IU Indianapolis ScholarWorks :: Browsing by Subject "Genome-wide association studies"

Browsing by Subject "Genome-wide association studies"

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Author Correction: FAM222A encodes a protein which accumulates in plaques in Alzheimer’s disease
(Springer Nature, 2022-07-11) Yan, Tingxiang; Liang, Jingjing; Gao, Ju; Wang, Luwen; Fujioka, Hisashi; The Alzheimer Disease Neuroimaging Initiative; Zhu, Xiaofeng; Wang, Xinglong; Radiology and Imaging Sciences, School of Medicine
Correction to: Nature Communications 10.1038/s41467-019-13962-0, published online 21 January 2020. In the original version of the manuscript, the image shown in Figure 4g, bottom row (Aβ1–42 + rAggregatin), under “6h” was incorrect. This image incorrectly showed the same sample as shown in the original Figure 4g, top row (Aβ1–42), under “0.5h”.
Author Correction: Whole-Genome Sequencing Analysis of Human Metabolome in Multi-Ethnic Populations
(Springer Nature, 2023-10-19) Feofanova, Elena V.; Brown, Michael R.; Alkis, Taryn; Manuel, Astrid M.; Li, Xihao; Tahir, Usman A.; Li, Zilin; Mendez, Kevin M.; Kelly, Rachel S.; Qi, Qibin; Chen, Han; Larson, Martin G.; Lemaitre, Rozenn N.; Morrison, Alanna C.; Grieser, Charles; Wong, Kari E.; Gerszten, Robert E.; Zhao, Zhongming; Lasky-Su, Jessica; NHLBI Trans-Omics for Precision Medicine (TOPMed); Yu, Bing; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
Correction to: Nature Communications 10.1038/s41467-023-38800-2, published online 30 May2023 In this article, the author name Robert E. Gerszten was incorrectly written as Robert E. Gersztern. The original article has been corrected.
Bayesian mixed model inference for genetic association under related samples with brain network phenotype
(Oxford University Press, 2024) Tian, Xinyuan; Wang, Yiting; Wang, Selena; Zhao, Yi; Zhao, Yize; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
Genetic association studies for brain connectivity phenotypes have gained prominence due to advances in noninvasive imaging techniques and quantitative genetics. Brain connectivity traits, characterized by network configurations and unique biological structures, present distinct challenges compared to other quantitative phenotypes. Furthermore, the presence of sample relatedness in the most imaging genetics studies limits the feasibility of adopting existing network-response modeling. In this article, we fill this gap by proposing a Bayesian network-response mixed-effect model that considers a network-variate phenotype and incorporates population structures including pedigrees and unknown sample relatedness. To accommodate the inherent topological architecture associated with the genetic contributions to the phenotype, we model the effect components via a set of effect network configurations and impose an inter-network sparsity and intra-network shrinkage to dissect the phenotypic network configurations affected by the risk genetic variant. A Markov chain Monte Carlo (MCMC) algorithm is further developed to facilitate uncertainty quantification. We evaluate the performance of our model through extensive simulations. By further applying the method to study, the genetic bases for brain structural connectivity using data from the Human Connectome Project with excessive family structures, we obtain plausible and interpretable results. Beyond brain connectivity genetic studies, our proposed model also provides a general linear mixed-effect regression framework for network-variate outcomes.
Collaborative meta-analysis finds no evidence of a strong interaction between stress and 5-HTTLPR genotype contributing to the development of depression
(Nature Publishing Group, 2018-01) Culverhouse, Robert C.; Saccone, Nancy L.; Horton, Amy C.; Ma, Yinjiao; Anstey, Kaarin J.; Banaschewski, Tobias; Burmeister, Margit; Cohen-Woods, Sarah; Etain, Bruno; Fisher, Helen L.; Goldman, Noreen; Guillaume, Sébastien; Horwood, John; Juhasz, Gabriella; Lester, Kathryn J.; Mandelli, Laura; Middeldorp, Christel M.; Olié, Emilie; Villafuerte, Sandra; Air, Tracy M.; Araya, Ricardo; Bowes, Lucy; Burns, Richard; Byrne, Enda M.; Coffey, Carolyn; Coventry, William L.; Gawronski, Katerina; Glei, Dana; Hatzimanolis, Alex; Hottenga, Jouke-Jan; Jaussent, Isabelle; Jawahar, Catharine; Jennen-Steinmetz, Christine; Kramer, John R.; Lajnef, Mohamed; Little, Keriann; zu Schwabedissen, Henriette Meyer; Nauck, Matthias; Nederhof, Esther; Petschner, Peter; Peyrot, Wouter J.; Schwahn, Christian; Sinnamon, Grant; Stacey, David; Tian, Yan; Toben, Catherine; Auwera, Sandra Van der; Wainwright, Nick; Wang, Jen-Chyong; Willemsen, Gonneke; Anderson, Ian M.; Arolt, Volker; Åslund, Cecilia; Bagdy, Gyorgy; Baune, Bernhard T.; Bellivier, Frank; Boomsma, Dorret I.; Courtet, Philippe; Dannlowski, Udo; de Geus, Eco J.C.; Deakin, John F. W.; Easteal, Simon; Eley, Thalia; Fergusson, David M.; Goate, Alison M.; Gonda, Xenia; Grabe, Hans J.; Holzman, Claudia; Johnson, Eric O.; Kennedy, Martin; Laucht, Manfred; Martin, Nicholas G.; Munafò, Marcus; Nilsson, Kent W.; Oldehinkel, Albertine J.; Olsson, Craig; Ormel, Johan; Otte, Christian; Patton, George C.; Penninx, Brenda W.J.H.; Ritchie, Karen; Sarchiapone, Marco; Scheid, JM; Serretti, Alessandro; Smit, Johannes H.; Stefanis, Nicholas C.; Surtees, Paul G.; Völzke, Henry; Weinstein, Maxine; Whooley, Mary; Nurnberger, John I., Jr.; Breslau, Naomi; Bierut, Laura J.; Psychiatry, School of Medicine
The hypothesis that the S allele of the 5-HTTLPR serotonin transporter promoter region is associated with increased risk of depression, but only in individuals exposed to stressful situations, has generated much interest, research, and controversy since first proposed in 2003. Multiple meta-analyses combining results from heterogeneous analyses have not settled the issue. To determine the magnitude of the interaction and the conditions under which it might be observed, we performed new analyses on 31 datasets containing 38 802 European-ancestry subjects genotyped for 5-HTTLPR and assessed for depression and childhood maltreatment or other stressful life events, and meta-analyzed the results. Analyses targeted two stressors (narrow, broad) and two depression outcomes (current, lifetime). All groups that published on this topic prior to the initiation of our study and met the assessment and sample size criteria were invited to participate. Additional groups, identified by consortium members or self-identified in response to our protocol (published prior to the start of analysis1) with qualifying unpublished data were also invited to participate. A uniform data analysis script implementing the protocol was executed by each of the consortium members. Our findings do not support the interaction hypothesis. We found no subgroups or variable definitions for which an interaction between stress and 5-HTTLPR genotype was statistically significant. In contrast, our findings for the main effects of life stressors (strong risk factor) and 5-HTTLPR genotype (no impact on risk) are strikingly consistent across our contributing studies, the original study reporting the interaction, and subsequent meta-analyses. Our conclusion is that if an interaction exists in which the S allele of 5-HTTLPR increases risk of depression only in stressed individuals, then it is not broadly generalizable, but must be of modest effect size and only observable in limited situations.
Comprehensive genetic analysis of the human lipidome identifies loci associated with lipid homeostasis with links to coronary artery disease
(Springer Nature, 2022-06-06) Cadby, Gemma; Giles, Corey; Melton, Phillip E.; Huynh, Kevin; Mellett, Natalie A.; Duong, Thy; Nguyen, Anh; Cinel, Michelle; Smith, Alex; Olshansky, Gavriel; Wang, Tingting; Brozynska, Marta; Inouye, Mike; McCarthy, Nina S.; Ariff, Amir; Hung, Joseph; Hui, Jennie; Beilby, John; Dubé, Marie-Pierre; Watts, Gerald F.; Shah, Sonia; Wray, Naomi R.; Lim, Wei Ling Florence; Chatterjee, Pratishtha; Martins, Ian; Laws, Simon M.; Porter, Tenielle; Vacher, Michael; Bush, Ashley I.; Rowe, Christopher C.; Villemagne, Victor L.; Ames, David; Masters, Colin L.; Taddei, Kevin; Arnold, Matthias; Kastenmüller, Gabi; Nho, Kwangsik; Saykin, Andrew J.; Han, Xianlin; Kaddurah-Daouk, Rima; Martins, Ralph N.; Blangero, John; Meikle, Peter J.; Moses, Eric K.; Radiology and Imaging Sciences, School of Medicine
We integrated lipidomics and genomics to unravel the genetic architecture of lipid metabolism and identify genetic variants associated with lipid species putatively in the mechanistic pathway for coronary artery disease (CAD). We quantified 596 lipid species in serum from 4,492 individuals from the Busselton Health Study. The discovery GWAS identified 3,361 independent lipid-loci associations, involving 667 genomic regions (479 previously unreported), with validation in two independent cohorts. A meta-analysis revealed an additional 70 independent genomic regions associated with lipid species. We identified 134 lipid endophenotypes for CAD associated with 186 genomic loci. Associations between independent lipid-loci with coronary atherosclerosis were assessed in ∼456,000 individuals from the UK Biobank. Of the 53 lipid-loci that showed evidence of association (P < 1 × 10-3), 43 loci were associated with at least one lipid endophenotype. These findings illustrate the value of integrative biology to investigate the aetiology of atherosclerosis and CAD, with implications for other complex diseases.
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 Initiative
Deep 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.
Editorial: Deciphering Non-Coding Regulatory Variants: Computational and Functional Validation
(Frontiers, 2021-11) Chen, Li; Li, Mulin Jun; Biostatistics, School of Public Health
Enhanced insights into the genetic architecture of 3D cranial vault shape using pleiotropy-informed GWAS
(Springer Nature, 2025-03-15) Goovaerts, Seppe; Naqvi, Sahin; Hoskens, Hanne; Herrick, Noah; Yuan, Meng; Shriver, Mark D.; Shaffer, John R.; Walsh, Susan; Weinberg, Seth M.; Wysocka, Joanna; Claes, Peter; Biology, School of Science
Large-scale GWAS studies have uncovered hundreds of genomic loci linked to facial and brain shape variation, but only tens associated with cranial vault shape, a largely overlooked aspect of the craniofacial complex. Surrounding the neocortex, the cranial vault plays a central role during craniofacial development and understanding its genetics are pivotal for understanding craniofacial conditions. Experimental biology and prior genetic studies have generated a wealth of knowledge that presents opportunities to aid further genetic discovery efforts. Here, we use the conditional FDR method to leverage GWAS data of facial shape, brain shape, and bone mineral density to enhance SNP discovery for cranial vault shape. This approach identified 120 independent genomic loci at 1% FDR, nearly tripling the number discovered through unconditioned analysis and implicating crucial craniofacial transcription factors and signaling pathways. These results significantly advance our genetic understanding of cranial vault shape and craniofacial development more broadly.
Fine-mapping genomic loci refines bipolar disorder risk genes
(medRxiv, 2024-02-13) Koromina, Maria; Ravi, Ashvin; Panagiotaropoulou, Georgia; Schilder, Brian M.; Humphrey, Jack; Braun, Alice; Bidgeli, Tim; Chatzinakos, Chris; Coombes, Brandon; Kim, Jaeyoung; Liu, Xiaoxi; Terao, Chikashi; O'Connell, Kevin S.; Adams, Mark; Adolfsson, Rolf; Alda, Martin; Alfredsson, Lars; Andlauer, Till F. M.; Andreassen, Ole A.; Antoniou, Anastasia; Baune, Bernhard T.; Bengesser, Susanne; Biernacka, Joanna; Boehnke, Michael; Bosch, Rosa; Cairns, Murray; Carr, Vaughan J.; Casas, Miquel; Catts, Stanley; Cichon, Sven; Corvin, Aiden; Craddock, Nicholas; Dafnas, Konstantinos; Dalkner, Nina; Dannlowski, Udo; Degenhardt, Franziska; Di Florio, Arianna; Dikeos, Dimitris; Fellendorf, Frederike Tabea; Ferentinos, Panagiotis; Forstner, Andreas J.; Forty, Liz; Frye, Mark; Fullerton, Janice M.; Gawlik, Micha; Gizer, Ian R.; Gordon-Smith, Katherine; Green, Melissa J.; Grigoroiu-Serbanescu, Maria; Guzman-Parra, José; Hahn, Tim; Henskens, Frans; Hillert, Jan; Jablensky, Assen V.; Jones, Lisa; Jones, Ian; Jonsson, Lina; Kelsoe, John R.; Kircher, Tilo; Kirov, George; Kittel-Schneider, Sarah; Kogevinas, Manolis; Landén, Mikael; Leboyer, Marion; Lenger, Melanie; Lissowska, Jolanta; Lochner, Christine; Loughland, Carmel; MacIntyre, Donald; Martin, Nicholas G.; Maratou, Eirini; Mathews, Carol A.; Mayoral, Fermin; McElroy, Susan L.; McGregor, Nathaniel W.; McIntosh, Andrew; McQuillin, Andrew; Michie, Patricia; Milanova, Vihra; Mitchell, Philip B.; Moutsatsou, Paraskevi; Mowry, Bryan; Müller-Myhsok, Bertram; Myers, Richard; Nenadić, Igor; Nöthen, Markus M.; O'Donovan, Claire; O'Donovan, Michael; Ophoff, Roel A.; Owen, Michael J.; Pantelis, Chris; Pato, Carlos; Pato, Michele T.; Patrinos, George P.; Pawlak, Joanna M.; Perlis, Roy H.; Porichi, Evgenia; Posthuma, Danielle; Ramos-Quiroga, Josep Antoni; Reif, Andreas; Reininghaus, Eva Z.; Ribasés, Marta; Rietschel, Marcella; Schall, Ulrich; Schulze, Thomas G.; Scott, Laura; Scott, Rodney J.; Serretti, Alessandro; Shannon Weickert, Cynthia; Smoller, Jordan W.; Soler Artigas, Maria; Stein, Dan J.; Streit, Fabian; Toma, Claudio; Tooney, Paul; Vieta, Eduard; Vincent, John B.; Waldman, Irwin D.; Weickert, Thomas; Witt, Stephanie H.; Hong, Kyung Sue; Ikeda, Masashi; Iwata, Nakao; Świątkowska, Beata; Won, Hong-Hee; Edenberg, Howard J.; Ripke, Stephan; Raj, Towfique; Coleman, Jonathan R. I.; Mullins, Niamh; Biochemistry and Molecular Biology, School of Medicine
Bipolar disorder (BD) is a heritable mental illness with complex etiology. While the largest published genome-wide association study identified 64 BD risk loci, the causal SNPs and genes within these loci remain unknown. We applied a suite of statistical and functional fine-mapping methods to these loci, and prioritized 22 likely causal SNPs for BD. We mapped these SNPs to genes, and investigated their likely functional consequences by integrating variant annotations, brain cell-type epigenomic annotations, brain quantitative trait loci, and results from rare variant exome sequencing in BD. Convergent lines of evidence supported the roles of SCN2A, TRANK1, DCLK3, INSYN2B, SYNE1, THSD7A, CACNA1B, TUBBP5, PLCB3, PRDX5, KCNK4, AP001453.3, TRPT1, FKBP2, DNAJC4, RASGRP1, FURIN, FES, YWHAE, DPH1, GSDMB, MED24, THRA, EEF1A2, and KCNQ2 in BD. These represent promising candidates for functional experiments to understand biological mechanisms and therapeutic potential. Additionally, we demonstrated that fine-mapping effect sizes can improve performance and transferability of BD polygenic risk scores across ancestrally diverse populations, and present a high-throughput fine-mapping pipeline (https://github.com/mkoromina/SAFFARI).
Genes identified in rodent studies of alcohol intake are enriched for heritability of human substance use
(Wiley, 2021) Huggett, Spencer B.; Johnson, Emma C.; Hatoum, Alexander S.; Lai, Dongbing; Srijeyanthan, Jenani; Bubier, Jason A.; Chesler, Elissa J.; Agrawal, Arpana; Palmer, Abraham A.; Edenberg, Howard J.; Palmer, Rohan H. C.; Medical and Molecular Genetics, School of Medicine
Background: Rodent paradigms and human genome-wide association studies (GWAS) on drug use have the potential to provide biological insight into the pathophysiology of addiction. Methods: Using GeneWeaver, we created rodent alcohol and nicotine gene-sets derived from 19 gene expression studies on alcohol and nicotine outcomes. We partitioned the SNP heritability of these gene-sets using four large human GWAS: (1) alcoholic drinks per week, (2) problematic alcohol use, (3) cigarettes per day, and (4) smoking cessation. We benchmarked our findings with curated human alcohol and nicotine addiction gene-sets and performed specificity analyses using other rodent gene-sets (e.g., locomotor behavior) and other human GWAS (e.g., height). Results: The rodent alcohol gene-set was enriched for heritability of drinks per week, cigarettes per day, and smoking cessation, but not problematic alcohol use. However, the rodent nicotine gene-set was not significantly associated with any of these traits. Both rodent gene-sets showed enrichment for several non-substance-use GWAS, and the extent of this relationship tended to increase as a function of trait heritability. In general, larger gene-sets demonstrated more significant enrichment. Finally, when evaluating human traits with similar heritabilities, both rodent gene-sets showed greater enrichment for substance use traits. Conclusion: Our results suggest that rodent gene expression studies can help to identify genes that contribute to the heritability of some substance use traits in humans, yet there was less specificity than expected. We outline various limitations, interpretations, and considerations for future research.

Browsing by Subject "Genome-wide association studies"

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