Sharon Browning

I am a research associate professor in the Department of Biostatistics at the University of Washington in Seattle.

E-mail: sguy@uw.edu
Phone: +1 206 616 5037.
Location: Health Sciences Building F-671.
For mailing/fax information see the Department of Biostatistics contact page.

Research interests

My research is focussed on population genetics methods for large scale genetic data sets. I am particularly interested in methods that make use of the correlation structure in closely spaced genetic markers, and that are fast enough to be applied to whole genome scans (1M+ markers) and sequence data.

BEAGLE software

The BEAGLE program implements much of my recent research work (in collaboration with Brian Browning, the author of this software). BEAGLE is useful for haplotypic association testing, haplotype phasing, imputation, LD-based genotype calling, and identity by descent segment detection, all on a genome-wide scale.

Browning research group

The Browning research group is run jointly by Brian and me, and includes the following talented students: Xiaowen Tian, Kelsey Grinde and Tracy Dong. Lisa Brown graduated this past summer. Ying (Joe) Zhou will be joining us as a postdoctoral fellow in March. The group photo was taken in Fall 2014.

CV and Publications

Link to CV.

To see a list of my peer-reviewed publications with citation statistics, go to my ResearcherID page or my Google Scholar page.

Selected publications

Browning, S. R. et al. 2016. Local Ancestry Inference in a large US-Based Hispanic/Latino Study: Hispanic Community Health Study / Study of Latinos (HCHS/SOL). G3, 6:1525-1534 (link to article).

Browning, B. L. and S. R. Browning, 2016. Genotype imputation with millions of reference samples. American Journal of Human Genetics, 98: 116-126 (link to article).

Browning, S. R. and B. L. Browning, 2015. Accurate Non-parametric Estimation of Recent Effective Population Size from Segments of Identity by Descent. American Journal of Human Genetics, 97:404-418 (link to article). IBDNe is a method for estimating recent (past 200 generations) effective population size from population samples of SNP array or sequence data.

Browning, B. L. and S. R. Browning, 2013. Detecting Identity by Descent and Estimating Genotype Error Rates in Sequence Data. American Journal of Human Genetics, 93: 840-851 (link to article). IBDseq is a method for finding IBD segments in sequence data. We also demonstrate how IBD segments can be used to estimate genotype error rates for low frequency variants in sequence data.

Browning, B. L. and S. R. Browning, 2013. Improving the Accuracy and Efficiency of Identity by Descent Detection in Population Data. Genetics, 194: 459-471 (link to open access article). Beagle's Refined IBD method finds candidate IBD segments using a hashing method and evaluates their significance via a likelihood ratio. The method is accurate, powerful, and relatively fast for large data sets.

Browning, S. R. and B. L. Browning, 2012. Identity by Descent Between Distant Relatives: Detection and Applications. Annual Review of Genetics, 46: 617-633 (link with free access to article). A review article.

Browning, S. R. and E. A. Thompson, 2012. Detecting Rare Variant Associations by Identity-by-Descent Mapping in Case-Control Studies. Genetics, 190: 1521-1531 (link to open access article). An investigation into the utility of IBD mapping. Link to associated analysis scripts.

Browning, S. R. and B. L. Browning, 2010. High-resolution detection of identity by descent in unrelated individuals. American Journal of Human Genetics, 86:526-539 (link to abstract/article). We can detect small regions of identity by descent in supposedly unrelated individuals.

Browning, B. L. and S. R. Browning, 2009. A Unified Approach to Genotype Imputation and Haplotype-Phase Inference for Large Data Sets of Trios and Unrelated Individuals. American Journal of Human Genetics, 84: 210-223 (link to abstract/article).We extended our earlier haplotype phasing work to imputation of ungenotyped markers and to phasing of parent-offspring trios. Our approach is applicable to whole genome association data, and has high computational efficiency as well as excellent accuracy.

Madsen, B. E. and S. R. Browning, 2009. A Groupwise Association Test for Rare Mutations Using a Weighted Sum Statistic. PLOS Genetics, 5: e1000384 (link to abstract/article). We provide a test for whether mutations are more common in cases than in controls (in a gene or set of genes), which provides a useful complement to single-marker association testing, particularly in those diseases for which de novo mutations play an important role.

Browning, S. R. and B. L. Browning, 2007. Rapid and accurate haplotype phasing and missing data inference for whole genome association studies using localized haplotype clustering. American Journal of Human Genetics, 81:1084-1097 (link to abstract/article). A method for haplotype phasing that is fast and accurate on genome wide SNP-chip data.

Browning, B. L. and S. R. Browning, 2007. Efficient multilocus association testing for whole genome association studies using localized haplotype clustering. Genetic Epidemiology, 31:365-375 (link to abstract/article). Provides efficient software implementation of the method proposed in Browning 2006, and extensive simulation results assessing the value of the method. This paper received the 2008 Best Paper Award from the International Genetic Epidemiology Society for best paper published in Genetic Epidemiology in 2007.

Browning, S. R., 2006. Multilocus association mapping using variable-length Markov chains. American Journal of Human Genetics, 78:903-913 (link to abstract/article). This is the linkage disequilibrium / haplotype frequency model behind BEAGLE.