Literature DB >> 20216460

Phenomics: expanding the role of clinical evaluation in genomic studies.

Matthew B Lanktree1, Reina G Hassell, Piya Lahiry, Robert A Hegele.   

Abstract

With advances in high-throughput genotyping technologies, the rate-limiting step of large-scale genetic investigations has become the collection of sensitive and specific phenotype information in large samples of study participants. Clinicians play a pivotal role for successful genetic studies because sound clinical acumen can substantially increase study power by reducing measurement error and improving diagnostic precision for translational research. Phenomics is the systematic measurement and analysis of qualitative and quantitative traits, including clinical, biochemical, and imaging methods, for the refinement and characterization of a phenotype. Phenomics requires deep phenotyping, the collection of a wide breadth of phenotypes with fine resolution, and phenomic analysis, composed of constructing heat maps, cluster analysis, text mining, and pathway analysis. In this article, we review the components of phenomics and provide examples of their application to genomic studies, specifically for implicating novel disease processes, reducing sample heterogeneity, hypothesis generation, integration of multiple types of data, and as an extension of Mendelian randomization studies.

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Mesh:

Year:  2010        PMID: 20216460     DOI: 10.231/JIM.0b013e3181d844f7

Source DB:  PubMed          Journal:  J Investig Med        ISSN: 1081-5589            Impact factor:   2.895


  19 in total

1.  An autosomal recessive syndrome of joint contractures, muscular atrophy, microcytic anemia, and panniculitis-associated lipodystrophy.

Authors:  Abhimanyu Garg; Maria Dolores Hernandez; Ana Berta Sousa; Lalitha Subramanyam; Laura Martínez de Villarreal; Heloísa G dos Santos; Oralia Barboza
Journal:  J Clin Endocrinol Metab       Date:  2010-06-09       Impact factor: 5.958

Review 2.  Functional genomics applied to cardiovascular medicine.

Authors:  Thomas P Cappola; Kenneth B Margulies
Journal:  Circulation       Date:  2011-07-05       Impact factor: 29.690

3.  Phenome-Wide Association Studies: Leveraging Comprehensive Phenotypic and Genotypic Data for Discovery.

Authors:  S A Pendergrass; M D Ritchie
Journal:  Curr Genet Med Rep       Date:  2015-06-01

4.  Cardiovascular Event Prediction by Machine Learning: The Multi-Ethnic Study of Atherosclerosis.

Authors:  Bharath Ambale-Venkatesh; Xiaoying Yang; Colin O Wu; Kiang Liu; W Gregory Hundley; Robyn McClelland; Antoinette S Gomes; Aaron R Folsom; Steven Shea; Eliseo Guallar; David A Bluemke; João A C Lima
Journal:  Circ Res       Date:  2017-08-09       Impact factor: 17.367

5.  The use of phenome-wide association studies (PheWAS) for exploration of novel genotype-phenotype relationships and pleiotropy discovery.

Authors:  S A Pendergrass; K Brown-Gentry; S M Dudek; E S Torstenson; J L Ambite; C L Avery; S Buyske; C Cai; M D Fesinmeyer; C Haiman; G Heiss; L A Hindorff; C-N Hsu; R D Jackson; C Kooperberg; L Le Marchand; Y Lin; T C Matise; L Moreland; K Monroe; A P Reiner; R Wallace; L R Wilkens; D C Crawford; M D Ritchie
Journal:  Genet Epidemiol       Date:  2011-05-18       Impact factor: 2.135

Review 6.  Heart Failure in Pediatric Patients With Congenital Heart Disease.

Authors:  Robert B Hinton; Stephanie M Ware
Journal:  Circ Res       Date:  2017-03-17       Impact factor: 17.367

Review 7.  New treatment paradigms for ADPKD: moving towards precision medicine.

Authors:  Matthew B Lanktree; Arlene B Chapman
Journal:  Nat Rev Nephrol       Date:  2017-10-09       Impact factor: 28.314

8.  Detecting and Characterizing Pleiotropy: New Methods for Uncovering the Connection Between the Complexity of Genomic Architecture and Multiple phenotypes.

Authors:  Anna L Tyler; Dana C Crawford; Sarah A Pendergrass
Journal:  Pac Symp Biocomput       Date:  2014-01

Review 9.  Neurogenomics in Africa: Perspectives, progress, possibilities and priorities.

Authors:  Rufus O Akinyemi; Mayowa O Owolabi; Tolulope Oyeniyi; Bruce Ovbiagele; Donna K Arnett; Hemant K Tiwari; Richard Walker; Adesola Ogunniyi; Raj N Kalaria
Journal:  J Neurol Sci       Date:  2016-05-06       Impact factor: 3.181

10.  The NKI-Rockland Sample: A Model for Accelerating the Pace of Discovery Science in Psychiatry.

Authors:  Kate Brody Nooner; Stanley J Colcombe; Russell H Tobe; Maarten Mennes; Melissa M Benedict; Alexis L Moreno; Laura J Panek; Shaquanna Brown; Stephen T Zavitz; Qingyang Li; Sharad Sikka; David Gutman; Saroja Bangaru; Rochelle Tziona Schlachter; Stephanie M Kamiel; Ayesha R Anwar; Caitlin M Hinz; Michelle S Kaplan; Anna B Rachlin; Samantha Adelsberg; Brian Cheung; Ranjit Khanuja; Chaogan Yan; Cameron C Craddock; Vincent Calhoun; William Courtney; Margaret King; Dylan Wood; Christine L Cox; A M Clare Kelly; Adriana Di Martino; Eva Petkova; Philip T Reiss; Nancy Duan; Dawn Thomsen; Bharat Biswal; Barbara Coffey; Matthew J Hoptman; Daniel C Javitt; Nunzio Pomara; John J Sidtis; Harold S Koplewicz; Francisco Xavier Castellanos; Bennett L Leventhal; Michael P Milham
Journal:  Front Neurosci       Date:  2012-10-16       Impact factor: 4.677

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