Andrew Nato

Andrew Q. Nato

Senior Fellow
Statistical Genetics Lab
Division of Medical Genetics
University of Washington

Tel: (206) 543-1471
Fax: (206) 616-1973
aqnato at uw dot edu


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Mailing Addresses:
Regular Mail:
Division of Medical Genetics
University of Washington
BOX 357720
Seattle, WA 98195-7720

Express Mail:
University of Washington Tower, T15
4333 Brooklyn Ave NE
BOX 359460
Seattle, WA 98195-9460


My current research interests span across human genetics, bioinformatics, and statistical genetics. I have been involved in developing computational tools and statistical approaches to aid in the identification of genes for human and mouse complex traits. I am currently a Senior Fellow in Dr. Ellen M. Wijsman's Statistical Genetics Lab at the University of Washington.

For my Ph.D. thesis under Dr. Tara C. Matise at Rutgers University, I defined and characterized putative schizophrenia candidate gene regions (SCRs)1 by comparing and combining results across 47 independent genomewide linkage studies. Under the guidance of Dr. Steven Buyske, I developed a novel integrative statistical method to define the SCRs. This method involves simulations and includes a single significant hit approach, disjoint approach, and smoothing method. Using the genes within these SCRs as input, I developed schizPrioritizer that identified high-ranking schizophrenia (SZ) candidate genes, and subsequently generated an interactome for SZ using Ingenuity® Pathway Analysis (IPA). As a graduate student, I have gained considerable expertise in bioinformatics and statistical genetics from other projects that I worked on in the Matise lab and with her collaborators. I updated the Rutgers combined linkage-physical map of the human genome by using CRI-MAP coupled with Perl and shell scripting2. I used the Rutgers Map to determine the boundaries of the SCRs. In collaboration with Dr. James Millonig, we have been developing a bioinformatics approach to determine candidate genes for mouse developmental QTL along with the development of a web tool (devQTL) that may be useful for other geneticists3. We are also studying genomewide association of alcoholism in outbred mice in collaboration with Drs. Lei Yu and Derek Gordon. In addition, I have also supervised an undergraduate student for her senior year project on SZ. During a two-month rotation under Dr. Jody Hey, I performed PCR and STR genotyping and analysis of chromosome Xp11 region in 500 individuals as part of worldwide survey of X-linked DNA variation4. In my former position as postdoctoral associate with Dr. Matise working for the Population Architecture using Genomics and Epidemiology (PAGE) study5, I performed quality control through a workflow that includes 40 data checks including consistency of genotyping strand orientation across multiple studies, prior to submission of study data to dbGaP. I also performed genotype calling using Sanger Institute's optiCall software. In addition, I was also involved in projects that aim to develop methods for analysis of next generation sequencing data.

Prior to coming to the United States, I was exposed to molecular biology and health physics where I performed BRCA1 mutation detection and environmental radioactivity measurements, etc. I also trained and advised undergraduate and graduate students undergoing on-the-job training or doing their theses.

My educational background and research experience in computational genetics, bioinformatics, statistical genetics, and molecular genetics, provides me with an innovative perspective for solving a variety of scientific problems. One potential project that I could work on would be to apply my novel method to determine candidate gene regions of a complex disease, prioritize genes within these candidate gene regions to determine high-ranking candidate genes, generate an interactome for that complex disease, and analyze that interactome to elucidate novel candidate genes. I am also quite interested in increasing my knowledge of (a) methods in statistical genetics and (b) computational and systems biology that will both be valuable in developing approaches for the identification of complex disease causal genes. My substantial experience working with and manipulating large genetics datasets, in particular flat-files of SNP, EST, gene information, and expression data from public databases will be useful in achieving these goals. I am proficient in using Linux and programming in Perl including CGI scripting and HTML. I am slightly familiar with R and I have implemented simple databases in both Windows (MS Access) and Linux (MySQL). Lastly, I am used to working both independently as well as being an integral part of a team.

4Mol Biol Evol. 24(3):687-698.