Tuesday, April 21, 2015

GWAS on SSRI treatment outcome in patients with depression: ISPC cohort and meta-analysis




The treatment response to selective serotonin reuptake inhibitors (SSRIs), a major class of antidepressant drugs, varies considerably between patients. The International SSRI PharmacogenomicsConsortium (ISPC) was established with the primary goal of identifying genetic variations that may contribute to SSRI treatment outcome in patients with depressive disorder.
  The ISPC includes eight research groups that contributed clinical and genetic data. A genome-wide association study of 4-week treatment outcomes, measured using the 17-item Hamilton Rating Scale for Depression (HRSD-17), was performed using data from 865 subjects from seven sites and the results are published in the journal Translational Psychiatry.
  Although many top association signals in the ISPC analysis for the primary outcomes percent change in HRSD-17 score and response map to interesting candidate genes (see Table 2 in the article), none were significant at the genome-wide level. The associations did not replicate using data from the Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomics Study (PGRN-AMPS) and the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study. 
Top association results in the meta-analysis of response based on data from the ISPC, PGRN-AMPS and STAR*D cohorts included SNPs in the HPRTP4 (hypoxanthine phosphoribosyltransferase pseudogene 4) /VSTM5 (V-set and transmembrane domain containing 5) region, which approached genome-wide significance (p=5.03E-08), and SNPs 5’ upstream of the neuregulin-1 gene, NRG1 (p=1.20E-06).


Tuesday, March 31, 2015

PharmGKB and Stanford Law School student collaboration

The Stanford Law School blog recently highlighted the collaboration between Stanford law students and PharmGKB to write a response to the FDA proposed regulation of laboratory developed tests.  Read more about the Juelsgaard Intellectual Property and Innovation Clinic students and their work.

Friday, March 27, 2015

NAT2 plays a role in insulin sensitivity


N-acetyltransferase 2 (NAT2) has long been known to metabolize xenobiotics but the endogenous substrates of NAT2 remain unknown. A new study published in “The Journal of Clinical Investigation” provides convincing evidence that NAT2 plays a role in the regulation of insulin sensitivity. The authors of the study reported that several SNPs, including the A allele at the single nucleotide polymorphism (SNP) rs1208 (c.803G>A, p.R268K) were associated with increased likelihood of insulin resistance (IR). The authors conducted a genome wide association study (GWAS) meta-analysis using genomic data and several measures of insulin sensitivity in multiple non-diabetic cohorts, including three European, one Hispanic and one East Asian cohort totaling 5,624 individuals. Although the A allele at rs1208 was not associated with IR at the genome wide significance level (P<0.05 x10^-8) in the discovery or replication cohorts it consistently showed the strongest association (P<6.4 x 10^-7) with increased degree of IR in all cohorts studied.

The authors confirmed the role of NAT2 as a modulator of insulin sensitivity by conducting in vitro studies in mouse adipocytes (3T3-L1) and myotubes. Administration of insulin caused a 50% decrease in expression of Nat1 (the mouse ortholog of NAT2) in both 3T3 cells and myotubes and siRNA mediated silencing of Nat1 caused a decrease in insulin stimulated glucose uptake, and an increase in basal lipolysis. These effects were reversed by over-expression of Nat1. In addition, Nat1 KO (-/-) and heterozygous (-/+) mice had higher fasting plasma glucose, insulin and triglyceride levels as well as decreased response to insulin during insulin tolerance tests as compared to wild-type (+/+) mice. 


These analyses provide convincing evidence that NAT2 plays a role in mediating insulin sensitivity, even though none of the NAT2 SNPs that were identified in the GWAS were significantly associated with IR at the genome wide level. Future studies could include more detailed functional analyses of individual SNPs on NAT2 expression and function, and may provide additional clues to identify the endogenous substrates of NAT2.

Read the original paper:

Knowles JW, Xie W, Zhang Z, Chennemsetty I, Assimes TL, Paananen J, Hansson O, Pankow J, Goodarzi MO, Carcamo-Orive I, Morris AP, Chen YI, Mäkinen VP, Ganna A, Mahajan A, Guo X, Abbasi F, Greenawalt DM, Lum P, Molony C, Lind L, Lindgren C, Raffel LJ, Tsao PS, Schadt EE, Rotter JI, Sinaiko A, Reaven G, Yang X, Hsiung CA, Groop L, Cordell HJ, Laakso M, Hao K, Ingelsson E, Frayling TM, Weedon MN, Walker M, Quertermous T.

J Clin Invest. 2015 Mar 23. pii: 74692. doi: 10.1172/JCI74692. [Epub ahead of print]

Thursday, March 26, 2015

New CPIC Guideline: CYP3A5 and Tacrolimus

Guidelines regarding the use of pharmacogenetic tests for CYP3A5 in dosing tacrolimus have been published in Clinical Pharmacology and Therapeutics by the Clinical Pharmacogenetics Implementation Consortium (CPIC).

Tacrolimus is an immunosuppressive agent administered to transplant recipients to prevent and treat allograft rejection. Clinical use of tacrolimus is complicated by its high inter-patient variability in pharmacokinetics and a narrow therapeutic index. As a result, management of tacrolimus usually includes therapeutic drug monitoring (TDM). Concentrations of tacrolimus are strongly influenced by CYP3A5 genotype - individuals with the CYP3A5 *1/*1 or CYP3A5 *1/*3 genotype (also known as extensive and intermediate metabolizers, respectively) have significantly lower concentrations of tacrolimus as compared to those with the *3/*3 genotype (poor metabolizers). In addition to standard TDM, adjusting the starting dose of tacrolimus based on CYP3A5 genotype may allow for a more rapid achievement of therapeutic drug concentrations. 

In the newly published guidelines, CPIC recommends increasing the starting dose by 1.5 to 2 times the recommended starting dose in CYP3A5 extensive and intermediate metabolizers. This particular CPIC dosing recommendation is unusual in that those with the extensive metabolizer phenotype (typically referred to as the "normal" metabolizer phenotype in other CYP enzymes) require an increase in dose, while those with the poor metabolizer phenotype do not require any change in dose. This is because, in the case of CYP3A5, extensive metabolizers are actually the minority in most worldwide populations (excluding those of African descent), while those with the poor metabolizer phenotype constitute the majority.

For details, see the CPIC guideline on PharmGKB.

For other CPIC guidelines see the list of CPIC publications and guidelines in progress.

Thursday, March 5, 2015

ABCB5 and haloperidol-induced toxicity: Results from a new study

Approximately 50% of patients treated with the antipsychotic drug haloperidol will develop extrapyramidal symptoms, a category that includes tremors, parkinsonism and decreased spontaneous movement. However, studies looking into the genetic variations associated with the development of these symptoms have been limited.

In a study recently published in PLOS Medicine, Zheng et al. used murine models and a human genetic association study to show a link between the ABCB5 gene and haloperidol-induced extrapyramidal symptoms (referred to as haloperidol-induced toxicity (HIT), and indicated in the murine models by "latency", or the time required for a mouse to move all four paws after being placed on an inclined wire-mesh screen). In the human genetic association study, it was the missense SNP rs17143212 in particular that was associated with haloperidol toxicities during the first 7 days of treatment, both before and after correcting for multiple testing using a permutation test.

ABCB5 is a member of the ATP-binding cassette (ABC) transporter family, and is responsible for the movement of substrates across cell membranes. Zheng et al. also used murine models to show that ABCB5 mRNA is expressed in brain capillaries, the location of the blood-brain barrier. This provides a possible mechanistic explanation for the association between the gene and HIT in mice - mouse strains with genetic variations that result in reduced ABCB5 activity may be more susceptible to HIT due to increased haloperidol concentrations in the brain. Furthermore, the authors suggest that this toxicity may actually be due to a metabolite of haloperidol, HPP+, which can induce mitochondrial toxicity that results in Parkinsonian-like symptoms.

While the authors conclude the paper by noting that other genetic factors are likely involved in the development of HIT in humans, the results from this study shed further light on the pharmacogenetics behind haloperidol-induced toxicity.

Read the original article:
The role of abcb5 alleles in susceptibility to haloperidol-induced toxicity in mice and humans.
Zheng M, Zhang H, Dill DL, Clark JB, Tu S, Yablonovitch AL, Tan MH, Zhang R, Rujescu D, Wu M, Tessarollo L, Vieira W, Gottesman MM, Deng S, Eberlin LS, Zare RN, Billard JM, Gillet JP, Li JB, Peltz G. PLoS Medicine. 2015 Feb 3;12(2):e1001782. PMID 25647612.

See the annotation for this paper on PharmGKB:
https://www.pharmgkb.org/pmid/25647612

Wednesday, February 25, 2015

Our 10-step guide to enabling your PGx study to be curated

At PharmGKB we manually curate pharmacogenetic (PGx) associations found in published articles and add them to our database. However, lack of information or clarity is unfortunately common in publications and these hurdles make it difficult or impossible to curate a particular association.

We have published a guideline in Clinical Pharmacology & Therapeutics of 10 simple rules to help your publication be curated:

Enabling the curation of your pharmacogenetic study

1. Use standard gene nomenclature. 

2. Provide dbSNP rsIDs, a reference sequence and mapping information for genetic variants. 

3. Clearly state which allele/genotype of the variant is associated with the phenotype and the direction of the association.  

4. Clearly state population size, ethnicity, gender and drug regimen. 

5. State the minor allele for the variant in the given population/cohort.

6. Clearly state on which chromosomal strand the alleles are reported. 

7. State all genetic variants that were screened (including SNPs, indels, copy number variations and structural variations). 

8. Specify how * alleles or haplotypes were defined. 

9. Report which genotypes/diplotypes were found in the study population and map them to phenotype groups. 

10. State the statistical tests used for each association analysis. Include methods used for multiple hypothesis correction. 



Why follow these steps? 

Here are some benefits to you for enabling us to add your study findings to our public database:
  • Be part of the largest collection of curated PGx literatures, which is often used for identifying clinically relevant variants, interpreting genomes and used as a standard for evaluating methods and models for mining scientific literatures as well as drug discovery.
  • Your results and a link to your publication on PubMed become accessible to a broad audience, including students, bioinformaticians, researchers and clinicians.
  • By becoming part of our data set, your results may contribute to further research studies.
  • Your findings can aid in the clinical interpretation of a PGx association (whether the association you found was negative or positive) by contributing evidence to a Clinical Annotation.
  • These in turn may aid towards establishing a PGx-based dosing guideline for a particular drug.

    Wednesday, February 18, 2015

    PharmGKB VIP summary for CFTR published

    The PharmGKB summary describing CFTR as a pharmacogene has been published by Pharmacogenetics & Genomics. Genetic variants within the CFTR gene are the target of new Cystic Fibrosis therapies and drugs in development. The hope is that patients will be better treated with personalized medicines tailored to the underlying defects within CFTR. The VIP summary details these treatment strategies and clinically important variants within the CFTR gene.

    • Read the article: 
    McDonagh EM, Clancy JP, Altman RB, Klein TE.   
    Pharmacogenetics & Genomics. 2014 Dec 15. (Epub ahead of print)