Skip to main content
SpringerLink
Log in

Using Heterogeneous Stocks for Fine-Mapping Genetically Complex Traits

  • Protocol
  • First Online:
Rat Genomics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2018))

Abstract

In this chapter we will review both the rationale and experimental design for using Heterogeneous Stock (HS) populations for fine-mapping of complex traits in mice and rats. We define an HS as an outbred population derived from an intercross between two or more inbred strains. HS have been used to perform genome-wide association studies (GWAS) for multiple behavioral, physiological, and gene expression traits. GWAS using HS require four key steps, which we review: selection of an appropriate HS population, phenotyping, genotyping, and statistical analysis. We provide advice on the selection of an HS, comment on key issues related to phenotyping, discuss genotyping methods relevant to these populations, and describe statistical genetic analyses that are applicable to genetic analyses that use HS.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Peirce JL, Lu L, Gu J, Silver LM, Williams RW (2004) A new set of BXD recombinant inbred lines from advanced intercross populations in mice. BMC Genet 5:7. https://doi.org/10.1186/1471-2156-5-7

    Article  PubMed  PubMed Central  Google Scholar 

  2. Churchill GA, Airey DC, Allayee H, Angel JM, Attie AD, Beatty J et al (2004) The Collaborative Cross, a community resource for the genetic analysis of complex traits. Nat Genet 36(11):1133–1137. https://doi.org/10.1038/ng1104-1133

    Article  CAS  PubMed  Google Scholar 

  3. Mott R, Flint J (2013) Dissecting quantitative traits in mice. Annu Rev Genomics Hum Genet 14:421–439. https://doi.org/10.1146/annurev-genom-091212-153419

    Article  CAS  PubMed  Google Scholar 

  4. Parker CC, Palmer AA (2011) Dark matter: are mice the solution to missing heritability? Front Genet 2:32. https://doi.org/10.3389/fgene.2011.00032

    Article  PubMed  PubMed Central  Google Scholar 

  5. Solberg Woods LC (2014) QTL mapping in outbred populations: successes and challenges. Physiol Genomics 46(3):81–90. https://doi.org/10.1152/physiolgenomics.00127.2013

    Article  PubMed  Google Scholar 

  6. Keele GR, Prokop JW, He H, Holl K, Littrell J, Deal A et al (2018) Genetic fine-mapping and identification of candidate genes and variants for adiposity traits in outbred rats. Obesity (Silver Spring) 26(1):213–222. https://doi.org/10.1002/oby.22075

    Article  CAS  Google Scholar 

  7. Svenson KL, Gatti DM, Valdar W, Welsh CE, Cheng R, Chesler EJ et al (2012) High-resolution genetic mapping using the Mouse Diversity outbred population. Genetics 190(2):437–447. https://doi.org/10.1534/genetics.111.132597

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Sittig LJ, Carbonetto P, Engel KA, Krauss KS, Barrios-Camacho CM, Palmer AA (2016) Genetic background limits generalizability of genotype-phenotype relationships. Neuron 91(6):1253–1259. https://doi.org/10.1016/j.neuron.2016.08.013

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Parker CC, Chen H, Flagel SB, Geurts AM, Richards JB, Robinson TE et al (2014) Rats are the smart choice: rationale for a renewed focus on rats in behavioral genetics. Neuropharmacology 76(Pt B):250–258. https://doi.org/10.1016/j.neuropharm.2013.05.047

    Article  CAS  PubMed  Google Scholar 

  10. Holl K, He H, Wedemeyer M, Clopton L, Wert S, Meckes JK et al (2018) Heterogeneous stock rats: a model to study the genetics of despair-like behavior in adolescence. Genes Brain Behav 17(2):139–148. https://doi.org/10.1111/gbb.12410

    Article  CAS  PubMed  Google Scholar 

  11. McClearn GE, Wilson JR, Meredith W (1970) The use of isogenic and heterogenic mouse stocks in behavioral research. In: Lindzey G, Thiessen D (eds) Contributions to behavior-genetic analysis: the mouse as a prototype. Appleton Century Crofts, New York, pp 3–22

    Google Scholar 

  12. Demarest K, Koyner J, McCaughran J Jr, Cipp L, Hitzemann R (2001) Further characterization and high-resolution mapping of quantitative trait loci for ethanol-induced locomotor activity. Behav Genet 31(1):79–91

    Article  CAS  Google Scholar 

  13. Talbot CJ, Nicod A, Cherny SS, Fulker DW, Collins AC, Flint J (1999) High-resolution mapping of quantitative trait loci in outbred mice. Nat Genet 21(3):305–308

    Article  CAS  Google Scholar 

  14. Valdar W, Solberg LC, Gauguier D, Burnett S, Klenerman P, Cookson WO et al (2006) Genome-wide genetic association of complex traits in heterogeneous stock mice. Nat Genet 38(8):879–887

    Article  CAS  Google Scholar 

  15. Talbot CJ, Radcliffe RA, Fullerton J, Hitzemann R, Wehner JM, Flint J (2003) Fine scale mapping of a genetic locus for conditioned fear. Mamm Genome 14(4):223–230

    Article  Google Scholar 

  16. Hitzemann R, Edmunds S, Wu W, Malmanger B, Walter N, Belknap J et al (2009) Detection of reciprocal quantitative trait loci for acute ethanol withdrawal and ethanol consumption in heterogeneous stock mice. Psychopharmacology 203(4):713–722. https://doi.org/10.1007/s00213-008-1418-y

    Article  CAS  PubMed  Google Scholar 

  17. Ahlqvist E, Ekman D, Lindvall T, Popovic M, Forster M, Hultqvist M et al (2011) High-resolution mapping of a complex disease, a model for rheumatoid arthritis, using heterogeneous stock mice. Hum Mol Genet 20(15):3031–3041. https://doi.org/10.1093/hmg/ddr206

    Article  CAS  PubMed  Google Scholar 

  18. Darvasi A, Soller M (1995) Advanced intercross lines, an experimental population for fine genetic mapping. Genetics 141(3):1199–1207

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Parker CC, Cheng R, Sokoloff G, Palmer AA (2012) Genome-wide association for methamphetamine sensitivity in an advanced intercross mouse line. Genes Brain Behav 11(1):52–61. https://doi.org/10.1111/j.1601-183X.2011.00747.x

    Article  CAS  PubMed  Google Scholar 

  20. Peirce JL, Broman KW, Lu L, Chesler EJ, Zhou G, Airey DC et al (2008) Genome Reshuffling for Advanced Intercross Permutation (GRAIP): simulation and permutation for advanced intercross population analysis. PLoS One 3(4):e1977. https://doi.org/10.1371/journal.pone.0001977

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Carroll AM, Cheng R, Collie-Duguid ES, Meharg C, Scholz ME, Fiering S et al (2017) Fine-mapping of genes determining extrafusal fiber properties in murine soleus muscle. Physiol Genomics 49(3):141–150. https://doi.org/10.1152/physiolgenomics.00092.2016

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Cheng R, Lim JE, Samocha KE, Sokoloff G, Abney M, Skol AD et al (2010) Genome-wide association studies and the problem of relatedness among advanced intercross lines and other highly recombinant populations. Genetics 185(3):1033–1044. https://doi.org/10.1534/genetics.110.116863

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Cheverud JM, Lawson HA, Bouckaert K, Kossenkov AV, Showe LC, Cort L et al (2014) Fine-mapping quantitative trait loci affecting murine external ear tissue regeneration in the LG/J by SM/J advanced intercross line. Heredity (Edinb) 112(5):508–518. https://doi.org/10.1038/hdy.2013.133

    Article  CAS  Google Scholar 

  24. Ehrich TH, Hrbek T, Kenney-Hunt JP, Pletscher LS, Wang B, Semenkovich CF et al (2005) Fine-mapping gene-by-diet interactions on chromosome 13 in a LG/J x SM/J murine model of obesity. Diabetes 54(6):1863–1872

    Article  CAS  Google Scholar 

  25. Gonzales NM, Seo J, Hernandez-Cordero AI, St. Pierre CL, Gregory JS, Distler MG et al (2018) Genome wide association study of behavioral, physiological and gene expression traits in a multigenerational mouse intercross. BioRxiv. https://doi.org/10.1101/230920

  26. Hernandez Cordero AI, Carbonetto P, Riboni Verri G, Gregory JS, Vandenbergh DJ, Gyekis JP et al (2018) Replication and discovery of musculoskeletal QTLs in LG/J and SM/J advanced intercross lines. Phys Rep 6(4). https://doi.org/10.14814/phy2.13561

    Article  Google Scholar 

  27. Lawson HA, Lee A, Fawcett GL, Wang B, Pletscher LS, Maxwell TJ et al (2011) The importance of context to the genetic architecture of diabetes-related traits is revealed in a genome-wide scan of a LG/J x SM/J murine model. Mamm Genome 22(3–4):197–208. https://doi.org/10.1007/s00335-010-9313-3

    Article  CAS  PubMed  Google Scholar 

  28. Lawson HA, Zelle KM, Fawcett GL, Wang B, Pletscher LS, Maxwell TJ et al (2010) Genetic, epigenetic, and gene-by-diet interaction effects underlie variation in serum lipids in a LG/JxSM/J murine model. J Lipid Res 51(10):2976–2984. https://doi.org/10.1194/jlr.M006957

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Norgard EA, Lawson HA, Pletscher LS, Wang B, Brooks VR, Wolf JB et al (2011) Genetic factors and diet affect long-bone length in the F34 LG,SM advanced intercross. Mamm Genome 22(3–4):178–196. https://doi.org/10.1007/s00335-010-9311-5

    Article  PubMed  Google Scholar 

  30. Parker CC, Carbonetto P, Sokoloff G, Park YJ, Abney M, Palmer AA (2014) High-resolution genetic mapping of complex traits from a combined analysis of F2 and advanced intercross mice. Genetics 198(1):103–116. https://doi.org/10.1534/genetics.114.167056

    Article  PubMed  PubMed Central  Google Scholar 

  31. Parker CC, Cheng R, Sokoloff G, Lim JE, Skol AD, Abney M et al (2011) Fine-mapping alleles for body weight in LG/J x SM/J F(2) and F(34) advanced intercross lines. Mamm Genome 22(9–10):563–571. https://doi.org/10.1007/s00335-011-9349-z

    Article  PubMed  PubMed Central  Google Scholar 

  32. Parker CC, Sokoloff G, Cheng R, Palmer AA (2012) Genome-wide association for fear conditioning in an advanced intercross mouse line. Behav Genet 42(3):437–448. https://doi.org/10.1007/s10519-011-9524-8

    Article  PubMed  PubMed Central  Google Scholar 

  33. Rai MF, Schmidt EJ, Hashimoto S, Cheverud JM, Sandell LJ (2015) Genetic loci that regulate ectopic calcification in response to knee trauma in LG/J by SM/J advanced intercross mice. J Orthop Res 33(10):1412–1423. https://doi.org/10.1002/jor.22944

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Samocha KE, Lim JE, Cheng R, Sokoloff G, Palmer AA (2010) Fine mapping of QTL for prepulse inhibition in LG/J and SM/J mice using F(2) and advanced intercross lines. Genes Brain Behav 9(7):759–767. https://doi.org/10.1111/j.1601-183X.2010.00613.x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Iancu OD, Darakjian P, Walter NA, Malmanger B, Oberbeck D, Belknap J et al (2010) Genetic diversity and striatal gene networks: focus on the heterogeneous stock-collaborative cross (HS-CC) mouse. BMC Genomics 11:585. https://doi.org/10.1186/1471-2164-11-585

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Consortium CC (2012) The genome architecture of the Collaborative Cross mouse genetic reference population. Genetics 190(2):389–401. https://doi.org/10.1534/genetics.111.132639

    Article  CAS  Google Scholar 

  37. Matsumoto Y, Goto T, Nishino J, Nakaoka H, Tanave A, Takano-Shimizu T et al (2017) Selective breeding and selection mapping using a novel wild-derived heterogeneous stock of mice revealed two closely-linked loci for tameness. Sci Rep 7(1):4607. https://doi.org/10.1038/s41598-017-04869-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Yalcin B, Flint J (2012) Association studies in outbred mice in a new era of full-genome sequencing. Mamm Genome 23(9–10):719–726. https://doi.org/10.1007/s00335-012-9409-z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Gatti D, French JE, Schughart K (2017) QTL Mapping and identification of candidate genes in DO mice: a use case model derived from a benzene toxicity experiment. Methods Mol Biol 1488:265–281. https://doi.org/10.1007/978-1-4939-6427-7_12

    Article  CAS  PubMed  Google Scholar 

  40. Logan RW, Robledo RF, Recla JM, Philip VM, Bubier JA, Jay JJ et al (2013) High-precision genetic mapping of behavioral traits in the diversity outbred mouse population. Genes Brain Behav 12(4):424–437. https://doi.org/10.1111/gbb.12029

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Recla JM, Robledo RF, Gatti DM, Bult CJ, Churchill GA, Chesler EJ (2014) Precise genetic mapping and integrative bioinformatics in Diversity Outbred mice reveals Hydin as a novel pain gene. Mamm Genome 25(5–6):211–222. https://doi.org/10.1007/s00335-014-9508-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Shorter JR, Huang W, Beak JY, Hua K, Gatti DM, de Villena FP et al (2018) Quantitative trait mapping in Diversity Outbred mice identifies two genomic regions associated with heart size. Mamm Genome 29(1–2):80–89. https://doi.org/10.1007/s00335-017-9730-7

    Article  PubMed  Google Scholar 

  43. Smallwood TL, Gatti DM, Quizon P, Weinstock GM, Jung KC, Zhao L et al (2014) High-resolution genetic mapping in the diversity outbred mouse population identifies Apobec1 as a candidate gene for atherosclerosis. G3 4(12):2353–2363. https://doi.org/10.1534/g3.114.014704

    Article  PubMed  Google Scholar 

  44. Winter JM, Gildea DE, Andreas JP, Gatti DM, Williams KA, Lee M et al (2017) Mapping complex traits in a diversity outbred F1 mouse population identifies germline modifiers of metastasis in human prostate cancer. Cell Syst 4(1):31–45 e36. https://doi.org/10.1016/j.cels.2016.10.018

    Article  CAS  PubMed  Google Scholar 

  45. Chesler EJ, Gatti DM, Morgan AP, Strobel M, Trepanier L, Oberbeck D et al (2016) Diversity Outbred mice at 21: maintaining allelic variation in the face of selection. G3 6(12):3893–3902. https://doi.org/10.1534/g3.116.035527

    Article  PubMed  Google Scholar 

  46. Hansen C, Spuhler K (1984) Development of the National Institutes of Health genetically heterogeneous rat stock. Alcohol Clin Exp Res 8(5):477–479

    Article  CAS  Google Scholar 

  47. Solberg Woods LC, Holl K, Tschannen M, Valdar W (2010) Fine-mapping a locus for glucose tolerance using heterogeneous stock rats. Physiol Genomics 41(1):102–108. https://doi.org/10.1152/physiolgenomics.00178.2009

    Article  CAS  PubMed  Google Scholar 

  48. Solberg Woods LC, Holl KL, Oreper D, Xie Y, Tsaih SW, Valdar W (2012) Fine-mapping diabetes-related traits, including insulin resistance, in heterogeneous stock rats. Physiol Genomics 44(21):1013–1026. https://doi.org/10.1152/physiolgenomics.00040.2012

    Article  CAS  PubMed  Google Scholar 

  49. Tsaih SW, Holl K, Jia S, Kaldunski M, Tschannen M, He H et al (2014) Identification of a novel gene for diabetic traits in rats, mice, and humans. Genetics 198(1):17–29. https://doi.org/10.1534/genetics.114.162982

    Article  PubMed  PubMed Central  Google Scholar 

  50. Baud A, Hermsen R, Guryev V, Stridh P, Graham D, McBride MW et al (2013) Combined sequence-based and genetic mapping analysis of complex traits in outbred rats. Nat Genet 45(7):767–775. https://doi.org/10.1038/ng.2644

    Article  CAS  PubMed  Google Scholar 

  51. Solberg Woods LC, Stelloh C, Regner KR, Schwabe T, Eisenhauer J, Garrett MR (2010) Heterogeneous stock rats: a new model to study the genetics of renal phenotypes. Am J Physiol Ren Physiol 298(6):F1484–F1491. https://doi.org/10.1152/ajprenal.00002.2010

    Article  CAS  Google Scholar 

  52. Alam I, Koller DL, Sun Q, Roeder RK, Canete T, Blazquez G et al (2011) Heterogeneous stock rat: a unique animal model for mapping genes influencing bone fragility. Bone 48(5):1169–1177. https://doi.org/10.1016/j.bone.2011.02.009

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. King CP, Palmer AA, Woods LC, Hawk LW, Richards JB, Meyer PJ (2016) Premature responding is associated with approach to a food cue in male and female heterogeneous stock rats. Psychopharmacology 233(13):2593–2605. https://doi.org/10.1007/s00213-016-4306-x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Richards JB, Lloyd DR, Kuehlewind B, Militello L, Paredez M, Solberg Woods L et al (2013) Strong genetic influences on measures of behavioral-regulation among inbred rat strains. Genes Brain Behav 12(5):490–502. https://doi.org/10.1111/gbb.12050

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Wang T, Han W, Wang B, Jiang Q, Solberg-Woods LC, Palmer AA et al (2014) Propensity for social interaction predicts nicotine-reinforced behaviors in outbred rats. Genes Brain Behav 13(2):202–212. https://doi.org/10.1111/gbb.12112

    Article  CAS  PubMed  Google Scholar 

  56. Diaz-Moran S, Palencia M, Mont-Cardona C, Canete T, Blazquez G, Martinez-Membrives E et al (2012) Coping style and stress hormone responses in genetically heterogeneous rats: comparison with the Roman rat strains. Behav Brain Res 228(1):203–210. https://doi.org/10.1016/j.bbr.2011.12.002

    Article  CAS  PubMed  Google Scholar 

  57. Lopez-Aumatell R, Guitart-Masip M, Vicens-Costa E, Gimenez-Llort L, Valdar W, Johannesson M et al (2008) Fearfulness in a large N/Nih genetically heterogeneous rat stock: differential profiles of timidity and defensive flight in males and females. Behav Brain Res 188(1):41–55. https://doi.org/10.1016/j.bbr.2007.10.015

    Article  PubMed  Google Scholar 

  58. Lopez-Aumatell R, Vicens-Costa E, Guitart-Masip M, Martinez-Membrives E, Valdar W, Johannesson M et al (2009) Unlearned anxiety predicts learned fear: a comparison among heterogeneous rats and the Roman rat strains. Behav Brain Res 202(1):92–101. https://doi.org/10.1016/j.bbr.2009.03.024

    Article  PubMed  Google Scholar 

  59. Bice PJ, Liang T, Zhang L, Graves TJ, Carr LG, Lai D et al (2010) Fine mapping and expression of candidate genes within the chromosome 10 QTL region of the high and low alcohol-drinking rats. Alcohol 44(6):477–485. https://doi.org/10.1016/j.alcohol.2010.06.004

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Foroud T, Bice P, Castelluccio P, Bo R, Miller L, Ritchotte A et al (2000) Identification of quantitative trait loci influencing alcohol consumption in the high alcohol drinking and low alcohol drinking rat lines. Behav Genet 30(2):131–140

    Article  CAS  Google Scholar 

  61. Spuhler K, Deitrich RA (1984) Correlative analysis of ethanol-related phenotypes in rat inbred strains. Alcohol Clin Exp Res 8(5):480–484

    Article  CAS  Google Scholar 

  62. Rockman MV, Kruglyak L (2008) Breeding designs for recombinant inbred advanced intercross lines. Genetics 179(2):1069–1078. https://doi.org/10.1534/genetics.107.083873

    Article  PubMed  PubMed Central  Google Scholar 

  63. Falconer DS (1960) Introduction to quantitative genetics. Ronald Press, New York

    Google Scholar 

  64. Wurbel H (2002) Behavioral phenotyping enhanced—beyond (environmental) standardization. Genes Brain Behav 1(1):3–8

    Article  CAS  Google Scholar 

  65. Mott R, Flint J (2002) Simultaneous detection and fine mapping of quantitative trait loci in mice using heterogeneous stocks. Genetics 160(4):1609–1618

    CAS  PubMed  PubMed Central  Google Scholar 

  66. Mott R, Talbot CJ, Turri MG, Collins AC, Flint J (2000) A method for fine mapping quantitative trait loci in outbred animal stocks. Proc Natl Acad Sci U S A 97(23):12649–12654

    Article  Google Scholar 

  67. Valdar WS, Flint J, Mott R (2003) QTL fine-mapping with recombinant-inbred heterogeneous stocks and in vitro heterogeneous stocks. Mamm Genome 14(12):830–838. https://doi.org/10.1007/s00335-003-3021-1

    Article  PubMed  Google Scholar 

  68. Valdar W, Holmes CC, Mott R, Flint J (2009) Mapping in structured populations by resample model averaging. Genetics 182(4):1263–1277. https://doi.org/10.1534/genetics.109.100727

    Article  PubMed  PubMed Central  Google Scholar 

  69. Valdar W, Flint J, Mott R (2006) Simulating the collaborative cross: power of quantitative trait loci detection and mapping resolution in large sets of recombinant inbred strains of mice. Genetics 172(3):1783–1797. https://doi.org/10.1534/genetics.104.039313

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Nicod J, Davies RW, Cai N, Hassett C, Goodstadt L, Cosgrove C et al (2016) Genome-wide association of multiple complex traits in outbred mice by ultra-low-coverage sequencing. Nat Genet 48(8):912–918. https://doi.org/10.1038/ng.3595

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Parker CC, Gopalakrishnan S, Carbonetto P, Gonzales NM, Leung E, Park YJ et al (2016) Genome-wide association study of behavioral, physiological and gene expression traits in outbred CFW mice. Nat Genet 48(8):919–926. https://doi.org/10.1038/ng.3609

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Davies RW, Flint J, Myers S, Mott R (2016) Rapid genotype imputation from sequence without reference panels. Nat Genet 48(8):965–969. https://doi.org/10.1038/ng.3594

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Browning BL, Browning SR (2016) Genotype imputation with millions of reference samples. Am J Hum Genet 98(1):116–126. https://doi.org/10.1016/j.ajhg.2015.11.020

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Fitzpatrick CJ, Gopalakrishnan S, Cogan ES, Yager LM, Meyer PJ, Lovic V et al (2013) Variation in the form of Pavlovian conditioned approach behavior among outbred male Sprague-Dawley rats from different vendors and colonies: sign-tracking vs. goal-tracking. PLoS One 8(10):e75042. https://doi.org/10.1371/journal.pone.0075042

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Cheng R, Abney M, Palmer AA, Skol AD (2011) QTLRel: an R package for genome-wide association studies in which relatedness is a concern. BMC Genet 12:66. https://doi.org/10.1186/1471-2156-12-66

    Article  PubMed  PubMed Central  Google Scholar 

  76. Cheng R, Palmer AA (2013) A simulation study of permutation, bootstrap, and gene dropping for assessing statistical significance in the case of unequal relatedness. Genetics 193(3):1015–1018. https://doi.org/10.1534/genetics.112.146332

    Article  PubMed  PubMed Central  Google Scholar 

  77. Cheng R, Parker CC, Abney M, Palmer AA (2013) Practical considerations regarding the use of genotype and pedigree data to model relatedness in the context of genome-wide association studies. G3 3(10):1861–1867. https://doi.org/10.1534/g3.113.007948

    Article  PubMed  Google Scholar 

  78. Gonzales NM, Palmer AA (2014) Fine-mapping QTLs in advanced intercross lines and other outbred populations. Mamm Genome 25(7–8):271–292. https://doi.org/10.1007/s00335-014-9523-1

    Article  PubMed  PubMed Central  Google Scholar 

  79. Gatti DM, Svenson KL, Shabalin A, Wu LY, Valdar W, Simecek P et al (2014) Quantitative trait locus mapping methods for diversity outbred mice. G3 4(9):1623–1633. https://doi.org/10.1534/g3.114.013748

    Article  PubMed  Google Scholar 

  80. Broman KW, Gatti DM, Simecek P, Furlotte NA, Prins P, Sen Ś, Yandell BS, Churchill GA (2018) R/qtl2: software for mapping quantitative trait loci with high-dimensional data and multi-parent populations. Genetics 211:495–502. https://doi.org/10.1534/genetics.118.301595. PMID: 30591514

    Article  CAS  PubMed  Google Scholar 

  81. Pallares LF, Carbonetto P, Gopalakrishnan S, Parker CC, Ackert-Bicknell CL, Palmer AA et al (2015) Mapping of craniofacial traits in outbred mice identifies major developmental genes involved in shape determination. PLoS Genet 11(11):e1005607. https://doi.org/10.1371/journal.pgen.1005607

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Broman KW (2016) qtl2geno: treatment of marker genotypes for QTL experiments. R package version 0.4-21. http://kbroman.org/qtl2

  83. Zhang Z, Wang W, Valdar W (2014) Bayesian modeling of haplotype effects in multiparent populations. Genetics 198(1):139–156. https://doi.org/10.1534/genetics.114.166249

    Article  PubMed  PubMed Central  Google Scholar 

  84. Risch N, Merikangas K (1996) The future of genetic studies of complex human diseases. Science 273(5281):1516–1517

    Article  CAS  Google Scholar 

  85. Hormozdiari F, van de Bunt M, Segre AV, Li X, Joo JWJ, Bilow M et al (2016) Colocalization of GWAS and eQTL signals detects target genes. Am J Hum Genet 99(6):1245–1260. https://doi.org/10.1016/j.ajhg.2016.10.003

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Yalcin B, Flint J, Mott R (2005) Using progenitor strain information to identify quantitative trait nucleotides in outbred mice. Genetics 171(2):673–681

    Article  CAS  Google Scholar 

Download references

Acknowledgments

L.S.W. and A.A.P. were both supported by P50DA037844. L.S.W. is also supported by R01 DK106386.

Author information

Authors and Affiliations

  1. Department of Internal Medicine, Section on Molecular Medicine, Wake Forest University School of Medicine, Winston Salem, NC, USA

    Leah C. Solberg Woods

  2. Department of Psychiatry, University of California San Diego, La Jolla, CA, USA

    Abraham A. Palmer

  3. Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA

    Abraham A. Palmer

Authors
  1. Leah C. Solberg Woods
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abraham A. Palmer
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Leah C. Solberg Woods .

Editor information

Editors and Affiliations

  1. Department of Biomedical Engineering, Rat Genome Database, Medical College of Wisconsin, Milwaukee, WI, USA

    G. Thomas Hayman

  2. Department of Biomedical Engineering, Rat Genome Database, Medical College of Wisconsin, Milwaukee, WI, USA

    Jennifer R. Smith

  3. Department of Physiology, Rat Genome Database, Genomic Sciences and Precision Medicine Center, Medical College of Wisconsin, Milwaukee, WI, USA

    Melinda R. Dwinell

  4. Department of Biomedical Engineering, Rat Genome Database, Medical College of Wisconsin, Milwaukee, WI, USA

    Mary Shimoyama

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Solberg Woods, L.C., Palmer, A.A. (2019). Using Heterogeneous Stocks for Fine-Mapping Genetically Complex Traits. In: Hayman, G., Smith, J., Dwinell, M., Shimoyama, M. (eds) Rat Genomics. Methods in Molecular Biology, vol 2018. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9581-3_11

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-9581-3_11

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-4939-9580-6

  • Online ISBN: 978-1-4939-9581-3

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation