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Introductory Methods for eQTL Analyses

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eQTL Analysis

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2082))

Abstract

Expression quantitative trait locus (eQTL) analysis has proven to be a powerful method to describe how variation in phenotypes may be attributed to a given genotype. While the field of bioinformatics and genomics has experienced exponential growth with modern technological advances, an unintended consequence arises as a lack of a gold standard for many applications and methods, which may be compounded with ever-improving computational capabilities. Researchers working on eQTL analysis have at their disposal a multitude of bioinformatics software, each with different assumptions and algorithms, which may produce confusion as to their respective applicability. In this chapter, we will introduce eQTLs, survey commonly used software to conduct a mapping study, as well as provide data correction methods to avoid the pitfalls of such analyses.

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References

  1. Gilad Y, Rifkin SA, Pritchard JK (2008) Revealing the architecture of gene regulation: the promise of eQTL studies. Trends Genet 24(8):408–415

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Kang HM et al (2007) Efficient control of population structure in model organism association mapping. Genetics 178:1709–1723

    Article  Google Scholar 

  3. Kang HM, Ye C, Eskin E (2008) Accurate discovery of expression quantitative trait loci under confounding from spurious and genuine regulatory hotspots. Genetics 180(4):1909–1925

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Leek JT, Johnson WE, Parker HS, Fertig EJ, Jaffe AE, Storey JD (2016) sva: surrogate variable analysis. R Package version 322.0

    Google Scholar 

  5. Johnson WE, Rabinovic A, Li C (2007) Adjusting batch effects in microarray expression data using Empirical Bayes methods. Biostatistics 8(1):118–127

    Article  PubMed  Google Scholar 

  6. Alberts R, Vera G, Jansen RC (2008) affyGG: computational protocols for genetical genomics with affymetrix arrays. Bioinformatics 24(3):433–434. https://doi.org/10.1093/bioinformatics/btm614

    Article  CAS  PubMed  Google Scholar 

  7. Chen L, Page GP, Mehta T, Feng R, Cui X (2009) Single nucleotide polymorphisms affect both cis- and trans-eQTLs. Genomics 93:501–508

    Article  CAS  PubMed  Google Scholar 

  8. Irizarry H, Collin B-B, Antonellis S, Speed TP (2003) Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4(1):249–264. https://doi.org/10.1093/biostatistics/4.2.249

    Article  PubMed  Google Scholar 

  9. Miller CJ (2017) Simpleaffy: very simple high level analysis of affymetrix data. http://www.bioconductor.org, http://bioinformatics.picr.man.ac.uk/simpleaffy/

  10. Wright FA, Shabalin AA, Rusyn I (2012) Computational tools for discovery and interpretation of expression quantitative trait loci. Pharmacogenomics 13(3):343–352. https://doi.org/10.2217/pgs.11.185

    Article  CAS  PubMed  Google Scholar 

  11. Abecasis G, Cherny S, Cookson W, Cardon L (2002) Merlin - rapid analysis of dense genetic maps using sparse gene flow trees. Nat Genet 30:97–101

    Article  CAS  PubMed  Google Scholar 

  12. Sen S, Churchill G (2001) A statistical framework for quantitative trait mapping. Genetics 159(1):371–387

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Clayton D, Leung H-T (2007) An R package for analysis of whole-genome association studies. Hum Hered 64:45–51

    Article  PubMed  Google Scholar 

  14. Sun W (2010) eMap http://www.bios.unc.edu/~weisun/software/

  15. Broman KW, Wu H, Sen Ś, Churchill GA (2003) R/qtl: QTL mapping in experimental crosses. Bioinformatics 19:889–890

    Article  CAS  PubMed  Google Scholar 

  16. Broman KW (2014) Fourteen years of R/QTL: just barely sustainable. J Open Res Softw 2(1):e11

    Article  PubMed  PubMed Central  Google Scholar 

  17. Haley CS, Knott SA (1992) A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity 69:315–324

    Article  CAS  PubMed  Google Scholar 

  18. Arends D, Prins P, Jansen RC, Broman KW (2010) R/qtl: High-throughput multiple QTL mapping. Bioinformatics 26:2990–2992

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Broman KW, Sen S (2009) A guide to QTL mapping with R/qtl. http://www.rqtl.org/book/rqtlbook_appB.pdf

    Book  Google Scholar 

  20. Van Ooijen JW (2009) MapQTL 6, Software for the mapping of quantitative trait loci in experimental populations of diploid species. Kyazma B.V, Wageningen, Netherlands

    Google Scholar 

  21. Seaton G, Haley CS, Knott SA, Kearsey M, Visscher PM (2002) QTL Express: mapping quantitative trait loci in simple and complex pedigrees. Bioinformatics 18:339–340

    Article  CAS  PubMed  Google Scholar 

  22. Seaton G, Hernandez J, Grunchec JA, White I, Allen J, De Koning DJ, Wei W, Berry D, Haley C, Knott S (2006) GridQTL: A Grid Portal for QTL Mapping of Compute Intensive Datasets. Proceedings of the 8th World Congress on Genetics Applied to Livestock Production, August 13–18, 2006. Belo Horizonte, Brazil

    Google Scholar 

  23. Allen J, Scott D, Illingworth M, Dobrzelecki B, Virdee D, Thorn S, Knott S (2012) CloudQTL: Evolving a Bioinformatics Application to the Cloud. Digital Research 2012, September 10–12, 2012. Oxford, UK

    Google Scholar 

  24. Le Roy P, Elsen JM, Gilbert H, Moreno C, Legarra A, Filangi O, INRA (2013) QTLMap https://forge-dga.jouy.inra.fr/projects/qtlmap

  25. Shabalin AA (2012) Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioinformatics 28(10):1353–1358. https://doi.org/10.1093/bioinformatics/bts163

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Roy Statistical Society B Meth 57:289–300

    Google Scholar 

  27. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreria M, Bender D et al (2007) PLINK: A tool set for whole-genome association and population-based linkage analysis. Am J Hum Genet 81:559–575

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Chang et al (2015) Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience 4:7

    Article  PubMed  PubMed Central  Google Scholar 

  29. Freidman J, Hastie T, Hofling H, Tibshirani R (2007) Pairwise coordinate optimization. Ann Appl Stat 1:302–332

    Article  Google Scholar 

  30. Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA, Handsaker B, Lunter G, Marth G, Sherry ST, McVean G, Durbin R and 1000 Genomes Project Analysis Group (2011) The variant call format and VCFtools. Bioinformatics 27(15):2156–2158

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Li H, Handsaker B, Wysoker A, Fennel T, Ruan J, Homer N, 1000 Genome Project Data Processing Subgroup, et al. (2009) The sequence alignment/map format and samtools. Bioinformatics 25:2078–2079

    Article  PubMed  PubMed Central  Google Scholar 

  32. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, Garimella K, Altshuler D, Gabriel S, Daly M, DePristo MA (2010) The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res 20:1297–1303

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Ongen H, Buil A, Brown AA, Dermitzakis ET, Delaneau O (2016) Fast and efficient QTL mapper for thousands of molecular phenotypes. Bioinformatics 32(10):1479–1485. https://doi.org/10.1093/bioinformatics/btv722

    Article  CAS  PubMed  Google Scholar 

  34. eQTL. http://eqtl.uchicago.edu/Home.html

  35. The GTEx Consortium (2013) The genotype-tissue expression (GTEx) project. Nat Genet 45(6):580–585. https://doi.org/10.1038/ng.2653

    Article  CAS  PubMed Central  Google Scholar 

  36. The GTEx Consortium (2017) Genetic effects on gene expression across human tissues. Nature 550:204–213. https://doi.org/10.1038/nature24277

    Article  Google Scholar 

  37. Jackson Lab Mouse Genomics Database: MGI http://www.informatics.jax.org/mgihome/projects/overview.shtml

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Author information

Authors and Affiliations

  1. Department of Bioinformatics and Genomics, College of Computing and Informatics, University of North Carolina at Charlotte, Charlotte, NC, USA

    Conor Nodzak

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  1. Conor Nodzak
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Correspondence to Conor Nodzak .

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Editors and Affiliations

  1. Temple University, Philadelphia, PA, USA

    Xinghua Mindy Shi

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Nodzak, C. (2020). Introductory Methods for eQTL Analyses. In: Shi, X. (eds) eQTL Analysis. Methods in Molecular Biology, vol 2082. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0026-9_1

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  • DOI: https://doi.org/10.1007/978-1-0716-0026-9_1

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-0025-2

  • Online ISBN: 978-1-0716-0026-9

  • eBook Packages: Springer Protocols

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