Systems Biology Data Analysis Methodology in Pharmacogenomics

Rodin, Andréi S.; Gogoshin, Grigoriy; Boerwinkle, Eric

doi:10.2217/pgs.11.76

“…Since only one of the 44 individual SNPs included in this study was associated with severity of depression (rs12248560, see Table 2) we used Random forest (RF) (Nguyen, Huang, Wu, Nguyen, & Li, 2015) as a SNP selection method. RF has the advantage of being able to rank variables with small effect sizes according to their importance (Rodin, Gogoshin, & Boerwinkle, 2011). The method has been successfully applied to evaluate the impact of BMI associated SNPs on weight loss after bariatric surgery (Bandstein et al, 2016) as well as the association between different genetic variants and migraine with or without aura (Pisanu et al, 2017).…”

Section: Random Forest Analyses and Development Of A Genetic Risk Smentioning

confidence: 99%

A pharmacogenetic risk score for the evaluation of major depression severity under treatment with antidepressants

Kanders

¹

,

Pisanu

²

,

Bandstein

³

et al. 2019

Drug Development Research

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The severity of symptoms as well as efficacy of antidepressants in major depressive disorder (MDD) is modified by single nucleotide polymorphisms (SNPs) in different genes, which may contribute in an additive or synergistic fashion. We aimed to investigate depression severity in participants with MDD under treatment with antidepressants in relation to the combinatory effect of selected genetic variants combined using a genetic risk score (GRS). The sample included 150 MDD patients on regular AD therapy from the population‐based Swiss PsyCoLaus cohort. We investigated 44 SNPs previously associated with antidepressant response by ranking them with regard to their association to the Center for Epidemiologic Studies Short Depression Scale (CES‐D) score using random forest. The three top scoring SNPs (rs12248560, rs878567, rs17710780) were subsequently combined into an unweighted GRS, which was included in linear and logistic regression models using the CES‐D score, occurrence of a major depressive episode (MDE) during follow‐up and regular antidepressant treatment during the 6 months preceding follow‐up assessment as outcomes. The GRS was associated with MDE occurrence (p = .02) and ln CES‐D score (p = .001). The HTR1A rs878567 variant was associated with ln CES‐D after adjustment for demographic and clinical variables [p = .02, lower scores for minor allele (G) carriers]. Additionally, rs12248560 (CYP2C19) CC homozygotes showed a six‐fold higher likelihood of regular AD therapy at follow‐up compared to minor allele homozygotes [TT; ultrarapid metabolizers (p = .03)]. Our study suggests that the cumulative consideration of pharmacogenetic risk variants more reliably reflects the impact of the genetic background on depression severity than individual SNPs.

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“…Systems biology is especially fitting for pharmacogenomics as it involves hierarchical levels of different phenotypes and pharmacogenomics typically involves multiple phenotypes such as a disease of interest and a drug response. 11 With an approach utilizing vast amounts of biological data to predict a phenotype, there exists the opportunity for more effective translation of biological data into clinical practice.…”

Section: Systems Appraoches and Pharmacogenomicsmentioning

confidence: 99%

Using systems approaches to address challenges for clinical implementation of pharmacogenomics

Karnes

¹

,

Driest

²

,

Bowton

³

et al. 2015

WIREs Mechanisms of Disease

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Many genetic variants have been shown to affect drug response through changes in drug efficacy and likelihood of adverse effects. Much of pharmacogenomic science has focused on discovering and clinically implementing single gene variants with large effect sizes. Given the increasing complexities of drug responses and their variability, a systems approach may be enabling for discovery of new biology in this area. Further, systems approaches may be useful in addressing challenges in moving these data to clinical implementation, including creation of predictive models of drug response phenotypes, improved clinical decision-making through complex biological models, improving strategies for integrating genomics into clinical practice, and evaluating the impact of implementation programs on public health. For decades, genetic variation has been clearly implicated as an important determinant in drug disposition and effects. 1 One of the promises of the completion of the human genome project is personalised medicine, one aspect of which is pharmacogenomics, or tailoring drug therapy to an individual's genetic makeup. 2 To date, the field has focused largely on the effect of individual genetic variants with large effect sizes. Extending this paradigm to large numbers of drugs will likely require consideration of the complex interplay of genetic, metabolic, environmental, and developmental factors on drug responses. 3 Enabling this type of analysis will be a framework that views drug response as a dynamic system and maps

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“…As a system is perturbed by medications, key nodes within the system can be identified and phenotypic response predicted. Systems biology is especially fitting for pharmacogenomics as it involves hierarchical levels of different phenotypes and pharmacogenomics typically involves multiple phenotypes such as a disease of interest and a drug response . With an approach utilizing vast amounts of biological data to predict a phenotype, there exists the opportunity for more effective translation of biological data into clinical practice.…”

Section: Systems Approaches and Pharmacogenomicsmentioning

confidence: 99%

“…Systems biology is especially fitting for pharmacogenomics as it involves hierarchical levels of different phenotypes and pharmacogenomics typically involves multiple phenotypes such as a disease of interest and a drug response. 11 With an approach utilizing vast amounts of biological data to predict a phenotype, there exists the opportunity for more effective translation of biological data into clinical practice. System approaches have been used to analyze determinants of clinical responses to cardiovascular drugs, including QT prolonging drugs (see Box 1).…”

Section: Systems Approaches and Pharmacogenomicsmentioning

confidence: 99%

Using systems approaches to address challenges for clinical implementation of pharmacogenomics

Karnes

¹

,

Driest

²

,

Bowton

³

et al. 2013

WIREs Mechanisms of Disease

View full text Add to dashboard Cite

Many genetic variants have been shown to affect drug response through changes in drug efficacy and likelihood of adverse effects. Much of pharmacogenomic science has focused on discovering and clinically implementing single gene variants with large effect sizes. Given the increasing complexities of drug responses and their variability, a systems approach may be enabling for discovery of new biology in this area. Further, systems approaches may be useful in addressing challenges in moving these data to clinical implementation, including creation of predictive models of drug response phenotypes, improved clinical decision-making through complex biological models, improving strategies for integrating genomics into clinical practice, and evaluating the impact of implementation programs on public health.

show abstract

Systems Biology Data Analysis Methodology in Pharmacogenomics

Cited by 14 publications

References 75 publications

A pharmacogenetic risk score for the evaluation of major depression severity under treatment with antidepressants

A pharmacogenetic risk score for the evaluation of major depression severity under treatment with antidepressants

Using systems approaches to address challenges for clinical implementation of pharmacogenomics

Using systems approaches to address challenges for clinical implementation of pharmacogenomics

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