Browsing by Subject "Genetic predisposition to disease"
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Item A Between-Sex Comparison of the Genomic Architecture of Asthma(American Thoracic Society, 2023) Zein, Joe G.; Bazeley, Peter; Meyers, Deborah; Bleecker, Eugene; Gaston, Benjamin; Hu, Bo; Attaway, Amy; Ortega, Victor; Pediatrics, School of MedicineItem A global view of the genetic basis of Alzheimer disease(Springer Nature, 2023) Reitz, Christiane; Pericak-Vance, Margaret A.; Foroud, Tatiana; Mayeux, Richard; Medical and Molecular Genetics, School of MedicineThe risk of Alzheimer disease (AD) increases with age, family history and informative genetic variants. Sadly, there is still no cure or means of prevention. As in other complex diseases, uncovering genetic causes of AD could identify underlying pathological mechanisms and lead to potential treatments. Rare, autosomal dominant forms of AD occur in middle age as a result of highly penetrant genetic mutations, but the most common form of AD occurs later in life. Large-scale, genome-wide analyses indicate that 70 or more genes or loci contribute to AD. One of the major factors limiting progress is that most genetic data have been obtained from non-Hispanic white individuals in Europe and North America, preventing the development of personalized approaches to AD in individuals of other ethnicities. Fortunately, emerging genetic data from other regions - including Africa, Asia, India and South America - are now providing information on the disease from a broader range of ethnicities. Here, we summarize the current knowledge on AD genetics in populations across the world. We predominantly focus on replicated genetic discoveries but also include studies in ethnic groups where replication might not be feasible. We attempt to identify gaps that need to be addressed to achieve a complete picture of the genetic and molecular factors that drive AD in individuals across the globe.Item A polygenic risk score for alcohol-associated cirrhosis among heavy drinkers with European ancestry(Wolters Kluwer, 2024-05-10) Schwantes-An, Tae-Hwi; Whitfield, John B.; Aithal, Guruprasad P.; Atkinson, Stephen R.; Bataller, Ramon; Botwin, Greg; Chalasani, Naga P.; Cordell, Heather J.; Daly, Ann K.; Darlay, Rebecca; Day, Christopher P.; Eyer, Florian; Foroud, Tatiana; Gawrieh, Samer; Gleeson, Dermot; Goldman, David; Haber, Paul S.; Jacquet, Jean-Marc; Lammert, Craig S.; 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: Polygenic Risk Scores (PRS) based on results from genome-wide association studies offer the prospect of risk stratification for many common and complex diseases. We developed a PRS for alcohol-associated cirrhosis by comparing single-nucleotide polymorphisms among patients with alcohol-associated cirrhosis (ALC) versus drinkers who did not have evidence of liver fibrosis/cirrhosis. Methods: Using a data-driven approach, a PRS for ALC was generated using a meta-genome-wide association study of ALC (N=4305) and an independent cohort of heavy drinkers with ALC and without significant liver disease (N=3037). It was validated in 2 additional independent cohorts from the UK Biobank with diagnosed ALC (N=467) and high-risk drinking controls (N=8981) and participants in the Indiana Biobank Liver cohort with alcohol-associated liver disease (N=121) and controls without liver disease (N=3239). Results: A 20-single-nucleotide polymorphisms PRS for ALC (PRSALC) was generated that stratified risk for ALC comparing the top and bottom deciles of PRS in the 2 validation cohorts (ORs: 2.83 [95% CI: 1.82 -4.39] in UK Biobank; 4.40 [1.56 -12.44] in Indiana Biobank Liver cohort). Furthermore, PRSALC improved the prediction of ALC risk when added to the models of clinically known predictors of ALC risk. It also stratified the risk for metabolic dysfunction -associated steatotic liver disease -cirrhosis (3.94 [2.23 -6.95]) in the Indiana Biobank Liver cohort -based exploratory analysis. Conclusions: PRSALC incorporates 20 single-nucleotide polymorphisms, predicts increased risk for ALC, and improves risk stratification for ALC compared with the models that only include clinical risk factors. This new score has the potential for early detection of heavy drinking patients who are at high risk for ALC.Item A Role for Germline Variants in Multiple Myeloma?(American Association for Cancer Research, 2024) Walker, Brian A.; Medicine, School of MedicineIn Blood Cancer Discovery, Thibaud and colleagues report the incidence of pathogenic germline variants (PGV) in patients with multiple myeloma and that these PGVs are associated with DNA repair pathway genes, including BRCA1 and BRCA2. They find an association of patients with PGVs and previous family or personal history of cancer, and that these patients are diagnosed slightly earlier than those without PGVs. Patients with PGVs had a longer progression-free survival than those without PGVs when they received high-dose melphalan and autologous stem cell transplant, providing a therapeutic rationale for diagnostic germline testing in myeloma. See related article by Thibaud et al., p. 428.Item Alcohol Use Disorder Polygenic Score Compared With Family History and ADH1B(American Medical Association, 2024-12-02) Lai, Dongbing; Zhang, Michael; Abreu, Marco; Schwantes-An, Tae-Hwi; Chan, Grace; Dick, Danielle M.; Kamarajan, Chella; Kuang, Weipeng; Nurnberger, John I.; Plawecki, Martin H.; Rice, John; Schuckit, Marc; Porjesz, Bernice; Liu, Yunlong; Foroud, Tatiana; Medical and Molecular Genetics, School of MedicineImportance: Identification of individuals at high risk of alcohol use disorder (AUD) and subsequent application of prevention and intervention programs has been reported to decrease the incidence of AUD. The polygenic score (PGS), which measures an individual's genetic liability to a disease, can potentially be used to evaluate AUD risk. Objective: To assess the estimability and generalizability of the PGS, compared with family history and ADH1B, in evaluating the risk of AUD among populations of European ancestry. Design, setting, and participants: This genetic association study was conducted between October 1, 2023, and May 21, 2024. A 2-stage design was used. First, the pruning and thresholding method was used to calculate PGSs in the screening stage. Second, the estimability and generalizability of the best PGS was determined using 2 independent samples in the testing stage. Three cohorts ascertained to study AUD were used in the screening stage: the Collaborative Study on the Genetics of Alcoholism (COGA), the Study of Addiction: Genetics and Environment (SAGE), and the Australian Twin-Family Study of Alcohol Use Disorder (OZALC). The All of Us Research Program (AOU), which comprises participants with diverse backgrounds and conditions, and the Indiana Biobank (IB), consisting of Indiana University Health system patients, were used to test the best PGS. For the COGA, SAGE, and OZALC cohorts, cases with AUD were determined using Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) or Fifth Edition (DSM-5) criteria; controls did not meet any criteria or did not have any other substance use disorders. For the AOU and IB cohorts, cases with AUD were identified using International Classification of Diseases, Ninth Revision (ICD-9) or International Classification of Diseases, Tenth Revision (ICD-10) codes; controls were aged 21 years or older and did not have AUD. Exposure: The PGS was calculated using single-nucleotide variants with concordant effects in 3 large-scale genome-wide association studies of AUD-related phenotypes. Main outcomes and measures: The main outcome was AUD determined with DSM-IV or DSM-5 criteria and ICD-9 or ICD-10 codes. Generalized linear mixed models and logistic regression models were used to analyze related and unrelated samples, respectively. Results: The COGA, SAGE, and OZALC cohorts included a total of 8799 samples (6323 cases and 2476 controls; 50.6% were men). The AOU cohort had a total of 116 064 samples (5660 cases and 110 404 controls; 60.4% were women). The IB cohort had 6373 samples (936 cases and 5437 controls; 54.9% were women). The 5% of samples with the highest PGS in the AOU and IB cohorts were approximately 2 times more likely to develop AUD (odds ratio [OR], 1.96 [95% CI, 1.78-2.16]; P = 4.10 × 10-43; and OR, 2.07 [95% CI, 1.59-2.71]; P = 9.15 × 10-8, respectively) compared with the remaining 95% of samples; these ORs were comparable to family history of AUD. For the 5% of samples with the lowest PGS in the AOU and IB cohorts, the risk of AUD development was approximately half (OR, 0.53 [95% CI, 0.45-0.62]; P = 6.98 × 10-15; and OR, 0.57 [95% CI, 0.39-0.84]; P = 4.88 × 10-3) compared with the remaining 95% of samples; these ORs were comparable to the protective effect of ADH1B. PGS had similar estimabilities in male and female individuals. Conclusions and relevance: In this study of AUD risk among populations of European ancestry, PGSs were calculated using concordant single-nucleotide variants and the best PGS was tested in targeted datasets. The findings suggest that the PGS may potentially be used to evaluate AUD risk. More datasets with similar AUD prevalence as in general populations are needed to further test the generalizability of PGS.Item Assessment and ascertainment in psychiatric molecular genetics: challenges and opportunities for cross-disorder research(Springer Nature, 2025) Cai, Na; Verhulst, Brad; Andreassen, Ole A.; Buitelaar, Jan; Edenberg, Howard J.; Hettema, John M.; Gandal, Michael; Grotzinger, Andrew; Jonas, Katherine; Lee, Phil; Mallard, Travis T.; Mattheisen, Manuel; Neale, Michael C.; Nurnberger, John I., Jr.; Peyrot, Wouter J.; Tucker-Drob, Elliot M.; Smoller, Jordan W.; Kendler, Kenneth S.; Biochemistry and Molecular Biology, School of MedicinePsychiatric disorders are highly comorbid, heritable, and genetically correlated [1-4]. The primary objective of cross-disorder psychiatric genetics research is to identify and characterize both the shared genetic factors that contribute to convergent disease etiologies and the unique genetic factors that distinguish between disorders [4, 5]. This information can illuminate the biological mechanisms underlying comorbid presentations of psychopathology, improve nosology and prediction of illness risk and trajectories, and aid the development of more effective and targeted interventions. In this review we discuss how estimates of comorbidity and identification of shared genetic loci between disorders can be influenced by how disorders are measured (phenotypic assessment) and the inclusion or exclusion criteria in individual genetic studies (sample ascertainment). Specifically, the depth of measurement, source of diagnosis, and time frame of disease trajectory have major implications for the clinical validity of the assessed phenotypes. Further, biases introduced in the ascertainment of both cases and controls can inflate or reduce estimates of genetic correlations. The impact of these design choices may have important implications for large meta-analyses of cohorts from diverse populations that use different forms of assessment and inclusion criteria, and subsequent cross-disorder analyses thereof. We review how assessment and ascertainment affect genetic findings in both univariate and multivariate analyses and conclude with recommendations for addressing them in future research.Item Association of Body Mass Index With Colorectal Cancer Risk by Genome-Wide Variants(Oxford University Press, 2021) Campbell, Peter T.; Lin, Yi; Bien, Stephanie A.; Figueiredo, Jane C.; Harrison, Tabitha A.; Guinter, Mark A.; Berndt, Sonja I.; Brenner, Hermann; Chan, Andrew T.; Chang-Claude, Jenny; Gallinger, Steven J.; Gapstur, Susan M.; Giles, Graham G.; Giovannucci, Edward; Gruber, Stephen B.; Gunter, Marc; Hoffmeister, Michael; Jacobs, Eric J.; Jenkins, Mark A.; Marchand, Loic Le; Li, Li; McLaughlin, John R.; Murphy, Neil; Milne, Roger L.; Newcomb, Polly A.; Newton, Christina; Ogino, Shuji; Potter, John D.; Rennert, Gad; Rennert, Hedy S.; Robinson, Jennifer; Sakoda, Lori C.; Slattery, Martha L.; Song, Yiqing; White, Emily; Woods, Michael O.; Casey, Graham; Hsu, Li; Peters, Ulrike; Epidemiology, School of Public HealthBackground: Body mass index (BMI) is a complex phenotype that may interact with genetic variants to influence colorectal cancer risk. Methods: We tested multiplicative statistical interactions between BMI (per 5 kg/m2) and approximately 2.7 million single nucleotide polymorphisms with colorectal cancer risk among 14 059 colorectal cancer case (53.2% women) and 14 416 control (53.8% women) participants. All analyses were stratified by sex a priori. Statistical methods included 2-step (ie, Cocktail method) and single-step (ie, case-control logistic regression and a joint 2-degree of freedom test) procedures. All statistical tests were two-sided. Results: Each 5 kg/m2 increase in BMI was associated with higher risks of colorectal cancer, less so for women (odds ratio [OR] = 1.14, 95% confidence intervals [CI] = 1.11 to 1.18; P = 9.75 × 10-17) than for men (OR = 1.26, 95% CI = 1.20 to 1.32; P = 2.13 × 10-24). The 2-step Cocktail method identified an interaction for women, but not men, between BMI and a SMAD7 intronic variant at 18q21.1 (rs4939827; Pobserved = .0009; Pthreshold = .005). A joint 2-degree of freedom test was consistent with this finding for women (joint P = 2.43 × 10-10). Each 5 kg/m2 increase in BMI was more strongly associated with colorectal cancer risk for women with the rs4939827-CC genotype (OR = 1.24, 95% CI = 1.16 to 1.32; P = 2.60 × 10-10) than for women with the CT (OR = 1.14, 95% CI = 1.09 to 1.19; P = 1.04 × 10-8) or TT (OR = 1.07, 95% CI = 1.01 to 1.14; P = .02) genotypes. Conclusion: These results provide novel insights on a potential mechanism through which a SMAD7 variant, previously identified as a susceptibility locus for colorectal cancer, and BMI may influence colorectal cancer risk for women.Item Association of Genetic Predisposition and Physical Activity With Risk of Gestational Diabetes in Nulliparous Women(American Medical Association, 2022-08-01) Pagel, Kymberleigh A.; Chu, Hoyin; Ramola, Rashika; Guerrero, Rafael F.; Chung, Judith H.; Parry, Samuel; Reddy, Uma M.; Silver, Robert M.; Steller, Jonathan G.; Yee, Lynn M.; Wapner, Ronald J.; Hahn, Matthew W.; Natarajan, Sriraam; Haas, David M.; Radivojac, Predrag; Obstetrics and Gynecology, School of MedicineImportance: Polygenic risk scores (PRS) for type 2 diabetes (T2D) can improve risk prediction for gestational diabetes (GD), yet the strength of the association between genetic and lifestyle risk factors has not been quantified. Objective: To assess the association of PRS and physical activity in existing GD risk models and identify patient subgroups who may receive the most benefits from a PRS or physical activity intervention. Design, settings, and participants: The Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be cohort was established to study individuals without previous pregnancy lasting at least 20 weeks (nulliparous) and to elucidate factors associated with adverse pregnancy outcomes. A subcohort of 3533 participants with European ancestry was used for risk assessment and performance evaluation. Participants were enrolled from October 5, 2010, to December 3, 2013, and underwent genotyping between February 19, 2019, and February 28, 2020. Data were analyzed from September 15, 2020, to November 10, 2021. Exposures: Self-reported total physical activity in early pregnancy was quantified as metabolic equivalents of task (METs). Polygenic risk scores were calculated for T2D using contributions of 84 single nucleotide variants, weighted by their association in the Diabetes Genetics Replication and Meta-analysis Consortium data. Main outcomes and measures: Estimation of the development of GD from clinical, genetic, and environmental variables collected in early pregnancy, assessed using measures of model discrimination. Odds ratios and positive likelihood ratios were used to evaluate the association of PRS and physical activity with GD risk. Results: A total of 3533 women were included in this analysis (mean [SD] age, 28.6 [4.9] years). In high-risk population subgroups (body mass index ≥25 or aged ≥35 years), individuals with high PRS (top 25th percentile) or low activity levels (METs <450) had increased odds of a GD diagnosis of 25% to 75%. Compared with the general population, participants with both high PRS and low activity levels had higher odds of a GD diagnosis (odds ratio, 3.4 [95% CI, 2.3-5.3]), whereas participants with low PRS and high METs had significantly reduced risk of a GD diagnosis (odds ratio, 0.5 [95% CI, 0.3-0.9]; P = .01). Conclusions and relevance: In this cohort study, the addition of PRS was associated with the stratified risk of GD diagnosis among high-risk patient subgroups, suggesting the benefits of targeted PRS ascertainment to encourage early intervention.Item Associations Between Cannabis Use, Polygenic Liability for Schizophrenia, and Cannabis-related Experiences in a Sample of Cannabis Users(Oxford University Press, 2023) Johnson, Emma C.; Colbert, Sarah M. C.; Jeffries, Paul W.; Tillman, Rebecca; Bigdeli, Tim B.; Karcher, Nicole R.; Chan, Grace; Kuperman, Samuel; Meyers, Jacquelyn L.; Nurnberger, John I.; Plawecki, Martin H.; Degenhardt, Louisa; Martin, Nicholas G.; Kamarajan, Chella; Schuckit, Marc A.; Murray, Robin M.; Dick, Danielle M.; Edenberg, Howard J.; D'Souza, Deepak Cyril; Di Forti, Marta; Porjesz, Bernice; Nelson, Elliot C.; Agrawal, Arpana; Medical and Molecular Genetics, School of MedicineBackground and hypothesis: Risk for cannabis use and schizophrenia is influenced in part by genetic factors, and there is evidence that genetic risk for schizophrenia is associated with subclinical psychotic-like experiences (PLEs). Few studies to date have examined whether genetic risk for schizophrenia is associated with cannabis-related PLEs. Study design: We tested whether measures of cannabis involvement and polygenic risk scores (PRS) for schizophrenia were associated with self-reported cannabis-related experiences in a sample ascertained for alcohol use disorders (AUDs), the Collaborative Study on the Genetics of Alcoholism (COGA). We analyzed 4832 subjects (3128 of European ancestry and 1704 of African ancestry; 42% female; 74% meeting lifetime criteria for an AUD). Study results: Cannabis use disorder (CUD) was prevalent in this analytic sample (70%), with 40% classified as mild, 25% as moderate, and 35% as severe. Polygenic risk for schizophrenia was positively associated with cannabis-related paranoia, feeling depressed or anhedonia, social withdrawal, and cognitive difficulties, even when controlling for duration of daily cannabis use, CUD, and age at first cannabis use. The schizophrenia PRS was most robustly associated with cannabis-related cognitive difficulties (β = 0.22, SE = 0.04, P = 5.2e-7). In an independent replication sample (N = 1446), associations between the schizophrenia PRS and cannabis-related experiences were in the expected direction and not statistically different in magnitude from those in the COGA sample. Conclusions: Among individuals who regularly use cannabis, genetic liability for schizophrenia-even in those without clinical features-may increase the likelihood of reporting unusual experiences related to cannabis use.Item The collaborative study on the genetics of alcoholism: an update(The National Institute on Alcohol Abuse and Alcoholism, 2002) Edenberg, Howard J.; Biochemistry and Molecular Biology, School of MedicineThe Collaborative Study on the Genetics of Alcoholism (COGA) is a large-scale family study designed to identify genes that affect the risk for alcoholism (i.e., alcohol dependence) and alcohol-related characteristics and behaviors (i.e., phenotypes1). This collaborative project is funded by the National Institute on Alcohol Abuse and Alcoholism. Data collection, analysis, and/or storage for this study take place at nine sites across the United States. Because alcoholism is a complex genetic disorder, the COGA researchers expected that multiple genes would contribute to the risk. In other words, there will be no single “gene for alcoholism” but rather variations in many different genes that together, interacting with the environment, place some people at significantly higher risk for the disease. This genetic and environmental variability (i.e., heterogeneity) makes the task of identifying individual genes difficult. However, the COGA project was designed with these difficulties in mind and incorporated strategies to meet the challenges. This article briefly reviews these strategies and summarizes some of the results already obtained in the ongoing COGA study.