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.
Leadership
Members
Workshops
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:
Current
- Genomics Research to Elucidate the Genetics of Rare diseases (GREGoR) Consortium Data Coordinating Center
- Polygenic Risk Methods in Diverse Populations (PRIMED) Consortium Coordinating Center
- Population Genetic Issues for forensic DNA Profiles (NIJ)
- Development of scalable and user friendly engine to support genotype-phenotype association testing on BioData Catalyst powered by Seven Bridges
- Theoretical Population Genetics
- CODIS Support (FBI Project)
- Statistical Evaluation of Forensic Sequencing Profiles (NIJ)
- Predoctoral Training in Statistical Genetics (NIH Training Grant)
Past
- Trans-Omics for Precision Medicine (TOPMed) Data Coordinating Center
- Hispanic Community Health Study/Study Of Latinos (HCHS/SOL) Genetic Analysis Center
- Center for Inherited Disease Research (CIDR)
- Genomics and Randomized Trial Networks (GARNET) Coordinating Center
- Gene Environment Association Studies (GENEVA) Coordinating Center
Activities
- 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
Overview
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
gdsfmt
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
GENESIS
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
GWASTools
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
SeqArray
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
SeqVarTools
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, https://github.com/smgogarten/SeqVarTools.
SNPRelate
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.
WGSAParsr
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.
Selected Publications
TOPMed
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 https://github.com/UW-GAC/topmed-dcc-harmonized-phenotypes.
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
HCHS/SOL
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
GENEVA
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