Life Sciences Commons^™

Extrachromosomal Dna (Ecdna): An Origin Of Tumor Heterogeneity, Genomic Remodeling, And Drug Resistance., Lauren T Pecorino, Roel G W Verhaak, Anton Henssen, Paul S Mischel

Faculty Research 2022

The genome of cancer cells contains circular extrachromosomal DNA (ecDNA) elements not found in normal cells. Analysis of clinical samples reveal they are common in most cancers and their presence indicates poor prognosis. They often contain enhancers and driver oncogenes that are highly expressed. The circular ecDNA topology leads to an open chromatin conformation and generates new gene regulatory interactions, including with distal enhancers. The absence of centromeres leads to random distribution of ecDNAs during cell division and genes encoded on them are transmitted in a non-mendelian manner. ecDNA can integrate into and exit from chromosomal DNA. The numbers of …

Taxonomic Assessment Of Two Wild House Mouse Subspecies Using Whole-Genome Sequencing., Raman Akinyanju Lawal, Verity L Mathis, Mary Barter, Jeremy R. Charette, Alexis Garretson, Beth L Dumont

Faculty Research 2022

The house mouse species complex (Mus musculus) is comprised of three primary subspecies. A large number of secondary subspecies have also been suggested on the basis of divergent morphology and molecular variation at limited numbers of markers. While the phylogenetic relationships among the primary M. musculus subspecies are well-defined, relationships among secondary subspecies and between secondary and primary subspecies remain less clear. Here, we integrate de novo genome sequencing of museum-stored specimens of house mice from one secondary subspecies (M. m. bactrianus) and publicly available genome sequences of house mice previously characterized as M. m. helgolandicus, with whole genome sequences …

Adding Gene Transcripts Into Genomic Prediction Improves Accuracy And Reveals Sampling Time Dependence., Bruno C Perez, Marco C A M Bink, Karen L. Svenson, Gary Churchill, Mario P L Calus

Faculty Research 2022

Recent developments allowed generating multiple high-quality 'omics' data that could increase the predictive performance of genomic prediction for phenotypes and genetic merit in animals and plants. Here, we have assessed the performance of parametric and nonparametric models that leverage transcriptomics in genomic prediction for 13 complex traits recorded in 478 animals from an outbred mouse population. Parametric models were implemented using the best linear unbiased prediction, while nonparametric models were implemented using the gradient boosting machine algorithm. We also propose a new model named GTCBLUP that aims to remove between-omics-layer covariance from predictors, whereas its counterpart GTBLUP does not do …

Functional Genomics Of Complex Cancer Genomes., Francesca Menghi, Edison Liu

Faculty Research 2022

Cancer functional genomics is the study of how genetic, epigenetic, and transcriptional alterations affect cancer phenotypes, such as growth and therapeutic response. Here, we comment on how, taking advantage of next generation sequencing, functional genomics, often combined with systems biology approaches, has revealed novel cancer vulnerabilities beyond the original paradigm of one gene-one phenotype.

A Standardized Nomenclature For Mammalian Histone Genes., Ruth L Seal, Paul Denny, Elspeth A Bruford, Anna K Gribkova, David Landsman, William F Marzluff, Monica Mcandrews, Anna R Panchenko, Alexey K Shaytan, Paul B Talbert

Faculty Research 2022

Histones have a long history of research in a wide range of species, leaving a legacy of complex nomenclature in the literature. Community-led discussions at the EMBO Workshop on Histone Variants in 2011 resulted in agreement amongst experts on a revised systematic protein nomenclature for histones, which is based on a combination of phylogenetic classification and historical symbol usage. Human and mouse histone gene symbols previously followed a genome-centric system that was not applicable across all vertebrate species and did not reflect the systematic histone protein nomenclature. This prompted a collaboration between histone experts, the Human Genome Organization (HUGO) Gene …

Phenotype-Driven Approaches To Enhance Variant Prioritization And Diagnosis Of Rare Disease., Julius O B Jacobsen, Catherine Kelly, Valentina Cipriani, Genomics England Research Consortium, Christopher J Mungall, Justin Reese, Daniel Danis, Peter N Robinson, Damian Smedley

Faculty Research 2022

Rare disease diagnostics and disease gene discovery have been revolutionized by whole-exome and genome sequencing but identifying the causative variant(s) from the millions in each individual remains challenging. The use of deep phenotyping of patients and reference genotype-phenotype knowledge, alongside variant data such as allele frequency, segregation, and predicted pathogenicity, has proved an effective strategy to tackle this issue. Here we review the numerous tools that have been developed to automate this approach and demonstrate the power of such an approach on several thousand diagnosed cases from the 100,000 Genomes Project. Finally, we discuss the challenges that need to be …

A Research Agenda To Support The Development And Implementation Of Genomics-Based Clinical Informatics Tools And Resources., Ken Wiley, Laura Findley, Madison Goldrich, Tejinder K Rakhra-Burris, Ana Stevens, Pamela Williams, Carol J Bult, Rex Chisholm, Patricia Deverka, Geoffrey S Ginsburg, Eric D Green, Gail Jarvik, George A Mensah, Erin Ramos, Mary V Relling, Dan M Roden, Robb Rowley, Gil Alterovitz, Samuel Aronson, Lisa Bastarache, James J Cimino, Erin L Crowgey, Guilherme Del Fiol, Robert R Freimuth, Mark A Hoffman, Janina Jeff, Kevin Johnson, Kensaku Kawamoto, Subha Madhavan, Eneida A Mendonca, Lucila Ohno-Machado, Siddharth Pratap, Casey Overby Taylor, Marylyn D Ritchie, Nephi Walton, Chunhua Weng, Teresa Zayas-Cabán, Teri A Manolio, Marc S Williams

Faculty Research 2022

OBJECTIVE: The Genomic Medicine Working Group of the National Advisory Council for Human Genome Research virtually hosted its 13th genomic medicine meeting titled "Developing a Clinical Genomic Informatics Research Agenda". The meeting's goal was to articulate a research strategy to develop Genomics-based Clinical Informatics Tools and Resources (GCIT) to improve the detection, treatment, and reporting of genetic disorders in clinical settings.

MATERIALS AND METHODS: Experts from government agencies, the private sector, and academia in genomic medicine and clinical informatics were invited to address the meeting's goals. Invitees were also asked to complete a survey to assess important considerations needed to …

The Rd-Connect Genome-Phenome Analysis Platform: Accelerating Diagnosis, Research, And Gene Discovery For Rare Diseases., Steven Laurie, Davide Piscia, Leslie Matalonga, Alberto Corvó, Marcos Fernández-Callejo, Carles Garcia-Linares, Carles Hernandez-Ferrer, Cristina Luengo, Inés Martínez, Anastasios Papakonstantinou, Daniel Picó-Amador, Joan Protasio, Rachel Thompson, Raul Tonda, Mònica Bayés, Gemma Bullich, Jordi Camps-Puchadas, Ida Paramonov, Jean-Rémi Trotta, Angel Alonso, Marcella Attimonelli, Christophe Béroud, Virginie Bros-Facer, Orion J Buske, Andrés Cañada-Pallarés, José M Fernández, Mats G Hansson, Rita Horvath, Julius O B Jacobsen, Rajaram Kaliyaperumal, Séverine Lair-Préterre, Luana Licata, Pedro Lopes, Estrella López-Martín, Deborah Mascalzoni, Lucia Monaco, Luis A Pérez-Jurado, Manuel Posada De La Paz, Jordi Rambla, Ana Rath, Olaf Riess, Peter N Robinson, David Salgado, Damian Smedley, Dylan Spalding, Peter A C 'T Hoen, Ana Töpf, Irina Zaharieva, Holm Graessner, Ivo G Gut, Hanns Lochmüller, Sergi Beltran

Faculty Research 2022

Rare disease patients are more likely to receive a rapid molecular diagnosis nowadays thanks to the wide adoption of next-generation sequencing. However, many cases remain undiagnosed even after exome or genome analysis, because the methods used missed the molecular cause in a known gene, or a novel causative gene could not be identified and/or confirmed. To address these challenges, the RD-Connect Genome-Phenome Analysis Platform (GPAP) facilitates the collation, discovery, sharing, and analysis of standardized genome-phenome data within a collaborative environment. Authorized clinicians and researchers submit pseudonymised phenotypic profiles encoded using the Human Phenotype Ontology, and raw genomic data which is …

Recurrent Inversion Polymorphisms In Humans Associate With Genetic Instability And Genomic Disorders., David Porubsky, Wolfram Höps, Hufsah Ashraf, Pinghsun Hsieh, Bernardo Rodriguez-Martin, Feyza Yilmaz, Jana Ebler, Pille Hallast, Flavia Angela Maria Maggiolini, William T Harvey, Barbara Henning, Peter A Audano, David S Gordon, Peter Ebert, Patrick Hasenfeld, Eva Benito, Qihui Zhu, Charles Lee, Francesca Antonacci, Matthias Steinrücken, Christine R Beck, Ashley D Sanders, Tobias Marschall, Evan E Eichler, Jan O Korbel

Faculty Research 2022

Unlike copy number variants (CNVs), inversions remain an underexplored genetic variation class. By integrating multiple genomic technologies, we discover 729 inversions in 41 human genomes. Approximately 85% of inversionsretrotransposition; 80% of the larger inversions are balanced and affect twice as many nucleotides as CNVs. Balanced inversions show an excess of common variants, and 72% are flanked by segmental duplications (SDs) or retrotransposons. Since flanking repeats promote non-allelic homologous recombination, we developed complementary approaches to identify recurrent inversion formation. We describe 40 recurrent inversions encompassing 0.6% of the genome, showing inversion rates up to 2.7 × 10

Combining Genomic And Epidemiological Data To Compare The Transmissibility Of Sars-Cov-2 Variants Alpha And Iota., Mary E Petrone, Jessica E Rothman, Mallery I Breban, Isabel M Ott, Alexis Russell, Erica Lasek-Nesselquist, Hamada Badr, Kevin Kelly, Gregory Omerza, Nicholas Renzette, Anne E Watkins, Chaney C Kalinich, Tara Alpert, Anderson F Brito, Rebecca Earnest, Irina R Tikhonova, Christopher Castaldi, John P Kelly, Matthew Shudt, Jonathan Plitnick, Erasmus Schneider, Steven Murphy, Caleb Neal, Eva Laszlo, Ahmad Altajar, Claire Pearson, Anthony Muyombwe, Randy Downing, Jafar Razeq, Linda Niccolai, Madeline S Wilson, Margaret L Anderson, Jianhui Wang, Chen Liu, Pei Hui, Shrikant Mane, Bradford P Taylor, William P Hanage, Marie L Landry, David R Peaper, Kaya Bilguvar, Joseph R Fauver, Chantal B F Vogels, Lauren M Gardner, Virginia E Pitzer, Kirsten St George, Mark D Adams, Nathan D Grubaugh

Faculty Research 2022

SARS-CoV-2 variants shaped the second year of the COVID-19 pandemic and the discourse around effective control measures. Evaluating the threat posed by a new variant is essential for adapting response efforts when community transmission is detected. In this study, we compare the dynamics of two variants, Alpha and Iota, by integrating genomic surveillance data to estimate the effective reproduction number (R_t) of the variants. We use Connecticut, United States, in which Alpha and Iota co-circulated in 2021. We find that the R_t of these variants were up to 50% larger than that of other variants. We then …

Svanna: Efficient And Accurate Pathogenicity Prediction Of Coding And Regulatory Structural Variants In Long-Read Genome Sequencing., Daniel Danis, Julius O B Jacobsen, Parithi Balachandran, Qihui Zhu, Feyza Yilmaz, Justin Reese, Matthias Haimel, Gholson J Lyon, Ingo Helbig, Christopher J Mungall, Christine R Beck, Charles Lee, Damian Smedley, Peter N Robinson

Faculty Research 2022

Structural variants (SVs) are implicated in the etiology of Mendelian diseases but have been systematically underascertained owing to sequencing technology limitations. Long-read sequencing enables comprehensive detection of SVs, but approaches for prioritization of candidate SVs are needed. Structural variant Annotation and analysis (SvAnna) assesses all classes of SVs and their intersection with transcripts and regulatory sequences, relating predicted effects on gene function with clinical phenotype data. SvAnna places 87% of deleterious SVs in the top ten ranks. The interpretable prioritizations offered by SvAnna will facilitate the widespread adoption of long-read sequencing in diagnostic genomics. SvAnna is available at https://github.com/TheJacksonLaboratory/SvAnn a …

Crispr-Mediated Multiplexed Live Cell Imaging Of Nonrepetitive Genomic Loci With One Guide Rna Per Locus., Patricia A Clow, Menghan Du, Nathaniel L. Jillette, Aziz Taghbalout, Jacqueline J Zhu, Albert Cheng

Faculty Research 2022

Three-dimensional (3D) structures of the genome are dynamic, heterogeneous and functionally important. Live cell imaging has become the leading method for chromatin dynamics tracking. However, existing CRISPR- and TALE-based genomic labeling techniques have been hampered by laborious protocols and are ineffective in labeling non-repetitive sequences. Here, we report a versatile CRISPR/Casilio-based imaging method that allows for a nonrepetitive genomic locus to be labeled using one guide RNA. We construct Casilio dual-color probes to visualize the dynamic interactions of DNA elements in single live cells in the presence or absence of the cohesin subunit RAD21. Using a three-color palette, we track …

Prediction Performance Of Linear Models And Gradient Boosting Machine On Complex Phenotypes In Outbred Mice., Bruno C Perez, Marco C A M Bink, Karen L. Svenson, Gary Churchill, Mario P L Calus

Faculty Research 2022

We compared the performance of linear (GBLUP, BayesB, and elastic net) methods to a nonparametric tree-based ensemble (gradient boosting machine) method for genomic prediction of complex traits in mice. The dataset used contained genotypes for 50,112 SNP markers and phenotypes for 835 animals from 6 generations. Traits analyzed were bone mineral density, body weight at 10, 15, and 20 weeks, fat percentage, circulating cholesterol, glucose, insulin, triglycerides, and urine creatinine. The youngest generation was used as a validation subset, and predictions were based on all older generations. Model performance was evaluated by comparing predictions for animals in the validation subset …

Mouse Genome Informatics (Mgi): Latest News From Mgd And Gxd., Martin Ringwald, Joel E Richardson, Richard M. Baldarelli, Judith A. Blake, James A. Kadin, Cynthia Smith, Carol J Bult

Faculty Research 2022

The Mouse Genome Informatics (MGI) database system combines multiple expertly curated community data resources into a shared knowledge management ecosystem united by common metadata annotation standards. MGI's mission is to facilitate the use of the mouse as an experimental model for understanding the genetic and genomic basis of human health and disease. MGI is the authoritative source for mouse gene, allele, and strain nomenclature and is the primary source of mouse phenotype annotations, functional annotations, developmental gene expression information, and annotations of mouse models with human diseases. MGI maintains mouse anatomy and phenotype ontologies and contributes to the development of …

Mako: A Graph-Based Pattern Growth Approach To Detect Complex Structural Variants., Jiadong Lin, Xiaofei Yang, Walter Kosters, Tun Xu, Yanyan Jia, Songbo Wang, Qihui Zhu, Mallory Ryan, Li Guo, Chengsheng Zhang, The Human Genome Structural Variation Consortium, Charles Lee, Scott E Devine, Evan E Eichler, Kai Ye

Faculty Research 2022

Complex structural variants (CSVs) are genomic alterations that have more than two breakpoints and are considered as the simultaneous occurrence of simple structural variants. However, detecting the compounded mutational signals of CSVs is challenging through a commonly used model-match strategy. As a result, there has been limited progress for CSV discovery compared with simple structural variants. Here, we systematically analyzed the multi-breakpoint connection feature of CSVs, and proposed Mako, utilizing a bottom-up guided model-free strategy, to detect CSVs from paired-end short-read sequencing. Specifically, we implemented a graph-based pattern growth approach, where the graph depicts potential breakpoint connections, and pattern growth …

The Human Disease Ontology 2022 Update., Lynn M Schriml, James B Munro, Mike Schor, Dustin Olley, Carrie Mccracken, Victor Felix, J Allen Baron, Rebecca Jackson, Susan M. Bello, Cynthia Bearer, Richard Lichenstein, Katharine Bisordi, Nicole Campion Dialo, Michelle Giglio, Carol Greene

Faculty Research 2022

The Human Disease Ontology (DO) (www.disease-ontology.org) database, has significantly expanded the disease content and enhanced our userbase and website since the DO's 2018 Nucleic Acids Research DATABASE issue paper. Conservatively, based on available resource statistics, terms from the DO have been annotated to over 1.5 million biomedical data elements and citations, a 10× increase in the past 5 years. The DO, funded as a NHGRI Genomic Resource, plays a key role in disease knowledge organization, representation, and standardization, serving as a reference framework for multiscale biomedical data integration and analysis across thousands of clinical, biomedical and computational research projects and …

Comprehensive Characterization Of 536 Patient-Derived Xenograft Models Prioritizes Candidatesfor Targeted Treatment., Hua Sun, Song Cao, R Jay Mashl, Chia-Kuei Mo, Simone Zaccaria, Michael C Wendl, Sherri R Davies, Matthew H Bailey, Tina M Primeau, Jeremy Hoog, Jacqueline L Mudd, Dennis A Dean, Rajesh Patidar, Li Chen, Matthew A Wyczalkowski, Reyka G Jayasinghe, Fernanda Martins Rodrigues, Nadezhda V Terekhanova, Yize Li, Kian-Huat Lim, Andrea Wang-Gillam, Brian A Van Tine, Cynthia X Ma, Rebecca Aft, Katherine C Fuh, Julie K Schwarz, Jose P Zevallos, Sidharth V Puram, John F Dipersio, Nci Pdxnet Consortium, Brandi Davis-Dusenbery, Matthew J Ellis, Michael T Lewis, Michael A Davies, Meenhard Herlyn, Bingliang Fang, Jack A Roth, Alana L Welm, Bryan E Welm, Funda Meric-Bernstam, Feng Chen, Ryan C Fields, Shunqiang Li, Ramaswamy Govindan, James H Doroshow, Jeffrey A Moscow, Yvonne A Evrard, Jeffrey Chuang, Benjamin J Raphael, Li Ding, Carol J Bult, Peter N Robinson

Faculty Research 2021

Development of candidate cancer treatments is a resource-intensive process, with the research community continuing to investigate options beyond static genomic characterization. Toward this goal, we have established the genomic landscapes of 536 patient-derived xenograft (PDX) models across 25 cancer types, together with mutation, copy number, fusion, transcriptomic profiles, and NCI-MATCH arms. Compared with human tumors, PDXs typically have higher purity and fit to investigate dynamic driver events and molecular properties via multiple time points from same case PDXs. Here, we report on dynamic genomic landscapes and pharmacogenomic associations, including associations between activating oncogenic events and drugs, correlations between whole-genome duplications …

User Testing Of A Diagnostic Decision Support System With Machine-Assisted Chart Review To Facilitate Clinical Genomic Diagnosis., Alanna Kulchak Rahm, Nephi A Walton, Lynn K Feldman, Conner Jenkins, Troy Jenkins, Thomas N Person, Joeseph Peterson, Jonathon C Reynolds, Peter N Robinson, Makenzie A Woltz, Marc S Williams, Michael M Segal

Faculty Research 2021

OBJECTIVES: There is a need in clinical genomics for systems that assist in clinical diagnosis, analysis of genomic information and periodic reanalysis of results, and can use information from the electronic health record to do so. Such systems should be built using the concepts of human-centred design, fit within clinical workflows and provide solutions to priority problems.

METHODS: We adapted a commercially available diagnostic decision support system (DDSS) to use extracted findings from a patient record and combine them with genomic variant information in the DDSS interface. Three representative patient cases were created in a simulated clinical environment for user …

Mouse Genome Database (Mgd): Knowledgebase For Mouse-Human Comparative Biology., Judith A. Blake, Richard M. Baldarelli, James A. Kadin, Joel E Richardson, Cynthia Smith, Carol J Bult, Mouse Genome Database Group

Faculty Research 2021

The Mouse Genome Database (MGD; http://www.informatics.jax.org) is the community model organism knowledgebase for the laboratory mouse, a widely used animal model for comparative studies of the genetic and genomic basis for human health and disease. MGD is the authoritative source for biological reference data related to mouse genes, gene functions, phenotypes and mouse models of human disease. MGD is the primary source for official gene, allele, and mouse strain nomenclature based on the guidelines set by the International Committee on Standardized Nomenclature for Mice. MGD's biocuration scientists curate information from the biomedical literature and from large and small datasets contributed …