Quantitative Trait Locus Mapping Methods for Diversity Outbred Mice

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.

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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.
   Chapter 11: Genome-Wide Association Studies · 2012

   No notice
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.
    Bioconductor: open software development for computational biology and bioinformatics · 2004

    No notice
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.
    Genetic diversity and striatal gene networks: focus on the heterogeneous stock-collaborative cross (HS-CC) mouse · 2010

    Not checked
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.
    A Multiparent Advanced Generation Inter-Cross to Fine-Map Quantitative Traits in Arabidopsis thaliana · 2009

    No notice
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.

    Mott R. Talbot C. J. Turri M. G. Collins A. C. Flint J., 2000 A method for fine mapping quantitative trait loci in outbred animal stocks. Proc. Natl. Acad. Sci. USA 97: 12649–12654

    Not in our library10.1073/pnas.230304397

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

Cited by papers we hold 5

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• Multi-omics approaches to disease · 2017
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• Genomes of the Mouse Collaborative Cross · 2017
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• The Mouse Universal Genotyping Array: From Substrains to Subspecies · 2016
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• Genetic properties of the MAGIC maize population: a new platform for high definition QTL mapping in Zea mays · 2015
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