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Bioinformatics challenges for personalized medicine.

Fernald GH, Capriotti E, Daneshjou R, Karczewski KJ, Altman RB - Bioinformatics (2011)

Bottom Line: Widespread availability of low-cost, full genome sequencing will introduce new challenges for bioinformatics.This review outlines recent developments in sequencing technologies and genome analysis methods for application in personalized medicine.New methods are needed in four areas to realize the potential of personalized medicine: (i) processing large-scale robust genomic data; (ii) interpreting the functional effect and the impact of genomic variation; (iii) integrating systems data to relate complex genetic interactions with phenotypes; and (iv) translating these discoveries into medical practice. russ.altman@stanford.edu

View Article: PubMed Central - PubMed

Affiliation: Biomedical Informatics Training Program, Stanford University School of Medicine, Department of Bioengineering, Stanford University, Stanford, CA, USA.

ABSTRACT

Motivation: Widespread availability of low-cost, full genome sequencing will introduce new challenges for bioinformatics.

Results: This review outlines recent developments in sequencing technologies and genome analysis methods for application in personalized medicine. New methods are needed in four areas to realize the potential of personalized medicine: (i) processing large-scale robust genomic data; (ii) interpreting the functional effect and the impact of genomic variation; (iii) integrating systems data to relate complex genetic interactions with phenotypes; and (iv) translating these discoveries into medical practice.

Contact: russ.altman@stanford.edu

Show MeSH
Personalized medicine. Personal genomics connect genotype to phenotype and provide insight into disease. Pharmacogenomics connect connects genotype to patient-specific treatment. Traditional medicine defines the pathologic states and clinical observations to evaluate and adjust treatments.
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Figure 1: Personalized medicine. Personal genomics connect genotype to phenotype and provide insight into disease. Pharmacogenomics connect connects genotype to patient-specific treatment. Traditional medicine defines the pathologic states and clinical observations to evaluate and adjust treatments.

Mentions: In the last decade, molecular science has made many advances to benefit medicine, including the Human Genome project, International HapMap project and genome wide association studies (GWASs) (International HapMap Consortium, 2005). Single nucleotide polymorphisms (SNPs) are now recognized as the main cause of human genetic variability and are already a valuable resource for mapping complex genetic traits (Collins et al., 1997). Thousands of DNA variants have been identified that are associated with diseases and traits (Hindorff et al., 2009). By combining these genetic associations with phenotypes and drug response, personalized medicine will tailor treatments to the patients' specific genotype (Fig. 1). Although whole genome sequences are not used in regular practice today (McGuire and Burke, 2008), there are already many examples of personalized medicine in current practice. Chemotherapy medications such as trastuzumab and imatinib target specific cancers (Gambacorti-Passerini, 2008; Hudis, 2007), a targeted pharmacogenetic dosing algorithm is used for warfarin (International Warfarin Pharmacogenetics Consortium et al., 2009; Sagreiya et al., 2010) and the incidence of adverse events is reduced by checking for susceptible genotypes for drugs like abacavir, carbamazepine and clozapine (Dettling et al., 2007; Ferrell and McLeod, 2008; Hetherington et al., 2002).Fig. 1.


Bioinformatics challenges for personalized medicine.

Fernald GH, Capriotti E, Daneshjou R, Karczewski KJ, Altman RB - Bioinformatics (2011)

Personalized medicine. Personal genomics connect genotype to phenotype and provide insight into disease. Pharmacogenomics connect connects genotype to patient-specific treatment. Traditional medicine defines the pathologic states and clinical observations to evaluate and adjust treatments.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3117361&req=5

Figure 1: Personalized medicine. Personal genomics connect genotype to phenotype and provide insight into disease. Pharmacogenomics connect connects genotype to patient-specific treatment. Traditional medicine defines the pathologic states and clinical observations to evaluate and adjust treatments.
Mentions: In the last decade, molecular science has made many advances to benefit medicine, including the Human Genome project, International HapMap project and genome wide association studies (GWASs) (International HapMap Consortium, 2005). Single nucleotide polymorphisms (SNPs) are now recognized as the main cause of human genetic variability and are already a valuable resource for mapping complex genetic traits (Collins et al., 1997). Thousands of DNA variants have been identified that are associated with diseases and traits (Hindorff et al., 2009). By combining these genetic associations with phenotypes and drug response, personalized medicine will tailor treatments to the patients' specific genotype (Fig. 1). Although whole genome sequences are not used in regular practice today (McGuire and Burke, 2008), there are already many examples of personalized medicine in current practice. Chemotherapy medications such as trastuzumab and imatinib target specific cancers (Gambacorti-Passerini, 2008; Hudis, 2007), a targeted pharmacogenetic dosing algorithm is used for warfarin (International Warfarin Pharmacogenetics Consortium et al., 2009; Sagreiya et al., 2010) and the incidence of adverse events is reduced by checking for susceptible genotypes for drugs like abacavir, carbamazepine and clozapine (Dettling et al., 2007; Ferrell and McLeod, 2008; Hetherington et al., 2002).Fig. 1.

Bottom Line: Widespread availability of low-cost, full genome sequencing will introduce new challenges for bioinformatics.This review outlines recent developments in sequencing technologies and genome analysis methods for application in personalized medicine.New methods are needed in four areas to realize the potential of personalized medicine: (i) processing large-scale robust genomic data; (ii) interpreting the functional effect and the impact of genomic variation; (iii) integrating systems data to relate complex genetic interactions with phenotypes; and (iv) translating these discoveries into medical practice. russ.altman@stanford.edu

View Article: PubMed Central - PubMed

Affiliation: Biomedical Informatics Training Program, Stanford University School of Medicine, Department of Bioengineering, Stanford University, Stanford, CA, USA.

ABSTRACT

Motivation: Widespread availability of low-cost, full genome sequencing will introduce new challenges for bioinformatics.

Results: This review outlines recent developments in sequencing technologies and genome analysis methods for application in personalized medicine. New methods are needed in four areas to realize the potential of personalized medicine: (i) processing large-scale robust genomic data; (ii) interpreting the functional effect and the impact of genomic variation; (iii) integrating systems data to relate complex genetic interactions with phenotypes; and (iv) translating these discoveries into medical practice.

Contact: russ.altman@stanford.edu

Show MeSH