Literature DB >> 16004563

Perlegen sciences, inc.

Eric Peacock1, Phyllis Whiteley.   

Abstract

For years we have recognized that using genetics to target the right medicines to the right patients at the right time could improve health care; however, technical and financial constraints have prevented researchers from conducting the comprehensive, whole-genome association studies required to make such advances. Now, Perlegen and its collaborators are making personalized medicine and high density whole-genome association studies a reality. Perlegen quickly and cost-effectively analyzes millions of unique genetic variations in thousands of patients to identify the genetic differences associated with efficacy and side effects. This enables the pharmaceutical drug response to be predicted, and the therapeutic window of a treatment to be expanded. Perlegen licenses compounds that may benefit from this approach, and collaborates with pharmaceutical companies to rescue and better position medicines in their portfolios. Furthermore, the company collaborates with researchers in the public sector to discover the genetic variations associated with common diseases, paving the way for new therapeutics and diagnostics.

Entities:  

Mesh:

Year:  2005        PMID: 16004563     DOI: 10.1517/14622416.6.4.439

Source DB:  PubMed          Journal:  Pharmacogenomics        ISSN: 1462-2416            Impact factor:   2.533


  6 in total

1.  Japanese population structure, based on SNP genotypes from 7003 individuals compared to other ethnic groups: effects on population-based association studies.

Authors:  Yumi Yamaguchi-Kabata; Kazuyuki Nakazono; Atsushi Takahashi; Susumu Saito; Naoya Hosono; Michiaki Kubo; Yusuke Nakamura; Naoyuki Kamatani
Journal:  Am J Hum Genet       Date:  2008-09-25       Impact factor: 11.025

2.  ENGINES: exploring single nucleotide variation in entire human genomes.

Authors:  Jorge Amigo; Antonio Salas; Christopher Phillips
Journal:  BMC Bioinformatics       Date:  2011-04-19       Impact factor: 3.169

3.  VKORC1-1639A allele influences warfarin maintenance dosage among Blacks receiving warfarin anticoagulation: a retrospective cohort study.

Authors:  Fatima Donia Mili; Tenecia Allen; Paula Weinstein Wadell; W Craig Hooper; Christine De Staercke; Christopher J Bean; Cathy Lally; Harland Austin; Nanette K Wenger
Journal:  Future Cardiol       Date:  2017-12-08

4.  Touring Ensembl: a practical guide to genome browsing.

Authors:  Giulietta M Spudich; Xosé M Fernández-Suárez
Journal:  BMC Genomics       Date:  2010-05-11       Impact factor: 3.969

5.  Viability of in-house datamarting approaches for population genetics analysis of SNP genotypes.

Authors:  Jorge Amigo; Christopher Phillips; Antonio Salas; Angel Carracedo
Journal:  BMC Bioinformatics       Date:  2009-03-19       Impact factor: 3.169

6.  SPSmart: adapting population based SNP genotype databases for fast and comprehensive web access.

Authors:  Jorge Amigo; Antonio Salas; Christopher Phillips; Angel Carracedo
Journal:  BMC Bioinformatics       Date:  2008-10-10       Impact factor: 3.169

  6 in total

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