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Quantitative Trait Loci for Regional Adiposityin Mouse Lines Divergently Selected forFood IntakeKellie A. Rance,*† Catherine Hambly,* Gillian Dalgleish,* Jean-Michel Fustin,* Lutz Bunger,‡§ andJohn R. Speakman*†

AbstractRANCE, KELLIE A., CATHERINE HAMBLY, GILLIANDALGLEISH, JEAN-MICHEL FUSTIN, LUTZBUNGER, AND JOHN R. SPEAKMAN. Quantitative traitloci for regional adiposity in mouse lines divergentlyselected for food intake. Obesity. 2007;15:?–?.Objective: Obesity is thought to result from an interactionbetween genotype and environment. Excessive adiposity isassociated with a number of important comorbidities; how-ever, the risk of obesity-related disease varies with thedistribution of fat throughout the body. The aim of thisstudy was to map quantitative trait loci (QTLs) associatedwith regional fat depots in mouse lines divergently selectedfor food intake corrected for body mass.Research Methods and Procedures: Using an F2 intercrossdesign (n � 457), the dry mass of regional white (subcuta-neous, gonadal, retroperitoneal, and mesenteric) adiposetissue (WAT) and brown adipose tissue (BAT) depots wereanalyzed to map QTLs.Results: The total variance explained by the mapped QTLvaried between 12% and 39% for BAT and gonadal fatdepots, respectively. Using the genome-wide significancethreshold, nine QTLs were associated with multiple fatdepots. Chromosomes 4 and 19 were associated with WAT

and BAT and chromosome 9 with WAT depots. Significantsex � QTL interactions were identified for gonadal fat onchromosomes 9, 16, and 19. The pattern of QTLs identifiedfor the regional deposits showed the most similarity be-tween retroperitoneal and gonadal fat, whereas BATshowed the least similarity to the WAT depots. Analysis oftotal fat mass explained in excess of 40% of total variance.Discussion: There was limited concordance between theQTLs mapped in our study and those reported previously.This is likely to reflect the unique nature of the mouse linesused. Results provide an insight into the genetic basis ofregional fat distribution.

Key words: quantitative trait loci, adipose tissue,appetite

IntroductionOver the past 30 years, there has been an enormous

increase in the number of people in the Western world whoare classed as either obese or overweight (1,2). Obesity is apredisposing factor for a number of serious chronic illnessesincluding type 2 diabetes, hypertension, cardiovascular dis-ease, and some cancers (3). Accordingly, the increasingprevalence of obesity has resulted in a dramatically elevatedburden on health care budgets (4). Moreover, it has beenestimated that obesity results in �300,000 excess deathsannually in the United States alone (5).

The increase in the prevalence of obesity has occurredwith such rapidity that it is extremely unlikely to have beencaused by changes in the genetic structure of the popula-tion—pointing to an environmental cause. However, whenstudies have attempted to partition the individual variance inbody fatness between environmental and genetic factors, theoverwhelming factor that emerges is genetic (6,7). Com-bined, these observations strongly suggest that the epidemicis a result of a genotype by environment interaction (8–11).Some individuals have a latent genetic predisposition to

Received for review March 6, 2007.Accepted in final form May 4, 2007.The costs of publication of this article were defrayed, in part, by the payment of pagecharges. This article must, therefore, be hereby marked “advertisement” in accordance with18 U.S.C. Section 1734 solely to indicate this fact.*Aberdeen Centre for Energy Regulation and Obesity, School of Biological Sciences,University of Aberdeen, Aberdeen, United Kingdom; †Aberdeen Centre for Energy Regu-lation and Obesity, Division of Vascular Health, Rowett Research Institute, Bucksburn,Aberdeen, United Kingdom; and ‡School of Biological Sciences, Institute of EvolutionaryBiology, University of Edinburgh, Edinburgh, United Kingdom.§Current address: Scottish Agricultural College, Sustainable Livestock Systems Group,Bush Estate, Penicuik, United Kingdom.Address correspondence to Kellie Rance, Rowett Research Institute, Greenburn Road,Bucksburn, Aberdeen, AB21 9SB, UK.E-mail: [email protected] © 2007 NAASO

OBESITY Vol. 15 No. 12 December 2007 1

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** AUTHORS: PLEASE RETURN PROOFS WITHIN 48 HOURS **

gain weight that is expressed in modern societies. Attemptsto resolve the genetic basis of the predisposition to obesityhave involved a combination of animal studies and humanmapping programs.

Observations indicate that not all people become obese inexactly the same way. In some individuals, adipose tissuemay be deposited primarily in the abdominal region,whereas in others, fat is deposited into stores in the uppersection of the lower limbs or around the buttocks. It hasbecome increasingly apparent that these different forms ofobesity do not involve the same level of risk for the devel-opment of disease (12–17). In particular, it has been notedthat high levels of abdominal fat pose a particularly severerisk for progress to metabolic disease (12–18), whereas fatdeposited subcutaneously is more benign (17,19) and mayactually be protective (20). Despite this emerging knowl-edge that all obesity does not pose equal risks, compara-tively few genetics studies have attempted to assess thegenetic basis of variability in the sizes of component fatstores (21–26), focusing rather on the levels of total bodyadiposity (27).

Studies that have been performed to date have identifieda number of potential loci that may have specific influenceson the sizes of individual fat pads. For example Stylianou etal. (23) used an intercross of the lean SM/J and obeseNZB/BINJ strains to identify multiple significant quantita-tive trait loci (QTLs)1 influencing the weight of four fatpads: mesenteric, inguinal, gonadal, and retroperitoneal.Epistatic QTL interactions were also identified: a QTL onchromosome 19 was shown to be at the center of a networkof eight interacting QTLs, a QTL on chromosome 4 was atthe center of six, and a QTL on chromosome 17 was at thecenter of four interacting QTLs. The same strains were usedby Taylor et al. (28) to distinguish QTL affecting adiposityindex (the sum of all four fat depots divided by body mass)and sizes of specific fat pads. Several QTLs were identifiedthat differentially affected specific fat depots. QTLs onchromosomes 2 and 7 had effects more on gonadal thaninguinal fat, whereas the converse was true for the QTLs onchromosome 17. In contrast, Reed et al. (25) used F2 micederived from crosses of C57BL/6 and 129P3/J mice andexamined effects on absolute and relative (corrected forbody mass) retroperitoneal and gonadal fat pads. Retroper-itoneal depot weight was linked to chromosomes 8, 11, 12,and 17, but the linkage effects for the gonadal depot werestronger for chromosomes 5, 7, and 9. Hence, both Reed etal. (25) and Taylor et al. (28), using very different originalcrosses, identified QTLs on chromosome 7 linked to vari-

ation in gonadal fat pad weight. Another common feature ofthese studies was that they both identified differential ef-fects of QTLs between sexes.

In addition to QTL mapping approaches, other studieshave identified candidate processes that may affect specificfat depots. Matsuzaki et al. (29), for example, noted thatvisceral obesity was correlated with levels of glucocorti-coids. Glucocorticoids can be produced locally from inac-tive 11-keto forms through the enzyme 11 � hydroxysteroiddehydrogenase type 1 (11 � HSD-1). By creating transgenicmice overexpressing 11 � HSD-1 selectively in adiposetissue, they were able to produce mice with elevated adiposelevels of corticosterone that developed a syndrome of spe-cific visceral obesity. Based on these data, they suggestedthat increased adipocyte 11 � HSD-1 activity may be acommon molecular etiology for visceral obesity. Becauseglucocorticoid metabolism may be modulated by multiplepathways (e.g., the growth hormone and insulin-like growthfactor-1 pathway) (30), this opens up several routeswhereby genetic polymorphisms may converge on a singlemechanism driving regional adiposity. Furthermore, differ-ences in glucocorticoid metabolism between the sexes maypartly explain the differential effects of some identifiedQTLs by sex.

We have been using a novel animal model system toexplore the genetic basis of various parameters related toenergy balance and storage (31–33). The model systeminvolves partially inbred lines derived from mice that hadbeen divergently selected for high and low food intakecorrected for body mass (34,35). This selection has resultedin divergence of various phenotypes including resting en-ergy expenditure, thermoregulation, and a small divergencein body mass (32,36). Using an F2 population, we used QTLmapping to identify regions of the genome associated withphenotypes of interest. The novel nature of the originalselection has already revealed a novel paternally imprintedQTL on chromosome 8 that is related to genetic determi-nation of body mass (33). In this study, we investigate QTLsfor adiposity, focusing in particular on identifying QTLs forregional fat stores.

Research Methods and ProceduresMouse Lines

The mouse lines used in this study (the Maintenance-linesor M-lines) have been divergently selected for food intakebetween 8 and 10 weeks of age, corrected by phenotypicregression on mean body weight (over both ages), therebychanging the maintenance needs and metabolic rate(32,34,35). The selection responses have been describedearlier (34,37). In brief, the base population was createdfrom a cross of the six control lines (random mating for 20generations) for the Edinburgh divergent selection lines,which were formed from an F1 between two inbred lines

1 Nonstandard abbreviations: QTL, quantitative trait loci; 11 � HSD-1, 11 � hydroxysteroiddehydrogenase type 1; M-line, Maintenance line; WAT, white adipose tissue; F-line, Fatpercentage line; BAT, brown adipose tissue; SUB, subcutaneous WAT; GOND, gonadalWAT; RP, retroperitoneal WAT; MES, adipose tissue that surrounded the mesentericvessels; Nnt, nicotinamide nucleotide transhyrogenase; totFM, sum of all dissected fatdepots; Mfat, M-line fat; SD, standard deviation; .

QTL for Adipose Tissue Depots, Rance et al.

2 OBESITY Vol. 15 No. 12 December 2007


Fn1

(JU and CBA) crossed with an outbred population (CFLP)(37). For the first 24 generations of selection, the M-lineswere maintained as three replicates in each direction ofselection. At this point, the three replicates were inter-crossed, and only a single line in each direction was main-tained. At generation 38, phenotypic selection was sus-pended, and from generation 43 onward, the lines weremaintained by full sib mating (MHi and MLi).

QTL mapping analysis was performed using an F2 pop-ulation that has been described previously (33). The F2

population was generated representing all reciprocal one-half F1 generation combinations (MHi � MLi and MLi �MHi). To ensure age homogeneity during measurements, asa result of the time-consuming nature of dissections andadditional measures taken on the F2 population, the largesample size was generated by batches at six different time-points. All F2 mice were maintained in their family groupsuntil 3 weeks old, after which they were weaned intosingle-sex groups and identified using unique ear clips.Single-sex groups were kept in MB1 cages (45 � 28 � 13cm; North Kent Plastics, Rochester, U.K.) under a photo-period of 12-hour:12-hour light:dark (lights on at 7:00 AM)at 20 � 2 °C. At �14 weeks of age, animals were individ-ually housed in M3 cages (48 � 15 � 13 cm; North KentPlastics) until the end of the experimental period at 18weeks. All individuals were housed on sawdust bedding andhad access to water and food (SDS Breeder and grower diet:Special Diet Services; BP Nutrition, U.K.) ad libitum. Char-acteristics of this diet have been previously published (38)and consists of 94.4% dry matter with a gross energycontent of 18.36 kJ/g dry. Approximately 70% of the calo-ries are provided from carbohydrate, 9% from fat, and 22%from protein. Apparent dry matter digestability in non-reproductive animals was 76.5%, and food spillage aver-aged 0.7% of the food missing each day (n � 93 cagessorted for food in the sawdust) (38). A total of 515 F2

individuals were produced from 51 F1 pairings, and dissec-tion data were available on 457 F2 individuals.

Phenotype MeasurementsTerminal measurements were taken at 18 weeks. Total

mass of each animal was recorded, and animals were dis-sected into 21 separate components, each weighed to fourdecimal places (Ohaus Analytical Plus) to determine freshmass. Body components were then oven dried (Gallen-kamp) at 60 °C for a minimum of 14 days until weightstable. Dry masses (four decimal places) were recordedimmediately after removal from the oven to prevent rehy-dration. Fat stores may differ significantly in their watercontent caused, in part, by differences in cellularity betweendepots, with the mesenteric fat depot generally having agreater water content than the other depots (39). Becausevariability in the water content of the depot might add to thevariation in total weight that was not reflective of adiposity,

we present data here on the results of QTL mapping analysisfor the dry masses of the major fat depots. However, dry andfresh mass were highly correlated (r � 0.95), and thesubsequently identified QTLs were all replicated in the wetand dry tissue analyses.

We dissected four separate white adipose tissues (WATs)depots and intrascapular brown adipose tissues (BATs).First we removed all of the white adipose tissue that laybetween the skin and the body cavity predominantly locatedin four axial fat pads around the base of the limbs. Wepooled fat from these four separate sources plus any otherfat under the skin surface into a single composite, which wetermed the subcutaneous WAT (SUB). We opened the ab-dominal cavity and removed the two fat pads that areassociated with the gonads. In males, these form two verydistinct pads, each associated with one of the testicles. Infemales, these pads are more diffuse and typically lie adja-cent to the uterine horns. These were pooled and termedgonadal WAT (GOND). We removed the alimentary tractfrom the abdomen and dissected any fat that was adjacent tothe dorsal surface of the peritoneum—retroperitoneal WAT(RP). We unraveled the alimentary tract and removed anyadipose tissue that surrounded the mesenteric vessels(MES). Finally, we removed the distinctive bilobular inter-scapular BAT that is located dorsally between the scapulae(BAT). We separated the BAT from any associated WATthat was included in SUB without the aid of a dissectingmicroscope. Although the BAT component of the interscap-ular fat pad is quite distinctive in these strains, this dissec-tion was made only at a gross level, and consequently, theremay have been error introduced by this procedure into theweight of the BAT. Because the associated WAT from thispad was only a very minor component of the total SUB fat,this error would not have been evident in the SUB fraction.We also calculated the sum of all dissected fat depots(totFM).

GenotypingThe genotyping protocols for the population have been

described previously (33). In brief, all animals were geno-typed using microsatellite markers purchased from ResGen(Huntsville, AL). All animals were genotyped at 87 infor-mative, evenly distributed MapPair primers giving an aver-age map distance of 23.6 cM (33). The average informationcontent of makers calculated according to Knott et al. (40)was 0.79 and 0.72 for additive and dominance components,respectively.

Data AnalysesFive separate fat depots and totFM were analyzed to

identify the magnitude and location of QTLs. Fresh and drymasses of the individual fat depots were highly correlated;we used dry mass in all analyses to reflect the adiposity ofthe animals. Using GenStat 7.1 (41), all data were studied




AQ: A

AQ: B

for normality and transformed by Box-Cox transformationwhere required. The effects studied included the fixed ef-fects associated with sex (levels � 2), number in litter atweaning (levels � 12), reciprocal half of mating structure(levels � 4), and batch effect (representing the six differentmating time-points); the random effects of dam (n � 51)and linear covariates of body mass and age in days atdissection. No significant effects were associated with mat-ing structure, batch, and age; these parameters were omittedfrom further analyses.

The QTL mapping model applied to the data have beendescribed previously by Rance et al. (33). Briefly, QTLmapping analysis was carried out as described by Haley etal. (42). The F-ratio test statistic was used to determine QTLposition. Background genetic effects were included in themodel to remove variance caused by QTLs elsewhere on thegenome (43). An interaction term for sex � QTL interac-tions was tested in all models. QTLs were also tested for theeffects associated with imprinting (44). To separate theprobability of an individual inheriting an allele from its sireor dam, saturated imprinting models including paternal,maternal, and dominance components were fitted. Whereimprinting effects were indicated, the imprinting model wascompared with the single Mendelian QTL model to test thesignificance of imprinting effect. Finally, models testing thepresence of two QTLs on a chromosome were tested.

Genome-wide empirical F-ratio thresholds were esti-mated using the permutation test (45). The empirical ge-nome-wide significance thresholds were 7.56 for significant(p � 0.05), 9.41 for highly significant (p � 0.01), and asuggestive threshold (46) of 4.32. Confidence intervals forQTL position were determined using the 1-limit of detectiondrop-off method approximate to a 90% to 95% confidenceinterval. All QTLs were named in line with the rules formouse genetic nomenclature as outlined by Mouse GenomeInformatics (http://www.informatics.jax.org/).

ResultsPhenotypes

Descriptive statistics for all WAT depots, BAT, andtotFM are presented in Table 1. All measures of adipositywere highly variable, with coefficients of variation rangingfrom 65% to 100% for BAT and SUB, respectively. Allmeasures of adiposity were highly correlated (Table 2).

QTLsA total of 27 QTLs on 11 chromosomes including the X

chromosome were detected for the individual fat depotsusing the suggestive threshold (F-ratio � 4.32; chromo-some-wide p � 0.05; Table 3). The total phenotypic vari-ance in the F2 explained by the mapped suggestive QTLswas 25% for SUB, 39% for GOND, 28% for RP, 19% forMES, and 12% for interscapular BAT. Of these 27 QTLs,21 surpassed the significant threshold (F-ratio � 7.56; p �0.05), and 14 were highly significant (F-ratio � 9.41; p �0.01).

Table 1. Descriptive statistics for fresh and dry fat mass traits

Traits N

Fresh mass Dry mass

Mean (SD) Range Mean (SD) Range

SUB (g) 457 0.83 (0.65) 0.124 to 7.79 0.55 (0.55) 0.024 to 6.58RP (g) 456 0.32 (0.23) 0.006 to 1.84 0.21 (0.18) 0.002 to 1.44MES (g) 456 0.30 (0.17) 0.010 to 1.35 0.19 (0.15) 0.007 to 1.18GOND (g) 457 0.55 (0.37) 0.04 to 2.78 0.45 (0.33) 0.016 to 2.37BAT (g) 457 0.18 (0.077) 0.058 to 0.897 0.10 (0.065) 0.011 to 0.719totFM (g) 456 2.18 (1.38) 0.49 to 13.96 1.50 (1.20) 0.13 to 11.82

SD, standard deviation; WAT, white adipose tissue; SUB, subcutaneous WAT; RP, retroperitoneal WAT; MES, adipose tissue thatsurrounded the mesenteric vessels; GOND, gonadal WAT; BAT, brown adipose tissue; totFM, sum of all dissected fat depots.

Table 2. Phenotypic correlations between dry fatmass traits

SUB RP MES GOND

RP 0.860*MES 0.880 0.899GOND 0.845 0.870 0.893BAT 0.869 0.822 0.884 0.831

WAT, white adipose tissue; SUB, subcutaneous WAT; RP, retro-peritoneal WAT; MES, adipose tissue that surrounded the mesen-teric vessels; GOND, gonadal WAT.* All Pearsons correlations were highly significant (p � 0.005).




T1

T2

T3

Table 3. QTL detected for dry mass of individual fat depots from the genome wide interval mapping scan bychromosome

Chromosomes TraitQTL

name*MaximumF-ratio†

Mapposition§

QTL associated effects

Additive‡ Dominance‡Percent

variance¶

1 GOND 5.28 38 �0.019 0.359 2.334 RP Mfat1 9.26 49 �0.191 �0.024 4.024 GOND Mfat1 13.33 47 (29 to 56) �0.196 �0.056 5.694 MES Mfat1 21.69 53 (46 to 60) �0.295 �0.114 8.884 SUB Mfat1 17.96 53 (37 to 61) �0.249 �0.173 7.214 BAT Mfat1 9.93 51 (28 to 62) �0.200 �0.125 4.244 totFM Mfat1 16.67 49 (42 to 58) �0.228 �0.109 7.019 RP Mfat2 17.54 34 (16 to 49) 0.305 �0.188 7.359 GOND Mfat2 20.66 41 (27 to 53) 0.344 �0.109 8.559 MES 5.30 26 0.149 �0.119 2.339 SUB Mfat2 11.96 34 (17 to 50) 0.280 �0.096 5.109 totFM Mfat2 21.46 36 (20 to 49) 0.312 �0.199 8.85

11 RP Mfat3 8.01 1 0.056 0.148 3.5011 GOND Mfat3 11.93 1 (�7) 0.078 0.181 5.1211 totFM Mfat3 11.43 1 (�9) 0.065 0.155 4.9212 RP Mfat4 8.83 0 �0.118 �0.136 3.8412 GOND Mfat4 8.37 0 �0.121 �0.103 3.6512 totFM Mfat4 7.62 0 �0.097 �0.116 3.3313 GOND Mfat5 11.15 67 (57�) 0.166 0.050 4.8013 SUB Mfat5 9.11 66 0.177 0.046 3.9313 BAT Mfat5 11.66 61 (46�) 0.227 0.057 4.9513 totFM Mfat5 7.80 65 0.138 0.035 3.4114 GOND 5.28 56 �0.096 0.126 2.3315 SUB 7.08 58 �0.180 �0.184 3.0815 totFM Mfat6 8.34 66 �0.115 �0.102 3.6416 GOND 5.68 63 0.170 �0.010 2.5119 RP Mfat7 11.20 52 (15 to 62) �0.209 �0.001 4.8219 GOND Mfat7 8.38 55 �0.156 �0.030 3.6519 MES Mfat7 9.62 53 (23 to 65) �0.203 �0.021 4.1519 SUB Mfat7 12.94 46 (26 to 58) �0.219 �0.001 5.5019 BAT 5.54 74 �0.162 �0.035 2.4119 totFM Mfat7 14.48 52 (28 to 62) �0.200 �0.011 6.15X RP Mfat8 9.56 38 (9 to 53) �0.141 0.096 4.15X MES Mfat8 7.66 17 �0.167 0.109 3.34X totFM Mfat8 8.49 40 �0.106 0.112 3.70

QTL, quantitative trait loci; WAT, white adipose tissue; GOND, gonadal WAT; RP, retroperitoneal WAT; MES, adipose tissue that surroundedthe mesenteric vessels; SUB, subcutaneous WAT; BAT, brown adipose tissue; totFM, sum of all dissected fat depots; LOD, limit of detection;SD, standard deviation.* QTL names assigned to significant (p � 0.05) QTLs.† Maximum F-ratio detected; those shown in italics reached only the suggestive linkage threshold.‡ Position in cM from the most proximal marker, for loci reaching the genome-wide highly significant level (p � 0.01); the 1-LOD supportinterval is given in parenthesis.§ Additive and dominance effects given in SD units; estimates shown in italics are not significantly different from zero (p � 0.05); positiveestimates are associated with the increasing allele coming from the high line; and negative effects associated with the increasing allele originatingfrom the low line.¶ Percentage of F2 phenotypic variance explained by the QTLs.




The results showed that QTLs for all four WAT traitswere located on chromosomes 4, 9, and 19 (Table 3). Onchromosomes 4 and 19, the QTLs seemed to be associatedwith general adiposity, with QTLs also being identified forBAT. The QTLs mapped on chromosome 4, Mfat1 (M-linefat QTL 1; Table 3) shows agreement for QTL position (47to 53 cM) across all WAT and BAT traits. The estimatedQTL effects showed the same direction of additive anddominance deviations for all traits. However, the central fatdepots of RP and GOND were associated with non-signif-icant dominance deviations, whereas MES and SUB wereassociated with larger additive effects [�0.295 and �0.249standard deviation (SD) units, respectively] and significantdominance deviations (�0.114 and �0.173, SD units).There was no evidence for imprinting, two-QTL, or QTL �sex interactions on chromosome 4 for any of the traitsanalyzed.

On chromosome 9, Mfat2 showed good agreement forestimates of QTL position and effects for the WAT traits,with the exception of MES, where only suggestive QTLeffects were detected. The QTL Mfat2, explained 8.6% ofthe total phenotypic variance for GOND, 7.4% for RP, and5.1% for SUB fat depots. Sex � QTL interactions weresignificant (p � 0.01) for GOND, where only females wereassociated with significant additive effects (0.548 � 0.086SD units) compared with males (0.178 � 0.115 SD units).

The final region where there was good agreement for allWAT traits was chromosome 19—Mfat7. Highly signifi-cant (p � 0.01) additive QTL effects were associated withRP, explaining 4.8%, MES 4.2%, and SUB 5.5% of theresidual phenotypic variance. The QTL for GOND on chro-mosome 19 explains 3.7% total variation and was associ-ated with significant (p � 0.01) sex � QTL interaction,where females were associated with significant additiveeffects (0.342 � 0.068 SD units) compared with males(0.026 � 0.092 SD units). Dominance effects were smalland did not differ significantly from zero. The chromosome19 BAT QTL explained less variance than the WAT traitsand seems to be in a position more distal to the estimatedposition for the WAT QTL.

Significant (p � 0.01) sex � QTL interactions were alsodetected for the GOND QTL on chromosome 16. Onceagain, female additive effects were significantly differentfrom zero (�0.265 � 0.061 SD units), whereas males didnot show significant effects (�0.057 � 0.084 SD units).

Correlation analysis of the WAT and BAT variablesindicated strong correlations between the individual dis-sected traits (Table 2). Additional multivariate cluster anal-ysis of the dissected fat traits indicated RP and GOND weremost related, showing 93.9% similarity; these traits clus-tered with SUB at 93.3% similarity, followed by MES at92.7%, and finally clustering with BAT at 90.5% similarity.This cluster analysis seems to be reflected in the QTLidentified. In particular, the RP and GOND traits show a

high degree of similarity in the identified QTL. In additionto QTLs already discussed, QTLs for RP and GOND weremapped on chromosomes 11 (Mfat3) and 12 (Mfat4), wherethe magnitude and direction of effects were similar for bothtraits. Significant QTLs were also mapped on chromosome13 (Mfat5) for GOND, SUB, and BAT, where there weresignificant additive effects and dominance deviations thatwere not significantly different from zero.

In addition to analyzing the individual dissected fat de-pots, we analyzed totFM and identified a total of eight QTLs(Table 3). All of the QTLs mapped using totFM seemed tobe represented by the individual WAT and BAT depots. TheQTL Mfat6 on chromosome 15 was significant for totFMbut was only significant at the suggestive level for thecomponent trait SUB. The total variance explained by QTLfor totFM was substantially higher than individual fat de-pots at 40%, suggesting an increase in power associatedwith this composite trait. Testing the model fitting sex �QTL interactions for the composite traits showed a signif-icant interaction on chromosome 9. This is likely to be as aresult of the female additive effects associated with thehighly significant (F-ratio � 20.66) QTL Mfat2 for GOND.There was no evidence to support models for imprinting andtwo-QTL for any of the traits analyzed.

DiscussionWe presented results from QTL mapping analyses of the

major white fat depots (WAT) of SUB, GOND, RP, andMES, interscapular BAT, and totFM. A general feature ofthe mapping studies, which have identified QTLs associatedwith fat mass, is that they have used mouse lines eitherdivergently selected for adiposity or which differ substan-tially for fat mass. A unique feature of this QTL mappingexperiment is that we used mouse lines divergently selectedfor food intake from 8 to 10 weeks of age, corrected formean 8- and 10-week weight. Consequently, the originallines used to establish the F1 generation were not verydifferent in their body and fat masses. Body weight differsby an average of 14% (34), and there are minor, but signif-icant, differences between body fat (32). However, in the F2

generation, we exposed an enormous variation in fat mass-es—varying more than two orders of magnitude.

A number of studies have identified QTLs associatedwith adiposity and shown that there is a degree of common-ality across lines of alternative genetic backgrounds. Theseregions have been referred to as “obesity genomic hot-spots”; however, Rocha et al. (22) stressed that cautionshould be taken when assuming that the underlying genes inthe QTL regions are identical in each genetic background.The M-lines used in this study were one of the Edinburghselection lines, where divergent selection was applied basedon a number of different phenotypes including high and lowfat percentage (37). The divergent F-lines have also beencharacterized for QTLs (47), and the QTLs detected were




Fob1 (chromosome 2, 4.9% of total variance), Fob2 (chro-mosome 12, 19.5% of total variance) Fob3 (chromosome15, 14.4% of total variance), and Fob4 (X chromosome,7.3% of total variance). In our study, no QTLs were de-tected on chromosome 2 where Fob1 resides. The QTLFob2 is unlikely to be in common with Mfat4 because,although confidence intervals do not preclude a commonbasis, Fob2 is associated with female-only effects in theF-line (47). The chromosome 15 QTL Fob3 has been fine-mapped and separated into two QTLs: Fob3a (Bw21d) andFob3b (gonadal fat pad weight) (48). The F-line QTLFob3b is associated with much larger additive effects (0.662SD units compared with 0.115 SD units for Mfat6), and theposition is more proximal (27 cM compared with 66 cM forMfat6). Finally, the QTL Fob4 does seem to be in commonwith Mfat8 because positions are very similar in both stud-ies (37 cM for Fob4 and 40 cM for totFM Mfat8), althoughthe variance explained is larger for Fob4 (7.3%) (47) com-pared with Mfat8 (3.6% of totFM). It is clear that there isonly limited concordance in the QTLs identified in theM-line and F-line Edinburgh selection lines. Of the sixhighly significant (p � 0.01) QTLs identified in our study(Table 3), only one (Mfat8) seems to be present in theF-line. The differences in the QTLs identified could becaused by drift or differences in the time at which measure-ments were taken (14 weeks for F-line, 18 weeks for M-line); however, it is more likely to be caused by the selectioncriterion applied to the two lines resulting in differences inthe divergence of genes underlying the QTL identified.Other potential factors include epistatic interactions be-tween the QTL underlying fat traits, the genetic backgroundof the lines, and the possibility of spontaneous mutation.

Although there was little commonality between our studyand the previous QTL mapping analyses carried out onother Edinburgh selection lines, there are some similaritieswith other QTL mapping studies. Our QTL mapping anal-yses indicated that there were a number of chromosomesthat are associated with general adiposity, and a large num-ber of functional and position candidates may be identified.Most notable were chromosomes 4 (Mfat1) and 19 (Mfat7),which were associated with both WAT and BAT depots.The QTL Mfat1 explained a relatively large proportion oftotal variance for totFM (�7%) and specific traits (8.9% ofMES). Chromosome 4 has previously been associated withgeneral adiposity (Table 4). There are a number of possiblecandidate genes on chromosome 4 that may be responsiblefor the general adiposity QTL Mfat1; however, the leptinreceptor (Lepr) at 46.7 cM provides an excellent functionaland positional candidate. The QTL Mfat7 on chromosome19 is also associated with general adiposity in our popula-tion. A number of QTLs for adiposity have previouslymapped to this location (Table 4), including Abfw4 (49),Nobq2 (50), and Obqw5 (23). Because of the wide confi-dence interval associated with our QTLs and the more

proximal QTLs Obq5 (51) and Afw8 (52), these QTLs maystill be in concordance with Mfat7. Our searches failed thusfar to isolate any feasible functional and positional candi-dates for this QTL. In addition to the two general adiposityQTLs, the chromosome 9 QTL Mfat2 is associated withWAT traits. Other QTLs have been detected concordantwith Mfat2, and Apoc3 is a good positional and functionalcandidate gene (knockout mice are associated with obesityon a high-fat diet) (53). For further potential candidategenes, see McDaniel et al. (54), who carried out an exten-sive review of this region. Of the remaining three significantQTLs, the X-linked Mfat8 is in good concordance withother QTL mapping studies (Table 4). Bombesin-like recep-tor 3 (Brs3) provides a good functional and positionalcandidate gene. Homozygote Brs3 knockout mice sufferfrom mild obesity, hypertension, impaired glucose metabo-lism, reduced metabolic rate, and hyperphagia (55). It isinteresting to note that this candidate gene is associated withfood intake, which formed part of the selection criteria inthe development of the M-line mice. Therefore, any poly-morphism in this gene may have diverged as part of theselection response in these lines.

The QTLs on chromosomes 11 and 13 show little con-cordance with other QTL mapping studies. Generally, QTLmapping studies established to identify obesity-relatedQTLs have used mouse lines that differ or diverge in bodyweight/composition (47,50,52,56,57). The lack of concor-dance between the QTLs Mfat3 and Mfat5 is likely to be afeature of the relatively unique genetic resource that theM-lines represent. There are a number of plausible candi-date genes for Mfat3 and Mfat5. For example, on chromo-some 11, glucagon receptor (Gcgr) is involved in glucosehomeostasis and Gcgr-null mice displayed reduced adipos-ity and leptin levels but normal body weight, food intake,and energy expenditure (58). Candidate genes for Mfat5 onchromosome 13 include cocaine- and amphetamine-regu-lated transcript (Cart) (59), which is a satiety factor and isclosely associated with the actions of two important regu-lators of food intake, leptin and neuropeptide Y. A furthercandidate for Mfat5 is nicotinamide nucleotide transhyro-genase (Nnt) (60), an integral inner mitochondrial mem-brane protein. Nnt forms part of the energy-transfer systemof the respiratory chain and catalyzes the transfer of ahydride ion between nicotinamide adenine dinucleotide,NAD(H), and oxidized nicotinamide dinucleotide phos-phate, NADP(H). Nnt was recently identified to underlaythe phenotype associated with the QTL Bglu2 blood glucoseQTL (61).

QTL analysis of regional fat depots has enabled us toidentify a number of QTLs influencing regional adiposity.Our data suggest that, although there seems to be somedifference in the genetic basis of variation in regional fatdepots, much of the genetic variation can be explained bygeneral adiposity loci. Many previous studies have also




T4

identified loci that are associated with general adiposity.Relatively few have identified QTLs that have specificregional effects. For example, Taylor et al. (28) identifiedQTLs on chromosomes 2 and 7 that had effects primarily ongonadal fat, and a QTL on chromosome 17 that had moreinfluence on inguinal fat. Reed et al. (25) found that retro-peritoneal depot weight was linked to QTLs on chromo-somes 8, 11, 12, and 17, whereas gonadal depot weight wasmore closely related to QTLs on chromosomes 5, 7, and 9.In contrast, we identified QTLs that seemed to have specificeffects on gonadal fat mass on chromosomes 1, 14, and 16,and no QTLs that specifically affected only the retroperito-neal fat pad independently of total adiposity. These QTLsfor gonadal fat did not cover regions with obvious candidate

genes affecting all or part of glucocorticoid metabolismpathways—particularly 11 � HSD-1.

Few studies have been able to examine the role of im-printing of QTLs in mouse populations. As a result of usingonly partially inbred mouse lines, we were able to test forimprinted QTLs, where parental-specific epigenetic modi-fications of DNA elements control the differential expres-sion of maternal and paternal alleles. Previously, we iden-tified a paternally imprinted QTL for body mass of largeeffect on chromosome 8. However, despite identifying anumber of significant QTLs for adiposity in this study, nonewere associated with imprinting. This is inconsistent withsome previous studies where imprinted QTL for adiposityhave already been identified in pig populations (44). In

Table 4. Adiposity QTLs mapped in other studies associated with the highly significant (p � 0.01) mapped QTL

Chr/QTL* Position† Symbol‡ Name Reference

4 Mfat1 30cM Pfat1 predicted fat percentage 1 6346cM Adip11 Adiposity 11 2350cM Dob1 Dietary obesity 1 6466cM Afpq2 Abdominal fat percent QTL 2 5264cM Abfw2 Abdominal fat weight 2 4970cM Tafat TallyHo associated mesenteric fat pad weight 65

9 Mfat2 19cM Obq5 Obesity QTL 5 5145.6cM Relative gonadal fat weight 5429cM Afw4 Abdominal fat weight QTL 4 6642cM Mob8 Multigenic obesity 8 6742cM Adip5 Adiposity 5 5660cM Dob2 Dietary obesity 6860cM Adip14 Adiposity 14 2365cM Obq18 Obesity QTL 18 69

11 Mfat3 10cM Afw5 Abdominal fat weight QTL 5 2619 Mfat7 19cM Obq5 Obesity QTL 5 51

26cM Afw8 Abdominal fat weight QTL 8 5243cM Abfw4 Abdominal fat weight 4 4947cM Nobq2 New Zealand obese QTL 2 5052cM Obqw5 Obesity and body weight QTL 5 23

X Mfat8 16cM Obq6 Obesity QTL 6 5117cM Afw11 Abdominal fat weight QTL 11 2634cM Obq22 Obesity QTL 22 2337cM Fob4 F-line obesity QTL 4 4752cM Obwq5 Obesity and body weight QTL 5 2359cM Dob7 Dietary obesity 7 70

QTL, quantitative trait loci.* Chromosome number and current study QTL name.† Position in cM from proximal chromosome end.‡ QTL name.




AQ: C

addition, imprinted genes, such as Igf2 and Igf2r, are knownto be associated with growth and adipose tissue deposition.No hypothesis as to why no imprinted QTLs for adipositywere identified can be offered. However, our failure todetect significant imprinted QTLs does not mean imprintingeffects are unimportant in the determination of obesity.Rather, the specific selection lines that we studied do notseem to include imprinted QTLs for obesity. This wasprobably not an idiosyncratic aspect of the unique selectionlines because of the previous determination of significantimprinted QTLs for body mass (62).

Caution should be taken in applying these results tohuman populations because these results represent a single,comparatively homogenous population and do not reflectthe genetic heterogeneity present in human populations.

AcknowledgmentsThis work was supported by Wellcome Grant 060,086/

Z/99/Z. The authors acknowledge the assistance of animalhouse staff (Duncan Wood, Shona Fleming, and Jim Le-vine) for care of the mice. L.B. is grateful to Biotechnologyand Biological Sciences Research Council, U.K., and Scot-tish Executive Environment and Rural Affairs Department,Scotland, for funding part of his work.

References1. Flegal KM, Carroll MD, Kuczmarski RJ, Johnson CL.

Overweight and obesity in the United States: prevalence andtrends, 1960–1994. Int J Obes Relat Metab Disord. 1998;22:39–47.

2. Flegal KM, Carroll MD, Ogden CL, Johnson CL. Preva-lence and trends in obesity among US adults, 1999–2000.JAMA. 2002;288:1723–7.

3. Lawrence VJ, Kopelman PG. Medical consequences of obe-sity. Clin Dermatol. 2004;22:296–302.

4. Wolf AM, Colditz GA. Current estimates of the economiccost of obesity in the United States. Obes Res. 1998;6:97–106.

5. Flegal KM. Excess deaths associated with obesity: cause andeffect. Int J Obes Relat Metab Disord. 2006;30:1171–2.

6. Pollex RL, Hegele RA. Genetic determinants of the metabolicsyndrome. Nat Clin Pract Cardiovasc Med. 2006;3:482–9.

7. Maes HHM, Neale MC, Eaves LJ. Genetic and environmen-tal factors in relative body weight and human adiposity. BehavGenet. 1997;27:325–51.

8. Karnehed N, Tynelius P, Heitmann BL, Rasmussen F.Physical activity, diet and gene-environment interactions inrelation to body mass index and waist circumference: TheSwedish Young Male Twins Study. Public Health Nutr. 2006;9:851–8.

9. Loos RJF, Rankinein T. Gene-diet interactions on bodyweight changes. J Am Dietet Assoc. 2005;105:S29–34.

10. Speakman JR. Obesity: the integrated roles of environmentand genetics. J Nutr. 2004;134:2090S–105S.

11. Speakman JR. Thrifty genes for obesity and the metabolicsyndrome—time to call off the search? Diab Vasc Dis Res.2006;3:7–11.

12. Vega GL, Adams-Huet B, Peshock R, Willett D, Shah B,

Grundy SM. Influence of body fat content and distribution onvariation in metabolic risk. J Clin Endocrinol Metab. 2006;91:4459–66.

13. Liu KH, Chan YL, Chan WB, Chan JC, Chu CW. Mes-enteric fat thickness is an independent determinant of meta-bolic syndrome and identifies subjects with increased carotidintima-media thickness. Diabetes Care. 2006;29:379–84.

14. Hayashi T, Boyko EJ, Leonetti DL, et al. Visceral adiposityis an independent predictor of incident hypertension in Japa-nese Americans. Ann Intern Med. 2004;140:992–1000.

15. Bergman RN, Van Citters GW, Mittelman SD, et al. Cen-tral role of the adipocyte in the metabolic syndrome. J InvestigMed. 2001;49:119–26.

16. Bergman RN, Kim SP, Catalano KJ, et al. Why visceral fatis bad: mechanisms of the metabolic syndrome. Obesity (Sil-ver Spring). 2006;14(Suppl 1):16S–9.

17. Albu JB, Murphy L, Frager DH, Johnson JA, Pi-SunyerFX. Visceral fat and race-dependent health risks in obesenondiabetic premenopausal women. Diabetes. 1997;46:456–62.

18. Kahn SE, Prigeon RL, Schwartz RS, et al. Obesity, body fatdistribution, insulin sensitivity and Islet beta-cell function asexplanations for metabolic diversity. J Nutr. 2001;131:354S–60S.

19. Kelley DE, Thaete FL, Troost F, Huwe T, Goodpaster BH.Subdivisions of subcutaneous abdominal adipose tissue andinsulin resistance. Am J Physiol Endocrinol Metab. 2000;278:E941–8.

20. Weber RV, Buckley MC, Fried SK, Kral JG. Subcutaneouslipectomy causes a metabolic syndrome in hamsters. Am JPhysiol Regul Integr Comp Physiol. 2000;279:R936–43.

21. Jerez-Timaure NC, Eisen EJ, Pomp D. Fine mapping of aQTL region with large effects on growth and fatness on mousechromosome 2. Physiol Genom. 2005;21:411–22.

22. Rocha JL, Eisen EJ, Van Vleck LD, Pomp D. A large-sample QTL study in mice. II. Body composition. MammalGenome. 2004;15:100–13.

23. Stylianou IM, Korstanje R, Li RH, Sheehan S, Paigen B,Churchill GA. Quantitative trait locus analysis for obesityreveals multiple networks of interacting loci. Mammal Ge-nome. 2006;17:22–36.

24. Taylor BA, Wnek C, Schroeder D, Phillips SJ. Multipleobesity QTLs identified in an intercross between the Nzo(New Zealand Obese) and the Sm (Small) mouse strains.Mammal Genome. 2001;12:95–103.

25. Reed DR, McDaniel AH, Li X, Tordoff MG, BachmanovAA. Quantitative trait loci for individual adipose depotweights in C57BL/6ByJ x 129P3/J F2 mice. Mammal Ge-nome. 2006;17:1065–77.

26. Brockmann GA, Kratzsch J, Haley CS, Renne U, SchwerinM, Karle S. Single QTL effects, epistasis, and pleiotropyaccount for two- thirds of the phenotypic F-2 variance ofgrowth and obesity in DIU6i X DBA/2 Mice. Genet Res.2000;10:1941–57.

27. Rankinen T, Zuberi A, Chagnon YC, et al. The humanobesity gene map: the 2005 update. Obesity. 2006;14:529–644.

28. Taylor BA, Wnek C, Schroeder D, Phillips SJ. Multipleobesity QTLs identified in an intercross between the NZO




(New Zealand obese) and the SM (small) mouse strains.Mammal Genome. 2001;12:95–103.

29. Masuzaki H, Paterson J, Shinyama H, et al. A transgenicmodel of visceral obesity and the metabolic syndrome.Science. 2001;294:2166–70.

30. Agha A, Monson JP. Modulation of glucocorticoid metabo-lism by the growth hormone - IGF-1 axis. Clin Endocrinol(Oxf). 2007;66:459–65.

31. Hambly C, Adams A, Fustin JM, Rance KA, Bunger L,Speakman JR. Mice with low metabolic rates are not sus-ceptible to weight gain when fed a high-fat diet. Obes Res.2005;13:556–66.

32. Selman C, Lumsden S, Bunger L, Hill WG, Speakman JR.Resting metabolic rate and morphology in mice (Mus muscu-lus) selected for high and low food intake. J Exp Biol. 2001;204:777–84.

33. Rance KA, Fustin JM, Dalgleish G, Hambly C, Bunger L,Speakman JR. A paternally imprinted QTL for mature bodymass on mouse chromosome 8. Mammal Genome. 2005;16:567–77.

34. Bunger L, MacLeod MG, Wallace CA, Hill WG. Direct andcorrelated effects of selection for food intake corrected forbody weight in the adult mouse. Proc 6th World Congress onGenetics applied to Livestock Production, Armidale. 1998;26:97–100.

35. Hastings IM, Moruppa SM, Bunger L, Hill WG. Effects ofselection on food intake in the adult mouse. J Anim BreedGenet. 1997;114:419–33.

36. Selman C, Korhonen TK, Bunger L, Hill WG, SpeakmanJR. Thermoregulatory responses of two mouse Mus musculusstrains selectively bred for high and low food intake. J CompPhysiol [B]. 2001;171:661–8.

37. Sharp GL, Hill WG, Robertson A. Effects of selection ongrowth, body composition and food intake in mice. 1. Re-sponses in selected traits. Genet Res. 1984;43:75–92.

38. Krol E, Speakman JR. Limits to sustained energy intake. VI.Energetics of lactation in laboratory mice at thermoneutrality.J Exp Biol. 2003;206:4255–66.

39. Speakman JR, Booles D, Butterwick R. Validation of dualenergy X-ray absorptiometry (DXA) by comparison withchemical analysis of dogs and cats. Int J Obes Relat MetabDisord. 2001;25:439–47.

40. Knott SA, Marklund L, Haley CS, et al. Multiple markermapping of quantitative trait loci in a cross between outbredwild boar and large white pigs. Genetics. 1998;149:1069–80.

41. GenStat 7 Committee. Genstat Release 7.1 Reference Man-ual. 2003.

42. Haley CS, Knott SA, Elsen JM. Mapping quantitative traitloci in crosses between outbred lines using least-squares.Genetics. 1994;136:1195–207.

43. Zeng Z-B. Precision mapping of quantitative trait loci.Genetics. 1994;136:1457–68.

44. De Koning DJ, Rattink AP, Harlizius B, Van ArendonkJAM, Brascamp EW, Groenen MAM. Genome-wide scanfor body composition in pigs reveals important role of im-printing. Proc Natl Acad Sci U.S.A. 2000;97:7947–50.

45. Churchill GA, Doerge RW. Empirical threshold values forquantitative trait mapping. Genetics. 1994;138:963–71.

46. Lander ES, Kruglyak L. Genetic dissection of complextraits: guidelines for interpreting and reporting linkage results.Nat Genet. 1995;11:241–7.

47. Horvat S, Bunger L, Falconer VM, et al. Mapping ofobesity QTLs in a cross between mouse lines divergentlyselected on fat content. Mammal Genome. 2000;11:2–7.

48. Stylianou IM, Clinton M, Keightley PD, et al. Microarraygene expression analysis of the Fob3b obesity QTL identifiespositional candidate gene Sqle and perturbed cholesterol andglycolysis. Physiol Genom. 2005;20:224–32.

49. Carlborg O, Brockmann GA, Haley CS. Simultaneous map-ping of epistatic QTL in DU6i x DBA/2 mice. MammalGenome. 2005;16:481–94.

50. Kluge R, Giesen K, Bahrenberg G, Plum L, Ortlepp JR,Joost HG. Quantitative trait loci for obesity and insulin re-sistance (Nob1, Nob2) and their interaction with the leptinreceptor allele (Lper(A720t/T1044i)) in New Zealand obesemice. Diabetologia. 2000;43:1565–72.

51. Taylor BA, Tarantino LM, Phillips SJ. Gender-influencedobesity QTLs identified in a cross involving the KK type IIdiabetes-prone mouse strain. Mammal Genome. 1999;10:963–8.

52. Brockmann GA, Renne U, Kopplow K, Das P. Genetic mark-ers for the detection of quantitative trait loci with special consid-eration of body weight and fat. Acta Theriol. 1998;53–62.

53. Jong MC, Rensen PC, Dahlmans VE, van der Boom H, vanBerkel TJ, Havekes LM. Apolipoprotein C-III deficiencyaccelerates triglyceride hydrolysis by lipoprotein lipase inwild-type and apoE knockout mice. J Lipid Res. 2001;42:1578–85.

54. McDaniel AH, Li X, Tordoff MG, Bachmanov AA, ReedDR. A locus on mouse chromosome 9 (Adip5) affects therelative weight of the gonadal but not retroperitoneal adiposedepot. Mammal Genome. 2006;17:1078–92.

55. Ohki-Hamazaki H, Watase K, Yamamoto K, et al. Micelacking bombesin receptor subtype-3 develop metabolic de-fects and obesity. Nature. 1997;390:165–9.

56. Cheverud JM, Vaughn TT, Pletscher LS, et al. Geneticarchitecture of adiposity in the cross of Lg/J and Sm/J inbredmice. Mammal Genome. 2001;12:3–12.

57. Taylor BA, Phillips SJ. Obesity QTLs on mouse chromo-somes 2 and 17. Genomics. 1997;43:249–57.

58. Gelling RW, Du XQ, Dichmann DS, et al. Lower bloodglucose, hyperglucagonemia, and pancreatic alpha cell hyper-plasia in glucagon receptor knockout mice. Proc Natl Acad SciU.S.A. 2003;100:1438–43.

59. Asnicar MA, Smith DP, Yang DD, et al. Absence ofcocaine- and amphetamine-regulated transcript results in obe-sity in mice fed a high caloric diet. Endocrinology. 2001;142:4394–400.

60. Arkblad EL, Helou K, Levan G, Rydstrom J. Mapping ofthe rat and mouse nicotinamide nucleotide transhydrogenasegene. Mammal Genome. 1997;8:703.

61. Kayo T, Fujita H, Nozaki J, E X, Koizumi A. Identificationof two chromosomal loci determining glucose intolerance in aC57BL/6 mouse strain. Comp Med. 2000;50:296–302.

62. Rance KA, Fustin JM, Dalgleish G, Hambly C, Bunger L,Speakman JR. A paternally imprinted QTL for mature bodymass on mouse chromosome 8. Mammal Genome. 2005;16:567–77.




AQ: D

AQ: E

AQ: F

AQ: G

AQ: H

63. Keightley PD, Morris KH, Ishikawa A, Falconer VM,Oliver F. Test of candidate gene quantitative trait locus asso-ciation applied to fatness in mice. Heredity. 1998;81:630–7.

64. West DB, Waguespack J, York B, Goudey-Lefevre J, PriceRA. Genetics of dietary obesity in AKR/J x SWR/J mice:segregation of the trait and identification of a linked locus onchromosome 4. Mammal Genome. 1994;5:546–52.

65. Kim JH, Sen S, Avery CS, et al. Genetic analysis of a newmouse model for non-insulin-dependent diabetes. Genomics.2001;74:273–86.

66. Brockmann GA, Haley CS, Renne U, Knott SA, SchwerinM. Quantitative trait loci affecting body weight and fatnessfrom a mouse line selected for extreme high growth. Genetics.1998;150:369–81.

67. Mehrabian M, Wen PZ, Fisler J, Davis RC, Lusis AJ.Genetic loci controlling body fat, lipoprotein metabolism, andinsulin levels in a multifactorial mouse model. J Clin Invest.1998;101:2485–96.

68. West DB, Goudeylefevre J, York B, Truett GE. Dietaryobesity linked to genetic-loci on chromosome-9 andchromosome-15 in a polygenic mouse model. J Clin Invest.1994;94:1410–6.

69. Ishimori N, Li R, Kelmenson PM, et al. Quantitative traitloci that determine plasma lipids and obesity in C57BL/6J and129S1/SvImJ inbred mice. J Lipid Res. 2004;45:1624–32.

70. York B, Lei KL, West DB. Inherited non-autosomal effectson body fat in F-2 mice derived from an AKR/J x SWR/Jcross. Mammal Genome. 1997;8:726–30.




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