Mammalian Genome

, Volume 14, Issue 11, pp 733–747

A strategy for the integration of QTL, gene expression, and sequence analyses

  • Robert Hitzemann
  • Barry Malmanger
  • Cheryl Reed
  • Maureen Lawler
  • Barbara Hitzemann
  • Shannon Coulombe
  • Kari Buck
  • Brooks Rademacher
  • Nicole Walter
  • Yekatrina Polyakov
  • James Sikela
  • Brenda Gensler
  • Sonya Burgers
  • Robert W. Williams
  • Ken Manly
  • Jonathan Flint
  • Christopher Talbot
Article

DOI: 10.1007/s00335-003-2277-9

Cite this article as:
Hitzemann, R., Malmanger, B., Reed, C. et al. Mamm Genome (2003) 14: 733. doi:10.1007/s00335-003-2277-9

Abstract

Although hundreds if not thousands of quantitative trait loci (QTL) have been described for a wide variety of complex traits, only a very small number of these QTLs have been reduced to quantitative trait genes (QTGs) and quantitative trait nucleotides (QTNs). A strategy, Multiple Cross Mapping (MCM), is described for detecting QTGs and QTNs that is based on leveraging the information contained within the haplotype structure of the mouse genome. As described in the current report, the strategy utilizes the six F2 intercrosses that can be formed from the C57BL/6J (B6), DBA/2J (D2), BALB/cJ (C), and LP/J (LP) inbred mouse strains. Focusing on the phenotype of basal locomotor activity, it was found that in all three B6 intercrosses, a QTL was detected on distal Chromosome (Chr) 1; no QTL was detected in the other three intercrosses, and thus, it was assumed that at the QTL, the C, D2, and LP strains had functionally identical alleles. These intercross data were used to form a simple algorithm for interrogating microsatellite, single nucleotide polymorphism (SNP), brain gene expression, and sequence databases. The results obtained point to Kcnj9 (which has a markedly lower expression in the B6 strain) as being the likely QTG. Further, it is suggested that the lower expression in the B6 strain results from a polymorphism in the 5′-UTR that disrupts the binding of at least three transcription factors. Overall, the method described should be widely applicable to the analysis of QTLs.

Copyright information

© Springer-Verlag New York Inc. 2003

Authors and Affiliations

  • Robert Hitzemann
    • 1
    • 2
  • Barry Malmanger
    • 2
  • Cheryl Reed
    • 2
  • Maureen Lawler
    • 1
  • Barbara Hitzemann
    • 2
  • Shannon Coulombe
    • 1
  • Kari Buck
    • 2
  • Brooks Rademacher
    • 2
  • Nicole Walter
    • 2
  • Yekatrina Polyakov
    • 2
  • James Sikela
    • 3
  • Brenda Gensler
    • 3
  • Sonya Burgers
    • 3
  • Robert W. Williams
    • 4
  • Ken Manly
    • 5
  • Jonathan Flint
    • 6
  • Christopher Talbot
    • 7
  1. 1.Research ServiceVeterans Affairs Medical Center, Portland, OregonUSA
  2. 2.Department of Behavioral Neuroscience L470Oregon Health and Science University, 3181 SW Sam Jackson Park Rd, Portland, Oregon 97239USA
  3. 3.Department of PharmacologyUniversity of Colorado Health Sciences Center, Denver, ColoradoUSA
  4. 4.Center for Genomics and BioinformaticsUniversity of Tennessee Health Science Center, Memphis, TennesseeUSA
  5. 5.Molecular and Cellular BiologyRoswell Park Cancer Institute, Buffalo, New YorkUSA
  6. 6.Wellcome Trust Centre for Human GeneticsRoosevelt Drive, Oxford, Ox3 7BNUK
  7. 7.Department of GeneticsUniversity of Leicester, Leicester LE1 7RHUK

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