Genetic Analysis Center

The Genetic Analysis Center (GAC) develops and applies statistical methods to genetic data with the aim of discovering how genetic variation contributes to human disease and well-being. We also provide scientific and administrative coordination to ensure the success of large-scale genomics research consortia and other programs.


We are the Data Coordinating Center for the NHGRI GREGoR (Genomics Research to Elucidate the Genetics of Rare diseases) Consortium and the Coordinating Center for the NIH PRIMED (Polygenic Risk Methods in Diverse Populations) Consortium.


About us

The GAC contributes to major genomic research initiatives, offering data analysis support, software and methods development, statistical consulting, study design, data coordination, and ongoing data quality assurance through the duration of a project. Research efforts are collaborative with University of Washington (UW) faculty and students who possess advanced expertise and a dedicated interest in biostatistics, statistical genetics, and public health genetics. Other collaborators come from other academic institutions, government, nonprofits, and the private sector.



Michael Bowers, Project Administrator
Matt Conomos, Research Scientist
Ben Heavner, Research Scientist
Sarah Lewandowski, Project Administrator
Susanne May, Faculty, PI
Sarah Catherine Nelson, Research Scientist, Project Manager
Sheryl Payne, Project Manager, Associate Director
Jenn Purnell, Project Administrator
Ken Rice, Faculty, PI
Bruce Weir, Faculty, PI


Sanne Aalbers, Student
Emily Bonkowski, Genetic Counselor
Stephanie Gogarten, Research Scientist
Deepti Jain, Research Scientist
Addison Keely, Student
Kathleen Kerr, Faculty
Alyna Khan, Research Scientist
Cathy Laurie, Senior Consultant
Cecelia Laurie, Senior Consultant
David Levine, Senior Consultant
Ali Shojaie, Faculty
Noah Simon, Faculty
Adrienne Stilp, Research Scientist
Catherine Tong, Program Analyst
Kate Wehr, Project Administrator
Ellen Wijsman, Faculty
Quenna Wong, Research Scientist


  • Data coordination
  • Data cleaning (Quality Assurance/Quality Control) and harmonization
  • Data analysis support and training
  • Statistical software and methods development
  • Consulting
  • Research study design and planning
  • Population and quantitative genetics methods and analysis
  • Forensic genetics methods and analysis

Areas of Expertise

  • Statistical genetics methods and analysis
  • Quantitative genetics methods and analysis
  • Population genetics methods and analysis
  • Forensic genetics methods and analysis
  • Ethical, Legal, and Social Implications (ELSI)
  • Cloud computing




Selected Publications


Stilp, A. M., Emery, L. S., Broome, J. G. et al. A System for Phenotype Harmonization in the National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine (TOPMed) Program. Am J Epidemiol 190, 1977–1992 (2021). PMID: 33861317. See associated harmonization documentation at

Hu, Y., Stilp, A. M., McHugh, C. P. et al. Whole-genome sequencing association analysis of quantitative red blood cell phenotypes: The NHLBI TOPMed program. Am J Hum Genet 108, 874–893 (2021). PMID: 33887194

Taliun, D., Harris, D. N., Kessler, M. D. et al. Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program. Nature 590, 290–299 (2021). PMID: 33568819

Statistical genetics and methods

Sofer, T., Zheng, X., Laurie, C. A. et al. Variant-specific inflation factors for assessing population stratification at the phenotypic variance level. Nat Commun 12, 3506 (2021). PMID: 34108454

Sofer, T., Zheng, X., Gogarten, S. M. et al. A fully adjusted two-stage procedure for rank-normalization in genetic association studies. Genet Epidemiol 43, 263–275 (2019). PMID: 30653739

Chen, H., Wang, C., Conomos, M. P. et al. Control for Population Structure and Relatedness for Binary Traits in Genetic Association Studies via Logistic Mixed Models. Am. J. Hum. Genet. 98, 653–666 (2016). PMID: 27018471

Conomos, M. P., Reiner, A. P., Weir, B. S. et al. Model-free Estimation of Recent Genetic Relatedness. Am. J. Hum. Genet. 98, 127–148 (2016). PMID: 26748516

Browning, B. L. & Browning, S. R. Genotype Imputation with Millions of Reference Samples. Am J Hum Genet 98, 116–126 (2016). PMID: 26748515

Buckleton, J., Curran, J., Goudet, J. et al. Population-specific FST values for forensic STR markers: A worldwide survey. Forensic Sci Int Genet 23, 91–100 (2016). PMID: 27082756

Graffelman, J. & Weir, B. S. Testing for Hardy-Weinberg equilibrium at biallelic genetic markers on the X chromosome. Heredity (Edinb) 116, 558–568 (2016). PMID: 27071844

Conomos, M. P., Miller, M. B. & Thornton, T. A. Robust inference of population structure for ancestry prediction and correction of stratification in the presence of relatedness. Genet. Epidemiol. 39, 276–293 (2015). PMID: 25810074

Browning, S. R. & Browning, B. L. Accurate Non-parametric Estimation of Recent Effective Population Size from Segments of Identity by Descent. Am J Hum Genet 97, 404–418 (2015). PMID: 26299365

Zheng, X. & Weir, B. S. Eigenanalysis of SNP data with an identity by descent interpretation. Theor Popul Biol 107, 65–76 (2016). PMID: 26482676

Zhu, Z., Bakshi, A., Vinkhuyzen, A. A. E. et al. Dominance genetic variation contributes little to the missing heritability for human complex traits. Am J Hum Genet 96, 377–385 (2015). PMID: 25683123

Nelson, S. C., Doheny, K. F., Pugh, E. W. et al. Imputation-based genomic coverage assessments of current human genotyping arrays. G3 (Bethesda) 3, 1795–1807 (2013). DOI: 10.1101/150219


Conomos, M. P., Laurie, C. A., Stilp, A. M. et al. Genetic Diversity and Association Studies in US Hispanic/Latino Populations: Applications in the Hispanic Community Health Study/Study of Latinos. Am. J. Hum. Genet. 98, 165–184 (2016). PMID: 26748518

Browning, S. R., Grinde, K., Plantinga, A. et al. Local Ancestry Inference in a Large US-Based Hispanic/Latino Study: Hispanic Community Health Study/Study of Latinos (HCHS/SOL). G3 (Bethesda) 6, 1525–1534 (2016). PMID: 27172203

Nelson, S. C., Stilp, A. M., Papanicolaou, G. J. et al. Improved imputation accuracy in Hispanic/Latino populations with larger and more diverse reference panels: applications in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). Hum. Mol. Genet. 25, 3245–3254 (2016). PMID: 27346520


Laurie, C. C., Laurie, C. A., Smoley, S. A. et al. Acquired chromosomal anomalies in chronic lymphocytic leukemia patients compared with more than 50,000 quasi-normal participants. Cancer Genet 207, 19–30 (2014). PMID: 24613276

Laurie, C. C., Laurie, C. A., Rice, K. et al. Detectable clonal mosaicism from birth to old age and its relationship to cancer. Nat Genet 44, 642–650 (2012). PMID: 22561516

Laurie, C. C., Doheny, K. F., Mirel, D. B. et al. Quality control and quality assurance in genotypic data for genome-wide association studies. Genet. Epidemiol. 34, 591–602 (2010). PMID: 20718045

See also Software for additional publications



We develop open source software for analyzing genetic data.

UW GAC GitHub Repository

Central collection of publicly available source code across various GAC projects.

Docker images

Docker images containing GAC software.

R Packages


The package gdsfmt provides a high-level R interface to CoreArray Genomic Data Structure (GDS) data files, which are portable across platforms and include hierarchical structure to store multiple scalable array-oriented data sets with metadata information.

Zheng, X., Levine, D., Shen, J. et al. A high-performance computing toolset for relatedness and principal component analysis of SNP data. Bioinformatics 28, 3326–3328 (2012). PMID: 23060615


An R package for single- and aggregate-variant genetic association testing using computationally efficient mixed models in samples with complex population and pedigree structure. Also provides tools for de-convoluting population and pedigree structure in genetic data.

Gogarten, S. M., Sofer, T., Chen, H. et al. Genetic association testing using the GENESIS R/Bioconductor package. Bioinformatics 35, 5346–5348 (2019). PMID: 31329242


Classes for storing very large GWAS data sets and annotation, and functions for GWAS data cleaning and analysis.

Gogarten, S. M., Bhangale, T., Conomos, M. P. et al. GWASTools: an R/Bioconductor package for quality control and analysis of genome-wide association studies. Bioinformatics 28, 3329–3331 (2012). PMID: 23052040


Big data management of whole-genome sequence variant calls with thousands of individuals: genotypic data (e.g., SNVs, indels and structural variation calls) and annotations in GDS files are stored in an array-oriented and compressed manner, with efficient data access using the R programming language.

Zheng, X., Gogarten, S. M., Lawrence, M. et al. SeqArray-a storage-efficient high-performance data format for WGS variant calls. Bioinformatics 33, 2251–2257 (2017). PMID: 28334390


An interface to the fast-access storage format for VCF data provided in SeqArray, with tools for common operations and analysis.

Gogarten SM, Zheng X, Stilp A (2021). SeqVarTools: Tools for variant data. R package version 1.30.0,


A parallel computing toolset for relatedness and principal component analysis of SNP data.

Zheng, X., Levine, D., Shen, J. et al. A high-performance computing toolset for relatedness and principal component analysis of SNP data. Bioinformatics 28, 3326–3328 (2012). PMID: 23060615

TOPMed WGS analysis pipeline

Analysis pipeline for TOPMed whole genome sequencing project.


An R package the TOPMed DCC developed and uses to parse genetic variant annotation files produced by the WGSA annotation tool.

Tools on BioData Catalyst powered by Seven Bridges

Ancestry and Relatedness workflows

Workflows for genetic ancestry and relatedness inference, implementing methods including LD-pruning, PC-AiR, PC-Relate, KING-robust, and KING-ibdseg.

Annotation Explorer

Interactive application to explore, query, and study characteristics of an inventory of annotations for all possible SNVs, indels in dbSNP and variants called in TOPMed studies. This application can be used pre-GWAS to generate annotation-informed variant filters and groups for rare variant association testing, and post-GWAS for fine-mapping and variant prioritization.

Data Management tools

Tools to manipulate and format data files, such as, tool for merging multiple VCF/BCF files and filtering monomorphic variants, and tool for converting variant calls from VCF into GDS format.

GENESIS Association Testing workflows

Workflows for genetic association testing using the GENESIS R package. Available workflows include: fitting a null model, single variant association testing, aggregate variant association testing (including burden, SKAT, fastSKAT, and SMMAT methods), sliding window association testing, and tools for making Manhattan, QQ, and LocusZoom plots.

Quality Control tools

Workflows for variant and sample QC using WGS data. Available workflows include: Pedigree check, Heterozygosity by sample and XY chromosome depth.



The GAC provides hands-on training to focused groups and the broad scientific community on topics related to quantitative genetics. Some of the workshops we have taught are:

  • Computational Pipeline for WGS Data Module at the UW Summer Institute for Statistical Genetics (SISG) (2021; 2020; 2019; 2018)