Browsing by Subject "Single nucleotide polymorphism"
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Item A Cohort Study to Evaluate Genetic Predictors of Aromatase Inhibitor Musculoskeletal Symptoms: Results from ECOG-ACRIN E1Z11(American Association for Cancer Research, 2024) Stearns, Vered; Jegede, Opeyemi A.; Chang, Victor T-S; Skaar, Todd C.; Berenberg, Jeffrey L.; Nand, Ranveer; Shafqat, Atif; Jacobs, Nisha L.; Luginbuhl, William; Gilman, Paul; Benson, Al B., III; Goodman, Judie R.; Buchschacher, Gary L., Jr.; Henry, N. Lynn; Loprinzi, Charles L.; Flynn, Patrick J.; Mitchell, Edith P.; Fisch, Michael J.; Sparano, Joseph A.; Wagner, Lynne I.; Pharmacology and Toxicology, School of MedicinePurpose: Aromatase inhibitor (AI)-associated musculoskeletal symptoms (AIMSS) are common and frequently lead to AI discontinuation. SNPs in candidate genes have been associated with AIMSS and AI discontinuation. E1Z11 is a prospective cohort study designed to validate the association between 10 SNPs and AI discontinuation due to AIMSS. Patients and methods: Postmenopausal women with stage I to III hormone receptor-positive breast cancer received anastrozole 1 mg daily and completed patient-reported outcome measures to assess AIMSS (Stanford Health Assessment Questionnaire) at baseline, 3, 6, 9, and 12 months. We estimated that 40% of participants would develop AIMSS and 25% would discontinue AI treatment within 12 months. Enrollment of 1,000 women with a fixed number per racial stratum provided 80% power to detect an effect size of 1.5 to 4. SNPs were found in ESR1 (rs2234693, rs2347868, and rs9340835), CYP19A1 (rs1062033 and rs4646), TCL1A (rs11849538, rs2369049, rs7158782, and rs7159713), and HTR2A (rs2296972). Results: Of the 970 evaluable women, 43% developed AIMSS and 12% discontinued AI therapy within 12 months. Although more Black and Asian women developed AIMSS than White women (49% vs. 39%, P = 0.017; 50% vs. 39%, P = 0.004, respectively), the AI discontinuation rates were similar across groups. None of the SNPs were significantly associated with AIMSS or AI discontinuation in the overall population or in distinct cohorts. The OR for rs2296972 (HTR2A) approached significance for developing AIMSS. Conclusions: We were unable to prospectively validate candidate SNPs previously associated with AI discontinuation due to AIMSS. Future analyses will explore additional genetic markers, patient-reported outcome predictors of AIMSS, and differences by race.Item A genetic risk score and diabetes predict development of alcohol-related cirrhosis in drinkers(Elsevier, 2022) Whitfield, John B.; Schwantes-An, Tae-Hwi; Darlay, Rebecca; Aithal, Guruprasad P.; Atkinson, Stephen R.; Bataller, Ramon; Botwin, Greg; Chalasani, Naga P.; Cordell, Heather J.; Daly, Ann K.; Day, Christopher P.; Eyer, Florian; Foroud, Tatiana; Gleeson, Dermot; Goldman, David; Haber, Paul S.; Jacquet, Jean-Marc; Liang, Tiebing; Liangpunsakul, Suthat; Masson, Steven; Mathurin, Philippe; Moirand, Romain; McQuillin, Andrew; Moreno, Christophe; Morgan, Marsha Y.; Mueller, Sebastian; Müllhaupt, Beat; Nagy, Laura E.; Nahon, Pierre; Nalpas, Bertrand; Naveau, Sylvie; Perney, Pascal; Pirmohamed, Munir; Seitz, Helmut K.; Soyka, Michael; Stickel, Felix; Thompson, Andrew; Thursz, Mark R.; Trépo, Eric; Morgan, Timothy R.; Seth, Devanshi; GenomALC Consortium; Medical and Molecular Genetics, School of MedicineBackground & aims: Only a minority of excess alcohol drinkers develop cirrhosis. We developed and evaluated risk stratification scores to identify those at highest risk. Methods: Three cohorts (GenomALC-1: n = 1,690, GenomALC-2: n = 3,037, UK Biobank: relevant n = 6,898) with a history of heavy alcohol consumption (≥80 g/day (men), ≥50 g/day (women), for ≥10 years) were included. Cases were participants with alcohol-related cirrhosis. Controls had a history of similar alcohol consumption but no evidence of liver disease. Risk scores were computed from up to 8 genetic loci identified previously as associated with alcohol-related cirrhosis and 3 clinical risk factors. Score performance for the stratification of alcohol-related cirrhosis risk was assessed and compared across the alcohol-related liver disease spectrum, including hepatocellular carcinoma (HCC). Results: A combination of 3 single nucleotide polymorphisms (SNPs) (PNPLA3:rs738409, SUGP1-TM6SF2:rs10401969, HSD17B13:rs6834314) and diabetes status best discriminated cirrhosis risk. The odds ratios (ORs) and (95% CIs) between the lowest (Q1) and highest (Q5) score quintiles of the 3-SNP score, based on independent allelic effect size estimates, were 5.99 (4.18-8.60) (GenomALC-1), 2.81 (2.03-3.89) (GenomALC-2), and 3.10 (2.32-4.14) (UK Biobank). Patients with diabetes and high risk scores had ORs of 14.7 (7.69-28.1) (GenomALC-1) and 17.1 (11.3-25.7) (UK Biobank) compared to those without diabetes and with low risk scores. Patients with cirrhosis and HCC had significantly higher mean risk scores than patients with cirrhosis alone (0.76 ± 0.06 vs. 0.61 ± 0.02, p = 0.007). Score performance was not significantly enhanced by information on additional genetic risk variants, body mass index or coffee consumption. Conclusions: A risk score based on 3 genetic risk variants and diabetes status enables the stratification of heavy drinkers based on their risk of cirrhosis, allowing for the provision of earlier preventative interventions. Lay summary: Excessive chronic drinking leads to cirrhosis in some people, but so far there is no way to identify those at high risk of developing this debilitating disease. We developed a genetic risk score that can identify patients at high risk. The risk of cirrhosis is increased >10-fold with just two risk factors - diabetes and a high genetic risk score. Risk assessment using this test could enable the early and personalised management of this disease in high-risk patients.Item Biomarker-And Pathway-Informed Polygenic Risk Scores for Alzheimer's Disease and Related Disorders(2022-05) Chasioti, Danai; Yan, Jingwen; Saykin, Andrew J.; Nho, Kwangsik; Risacher, Shannon L.; Wu, HuanmeiDetermining an individual’s genetic susceptibility in complex diseases like Alzheimer’s disease (AD) is challenging as multiple variants each contribute a small portion of the overall risk. Polygenic Risk Scores (PRS) are a mathematical construct or composite that aggregates the small effects of multiple variants into a single score. Potential applications of PRS include risk stratification, biomarker discovery and increased prognostic accuracy. A systematic review demonstrated that methodological refinement of PRS is an active research area, mostly focused on large case-control genome-wide association studies (GWAS). In AD, where there is considerable phenotypic and genetic heterogeneity, we hypothesized that PRS based on endophenotypes, and pathway-relevant genetic information would be particularly informative. In the first study, data from the NIA Alzheimer’s Disease Neuroimaging Initiative (ADNI) was used to develop endophenotype-based PRS based on amyloid (A), tau (T), neurodegeneration (N) and cerebrovascular (V) biomarkers, as well as an overall/combined endophenotype-PRS. Results indicated that combined phenotype-PRS predicted neurodegeneration biomarkers and overall AD risk. By contrast, amyloid and tau-PRSs were strongly linked to the corresponding biomarkers. Finally, extrinsic significance of the PRS approach was demonstrated by application of AD biological pathway-informed PRS to prediction of cognitive changes among older women with breast cancer (BC). Results from PRS analysis of the multicenter Thinking and Living with Cancer (TLC) study indicated that older BC patients with high AD genetic susceptibility within the immune-response and endocytosis pathways have worse cognition following chemotherapy±hormonal therapy rather than hormonal-only therapy. In conclusion, PRSs based on biomarker- or pathway- specific genetic information may provide mechanistic insights beyond disease susceptibility, supporting development of precision medicine with potential application to AD and other age-associated cognitive disorders.Item Genetic Associations With Toxicity-related Discontinuation of Aromatase Inhibitor Therapy for Breast Cancer(Breast Cancer Research and Treatment, 2013-04-02) Henry, N. Lynn; Skaar, Todd C.; Dantzer, Jessica; Li, Lang; Kidwell, Kelley; Gersch, Christina; Nguyen, Anne T.; Rae, James M.; Desta, Zeruesenay; Oesterreich, Steffi; Philips, Santosh; Carpenter, Janet S.; Storniolo, Anna M.; Stearns, Vered; Hayes, Daniel F.; Flockhart, David A.Up to 25 % of patients discontinue adjuvant aromatase inhibitor (AI) therapy due to intolerable symptoms. Predictors of which patients will be unable to tolerate these medications have not been defined. We hypothesized that inherited variants in candidate genes are associated with treatment discontinuation because of AI-associated toxicity. We prospectively evaluated reasons for treatment discontinuation in women with hormone receptor-positive breast cancer initiating adjuvant AI through a multicenter, prospective, randomized clinical trial of exemestane versus letrozole. Using multiple genetic models, we evaluated potential associations between discontinuation of AI therapy because of toxicity and 138 variants in 24 candidate genes, selected a priori, primarily with roles in estrogen metabolism and signaling. To account for multiple comparisons, statistical significance was defined as p < 0.00036. Of the 467 enrolled patients with available germline DNA, 152 (33 %) discontinued AI therapy because of toxicity. Using a recessive statistical model, an intronic variant in ESR1 (rs9322336) was associated with increased risk of musculoskeletal toxicity-related exemestane discontinuation [HR 5.0 (95 % CI 2.1-11.8), p < 0.0002]. An inherited variant potentially affecting estrogen signaling may be associated with exemestane-associated toxicity, which could partially account for intra-patient differences in AI tolerability. Validation of this finding is required.Item Genetic variants for Alzheimer’s disease and comorbid conditions(Sage, 2024) Pan, Minmin; Lai, Dongbing; Unverzagt, Frederick; Apostolova, Liana; Hendrie, Hugh C.; Saykin, Andrew; Foroud, Tatiana; Gao, Sujuan; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public HealthBackground: Alzheimer's disease and related dementias (ADRD) frequently co-occur with comorbidities such as diabetes and cardiovascular diseases in elderly populations. Objective: Utilize a life-course approach to identify genetic variants that are associated with the co-occurrence of ADRD and another comorbid condition. Methods: Research data from African American participants of the Indianapolis-Ibadan Dementia Project (IIDP) linked with electronic medical record (EMR) data and genome-wide association study (GWAS) data were utilized. The age of onset for ADRD was obtained from longitudinal follow-up of the IIDP study. Age of onset for comorbid conditions was obtained from EMR. The analysis included 1177 African Americans, among whom 174 were diagnosed with ADRD. A semi-parametric marginal bivariate survival model was used to examine the influence of single nucleotide polymorphisms (SNPs) on dual time-to-event outcomes while adjusting for sex, years of education, and the first principal component of GWAS data. Results: Targeted analysis of 20 SNPs that were reported to be associated with ADRD revealed that six were significantly associated with dual-disease outcomes, specifically congestive heart failure and cancer. In addition, eight novel SNPs were identified for associations with both ADRD and a comorbid condition. Conclusions: Using a bivariate survival model approach, we identified genetic variants associated not only with ADRD, but also with comorbid conditions. Our utilization of dual-disease models represents a novel analytic strategy for uncovering shared genetic variants for multiple disease phenotypes.Item Genome-wide association study of stimulant dependence(Springer Nature, 2021-06-29) Cox, Jiayi; Sherva, Richard; Wetherill, Leah; Foroud, Tatiana; Edenberg, Howard J.; Kranzler, Henry R.; Gelernter, Joel; Farrer, Lindsay A.; Medical and Molecular Genetics, School of MedicineStimulant dependence is heritable, but specific genetic factors underlying the trait have not been identified. A genome-wide association study for stimulant dependence was performed in a discovery cohort of African- (AA) and European-ancestry (EA) subjects ascertained for genetic studies of alcohol, opioid, and cocaine use disorders. The sample comprised individuals with DSM-IV stimulant dependence (393 EA cases, 5288 EA controls; 155 AA cases, 5603 AA controls). An independent cohort from the family-based Collaborative Study on the Genetics of Alcoholism (532 EA cases, 7635 EA controls; 53 AA cases, AA 3352 controls) was used for replication. One variant in SLC25A16 (rs2394476, p = 3.42 × 10-10, odds ratio [OR] = 3.70) was GWS in AAs. Four other loci showed suggestive evidence, including KCNA4 in AAs (rs11500237, p = 2.99 × 10-7, OR = 2.31) which encodes one of the potassium voltage-gated channel protein that has been linked to several other substance use disorders, and CPVL in the combined population groups (rs1176440, p = 3.05 × 10-7, OR = 1.35), whose expression was previously shown to be upregulated in the prefrontal cortex from users of cocaine, cannabis, and phencyclidine. Analysis of the top GWAS signals revealed a significant enrichment with nicotinic acetylcholine receptor genes (adjusted p = 0.04) and significant pleiotropy between stimulant dependence and alcohol dependence in EAs (padj = 3.6 × 10-3), an anxiety disorder in EAs (padj = 2.1 × 10-4), and ADHD in both AAs (padj = 3.0 × 10-33) and EAs (padj = 6.7 × 10-35). Our results implicate novel genes and pathways as having roles in the etiology of stimulant dependence.Item Integrative analysis of eQTL and GWAS summary statistics reveals transcriptomic alteration in Alzheimer brains(BMC, 2022-04-23) Varathan, Pradeep; Gorijala, Priyanka; Jacobson, Tanner; Chasioti, Danai; Nho, Kwangsik; Risacher, Shannon L.; Saykin, Andrew J.; Yan, Jingwen; Radiology and Imaging Sciences, School of MedicineBackground: Large-scale genome-wide association studies have successfully identified many genetic variants significantly associated with Alzheimer's disease (AD), such as rs429358, rs11038106, rs723804, rs13591776, and more. The next key step is to understand the function of these SNPs and the downstream biology through which they exert the effect on the development of AD. However, this remains a challenging task due to the tissue-specific nature of transcriptomic and proteomic data and the limited availability of brain tissue.In this paper, instead of using coupled transcriptomic data, we performed an integrative analysis of existing GWAS findings and expression quantitative trait loci (eQTL) results from AD-related brain regions to estimate the transcriptomic alterations in AD brain. Results: We used summary-based mendelian randomization method along with heterogeneity in dependent instruments method and were able to identify 32 genes with potential altered levels in temporal cortex region. Among these, 10 of them were further validated using real gene expression data collected from temporal cortex region, and 19 SNPs from NECTIN and TOMM40 genes were found associated with multiple temporal cortex imaging phenotype. Conclusion: Significant pathways from enriched gene networks included neutrophil degranulation, Cell surface interactions at the vascular wall, and Regulation of TP53 activity which are still relatively under explored in Alzheimer's Disease while also encouraging a necessity to bind further trans-eQTL effects into this integrative analysis.Item Multidrug resistance-associated protein 1 (MRP1/ABCC1) polymorphism: from discovery to clinical application(Wanfang Med Online, 2011-10) Yin, Jiye; Zhang, Jianting; Department of Pharmacology and Toxicology, IU School of MedicineMultidrug resistance-associated protein 1 (MRP1/ABCC1) is the first identified member of ABCC subfamily which belongs to ATP-binding cassette (ABC) transporter superfamily. It is ubiquitously expressed in almost all human tissues and transports a wide spectrum of substrates including drugs, heavy metal anions, toxicants, and conjugates of glutathione, glucuronide and sulfate. With the advance of sequence technology, many MRP1/ABCC1 polymorphisms have been identified. Accumulating evidences show that some polymorphisms are significantly associated with drug resistance and disease susceptibility. In vitro reconstitution studies have also unveiled the mechanism for some polymorphisms. In this review, we present recent advances in understanding the role and mechanism of MRP1/ABCC1 polymorphisms in drug resistance, toxicity, disease susceptibility and severity, prognosis prediction, and methods to select and predict functional polymorphisms.Item PancanQTLv2.0: a comprehensive resource for expression quantitative trait loci across human cancers(Oxford University Press, 2024) Chen, Chengxuan; Liu, Yuan; Luo, Mei; Yang, Jingwen; Chen, Yamei; Wang, Runhao; Zhou, Joseph; Zang, Yong; Diao, Lixia; Han, Leng; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public HealthExpression quantitative trait locus (eQTL) analysis is a powerful tool used to investigate genetic variations in complex diseases, including cancer. We previously developed a comprehensive database, PancanQTL, to characterize cancer eQTLs using The Cancer Genome Atlas (TCGA) dataset, and linked eQTLs with patient survival and GWAS risk variants. Here, we present an updated version, PancanQTLv2.0 (https://hanlaboratory.com/PancanQTLv2/), with advancements in fine-mapping causal variants for eQTLs, updating eQTLs overlapping with GWAS linkage disequilibrium regions and identifying eQTLs associated with drug response and immune infiltration. Through fine-mapping analysis, we identified 58 747 fine-mapped eQTLs credible sets, providing mechanic insights of gene regulation in cancer. We further integrated the latest GWAS Catalog and identified a total of 84 592 135 linkage associations between eQTLs and the existing GWAS loci, which represents a remarkable ∼50-fold increase compared to the previous version. Additionally, PancanQTLv2.0 uncovered 659516 associations between eQTLs and drug response and identified 146948 associations between eQTLs and immune cell abundance, providing potentially clinical utility of eQTLs in cancer therapy. PancanQTLv2.0 expanded the resources available for investigating gene expression regulation in human cancers, leading to advancements in cancer research and precision oncology.Item RegSNPs-intron: a computational framework for predicting pathogenic impact of intronic single nucleotide variants(BMC, 2019-11-28) Lin, Hai; Hargreaves, Katherine A.; Li, Rudong; Reiter, Jill L.; Wang, Yue; Mort, Matthew; Cooper, David N.; Zhou, Yaoqi; Zhang, Chi; Eadon, Michael T.; Dolan, M. Eileen; Ipe, Joseph; Skaar, Todd C.; Liu, Yunlong; Medical and Molecular Genetics, School of MedicineSingle nucleotide variants (SNVs) in intronic regions have yet to be systematically investigated for their disease-causing potential. Using known pathogenic and neutral intronic SNVs (iSNVs) as training data, we develop the RegSNPs-intron algorithm based on a random forest classifier that integrates RNA splicing, protein structure, and evolutionary conservation features. RegSNPs-intron showed excellent performance in evaluating the pathogenic impacts of iSNVs. Using a high-throughput functional reporter assay called ASSET-seq (ASsay for Splicing using ExonTrap and sequencing), we evaluate the impact of RegSNPs-intron predictions on splicing outcome. Together, RegSNPs-intron and ASSET-seq enable effective prioritization of iSNVs for disease pathogenesis.