<div>August 21, 2017</div>


<div>To Whom it may concern,</div>


<div>We would like to request continued access to the STAR*D: Treatment
Resistant Depression dataset (Depression Distribution 2.0 &amp; 3.0, study
18). We were previously granted access to this dataset with the goal of
using a bioinformatic technique, genomic-relatedness-matrix
restricted maximum-likelihood analysis (GREML), to estimate the
aggregate effect of single-nucleotide polymorphisms (SNPs) on
antidepressant treatment response.</div>


<div>This project is now complete, resulting in two publications. We used
GREML to examine the SNP heritability of pretreatment depression
severity and treatment response and the extent to which observed SNP
heritability could be attributed to variation in the dopaminergic and
serotonergic systems (Palmer, Beevers, McGeary, Brick &amp; Knopik, 2016).
Subsequently, we used GREML to investigate the extent to which
variance in depression symptoms and symptom clusters could be explained
by variation in common SNPs (Pearson, Palmer, Brick, McGeary, Knopik &amp;
Beevers, 2016; citations provided below).</div>


<div>We hope to have continued access to the STAR*D dataset for a novel
project which aims to use machine learning models to predict
anti-depressant treatment response using clinical predictors and genetic
information. We have current NIMH funding for this project (R21MH110758)
and we are eager to start these analyses.</div>


<div>This project includes Jason Shumake, Ph.D. as a Co-Investigator. Dr.
Shumake is a research assistant professor and biostatistician for the
Institute for Mental Health Research at the University of Texas at
Austin. He has strong expertise in data analysis, including machine
learning methods, randomized trials with longitudinal data, mediation
analyses, structural equation analyses, and power analyses. He also has
extensive experience managing and cleaning large, complex and
multidimensional data. Dr. Shumake is the ideal biostatistician for this
project and he will take the lead on the proposed data analyses.</div>


<div>I appreciate your consideration of our data access request. Please do
not hesitate to contact me with any additional questions.</div>


<div>Sincerely,</div>