CREAL. Centre de Recerca en Epidemiologia Ambiental


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Juan R Gonzalez's biosketch

Juan R Gonzalez is an Associate Reseach Professor at CREAL where leads a bioinformatic group in genetic epidemiology (BRGE). He is also leading GENOmet, a Spanish network for developing bioinformatic tools and statistical methos in genomics. He has co-authored more than 90 scientific papers published in peer-reviewed journals and has ample experience in large international research projects.As an Assistant Professor at the Department of Mathematics at Autonomous University of Barcelona, his educational activities focuses on providing post-graduate lectures in biostatistics and in 'Omic' data analysis (Master in Bioinformatics at UAB and Master in 'Omic' data analysis at UVic-UPF). After getting his PhD in Biostatistics supervised by Prof Edsel Peña from University of South Carolina Columbia, he spent 2 years as a post-doc at Center for Genomic Regulation (CRG). There, he worked in genetic causes of psyquiatric disorders and developing tools for analyzing GWAS data as the case of SNPassoc R package, one of the widely used tools to perform genetic association studies. During last years, working at CREAL, his work focuses on developing new statistical methods and implementing tools for analyzing different 'omic' data including genomic (CNVs, inversions, mosaicisms), and transcriptomic (RNA-seq and alternative splicing). These R packages can be overview at /jrgonzalez/software.htm.

He is currently in charge of developing new statistical methods and bioinformatic tools to link the early-life exposome to several omic and exposure data (i.e. exposome) as part of 7th Framework Program project The Human Early-Life Exposome (HELIX). He has also been involved on research linking neurodevelopment to air pollution and other environmental determinants as part of the projects INMA and BREATHE. He is collaborating with Gendiag to develop algorithms to provide genetic advice in complex diseases such as obesity (Nutri inCode®), thrombosis (Thrombo inCode®) and cardiovascular disease (Cardio inCode®). He is highly skilled in providing R courses for analyzing genetic data. During last 4 years, he has organized 14 editions of the course 'Genetic association studies' and 3 editions of the course 'Statistical methods to analyze complex genetic data' as well as 2 courses on 'Statistical methods to analyze next generation sequencing data'.

Bioinformatics Research Group in Epidemiology's Web Page can be found here.

Selected Publications

  1. González JR, Subirana I, Escaramís G, Peraza S, Cáceres A, Estivill X, Armengol L. Accounting for uncertainty when assessing association between copy number and disease: a latent class model. BMC Bioinformatics, 2009;10:172.
  2. Armengol L, Villatoro S, González JR, Pantano L, García-Aragonés M, Rabionet R, Cáceres M, Estivill X. Identification of copy number variants defining genomic differences among major human groups. PLoS One, 2009;30;4(9):e7230.
  3. González JR, Carrasco JL, Armengol L, Villatoro S, Jover L, Yasui Y, Estivill X. Probe-specific mixed-model approach to detect copy number differences using multiplex ligation-dependent probe amplification (MLPA). BMC Bioinformatics, 2008;9:261
  4. González JR, Carrasco JL, Dudbridge F, Armengol L, Estivill X, Moreno V. Maximizing association statistics over genetic models. Genet Epidemiol, 2008;32:246-54.
  5. Gonzalez JR, Armengol L, Solé X, Guino E, Mercader JM, Estivill X, Moreno V.  SNPassoc: an R package to perform whole genome association studies. Bioinformatics, 2007;23(5):644-5

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