Advanced

Cohort-specific imputation of gene expression improves prediction of warfarin dose for African Americans

Gottlieb, Assaf; Daneshjou, Roxana; DeGorter, Marianne; Bourgeois, Stephane; Svensson, Peter J. LU ; Wadelius, Mia; Deloukas, Panos; Montgomery, Stephen B. and Altman, Russ B. (2017) In Genome Medicine 9(1).
Abstract

Background: Genome-wide association studies are useful for discovering genotype-phenotype associations but are limited because they require large cohorts to identify a signal, which can be population-specific. Mapping genetic variation to genes improves power and allows the effects of both protein-coding variation as well as variation in expression to be combined into "gene level" effects. Methods: Previous work has shown that warfarin dose can be predicted using information from genetic variation that affects protein-coding regions. Here, we introduce a method that improves dose prediction by integrating tissue-specific gene expression. In particular, we use drug pathways and expression quantitative trait loci knowledge to impute gene... (More)

Background: Genome-wide association studies are useful for discovering genotype-phenotype associations but are limited because they require large cohorts to identify a signal, which can be population-specific. Mapping genetic variation to genes improves power and allows the effects of both protein-coding variation as well as variation in expression to be combined into "gene level" effects. Methods: Previous work has shown that warfarin dose can be predicted using information from genetic variation that affects protein-coding regions. Here, we introduce a method that improves dose prediction by integrating tissue-specific gene expression. In particular, we use drug pathways and expression quantitative trait loci knowledge to impute gene expression-on the assumption that differential expression of key pathway genes may impact dose requirement. We focus on 116 genes from the pharmacokinetic and pharmacodynamic pathways of warfarin within training and validation sets comprising both European and African-descent individuals. Results: We build gene-tissue signatures associated with warfarin dose in a cohort-specific manner and identify a signature of 11 gene-tissue pairs that significantly augments the International Warfarin Pharmacogenetics Consortium dosage-prediction algorithm in both populations. Conclusions: Our results demonstrate that imputed expression can improve dose prediction and bridge population-specific compositions. MATLAB code is available at https://github.com/assafgo/warfarin-cohort.

(Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
African Americans, International Warfarin Pharmacogenetics Consortium, Pharmacogenomics, Warfarin dose
in
Genome Medicine
volume
9
issue
1
publisher
BioMed Central
external identifiers
  • scopus:85034866990
  • wos:000416455600001
ISSN
1756-994X
DOI
10.1186/s13073-017-0495-0
language
English
LU publication?
yes
id
5cb9df5b-afc0-445f-9cdc-b8e06462bc7a
date added to LUP
2017-12-14 14:05:00
date last changed
2018-01-16 13:27:52
@article{5cb9df5b-afc0-445f-9cdc-b8e06462bc7a,
  abstract     = {<p>Background: Genome-wide association studies are useful for discovering genotype-phenotype associations but are limited because they require large cohorts to identify a signal, which can be population-specific. Mapping genetic variation to genes improves power and allows the effects of both protein-coding variation as well as variation in expression to be combined into "gene level" effects. Methods: Previous work has shown that warfarin dose can be predicted using information from genetic variation that affects protein-coding regions. Here, we introduce a method that improves dose prediction by integrating tissue-specific gene expression. In particular, we use drug pathways and expression quantitative trait loci knowledge to impute gene expression-on the assumption that differential expression of key pathway genes may impact dose requirement. We focus on 116 genes from the pharmacokinetic and pharmacodynamic pathways of warfarin within training and validation sets comprising both European and African-descent individuals. Results: We build gene-tissue signatures associated with warfarin dose in a cohort-specific manner and identify a signature of 11 gene-tissue pairs that significantly augments the International Warfarin Pharmacogenetics Consortium dosage-prediction algorithm in both populations. Conclusions: Our results demonstrate that imputed expression can improve dose prediction and bridge population-specific compositions. MATLAB code is available at https://github.com/assafgo/warfarin-cohort.</p>},
  articleno    = {98},
  author       = {Gottlieb, Assaf and Daneshjou, Roxana and DeGorter, Marianne and Bourgeois, Stephane and Svensson, Peter J. and Wadelius, Mia and Deloukas, Panos and Montgomery, Stephen B. and Altman, Russ B.},
  issn         = {1756-994X},
  keyword      = {African Americans,International Warfarin Pharmacogenetics Consortium,Pharmacogenomics,Warfarin dose},
  language     = {eng},
  month        = {11},
  number       = {1},
  publisher    = {BioMed Central},
  series       = {Genome Medicine},
  title        = {Cohort-specific imputation of gene expression improves prediction of warfarin dose for African Americans},
  url          = {http://dx.doi.org/10.1186/s13073-017-0495-0},
  volume       = {9},
  year         = {2017},
}