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@article{Malki:2016:10.1002/ajmg.b.32494,
author = {Malki, K and Tosto, MG and Mouriño-Talín, H and Rodríguez-Lorenzo, S and Pain, O and Jumhaboy, I and Liu, T and Parpas, P and Newman, S and Malykh, A and Carboni, L and Uher, R and McGuffin, P and Schalkwyk, LC and Bryson, K and Herbster, M},
doi = {10.1002/ajmg.b.32494},
journal = {American Journal of Medical Genetics Part B-Neuropsychiatric Genetics},
pages = {235--250},
title = {Highly polygenic architecture of antidepressant treatment response: Comparative analysis of SSRI and NRI treatment in an animal model of depression},
url = {http://dx.doi.org/10.1002/ajmg.b.32494},
volume = {174},
year = {2016}
}

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TY  - JOUR
AB  - Response to antidepressant (AD) treatment may be a more polygenic trait than previously hypothesized, with many genetic variants interacting in yet unclear ways. In this study we used methods that can automatically learn to detect patterns of statistical regularity from a sparsely distributed signal across hippocampal transcriptome measurements in a large-scale animal pharmacogenomic study to uncover genomic variations associated with AD. The study used four inbred mouse strains of both sexes, two drug treatments, and a control group (escitalopram, nortriptyline, and saline). Multi-class and binary classification using Machine Learning (ML) and regularization algorithms using iterative and univariate feature selection methods, including InfoGain, mRMR, ANOVA, and Chi Square, were used to uncover genomic markers associated with AD response. Relevant genes were selected based on Jaccard distance and carried forward for gene-network analysis. Linear association methods uncovered only one gene associated with drug treatment response. The implementation of ML algorithms, together with feature reduction methods, revealed a set of 204 genes associated with SSRI and 241 genes associated with NRI response. Although only 10% of genes overlapped across the two drugs, network analysis shows that both drugs modulated the CREB pathway, through different molecular mechanisms. Through careful implementation and optimisations, the algorithms detected a weak signal used to predict whether an animal was treated with nortriptyline (77%) or escitalopram (67%) on an independent testing set. The results from this study indicate that the molecular signature of AD treatment may include a much broader range of genomic markers than previously hypothesized, suggesting that response to medication may be as complex as the pathology. The search for biomarkers of antidepressant treatment response could therefore consider a higher number of genetic markers and their interactions. Through predominat
AU  - Malki,K
AU  - Tosto,MG
AU  - Mouriño-Talín,H
AU  - Rodríguez-Lorenzo,S
AU  - Pain,O
AU  - Jumhaboy,I
AU  - Liu,T
AU  - Parpas,P
AU  - Newman,S
AU  - Malykh,A
AU  - Carboni,L
AU  - Uher,R
AU  - McGuffin,P
AU  - Schalkwyk,LC
AU  - Bryson,K
AU  - Herbster,M
DO  - 10.1002/ajmg.b.32494
EP  - 250
PY  - 2016///
SN  - 1552-485X
SP  - 235
TI  - Highly polygenic architecture of antidepressant treatment response: Comparative analysis of SSRI and NRI treatment in an animal model of depression
T2  - American Journal of Medical Genetics Part B-Neuropsychiatric Genetics
UR  - http://dx.doi.org/10.1002/ajmg.b.32494
UR  - http://hdl.handle.net/10044/1/41792
VL  - 174
ER  -

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