BACKGROUND: To assess the utility of haplotype association mapping (HAM) as a quantitative trait locus (QTL) discovery tool, we conducted HAM analyses for red blood cell count (RBC) and high density lipoprotein cholesterol (HDL) in mice. We then experimentally tested each HAM QTL using published crosses or new F2 intercrosses guided by the haplotype at the HAM peaks. RESULTS: The HAM for RBC, using 33 classic inbred lines, revealed 8 QTLs; 2 of these were true positives as shown by published crosses. A HAM-guided (C57BL/6J x CBA/J)F2 intercross we carried out verified 2 more as true positives and 4 as false positives. The HAM for HDL, using 81 strains including recombinant inbred lines and chromosome substitution strains, detected 46 QTLs. Of these, 36 were true positives as shown by published crosses. A HAM-guided (C57BL/6J x A/J)F2 intercross that we carried out verified 2 more as true positives and 8 as false positives. By testing each HAM QTL for RBC and HDL, we demonstrated that 78% of the 54 HAM peaks were true positives and 22% were false positives. Interestingly, all false positives were in significant allelic association with one or more real QTL. CONCLUSION: Because type I errors (false positives) can be detected experimentally, we conclude that HAM is useful for QTL detection and narrowing. We advocate the powerful and economical combined approach demonstrated here: the use of HAM for QTL discovery, followed by mitigation of the false positive problem by testing the HAM-predicted QTLs with small HAM-guided experimental crosses.
BACKGROUND: To assess the utility of haplotype association mapping (HAM) as a quantitative trait locus (QTL) discovery tool, we conducted HAM analyses for red blood cell count (RBC) and high density lipoprotein cholesterol (HDL) in mice. We then experimentally tested each HAM QTL using published crosses or new F2 intercrosses guided by the haplotype at the HAM peaks. RESULTS: The HAM for RBC, using 33 classic inbred lines, revealed 8 QTLs; 2 of these were true positives as shown by published crosses. A HAM-guided (C57BL/6J x CBA/J)F2 intercross we carried out verified 2 more as true positives and 4 as false positives. The HAM for HDL, using 81 strains including recombinant inbred lines and chromosome substitution strains, detected 46 QTLs. Of these, 36 were true positives as shown by published crosses. A HAM-guided (C57BL/6J x A/J)F2 intercross that we carried out verified 2 more as true positives and 8 as false positives. By testing each HAM QTL for RBC and HDL, we demonstrated that 78% of the 54 HAM peaks were true positives and 22% were false positives. Interestingly, all false positives were in significant allelic association with one or more real QTL. CONCLUSION: Because type I errors (false positives) can be detected experimentally, we conclude that HAM is useful for QTL detection and narrowing. We advocate the powerful and economical combined approach demonstrated here: the use of HAM for QTL discovery, followed by mitigation of the false positive problem by testing the HAM-predicted QTLs with small HAM-guided experimental crosses.
Authors: L L Peters; H K Jindel; B Gwynn; C Korsgren; K M John; S E Lux; N Mohandas; C M Cohen; M R Cho; D E Golan; C Brugnara Journal: J Clin Invest Date: 1999-06 Impact factor: 14.808
Authors: Gary A Churchill; David C Airey; Hooman Allayee; Joe M Angel; Alan D Attie; Jackson Beatty; William D Beavis; John K Belknap; Beth Bennett; Wade Berrettini; Andre Bleich; Molly Bogue; Karl W Broman; Kari J Buck; Ed Buckler; Margit Burmeister; Elissa J Chesler; James M Cheverud; Steven Clapcote; Melloni N Cook; Roger D Cox; John C Crabbe; Wim E Crusio; Ariel Darvasi; Christian F Deschepper; R W Doerge; Charles R Farber; Jiri Forejt; Daniel Gaile; Steven J Garlow; Hartmut Geiger; Howard Gershenfeld; Terry Gordon; Jing Gu; Weikuan Gu; Gerald de Haan; Nancy L Hayes; Craig Heller; Heinz Himmelbauer; Robert Hitzemann; Kent Hunter; Hui-Chen Hsu; Fuad A Iraqi; Boris Ivandic; Howard J Jacob; Ritsert C Jansen; Karl J Jepsen; Dabney K Johnson; Thomas E Johnson; Gerd Kempermann; Christina Kendziorski; Malak Kotb; R Frank Kooy; Bastien Llamas; Frank Lammert; Jean-Michel Lassalle; Pedro R Lowenstein; Lu Lu; Aldons Lusis; Kenneth F Manly; Ralph Marcucio; Doug Matthews; Juan F Medrano; Darla R Miller; Guy Mittleman; Beverly A Mock; Jeffrey S Mogil; Xavier Montagutelli; Grant Morahan; David G Morris; Richard Mott; Joseph H Nadeau; Hiroki Nagase; Richard S Nowakowski; Bruce F O'Hara; Alexander V Osadchuk; Grier P Page; Beverly Paigen; Kenneth Paigen; Abraham A Palmer; Huei-Ju Pan; Leena Peltonen-Palotie; Jeremy Peirce; Daniel Pomp; Michal Pravenec; Daniel R Prows; Zhonghua Qi; Roger H Reeves; John Roder; Glenn D Rosen; Eric E Schadt; Leonard C Schalkwyk; Ze'ev Seltzer; Kazuhiro Shimomura; Siming Shou; Mikko J Sillanpää; Linda D Siracusa; Hans-Willem Snoeck; Jimmy L Spearow; Karen Svenson; Lisa M Tarantino; David Threadgill; Linda A Toth; William Valdar; Fernando Pardo-Manuel de Villena; Craig Warden; Steve Whatley; Robert W Williams; Tim Wiltshire; Nengjun Yi; Dabao Zhang; Min Zhang; Fei Zou Journal: Nat Genet Date: 2004-11 Impact factor: 38.330
Authors: A Grupe; S Germer; J Usuka; D Aud; J K Belknap; R F Klein; M K Ahluwalia; R Higuchi; G Peltz Journal: Science Date: 2001-06-08 Impact factor: 47.728
Authors: Jonathan D Smith; Daylon James; Hayes M Dansky; Knut M Wittkowski; Karen J Moore; Jan L Breslow Journal: Arterioscler Thromb Vasc Biol Date: 2003-01-01 Impact factor: 8.311
Authors: J Timothy Lightfoot; Larry Leamy; Daniel Pomp; Michael J Turner; Anthony A Fodor; Amy Knab; Robert S Bowen; David Ferguson; Trudy Moore-Harrison; Alicia Hamilton Journal: J Appl Physiol (1985) Date: 2010-06-10
Authors: Marcin Krawczyk; Roman Müllenbach; Susanne N Weber; Vincent Zimmer; Frank Lammert Journal: Nat Rev Gastroenterol Hepatol Date: 2010-11-02 Impact factor: 46.802
Authors: George D Leikauf; Hannah Pope-Varsalona; Vincent J Concel; Pengyuan Liu; Kiflai Bein; Annerose Berndt; Timothy M Martin; Koustav Ganguly; An Soo Jang; Kelly A Brant; Richard A Dopico; Swapna Upadhyay; Y P Peter Di; Qian Li; Zhen Hu; Louis J Vuga; Mario Medvedovic; Naftali Kaminski; Ming You; Danny C Alexander; Jonathan E McDunn; Daniel R Prows; Daren L Knoell; James P Fabisiak Journal: Am J Respir Cell Mol Biol Date: 2012-03-23 Impact factor: 6.914
Authors: Luanne L Peters; Jordan A Shavit; Amy J Lambert; Shirng-Wern Tsaih; Qian Li; Zhiguang Su; Magalie S Leduc; Beverly Paigen; Gary A Churchill; David Ginsburg; Carlo Brugnara Journal: Blood Date: 2010-09-10 Impact factor: 22.113
Authors: Darryl L Hadsell; Louise A Hadsell; Walter Olea; Monique Rijnkels; Chad J Creighton; Ian Smyth; Kieran M Short; Liza L Cox; Timothy C Cox Journal: Mamm Genome Date: 2015-01-01 Impact factor: 2.957
Authors: George D Leikauf; Vincent J Concel; Pengyuan Liu; Kiflai Bein; Annerose Berndt; Koustav Ganguly; An Soo Jang; Kelly A Brant; Maggie Dietsch; Hannah Pope-Varsalona; Richard A Dopico; Y P Peter Di; Qian Li; Louis J Vuga; Mario Medvedovic; Naftali Kaminski; Ming You; Daniel R Prows Journal: Am J Respir Crit Care Med Date: 2011-02-04 Impact factor: 21.405
Authors: Stela McLachlan; Seung-Min Lee; Teresa M Steele; Paula L Hawthorne; Matthew A Zapala; Eleazar Eskin; Nicholas J Schork; Gregory J Anderson; Chris D Vulpe Journal: Physiol Genomics Date: 2010-11-09 Impact factor: 3.107
Authors: Darryl L Hadsell; Louise A Hadsell; Monique Rijnkels; Yareli Carcamo-Bahena; Jerry Wei; Peter Williamson; Michael A Grusak Journal: Mamm Genome Date: 2018-08-02 Impact factor: 2.957