Aaron Quinlan, Ph.D.

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 Utah 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, PEDDY, and GQT.

Sasani TA, Pedersen BS, Gao Z, Baird L, Przeworski M, Jorde LB, Quinlan AR*. Large, three-generation CEPH families reveal post-zygotic mosaicism and variability in germline mutation accumulation. eLife, 2019. PMC6759356.

Ostrander BEP, Butterfield RJ, Pedersen BS, Farrell AJ, Layer RM, Ward A, Miller C, DiSera T, Filloux FM, Candee MS, Newcomb T, Bonkowsky JL, Marth GT, Quinlan AR*. Whole-genome analysis for effective clinical diagnosis and gene discovery in early infantile epileptic encephalopathy. Nature Genomic Medicine, (2018), doi: 10.1038/s41525-018-0061-8. PMC6089881.

Havrilla JM, Pedersen BS, Layer RM, Quinlan AR. A map of constrained coding regions in the human genome. Nature Genetics, 2019. PMC6589356.

Richard M. Cawthon, Huong D. Meeks, Thomas A. Sasani, Ken R. Smith, Richard A. Kerber, Elizabeth O’Brien, Lisa Baird, Melissa M. Dixon, Andreas P. Peiffer, Mark F. Leppert, Aaron R. Quinlan, Lynn B. Jorde. Germline mutation rates in young adults predict longevity and reproductive lifespan. Scientific Reports, 2020; 10 (1) DOI: 10.1038/s41598-020-66867-0

Paila U, Chapman BA, Kirchner R, and Quinlan AR*. GEMINI: Integrative Exploration of Genetic Variation and Genome Annotations PLoS Computational Biology, (2013), 9(7): e1003153.  doi:10.1371/journal.pcbi.1003153. PMC3715403

Layer R, Kindlon N., Karcewski K, ExAC Consortium, Quinlan AR*. Efficient genotype compression and analysis of large genetic variation datasets. Nature Methods, (2016), 13(1):63-5. PMC4697868.

Onengut-Gumuscu A, Chen WM, Quinlan AR, et al. Comparison of type 1 diabetes loci with 15 other immune diseases and evidence for co-localisation of diabetes causal variants with lymphoid gene enhancers. Nature Genetics. (2015), 47(4), pp. 381-6. PMC25751624.

Martin NT, Nakamura K, Paila U, Woo J, Brown C, Wright JA, Teraoka SN, Haghayegh S, McCurdy D, Schneider M, Hu H, Quinlan AR, Gatti RA, Concannon P. Homozygous mutation of MTPAP causes cellular radiosensitivity and persistent DNA double-strand breaks. Cell Death Dis., (2014). PMC3973239

a. Loman N and Quinlan AR*. PORETOOLS: a toolkit for working with nanopore sequencing data from Oxford Nanopore. Bioinformatics, (2014), doi:10.1093/bioinformatics/btu555

1000 Genomes Project Consortium et al. A map of human genome variation from population-scale sequencing. Nature, (2010) vol. 467 (7319) pp. 1061-73. PMC3042601

Quinlan A.R., and Ira M. Hall. BEDTools: A flexible framework for comparing genomic features.  Bioinformatics, (2010), 26, 6. PMC2.832824

Quinlan A.R., Stewart D., Stromberg M., Marth G.T. PyroBayes: Accurate quality scores for 454 Life Science pyrosequences. Nature Methods, (2008), 5, 179

Layer R, Chiang C, Quinlan AR*, Hall IM*. LUMPY: A probabilistic framework for structural variant discovery. Genome Biology, (2014), doi:10.1186/gb-2014-15-6-r84. PMC4197822

Malhotra A, Lindberg M, Faust G, Leibowitz M, Clark R, Layer R, Quinlan AR*, and Hall IM*. Breakpoint profiling of 64 cancer genomes reveals numerous complex rearrangements spawned by homology-independent mechanisms. Genome Research, (2013), doi:10.1101/gr.143677.112. PMC3638133.

Quinlan AR, Boland M, et al. Genome Sequencing of Mouse Induced Pluripotent Stem Cells Reveals Retroelement Stability and Infrequent DNA Rearrangement during Reprogramming. Cell Stem Cell (2011), 4, 366-73. PMC3975295.

Quinlan A.R., Clark R.A., et al. Genome-wide mapping and assembly of structural variant breakpoints in the mouse genome. Genome Research (2010), 20, 623. PMC2860164.