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Browsing by Author "Lucente, Diane"
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Item A cross-disorder dosage sensitivity map of the human genome(Elsevier, 2022) Collins, Ryan L.; Glessner, Joseph T.; Porcu, Eleonora; Lepamets, Maarja; Brandon, Rhonda; Lauricella, Christopher; Han, Lide; Morley, Theodore; Niestroj, Lisa-Marie; Ulirsch, Jacob; Everett, Selin; Howrigan, Daniel P.; Boone, Philip M.; Fu, Jack; Karczewski, Konrad J.; Kellaris, Georgios; Lowther, Chelsea; Lucente, Diane; Mohajeri, Kiana; Nõukas, Margit; Nuttle, Xander; Samocha, Kaitlin E.; Trinh, Mi; Ullah, Farid; Võsa, Urmo; Epi25 Consortium; Estonian Biobank Research Team; Hurles, Matthew E.; Aradhya, Swaroop; Davis, Erica E.; Finucane, Hilary; Gusella, James F.; Janze, Aura; Katsanis, Nicholas; Matyakhina, Ludmila; Neale, Benjamin M.; Sanders, David; Warren, Stephanie; Hodge, Jennelle C.; Lal, Dennis; Ruderfer, Douglas M.; Meck, Jeanne; Mägi, Reedik; Esko, Tõnu; Reymond, Alexandre; Kutalik, Zoltán; Hakonarson, Hakon; Sunyaev, Shamil; Brand, Harrison; Talkowski, Michael E.; Medical and Molecular Genetics, School of MedicineRare copy-number variants (rCNVs) include deletions and duplications that occur infrequently in the global human population and can confer substantial risk for disease. In this study, we aimed to quantify the properties of haploinsufficiency (i.e., deletion intolerance) and triplosensitivity (i.e., duplication intolerance) throughout the human genome. We harmonized and meta-analyzed rCNVs from nearly one million individuals to construct a genome-wide catalog of dosage sensitivity across 54 disorders, which defined 163 dosage sensitive segments associated with at least one disorder. These segments were typically gene dense and often harbored dominant dosage sensitive driver genes, which we were able to prioritize using statistical fine-mapping. Finally, we designed an ensemble machine-learning model to predict probabilities of dosage sensitivity (pHaplo & pTriplo) for all autosomal genes, which identified 2,987 haploinsufficient and 1,559 triplosensitive genes, including 648 that were uniquely triplosensitive. This dosage sensitivity resource will provide broad utility for human disease research and clinical genetics.Item Systematic evaluation of genome sequencing for the diagnostic assessment of autism spectrum disorder and fetal structural anomalies(Elsevier, 2023) Lowther, Chelsea; Valkanas, Elise; Giordano, Jessica L.; Wang, Harold Z.; Currall, Benjamin B.; O'Keefe, Kathryn; Pierce-Hoffman, Emma; Kurtas, Nehir E.; Whelan, Christopher W.; Hao, Stephanie P.; Weisburd, Ben; Jalili, Vahid; Fu, Jack; Wong, Isaac; Collins, Ryan L.; Zhao, Xuefang; Austin-Tse, Christina A.; Evangelista, Emily; Lemire, Gabrielle; Aggarwal, Vimla S.; Lucente, Diane; Gauthier, Laura D.; Tolonen, Charlotte; Sahakian, Nareh; Stevens, Christine; An, Joon-Yong; Dong, Shan; Norton, Mary E.; MacKenzie, Tippi C.; Devlin, Bernie; Gilmore, Kelly; Powell, Bradford C.; Brandt, Alicia; Vetrini, Francesco; DiVito, Michelle; Sanders, Stephan J.; MacArthur, Daniel G.; Hodge, Jennelle C.; O'Donnell-Luria, Anne; Rehm, Heidi L.; Vora, Neeta L.; Levy, Brynn; Brand, Harrison; Wapner, Ronald J.; Talkowski, Michael E.; Medical and Molecular Genetics, School of MedicineShort-read genome sequencing (GS) holds the promise of becoming the primary diagnostic approach for the assessment of autism spectrum disorder (ASD) and fetal structural anomalies (FSAs). However, few studies have comprehensively evaluated its performance against current standard-of-care diagnostic tests: karyotype, chromosomal microarray (CMA), and exome sequencing (ES). To assess the clinical utility of GS, we compared its diagnostic yield against these three tests in 1,612 quartet families including an individual with ASD and in 295 prenatal families. Our GS analytic framework identified a diagnostic variant in 7.8% of ASD probands, almost 2-fold more than CMA (4.3%) and 3-fold more than ES (2.7%). However, when we systematically captured copy-number variants (CNVs) from the exome data, the diagnostic yield of ES (7.4%) was brought much closer to, but did not surpass, GS. Similarly, we estimated that GS could achieve an overall diagnostic yield of 46.1% in unselected FSAs, representing a 17.2% increased yield over karyotype, 14.1% over CMA, and 4.1% over ES with CNV calling or 36.1% increase without CNV discovery. Overall, GS provided an added diagnostic yield of 0.4% and 0.8% beyond the combination of all three standard-of-care tests in ASD and FSAs, respectively. This corresponded to nine GS unique diagnostic variants, including sequence variants in exons not captured by ES, structural variants (SVs) inaccessible to existing standard-of-care tests, and SVs where the resolution of GS changed variant classification. Overall, this large-scale evaluation demonstrated that GS significantly outperforms each individual standard-of-care test while also outperforming the combination of all three tests, thus warranting consideration as the first-tier diagnostic approach for the assessment of ASD and FSAs.Item Temporal order of clinical and biomarker changes in familial frontotemporal dementia(Springer Nature, 2022) Staffaroni, Adam M.; Quintana, Melanie; Wendelberger, Barbara; Heuer, Hilary W.; Russell, Lucy L.; Cobigo, Yann; Wolf, Amy; Goh, Sheng-Yang Matt; Petrucelli, Leonard; Gendron, Tania F.; Heller, Carolin; Clark, Annie L.; Taylor, Jack Carson; Wise, Amy; Ong, Elise; Forsberg, Leah; Brushaber, Danielle; Rojas, Julio C.; VandeVrede, Lawren; Ljubenkov, Peter; Kramer, Joel; Casaletto, Kaitlin B.; Appleby, Brian; Bordelon, Yvette; Botha, Hugo; Dickerson, Bradford C.; Domoto-Reilly, Kimiko; Fields, Julie A.; Foroud, Tatiana; Gavrilova, Ralitza; Geschwind, Daniel; Ghoshal, Nupur; Goldman, Jill; Graff-Radford, Jonathon; Graff-Radford, Neill; Grossman, Murray; Hall, Matthew G. H.; Hsiung, Ging-Yuek; Huey, Edward D.; Irwin, David; Jones, David T.; Kantarci, Kejal; Kaufer, Daniel; Knopman, David; Kremers, Walter; Lago, Argentina Lario; Lapid, Maria I.; Litvan, Irene; Lucente, Diane; Mackenzie, Ian R.; Mendez, Mario F.; Mester, Carly; Miller, Bruce L.; Onyike, Chiadi U.; Rademakers, Rosa; Ramanan, Vijay K.; Ramos, Eliana Marisa; Rao, Meghana; Rascovsky, Katya; Rankin, Katherine P.; Roberson, Erik D.; Savica, Rodolfo; Tartaglia, M. Carmela; Weintraub, Sandra; Wong, Bonnie; Cash, David M.; Bouzigues, Arabella; Swift, Imogen J.; Peakman, Georgia; Bocchetta, Martina; Todd, Emily G.; Convery, Rhian S.; Rowe, James B.; Borroni, Barbara; Galimberti, Daniela; Tiraboschi, Pietro; Masellis, Mario; Finger, Elizabeth; van Swieten, John C.; Seelaar, Harro; Jiskoot, Lize C.; Sorbi, Sandro; Butler, Chris R.; Graff, Caroline; Gerhard, Alexander; Langheinrich, Tobias; Laforce, Robert; Sanchez-Valle, Raquel; de Mendonça, Alexandre; Moreno, Fermin; Synofzik, Matthis; Vandenberghe, Rik; Ducharme, Simon; Le Ber, Isabelle; Levin, Johannes; Danek, Adrian; Otto, Markus; Pasquier, Florence; Santana, Isabel; Kornak, John; Boeve, Bradley F.; Rosen, Howard J.; Rohrer, Jonathan D.; Boxer, Adam L.; Frontotemporal Dementia Prevention Initiative (FPI) Investigators; Medicine, School of MedicineUnlike familial Alzheimer’s disease, we have been unable to accurately predict symptom onset in presymptomatic familial frontotemporal dementia (f-FTD) mutation carriers, which is a major hurdle to designing disease prevention trials. We developed multimodal models for f-FTD disease progression and estimated clinical trial sample sizes in C9orf72, GRN, and MAPT mutation carriers. Models included longitudinal clinical and neuropsychological scores, regional brain volumes, and plasma neurofilament light chain (NfL) in 796 carriers and 412 non-carrier controls. We found that the temporal ordering of clinical and biomarker progression differed by genotype. In prevention-trial simulations employing model-based patient selection, atrophy and NfL were the best endpoints, whereas clinical measures were potential endpoints in early symptomatic trials. F-FTD prevention trials are feasible but will likely require global recruitment efforts. These disease progression models will facilitate the planning of f-FTD clinical trials, including the selection of optimal endpoints and enrollment criteria to maximize power to detect treatment effects.