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Link V, Schraiber JG, Fan C, Dinh B, Mancuso N, Chiang CWK, Edge MD. Tree-based QTL mapping with expected local genetic relatedness matrices. Am J Hum Genet 2023; 110:2077-2091. [PMID: 38065072 PMCID: PMC10716520 DOI: 10.1016/j.ajhg.2023.10.017] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Revised: 10/26/2023] [Accepted: 10/27/2023] [Indexed: 12/18/2023] Open
Abstract
Understanding the genetic basis of complex phenotypes is a central pursuit of genetics. Genome-wide association studies (GWASs) are a powerful way to find genetic loci associated with phenotypes. GWASs are widely and successfully used, but they face challenges related to the fact that variants are tested for association with a phenotype independently, whereas in reality variants at different sites are correlated because of their shared evolutionary history. One way to model this shared history is through the ancestral recombination graph (ARG), which encodes a series of local coalescent trees. Recent computational and methodological breakthroughs have made it feasible to estimate approximate ARGs from large-scale samples. Here, we explore the potential of an ARG-based approach to quantitative-trait locus (QTL) mapping, echoing existing variance-components approaches. We propose a framework that relies on the conditional expectation of a local genetic relatedness matrix (local eGRM) given the ARG. Simulations show that our method is especially beneficial for finding QTLs in the presence of allelic heterogeneity. By framing QTL mapping in terms of the estimated ARG, we can also facilitate the detection of QTLs in understudied populations. We use local eGRM to analyze two chromosomes containing known body size loci in a sample of Native Hawaiians. Our investigations can provide intuition about the benefits of using estimated ARGs in population- and statistical-genetic methods in general.
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Affiliation(s)
- Vivian Link
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
| | - Joshua G Schraiber
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
| | - Caoqi Fan
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA; Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Bryan Dinh
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA; Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Nicholas Mancuso
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA; Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Charleston W K Chiang
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA; Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Michael D Edge
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA.
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Burkett KM, McNeney B, Graham J. Sampletrees and Rsampletrees: sampling gene genealogies conditional on SNP genotype data. Bioinformatics 2016; 32:1580-2. [PMID: 26787665 DOI: 10.1093/bioinformatics/btv763] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2015] [Accepted: 12/23/2015] [Indexed: 11/14/2022] Open
Abstract
UNLABELLED : The program sampletrees is a Markov chain Monte Carlo sampler of gene genealogies conditional on either phased or unphased SNP genotype data. The companion program Rsampletrees is for pre- and post-processing of sampletrees files, including setting up the files for sampletrees and storing and plotting the output of a sampletrees run. AVAILABILITY AND IMPLEMENTATION sampletrees is implemented in C ++. The source code, documentation and test files are available at http://stat.sfu.ca/statgen/research/sampletrees.html The R package Rsampletrees is available on CRAN http://cran.r-project.org/web/packages/Rsampletrees/index.html CONTACT : kburkett@uottawa.ca SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Kelly M Burkett
- Department of Statistics, Simon Fraser University, Burnaby V5A 1S6, Canada and Department of Mathematics and Statistics, University of Ottawa, Ottawa K1N 6N5, Canada
| | - Brad McNeney
- Department of Statistics, Simon Fraser University, Burnaby V5A 1S6, Canada and
| | - Jinko Graham
- Department of Statistics, Simon Fraser University, Burnaby V5A 1S6, Canada and
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Wang X, Biernacka JM. Assessing the effects of multiple markers in genetic association studies. Front Genet 2015; 6:66. [PMID: 25759719 PMCID: PMC4338793 DOI: 10.3389/fgene.2015.00066] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2015] [Accepted: 02/09/2015] [Indexed: 11/13/2022] Open
Affiliation(s)
- Xuefeng Wang
- Department of Preventive Medicine, Stony Brook University Stony Brook, NY, USA
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