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Item The collaborative study on the genetics of alcoholism: Sample and clinical data(Wiley, 2023) Dick, Danielle M.; Balcke, Emily; McCutcheon, Vivia; Francis, Meredith; Kuo, Sally; Salvatore, Jessica; Meyers, Jacquelyn; Bierut, Laura J.; Schuckit, Marc; Hesselbrock, Victor; Edenberg, Howard J.; Porjesz, Bernice; COGA Collaborators; Kuperman, Samuel; Kramer, John; Bucholz, Kathleen; Medical and Molecular Genetics, School of MedicineThe collaborative study on the genetics of alcoholism (COGA) is a multi-site, multidisciplinary project with the goal of identifying how genes are involved in alcohol use disorder and related outcomes, and characterizing how genetic risk unfolds across development and in conjunction with the environment and brain function. COGA is a multi-generational family-based study in which probands were recruited through alcohol treatment centers, along with a set of community comparison families. Nearly 18,000 individuals from >2200 families have been assessed over a period of over 30 years with a rich phenotypic battery that includes semi-structured psychiatric interviews and questionnaire measures, along with DNA collection and electrophysiological data on a large subset. Participants range in age from 7 to 97, with many having longitudinal assessments, providing a valuable opportunity to study alcohol use and problems across the lifespan. Here we provide an overview of data collection methods for the COGA sample, and details about sample characteristics and comorbidity. We also review key research findings that have emerged from analyses of the COGA data. COGA data are available broadly to researchers, and we hope this overview will encourage further collaboration and use of these data to advance the field.Item The Collaborative Study on the Genetics of Alcoholism: Overview(Wiley, 2023) Agrawal, Arpana; Brislin, Sarah J.; Bucholz, Kathleen K.; Dick, Danielle; Hart, Ronald P.; Johnson, Emma C.; Meyers, Jacquelyn; Salvatore, Jessica; Slesinger, Paul; COGA Collaborators; Almasy, Laura; Foroud, Tatiana; Goate, Alison; Hesselbrock, Victor; Kramer, John; Kuperman, Samuel; Merikangas, Alison K.; Nurnberger, John I.; Tischfield, Jay; Edenberg, Howard J.; Porjesz, Bernice; Medical and Molecular Genetics, School of MedicineAlcohol use disorders (AUD) are commonly occurring, heritable and polygenic disorders with etiological origins in the brain and the environment. To outline the causes and consequences of alcohol-related milestones, including AUD, and their related psychiatric comorbidities, the Collaborative Study on the Genetics of Alcoholism (COGA) was launched in 1989 with a gene-brain-behavior framework. COGA is a family based, diverse (~25% self-identified African American, ~52% female) sample, including data on 17,878 individuals, ages 7-97 years, in 2246 families of which a proportion are densely affected for AUD. All participants responded to questionnaires (e.g., personality) and the Semi-Structured Assessment for the Genetics of Alcoholism (SSAGA) which gathers information on psychiatric diagnoses, conditions and related behaviors (e.g., parental monitoring). In addition, 9871 individuals have brain function data from electroencephalogram (EEG) recordings while 12,009 individuals have been genotyped on genome-wide association study (GWAS) arrays. A series of functional genomics studies examine the specific cellular and molecular mechanisms underlying AUD. This overview provides the framework for the development of COGA as a scientific resource in the past three decades, with individual reviews providing in-depth descriptions of data on and discoveries from behavioral and clinical, brain function, genetic and functional genomics data. The value of COGA also resides in its data sharing policies, its efforts to communicate scientific findings to the broader community via a project website and its potential to nurture early career investigators and to generate independent research that has broadened the impact of gene-brain-behavior research into AUD.Item Parkinson's Progression Markers Initiative: A Milestone-Based Strategy to Monitor Parkinson's Disease Progression(IOS Press, 2023) Brumm, Michael C.; Siderowf, Andrew; Simuni, Tanya; Burghardt, Elliot; Choi, Seung Ho; Caspell-Garcia, Chelsea; Chahine, Lana M.; Mollenhauer, Brit; Foroud, Tatiana; Galasko, Douglas; Merchant, Kalpana; Arnedo, Vanessa; Hutten, Samantha J.; O’Grady, Alyssa N.; Poston, Kathleen L.; Tanner, Caroline M.; Weintraub, Daniel; Kieburtz, Karl; Marek, Kenneth; Coffey, Christopher S.; Parkinson’s Progression Markers Initiative; Medical and Molecular Genetics, School of MedicineBackground: Identifying a meaningful progression metric for Parkinson's disease (PD) that reflects heterogeneity remains a challenge. Objective: To assess the frequency and baseline predictors of progression to clinically relevant motor and non-motor PD milestones. Methods: Using data from the Parkinson's Progression Markers Initiative (PPMI) de novo PD cohort, we monitored 25 milestones across six domains ("walking and balance"; "motor complications"; "cognition"; "autonomic dysfunction"; "functional dependence"; "activities of daily living"). Milestones were intended to be severe enough to reflect meaningful disability. We assessed the proportion of participants reaching any milestone; evaluated which occurred most frequently; and conducted a time-to-first-event analysis exploring whether baseline characteristics were associated with progression. Results: Half of participants reached at least one milestone within five years. Milestones within the cognitive, functional dependence, and autonomic dysfunction domains were reached most often. Among participants who reached a milestone at an annual follow-up visit and remained active in the study, 82% continued to meet criteria for any milestone at one or more subsequent annual visits and 55% did so at the next annual visit. In multivariable analysis, baseline features predicting faster time to reaching a milestone included age (p < 0.0001), greater MDS-UPDRS total scores (p < 0.0001), higher GDS-15 depression scores (p = 0.0341), lower dopamine transporter binding (p = 0.0043), and lower CSF total α-synuclein levels (p = 0.0030). Symptomatic treatment was not significantly associated with reaching a milestone (p = 0.1639). Conclusion: Clinically relevant milestones occur frequently, even in early PD. Milestones were significantly associated with baseline clinical and biological markers, but not with symptomatic treatment. Further studies are necessary to validate these results, further assess the stability of milestones, and explore translating them into an outcome measure suitable for observational and therapeutic studies.Item Targeting Mosquitoes through Generation of an Insecticidal RNAi Yeast Strain Using Cas-CLOVER and Super PiggyBac Engineering in Saccharomyces cerevisiae(MDPI, 2023-10-27) Brizzee, Corey; Mysore, Keshava; Njoroge, Teresia M.; McConnell, Seth; Hamid-Adiamoh, Majidah; Stewart, Akilah T. M.; Kinder, J. Tyler; Crawford, Jack; Duman-Scheel, Molly; Medical and Molecular Genetics, School of MedicineThe global deployment of RNAi yeast insecticides involves transitioning from the use of laboratory yeast strains to more robust strains that are suitable for scaled fermentation. In this investigation, the RNA-guided Cas-CLOVER system was used in combination with Piggybac transposase to produce robust Saccharomyces cerevisiae strains with multiple integrated copies of the Sh.463 short hairpin RNA (shRNA) insecticide expression cassette. This enabled the constitutive high-level expression of an insecticidal shRNA corresponding to a target sequence that is conserved in mosquito Shaker genes, but which is not found in non-target organisms. Top-expressing Cas-CLOVER strains performed well in insecticide trials conducted on Aedes, Culex, and Anopheles larvae and adult mosquitoes, which died following consumption of the yeast. Scaled fermentation facilitated the kilogram-scale production of the yeast, which was subsequently heat-killed and dried. These studies indicate that RNAi yeast insecticide production can be scaled, an advancement that may one day facilitate the global distribution of this new mosquito control intervention.Item Read-depth based approach on whole genome resequencing data reveals important insights into the copy number variation (CNV) map of major global buffalo breeds(BMC, 2023-10-16) Ahmad, Sheikh Firdous; Shailaja, Celus Chandrababu; Vaishnav, Sakshi; Kumar, Amit; Gaur, Gyanendra Kumar; Janga, Sarath Chandra; Ahmad, Syed Mudasir; Malla, Waseem Akram; Dutt, Triveni; Medical and Molecular Genetics, School of MedicineBackground: Elucidating genome-wide structural variants including copy number variations (CNVs) have gained increased significance in recent times owing to their contribution to genetic diversity and association with important pathophysiological states. The present study aimed to elucidate the high-resolution CNV map of six different global buffalo breeds using whole genome resequencing data at two coverages (10X and 30X). Post-quality control, the sequence reads were aligned to the latest draft release of the Bubaline genome. The genome-wide CNVs were elucidated using a read-depth approach in CNVnator with different bin sizes. Adjacent CNVs were concatenated into copy number variation regions (CNVRs) in different breeds and their genomic coverage was elucidated. Results: Overall, the average size of CNVR was lower at 30X coverage, providing finer details. Most of the CNVRs were either deletion or duplication type while the occurrence of mixed events was lesser in number on a comparative basis in all breeds. The average CNVR size was lower at 30X coverage (0.201 Mb) as compared to 10X (0.013 Mb) with the finest variants in Banni buffaloes. The maximum number of CNVs was observed in Murrah (2627) and Pandharpuri (25,688) at 10X and 30X coverages, respectively. Whereas the minimum number of CNVs were scored in Surti at both coverages (2092 and 17,373). On the other hand, the highest and lowest number of CNVRs were scored in Jaffarabadi (833 and 10,179 events) and Surti (783 and 7553 events) at both coverages. Deletion events overnumbered duplications in all breeds at both coverages. Gene profiling of common overlapped genes and longest CNVRs provided important insights into the evolutionary history of these breeds and indicate the genomic regions under selection in respective breeds. Conclusion: The present study is the first of its kind to elucidate the high-resolution CNV map in major buffalo populations using a read-depth approach on whole genome resequencing data. The results revealed important insights into the divergence of major global buffalo breeds along the evolutionary timescale.Item MicroRNAs Signature Panel Identifies Heavy Drinkers with Alcohol-Associated Cirrhosis from Heavy Drinkers without Liver Injury(MDPI, 2023-10-08) Shihana, Fathima; Joglekar, Mugdha V.; Schwantes-An, Tae-Hwi; Hardikar, Anandwardhan A.; Seth, Devanshi; Medical and Molecular Genetics, School of MedicineBackground: Alcohol-associated liver disease (ALD) is the most common disorder of prolonged drinking. Mechanisms underlying cirrhosis in such patients remain unclear. MicroRNAs play regulatory role in several diseases, are affected by alcohol and may be important players in alcohol use disorders, such as cirrhosis. Methods: We investigated serum samples from heavy chronic alcohol users (80 g/day (male) and 50 g/day (female) for ≥10 years) that were available from our previously reported GenomALC study. A subset of GenomALC drinkers with liver cirrhosis (cases, n = 24) and those without significant liver disease (drinking controls, n = 23) were included. Global microRNA profiling was performed using high-throughput real-time quantitative PCR to identify the microRNA signatures associated with cirrhosis. Ingenuity Pathway Analysis (IPA) software was utilized to identify target mRNAs of significantly altered microRNAs, and molecular pathways were analysed. Identified microRNAs were analysed for correlation with traditional liver disease biomarkers and risk gene variants previously reported from GenomALC genome-wide association study. Results: The expression of 21 microRNAs was significantly downregulated in cases compared to drinking controls (p < 0.05, ∆∆Ct > 1.5-fold). Seven microRNAs (miR-16, miR-19a, miR-27a, miR-29b, miR-101, miR-130a, and miR-191) had a highly significant correlation (p < 0.001) with INR, bilirubin and MELD score. Three microRNAs (miR-27a, miR-130a and miR-191) significantly predicted cases with AUC-ROC 0.8, 0.78 and 0.85, respectively (p < 0.020); however, INR performed best (0.97, p < 0.001). A different set of six microRNAs (miR-19a, miR-26a, miR-101, miR-151-3p, miR-221, and miR-301) showed positive correlation (ranging from 0.32 to 0.51, p < 0.05) with rs10433937:HSD17B13 gene variant, associated with the risk of cirrhosis. IPA analysis revealed mRNA targets of the significantly altered microRNAs associated with cell death/necrosis, fibrosis and increased steatosis, particularly triglyceride metabolism. Conclusions: MicroRNA signatures in drinkers distinguished those with liver cirrhosis from drinkers without liver disease. We identified mRNA targets in liver functions that were enriched for disease pathogenesis pathways.Item Macrocephaly and developmental delay caused by missense variants in RAB5C(Oxford University Press, 2023) Koop, Klaas; Yuan, Weimin; Tessadori, Federico; Rodriguez-Polanco, Wilmer R.; Grubbs, Jeremy; Zhang, Bo; Osmond, Matt; Graham, Gail; Sawyer, Sarah; Conboy, Erin; Vetrini, Francesco; Treat, Kayla; Płoski, Rafal; Pienkowski, Victor Murcia; Kłosowska, Anna; Fieg, Elizabeth; Krier, Joel; Mallebranche, Coralie; Alban, Ziegler; Aldinger, Kimberly A.; Ritter, Deborah; Macnamara, Ellen; Sullivan, Bonnie; Herriges, John; Alaimo, Joseph T.; Helbig, Catherine; Ellis, Colin A.; van Eyk, Clare; Gecz, Jozef; Farrugia, Daniel; Osei-Owusu, Ikeoluwa; Adès, Lesley; van den Boogaard, Marie-Jose; Fuchs, Sabine; Bakker, Jeroen; Duran, Karen; Dawson, Zachary D.; Lindsey, Anika; Huang, Huiyan; Baldridge, Dustin; Silverman, Gary A.; Grant, Barth D.; Raizen, David; Undiagnosed Diseases Network; van Haaften, Gijs; Pak, Stephen C.; Rehmann, Holger; Schedl, Tim; van Hasselt, Peter; Medical and Molecular Genetics, School of MedicineRab GTPases are important regulators of intracellular vesicular trafficking. RAB5C is a member of the Rab GTPase family that plays an important role in the endocytic pathway, membrane protein recycling and signaling. Here we report on 12 individuals with nine different heterozygous de novo variants in RAB5C. All but one patient with missense variants (n = 9) exhibited macrocephaly, combined with mild-to-moderate developmental delay. Patients with loss of function variants (n = 2) had an apparently more severe clinical phenotype with refractory epilepsy and intellectual disability but a normal head circumference. Four missense variants were investigated experimentally. In vitro biochemical studies revealed that all four variants were damaging, resulting in increased nucleotide exchange rate, attenuated responsivity to guanine exchange factors and heterogeneous effects on interactions with effector proteins. Studies in C. elegans confirmed that all four variants were damaging in vivo and showed defects in endocytic pathway function. The variant heterozygotes displayed phenotypes that were not observed in null heterozygotes, with two shown to be through a dominant negative mechanism. Expression of the human RAB5C variants in zebrafish embryos resulted in defective development, further underscoring the damaging effects of the RAB5C variants. Our combined bioinformatic, in vitro and in vivo experimental studies and clinical data support the association of RAB5C missense variants with a neurodevelopmental disorder characterized by macrocephaly and mild-to-moderate developmental delay through disruption of the endocytic pathway.Item An Efficient Binary Sand Cat Swarm Optimization for Feature Selection in High-Dimensional Biomedical Data(MDPI, 2023-09-25) Pashaei, Elnaz; Medical and Molecular Genetics, School of MedicineRecent breakthroughs are making a significant contribution to big data in biomedicine which are anticipated to assist in disease diagnosis and patient care management. To obtain relevant information from this data, effective administration and analysis are required. One of the major challenges associated with biomedical data analysis is the so-called “curse of dimensionality”. For this issue, a new version of Binary Sand Cat Swarm Optimization (called PILC-BSCSO), incorporating a pinhole-imaging-based learning strategy and crossover operator, is presented for selecting the most informative features. First, the crossover operator is used to strengthen the search capability of BSCSO. Second, the pinhole-imaging learning strategy is utilized to effectively increase exploration capacity while avoiding premature convergence. The Support Vector Machine (SVM) classifier with a linear kernel is used to assess classification accuracy. The experimental results show that the PILC-BSCSO algorithm beats 11 cutting-edge techniques in terms of classification accuracy and the number of selected features using three public medical datasets. Moreover, PILC-BSCSO achieves a classification accuracy of 100% for colon cancer, which is difficult to classify accurately, based on just 10 genes. A real Liver Hepatocellular Carcinoma (TCGA-HCC) data set was also used to further evaluate the effectiveness of the PILC-BSCSO approach. PILC-BSCSO identifies a subset of five marker genes, including prognostic biomarkers HMMR, CHST4, and COL15A1, that have excellent predictive potential for liver cancer using TCGA data.Item Osteogenic Differentiation Potential of Mesenchymal Stem Cells Using Single Cell Multiomic Analysis(MDPI, 2023-09-26) Chen, Duojiao; Liu, Sheng; Chu, Xiaona; Reiter, Jill; Gao, Hongyu; McGuire, Patrick; Yu, Xuhong; Xuei, Xiaoling; Liu, Yichen; Wan, Jun; Fang, Fang; Liu, Yunlong; Wang, Yue; Medical and Molecular Genetics, School of MedicineMesenchymal stem cells (MSC) are multipotent stem cells that can differentiate into multiple cell types, including osteoblasts, chondrocytes, and adipocytes. Osteoblast differentiation is reduced during osteoporosis development, resulting in reduced bone formation. Further, MSC isolated from different donors possess distinct osteogenic capacity. In this study, we used single-cell multiomic analysis to profile the transcriptome and epigenome of MSC from four healthy donors. Data were obtained from ~1300 to 1600 cells for each donor. These cells were clustered into four groups, indicating that MSC from different donors have distinct chromatin accessible regulatory elements for regulating gene expression. To investigate the mechanism by which MSC undergo osteogenic differentiation, we used the chromatin accessibility data from the single-cell multiome data to identify individual-specific enhancer–promoter pairs and evaluated the expression levels and activities of the transcriptional regulators. The MSC from four donors showed distinct differentiation potential into osteoblasts. MSC of donor 1 showed the largest average motif activities, indicating that MSC from donor 1 was most likely to differentiate into osteoblasts. The results of our validation experiments were consistent with the bioinformatics prediction. We also tested the enrichment of genome-wide association study (GWAS) signals of several musculoskeletal disease traits in the patient-specific chromatin accessible regions identified in the single-cell multiome data, including osteoporosis, osteopenia, and osteoarthritis. We found that osteoarthritis-associated variants were only enriched in the regions identified from donor 4. In contrast, osteoporosis and osteopenia variants were enriched in regions from donor 1 and least enriched in donor 4. Since osteoporosis and osteopenia are related to the density of bone cells, the enrichment of variants from these traits should be correlated with the osteogenic potential of MSC. In summary, this study provides large-scale data to link regulatory elements with their target genes to study the regulatory relationships during the differentiation of mesenchymal stem cells and provide a deeper insight into the gene regulatory mechanism.Item Genome-Wide Association Analysis across Endophenotypes in Alzheimer's Disease: Main Effects and Disease Stage-Specific Interactions(MDPI, 2023-10-27) Rosewood, Thea J.; Nho, Kwangsik; Risacher, Shannon L.; Gao, Sujuan; Shen, Li; Foroud, Tatiana; Saykin, Andrew J.; Alzheimer’s Disease Neuroimaging Initiative; Medical and Molecular Genetics, School of MedicineThe underlying genetic susceptibility for Alzheimer's disease (AD) is not yet fully understood. The heterogeneous nature of the disease challenges genetic association studies. Endophenotype approaches can help to address this challenge by more direct interrogation of biological traits related to the disease. AD endophenotypes based on amyloid-β, tau, and neurodegeneration (A/T/N) biomarkers and cognitive performance were selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort (N = 1565). A genome-wide association study (GWAS) of quantitative phenotypes was performed using an SNP main effect and an SNP by Diagnosis interaction (SNP × DX) model to identify disease stage-specific genetic effects. Nine loci were identified as study-wide significant with one or more A/T/N endophenotypes in the main effect model, as well as additional findings significantly associated with cognitive measures. These nine loci include SNPs in or near the genes APOE, SRSF10, HLA-DQB1, XKR3, and KIAA1671. The SNP × DX model identified three study-wide significant genetic loci (BACH2, EP300, and PACRG-AS1) with a neuroprotective effect in later AD stage endophenotypes. An endophenotype approach identified novel genetic associations and provided insight into the molecular mechanisms underlying the genetic associations that may otherwise be missed using conventional case-control study designs.