Congenic and bioinformatics analyses resolved a major effect Fob3b QTL on mouse Chr 15 into two closely linked loci

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Congenic and bioinformatics analyses resolved a major effect Fob3b QTL on1

mouse Chr 15 into two closely linked loci2

3

Running HEAD: Congenic mapping resolved Fob3b QTL into linked loci 4

Keywords: QTL, congenic, obesity, mapping, mice5

Zala Prevoršek 1

· Gregor Gorjanc1

· Beverly Paigen2

· Simon Horvat1

6

1University of Ljubljana, Biotechnical Faculty, Department of Animal Science, Groblje 3, 12307

Domžale, Slovenia8

Fax: +386 1 72 17 8889

Tel: +386 1 72 17 71910

E-mail: [email protected] 11

12

2The Jackson Laboratory, 600 Main St, Bar Harbor, ME 04609, USA13



Abstract14

We previously identified a Chr 15 quantitative trait locus (QTL) Fob3b in lines of mice selected15on high (Fat line) and low (Lean line) body fat content that represent a unique model of polygenic16

obesity. Here we genetically dissected the Fob3b interval by analyzing phenotypes of eight17overlapping congenic strains and four F2 congenic intercrosses and prioritized candidates by18

bioinformatics approaches. Analyses revealed that the Fob3b QTL consists of at least two19separate linked QTLs Fob3b1 and Fob3b2. They exhibit additive inheritance and are linked in20

coupling with alleles originating from the Lean line decreasing obesity related traits. In further 21

analyses we focused on Fob3b1 because it had a larger effect on obesity related traits than22

Fob3b2. – e.g., the difference between homozygotes for adiposity index percentage was 1.22%23

and 0.77% for Fob3b1 and Fob3b2, respectively. A set of bioinformatics tools was used to24

narrow down positional candidates from 85 to four high priority Fob3b1 candidates. Previous25

single Fob3b QTL was therefore resolved into a further two closely linked QTLs, confirming the26fractal nature of QTL mapped at the low resolution. The interval of the original Fob3b QTL was27

narrowed from 22.39 Mbp to 4.98 Mbp for Fob3b1 and to 7.68 Mbp for Fob3b2, which excluded28

previously assigned candidate squalene epoxidase (Sqle) as the causal gene because it maps29

proximal to refined Fob3b1 and Fob3b2 intervals. High resolution map along with prioritization30of Fob3b1 candidates by bioinformatics represents an important step forward to final31

identification of the Chr 15 obesity QTL.32

33

Introduction34

Obesity is a major risk factor for a number of chronic diseases including diabetes, cardiovascular 35diseases and cancer. It is a complex trait and has varying contributions of genetic and interacting36

environmental factors (Pomp et al. 2008). Heritability estimates as high as 50-75% in mammals37

suggest a strong genetic basis for this phenotype (Maes et al. 1997). Identification of genes38contributing to obesity would help to uncover the molecular basis and treatment for this condition39

and also enable manipulation of body composition through marker assisted selection in farm40

animals (e.g., Jerez-Timaure et al. 2005). Identification of genes underlying QTLs has proven to41

be difficult and despite an increasing number of successes in QTL identification (reviewed by42

Rankinen et al. 2006) little progress has been achieved in determining the identity of the43

underlying genes, each of which make only a modest contribution to overall heritability44(Wuschke et al. 2007; Mott and Flint 2008). In contrast to human studies, fine mapping of QTLs45in rodent animal models is possible through the use of different crosses and lines. Congenic46

mouse lines represent one type of genetic resource suitable for high resolution genetic mapping47

of QTLs as well as for physiological studies of QTL effects on obesity. Congenic lines harbor a48

chromosomal segment from a donor line in a different genetic background transferred through49

several generations of backcrossing (Silver 1995). Fine mapping of QTLs can be performed using50a number of overlapping congenic lines that span the entire donor region. Further high resolution51

mapping can be achieved by increasing the number of congenic lines containing even smaller,52

overlapping donor segments (Farber and Medrano 2007). Congenic studies have been used to53



detect and fine map several complex trait QTLs (e.g., Warden et al. 2004; Graham et al. 2005;54

Dokmanovic-Chouinard et al. 2008; Scherneck et al. 2009).55

This study is focused on the previously discovered Fob3 obesity QTL region on mouse Chr 1556that segregated between Fat and Lean mouse lines (Horvat et al. 2000), which were selected on57

body fat content by long term divergent selection (Sharp et al. 1984; Bunger and Hill 1999). In an58

earlier study we mapped Fob3 to two separate QTLs: Fob3a and Fob3b (Stylianou et al. 2004).59

The current study represents the development and characterization of congenic lines and fine60mapping of the Fob3b obesity QTL region and the application of the bioinformatics tools to61

narrow down the list of possible candidates for QTLs.62

Materials and methods63

Mouse lines64

An F2 cross between outbred Fat and Lean lines was previously used for a genome wide QTL65detection (Horvat et al. 2000). From this study, one recombinant individual within Fob3b QTL66

region was used as a founder for five subsequent backcrosses to the Fat line to produce congenic67

lines with Lean donor alleles in the Fat line background, Fchrl5L

B, and Fchrl5L

D (Stylianou et al.68

2004). At the fifth backcross, Lean line alleles at three other QTL on chromosomes 2, 12, and X69

( Fob1, Fob2, and Fob4, respectively) were selected against. For our study, the congenic lines70

Fchrl5L

B and Fchrl5L

D were backcrossed to the recipient Fat line for further five to seven71generations. In this way, the interval specific congenic lines used here were backcrossed in total72

10-12 generations with additional marker-assisted elimination of other QTL regions. Hence it is73

anticipated that regions unlinked to Fob3b originate from the Fat line. The F

chrl5L

B line used in74 this study retained Lean line alleles between markers D15Mit26.1 and D15Mit70 after additional75

backcrosses. A recombinant mouse, found during this backcrossing, was used to develop76

congenic line M. After further 5-12 generations of backcrossing more recombinants within the77 Fob3b QTL region were identified from the line F

chrl5LD that, at the fifth backcross (Stylianou et78

al. 2004), carried Lean line alleles between D15Mit184 and D15Mit73 to develop congenic lines79

D12, E, P, G, and K. The congenic line D6 was from the study of Stylianou et al. (2004; line was80then called F

chrl5LD) having 6 backcrosses only and was used here as additional control as it was81

shown that it retained the Fob3b QTL effect (Stylianou et al. 2004). All congenic lines were82

eventually fixed for overlapping segments (Fig. 2) and used for fine mapping of the Fob3b QTL83region. The Fat line was bred concomitantly using the same husbandry and phenotyping84

procedures to serve as a control. Positions of Lean donor segments in congenic lines were defined85

based on SNPs (Fig. 2) with the exception of the G line, where intervals were defined based on86

microsatellite markers (SNPs were not available for this line).87

Husbandry88

Mice were housed in polycarbonate cages (Techniplast Inc., Italy) containing wood chips89

bedding (Lignocel, J.Rettenmaier & Sohne, Germany) and maintained under controlled90conditions: temperature (21°C ± 2°C), humidity (40-70%) and lighting (12 h light: 12 h dark 91cycle). Food chow (1324 Maintenance diet for rats and mice, Altromin, Germany) and acidified92

water were offered ad libitum. Mice were weaned at 3 weeks of age and housed in pairs. Only93



male mice were used for the phenotyping of homozygous congenic lines, while both sexes were94

used for the F2 congenic intercrosses. All the procedures involving animals were performed95

according to local ethical and regulatory guidelines which are all in compliance with the EU96

regulations regarding research on experimental animals (project license number 34401-3/2007/4). 97

Genotyping98

Genetic screening and monitoring of congenic lines was performed by microsatellite analysis on99murine Chr 15 (for primer sequences see Table 1). Genomic DNA was extracted from mouse ear 100

clips by a modification of the protocol described by Laird et al. (1991). Each ear clip was101

incubated in 60 µL of lysis buffer (1M Tris-HCl pH=8.3, 0.5 M EDTA, 20% SDS, 5 M NaCl)102and Proteinase K [5 µL PK (10 mg/mL) / 1 mL lysis buffer] at 55°C for at least 4 hours or 103

overnight. The lysate was then diluted 1:25 in distilled water and the resulting solution was used104

for genotyping without further purification. Polymerase chain reaction (PCR) conditions were105

optimized (Table 1) for each primer pair in 10 µL reaction consisting of: 5 µL diluted lysate106(1:25) DNA template and 5 µL 2× PCR premix. 96-well polypropylene PCR plates (Greiner Bio-107One, Germany) and MJ Research thermocyclers were used for running the PCR reactions. PCR 108

products were separated on 4% agarose gels (SeaKem LE agarose, Cambrex), with 0.5 TBE109

buffer (0.5 M Tris, 0.5 M boric acid, 10 mM EDTA) containing ethidium bromide (20 µL /1L110

TBE buffer).111

Phenotyping112

Mice were weighed at 14 weeks of age and sacrificed by CO2 euthanasia. Abdominal (ABD),113

gonadal (epididymal, EPI), femoral (FEM), and mesenteric (MES) adipose tissues were dissected114

and weighed. Fat tissue surrounding the kidney and attached to the dorsal wall of the abdomen115was considered abdominal fat depot (ABD) as described by McDaniel et al. (2006). The116

epididymal (EPI) fat depot included the fat tissue attached to the epididymis, vas deferens and117

testicles in males. Femoral (FEM) fat was subcutaneous fat from the outer thigh. For mesenteric118

(MES) fat, all the adipose tissue surrounding the gastrointestinal tract from the gastroesophageal119

sphincter to the end of the rectum was dissected. The sum of all collected fat depot weights was120used to calculate the adiposity index (ADI) as:121

( ) .2 MES FEM EPI ABD ADI +++= 122

Every fat depot weight and adiposity index was divided by the body weight of a mouse. For the123

experiment comparing homozygous congenic lines, 383 males were phenotyped involving the124 original Fat line (75 animals), Lean line (72 animals), and congenic lines D6 (34), D12 (36), B125

(28), E (29), P (20), G (28), K (30), and M (31). Additionally, 286 animals (135 males and 151126females) from the F2 generation of the crosses between the Fat line and the congenic lines D12127

(47), P (45), G (70), and M (124) were phenotyped.128

Statistical analyses129

Empirical distributions of collected phenotypic data suggested a normal distribution for each130analyzed variable. Therefore, a statistical model (Eq [1]) with multivariate normal distribution131

was fitted:132



( ),,~,,| R ZsXbR sby + MVN [1]133

where y is an ny×5 matrix of phenotypic values (n

y=383 for congenic lines and n

y=286 for F

2 134

congenic intercrosses); b is the vector of location parameters for effects that differed between the135

data sets; s is the vector of location parameters for ns seasons of dissection defined as year-136

month interaction, and 0R IR ⊗= yn is residual covariance matrix. For congenic lines the vector 137

b involved the effect of parity (1, 2, and 3+), the number of pups per litter (1-2, 3, 4, 5, 6, 7, and138

8+), and line (Fat, Lean, D6, D12, B, E, P, G, K, and M). For F 2 congenic intercrosses the vector 139

b involved the effect of sex (males and females), parity (1, 2, and 3+), the number of pups per 140litter (1-2, 3, 4, 5, 6, and 7+), line (D12, P, G, and M), and genotype within line (homozygotes for 141

Fat line alleles or Lean line alleles and heterozygotes). For the latter the additive and dominance142

effect was tested using the DIC statistic (Spiegelhalter et al. 2002).143

A full Bayesian approach was used with “non-informative” priors (Eq [2]) following Gelman and144Hill (2007). The number of records varied between seasons. In order to shrink excessive145

deviations for seasons with small number of records a hierarchical prior was used for this effect.146

( )

( ) ( )

.6,001.0

,~,,~

,,,~|

.,

5

00

0

=×=

⊗=

∝

df

df Wishart df Wishart

MVN

const

sn

IW

WR WS

SISS0Ss

b

[2]147

Markov chain Monte Carlo method was used to infer model parameters. Length of three chains148

was conservatively set to 1,000,000 with burn-in of 100,000 and every 100th

sample was saved149for the analysis of posterior distributions. Computations were performed using the R and150

WinBUGS software (R Development Core Team 2008; Sturtz et al. 2005; Lunn et al. 2000;151

Plummer et al. 2006).152

Posterior distributions were summarized with mean and standard deviation for each line or 153

genotype as evaluated at the first parity and five pups in the litter. Additionally, posterior 154

probability that a particular line, say X, is leaner than the Fat line was computed and denoted as155Pr(X < Fat). High probability, say 0.95, would suggest that line X is leaner than the Fat line,156

which in turn would suggest that the line X contains a QTL region associated with decreasing157

obesity related traits. For the F2 congenic intercrosses posterior probability of difference between158

homozygous genotypes Lean/Lean (LL) vs. Fat/Fat (FF) was computed.159

160

Bioinformatics analyses for QTL fine mapping161

Comparative genomics162

To perform comparative analysis within species, all obesity QTLs on mouse Chr 15 published till163

October 2009 were considered in order to find QTLs overlapping with Fob3b QTL and164 potentially causal orthologous segments. Region of overlap, common to all mouse obesity QTLs165



in this region, is most likely to contain the causal gene and can therefore help us narrow the166

observed QTL region. Published mouse obesity QTL positions were obtained from the MGI167

database or Wuschke et al. (2007). Pig, chicken and cattle QTLs were extracted from the Animal168

QTL database (http://www.animalgenome.org/QTLdb/, Hu et al. 2007). Human QTL assembly169was based on the 2005 update of the Human obesity gene map (Rankinen et al. 2006). Recent170

literature, published after 2005, was also examined for possible newly published human obesity171

QTLs. The procedures involved in whole genome comparative maps and results assembly172

(Fig. 3) were previously described by Burgess-Herbert et al. (2008).173

Interval-specific haplotype block analysis174

Haplotype analysis identifies haplotype blocks that are not identical by descent (non-IBD)175

between two mouse lines because they are likely to contain the causal polymorphism(s), that176

cause differences in phenotype in two observed lines (DiPetrillo et al. 2005). Within the QTL-177

allele-containing interval, it is expected that “high body fat allele lines” will share the haplotype,178which is different from the “low body fat allele lines” haplotype.179

In this QTL study Fat line haplotypes were compared to Lean line haplotypes. A low density180

SNP set was used with 1401 SNPs located within the reduced Fob3b interval. Genes located in181

non-IBD regions between the Fat and Lean line, were considered possible candidate genes. If a182

certain candidate (a gene with positive hits from other bioinformatics analyses) wasn’t located in183

a non-IBD haplotype block it was not excluded from our high priority list of genes, because of 184the likelihood that undetected coding non-synonymous SNPs (CnSNP) may exist (CnSNP can185

cause changes in the amino acid sequences of proteins).186

Identification of high priority positional candidate genes within the reduced Fob3b QTL interval187

Mouse phenome database (MPD; http://phenome.jax.org/phenome; Grubb et al. 2009) Mouse188

SNP Wizard is an MPD based online tool for simple comparison of mouse SNPs and for 189

detection of CnSNPs and UTR region SNPs. CnSNPs between high density SNP set of C57BL/6J190

and DBA/2J – (a cross used to locate a Fob3b overlapping obesity QTL) were retrieved.191

Uniprot database (http://www.uniprot.org) . This publicly available database was used to192ascertain whether CnSNPs cause changes in polarity of the amino acids and if they are located in193

the functional domains of a protein.194

Bio GPS Expression database (http://biogps.gnf.org) provides expression data, acquired by195

direct measurements of gene expression by microarrays. Positional candidate genes were196screened for high expression in tissues involved in energy metabolism and fat storage.197

Gene ontology (GO) (http://amigo.geneontology.org) was used for identification of candidates,198

having their function assigned to obesity, energy/lipid metabolism, adipogenesis and fat storage.199

Mouse Genome Informatics (MGI) (http://www.informatics.jax.org) was used to search for 200

mouse knockout and transgenic models for selected high priority candidate genes. If genetically201modified mice were available, we checked if transgenic modification of our candidate gene202

affects systems or tissues, involved in obesity.203



Results204

Development and characterization of interval specific congenic lines for Fob3b QTL on mouse205

Chr 15206

A panel of eight congenic lines (D6, D12, B, E, P, G, K, and M) covering the Fob3b QTL was207generated for fine genetic mapping. These lines had Lean-line donor regions of various lengths208

which were stabilized after 10-12 backcrosses to the Fat line, apart from line D6 that stayed at the2096th backcross generation. Characterization of obesity-related traits for these lines is shown in210

Table 2, while SNP and microsatellite marker-defined intervals are shown in Fig. 2.211

Fob3b congenic lines D6, D12, and B had a Lean segment that covered the entire interval of the212 Fob3b QTL (Stylianou et al. 2004) and can be considered here as positive control lines for the213

Fob3b QTL effect in addition to the Lean line. The later deviated from the Fat line for -214

0.82±0.03% in ABD, -2.02±0.06% in EPI, -1.34±0.05% in FEM, -1.66±0.08% in MES, and -21510.04±0.30% in ADI. Lines D6 and D12 had the largest decrease among congenic lines in216comparison to the Fat line (from -0.15 to -3.06%) in all analyzed traits (Table 2). Consistent217

decrease for all traits was also observed in the congenic lines B, G, and K suggesting that the218

Fob3b QTL is retained in their congenic intervals. Results for line E varied between fat depots -219

the difference was large for EPI (-0.22±0.10%) with some trends indicated for MES (-220

0.13±0.10%) and ADI (-0.67±42%), and no differences in ABD (-0.02±0.04%) and FEM (-221

0.04±0.07%). Lines P and M did not differ from Fat line in any trait analyzed.222

223

Fine mapping analysis using F2 congenic intercrosses reveals that Fob3b QTL is composed of at224least two linked QTLs, Fob3b1 and Fob3b2 225

To confirm the localization of QTL effects based on the analysis of homozygous congenic lines226

above, we developed F2 crosses between the Fat line and congenic lines D12, P, G, and M. The227comparison of additive vs. additive and dominance models for F2 data using the DIC statistics228

(smaller value indicates better fit) for all lines jointly showed that the simpler additive model had229

better fit (Table 3). This was also manifested by the fact that genotype means and the230corresponding intervals from both models almost completely overlapped in all analyzed traits231

(see Fig. 1 for adiposity index). Hence, only results based on the additive model are presented232

below. The F2 cross between the congenic D12 line and the Fat line served as a control and, as233

expected, expressed negative differences between the F2 Lean/Lean and Fat/Fat genotypes for all234

traits (Table 4). In this line the differences ranged from -0.16±0.09% for ABD to -1.70±0.74%235for ADI. Results for the F2 cross between the P and Fat line showed no difference between the F 2 236genotypes for all analyzed traits. In contrast, the F2 cross of congenic G and Fat lines exhibited a237genotype effect similar or larger than in the F2 intercross of D12 congenic and Fat line - the238

difference between the F2 homozygotes for ADI was -1.99±0.64% (Table 4). Finally, the results239

for the F2 cross involving the M line showed differences for EPI (-0.14±0.09%), MES (-240

0.14±0.08), and ADI (-0.77±0.46%) though the size of the effect was smaller than in the F2 241

crosses involving G or D12 lines. F2 intercrosses involving lines D12, P, and G lines confirmed242results obtained by homozygous line analysis (previous subsection). However, the F2 congenic243intercross with the M line revealed a difference between the genotypes, which was not detected in244



the comparison of homozygous line M to the Fat line (Table 2). This could be attributed to larger 245

sample size in the F2 analysis and elimination of confounding maternal and genetic background246

effects. Additionally, the homozygous line analysis of the M line did show the same direction of 247

differences as in the G and Fat line intercross, further supporting the results obtained in the F2 248analysis.249

Comparison of homozygous congenic lines to the Fat line and analysis of F2 congenic250

intercrosses indicate that the P line does not harbor the Lean line region with a QTL effect, while251

the G line contains the Lean line donor segment, producing a reduction in obesity traits (four fat252

depots and consequently also adiposity index percentage). The M line contains a smaller donor 253

segment within the larger G line segment but the phenotypic effect of the M line segment is also254

smaller (e.g. , for ADI -0.77±0.46%) in comparison to the segment in the G line (e.g., for ADI -2551.99±0.64%). These results indicate that the Fob3b QTL region of Stylianou et al. (2004) is256

composed of at least two QTL regions. Additional pairwise comparisons of congenic lines and257

QTL effects between different F2 congenic intercrosses (see supplementary Table 1) support this258finding. In accordance with the International Committee on Standardized Genetic Nomenclature259

for Mice we have named these new QTLs: Fob3b1 (71.38 Mbp to 76.36 Mbp) and Fob3b2 260

(75.25 Mbp to 82.93 Mbp) (Fig. 2). The effect of each QTL for ADI amounts to 1.22% and2610.77% in Fob3b1 and Fob3b2, respectively.262

Identification of Fob3b1 high priority candidate genes using bioinformatics tools263

Our bioinformatics analyses were focused on the Fob3b1 interval due to the large observed effect264

on obesity related traits (Table 4). The Fob3b interval contains 255 genes, while the reduced265

interval of Fob3b1 contains 85 genes. Instead of expensive and time consuming experimental266

testing of every candidate gene, several publicly available bioinformatics resources were used to267narrow the gene list to a manageable number of candidates, which can be further examined by268experimental methods. Candidate genes were ranked by priority, depending on the number of 269 positive hits from bioinformatics methods. Candidate genes which demonstrated effect on obesity270

traits or function, genes with knockout or transgenic models that confirmed their obesity-related271

functions and genes expressed in obesity-relevant tissues were considered high priority272

candidates. Genes with unknown functions, expressed in obesity-related tissues and positive hits273

from comparative genomics and haplotype analysis, were considered medium priority candidates.274

Comparative genomics within species revealed three mouse obesity QTLs that have previously275

been mapped to the Fob3b region. The Pfat4 (predicted fat percentage 4) QTL was identified in a276cross between C57BL/6J and DBA/2J by Keightley et al. (1998). Previously published Fob3b 277

QTL and Pfat4 QTL peaks both map to 74 Mbp on Chr 15, which is concordant with our 278narrowed Fob3b interval (Fig. 3A). The Mfat6 (for total fat mass) was mapped to the same region279

by Rance et al. (2007) and the Epf%q4 (for gonadal and femoral fat percentage) by Rocha et al.280

(2004). A region of overlap for these QTLs spans from 58 Mbp to 79 Mbp. As can be seen in Fig.281

3A (1/; marked with dashed rectangle) comparative genomics within species only narrowed the282 Fob3b interval for 2 Mbp.283

Comparative mapping between species identified two homologous cattle QTLs for fat thickness284

overlapping with the reduced Fob3b QTL, whereas no homologous human and chicken obesity285

QTLs map to this region. Cattle fat thickness QTLs were not named in the original publications.286



Here (Fig. 3B) we named them Cattle QTL 1 (Casas et al. 2000) and Cattle QTL 2 (Casas et al.287

2003). In the relevant homologous bovine regions Cattle QTL 1 spans from 76.12 to 76.19 Mbp288

and cattle QTL 2 spans from 74.34 to 74.67 and from 75.53 to 76.32 Mbp on mouse Chr 15. Both289

peaks overlap at 76.10 Mbp (Fig. 3B).290

Haplotype analysis identified non-IBD haplotype blocks between the Fat and Lean lines291(Fig. 3B). We also performed this analysis on C57BL6/J and DBA/2J because this cross was used292

to detect Pfat4 QTL, which completely overlaps with Fob3b (Fig 3). Based on this we can293

assume that the same polymorphisms underlie this QTL and that the haplotype analysis of high294

density SNP set between C57BL6/J and DBA/2J might disclose a causing SNP. It is possible that295

we missed the latter in haplotype analysis of Fat and Lean lines, because we genotyped these296

lines only for a low density SNP set.297A high density SNP set search in MPD SNP wizard revealed three genes with CnSNPs between298

C57BL/6J and DBA/2J: Col22a1, Ly6a, and Rhpn1. Two CnSNPs (Cn E58A in Ly6a gene and299

Cn S508G in Rhpn1 gene) cause amino acid property changes from polar to non-polar and they300are both located within the functional domain of a protein (Table 5). Three candidates (Chrac1,301

Gm628, and Ly6a) had SNPs also located in their UTR regions (data not shown). A BioGPS302

search revealed two candidate genes that are highly expressed in adipose tissue: Cyc1, Gpihbp1303and Dgat1 (Table 6). High expression levels of Dgat1 are also reported in other tissues (e.g.,304

skeletal muscle, small intestine) in which triglyceride metabolism is prominent (Chen et al.305

2002). Six positional candidate genes are highly expressed in the intestine (Gpr20, Gsdmdc1,306

Naprt1, Tsta3, BC025446 and Dgat1), one in the stomach ( Psca), two in the adrenal gland307

(Cyp11b1 and Cyp11b2) and two in the olfactory bulb in the brain ( Bai1 and Arc) (Table 6).308

Dgat1 is one of two Dgat enzymes known to catalyze the final step in mammalian triglyceride309

synthesis (Chen et al. 2002). A search for knockout and transgenic models in the MGI database310confirmed Dgat1 as high priority candidate, because Dgat1-deficient mice are lean (decreased311

body fat percentage, decreased adipose tissue and body weight) and resistant to diet-induced312

obesity (Smith et al. 2000).313

Rho-GTPase-binding protein-1 ( Rhpn1) is reported to bind to the glucose response element and314

triggers genes for lipogenesis (Hasegawa et al. 1999). Gpihbp1 (glycosylphosphatidylinositol-315

anchored high density lipoprotein–binding protein 1) is reported to be involved in lipid and316cholesterol transport (Beigneux et al. 2007). Absence of Ly6a (lymphocyte antigen 6 complex,317

locus 6a) gene in mice caused abnormal fat cell morphology (Stanford et al. 1997). Mice lacking318

Tsta3 (tissue specific transplantation antigen P35B) had abnormal colon morphology and319

decreased body size (Smith et al. 2002).320

In summary, bioinformatics analyses helped to rank 85 positional candidates within the Fob3b1 321

region based on the amount and type of evidence (bioinformatics hits). Four candidates,322

positioned within a 2 Mbp region (from 74.5 to 76.5 Mbp), were given a high priority (Table 7)323

and are the strongest Fob3b1 candidates. Medium priority was given to 5 genes while genes with324fewer hits were given a low priority and are not listed here. Therefore, the bioinformatics325

analyses have reduced our Fob3b1 positional candidate list of 85 genes to 9 high or medium326

priority candidates (Table 7).327



Discussion328

Since several studies identified linkages for body weight and body fat traits on mouse Chr 15,329

this chromosome is referred to as one of the most important “obesity hot-spots" in the mouse330genome (Rocha et al. 2004; Wuschke et al. 2007). However, very few QTLs that were mapped to331

Chr 15 at a low resolution have been advanced to a finer genetic mapping stage. In the present332study we have focused on a high resolution mapping of a Fob3b obesity QTL region on Chr 15333

by congenic mapping and bioinformatics analyses.334

Congenic mapping335

Complex traits are often under the control of multiple genetic and environmental factors (Rogner 336and Avner 2008). Congenic lines represent an important genetic resource for QTL analysis and337

have been utilized to discover many obesity and body weight QTLs.338

Our study concentrated on Fob3b, obesity QTL on mouse Chr 15 originally detected in an F2 339

cross as a large effect QTL region with a LOD score of 11.3 for body fat percentage with a two-340LOD support interval spanning 10–45 cM (Horvat et al. 2000). Using an increased number of 341markers and a congenic line cross, the Fob3 region has been further dissected into two smaller 342

QTLs, Fob3a (27 cM; 22–32 cM; 14.16–56.07 Mbp) and Fob3b (68 cM; 44–70 cM; 56.15– 343

78.50 Mbp) (Stylianou et al. 2004).344

We developed and characterized eight overlapping congenic lines for Fob3b, five of which345

showed an effect on most of the studied traits. Congenic F2 intercrosses with lines D12, G, P, and346M were produced and analyzed in order to confirm the results of homozygous congenic line347

phenotype analyses and dissect possible linked QTL clusters. QTLs were partitioned within a348single line or a set of overlapping lines and their additive and dominance effects were estimated349

by comparing the three F2 genotypes - in our case Lean/Lean, Fat/Lean and Fat/Fat. In the future,350

the F2 intercrosses should be produced also for other lines to verify results obtained by congenic351

line comparisons - especially those lines that show some deviations from expected results (e.g.,352

line E). By congenic F2 mapping one can treat a complex trait as a simple Mendelian trait, which353increases the statistical power to detect QTLs with smaller effects. In addition, contaminating354

donor alleles and environmental effects (such as litter size and maternal effect) are randomized355

across F2 genotypes to reduce non-genetic variance and or variance due to contaminating356

background donor alleles (Farber and Medrano 2007).357

Our F2 congenic intercrosses confirmed the presence of QTL effect in lines D12 and G line and358

absence of the QTL effect in the P line. Consequently, the original Fob3b QTL interval could be359narrowed down by excluding the congenic segment from the P line. Additionally, the remaining360 Fob3b QTL (22.39 Mbp) was dissected into two separate QTLs: a proximal Fob3b1 (4.98 Mbp)361with a large effect and Fob3b2 (7.68 Mbp) with a smaller, but detectable effect on obesity– 362

related traits.363

These two loci are linked in coupling where the Fob3b1 and Fob3b2 alleles originating from the364

Lean line decrease obesity related traits. Some previous obesity mapping studies have also365

demonstrated genetic dissection into separate closely linked obesity QTLs (Farber and Medrano366

2007; Chiu et al. 2007) as well as studies of other complex traits (e.g., Lyons et al. 2001;367



Christians and Keightley 2004; Zhang et al. 2005; Christians et al. 2006; Fawcett et al. 2009).368

The phenomenon of numerous QTLs with smaller effects makes fine mapping of causal genes369

much more difficult, requiring alternative mapping strategies and/or refined phenotyping to be370

able to identify such loci. Some investigators have proposed that increasing the sample size or 371feeding mice a higher or lower fat

diet in case of obesity-related studies might help discover small372

effect QTLs (Chiu et al. 2007).373

High priority positional candidates for Fob3b1 374

Bioinformatics methods can be used for reducing candidate QTL- genes to a small list of testable375

candidate genes. An example of effective use of the bioinformatics toolbox is a reduction of two376

HDL QTLs presented by Burgess-Herbert et al. (2008), where one QTL containing 750377

positional candidates was reduced down to 7 candidates and another from 147 to 7 candidates. In378

the present study of the Fob3b1 QTL a list of 85 genes was reduced to 9 candidates, 4 of which379

we regard as high priority ( Dgat1, Ly6a, Gpihp1, and Rhpn1) and 5 as medium priority380candidates (Cyp11b1, Cyp11b2, Gpr20, Bai1, and Tsta3). High priority positional candidate381

genes for Fob3b1 are briefly described.382

Dgat1 Obesity is characterized by the accumulation of triacylglycerol in adipocytes and Dgat1 is383

known to catalyze the final reaction in mammalian triglyceride synthesis. Because the ability to384

make triglycerides is essential for the accumulation of adipose tissue, inhibition of triglyceride385

synthesis may ameliorate obesity and its related medical consequences (Chen 2006; Matsuda and386Tomoda 2007). Studies of Dgat1 in cattle discovered polymorphisms in both coding and non-387

coding sequences of the gene linked to muscle fat content (Thaller et al. 2003). Transgenic mice388

over expressing Dgat1 demonstrated increased adiposity (Yen et al. 2008). In contrast, Dgat1-389

deficient mice have a 50% reduction in adiposity and are resistant to diet-induced obesity (Chen390et al. 2002). Since resistance to diet induced obesity has also been observed in our Lean line391(Morton et al. 2005), the aforementioned data make Dgat1 an attractive positional candidate.392However, previous microarray analyses (Stylianou et al. 2005) did not find differential393

expression of Dgat1 in the liver and brown adipose tissue, although Dgat1 also mapped to the394

Fob3 region in this independent mapping study. Another preliminary microarray study using395

Affymetrix genome wide expression arrays also failed to uncover differential expression between396

the Fat line and congenic line G in various tissues (Horvat et al. unpublished). Verifying enzyme397activity and Dgat1 polymorphisms between congenic pairs in future analyses can still be398

illuminating.399

Gpihbp1 encodes a protein involved in lipid and cholesterol transport. It plays a key role in the400

lipolytic processing of large lipoprotein particles that transport dietary lipids from the intestines401to other locations in the body (Beigneux et al. 2007). Studies of functional importance of 402

Gpihbp1 in plasma lipid metabolism proved that it plays a critical role in lipolysis of triglyceride-403

rich lipoproteins, which is a central process in lipoprotein metabolism and in the delivery of lipid404

nutrients to tissues (Young et al. 2007). Mice lacking Gpihbp1 have abnormal circulating lipid405level, decreased circulating HDL cholesterol level, increased circulating total cholesterol level,406

increased circulating VLDL cholesterol level, and increased circulating triglyceride level407

(Beigneux et al. 2007).408

409



Rhpn1 and Ly6a Hasegawa et al. (1999) discovered that Rhpn1 binds to the glucose response410

element and hence triggers genes for lipogenesis. Absence of Ly6a gene in mice causes abnormal411

fat cell morphology (Stanford et al. 1997) potentially linking this gene to obesity related traits.412

Despite very few published studies about Ly6a and Rhpn1 function, these two genes do contain413CnSNPs between original Fat and Lean lines and congenics, used to detect Fob3b1. Future414

studies should determine possible causality of identified CnSNPs in Ly6a and Rhpn1, which 415

cause amino acid property change from polar to non-polar and are located in the functional416

domain.417

418Bioinformatics strategies used here can complement existing experimental methods, but they419have limitations. The utility of approaches based on sequence databases depends on the quality of 420

data uploaded in the database. When searching for potential causal polymorphisms, successful421

detection of functional polymorphism is still hampered by very limited knowledge of regulatory422

elements. Detecting coding SNPs can miss the causal SNP in cases of insufficient density of the423

data and errors. In the case of less popular inbred mouse lines only less dense SNP sets are424available and in cases of non-commercial mouse lines there might not be any SNPs available in425the databases. The absence of functional polymorphisms in regulatory or coding regions found in426

the databases therefore should not be a reason to exclude a candidate gene from further analyses.427

The same considerations apply to the expression databases - the quality of deposited data428

determines the quality of the results (DiPetrillo et al. 2005). In addition, when screening429

expression databases for predicted genes (according to Ensembl and MGI) there are usually no430

records about these genes. Hence they cannot be either nominated or excluded as important431candidates. Despite these limitations, bioinformatics can effectively prioritize candidate genes432

and reduce the list to a testable number of main candidates among currently known genes. The433ongoing development of expansive expression databases with additional genomic sequence data434

will greatly enhance the importance of bioinformatics in future QTL narrowing studies.435

The results of our fine mapping study illustrate the fractal nature of QTLs (Flint and Mackay436

2009). Fob3 QTL (Horvat et al. 2000), that initially appeared to be a single QTL detected in an F2 437cross of outbred selection lines was later resolved into two QTL Fob3a and Fob3b by Stylianou438et al. (2004). In our current study, Fob3b was further dissected into two separate QTL Fob3b1 439

and Fob3b2. Therefore, our high resolution mapping of Chr 15 obesity-related QTLs440

demonstrates that genetic architecture is much more complex than suggested in our initial whole441

genome QTL scan. What appeared as a single QTL of large effect has in two additional genetic442

mapping experiments decomposed into three linked QTLs. An independent fine mapping study443of obesity-related QTLs in mice also observed significantly narrowed QTL confidence regions444

and resolved many single QTLs into multiple QTL peaks (Fawcett et al. 2009). From recent SNP445genome wide association studies (GWAS) in humans it also appears that quantitative variation in446

obesity is characterized by QTLs of relatively small effect. For example Willer et al. (2009)447

determined that each of the known obesity loci account for less than 1% of the phenotypic448

variance. Our current study and GWAS studies in other species therefore support the view that a449

large number of loci with small effect determine quantitative genetic variation (Flint and Mackay4502009).451

Sequencing of high priority candidate genes will have to be carried out in order to identify452

additional polymorphisms between Fat and Lean line segments not captured by our current SNP453

genotyping. Since all the remaining positional candidates cannot be excluded based on454



bioinformatics, one complimentary approach involves the construction of new subcongenic lines455

with smaller overlapping Lean segments within Fob3b1 or Fob3b2. However, given that the456

present congenic lines already possess very small genetic intervals (e.g. , M line that encompasses457

Fob3b2 contains a Lean segment of only 7.68 Mbp, around 3 cM) it might be difficult or 458impractical to generate new recombinants. Mapping with new subcongenic lines would however 459

further decrease the candidate list or candidate polymorphisms. Whole genome microarray460

expression studies of newly developed congenic lines (e.g., line G for Fob3b1 and line M for 461

Fob3b2) and the Fat line in several different tissues of interest are in progress at the present time462

to help identify positional candidates exhibiting differential expression and perturbed pathways463due to the action of Fob3b1 or Fob3b2. However, on the basis of identified SNPs, differential464expression and functional relevance of positional candidates one should still be cautious when465

making claims of causality. For example, previous mapping identified Sqle, an enzyme in466

cholesterol synthesis, as a strong positional candidate (Stylianou et al 2005). A follow up467

microarray and RT-PCR study of liver tissue (Simoncic et al. 2008) revealed that Sqle was not468

differentially expressed in congenic line U12 (same line as D12 in this study). It is possible that469this discrepancy is a result of six more backcrosses performed with the line used in current vs.470Stylianou et al. (2005) study (i.e. differences in genetic background effect) or there are other 471

unknown genotype-environment interactions at play. Whatever the reason for this discrepant472

result, our current study clearly excluded the previous positional candidate Sqle as a possible473

causal gene based on genetic evidence that Sqle maps outside refined intervals of Fob3b1 or 474

Fob3b2 QTL. An optimal test to prove that a candidate gene is causal for the QTL effect is so475

called quantitative complementation test (Flint and Mott 2001). This approach is based on a set of 476crosses between each QTL allele (high and low), knockout and a wild type allele at every477

candidate gene. A QTL is recognized if there is a difference between the effects of the high and478low QTL allele in combination with the knockout allele at the causal candidate gene. Though479

successful in some cases (e.g., Yalcin et al. 2004), for such a test, appropriate knockout models480and genetic backgrounds for the candidate QTL are required. These resources are often481

unavailable as in our case. Therefore, the approach of developing novel subcongenic lines with482ever smaller donor segments combined with bioinformatics, expression and functional analysis483

still seems to be a general and potentially successful approach to finally identify causal QTL484

nucleotide sequence variation.485

In summary, we have successfully generated a panel of congenic lines and F2 congenic line486

crosses to generate a fine genetic map of obesity QTL on mouse Chr 15. By utilizing recently487

developed bioinformatics tools we nominate four high priority candidate genes. Our study488revealed that the original Fob3b QTL consists of at least two closely linked QTL ( Fob3b1 and489

Fob3b2) and represents an important step for future identification of causal alleles for these two490QTLs. Elucidating the molecular basis of common polygenic obesity in animal models should491

reveal important inherited risk factors as well as enable targeted development of drugs to cure492

and prevent obesity in humans.493

Acknowledgments494

This project was supported by the Slovenian Research Agency (ARRS) young investigator grant495

and programme grant P4-0220. Sincere thanks to all members of Beverly Paigen lab at the496

Jackson Laboratory for help with the bioinformatics. We thank Dr. McWhir for critical review of 497



this manuscript and Ana Zanjkovic for excellent technical assistance in the mouse colony.498

Finally, we are gratefull to anonymous reviewer for meticulous review and constructive criticism499

that helped us to improve this manuscript.500

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723Table 1 Microsatellite marker names, genetic and physical positions and PCR annealing temperatures of 724microsatellites used in congenic line development and genetic monitoring725

Markers PCR a cM b Mbpc

D15Mit113 62+Fd 22.2 50.225

D15Mit221 62+Fd 22.0 51.987

D15Mit231 60 27.5 54.780

D15Mit26.1 62 29.0 55.098

D15Mit184 60/62 25.4 56.067

D15Mit115 62 24.0 56.153

D15Mit278 62 7.4 56.998

D15Mit46 62 27.5 60.656

D15Mit88 62 27.6 61.183

D15Mit207 62 27.6 61.477

D15Mit209 60/62 32.0 61.511

D15Mit270 62 28.4 63.347D15Mit234 60/62 34.2 64.902

D15Mit63 62 29.2 65.074

D15Mit64 60/62 29.0 65.352

D15Mit102 60/62 6.7 65.395

D15Mit233 62 34.2 66.076

D15Mit211 60 30.2 66.584

D15Mit123 60/62 30.6 67.124

D15Mit144 60/62 32.2 68.118

D15Mit66 60/62 32.2 68.376

D15Mit90 62+Fd 36.0 68.968

D15Mit156 62 39.1 71.155

D15Mit105 62+Fd 47.9 72.327

D15Mit28 60/62 43.7 74.742D15Mit68 62 44.1 76.737

D15Mit239 60 48.2 78.407

D15Mit1 62 46.3 78.547

D15Mit238 60 46.6 79.097

D15Mit261 60/62 46.7 80.063

D15Mit2 60 46.9 80.106

D15Mit70 62+Fd 47.7 81.029

D15Mit107 62+Fd 49.0 84.214

D15Mit72 62+Fd 49.0 84.543

D15Mit241 62+Fd 50.2 86.997

D15Mit73 62+Fd 52.8 88.738

D15Mit243 62+Fd 56.7 92.841

D15Mit39 62+Fd 56.6 96.093a PCR annealing temperature (°C)726

b Genetic positions of microsatellite markers in cM (Ensembl 46, NCBI36)727

c Mbp position according to Ensembl 46, which is based on NCBI mouse assembly m36728

d PCR specificity was improved by adding 5% of formamide729

e PCR reaction works with 60 or 62°C annealing temperature730

731



Table 2 Obesity trait analyses in homozygous congenic lines in comparison to parental Fat line: posterior means (±732standard deviation) by line, difference between the Lean or congenic line and the Fat line with associated posterior 733 probability for the percentage of fat depots (ABD - abdominal, EPI - epididymal, FEM - femoral, and MES -734

mesenteric) and adiposity index (ADI) in congenic lines735

Trait

Line ABD (%) EPI (%) FEM (%) MES (%) ADI (%)

Fat 1.03±0.04 2.30±0.08 1.78±0.06 2.30±0.09 12.52±0.33

Lean 0.21±0.04 0.28±0.08 0.44±0.06 0.64±0.09 2.48±0.34

Difference -0.82±0.03 -2.02±0.06 -1.34±0.05 -1.66±0.08 -10.04±0.30 Pr(Lean < Fat) 1.00 1.00 1.00 1.00 1.00

D6 0.88±0.06 1.58±0.11 1.34±0.08 1.86±0.12 9.46±0.48

Difference -0.15±0.04 -0.72±0.09 -0.44±0.06 -0.44±0.10 -3.06±0.39

Pr(D6 < Fat) 1.00 1.00 1.00 1.00 1.00

D12 0.85±0.05 1.66±0.10 1.43±0.08 1.83±0.11 9.70±0.44

Difference -0.18±0.04 -0.65±0.08 -0.35±0.06 -0.47±0.09 -2.82±0.38

Pr(D12 < Fat) 1.00 1.00 1.00 1.00 1.00B 0.91±0.05 2.08±0.09 1.63±0.07 2.14±0.10 11.38±0.40

Difference -0.12±0.04 -0.22±0.09 -0.15±0.06 -0.16±0.09 -1.14±0.41

Pr(B < Fat) 1.00 0.99 0.98 0.96 1.00

E 1.01±0.06 2.08±0.10 1.74±0.08 2.17±0.11 11.85±0.45

Difference -0.02±0.04 -0.22±0.10 -0.04±0.07 -0.13±0.10 -0.67±0.42Pr(E < Fat) 0.65 0.99 0.69 0.90 0.94

P 1.04±0.06 2.44±0.11 1.78±0.09 2.44±0.12 12.96±0.50

Difference 0.01±0.05 0.14±0.11 0.00±0.08 0.14±0.12 0.44±0.50

Pr(P < Fat) 0.41 0.10 0.47 0.12 0.18

G 0.89±0.05 2.02±0.11 1.67±0.07 2.12±0.11 11.27±0.43

Difference -0.14±0.04 -0.28±0.09 0.11±0.06 -0.18±0.09 -1.25±0.40

Pr(G < Fat) 1.00 1.00 0.95 0.98 1.00

K 0.88±0.05 1.92±0.09 1.53±0.07 1.94±0.10 10.59±0.41Difference -0.15±0.04 -0.38±0.08 -0.25±0.06 -0.36±0.09 -1.93±0.38

Pr(K < Fat) 1.00 1.00 1.00 1.00 1.00

M 0.99±0.05 2.22±0.10 1.76±0.07 2.25±0.10 12.17±0.41

Difference -0.04±0.04 -0.08±0.08 0.02±0.06 -0.05±0.09 -0.35±0.39

Pr(M < Fat) 0.86 0.84 0.63 0.71 0.81

Pr(X < Fat) –posterior probability that a homozygous congenic line is leaner than the Fat line736

Table 3 Model comparison (additive and additive+dominance) by DIC statistics (smaller value indicates better fit)737for the percentage of fat depots (ABD - abdominal, EPI - epididymal, FEM - femoral, and MES - mesenteric) and738adiposity index (ADI) in F2 congenic intercrosses739

Model

TraitAdditive

Additive and

dominance

ABD (%) 11.4 12.4

EPI (%) 254.6 260.6

FEM (%) 181.2 188.4

MES (%) 217.2 221.6

ADI (%) 1183.8 1190.2

740



741

Table 4 Obesity trait analyses in F2 congenic intercrosses: posterior means (±standard deviation) of difference742

between the F2 Lean/Lean (LL) and F2 Fat/Fat (FF) genotypes with associated posterior probability for the percentage743 of fat depots (ABD - abdominal, EPI - epididymal, FEM - femoral, and MES - mesenteric) and adiposity index744(ADI)745

Trait

Line ABD (%) EPI (%) FEM (%) MES (%) ADI (%)

D12 -0.16±0.09 -0.36±0.14 -0.24±0.12 -0.19±0.13 -1.70±0.74

Pr(LL < FF) 0.95 0.99 0.97 0.92 0.99

P 0.00±0.11 -0.02±0.16 0.12±0.14 0.08±0.15 0.26±0.83

Pr(LL < FF) 0.51 0.56 0.21 0.29 0.38

G -0.15±0.08 -0.37±0.12 -0.32±0.11 -0.28±0.12 -1.99±0.64

Pr(LL < FF) 0.97 1.00 1.00 0.99 1.00

M -0.08±0.06 -0.14±0.09 -0.09±0.08 -0.14±0.08 -0.77±0.46

Pr(LL < FF) 0.91 0.95 0.88 0.95 0.95Pr(LL < FF) –posterior probability that an F2 individual of Lean/Lean (LL) genotype is leaner than an F2 individual746of Fat/Fat (FF) genotype747

748

Table 5 Coding nonsynonymous SNPs (CnSNPs) in candidate genes and their effect on amino acid/protein749 properties750

NCBI36

Gene IDaCnSNP b

(C57xDBA/2J) Amino acid changec

C57 / AA / DBA Amino acid propertyd

(C57xDBA/2J) Functional domaine

Col22a1 Cn P477A Pro / 477 / Ala nonpolar / nonpolar /

Col22a1 Cn A453V Ala / 453 / Val nonpolar / nonpolar /

Ly6a Cn E58A Glu / 58 / Ala polar / nonpolar UPAR/Ly6

Ly6a Cn V10L Val / 10 / Leu nonpolar / nonpolar /

Rhpn1 Cn S508G Ser / 508 / Gly polar / nonpolar PDZa Ensembl gene identification symbol.751

b Nonsynonymous single nucleotide polymorphism.752

c Amino acid for C57BL/6J and DBA/2J lines at the protein position number shown (AA no.).753

d Amino acid side chain property in C57BL/6J and DBA/2J lines.754

e Functional domain of a protein (/ not known)755



756

Table 6 Candidate genes, selected based on their tissue of expression and function757

NCBI36 Gene ID Tissue of expression (BIO GPS) Function (GO)

Dgat1 intestine, adipose tissue, brown adipose transferase activity

Gpihbp1 pancreas, adipose tissue lipid transport, cholesterol transport

Rhpn1 / b signal transductiona

Cyp11b1 adrenal gland aldosterone biosynthetic process

Cyp11b2 adrenal gland glucocorticoid biosynthetic process

Tsta3 large intestine metabolic process

Gpr20 large intestine receptor activity

Bai1 olfactory bulb (brain) neuropeptide signaling pathway

Arc olfactory bulb (brain) / b

Psca stomach / b

BC025446 liver, kidney, intestine, thyroid no records found

Gsdmdc1 small intestine no records found

Naprt1 small intestine, liver / b

Cyc1 brown adipose tissue transporta binds to the glucose response element and hence triggers genes for lipogenesis758

b not obesity related759

760

Table 7 Priority ranking of Fob3b1 candidate genes based on bioinformatics results761

Priority NCBI36Gene ID

EnsemblGene ID

NCBI36Gene Start (bp)

NCBI36Gene End (bp)

High Dgat1 ENSMUSG00000022555 76329270 76339073

High Gpihbp1 ENSMUSG00000022579 75423897 75425467

High

Rhpn1 ENSMUSG00000022580 75531626 75541501

High Ly6a ENSMUSG00000075602 74825308a 74879243 a

Medium Cyp11b1 ENSMUSG00000075604 74662150 74668874

Medium Cyp11b2 ENSMUSG00000022589 74678294 74683480

Medium Gpr20 ENSMUSG00000045281 73521862 73534760

Medium

Bai1 ENSMUSG00000034730 74344086 74416719Medium Tsta3 ENSMUSG00000022570 75751939 75757008a Ly6a is not mapped according to NCBI 36. Bp positions are in NCBI 37762



763

Fig. 1 Genotype effect (F - Fat line allele and L - Lean line allele) in F2 congenic intercrosses on adiposity index (see764also Table 4) – lines with gray area represent estimates and 95% interval according to the additive model, while dots765with thick and thin vertical lines represent genotype estimates and 50% and 95% intervals according to the additive766and dominance model.767



768

769

770

Position (Mbp)

L i n e

50 60 70 80 90

M

K

G

P

E

B

D 1 2

D 6

Fob3b

Fob3b1

Fob3b2QTL (this study)

QTL (Stylianou et al. 2004) Line results

Inbred F2

AEFMI /

AEFMI AEFmI

AEFMI /

−E−mi /

−−−−− −−−−−

AEFMI AEFMI

AEFMI /

−−−−− aE−MI

771

Fig. 2 The genetic map of Fob3b QTL region on Chr 15 based on congenic mapping analysis (thick black stripe – 772

genome segment from the Fat line; gray stripe – region with uncertain origin; thin black stripe – genome segment773 from the Lean line). On the top the lengths of QTL segments from Stylianou et al. (2004) and this study are shown.774Characters on the right side denote the difference between the (inbred) congenic line and the Fat line as well as the775difference between the Lean/Lean (LL) and Fat/Fat (FF) genotype in F2 congenic intercrosses (upper case letters776indicate Pr(X < Fat) or Pr(LL < FF) ≥ 0.95, while lower case letters indicate Pr(X < Fat) or Pr(LL < FF) ≥ 0.90) as777detailed in Table 2 and 4: abdominal (A), epididymal (E), femoral (Fat) and mesenteric (M) fat depot, adipose index778(I), no difference (-), and no data (/).779

780

781



782

Fig. 3A Fob3b1 high resolution mapping. Parental mouse strain with high allele for each QTL is shown in bold, and783 parental strain with low allele is shown in normal font. Next to mouse cross, there is a name of each QTL. Grey bars784represent 95% confidence intervals of the QTLs, black vertical lines represent QTL peaks. Interval size in Mbp and785number of genes are indicated in the right hand columns.786

Fig. 3B High resolution map of the reduced Fob3b1 interval. Bioinformatics methods are shown in the left hand787column, results are shown with shaded regions on the map and corresponding interval size and number of genes are788indicated on the right side.789

790



Supplementary table 1 Additional comparisons of obesity trait analyses in homozygous congenic lines: posterior 791means (± standard deviation) by line for the percentage of fat depots (ABD - abdominal, EPI - epididymal, FEM -792femoral, and MES - mesenteric) and adiposity index (ADI) in congenic lines793

Trait

ABD (%) EPI (%) FEM (%) MES (%) ADI (%)

Line results

B 0.91±0.05 2.08±0.09 1.63±0.07 2.14±0.10 11.38±0.40

P 1.04±0.06 2.44±0.11 1.78±0.09 2.30±0.12 12.96±0.50

ComparisonsP – B 0.13±0.06 0.36±0.12 0.15±0.09 0.26±0.13 1.58±0.56

Pr(P > B) 0.99 1.00 0.95 0.99 1.00

Line results

G 0.89±0.05 2.02±0.11 1.67±0.07 2.12±0.11 11.27±0.43

K 0.88±0.05 1.92±0.09 1.53±0.07 1.94±0.10 10.59±0.41

M 0.99±0.05 2.22±0.10 1.76±0.07 2.25±0.10 12.17±0.41

ComparisonsK – G -0.01±0.04 -0.10±0.10 -0.14±0.07 -0.18±0.10 -0.68±0.43

Pr(K > G) 0.43 0.16 0.02 0.03 0.06

M – G 0.10±0.04 0.20±0.10 0.09±0.07 0.13±0.10 0.90±0.44

Pr(M > G) 0.99 0.98 0.89 0.91 0.98M – K 0.11±0.04 0.30±0.09 0.23±0.07 0.31±0.10 1.58±0.43

Pr(M > K) 0.99 1.00 1.00 1.00 1.00

X – Y – difference between the lines X and Y; Pr(X > Y) – posterior probability that the X line is fatter than the Y794line 795



Supplementary table 1 Comparison of QTL effects (posterior means ±standard deviation of difference between the796Lean/Lean (LL) and Fat/Fat (FF) genotypes) in F2 congenic intercrosses for the percentage of fat depots (ABD -797abdominal, EPI - epididymal, FEM - femoral, and MES - mesenteric) and adiposity index (ADI)798

Trait

ABD (%) EPI (%) FEM (%) MES (%) ADI (%)

QTL effects

D12 -0.16±0.09 -0.36±0.14 -0.24±0.12 -0.19±0.13 -1.70±0.74

P 0.00±0.11 -0.02±0.16 0.12±0.14 0.08±0.15 0.26±0.83

G -0.15±0.08 -0.37±0.12 -0.32±0.11 -0.28±0.12 -1.99±0.64M -0.08±0.06 -0.14±0.09 -0.09±0.08 -0.14±0.08 -0.77±0.46

Comparisons

P – D12 0.16±0.14 0.34±0.21 0.36±0.19 0.27±0.20 1.96±1.10

Pr(|D12| > |P|) 0.87 0.94 0.97 0.92 0.96M – D12 0.08±0.11 0.21±0.17 0.15±0.15 0.05±0.16 0.93±0.87

Pr(|D12| > |M|) 0.77 0.90 0.83 0.63 0.86

G – D12 0.01±0.12 -0.01±0.19 -0.08±0.16 -0.09±0.18 -0.29±0.97Pr(|G| > |D12|) 0.48 0.53 0.70 0.70 0.62

M – G 0.07±0.10 0.23±0.15 0.23±0.13 0.14±0.14 1.22±0.78

Pr(|G| > |M|) 0.78 0.93 0.96 0.85 0.94

X – Y – difference between the lines X and Y; Pr(|X| > |Y|) – posterior probability that QTL effect is larger in the X799line than in the Y line800

Documents

Congenic and bioinformatics analyses resolved a major effect Fob3b QTL on mouse Chr 15 into two closely linked loci