University of Washington School of Public Health
Data Analyst - UW Genetic Analysis Center
Closing Date: open until filled
Posted: June 4, 2018
The Genetic Analysis Center (GAC, www.biostat.washington.edu/research/centers/gac) in the
Department of Biostatistics develops and applies statistical methods to genetic and health-related data with the aim of discovering how genetic variation contributes to human disease and well-being. The genetic data include variations assayed by whole-genome sequencing or micro-arrays, and the
health data cover a broad range of health-related traits, particularly complex diseases such as diabetes, asthma, atherosclerosis, and cancer; as well as responses to pharmaceuticals. The projects are funded by academic, non-profit and industrial institutions. A major current project is the Trans-Omics for Precision Medicine (TOPMed) Data Coordinating Center (www.nhlbiwgs.org).
Primary responsibilities include management, quality control, and analysis of genetic and health-related phenotypic data. Two current project examples: (1) Using health-related survey and medical exam data from a variety of clinical research studies, manage and organize the data; perform quality control analyses; identify common traits across diverse studies and perform
transformations to harmonize those data; prepare harmonized data files and associated documentation. (2) Using genetic data from micro-array or sequencing platforms, format data; perform quality control analyses; perform genotype-phenotype association analyses to identify genetic risk factors for disease-related phenotypes; prepare results files and associated documentation.
These responsibilities involve the following activities:
• Perform statistical data analysis of large data sets using parallel computing.
• Work with GAC faculty and staff to develop goals and statistical methods for data management and analysis.
• Construct, manipulate and interpret large and complex data sets.
• Using statistical algorithms, Identify problematic records in data sets and determine the best approach to handling them.
• Using statistically sound methodologies, perform quality control on genotypic and phenotypic data.
• Program data transformations and analyses using the R statistical environment.
• Summarize and communicate results of analyses to GAC staff and clinical collaborators.
Specific training will be provided for each of these activities, through working closely with more
experienced team members. The GAC provides a highly collaborative environment in which team members share expertise and mentor new research staff. Continuing education opportunities