We combine computational and genomic techniques to explore genome biology and the genetic basis of traits.


We are looking to hire a talented postdoctoral scientist who has a passion for genomics and the study of human disease. This scientist will lead the application and development of computational methods to multiple ongoing studies of rare human disorders that each leverage the power of family genetics. We are committed to building a diverse laboratory and we strongly encourage applications from female and minority candidates. If interested, please email Aaron Quinlan and apply here.


The research in our laboratory is focused on the application of computational methods to develop a deeper understanding of genetic variation in diverse contexts. Modern experimental methods allow us to examine entire genomes with exquisite detail. Perhaps not surprisingly, staggering complexity is revealed as we look more closely at how genetic variation (both inherited and somatic) contributes to phenotypes. Modern genomic technologies necessitate efficient approaches for exploring, manipulating and comparing large genomic datasets. We develop such methods so that we and others may apply them to experiments investigating the impact of genetic variation on human disease, evolution, and somatic differentiation. Genome research is difficult - we strive to develop computational means that make it easier.

  • Rare disease genetics

    We develop and apply new software for identifying causal genetic variants in studies of rare familial disease. The University of Utah has a long history of expertise in this area and we work closely with many clinical collaborators to solve rare disease. Our GEMINI software is central to these efforts, and our laboratory collaborates with other members of the USTAR Center for Genetic Discovery to study familial disease among the large pedigrees in the Utah Genome Project.

  • Structural variation

    Human chromosomes harbor hundreds of structural differences including deletions, insertions, duplications, inversions, and translocations. Collectively, these differences are known as "structural variation" (or, "SV"). Any two humans differ by thousands of structural variants which vary greatly in size and phenotypic consequence. However, we are just beginning to understand the contribution of SV to evolution, development, and complex disease. Our laboratory continues to develop new methods such as LUMPY for detecting and understanding structural variation using modern DNA sequencing techniques.

  • Cancer genomics and genome evolution

    Massively parallel DNA sequencing has yielded detailed maps of clonal variation in human cancer, through an inference of clonal substructure by analysis of variant allele frequencies in bulk tumor cell populations and direct sequencing of single cells. Dynamic changes in clonal structure over time and under the selective pressure of treatment have been extensively studied in hematologic malignancies, but are less well characterized in solid cancers. Our understanding of the dynamics of clonal change and its role in therapeutic response and the emergence of resistance is in its infancy. However, deeper insight is accessible via significant advances in sequencing and new algorithms. We are developing new methods to identify genomic changes that are responsible for clonal evolution, chemoresistance, and relapse.

  • Algorithm and Software Development

    Broadly speaking, the research in my laboratory marries genetics with genomics technologies, computer science, and machine learning techniques to develop new strategies for gaining insight into genome biology. We try to tackle challenging problems with practical importance to understanding genome variation in the context of human disease. We actively maintain a broad range of widely used tools for genome research including: BEDTOOLS, GEMINI, LUMPY, VCFANNO, PEDAGREE, and GQT



We strive to develop innovative, well-tested, and well-documented tools for genome research.


a swiss-army knife for genome intervals


a probabilistic framework for SV discovery


a flexible framework for exploring genome variation


Genotype Query Tools


A toolkit for working with nanopore sequencing data from Oxford Nanopore.


ultra-fast personal genome analysis and interpretation


A fast, flexible toolset for annotating VCF files.


Detect sample mixups in family based studies of disease.



Efficient genotype compression and analysis of large genetic-variation data sets.

Layer RM, Kindlon N, Karczewski K, Exome Aggregation Consortium, Quinlan AR†

Nature Methods. doi:10.1038/nmeth.3654

Targeted Deep Sequencing in Multiple-Affected Sibships of European Ancestry Identifies Rare Deleterious Variants in PTPN22 that Confer Risk for Type 1 Diabetes.

Ge Y, Onengut-Gumuscu S, Quinlan AR, Mackey AJ, Wright JA, Buckner JH, Habib T, Rich SS, Concannon P.

Diabetes. pii: db150322

A parallel algorithm for N-way interval set intersection.

Layer RM, Quinlan AR†

IEEE Proceedings.

Speedseq: Ultra-fast personal genome analysis and interpretation.

Chiang C, Layer RM, Faust GG, Lindberg MR, Rose DB, Garrison EP, Marth GT, Quinlan AR, Hall IM.

Nature Methods. doi:10.1038/nmeth.3505

Fine mapping of type 1 diabetes susceptibility loci and evidence for colocalization of causal variants with lymphoid gene enhancers.

Onengut-Gumuscu S, Chen WM, Burren O, Cooper NJ, Quinlan AR, et al.

Nature Genetics. doi:10.1038/ng.3245

Population-based structural variation discovery with Hydra-Multi.

Lindberg MR, Hall IM, Quinlan AR†, et al.

Bioinformatics. doi:10.1093/bioinformatics/btu771

Extending reference assembly models.

Church DM, Schneider VA, Steinberg KM, Schatz MC, Quinlan AR, Chin CS, Kitts PA, Aken B, Marth GT, Hoffman MM, Herrero J, Mendoza ML, Durbin R, Flicek P.

Genome Biology. doi:10.1186/s13059-015-0587-3.


Genetics of Systemic Lupus Erythematosus: Immune Responses and End Organ Resistance to Damage.

Dai C, Deng Y, Quinlan AR, Gaskin F, Tsao B, Fu SM.

Current Opinion in Immunology. doi:10.1016/j.coi.2014.10.004

A reference bacterial genome dataset generated on the MinIONTM portable single-molecule nanopore sequencer.

Quick J, Quinlan AR, Loman N.

GigaScience. doi: 10.1186/2047-217X-3-22

PORETOOLS: a toolkit for working with nanopore sequencing data from Oxford Nanopore

Loman N, Quinlan AR†, Loman N.

Bioinformatics. doi:10.1093/bioinformatics/btu555

SubcloneSeeker: a computational framework for reconstructing tumor clone structure for cancer variant interpretation and prioritization.

Qiao Y, Quinlan AR, Jazaeri A, Verhaak R, Wheeler D, Marth G.

Genome Biology. doi:10.1186/s13059-014-0443-x

BEDTools: the Swiss-army tool for genome interval arithmetic.

Quinlan AR†

Current Protocols in Bioinformatics. doi: 10.1002/0471250953.bi1112s47

LUMPY: A probabilistic framework for sensitive detec- tion of chromosomal rearrangements.

Layer RM, Quinlan AR†, Hall IM.

Genome Biology. doi:10.1186/gb-2014-15-6-r84

Homozygous mutation of MTPAP causes cellular radiosensitivity and persistent DNA double strand breaks.

Martin N, Nakamura K, Paila U, Woo J, Brown C, Wright J, Teraoka S, Haghayegh S, Mc- Curdy D, Schneider M, Hu H, Quinlan AR, Gatti R, and Concannon P.

Cell Death Dis. doi: 10.1038/cddis.2014.99

A Novel IFITM5 Mutation in Severe Osteogenesis Imperfecta Decreases PEDF Secretion by Osteoblasts.

Farber CR, Reich A, Barnes AM, Becerra P, Rauch F, Cabral WA, Bae A, Quinlan AR, Glorieux FH, Clemens TL, and Marini JC.

J Bone Miner Res. doi: 10.1002/jbmr.2173

Pathogenic variants for Mendelian and complex traits in exomes of 6,517 European and African Ameri- cans: implications for the return of incidental results.

Tabor HK, Auer PL, Jamal SM, Chong JX, Yu JH, Gordon AS, Graubert TA, O’Donnell CJ, Rich SS, Nickerson DA; NHLBI Exome Sequencing Project, Bamshad MJ.

Am J Hum Genet. doi: 10.1016/j.ajhg.2014.07.006

Whole-exome sequencing identifies rare and low-frequency coding variants associated with LDL cholesterol.

Lange LA, Hu Y, Zhang H, NHLBI Grand Opportunity Exome Sequencing Project, et al.

Am J Hum Genet. doi: 10.1016/j.ajhg.2014.01.010

Quantifying rare, deleterious variation in 12 human cytochrome P450 drug-metabolism genes in a large-scale exome dataset.

Gordon AS, Tabor HK, Johnson AD, Snively BM, NHLBI GO Exome Sequencing Project, et al.

Hum Mol Genet, doi: 10.1093/hmg/ddt588


GEMINI: Integrative Exploration of Genetic Variation and Genome Annotations.

Paila U, Chapman BA, Kirchner R, Quinlan AR†.

PLoS Comput Biol. doi:10.1371/journal.pcbi.1003153

Joint linkage and association analysis with exome sequence data implicates SLC25A40 in hypertriglyceridemia.

Rosenthal EA, Ranchalis J, Crosslin DR, Burt A, Brunzell JD, Motulsky AG, Nickerson DA; NHLBI GO Exome Sequencing Project, Wijsman EM, Jarvik GP.

Am J Hum Genet., doi: 10.1016/j.ajhg.2013.10.019

Recurrent gain-of-function mutation in PRKG1 causes thoracic aortic aneurysms and acute aortic dissections.

Guo DC, Regalado E, NHLBI Grand Opportunity Exome Sequencing Project, et al.

Am J Hum Genet., doi: 10.1016/j.ajhg.2013.06.019

Fine-scale patterns of population stratification confound rare variant association tests.

O’Connor TD, Kiezun A, Bamshad M, Rich SS, Smith JD, Turner E; NHLBIGO Exome Sequencing Project; ESP Population Genetics, Statistical Analysis Working Group, Leal SM, Akey JM.

PLoS One. doi:10.1371/journal.pone.0065834

Common and rare von Willebrand factor (VWF) coding variants, VWF levels, and factor VIII levels in African Americans: the NHLBI Exome Sequencing Project.

Johnsen JM, Auer PL, Morrison AC, Jiao S, Wei P, Haessler J, Fox K, McGee SR, Smith JD, Carlson CS, Smith N, Boerwinkle E, Kooperberg C, Nickerson DA, Rich SS, Green D, Peters U, Cushman M, Reiner AP; NHLBI Exome Sequencing Project.

Blood. doi:10.1182/blood-2013-02-485094

Exome sequencing and genome-wide linkage analysis in 17 families illustrate the complex contribution of TTN truncating variants to dilated cardiomy- opathy.

Norton N, Li D, Rampersaud E, Morales A, Martin ER, Zuchner S, Guo S, Gonzalez M, Hedges DJ, Robertson PD, Krumm N, Nickerson DA, Hershberger RE; National Heart, Lung, and Blood Institute GO Exome Sequencing Project and the Exome Sequencing Project Family Studies Project Team.

Circ Cardiovasc Genet. doi:10.1161/CIRCGENETICS.111.000062

Breakpoint profiling of 64 cancer genomes reveals numerous complex rearrangements spawned by homology-independent mechanisms.

Malhotra A, Lindberg M, Leibowitz M, Clark R, Faust G, Layer R, Quinlan AR†, and Hall IM†.

Genome Research, doi:10.1101/gr.143677.112

Analysis of 6,515 exomes reveals the recent origin of most human protein-coding variants

Fu W, O’Connor TD, Jun G, Kang HM, Abecasis G, Leal SM, Gabriel S, Rieder MJ, Altshuler D, Shendure J, Nickerson DA, Bamshad MJ; NHLBI Exome Sequencing Project, Akey JM.

Nature. doi:10.1038/nature11690

Binary Interval Search (BITS): A Scalable Algorithm for Counting Interval Intersections.

Layer R, Robins G, Skadron K, Quinlan AR†

Bioinformatics. doi: 10.1093/bioinformatics/bts652

TGFB2 mutations cause familial thoracic aortic aneurysms and dissections associated with mild systemic features of Marfan syndrome.

Boileau C, Guo DC, Hanna N, Regalado ES, D, NHLBI Go Exome Sequencing Project, et al.

Nature Genetics. doi:10.1038/ng.2348

Exome sequencing of extreme phenotypes identifies DCTN4 as a modifier of chronic Pseudomonas aeruginosa infection in cystic fibrosis.

Emond MJ, Louie T, Emerson J, Zhao W, NHLBI Exome Sequencing Project; Lung GO, Gibson RL, Bamshad MJ.

Nature Genetics. doi:10.1038/ng.2344


Copy number variation detection and genotyping from exome sequence data.

Krumm N, Sudmant PH, Ko A, O‘Roak BJ, NHLBI Exome Sequencing Project, Quinlan AR, Nickerson DA, Eichler EE.

Genome Research. doi: 10.1101/gr.138115.112

Characterizing complex structural variation in germline and somatic genomes.

Quinlan AR and Hall IM.

Trends in Genetics. doi: http://dx.doi.org/10.1016/j.tig.2011.10.002

Detection and interpretation of genomic structural variation in mammals.

Quinlan AR and Hall IM.

Methods in Molecular Biology

Genome sequencing of mouse induced pluripotent stem cells reveals retroelement stability and infrequent DNA rearrangement during reprogramming.

Quinlan AR, Boland MJ, Leibowitz ML, Shumilina S, Pehrson SM, Baldwin KK, Hall IM.

Cell Stem Cell. doi: 10.1016/j.stem.2011.07.018

Evidence for two independent associations with type 1 diabetes at the 12q13 locus.

Keene KL, Quinlan AR, Hou X, Hall IM, Mychaleckyj, Onengut-Gumuscu S, Concannon P.

Genes and Immunity. doi: 10.1038/gene.2011.56

Pybedtools: a flexible Python library for manipulating genomic datasets and annotations.

Dale R, Pedersen B, Quinlan AR†.

Bioinformatics. doi: 10.1093/bioinformatics/btr539

BamTools: a C++ API and toolkit for analyzing and managing BAM files.

Barnett D, Garrison E, Quinlan AR, Stromberg M, Marth G.

Bioinformatics. doi: 10.1093/bioinformatics/btr174

A map of human genome variation from population-scale sequencing.

1000 Genomes Project Consortium.

Nature. doi: 10.1038/nature09534

BEDTools: A flexible framework for comparing genomic features.

Quinlan AR and Hall IM.

Bioinformatics. doi: 10.1093/bioinformatics/btq033

Genome-wide mapping and assembly of structural variant breakpoints in the mouse genome.

Quinlan AR, Clark RA, Sokolova, S, Leibowitx ML, Zhang Y, Hurles ME, Mell JC, Hall IM.

Genome Research. doi: 10.1101/gr.102970.109

Population Genomic Inferences from Sparse High-Throughput Sequencing of Two Populations of Drosophila melanogaster.

Sackton, TB, Kulathinal RJ, Bergman CM, Quinlan AR, Dopman E, Marth GT, Hartl DL, Clark AG.

Genome Biol Evol. doi: 10.1093/gbe/evp048

Rapid whole-genome mutational profiling using next-generation sequencing technologies.

Smith D, Quinlan AR, Peckham HR, et al.

Genome Research

Whole Genome Sequencing and SNP Discovery for C. elegans using massively parallel sequencing-by-synthesis.

Hillier LW, Marth GT, Quinlan AR, et al.

Nature Methods. doi: 10.1101/gr.077776.108

PyroBayes: Accurate quality scores for 454 Life Science pyrosequences.

Quinlan AR, Stewart D, Stromberg M, Marth GT

Nature Methods. doi:10.1038/nmeth.1172

Primer-site SNPs mask mutations.

Quinlan AR and Marth GT.

Nature Methods. doi:10.1038/nmeth0307-192

The Lab

Aaron Quinlan

Principal Investigator

Brent Pedersen

Senior Programmer

Ryan Layer

Postdoctoral Research Associate

Jim Havrilla

Graduate Student

Tom Sasani

Graduate Student


Uma Paila

Postdoctoral Scientist

Neil Kindlon


John Kubinski

Undergraduate Researcher

Phanwadee Sinthong

Undergraduate Researcher


Contact Us

Aaron Quinlan is an Associate Professor in the Department of Human Genetics and the Department of Biomedical Informatics at the University of Utah. Our lab is located on the 7th floor of The Eccles Institute for Human Genetics at The University of Utah. We are a part of the USTAR Center for Genetic Discovery.
Eccles Institute for Human Genetics
15 North 2030 East, Room 7160B
aquinlan at genetics dot utah dot edu