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Dr. Mohamad Saad
Research Scientist
Dr. Mohamad Saad
Research Scientist
Educational Qualifications
PhD in Statistical Genetics/Biostatistics
MSc in Statistics/Biostatistics
Entity
Qatar Computing Research Institute
Divison
Qatar Center for Artificial Intelligence
Biography
Dr. Mohamad Saad is a Research Scientist in the Data Analytics group at Qatar Computing Research Institute. He joined QCRI in February 2017 and works on topics in Statistical Genetics, Biostatistics, and Bioinformatics. Dr. Saad has a background in applied mathematics and statistics. He obtained his bachelor's degree in Applied Mathematics (Majoring in Statistics) at the Lebanese University in 2006, before he moved to France where he obtained his master's degree in Statistics/Biostatistics from the University of Montpellier II, Monpellier in 2007, and his PhD in Statistical Genetics/Biostatistics/Bioinformatics from the University of Paul Sabatier III, Toulouse, in 2012. In Summer 2012, he moved to the United States for a Postdoctoral Senior Fellow position at the Department of Biostatistics at the University of Washington, Seattle, as a Postdoctoral Senior Fellow and stayed until Fall 2016. Dr. Saad has many peer-reviewed articles in top-tier journals including Nature Genetics, The Lancet, and Genome Research.
PhD in Statistical Genetics/Biostatistics
University of Paul Sabatier III & National Institute of Health and Medical Research, Toulouse, France.
2009 - 2012
MSc in Statistics/Biostatistics
Montpellier, France.
2006 - 2007
BSc in Applied Mathematics
Major in Statistics, Beirut, Lebanon.
2002 - 2006
Research Scientist
Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.
2017 - Present
Postdoctoral Senior Fellow
Department of Biostatistics, University of Washington, Seattle, USA.
2012 - 2016
Adjunct Assistant Professor
Faculty of Medical Sciences, Lebanese University, Beirut, Lebanon.
2014 - Present
- Large Scale Meta Analysis of Genome-wide Association Data in Parkinson’s Disease Reveals 28 Distinct Risk Loci. Nature Genetics, 46(9):989-93
- Wood NW, Imputation of sequence variants for identification of genetic risks for Parkinson’s disease: a meta-analysis of genome-wide association studies. Lancet, 377(976, 2011
- Comparison and assessment of family- and population-based genotype imputation methods in large pedigrees, Genome Research, 125-134, 10.1101/gr.236315.118.
- GIGI-Quick: a fast approach to impute missing genotypes in genome-wide association family data. Bioinformatics, 1;34(9):1591-1593, 10.1093/bioinformatics/btx782.
- Association score testing for rare variants and binary traits in family data with shared controls. Briefings in Bioinformatics, 18;20(1):245-253, 10.1093/bib/bbx107.
- Power of Family-Based Association Designs To Detect Rare Variants in Large Pedigrees Using Imputed Genotypes. Genet Epidemiol, 38(1):1-9.
- Genome-wide association study confirms BST1 and suggests a locus on 12q24 as the risk loci for Parkinson’s disease in the European population. Hum Mol Genet, 20(3):615-27.
- Use of Support Vector Machines for disease risk prediction in genome-wide association studies: concerns and opportunities. Human Mutation, 33 (12), 1708-1718.
2014: James V. Neel Young Investigator Award for the Best Presentation by a Young Investigator at the International Genetic Epidemiology Society, Vienna, Austria, August 2014.