target combination is a better strategy than a single target in developing cancer therapies. However, target combinations are generally more difficult to be predicted. Current CRISPR-cas9 technology enables genome-wide screening for potential targets, but only a handful of genes have been screend as target combinations. Thus, an effective computational approach for selecting candidate target combinations is highly desirable. Selected target combinations also need to be translational between cell lines and cancer patients. We have therefore developed DSCN (double-target selection guided by CRISPR screening and network), a method that matches expression levels in patients and gene essentialities in cell lines through spectral-clustered protein-protein interaction (PPI) network. In DSCN, a sub-sampling approach is developed to model first-target knockdown and its impact on the PPI network, and it also facilitates the selection of a second target. Our analysis first demonstrated a high correlation of the DSCN sub-sampling-based gene knockdown model and its predicted differential gene expressions using observed gene expression in 22 pancreatic cell lines before and after MAP2K1 and MAP2K2 inhibition (R2 = 0.75). In DSCN algorithm, various scoring schemes were evaluated. The 'diffusion-path' method showed the most significant statistical power of differentialting known synthetic lethal (SL) versus non-SL gene pairs (P = 0.001) in pancreatic cancer. The superior performance of DSCN over existing network-based algorithms, such as OptiCon and VIPER, in the selection of target combinations is attributable to its ability to calculate combinations for any gene pairs, whereas other approaches focus on the combinations among optimized regulators in the network. DSCN's computational speed is also at least ten times fast than that of other methods. Finally, in applying DSCN to predict target combinations and drug combinations for individual samples (DSCNi), DSCNi showed high correlation between target combinations predicted and real synergistic combinations (P = 1e-5) in pancreatic cell lines. In summary, DSCN is a highly effective computational method for the selection of target combinations.
  • Thumbnail Image
    Item
    Entinostat plus Pembrolizumab in Patients with Metastatic NSCLC Previously Treated with Anti-PD-(L)1 Therapy
    (American Association for Cancer Research, 2021) Hellmann, Matthew D.; Jänne, Pasi A.; Opyrchal, Mateusz; Hafez, Navid; Raez, Luis E.; Gabrilovich, Dmitry; Wang, Fang; Trepel, Jane B.; Lee, Min-Jung; Yuno, Akira; Lee, Sunmin; Brouwer, Susan; Sankoh, Serap; Wang, Lei; Tamang, David; Schmidt, Emmett; Meyers, Michael L.; Ramalingam, Suresh S.; Shum, Elaine; Ordentlich, Peter; Medicine, School of Medicine
    Purpose: New therapies are needed to treat immune checkpoint inhibitor-resistant non-small cell lung cancer (NSCLC) and identify biomarkers to personalize treatment. Epigenetic therapies, including histone deacetylase inhibitors, may synergize with programmed cell death-1 (PD-1) blockade to overcome resistance. We report outcomes in patients with anti-programmed cell death ligand-1 [PD-(L)1]-resistant/refractory NSCLC treated with pembrolizumab plus entinostat in ENCORE 601. Patients and methods: The expansion cohort of ENCORE 601 included patients with NSCLC who previously experienced disease progression with immune checkpoint inhibitors. The primary endpoint for the phase II expansion cohort is overall response rate (ORR); safety, tolerability, and exploratory endpoints are described. Results: Of 76 treated patients, 71 were evaluable for efficacy. immune-regulated RECIST-assessed ORR was 9.2% [95% confidence interval (CI): 3.8-18.1], which did not meet the prespecified threshold for positivity. Median duration of response was 10.1 months (95% CI: 3.9-not estimable), progression-free survival (PFS) at 6 months was 22%, median PFS was 2.8 months (95% CI: 1.5-4.1), and median overall survival was 11.7 months (95% CI: 7.6-13.4). Benefit was enriched among patients with high levels of circulating classical monocytes at baseline. Baseline tumor PD-L1 expression and IFNγ gene expression were not associated with benefit. Treatment-related grade ≥3 adverse events occurred in 41% of patients. Conclusions: In anti-PD-(L)1-experienced patients with NSCLC, entinostat plus pembrolizumab did not achieve the primary response rate endpoint but provided a clinically meaningful benefit, with objective response in 9% of patients. No new toxicities, including immune-related adverse events, were seen for either drug. Future studies will continue to evaluate the association of monocyte levels and response.
  • Thumbnail Image
    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 Medicine
    Alzheimer'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.
  • Thumbnail Image
    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 Medicine
    The 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.
  • Thumbnail Image
    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 Medicine
    For 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.
  • Thumbnail Image
    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; ADNI
    Genetic 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.