DUTIES:
The Associate Specialist will assist PI by applying known and novel methods to analyze data from genome-wide association studies, reviewing their design and effectiveness. The candidate will apply known, and assist in developing novel, computational and statistical methods to help analyze genomic data and associated information, including data related to genotyping, copy number variation, high-throughput sequencing, and transcriptomic and methylomic studies.

The successful candidate will assist in building software infrastructure, documentation, and tools to facilitate analysis. Oversee database quality control and data integrity. Analyze associations between genetic variants and disease risk using genomic and phenotype data. Identify functional consequences of these variants. Apply known and novel statistical analyses to address cofounders such as population stratification and cryptic relatedness. Utilize and review alternative haplotype analyses and genotype imputation on public-use data. Use and review alternative statistical methods for identifying epistatic and gene-environment interactions. Implement an infrastructure to integrate high throughput transcriptomic and methylomic data in GWAS. Generate gene ontology and pathway reports. Compile analyses and create reports on research findings for peer-reviewed scientific manuscripts and presentations, making presentations as needed to scientific groups. Draft and edit scientific manuscripts for review.

MINIMUM QUALIFICATIONS:
MPH or MS in biostatistics, epidemiology, or a relevant field and 3-4 years relevant experience in bioinformatics research, analysis of genetic polymorphisms, and genome-wide association studies in complex genetic diseases. Requires extensive knowledge of informatics tools and databases including HapMap, dbSNP, PLINK, IMPUTE and/or Haploview and at least 2 years experience programming in Perl, C/C+, Unix shell scripting, and a statistical computing platform, such as Matlab or R. Proficiency in written, spoken, and technical English. Prefer programming experience using Ensembl API and Bioconductor software. Advanced computing skills, and extensive knowledge of statistical analyses are highly desirable.

Anticipated start date: 04/01/09. Apply by 1/30/09 with copies of two publications and two sources from whom we may solicit letters of recommendation to: Bill Brockett; School of Public Health; 50 University Hall, MC 7356; Berkeley, CA 94720-7356. UC Berkeley is an Equal Opportunity/Affirmative Action employer. For the UC Berkeley Statement of Confidentiality, please view http://apo.chance.berkeley.edu/evalltr.html