Browsing by Subject "Genetic variants"
Now showing 1 - 10 of 11
Results Per Page
Sort Options
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 Hypobaric hypoxia drives selection of altitude-associated adaptative alleles in the Himalayan population(Elsevier, 2024) Sharma, Samantha; Koshy, Remya; Kumar, Rahul; Mohammad, Ghulam; Thinlas, Tashi; Graham, Brian B.; Pasha, Qadar; Medical and Molecular Genetics, School of MedicineGenetic variants play a crucial role in shaping the adaptive phenotypes associated with high-altitude populations. Nevertheless, a comprehensive understanding of the specific impacts of different environments associated with increasing altitudes on the natural selection of these genetic variants remains undetermined. Hence, this study aimed to identify genetic markers responsible for high-altitude adaptation with specific reference to different altitudes, majorly focussing on an altitude elevation range of ∼1500 m and a corresponding decrease of ≥5 % in ambient oxygen availability. We conducted a comprehensive genome-wide investigation (n = 192) followed by a validation study (n = 514) in low-altitude and three high-altitude populations (>2400 m) of Nubra village (NU) (3048 m), Sakti village (SKT) (3812 m), and Tso Moriri village (TK) (4522 m). Extensive genetic analysis identified 86 SNPs that showed significant associations with high-altitude adaptation. Frequency mapping of these SNPs revealed 38 adaptive alleles and specific haplotypes that exhibited a strong linear correlation with increasing altitude. Notably, these SNPs spanned crucial genes, such as ADH6 and NAPG along with the vastly studied genes like EGLN1 and EPAS1, involved in oxygen sensing, metabolism, and vascular homeostasis. Correlation analyses between these adaptive alleles and relevant clinical and biochemical markers provided evidence of their functional relevance in physiological adaptation to hypobaric hypoxia. High-altitude population showed a significant increase in plasma 8-isoPGF2α levels as compared to low-altitude population. Similar observation showcased increased blood pressure in NU as compared to TK (P < 0.0001). In silico analyses further confirmed that these alleles regulate gene expression of EGLN1, EPAS1, COQ7, NAPG, ADH6, DUOXA1 etc. This study provides genetic insights into the effects of hypobaric-hypoxia on the clinico-physiological characteristics of natives living in increasing high-altitude regions. Overall, our findings highlight the synergistic relationship between environment and evolutionary processes, showcasing physiological implications of genetic variants in oxygen sensing and metabolic pathway genes in increasing high-altitude environments.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 Polygenic scores for Alzheimer’s disease risk and resilience predict age at onset of amyloid‐β(Wiley, 2025-01-03) O’Brien, Eleanor K.; Porter, Tenielle; Fernandez, Shane; Cox, Timothy; Dore, Vincent; Bourgeat, Pierrick; Goudey, Benjamin; Doecke, James D.; Masters, Colin L.; Rowe, Christopher C.; Villemagne, Victor L.; Cruchaga, Carlos; Saykin, Andrew J.; Laws, Simon M.; ADOPIC Consortium (AIBL, ADNI, OASIS); Radiology and Imaging Sciences, School of MedicineBackground: Genome‐wide association studies (GWAS) have identified numerous genetic variants associated with Alzheimer’s disease (AD) risk, but genetic variation in the onset and progression of AD pathology is less understood. Accumulation of amyloid‐β (Aβ) in the brain is a key pathological hallmark of AD beginning 10 – 20 years prior to cognitive symptoms. We investigated the genetic basis of variation in age at onset (AAO) of brain Aβ by comparing the performance of polygenic scores (PGSs) based on AD risk and resilience with a Aβ‐AAO trait‐specific PGS. Method: 1122 participants from the Alzheimer’s Dementia Onset and Progression in International Cohorts (ADOPIC) study underwent genome‐wide SNP genotyping and assessment of brain Aβ using positron emission tomography (PET) imaging at two or more timepoints. AAO was the age at which participants were estimated to have crossed the 20 centiloid (CL) threshold for high Aβ. We utilised AD risk and resilience GWAS summary statistics and conducted a GWAS for AAO using a cross‐validation approach (10 test‐validation folds). We used PRSice to identify optimal PGSs for Aβ‐AAO for risk (PGSRisk), resilience (PGSResilience) and Aβ‐AAO (PGSAAO). Result: PGSRisk and PGSResilience were both significantly associated with Aβ‐AAO, such that higher PGSRisk and lower PGSResilience were associated with an earlier Aβ‐AAO. PGSRisk showed the strongest association and explained more variance in Aβ‐AAO than did PGSAAO. When stratified by APOE ε4 carriage, the strongest genetic risk factor for AD, the association of PGSRisk with Aβ‐AAO was stronger among ε4 non‐carriers, whilst PGSResilience, was more strongly associated with Aβ‐AAO in ε4 carriers. Conclusion: PGS based on genetic risk and resilience for AD are both significant predictors of the age at which people are estimated to cross the threshold for high brain Aβ burden. Predicting the age at which a person will pass this threshold would enable treatment at an earlier stage, when it may more effectively delay or prevent symptom onset.