Literature DB >> 18388695

'Deep phenotyping': characterizing populations in the era of genomics and systems biology.

Russell P Tracy1.   

Abstract

PURPOSE OF REVIEW: Large-scale genomic studies establish genotype-phenotype associations, but they use phenotypes that represent current views of disease. There is an opportunity to enhance our understanding of genotype-phenotype associations by extending phenotypes into much greater detail ('deep phenotyping'). RECENT
FINDINGS: We should engage in deep phenotyping for the following reasons. First, the current emphasis on clinical outcomes, although necessary for the advancement of clinical medicine, is not sufficient. Second, analytical and biological variance embedded in traditional phenotypes dilutes statistical power and strength of association. Finally, even relatively precise phenotypes may vary in terms of underlying pathophysiology across an individual's life history. Deep phenotyping focuses on the biological relevance of pathways and metabolic flux, increasing the 'granularity' of phenotypes.
SUMMARY: Focus on medical phenotypes is critical, but long-term interests require additional studies that illuminate underlying biology. Deep phenotyping is less likely to yield dramatic changes in current medical practice but it offers an opportunity to gain scientific insight in an incremental manner and to make progress in redefining clinical outcomes with greater precision. It is expensive, and debate is needed to determine when and how it should be applied.

Mesh:

Year:  2008        PMID: 18388695     DOI: 10.1097/MOL.0b013e3282f73893

Source DB:  PubMed          Journal:  Curr Opin Lipidol        ISSN: 0957-9672            Impact factor:   4.776


  34 in total

1.  Diving through the "-omics": the case for deep phenotyping and systems epidemiology.

Authors:  Robin Haring; Henri Wallaschofski
Journal:  OMICS       Date:  2012-02-09

2.  Genome-wide association studies and large-scale collaborations in epidemiology.

Authors:  Bruce M Psaty; Albert Hofman
Journal:  Eur J Epidemiol       Date:  2010-07-11       Impact factor: 8.082

3.  Web-enabled and improved software tools and data are needed to measure nutrient intakes and physical activity for personalized health research.

Authors:  Phyllis J Stumbo; Rick Weiss; John W Newman; Jean A Pennington; Katherine L Tucker; Paddy L Wiesenfeld; Anne-Kathrin Illner; David M Klurfeld; Jim Kaput
Journal:  J Nutr       Date:  2010-10-27       Impact factor: 4.798

4.  Combining billing codes, clinical notes, and medications from electronic health records provides superior phenotyping performance.

Authors:  Wei-Qi Wei; Pedro L Teixeira; Huan Mo; Robert M Cronin; Jeremy L Warner; Joshua C Denny
Journal:  J Am Med Inform Assoc       Date:  2015-09-02       Impact factor: 4.497

5.  Don't throw the baby out with the bathwater: enabling a bottom-up approach in genome-wide association studies.

Authors:  Sean E McGuire; Amy L McGuire
Journal:  Genome Res       Date:  2008-11       Impact factor: 9.043

6.  Defining a comprehensive verotype using electronic health records for personalized medicine.

Authors:  Mary Regina Boland; George Hripcsak; Yufeng Shen; Wendy K Chung; Chunhua Weng
Journal:  J Am Med Inform Assoc       Date:  2013-09-03       Impact factor: 4.497

7.  Analysis of medication and indication occurrences in clinical notes.

Authors:  Sunghwan Sohn; Hongfang Liu
Journal:  AMIA Annu Symp Proc       Date:  2014-11-14

Review 8.  Phenotypic spectrum of heart failure with preserved ejection fraction.

Authors:  Sanjiv J Shah; Daniel H Katz; Rahul C Deo
Journal:  Heart Fail Clin       Date:  2014-05-22       Impact factor: 3.179

9.  An official American Thoracic Society Statement: pulmonary hypertension phenotypes.

Authors:  Raed A Dweik; Sharon Rounds; Serpil C Erzurum; Stephen Archer; Karen Fagan; Paul M Hassoun; Nicholas S Hill; Marc Humbert; Steven M Kawut; Michael Krowka; Evangelos Michelakis; Nicholas W Morrell; Kurt Stenmark; Rubin M Tuder; John Newman
Journal:  Am J Respir Crit Care Med       Date:  2014-02-01       Impact factor: 21.405

10.  Mass-spectrometry-based metabolomics: limitations and recommendations for future progress with particular focus on nutrition research.

Authors:  Augustin Scalbert; Lorraine Brennan; Oliver Fiehn; Thomas Hankemeier; Bruce S Kristal; Ben van Ommen; Estelle Pujos-Guillot; Elwin Verheij; David Wishart; Suzan Wopereis
Journal:  Metabolomics       Date:  2009-06-12       Impact factor: 4.290

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