Skip to content
naulon

No retraction, correction or concern found when last checked on Nov 2, 2026.

Quantitative Trait Locus Mapping Methods for Diversity Outbred Mice

Daniel M Gatti, Karen L Svenson, Andrey Shabalin and 10 more

G3 Genes|Genomes|Genetics · 2014

doi:10.1534/g3.114.013748

Abstract

Genetic mapping studies in the mouse and other model organisms are used to search for genes underlying complex phenotypes. Traditional genetic mapping studies that employ single-generation crosses have poor mapping resolution and limit discovery to loci that are polymorphic between the two parental strains. Multiparent outbreeding populations address these shortcomings by increasing the density of recombination events and introducing allelic variants from multiple founder strains. However, multiparent crosses present new analytical challenges and require specialized software to take full advantage of these benefits. Each animal in an outbreeding population is genetically unique and must be genotyped using a high-density marker set; regression models for mapping must accommodate multiple founder alleles, and complex breeding designs give rise to polygenic covariance among related animals that must be accounted for in mapping analysis. The Diversity Outbred (DO) mice combine the genetic diversity of eight founder strains in a multigenerational breeding design that has been maintained for >16 generations. The large population size and randomized mating ensure the long-term genetic stability of this population. We present a complete analytical pipeline for genetic mapping in DO mice, including algorithms for probabilistic reconstruction of founder haplotypes from genotyping array intensity data, and mapping methods that accommodate multiple founder haplotypes and account for relatedness among animals. Power analysis suggests that studies with as few as 200 DO mice can detect loci with large effects, but loci that account for <5% of trait variance may require a sample size of up to 1000 animals. The methods described here are implemented in the freely available R package DOQTL.

Shown for reading. Its publisher allows quoting it, not republishing it.

References 48

  1. 1.

    Baud A. Hermsen R. Guryev V. Stridh P. Graham D., 2013 Combined sequence-based and genetic mapping analysis of complex traits in outbred rats. Nat. Genet. 45: 767–775

    Not in our library10.1038/ng.2644

  2. 2.

    Baum L. E., Petrie, T., Soules, G., and Weiss, N., 1970 A maximization technique occuring in the statistical analysis of probabilistic functions of Markov chains. Ann. Math. Stat. 41: 164–171

    Not in our library

  3. 3.

    Billings T. Sargent E. E. Szatkiewicz J. P. Leahy N. Kwak I. Y., 2010 Patterns of recombination activity on mouse chromosome 11 revealed by high resolution mapping. PLoS ONE 5: e15340

    Not in our library10.1371/journal.pone.0015340

  4. 4.

    Broman, K. W., 2012 Haplotype probabilities in advanced intercross populations. G3 Genes Genomes Genet. 2: 199–202.

    Not in our library10.1534/g3.111.001818

  5. 5.

    Broman K. W. Sen S. Owens S. E. Manichaikul A. Southard-Smith E. M., 2006 The X chromosome in quantitative trait locus mapping. Genetics 174: 2151–2158

    Not in our library10.1534/genetics.106.061176

  6. 6.
  7. 7.

    Cheng R. Palmer A. A., 2013 A simulation study of permutation, bootstrap, and gene dropping for assessing statistical significance in the case of unequal relatedness. Genetics 193: 1015–1018

    Not in our library10.1534/genetics.112.146332

  8. 8.

    Cheng R. Lim J. E. Samocha K. E. Sokoloff G. Abney M., 2010 Genome-wide association studies and the problem of relatedness among advanced intercross lines and other highly recombinant populations. Genetics 185: 1033–1044

    Not in our library10.1534/genetics.110.116863

  9. 9.

    Cheng R. Abney M. Palmer A. A. Skol A. D., 2011 QTLRel: an R package for genome-wide association studies in which relatedness is a concern. BMC Genet. 12: 66

    Not in our library10.1186/1471-2156-12-66

  10. 10.

    Cheng R. Parker C. C. Abney M. Palmer A. A., 2013 Practical considerations regarding the use of genotype and pedigree data to model relatedness in the context of genome-wide association studies. G3 3: 1861–1867.

    Not in our library10.1534/g3.113.007948

  11. 11.

    Churchill G. A., 1989 Stochastic models for heterogeneous DNA sequences. Bull. Math. Biol. 51: 79–94

    Not in our library10.1007/bf02458837

  12. 12.

    Churchill G. A. Doerge R. W., 1994 Empirical threshold values for quantitative trait mapping. Genetics 138: 963–971

    Not in our library10.1093/genetics/138.3.963

  13. 13.

    Churchill G. A. Doerge R. W., 2008 Naive application of permutation testing leads to inflated type I error rates. Genetics 178: 609–610

    Not in our library10.1534/genetics.107.074609

  14. 14.

    Churchill G. A. Airey D. C. Allayee H. Angel J. M. Attie A. D., 2004 The Collaborative Cross, a community resource for the genetic analysis of complex traits. Nat. Genet. 36: 1133–1137

    Not in our library10.1038/ng1104-1133

  15. 15.

    Clifford D. McCullagh P., 2006 The regress function. R News, 6

    Not in our library

  16. 16.

    Collaborative Cross Consortium, 2012 The genome architecture of the Collaborative Cross mouse genetic reference population. Genetics 190: 389–401

    Not in our library10.1534/genetics.111.132639

  17. 17.

    Cox A. Ackert-Bicknell C. L. Dumont B. L. Ding Y. Bell J. T., 2009 A new standard genetic map for the laboratory mouse. Genetics 182: 1335–1344

    Not in our library10.1534/genetics.109.105486

  18. 18.

    Didion J. P. Yang H. Sheppard K. Fu C. P. McMillan L., 2012 Discovery of novel variants in genotyping arrays improves genotype retention and reduces ascertainment bias. BMC Genomics 13: 34

    Not in our library10.1186/1471-2164-13-34

  19. 19.

    Eash K. J. Means J. M. White D. W. Link D. C., 2009 CXCR4 is a key regulator of neutrophil release from the bone marrow under basal and stress granulopoiesis conditions. Blood 113: 4711–4719

    Not in our library10.1182/blood-2008-09-177287

  20. 20.

    Flint J. Valdar W. Shifman S. Mott R., 2005 Strategies for mapping and cloning quantitative trait genes in rodents. Nat. Rev. Genet. 6: 271–286

    Not in our library10.1038/nrg1576

  21. 21.

    Fraley, C., A. E. Raferty, T. B. Murphy, and L. Scrucca, 2012 mclust, version 4 for R: Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation University of Washington, Seattle.

    Not in our library

  22. 22.

    Fu, C., C. E. Welsh, F. Pardo-Manuel de Villena, and L. McMillan, 2012 Inferring ancestry in admixed populations using microaray probe intensities, pp. in ACM-BCB 2012 Association for Computing Machinery, Orlando, FL.

    Not in our library

  23. 23.
  24. 24.

    Haley C. S. Knott S. A., 1992 A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity 69: 315–324

    Not in our library10.1038/hdy.1992.131

  25. 25.

    Hitzemann B. Dains K. Kanes S. Hitzemann R., 1994 Further studies on the relationship between dopamine cell density and haloperidol-induced catalepsy. J. Pharmacol. Exp. Ther. 271: 969–976

    Not in our library

  26. 26.
  27. 27.

    Kang H. M. Zaitlen N. A. Wade C. M. Kirby A. Heckerman D., 2008 Efficient control of population structure in model organism association mapping. Genetics 178: 1709–1723

    Not in our library10.1534/genetics.107.080101

  28. 28.

    Keane T. M. Goodstadt L. Danecek P. White M. A. Wong K., 2011 Mouse genomic variation and its effect on phenotypes and gene regulation. Nature 477: 289–294

    Not in our library10.1038/nature10413

  29. 29.

    King E. G. Merkes C. M. McNeil C. L. Hoofer S. R. Sen S., 2012 Genetic dissection of a model complex trait using the Drosophila synthetic population resource. Genome Res. 22: 1558–1566

    Not in our library10.1101/gr.134031.111

  30. 30.
  31. 31.

    Lander E. S. Botstein D., 1989 Mapping mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics 121: 185–199

    Not in our library10.1093/genetics/121.1.185

  32. 32.

    Li T. K. Lumeng L., 1984 Alcohol preference and voluntary alcohol intakes of inbred rat strains and the National Institutes of Health heterogeneous stock of rats. Alcohol. Clin. Exp. Res. 8: 485–486

    Not in our library10.1111/j.1530-0277.1984.tb05708.x

  33. 33.

    Liu E. Y. Morgan A. P. Chesler E. J. Wang W. Churchill G. A., 2014 High-resolution sex-specific linkage maps of the mouse reveal polarized distribution of crossovers in male germline. Genetics 197: 91–106

    Not in our library10.1534/genetics.114.161653

  34. 34.
  35. 35.

    Parvanov E. D. Petkov P. M. Paigen K., 2010 Prdm9 controls activation of mammalian recombination hotspots. Science 327: 835

    Not in our library10.1126/science.1181495

  36. 36.

    Pyke R., 1965 Spacings. J. R. Stat. Soc. B 27: 395–449

    Not in our library

  37. 37.

    Rabiner L. R., 1989 A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 77: 257–286

    Not in our library

  38. 38.

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

    Not in our library10.1093/genetics/159.1.371

  39. 39.

    Shabalin A. A., 2012 Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioinformatics 28: 1353–1358

    Not in our library10.1093/bioinformatics/bts163

  40. 40.

    Svenson K. L. Gatti D. M. Valdar W. Welsh C. E. Cheng R., 2012 High-resolution genetic mapping using the mouse diversity outbred population. Genetics 190: 437–447

    Not in our library10.1534/genetics.111.132597

  41. 41.

    Van der Bliek A. M. Baas F. Van der Velde-Koerts T. Biedler J. L. Meyers M. B., 1988 Genes amplified and overexpressed in human multidrug-resistant cell lines. Cancer Res. 48: 5927–5932

    Not in our library

  42. 42.

    Visscher P. M. Hill W. G. Wray N. R., 2008 Heritability in the genomics era–concepts and misconceptions. Nat. Rev. Genet. 9: 255–266

    Not in our library10.1038/nrg2322

  43. 43.

    Viterbi A. J., 1967 Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Trans. Inf. Theory 13: 260–269

    Not in our library

  44. 44.
  45. 45.

    Yang H. Wang J. R. Didion J. P. Buus R. J. Bell T. A., 2011 Subspecific origin and haplotype diversity in the laboratory mouse. Nat. Genet. 43: 648–655

    Not in our library10.1038/ng.847

  46. 46.

    Yang J. Zaitlen N. A. Goddard M. E. Visscher P. M. Price A. L., 2014 Advantages and pitfalls in the application of mixed-model association methods. Nat. Genet. 46: 100–106

    Not in our library10.1038/ng.2876

  47. 47.

    Zhaojun Z. Wang W. Valdar W., 2014 Bayesian modeling of haplotype effects in multiparent populations. Genetics 139–156

    Not in our library10.1534/genetics.114.166249

  48. 48.

    Zhou X. Stephens M., 2012 Genome-wide efficient mixed-model analysis for association studies. Nat. Genet. 44: 821–824

    Not in our library10.1038/ng.2310

Cited by papers we hold 5

Reads

Read by people on this page

None yet.

Checked or read by AI through naulon

None yet.

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.

Are you an author?

Is this your paper?

Claim it to add your note and be paid when agents cite it. Free.