Title: Recalculation of 23 mouse HDL QTL datasets improves accuracy and allows for better candidate gene analysis
Abstract: In the past 15 years, the quantitative trait locus (QTL) mapping approach has been applied to crosses between different inbred mouse strains to identify genetic loci associated with plasma HDL cholesterol levels. Although successful, a disadvantage of this method is low mapping resolution, as often several hundred candidate genes fall within the confidence interval for each locus. Methods have been developed to narrow these loci by combining the data from the different crosses, but they rely on the accurate mapping of the QTL and the treatment of the data in a consistent manner. We collected 23 raw datasets used for the mapping of previously published HDL QTL and reanalyzed the data from each cross using a consistent method and the latest mouse genetic map. By utilizing this approach, we identified novel QTL and QTL that were mapped to the wrong part of chromosomes. Our new HDL QTL map allows for reliable combining of QTL data and candidate gene analysis, which we demonstrate by identifying Grin3a and Etv6, as candidate genes for QTL on chromosomes 4 and 6, respectively. In addition, we were able to narrow a QTL on Chr 19 to five candidates. In the past 15 years, the quantitative trait locus (QTL) mapping approach has been applied to crosses between different inbred mouse strains to identify genetic loci associated with plasma HDL cholesterol levels. Although successful, a disadvantage of this method is low mapping resolution, as often several hundred candidate genes fall within the confidence interval for each locus. Methods have been developed to narrow these loci by combining the data from the different crosses, but they rely on the accurate mapping of the QTL and the treatment of the data in a consistent manner. We collected 23 raw datasets used for the mapping of previously published HDL QTL and reanalyzed the data from each cross using a consistent method and the latest mouse genetic map. By utilizing this approach, we identified novel QTL and QTL that were mapped to the wrong part of chromosomes. Our new HDL QTL map allows for reliable combining of QTL data and candidate gene analysis, which we demonstrate by identifying Grin3a and Etv6, as candidate genes for QTL on chromosomes 4 and 6, respectively. In addition, we were able to narrow a QTL on Chr 19 to five candidates. In the past decade, high-density lipoprotein (HDL) cholesterol has emerged as a new potential therapeutic target for the treatment of cardiovascular disease (CVD). The key role of HDL as a carrier of excess cellular cholesterol in the reverse cholesterol transport pathway is believed to provide protection against atherosclerosis (1Brewer H.B. High-density lipoproteins: a new potential therapeutic target for the prevention of cardiovascular disease.Arterioscler. Thromb. Vasc. Biol. 2004; 24: 387-391Crossref PubMed Scopus (122) Google Scholar). Several epidemiologic studies have demonstrated that HDL is a strong inverse predictor of CVD risk (2Gordon T. Castelli W.P. Hjortland M.C. Kannel W.B. Dawber T.R. High density lipoprotein as a protective factor against coronary heart disease. The Framingham Study.Am. J. Med. 1977; 62: 707-714Abstract Full Text PDF PubMed Scopus (4086) Google Scholar, 3Castelli W.P. Garrison R.J. Wilson P.W. Abbott R.D. Kalousdian S. Kannel W.B. Incidence of coronary heart disease and lipoprotein cholesterol levels. The Framingham Study.JAMA. 1986; 256: 2835-2838Crossref PubMed Scopus (2079) Google Scholar, 4Assmann G. Schulte H. von Eckardstein A. Huang Y. High-density lipoprotein cholesterol as a predictor of coronary heart disease risk. The PROCAM experience and pathophysiological implications for reverse cholesterol transport.Atherosclerosis. 1996; 124: S11-S20Abstract Full Text PDF PubMed Scopus (631) Google Scholar, 5Sharrett A.R. Ballantyne C.M. Coady S.A. Heiss G. Sorlie P.D. Catellier D. Patsch W. Atherosclerosis Risk in Communities Study Group Coronary heart disease prediction from lipoprotein cholesterol levels, triglycerides, lipoprotein(a), apolipoproteins A-I and B, and HDL density subfractions: The Atherosclerosis Risk in Communities (ARIC) Study.Circulation. 2001; 104: 1108-1113Crossref PubMed Scopus (781) Google Scholar). Although much is known about HDL metabolism and reverse cholesterol transport, we still do not have a complete understanding of all the genetic factors involved in the regulation of HDL levels. Also, HDL is not a single entity but can be subdivided into different subpopulations. The relationships among these different subpopulations are not understood, and the genes responsible for the subpopulation differences among individuals are unknown. Several approaches, including genetic mapping studies, have been used to identify novel genes involved in the regulation of HDL levels. In mice, the technique of quantitative trait locus (QTL) mapping, which uses crosses between different inbred strains of mice, has been heavily employed over the past 15 years. These results have been summarized in several reviews (6Wang X. Paigen B. Quantitative trait loci and candidate genes regulating HDL cholesterol: a murine chromosome map.Arterioscler. Thromb. Vasc. Biol. 2002; 22: 1390-1401Crossref PubMed Scopus (65) Google Scholar, 7Wang X. Paigen B. Genetics of variation in HDL cholesterol in humans and mice.Circ. Res. 2005; 96: 27-42Crossref PubMed Scopus (120) Google Scholar). One limitation of traditional QTL analysis is low mapping resolution, which is a result of the limited genetic recombination possible in one-generation backcrosses [i.e., A×(A×B)] and two-generation intercrosses [i.e., (A×B)×(A×B)]. The 95% confidence interval (CI), the interval in which the causative gene is most likely to reside, is usually very broad. For example, one large study of bone mineral density QTL found that the average CI width for traditionally mapped QTL was 32 cM (8Ackert-Bicknell C.L. Karasik D. Li Q. Smith R.V. Hsu Y-H. Churchill G.A. Paigen B.J. Tsaih S-W. Mouse BMD quantitative trait loci show improved concordance with human genome-wide association loci when recalculated on a new, common mouse genetic map.J. Bone Miner. Res. 2010; 25: 1808-1820Crossref PubMed Scopus (40) Google Scholar). As it can be assumed that there are, on average, 20 genes per cM (9Ridgway W.M. Healy B. Smink L.J. Rainbow D. Wicker L.S. New tools for defining the "genetic background" of inbred mouse strains.Nat. Immunol. 2007; 8: 669-673Crossref PubMed Scopus (23) Google Scholar), the number of candidate genes per QTL can be very large, making the identification of the causative gene very difficult. In the past few years, several methods have been developed that combine accumulated data from the different crosses, allowing for narrowing of the CI for QTL and reducing candidate gene lists (10DiPetrillo K. Wang X. Stylianou I.M. Paigen B. Bioinformatics toolbox for narrowing rodent quantitative trait loci.Trends Genet. 2005; 21: 683-692Abstract Full Text Full Text PDF PubMed Scopus (107) Google Scholar). However, the success of these methods heavily depends on the accuracy of the QTL mapping. The current standard genetic map for the mouse is curated and maintained by the Mouse Genome Informatics (MGI) Group at The Jackson Laboratory (www.informatics.jax.org) (11Blake J.A. Richardson J.E. Davisson M.T. Eppig J.T. The Mouse Genome Database (MGD). A comprehensive public resource of genetic, phenotypic and genomic data. The Mouse Genome Informatics Group.Nucleic Acids Res. 1997; 25: 85-91Crossref PubMed Scopus (48) Google Scholar). Mapping QTL requires accurate genetic map information for both the relative order of markers and the distances between them (12Broman K.W. Sen S. A Guide to QTL Mapping with R/qtl. Springer, New York2009Crossref Google Scholar). Recently, Shifman and colleagues published a new genetic map based on a large population of a heterogeneous stock (13Shifman S. Bell J.T. Copley R.R. Taylor M.S. Williams R.W. Mott R. Flint J. A high-resolution single nucleotide polymorphism genetic map of the mouse genome.PLoS Biol. 2006; 4: e395Crossref PubMed Scopus (209) Google Scholar). Cox and colleagues integrated a total of 7,080 standard, simple-sequence length polymorphism (SSLP) markers to this single-nucleotide polymorphism (SNP)-based map, generating a corrected mouse genetic map (14Cox A. Ackert-Bicknell C.L. Dumont B.L. Ding Y. Bell J.T. Brockmann G.A. Wergedal J.E. Bult C.J. Paigen B. Flint J. et al.A new standard genetic map for the laboratory mouse.Genetics. 2009; 182: 1335-1344Crossref PubMed Scopus (173) Google Scholar). This new map resolved inconsistencies between the physical and genetic maps and is now the standard MGI genetic map, providing highly accurate genetic distances. A recent mapping study, in which the new and traditional genetic maps were compared, suggests that up to 20% of published QTL may have been mislocalized due to marker order and positioning errors in the old genetic map (14Cox A. Ackert-Bicknell C.L. Dumont B.L. Ding Y. Bell J.T. Brockmann G.A. Wergedal J.E. Bult C.J. Paigen B. Flint J. et al.A new standard genetic map for the laboratory mouse.Genetics. 2009; 182: 1335-1344Crossref PubMed Scopus (173) Google Scholar). In addition, many advances have been made over the years in the statistics and insights underlying QTL mapping. For example, in the past, males and females were analyzed as one population, disregarding possible sex-associated differences in the phenotype, or as two separate populations, resulting in a loss of power. We now analyze males and females as one population, using sex as a covariate. This method results in power compared with analyzing them separately, and it takes into account the obvious differences between males and females. Another issue is the direction of the cross. While, for most crosses, all F1 animals were produced by crossing a female from strain A with a male from strain B, in some crosses, the F1 animals originated from reciprocal crosses (A×B and B×A) and the F2 progeny were analyzed regardless of the parental grandmother. Now that we are aware of the epigenetic differences that can occur depending on the direction of the cross, we use the paternal grandmother as a covariate when analyzing the data (12Broman K.W. Sen S. A Guide to QTL Mapping with R/qtl. Springer, New York2009Crossref Google Scholar). The different HDL QTL datasets have been analyzed in various ways using different genetic maps and often without the above-mentioned covariates. This has resulted in misplaced QTL, missed QTL, or QTL that were only suggestive because of the loss of power. In this study, we made a concerted effort to identify and collect as many published, historical HDL QTL datasets as possible and reanalyze them. While most of the data used in this study were generated by our research group, several datasets were kindly provided by other investigators. All data underwent rigorous quality control and were then analyzed using current statistical methods. In all cases, the new corrected genetic mouse map was used. By analyzing the different datasets in the same way and by using the same genetic map, we created a more consistent and complete mouse HDL QTL map. As we demonstrate in our study, this improved map will aid in the search for novel HDL genes. First, a literature search to identify published reports of mouse HDL QTL was done using the following key words: "HDL,""QTL," and "mouse." Second, the Mouse Genome Informatics Database (www.informatics.jax.org/) was searched for HDL QTL using the Genes and Markers Query form. Specifically, this database was searched using the keyword "HDL" in the Gene/Marker Symbol/Name field, "QTL" in the Type field, and "Any" in the 'Chromosome field; the No Limit box was checked under the heading of Maximum Returned. The cross in which each HDL QTL was mapped was identified and compared with the list of crosses identified by conventional literature search. In short, the result was a list of mouse mapping crosses in which HDL QTL were mapped regardless of the method used to measure HDL. Map positions for the markers for all datasets were updated to the new mouse genetic map using a mouse map converter tool (http://cgd.jax.org). All QTL analyses were done using the R/qtl software package (12Broman K.W. Sen S. A Guide to QTL Mapping with R/qtl. Springer, New York2009Crossref Google Scholar) (R Version 2.6.2, qtl Library Version 1.09-43, www.rqtl.org/). Data were examined for phenotypic outliers and for genotyping errors, as previously described (8Ackert-Bicknell C.L. Karasik D. Li Q. Smith R.V. Hsu Y-H. Churchill G.A. Paigen B.J. Tsaih S-W. Mouse BMD quantitative trait loci show improved concordance with human genome-wide association loci when recalculated on a new, common mouse genetic map.J. Bone Miner. Res. 2010; 25: 1808-1820Crossref PubMed Scopus (40) Google Scholar). A single locus main-scan for QTL was performed, and LOD scores were calculated at 2 cM intervals across the genome using the EM (or expectation-maximization) method in R/qtl for all datasets. The LOD thresholds for significant and suggestive QTL were determined in a cross-specific manner based on 1,000 permutations of the data. A QTL was considered to be suggestive if the LOD score exceeded the P < 0.63 threshold and significant if it exceeded the P < 0.05 threshold. These thresholds were chosen because they are the widely accepted cutoffs for suggestive and significant QTL. For 5 of the 23 crosses, data were available for both male and female mice (Table 1). To account for the average differences between the males and females, we carried out scans using sex as an additive covariate. To identify sex-dependent QTL effects, we carried out additional scans using sex as an interactive covariate and computed the differences in LOD scores between these two scans (i.e., the ΔLOD) (12Broman K.W. Sen S. A Guide to QTL Mapping with R/qtl. Springer, New York2009Crossref Google Scholar). The interactive scan model identified the most likely position of the sex-specific QTL. Calculating the ΔLOD score at the peak position is the secondary test for the QTL-by-sex interaction. This secondary test is carried out with no adjustment for multiple testing, and the threshold, based on the usual chi-square distribution of the likelihood ratio, is 2.0 on the LOD scale. However, sex specificity of QTL was not confirmed by further analysis, and thus all "sex-specificity" of QTL should be considered putative. For 6 of the 23 crosses, data were available for two different cross directions (i.e., B×A and A×B, Table 1). Putative cross-direction-specific QTL were identified using the strain of paternal grandmother (pgm) for each mouse as both an additive and interactive covariate, as was done for sex-specific QTL. In only one cross, both males and females were examined in crosses generated in a reciprocal fashion. In this last dataset, the interactive term of sex:pgm was examined to identify QTL that interacted with sex and were cross-direction specific; no such QTL, however, were identified in this study.TABLE 1Description of the cross datasetsCrossYear (Reference)Number (Sex)DirectaUni = all F1 animals were produced using the direction as indicated in the first column, with the first strain being the female; bi = F1 animals were produced using both directions.Age (Weeks)Notes on Diet(NZB×SM)NZB2002 (40Pitman W.A. Korstanje R. Churchill G.A. Nicodeme E. Albers J.J. Cheung M.C. Staton M.A. Sampson S.S. Harris S. Paigen B. Quantitative trait locus mapping of genes that regulate HDL cholesterol in SM/J and NZB/B1NJ inbred mice.Physiol. Genomics. 2002; 9: 93-102Crossref PubMed Scopus (22) Google Scholar)90 (F)Uni8, 12, 16, 26Chow diet first 8 weeks, followed by atherogenic diet for 18 weeksD2×CAST2003 (41Lyons M.A. Wittenburg H. Li R. Walsh K.A. Churchill G.A. Carey M.C. Paigen B. Quantitative trait loci that determine lipoprotein cholesterol levels in DBA/2J and CAST/Ei inbred mice.J. Lipid Res. 2003; 44: 953-967Abstract Full Text Full Text PDF PubMed Scopus (43) Google Scholar)278 (M)Bi16Chow diet first 8 weeks, followed by atherogenic diet for 8 weeksB6×CASA2003 (15Sehayek E. Duncan E.M. Yu H.J. Petukhova L. Breslow J.L. Loci controlling plasma non-HDL and HDL cholesterol levels in a C57BL/6J x CASA/Rk intercross.J. Lipid Res. 2003; 44: 1744-1750Abstract Full Text Full Text PDF PubMed Scopus (17) Google Scholar)184 (F) 185 (M)Uni11Chow dietB6×D22003 (16Colinayo V.V. Qiao J-H. Wang X. Krass K.L. Schadt E. Lusis A.J. Drake T.A. Genetic loci for diet-induced atherosclerotic lesions and plasma lipids in mice.Mamm. Genome. 2003; 14: 464-471Crossref PubMed Scopus (55) Google Scholar)111 (F)Uni52, 68Chow diet first 52 weeks, followed by atherogenic diet for 16 weeks(B6×NZB)B62003 (42Wang X. Le Roy I. Nicodeme E. Li R. Wagner R. Petros C. Churchill G.A. Harris S. Darvasi A. Kirilovsky J. et al.Using advanced intercross lines for high-resolution mapping of HDL cholesterol quantitative trait loci.Genome Res. 2003; 13: 1654-1664Crossref PubMed Scopus (78) Google Scholar)100 (F)Uni8, 12, 23Chow diet first 8 weeks, followed by atherogenic diet for 15 weeksSM×NZB2004 (43Korstanje R. Li R. Howard T. Kelmenson P. Marshall J. Paigen B. Churchill G. Influence of sex and diet on quantitative trait loci for HDL cholesterol levels in an SM/J by NZB/BlNJ intercross population.J. Lipid Res. 2004; 45: 881-888Abstract Full Text Full Text PDF PubMed Scopus (74) Google Scholar)259 (F) 254 (M)Uni8, 14, 20, 24Chow diet first 8 weeks, followed by atherogenic diet for 16 weeksB6×129S12004 (44Ishimori N. Li R. Kelmenson P.M. Korstanje R. Walsh K.A. Churchill G.A. Forsman-Semb K. Paigen B. Quantitative trait loci analysis for plasma HDL-cholesterol concentrations and atherosclerosis susceptibility between inbred mouse strains C57BL/6J and 129S1/SvImJ.Arterioscler. Thromb. Vasc. Biol. 2004; 24: 161-166Crossref PubMed Scopus (63) Google Scholar)294 (F)Uni20Chow diet first 6 weeks, followed by atherogenic diet for 14 weeks129S1×CAST2004 (45Lyons M.A. Quantitative trait loci that determine lipoprotein cholesterol levels in an intercross of 129S1/SvImJ and CAST/Ei inbred mice.Physiol. 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QTL mapping for genetic determinants of lipoprotein cholesterol levels in combined crosses of inbred mouse strains.J. Lipid Res. 2006; 47: 1780-1790Abstract Full Text Full Text PDF PubMed Scopus (25) Google Scholar)166 (M) 158 (M)Bi16Chow diet first 8 weeks, followed by atherogenic diet for 8 weeksB6×A2006 (48Stylianou I.M. Tsaih S-W. Dipetrillo K. Ishimori N. Li R. Paigen B. Churchill G.A. Complex genetic architecture revealed by analysis of high-density lipoprotein cholesterol in chromosome substitution strains and F2 crosses.Genetics. 2006; 174: 999-1007Crossref PubMed Scopus (28) Google Scholar)343 (M)Uni10Chow dietB6×A2006 (48Stylianou I.M. Tsaih S-W. Dipetrillo K. Ishimori N. Li R. Paigen B. Churchill G.A. Complex genetic architecture revealed by analysis of high-density lipoprotein cholesterol in chromosome substitution strains and F2 crosses.Genetics. 2006; 174: 999-1007Crossref PubMed Scopus (28) Google Scholar)271 (F)Uni8Chow dietNZB×RF2007 (49Wergedal J.E. Ackert-Bicknell C.L. Beamer W.G. Mohan S. Baylink D.J. Srivastava A.K. Mapping genetic loci that regulate lipid levels in a NZB/B1NJxRF/J intercross and a combined intercross involving NZB/B1NJ, RF/J, MRL/MpJ, and SJL/J mouse strains.J. Lipid Res. 2007; 48: 1724-1734Abstract Full Text Full Text PDF PubMed Scopus (18) Google Scholar)542 (F)Uni10Chow dietD2×DU6i2007 (50Brockmann G.A. Karatayli E. Neuschl C. Stylianou I.M. Aksu S. Ludwig A. Renne U. Haley C.S. Knott S. Genetic control of lipids in the mouse cross DU6i x DBA/2.Mamm. Genome. 2007; 18: 757-766Crossref PubMed Scopus (6) Google Scholar)228 (M) 174 (F)Uni6Chow dietB6.Apoe−/−×C3H.Apoe−/−2008 (51Li Q. Li Y. Zhang Z. Gilbert T.R. Matsumoto A.H. Dobrin S.E. Shi W. Quantitative trait locus analysis of carotid atherosclerosis in an intercross between C57BL/6 and C3H apolipoprotein E-deficient mice.Stroke. 2008; 39: 166-173Crossref PubMed Scopus (21) Google Scholar)241(F)Uni18Chow diet first 6 weeks, followed by western diet for 12 weeksB6×C3H2009 (52Su Z. Ishimori N. Chen Y. Leiter E.H. Churchill G.A. Paigen B. Stylianou I.M. Four additional mouse crosses improve the lipid QTL landscape and identify Lipg as a QTL gene.J. Lipid Res. 2009; 50: 2083-2094Abstract Full Text Full Text PDF PubMed Scopus (29) Google Scholar)277 (F)Uni14Chow diet first 8 weeks, followed by atherogenic diet for 6 weeks(NZO×NON)NON2009 (52Su Z. Ishimori N. Chen Y. Leiter E.H. Churchill G.A. Paigen B. Stylianou I.M. Four additional mouse crosses improve the lipid QTL landscape and identify Lipg as a QTL gene.J. Lipid Res. 2009; 50: 2083-2094Abstract Full Text Full Text PDF PubMed Scopus (29) Google Scholar)204 (M)Bi24Chow dietB6×D22009 (52Su Z. Ishimori N. Chen Y. Leiter E.H. Churchill G.A. Paigen B. Stylianou I.M. Four additional mouse crosses improve the lipid QTL landscape and identify Lipg as a QTL gene.J. Lipid Res. 2009; 50: 2083-2094Abstract Full Text Full Text PDF PubMed Scopus (29) Google Scholar)340 (M)Uni8Chow dietB6×129S12009 (19Su Z. Wang X. Tsaih S-W. Zhang A. Cox A. Sheehan S. Paigen B. Genetic basis of HDL variation in 129/SvImJ and C57BL/6J mice: importance of testing candidate genes in targeted mutant mice.J. Lipid Res. 2009; 50: 116-125Abstract Full Text Full Text PDF PubMed Scopus (20) Google Scholar)242 (F) 249 (M)Uni11, 18Chow diet first 11 weeks, followed by atherogenic diet for 7 weeksNOD(NOD×129.H2g7)2009 (53Leiter E.H. Reifsnyder P.C. Wallace R. Li R. King B. Churchill G.C. NOD x 129.H2(g7) backcross delineates 129S1/SvImJ-derived genomic regions modulating type 1 diabetes development in mice.Diabetes. 2009; 58: 1700-1703Crossref PubMed Scopus (16) Google Scholar)159 (F)Uni40Chow dietB6×NODUnpublished139 (M)Uni10Chow dietB6×NZWUnpublished143 (M)Uni10Chow dieta Uni = all F1 animals were produced using the direction as indicated in the first column, with the first strain being the female; bi = F1 animals were produced using both directions. Open table in a new tab High-quality genomic DNA from the different inbred mouse strains was obtained from the Jackson Laboratory's DNA Resource (www.jax.org/dnares/). Primers were designed spanning the exon that contained the SNP and ordered from Integrated DNA Technologies. PCR and Sanger sequencing were performed using standard protocols. Sequence data were analyzed using Sequencher 4.9. From our literature search, we determined that 28 datasets would be of interest to us for this project. However, 5 of these datasets were no longer available due to loss of records or the inability to contact the lead authors. In sum, we obtained a total of 23 independent datasets (summarized in Table 1). Of these datasets, 19 were from our own laboratory. Of the remaining 4 datasets, 2 were kindly provided by Drs. Breslow and Lusis, respectively (15Sehayek E. Duncan E.M. Yu H.J. Petukhova L. Breslow J.L. Loci controlling plasma non-HDL and HDL cholesterol levels in a C57BL/6J x CASA/Rk intercross.J. Lipid Res. 2003; 44: 1744-1750Abstract Full Text Full Text PDF PubMed Scopus (17) Google Scholar, 16Colinayo V.V. Qiao J-H. Wang X. Krass K.L. Schadt E. Lusis A.J. Drake T.A. Genetic loci for diet-induced atherosclerotic lesions and plasma lipids in mice.Mamm. Genome. 2003; 14: 464-471Crossref PubMed Scopus (55) Google Scholar), and the other two were obtained from the QTL archive (www.qtlarchive.org), a free public access repository for QTL mapping datasets. The analyses for single main effect QTL resulted in 143 QTL (Table 2, Fig. 1). In many instances, our analyses confirmed QTL that had been previously reported in the literature, but in other instances, our analyses yielded disparate results when compared with the published findings (Figs. 2, 3). For example, we observed that some QTL moved significantly on the same chromosome, the confidence interval width changed for some QTL, and peak LOD scores were different for many QTL. These dissimilarities were caused by the discrepancies between the genetic map we used and those used in the original studies (14Cox A. Ackert-Bicknell C.L. Dumont B.L. Ding Y. Bell J.T. Brockmann G.A. Wergedal J.E. Bult C.J. Paigen B. Flint J. et al.A new standard genetic map for the laboratory mouse.Genetics. 2009; 182: 1335-1344Crossref PubMed Scopus (173) Google Scholar) and because of methodological differences in the way we conducted our analyses compared with the original (8Ackert-Bicknell C.L. Karasik D. Li Q. Smith R.V. Hsu Y-H. Churchill G.A. Paigen B.J. Tsaih S-W. Mouse BMD quantitative trait loci show improved concordance with human genome-wide association loci when recalculated on a new, common mouse genetic map.J. Bone Miner. Res. 2010; 25: 1808-1820Crossref PubMed Scopus (40) Google Scholar).TABLE 2Main scan QTL peaksChrPeak (cM)Interval (cM)LODSexAge (Weeks)DietHighCrossRemark14418–661.9F (M?)18AthSM(NZB×SM)NZB4529–762.2M+F11ChowCASAB6×CASAAdd6316–743.2F10ChowRFNZB×RFAdd6854–884.5M (F?)8ChowCASTCAST×129S17052–807.4F (M?)8ChowAB×A7046–822.8M+F10ChowNODB6×NOD7063–883.9M (F?)8ChowRIII129×RIII7262–765.8M+F16AthPERAD2×PERA7864–902.7M (F?)8ChowDBAB6×D27874–8027.9M+F16Ath129B6×129S18078–8051.2M+F8Chow129B6×129S18076–8415.0M+F10ChowNZWB6×NZW8076–828.8F (M?)40ChowHetNOD×129S18173–877.3F (M?)8ChowHet(B6×NZB)B68173–858.3M+F8ChowNZBNZB×SM8171–914.5M (F?)16AthRIII129×RIII8274–889.8F (M?)14WesternC3HB6×C3H8266–866.8F (M?)18AthC3HB6×C3H-Apoe KO8484–9010.6F (M?)20Ath129B6×129S18563–892.2F (M?)23AthHet(B6×NZB)B68575–894.6M+F20AthNZBNZB×SM8577–913.0M+F24AthNZBNZB×SM852–852.2M (F?)24ChowNON(NON×NZO)NON8767–913.1M+F14AthNZBNZB×SM8975–895.6F (M?)23AthHetB6×NZB24428–842.3M+F14AthNZBNZB×SM4637–951.8F (M?)40ChowNODNOD×129S14642–606.4M (F?)8ChowCASTD2×CAST6032–1002.5M+F10ChowNZWB6×NZW8365–902.3M (F?)8ChowD2B6×D28378–884.1F (M?)18WesternHetB6×C3H-Apoe KO8374–1032.8M+F11ChowB6B6×CASA842–1022.2F (M?)68AthD2B6×D2968–962.3F (M?)14AthC3HB6×C3H10022–1002.4F10ChowRFNZB×RF352–542.8M+F8ChowNZBNZB×SM52–263.0M+F24AthNZBNZB×SMAdd1713–792.1M+F16AthPERAPERA×ISex-specific3323–792.6F (M?)52ChowB6B6×D2Add3420–442.2M (F?)16AthHet129×RIII422–523.0M+F14AthNZBNZB×SM5553–595.4M8ChowB6B6×1295644–682.7M (F?)8ChowB6B6×D26464–742.8M+F11ChowB6B6×CASA4177–293.9M10ChowB6B6×NODSex-specific2111–314.3M+F16AthPERAD2×PERAAd2612–383.9M+F16AthPERAPERA×IAd2711–553.1M (F?)10ChowB6B6×A2715–317.4M (F?)8ChowD2D2×CAST2920–403.5M (F?)8Chow129CAST×129S14816–762.7F8ChowB6B6×A6149–812.2M (F?)8ChowD2B6×D26254–683.7F (M?)68AthB6B6×D2563–116.3M+F16AthPERAPERA×I103–494.3M+F16Ath129B6×1293511–434.6F (M?)23AthHet(B6×NZB)B64131–473.4M (F?)24ChowHet(NON×NZO)NON459–613.3M+F8ChowB6B6×1294742–4910.3M+F20AthNZBNZB×SM4721–498.7M+F24AthNZBNZB×SM4923–582.3F (M?)23AthHetB6×NZB5123–652.8F (M?)8ChowHetB6×NZB5245–673.5F (M?)18AthNZB(NZB×SM)NZB5249–674.6F (M?)8ChowNZB(NZB×SM)NZB5347–5720F10ChowNZBNZB×RF5941–6110.6M+F14AthNZBNZB×SM6159–719.1M+F8ChowNZBNZB×SM6551–802.2F (M?)18AthNZB(NZB×SM)NZB7955–802.8F (M?)8ChowNZB(NZB×SM)NZB62916–672.3M+F10ChowNODB6×NODAdd3320–462.7M (F?)8ChowD2B6×D2Add4634–782.1M+F16AthPERAPERA×IAdd6040–722.7F (M?)14AthB6B6×C3HAdd7058–764.0M+F8ChowNZBNZB×SM7038–782.1M+F14AthNZBNZB×SM7248–784.4M (F?)8ChowCASTD2×CAST7464–782.9F (M?)52ChowHetB6×D2782–782.4F (M?)20AthB6B6×129S1799–762.2M (F?)8ChowHetB6×A7462–804.0M10ChowHetB6×NODSex-specific8775–893.0M+F24AthSMNZB×SMAdd8850–883.9M+F16AthPERAPERA×I822–342.7F10ChowNZBNZB×RF32–523.3M+F24AthNZBNZB×SMAdd32–523.6M+F14AthNZBNZB×SM3626–446.1M (F?)10ChowB6B6×A3824–464.6F (M?)8ChowB6B6×A4634–662.3F (M?)20Ath129B6×129S15034–663.8M+F10ChowB6B6×NOD5339–653.9M+F8ChowB6B6×129S15646–685.0M (F?)8ChowB6B6×D2733–764.3M+F11ChowB6B6×CASA922–343.8M24AthNZBNZB×SMSex-specific142–263.1M (F?)16AthRIII129×RIIIAdd1818–258.1M+F11ChowCASAB6×CASA2113–276.2M+F8Chow129B6×129S1226–343.0F (M?)20Ath129B6×1292321–591.6F (M?)18AthHetNZB×SM2518–259.0M+F11ChowCASAB6×CASA2522–506.4F (M?)40ChowHetNOD×1292512–326.6M (F?)16AthRIII129×RIII3312–502.6M+F24AthNZBNZB×SM3717–495.4M+F16Ath129B6×1291022–662.4M+F10ChowHetB6×NZW6353–723.2F/M8ChowNZB/SMNZB×SMSex-specific6839–682.2M (F?)10ChowB6B6×ASex-specific7261–723.9F14AthNZBNZB×SM111814–662.7M+F14AthHetNZB×SM2717–432.7M (F?)10ChowB6B6×AAdd3018–543.4M+F16AthPERAPERA×IAdd3614–503.4M+F16AthPERAD2×PERAAdd4135–554.0M+F6ChowDU6iD2×DU6i5643–722.9F (M?)14AthC3HB6×C3H7658–832.5F (M?)68AthD2B6×D28173–833.9M+F16Ath129B6×129813–812.2M+F10ChowHetB6×NZW12106–443.4F10ChowNZBNZB×RFSex-specific2012–285.9F (M?)20Ath129B6×129S12810–404.8M (F?)16AthRIII129×RIII3017–457.4M8ChowIPERA×I3812–542.1M (F?)8ChowB6B6×D25426–682.2M+F8ChowNZBNZB×SM5610–742.8M+F20AthNZBNZB×SM13102–283.0M+F8Chow129B6×129Add141915–293.1M (F?)8ChowB6B6×D2Add3818–562.6M+F10ChowHetB6×NOD15164–342.6F (M?)14AthB6B6×C3HAdd188–283.0M (F?)8ChowCASTCAST×1292315–353.5F (M?)8ChowB6B6×A3837–542.6M+F8ChowNZBNZB×SM5424–592.1M+F16AthPERAPERA×I16117–212.