The document is a CV for Thomas S. Price, who has expertise in statistical genetics, pharmacogenetics, genetic epidemiology, and bioinformatics. He has analyzed high-dimensional biological datasets using statistical methods like structural equation models and machine learning. He has worked as a researcher at universities in the UK and US, developing novel analysis methods and publishing numerous papers on genetics and genomics topics.

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Thomas S. Price, Ph.D.
tom@tomprice.net  251 Park Drive, Downingtown, PA 19335, U.S.A.  (267) 282 1675
I analyze data and develop statistical methods for genetics, genomics, and systems biology
SUMMARY
 The focus of my academic career has been the analysis of high dimensional biological datasets.
 My interests are statistical genetics, pharmacogenetics, genetic epidemiology, and bioinformatics.
 I have technical expertise in frequentist and Bayesian statistics, computationally intensive statistics,
and programming R, C/C++, Python, and Perl.
 My data analyses have employed a variety of modern statistical methods including structural
equation models, generalized estimating equations, non-parametric and semi-parametric models,
time series analysis, wavelet transforms, machine learning, and decision theory.
EDUCATION
Ph.D. Behavioral Genetics (2002). King’s College London, UK.
M.Sc. Applied Statistics, Honors with Distinction (2003). Oxford University, UK.
B.A. Psychology with Mathematics, Honors 1st Class (1993). Cambridge University, UK.
EXPERIENCE
ResearchAssistant Professor, Department of Pharmacology, University of Pennsylvania (2012-2015)
 Designed high-dimensional pharmacological experiments, analyzed datasets, and interpreted
results for medics and biologists
 Integrated data for systems pharmacology of NSAID response (http://www.pentaconhq.org/)
 Developed pipeline for RNAseq data: evaluated algorithms for normalization and inference
 Supervised junior statisticians and bioinformaticians
 Attracted funding as Bioinformatics core leader of CEET (http://ceet.upenn.edu/)
Lecturer in Statistical Genetics, SDGPRC, King’s College London (2008-2012)
 Developed research program in statistical genetics and gene-environment interplay
 Designed, analyzed, and meta-analyzed genome-wide association (GWAS) experiments
 Successfully supervised doctoral student to perform the first genome-wide meta-analysis of a
genotype-environment interaction (GxE): effect of prenatal tobacco exposure on birth weight
 Lectured on statistics and genetics to graduate researchers and medical students
ResearchAssociate, Department of Pharmacology, University of Pennsylvania (2005-2007)
 Designed and analyzed of high-dimensional microarray and proteomics experiments
 Devised and coded new algorithm for calling protein expression in mass spectrometry data
 Published novel method for detecting circadian rhythms in non-stationary time series
 Designed, funded, performed, and published GxE study of maternal smoking and birthweight
 Attracted funding as lead bioinformatician for a multidisciplinary study of platelet function
Postdoctoral Researcher, Wellcome Trust Centre for Human Genetics, Oxford University (2003-2004)
 Obtained funding as a Wellcome Trust training fellow in bioinformatics
 Devised and published novel methods for detecting and quantifying changes in gene
expression and genetic copy number variation

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SELECTED PUBLICATIONS
Further details on publications at http://www.researcherid.com/rid/B-7372-2008
Hinney A, et al. (2016). Evidence for three genetic loci involved in both anorexia nervosa risk and
variation of body mass index. Mol Psychiatry. doi:10.1038/mp.2016.71
St Pourcain B, et al. (2015). Heritability and genome-wide analyses of problematic peer relationships
during childhood and adolescence. Human Genetics, 134 539-551. doi:10.1007/s00439-014-1514-5.
St Pourcain B, et al. (2014). Common variation near ROBO2 is associated with expressive vocabulary in
infancy. Nat Commun, 5:4831. doi:10.1038/ncomms5831.
Davis OS, et al. (2014). The correlation between reading and mathematics ability at age twelve has a
substantial genetic component. Nat Commun, 5:4204. doi:10.1038/ncomms5204.
Wong CC, et al. (2014). Methylomic analysis of monozygotic twins discordant for autism spectrum
disorder and related behavioural traits. Mol Psychiatry, 19(4):495-503. doi:10.1038/mp.2013.41
Tragante V, et al. (2014). Gene-centric meta-analysis in 87,736 individuals of European ancestry
identifies multiple blood-pressure-related loci. Am J Hum Genet, 94(3):349-360.
doi:10.1016/j.ajhg.2013.12.016.
Holmes MV, et al. (2014). Causal effects of body mass index on cardiometabolic traits and events: a
Mendelian randomization analysis. Am J Hum Genet, 94(2):198-208. doi:10.1016/j.ajhg.2013.12.014.
Erratum in: Am J Hum Genet, 2014; 94(2):312.
Benyamin B, et al. (2014). Childhood intelligence is heritable, highly polygenic and associated with
FNBP1L. Mol Psychiatry, 19(2):253-258. doi:10.1038/mp.2012.184.
Guo Y, et al. & The IBC 50K SNP array BMI Consortium. (2013). Gene-centric meta-analyses of
108912 individuals confirm known body mass index loci and reveal three novel signals. Hum Mol
Genet, 22(1):184-201. doi:10.1093/hmg/dds396.
Ganesh SK, et al. (2013). Loci influencing blood pressure identified using a cardiovascular gene-centric
array. Hum Mol Genet, 22(8):1663-1678. doi:10.1093/hmg/dds555. Erratum in: Hum Mol Genet, 2013;
22(16): 3394-3395.
Benyamin B, et al. (2013). Childhood intelligence is heritable, highly polygenic and associated with
FNBP1L. Mol Psychiatry, 19(4):253-258. doi:10.1038/mp.2012.184.
Taal HR, et al. & Early Growth Genetics Consortium. (2012). Common variants at 12q15 and 12q24 are
associated with infant head circumference. Nat Genet, 44(5):532-538. doi:10.1038/ng.2238. Erratum in:
Nat Genet, 2013; 45(6):713.
Ikram MA, et al. for the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE)
Consortium & Early Growth Genetics (EGG) Consortium. (2012). Common variants at 6q22 and 17q21
are associated with intracranial volume. Nat Genet, 44(5):539-544. doi:10.1038/ng.2245. Erratum in:
Nat Genet, 2012; 44(6):732. Nat Genet, 2013; 45(6):713.
Bradfield JP, et al. for the Early Growth Genetics (EGG) Consortium. (2012). A genome-wide
association meta-analysis identifies new childhood obesity loci. Nat Genet, 44(5):526–531.
doi:10.1038/ng.2247.
Saxena R, et al. (2012). Large-scale gene-centric meta-analysis across 39 studies identifies type 2
diabetes loci. Am J Hum Genet, 90(3):410-425. doi:10.1016/j.ajhg.2011.12.022. Erratum in: Am J Hum
Genet, 2012; 90(4):753.