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 are the coordinating center for the National Heart, Lung and Blood Institute’s (NHLBI) TOPMed Whole Genome Sequencing Project which is part of the broader Precision Medicine Initiative. Learn more


About us

The GAC contributes to major genomic research initiatives, offering 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.  If you have genetic data or are pursuing a research project involving genetic data, contact us.


What we do

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

Selected Publications

HCHS/SOL papers

Schick UM, Jain D, Hodonsky CJ, et al. Genome-wide Association Study of Platelet Count Identifies Ancestry-Specific Loci in Hispanic/Latino Americans. Am J Hum Genet. 2016 Feb
4;98(2):229-42. doi: 10.1016/j.ajhg.2015.12.003. Epub 2016 Jan 21. PubMed PMID: 26805783; PubMed Central PMCID: PMC4746331.

Conomos MP, Laurie CA, Stilp AM, 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. 2016 Jan 7;98(1):165-84. doi: 10.1016/j.ajhg.2015.12.001. PubMed PMID: 26748518; PubMed Central PMCID: PMC4716704.

GENEVA papers

Laurie CC, Doheny KF, Mirel DB, et al; GENEVA Investigators. Quality control and quality assurance in genotypic data for genome-wide association studies. Genet Epidemiol. 2010 Sep;34(6):591-602. doi: 10.1002/gepi.20516. PubMed PMID: 20718045; PubMed Central PMCID: PMC3061487.

Laurie CC, Laurie CA, Rice K, et al. Detectable clonal mosaicism from birth to old age and its relationship to cancer. Nat Genet. 2012 May 6;44(6):642-50. doi: 10.1038/ng.2271. PubMed PMID: 22561516; PubMed Central PMCID: PMC3366033.

Laurie CC, Laurie CA, Smoley SA, et al. Acquired chromosomal anomalies in chronic lymphocytic leukemia patients compared with more than 50,000 quasi-normal participants. Cancer Genet. 2014 Jan-Feb;207(1-2):19-30. doi: 10.1016/j.cancergen.2014.01.004. PubMed PMID: 24613276; PubMed Central PMCID: PMC4074414.

Imputation papers

Nelson SC, Doheny KF, Laurie CC, Mirel DB. Is 'forward' the same as 'plus'?…and other adventures in SNP allele nomenclature. Trends Genet. 2012 Aug;28(8):361-3. doi: 10.1016/j.tig.2012.05.002. PubMed PMID: 22658725.

Nelson SC, Doheny KF, Pugh EW, et al.. Imputation-based genomic coverage assessments of current human genotyping arrays. G3 (Bethesda). 2013 Oct 3;3(10):1795-807. doi: 10.1534/g3.113.007161. PubMed PMID: 23979933; PubMed Central PMCID: PMC3789804.

Nelson SC, Stilp AM, Papanicolaou GJ, et al. Improved Imputation Accuracy in Hispanic/Latino Populations with Larger and More Diverse Reference Panels: Applications

Recent statistical genetic methodology papers

Browning BL, Browning SR. 2016. Genotype imputation with millions of reference samples. American Journal of Human Genetics, 98: 116-126. PubMed Central PMCID: PMC4716681

Browning SR, Browning BL. 2015. Accurate non-parametric estimation of recent effective population size from segments of identity by descent. American Journal of Human Genetics, 97:404-418. PubMed Central PMCID: PMC4564943.

Buckleton JS, Curran JM, Goudet J et al, 2016. Population-specific Fst values: A worldwide survey. Forensic Science International: Genetics 23:91-100.

Conomos MP, Reiner AP, Weir BS et al. 2016. Model-free estimation of recent genetic relatedness. American Journal of Human Genetics 98:127--148. PubMed Central PMCID: PMC4716688

Graffelman J, Weir BS. 2016. Testing for Hardy-Weinberg equilibrium at bi-allelic genetic markers on the X chromosome. 2016. Heredity 116:558-568

Zheng X, Weir BS. 2015. Eigenanalysis of SNP data with an interpretation of identity by descent. Theoretical Population Biology 107:65-76. PubMed Central PMCID: PMC4716003

Zhu ZH, Bakshi A, Vinkhuyzen AE et al. 2015. Dominance genetic variation contributes little to the missing heritability for human complex traits. American  Journal of Human Genetics 96:377-385. PubMed Central PMCID: PMC4375616


Tools we offer

We develop software for analyzing genomic and genetic data which we make publicly available as open source.


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


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


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


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.


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.


The GENESIS package provides methodology for estimating, inferring, and accounting for population and pedigree structure in genetic analyses.


R package for creating genotyping plate maps

TOPMed WGS analysis pipeline

Analysis pipeline for TOPMed whole genome sequencing project

HierFstat [PDF]

An R package to compute and test hierarchical F-statistics.