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Item Altered bile acid profile associates with cognitive impairment in Alzheimer's disease—An emerging role for gut microbiome(Elsevier, 2019-01) MahmoudianDehkordi, Siamak; Arnold, Matthias; Nho, Kwangsik; Ahmad, Shahzad; Jia, Wei; Xie, Guoxiang; Louie, Gregory; Kueider‐Paisley, Alexandra; Moseley, M. Arthur; Thompson, J. Will; St John Williams, Lisa; Tenenbaum, Jessica D.; Blach, Colette; Baillie, Rebecca; Han, Xianlin; Bhattacharyya, Sudeepa; Toledo, Jon B.; Schafferer, Simon; Klein, Sebastian; Koal, Therese; Risacher, Shannon L.; Kling, Mitchel Allan; Motsinger‐Reif, Alison; Rotroff, Daniel M.; Jack, John; Hankemeier, Thomas; Bennett, David A.; De Jager, Philip L.; Trojanowski, John Q.; Shaw, Leslie M.; Weiner, Michael W.; Doraiswamy, P. Murali; van Duijn, Cornelia M.; Saykin, Andrew J.; Kastenmüller, Gabi; Kaddurah‐Daouk, Rima; Radiology and Imaging Sciences, School of MedicineIntroduction Increasing evidence suggests a role for the gut microbiome in central nervous system disorders and a specific role for the gut‐brain axis in neurodegeneration. Bile acids (BAs), products of cholesterol metabolism and clearance, are produced in the liver and are further metabolized by gut bacteria. They have major regulatory and signaling functions and seem dysregulated in Alzheimer's disease (AD). Methods Serum levels of 15 primary and secondary BAs and their conjugated forms were measured in 1464 subjects including 370 cognitively normal older adults, 284 with early mild cognitive impairment, 505 with late mild cognitive impairment, and 305 AD cases enrolled in the AD Neuroimaging Initiative. We assessed associations of BA profiles including selected ratios with diagnosis, cognition, and AD‐related genetic variants, adjusting for confounders and multiple testing. Results In AD compared to cognitively normal older adults, we observed significantly lower serum concentrations of a primary BA (cholic acid [CA]) and increased levels of the bacterially produced, secondary BA, deoxycholic acid, and its glycine and taurine conjugated forms. An increased ratio of deoxycholic acid:CA, which reflects 7α‐dehydroxylation of CA by gut bacteria, strongly associated with cognitive decline, a finding replicated in serum and brain samples in the Rush Religious Orders and Memory and Aging Project. Several genetic variants in immune response–related genes implicated in AD showed associations with BA profiles. Discussion We report for the first time an association between altered BA profile, genetic variants implicated in AD, and cognitive changes in disease using a large multicenter study. These findings warrant further investigation of gut dysbiosis and possible role of gut‐liver‐brain axis in the pathogenesis of AD.Item Associations between menarche-related genetic variants and pubertal growth in male and female adolescents(Elsevier, 2015-01) Tu, Wanzhu; Wagner, Erin K.; Eckert, George J.; Yu, Zhangsheng; Hannon, Tamara; Pratt, J. Howard; He, Chunyan; Department of Epidemiology, School of Public HealthPURPOSE: Previous studies have identified novel genetic variants associated with age at menarche in females of European descent. The pubertal growth effects of these variants have not been carefully evaluated in non-European descent groups. We aimed to examine the effects of 31 newly identified menarche-related single-nucleotide polymorphisms (SNPs) on growth outcomes in African-American (AA) and European-American (EA) children in a prospective cohort. METHODS: We analyzed longitudinal data collected from 263 AAs and 338 EAs enrolled between ages 5 and 17 years; the subjects were followed semiannually for an average of 6 years. The associations between the SNPs and growth-related outcomes, including weight, height, and body mass index (BMI), were examined using mixed-effect models. RESULTS: Longitudinal analyses revealed that 4 (near or in genes VGLL3, PEX2, CA10, and SKOR2) of the 14 menarche-only-related SNPs were associated with changes in weight and BMI in EA and AA (p ≤ .0032), but none of them was associated with changes in height. Of the eight menarche-timing and BMI-related SNPs, none was associated with changes in height, but three (in or near genes NEGR1, ETV5, and FTO) were associated with more rapid increases in weight and/or BMI in EA (p ≤ .0059). Among the nine menarche-timing and height-related SNPs, four (in or near genes ZBTB38, LOC728666, TBX2, and CABLES) were associated with changes in weight or height in EA and AA (p ≤ .0042). CONCLUSIONS: Genetic variants related to age at menarche were found to be associated with various growth parameters in healthy adolescents. The identified associations were often race and sex specific.Item Deep learning-based identification of genetic variants: application to Alzheimer’s disease classification(Oxford University Press, 2022) Jo, Taeho; Nho, Kwangsik; Bice, Paula; Saykin, Andrew J.; Alzheimer’s Disease Neuroimaging InitiativeDeep learning is a promising tool that uses nonlinear transformations to extract features from high-dimensional data. Deep learning is challenging in genome-wide association studies (GWAS) with high-dimensional genomic data. Here we propose a novel three-step approach (SWAT-CNN) for identification of genetic variants using deep learning to identify phenotype-related single nucleotide polymorphisms (SNPs) that can be applied to develop accurate disease classification models. In the first step, we divided the whole genome into nonoverlapping fragments of an optimal size and then ran convolutional neural network (CNN) on each fragment to select phenotype-associated fragments. In the second step, using a Sliding Window Association Test (SWAT), we ran CNN on the selected fragments to calculate phenotype influence scores (PIS) and identify phenotype-associated SNPs based on PIS. In the third step, we ran CNN on all identified SNPs to develop a classification model. We tested our approach using GWAS data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) including (N = 981; cognitively normal older adults (CN) = 650 and AD = 331). Our approach identified the well-known APOE region as the most significant genetic locus for AD. Our classification model achieved an area under the curve (AUC) of 0.82, which was compatible with traditional machine learning approaches, random forest and XGBoost. SWAT-CNN, a novel deep learning-based genome-wide approach, identified AD-associated SNPs and a classification model for AD and may hold promise for a range of biomedical applications.Item In silico generation and augmentation of regulatory variants from massively parallel reporter assay using conditional variational autoencoder(bioRxiv, 2024-06-29) Jin, Weijia; Xia, Yi; Thela, Sai Ritesh; Liu, Yunlong; Chen, Li; Medical and Molecular Genetics, School of MedicinePredicting the functional consequences of genetic variants in non-coding regions is a challenging problem. Massively parallel reporter assays (MPRAs), which are an in vitro high-throughput method, can simultaneously test thousands of variants by evaluating the existence of allele specific regulatory activity. Nevertheless, the identified labelled variants by MPRAs, which shows differential allelic regulatory effects on the gene expression are usually limited to the scale of hundreds, limiting their potential to be used as the training set for achieving a robust genome-wide prediction. To address the limitation, we propose a deep generative model, MpraVAE, to in silico generate and augment the training sample size of labelled variants. By benchmarking on several MPRA datasets, we demonstrate that MpraVAE significantly improves the prediction performance for MPRA regulatory variants compared to the baseline method, conventional data augmentation approaches as well as existing variant scoring methods. Taking autoimmune diseases as one example, we apply MpraVAE to perform a genome-wide prediction of regulatory variants and find that predicted regulatory variants are more enriched than background variants in enhancers, active histone marks, open chromatin regions in immune-related cell types, and chromatin states associated with promoter, enhancer activity and binding sites of cMyC and Pol II that regulate gene expression. Importantly, predicted regulatory variants are found to link immune-related genes by leveraging chromatin loop and accessible chromatin, demonstrating the importance of MpraVAE in genetic and gene discovery for complex traits.Item Mining Outcome-relevant Brain Imaging Genetic Associations via Three-way Sparse Canonical Correlation Analysis in Alzheimer’s Disease(SpringerNature, 2017-03-14) Hao, Xiaoke; Liu, Chanxiu; Du, Lei; Yao, Xiaohui; Yan, Jingwen; Risacher, Shannon L.; Saykin, Andrew J.; Shen, Li; Zhang, Daoqiang; Department of Radiology and Imaging Sciences, IU School of MedicineNeuroimaging genetics is an emerging field that aims to identify the associations between genetic variants (e.g., single nucleotide polymorphisms (SNPs)) and quantitative traits (QTs) such as brain imaging phenotypes. In recent studies, in order to detect complex multi-SNP-multi-QT associations, bi-multivariate techniques such as various structured sparse canonical correlation analysis (SCCA) algorithms have been proposed and used in imaging genetics studies. However, associations between genetic markers and imaging QTs identified by existing bi-multivariate methods may not be all disease specific. To bridge this gap, we propose an analytical framework, based on three-way sparse canonical correlation analysis (T-SCCA), to explore the intrinsic associations among genetic markers, imaging QTs, and clinical scores of interest. We perform an empirical study using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort to discover the relationships among SNPs from AD risk gene APOE, imaging QTs extracted from structural magnetic resonance imaging scans, and cognitive and diagnostic outcomes. The proposed T-SCCA model not only outperforms the traditional SCCA method in terms of identifying strong associations, but also discovers robust outcome-relevant imaging genetic patterns, demonstrating its promise for improving disease-related mechanistic understanding.Item MPRAVarDB: an online database and web server for exploring regulatory effects of genetic variants(bioRxiv, 2024-04-03) Nizomov, Javlon; Jin, Weijia; Xia, Yi; Liu, Yunlong; Li, Zhigang; Chen, Li; Medical and Molecular Genetics, School of MedicineMassively parallel reporter assay (MPRA) is an important technology to evaluate the impact of genetic variants on gene regulation. Here, we present MPRAVarDB, an online database and web server, for exploring regulatory effects of genetic variants. MPRAVarDB harbors 18 MPRA experiments designed to assess the regulatory effects of genetic variants associated with GWAS loci, eQTLs and various genomic features, resulting in a total of 242,818 variants tested across more than 30 cell lines and 30 human diseases or traits. MPRAVarDB empowers the query of MPRA variants by genomic region, disease and cell line or by any combination of these query terms. Notably, MPRAVarDB offers a suite of pretrained machine learning models tailored to the specific disease and cell line, facilitating the genome-wide prediction of regulatory variants. MPRAVarDB is friendly to use, and users only need a few clicks to receive query and prediction results.Item PASSPORT-seq: A Novel High-Throughput Bioassay to Functionally Test Polymorphisms in Micro-RNA Target Sites(Frontiers Media, 2018-06-15) Ipe, Joseph; Collins, Kimberly S.; Hao, Yangyang; Gao, Hongyu; Bhatia, Puja; Gaedigk, Andrea; Liu, Yunlong; Skaar, Todd C.; Pharmacology and Toxicology, School of MedicineNext-generation sequencing (NGS) studies have identified large numbers of genetic variants that are predicted to alter miRNA-mRNA interactions. We developed a novel high-throughput bioassay, PASSPORT-seq, that can functionally test in parallel 100s of these variants in miRNA binding sites (mirSNPs). The results are highly reproducible across both technical and biological replicates. The utility of the bioassay was demonstrated by testing 100 mirSNPs in HEK293, HepG2, and HeLa cells. The results of several of the variants were validated in all three cell lines using traditional individual luciferase assays. Fifty-five mirSNPs were functional in at least one of three cell lines (FDR ≤ 0.05); 11, 36, and 27 of them were functional in HEK293, HepG2, and HeLa cells, respectively. Only four of the variants were functional in all three cell lines, which demonstrates the cell-type specific effects of mirSNPs and the importance of testing the mirSNPs in multiple cell lines. Using PASSPORT-seq, we functionally tested 111 variants in the 3' UTR of 17 pharmacogenes that are predicted to alter miRNA regulation. Thirty-three of the variants tested were functional in at least one cell line.Item Pharmacogenomics of Novel Direct Oral Anticoagulants: Newly Identified Genes and Genetic Variants(MDPI, 2019-01-17) Kanuri, Sri H.; Kreutz, Rolf P.; Pharmacology and Toxicology, School of MedicineDirect oral anticoagulants (DOAC) have shown an upward prescribing trend in recent years due to favorable pharmacokinetics and pharmacodynamics without requirement for routine coagulation monitoring. However, recent studies have documented inter-individual variability in plasma drug levels of DOACs. Pharmacogenomics of DOACs is a relatively new area of research. There is a need to understand the role of pharmacogenomics in the interpatient variability of the four most commonly prescribed DOACs, namely dabigatran, rivaroxaban, apixaban, and edoxaban. We performed an extensive search of recently published research articles including clinical trials and in-vitro studies in PubMed, particularly those focusing on genetic loci, single nucleotide polymorphisms (SNPs), and DNA polymorphisms, and their effect on inter-individual variation of DOACs. Additionally, we also focused on commonly associated drug-drug interactions of DOACs. CES1 and ABCB1 SNPs are the most common documented genetic variants that contribute to alteration in peak and trough levels of dabigatran with demonstrated clinical impact. ABCB1 SNPs are implicated in alteration of plasma drug levels of rivaroxaban and apixaban. Studies conducted with factor Xa, ABCB1, SLCOB1, CYP2C9, and VKORC1 genetic variants did not reveal any significant association with plasma drug levels of edoxaban. Pharmacokinetic drug-drug interactions of dabigatran are mainly mediated by p-glycoprotein. Strong inhibitors and inducers of CYP3A4 and p-glycoprotein should be avoided in patients treated with rivaroxaban, apixaban, and edoxaban. We conclude that some of the inter-individual variability of DOACs can be attributed to alteration of genetic variants of gene loci and drug-drug interactions. Future research should be focused on exploring new genetic variants, their effect, and molecular mechanisms that contribute to alteration of plasma levels of DOACs.Item The Genetic, Pharmacogenomic, and Immune Landscapes Associated with Protein Expression across Human Cancers(American Association for Cancer Research, 2023) Chen, Chengxuan; Liu, Yuan; Li, Qiang; Zhang, Zhao; Luo, Mei; Liu, Yaoming; Han, Leng; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public HealthProteomics is a powerful approach that can rapidly enhance our understanding of cancer development. Detailed characterization of the genetic, pharmacogenomic, and immune landscape in relation to protein expression in cancer patients could provide new insights into the functional roles of proteins in cancer. By taking advantage of the genotype data from The Cancer Genome Atlas (TCGA) and protein expression data from The Cancer Proteome Atlas (TCPA), we characterized the effects of genetic variants on protein expression across 31 cancer types and identified approximately 100,000 protein quantitative trait loci (pQTL). Among these, over 8000 pQTL were associated with patient overall survival. Furthermore, characterization of the impact of protein expression on more than 350 imputed anticancer drug responses in patients revealed nearly 230,000 significant associations. In addition, approximately 21,000 significant associations were identified between protein expression and immune cell abundance. Finally, a user-friendly data portal, GPIP (https://hanlaboratory.com/GPIP), was developed featuring multiple modules that enable researchers to explore, visualize, and browse multidimensional data. This detailed analysis reveals the associations between the proteomic landscape and genetic variation, patient outcome, the immune microenvironment, and drug response across cancer types, providing a resource that may offer valuable clinical insights and encourage further functional investigations of proteins in cancer.