An Integrative Genetics Approach to Identify Candidate Genes Regulating BMD: Combining Linkage, Gene Expression, and Association

Abstract

Numerous quantitative trait loci (QTLs) affecting bone traits have been identified in the mouse; however, few of the underlying genes have been discovered. To improve the process of transitioning from QTL to gene, we describe an integrative genetics approach, which combines linkage analysis, expression QTL (eQTL) mapping, causality modeling, and genetic association in outbred mice. In C57BL/6J × C3H/HeJ (BXH) F₂ mice, nine QTLs regulating femoral BMD were identified. To select candidate genes from within each QTL region, microarray gene expression profiles from individual F₂ mice were used to identify 148 genes whose expression was correlated with BMD and regulated by local eQTLs. Many of the genes that were the most highly correlated with BMD have been previously shown to modulate bone mass or skeletal development. Candidates were further prioritized by determining whether their expression was predicted to underlie variation in BMD. Using network edge orienting (NEO), a causality modeling algorithm, 18 of the 148 candidates were predicted to be causally related to differences in BMD. To fine-map QTLs, markers in outbred MF1 mice were tested for association with BMD. Three chromosome 11 SNPs were identified that were associated with BMD within the Bmd11 QTL. Finally, our approach provides strong support for Wnt9a, Rasd1, or both underlying Bmd11. Integration of multiple genetic and genomic data sets can substantially improve the efficiency of QTL fine-mapping and candidate gene identification.

INTRODUCTION

During the early 1990s, the development of genome-wide genetic markers made it possible to identify chromosomal regions (referred to as quantitative trait loci [QTLs]) harboring genetic variation contributing to common diseases such as osteoporosis.(1) In the years since, numerous studies in humans and mice have identified QTLs affecting a wide spectrum of osteoporosis-related traits, including BMD.(2) In the mouse, many strategies exist for QTL fine-mapping, including congenic strain analysis and recombinant progeny testing; however, these are time consuming, quite laborious, and are typically only intermediate steps in the gene identification process.(3) However, with the advancement of genomic technologies, tools such as high-throughput genotyping and global gene expression profiling provide the opportunity to significantly accelerate gene discovery.

A gene's expression represents a quantifiable intermediate phenotype and can provide key links between genetic perturbations and disease.(4) Now, with the advent of DNA microarrays, gene expression can be quantified on a global scale.(5,6) One exciting application of transcriptomics is its integration with traditional genetic analysis to study the “genetics of gene expression.”(7–10) In the same way that QTLs are mapped for clinical traits, expression QTLs (eQTLs) can be mapped for transcript levels. eQTLs are classified as either local or distant, and these terms describe the proximity of eQTL and gene.(11) Local eQTLs are located near the transcript they regulate, whereas distant eQTLs are removed (typically on different chromosomes) from their structural locus. We have shown that at least two thirds of local eQTLs represent allele-specific cis effects, and therefore, the genes they regulate are strong positional candidates for overlapping clinical trait QTLs.(12)

The ability to establish causal links between gene expression and clinical traits is an additional advantage of measuring gene expression in a segregating population. In biological systems, the flow of cellular information always begins with DNA. This knowledge can be leveraged to orient the downstream relationships between genes and complex traits. Recently, causality modeling algorithms have been developed(13,14) and shown effective in establishing causality in mouse crosses using microarray-generated gene expression profiles.(15)

The main disadvantage of using traditional mapping populations, such as backcrosses, intercrosses, and recombinant inbred panels, is their lack of genetic resolution. QTLs identified in these crosses typically have confidence intervals in the range of 20–40 cM (∼40–80 Mbp), which correspond to regions containing hundreds of genes. Recently, several elegant studies have shown that association mapping in outbred and heterogeneous stock (HS) mice can provide substantial increases in mapping resolution.(16–18) This increase is caused by the accumulation of recombinations over many generations of random breeding. HS mice have recently been used to map 843 QTLs for ∼100 human disease traits with an average 95% CI of 2.8 Mbp, showing the effectiveness of HS mice for high-resolution mapping.(17) Therefore, the use of outbred mice offers substantial improvements in QTL localization, relative to traditional crosses, and makes downstream quantitative trait gene (QTG) identification much more rapid.

In this study, we outline an approach that integrates linkage in an F₂ intercross, eQTL analysis, high-density SNP maps, causality modeling, and association in outbred mice to identify candidate genes for BMD. Although we have applied this approach to BMD, it is, in theory, extensible to the analysis of any complex clinical or gene expression trait.

MATERIALS AND METHODS

Mapping populations

C57BL/6J × C3H/HeJ (BXH) F₂ mice (N = 309, 164 males and 145 females) were generated by intercrossing F₁s. Mice were fed a chow diet containing 4% fat (Ralston-Purina, St Louis, MO, USA) until 8 wk of age and were placed on a high-fat “Western” diet containing 42% fat and 0.15% cholesterol (Teklad 88137; Harlan Teklad, Madison, WI, USA) for 12 wk. At 20 wk, mice were killed after a 12-h fast, and adipose tissue was dissected, flash frozen in LN₂, and stored at −80°C. Female MF1 mice (N = 97) were purchased from Harlan (Indianapolis, IN, USA) at ∼4–6 wk of age. MF1s were fed the same chow diet until 19 wk of age and the Western diet for 14 wk until they were killed at 35 wk. All mice were maintained on a 12-h light/dark cycle. All mouse protocols were managed according to the guidelines of the American Association for Accreditation of Laboratory Animal Care (AAALAC).

Genotyping

Genomic DNA was isolated from BXH F₂ kidneys by phenol-chloroform extraction. Genotyping was conducted by ParAllele (South San Francisco, CA, USA) using the molecular-inversion probe (MIB) multiplex technique.(19) MF1 genomic DNA was isolated from tail clips using the Qiagen DNeasy tissue kit (Qiagen, Valencia, CA, USA). Genotyping was conducted by Affymetrix (Santa Clara, CA, USA) using the Affymetrix GeneChip Mouse Mapping 5K SNP platform. SNPs in both populations were annotated using the NCBI Build 37.1 genome assembly.

BMD determination

All carcasses were stored at −20°C after death and thawed overnight at 4°C before BMD scans. The left and right femurs of BXH F₂ mice were removed, partially defleshed, and scanned. For MF1 mice, the entire thawed carcass was scanned. BMD scans were preformed using a Lunar PIXImus II Densitometer (GE Healthcare, Piscataway, NJ, USA). Positional scanning effects have been documented for PIXImus Densitometers.(20) To determine whether scanning position affected the DXA used in this study, we recorded the x and y coordinates for each scan. Using ANOVA, we found no difference in BMD dependent on x-axis position; however, there was a small increase in BMD from top to bottom on the y-axis (right femur: r ² = 0.01, p = 0.04; left femur: r ² = 0.02, p = 0.006). To determine whether this significantly affected the linkage analysis, BMD (right and left femurs) was adjusted for y-axis position, and the average of the residuals was calculated. We preformed the QTL analysis (methods to be described below) with y-axis adjusted and unadjusted BMD data; however, we observed no difference in the LOD score profiles. Therefore, for all the analyses described below, BMD was calculated as the average of the right and left femurs unadjusted for y-axis position effects. For the MF1 scans, BMD was calculated for the whole body, the average of both femurs, and the L₂–L₆ lumbar vertebrae. These data were not corrected for the position effect.

BMD linkage analysis

All statistical analyses for the project were performed using the R language and environment for statistical computing (http://cran.r-project.org/).(21) Specifically, the R/qtl package was used for the linkage analysis.(22) Initially, missing genotypes were imputed with the sim.geno function. Marker regression for each SNP (N = 1486; scanone function) was performed using two statistical models. The first was an additive model that included sex, body weight at death (weight), and cross direction (F₂ mice were generated using F₁s from either maternal C57BL/6J × paternal C3H/HeJ [BXH; N = 161] or maternal C3H/HeJ × paternal C57BL/6J [HXB; N = 148] matings) as additive covariates. A sex interaction model was also used and contained the same additive covariates plus a sex × QTL interaction term. Sex-specific genome scans were also performed using an additive model adjusting for weight and cross direction. We also evaluated a model including a cross × QTL interaction term. However, no significant differences in LOD scores were observed, indicating the lack of cross × QTL interactions for BMD in our cross (data not shown). The 1.5 LOD-drop support intervals (1.5 SIs), which have been shown to approximate 95% CIs for densely genotyped intercrosses (markers every ∼1 cM),(23) were calculated using the lodint function. Significant (p < 0.05) and suggestive (p < 0.63) LOD thresholds were empirically determined for each analysis using 1000 permutations. A multiple QTL model comprised of covariates (sex, weight, and cross direction), all significant and suggestive QTLs, and all possible sex × QTL interaction terms was generated for BMD using the fitqtl function. Novel QTLs were named according to Mouse Genome Informatics (MGI) nomenclature guidelines (http://www.informatics.jax.org/mgihome/nomen/gene.shtml) and previously assigned names were used for loci identified by Beamer et al.(24)

DNA microarray profiling

Microarray gene expression profiling was performed as described previously.(25) Briefly, RNA was isolated from the gonadal fat pads (N = 293) of F₂ mice using the TRIzol method. 60-mer oligonucleotide chips were used (Agilent Technologies, Santa Clara, CA, USA), and all hybridizations were performed in duplicate with fluor reversal. Each individual sample was hybridized against a pool of 150 F₂ samples. The expression of each probe was represented as the mean log ratio (mlratio) of an individual's expression relative to the pool.

Expression QTL analysis

The Agilent arrays contained probe sets for a total of 23,574 transcripts. Probes were annotated using the NCBI Build 37.1 genome assembly. To eliminate nonexpressed probes, we used the 12,932 “active” genes(26) for all downstream analyses. Briefly, “active” genes are those detected as expressed, are correlated with at least one (of ∼25) metabolic trait in BXH F₂ mice, and have at least one eQTL with a LOD >4.3. From the “active” list, we selected genes with nominally significant (p < 0.01) Pearson partial correlations with BMD in either the entire cross (after correcting for weight and sex), males (after adjusting for weight), or females (after adjusting for weight). False discovery rates (FDRs) for correlations at p < 0.01 were estimated by creating 1000 permuted BMD vectors and repeating the correlation analysis for each vector. FDR was calculated by dividing the average number of genes, across all 1000 permutations, significant at p < 0.01, by the number of genes correlated at p < 0.01 in the real data. Using the mlratios for each of the correlated (p < 0.01) transcripts, we performed an eQTL analysis after correcting for sex, weight, and cross direction. Only markers on chromosomes harboring BMD QTL were used in the analysis. The statistical threshold (p < 0.05) was determined using 500 permutations of 500 randomly selected “active” genes. The mean significant LOD score across all 500 genes (LOD = 3.39 ± 0.01) was used as the experiment-wise significance threshold. The cumulative mean threshold was stable by the 500th gene, indicating that permuting additional genes would not significantly alter the results (data not shown). Local eQTLs were defined as those whose peak was located ±20 Mbp of the structural gene they regulated.(11,14)

Causality modeling using network edge orienting

Network edge orienting (NEO) is a recently developed R function designed to orient the relationships between genetic markers, gene expression traits, and clinical traits.(13) Tutorials outlining the use of NEO can be found at http://www.modelgenetics.com. NEO uses the fact that all cellular information begins with DNA and therefore the many possible relationships that can exist between DNA variation, gene expression, and clinical traits can be distilled to three. The three relationships (or models) are (1) causal—flow of information goes from DNA to gene to BMD (gene's expression is causing the change in the trait); (2) reactive—flow of information goes from DNA to BMD to gene (gene's expression is reacting to the change in the trait) and (3) independent—DNA variation affects both traits independently. NEO uses structural equation modeling to estimate the probabilities for each of the three relationships. The log₁₀ ratio of the causal model probability relative to the next best model probability (of the two remaining) is calculated. This ratio (referred to as the LEO next best or LEO.NB score) quantifies the relative likelihood that a gene's expression is causal for a trait such as BMD. Simulation studies have shown that single marker LEO.NB scores >1.0 are highly suggestive of causal relationships.(13)

For this experiment, we ran NEO for each gene correlated with BMD and regulated by a local eQTL. Each of the nine BMD peak QTL markers were used as anchors for the appropriate genes (e.g., the peak marker for Bmd11 on Chr 11 was used as an anchor for each gene correlated with BMD and regulated by a local eQTL on Chr 11). The analysis was preformed using data from the entire cross for all nine QTLs. We also preformed a sex-specific analysis for those QTL displaying sex-biased expression. LEO.NB scores >1.0 were considered significant evidence of a causal relationship.

Association analysis in MF1s

For the association analysis with BMD in MF1 mice, only markers (N = 398) located within one of the BMD QTL 1.5 SIs and with a minor allele frequency of >5% were tested. The association was performed using three traits: whole body, femoral, and spinal BMD. Whole body and spinal BMD were not correlated with body weight (p > 0.05), so raw unadjusted traits were used for association. Femoral BMD was highly correlated with weight (r = 0.48, p < 0.001); therefore, weight-corrected residuals were used for the association. The relationship between SNP genotype and BMD was measured using ANOVA. To guard against small sample size biasing the ANOVA p values, any genotype class with less than five samples was eliminated from the analysis. Adjusted p values were generated for all SNPs by repeating the ANOVA analysis using 1000 permuted BMD vectors and nonpermuted MF1 genotypes. The 95% CI for the location of an associated region was calculated using 1000 bootstrap samples drawn with replacement.(27) For the Bmd11 association, 11 SNPs surrounding the peak were used for the bootstrap analysis. The marker with the maximum –logP (negative log₁₀ of the association p value) for each bootstrap was recorded, and the markers at the top and bottom 2.5% of the distribution defined the 95% CI.

Defining regions of high and low SNP density between C57BL/6J and C3H/HeJ

SNPs polymorphic between C57BL/6J and C3H/HeJ were downloaded from the Mouse Phenome Database (MPD; http://phenome.jax.org/pub-cgi/phenome/mpdcgi?rtn=snps/door). SNPs were annotated using the NCBI Build 37.1 genome assembly. A custom R function was used to calculate and plot the SNP frequency for 25-kbp intervals across the Bmd11 region.(28)

RESULTS

Genetic regulation of BMD in C57BL/6J × C3H/HeJ F₂ mice

To identify QTLs regulating femoral BMD in C57BL/6J × C3H/HeJ (BXH) F₂ mice (N = 309), we performed genome scans using an additive (sex, weight, and cross direction used as additive covariates) and a sex interaction model (same additive covariates plus a sex × QTL interaction term). Using the additive model, we identified eight QTLs surpassing the significant (p < 0.05; LOD = 3.9) or suggestive (p < 0.63; LOD = 2.4) LOD thresholds. The QTLs were located on chromosomes (Chrs) (QTL names) 1 (Bmd5), 3 (Bmd40), 4 (Bmd7), 7 (Bmd41), 11 (Bmd11), 13 (Bmd13), 14 (Bmd14), and 18 (Bmd43) (Fig. 1). We repeated the genome scan using the sex interaction model, and this resulted in the identification of an additional suggestive (p < 0.63; LOD = 3.53) QTL located on Chr 10 (Bmd42) (Fig. 1).

FIG. 1.

Genome scan results for BMD in BXH F₂ mice. (Top) LOD score plots for BMD using an additive model adjusting for sex, cross direction, and weight. The dashed horizontal lines represent the permutationally derived significant (p < 0.05) and suggestive (p < 0.63) LOD thresholds. (Bottom) LOD score plots for BMD using a sex interaction model adjusting for sex, cross direction, weight, and sex × QTL. The dashed horizontal lines represent the permutationally derived significant (p < 0.05) and suggestive (p < 0.63) LOD thresholds.

Of the nine QTLs, five (Bmd5, Bmd7, Bmd11, Bmd13, and Bmd14) have been previously reported in a distinct BXH F₂ cross.(24) A QTL on Chr 18 (Bmd16) was reported by Beamer et al.,(24) but it did not overlap with the Chr 18 QTL (Bmd43) identified in this study (the peaks for Bmd16 and Bmd43 lie on opposite ends of Chr 18 and their 95% CIs do not overlap). Bmd40 and Bmd42 were novel with regard to their effect on femoral BMD, although QTLs in similar locations have been found to regulate tibial BMD.(29) Bmd41 is unique to this study.

The reason for performing the analysis using two models was to determine the effect of sex. If LOD scores improve after adjusting for a sex × QTL interaction, this is evidence, but not proof, that the QTL in question is expressed differently between the sexes. By comparing the two genome scans, modest increases in LOD scores for Bmd40 (Chr 3), Bmd42 (Chr 10), and Bmd11 (Chr 11) were observed (data not shown). To formally address the effect of sex, we generated a multiple regression model comprised of terms for covariates, the nine QTLs, and all possible sex × QTL interaction terms. The final model, which included all QTL terms and significant sex × QTL interactions, was highly significant (p < 0.0001), accounting for 63.3% of the variance in BMD (Table 1). As suggested above, the sex × QTL interaction terms for Bmd40, Bmd42, and Bmd11 were significant (p < 0.05; Table 1).

Table 1.

Results of a Multiple QTL Model Generated for BMD

QTL (Chr@cM)	QTL name	%var	Genetic effects		p
			a (μg/cm2)	d (μg/cm2)
Chr1@46.6353	Bmd5	2.12	1.47	−0.23	4.19 × 10−4
Chr3@27.70306	Bmd40	3.18	0.51	0.49	1.21 × 10−4
Chr4@56.43302	Bmd7	10.95	3.50	−0.51	2.22 × 10−16
Chr7@26.06836	Bmd41	2.47	1.51	0.46	1.22 × 10−4
Chr10@45.47224	Bmd42	1.31	0.25	−0.53	4.46 × 10−2
Chr11@26.8728	Bmd11	6.01	1.12	0.60	1.57 × 10−8
Chr13@51.23638	Bmd13	1.60	1.32	−0.02	2.77 × 10−7
Chr14@14.93824	Bmd14	3.67	1.65	−1.25	1.88 × 10−6
Chr18@39.42883	Bmd43	0.68	0.83	0.12	7.93 × 10−2
QTL (Chr@cM) × sex interactions
Chr3@27.70306 × sex		0.97	1.82	−0.91	2.71 × 10−2
Chr10@45.47224 × sex		0.87	−1.52	1.54	3.95 × 10−2
Chr11@26.8728 × sex		1.26	0.80	3.04	9.39 × 10−3
Covariates
Sex		3.63			4.42 × 10−4
Cross		0.63			3.03 × 10−2
Weight		13.04			0

Previously identified BXH F₂ BMD QTLs are referred to using names assigned by MGI. Novel loci (names in bold italics) were named according to MGI nomenclature guidelines. %var, percent of the phenotypic variance explained by each QTL, sex × QTL interaction or covariate; a and d, additive and dominance effect (a = the average difference in trait means between homozygous genotypes and d = is the deviation from the average difference between homozygotes; positive and negative a values indicate an increase and decrease, respectively, in the trait mean because of replacing C57BL/6J with C3H/HeJ alleles).

We also performed sex-specific QTL scans and generated sex-specific effect plots for each QTL (Figs. 2 and 3). These analyses supported the results of the regression model and indicated that Bmd40, Bmd42, and Bmd11 were male-biased (effect was more pronounced in males). Although the regression-based sex × QTL interaction p values were not significant, the sex-specific scans and effect plots also suggested that Bmd5, Bmd14, and Bmd43 were female-biased (Figs. 2 and 3). The inability to detect significant interaction terms for these three loci may be because of a lack of statistical power.

FIG. 2.

Sex-specific BMD QTL analysis in BXH F₂ mice. LOD score plots for BMD using an additive model (adjusting for cross direction and weight) in males (solid line) and females (dashed line) separately. The dashed horizontal lines represent the permutationally derived significant (p < 0.05) and suggestive (p < 0.63) LOD thresholds for males (male and female thresholds were only slightly different).

FIG. 3.

Effect plots for BMD QTLs in BXH F₂ mice. Genotypic contrasts are plotted for each of the nine BMD QTLs identified in Fig. 1. The mean (± SE) BMD residuals (mg/cm²) (after adjusting for body weight and cross direction) are plotted for B6/B6 homozygotes (black line), B6/C3H heterozygotes (wide dashed line), and C3H/C3H homozygotes (narrow dashed line) for each sex and QTL.

Identification of gene expression traits correlated with BMD

To identify genes correlated with BMD, we first selected probes that were identified as “active.”(26) This reduced the number of array probes from 23,574 to 12,932. Correlated genes were identified as those with nominally significant (p < 0.01) partial correlations (adjusted for sex and weight) in the entire cross or each sex separately (adjusted for weight). We choose to consider all three groups of correlations instead of only those from the whole cross, because some genes may be either (1) correlated in one sex but not the entire cross or (2) not correlated in the whole cross because of correlations that go in the opposite directions in the sexes. Of the active genes, 3037 in the entire cross, 1335 in males, and 736 in females were correlated with BMD (p < 0.01). At p < 0.01, the FDR was 4.3%, 8.9%, and 16.2% in the entire cross, males, and females, respectively. In total, 3338 genes were correlated (p < 0.01) in one or more of the groups.

Identifying local eQTLs coincident with BMD loci

Local eQTL represent genetic variation perturbing the expression of a nearby gene.(11) By definition, local eQTLs coincident with BMD loci (referred to throughout as “coincident local eQTL”) are strong positional and functional candidates. To identify coincident local eQTLs, we performed an eQTL analysis using microarray expression profiles of adipose tissue from individual BXH F₂ mice (N = 293). Local eQTL were identified for the 3338 correlated gene expression traits using a model adjusting for sex, cross direction, and weight. Only markers on the nine chromosomes harboring BMD QTLs were used in the analysis. Coincident local eQTLs were defined as those (1) with LOD scores surpassing the significant (p < 0.05; LOD = 3.39) LOD threshold and (2) with peaks within one of the defined BMD QTL 1.5 LOD-drop support intervals (1.5 SIs; as determined using physical location [Mbp] for both the 1.5 SIs and QTL peak markers; Table 2). Using these criteria, 150 gene expression traits (148 unique genes) were regulated by a coincident local eQTL (Supplemental Table 1).

Table 2.

Support Intervals and Peak Markers for the Nine BXH F₂ BMD QTLs

QTL	Marker	Chr	Mbp	1.5 SI (Mbp)	cM	1.5 SI (cM)	LOD
Bmd5	rs3690322	1	20.1	158.8	3.7	64.7	2.0
	rs3719973	1	126.8		46.6		3.6
	rs3702990	1	178.9		68.4		1.7
Bmd40	rs3687193	3	39.8	61.3	13.4	25.7	3.9
	rs3714671	3	78.0		27.7		5.7
	rs3708129	3	101.1		39.1		3.4
Bmd7	rs3666537	4	126.2	11.0	53.6	7.7	14.6
	rs3023007	4	129.2		56.4		16.3
	rs3661463	4	137.2		61.3		13.0
Bmd41	rs3665051	7	24.9	80.0	1.6	39.4	2.1
	rs3688333	7	61.2		26.1		4.1
	rs3660732	7	104.9		40.9		1.9
Bmd42	rs3672342	10	28.3	100.8	11.4	56.2	1.8
	rs3694232	10	102.3		45.5		3.5
	rs3706493	10	129.1		67.6		2.6
Bmd11	rs3727156	11	27.7	36.4	10.9	21.1	5.0
	rs4228731	11	55.2		26.9		6.5
	rs3714311	11	64.1		32.0		4.6
Bmd13	rs3682400	13	47.8	72.4	14.8	44.1	0.9
	rs3656262	13	117.3		51.2		2.8
	rs3675054	13	120.2		58.9		2.3
Bmd14	rs3663068	14	8.9	81.5	0.0	34.2	3.4
	rs3671357	14	46.7		14.9		4.2
	rs3670872	14	90.4		34.2		2.5
Bmd43	rs3678691	18	42.6	43.5	18.6	32.4	0.9
	rs3669949	18	69.5		39.4		2.5
	rs3655977	18	86.1		51.0		0.2

The 1.5 LOD-drop support intervals (1.5 SIs) were calculated using LOD score profiles from sex-interaction model for Bmd5, Bmd40, Bmd7, Bmd41, Bmd42, Bmd11, and Bmd14. The 1.5 SIs for Bmd13 and Bmd43 were calculated using LOD score profiles from the additive model.

Supplemental Table 1.

Genes with local eQTL coincident with BMD QTL ranked based on maximum correlation within each locus cM = eQTL peak location in cM; wc.cor=partial Pearson correlation between gene and BMD in the whole cross; wc.p=wc.cor P value; m.cor=partial Pearson correlation between gene and BMD in the males; m.p=m.cor P value; f.cor=partial Pearson correlation between gene and BMD in females; f.p=f.cor P value; max.cor=the maximum correlation of the three groups

Gene	QTL	Chr	Mbp	cM	LOD	Peak marker	Mbp	wc.cor	wc.p	m.cor	m.p	f.cor	f.p	max.cor
Grem2	Bmd5	1	176.76	66.6	16.5	rs3659655	175.34	0.29	0.00	0.40	0.00	0.29	0.00	0.40
Twist2	Bmd5	1	93.70	43.6	11.7	rs3664662	106.45	0.34	0.00	0.35	0.00	0.31	0.00	0.35
Map4k4	Bmd5	1	39.96	15.7	3.6	s3723444	40.75	−0.26	0.00	−0.35	0.00	−0.11	0.20	.35
3110009E18Rik	Bmd5	1	122.02	45.0	49.2	rs3667720	122.82	0.25	0.00	0.18	0.03	0.31	0.00	0.31
Tmeff2	Bmd5	1	50.97	19.7	12.1	rs3724707	50.30	0.15	0.01	0.07	0.38	0.28	0.00	0.28
Dusp12	Bmd5	1	172.80	64.8	11.0	rs3696419	171.78	0.26	0.00	0.21	0.01	0.28	0.00	0.28
Pla2g4a	Bmd5	1	151.68	54.6	4.2	rs4136715	150.48	0.22	0.00	0.27	0.00	0.14	0.10	0.27
Ifi202b	Bmd5	1	175.89	66.8	149.9	rs3685549	176.44	0.17	0.00	0.08	0.34	0.27	0.00	0.27
Dars2	Bmd5	1	162.97	54.0	6.7	rs3701166	145.19	−0.25	0.00	−0.26	0.00	−0.24	0.00	0.26
Lancl1	Bmd5	1	67.05	27.2	19.8	rs4222406	66.84	0.04	0.48	−0.03	0.68	0.26	0.00	0.26
Dusp12	Bmd5	1	172.80	64.8	11.6	rs3696419	171.78	0.20	0.00	0.12	0.14	0.26	0.00	0.26
Ngef	Bmd5	1	89.37	36.3	7.9	rs3678377	87.69	0.11	0.05	0.25	0.00	0.00	0.98	0.25
1110058L19Rik	Bmd5	1	24.00	16.3	12.0	rs3667075	41.98	−0.21	0.00	−0.25	0.00	−0.13	0.12	0.25
Adamts4	Bmd5	1	173.18	66.6	24.1	rs3659655	175.34	0.15	0.01	0.07	0.38	0.24	0.00	0.24
Slc39a10	Bmd5	1	46.86	19.5	8.3	rs3726360	43.79	0.15	0.01	0.07	0.40	0.23	0.01	0.23
Thap4	Bmd5	1	95.60	44.1	11.3	rs3663003	110.65	0.23	0.00	0.23	0.01	0.19	0.03	0.23
Uap1	Bmd5	1	172.07	64.5	23.4	rs3720335	170.78	0.12	0.04	0.23	0.01	−0.03	0.76	0.23
Prkag3	Bmd5	1	74.79	24.6	3.9	rs3659238	60.05	−0.22	0.00	−0.23	0.01	−0.16	0.06	0.23
Glb1l	Bmd5	1	75.19	26.8	4.0	rs3726861	64.73	−0.18	0.00	−0.23	0.01	−0.11	0.18	0.23
Sdpr	Bmd5	1	51.35	24.6	4.1	rs3659238	60.05	−0.10	0.08	−0.02	0.77	−0.22	0.01	0.22
Ppfia4	Bmd5	1	136.19	51.0	46.3	rs3721530	136.69	−0.22	0.00	−0.21	0.01	−0.21	0.01	0.22
Serpine2	Bmd5	1	79.79	33.1	16.4	rs3706465	81.03	−0.08	0.15	−0.01	0.87	−0.22	0.01	0.22
Usf1	Bmd5	1	173.34	66.6	44.1	rs3659655	175.34	0.16	0.01	0.09	0.28	0.22	0.01	0.22
Sept2	Bmd5	1	95.38	41.3	7.4	rs3688242	96.20	−0.14	0.01	−0.22	0.01	−0.06	0.46	0.22
Fmo1	Bmd5	1	164.76	54.8	5.1	rs3688042	153.10	0.11	0.07	0.21	0.01	−0.01	0.91	0.21
Adipor1	Bmd5	1	136.31	46.6	4.1	rs3687720	126.91	−0.17	0.00	−0.18	0.03	−0.20	0.02	0.20
Tsn	Bmd5	1	120.19	44.7	10.4	rs3694901	118.66	0.18	0.00	0.20	0.02	0.11	0.20	0.20
E430029J22Rik	Bmd5	1	175.52	66.8	15.5	rs3705656	176.08	−0.17	0.00	−0.18	0.03	−0.16	0.05	0.18
Gas5	Bmd5	1	162.97	60.7	83.5	rs4135672	162.99	−0.15	0.01	−0.17	0.04	−0.15	0.08	0.17
Sned1	Bmd5	1	95.13	39.3	10.4	rs3719888	93.20	0.15	0.01	0.16	0.05	0.12	0.17	0.16
Mgst2	Bmd40	3	51.47	14.3	41.8	rs3725706	41.55	−0.26	0.00	−0.37	0.00	−0.16	0.07	0.37
Ttc14	Bmd40	3	33.70	16.4	8.7	rs3689832	51.09	0.26	0.00	0.29	0.00	0.18	0.03	0.29
3110057O12Rik	Bmd40	3	40.65	15.0	7.5	rs3678630	47.98	0.24	0.00	0.28	0.00	0.22	0.01	0.28
Man1a2	Bmd40	3	100.37	37.0	6.5	rs3687989	96.76	0.23	0.00	0.21	0.01	0.27	0.00	0.27
Pet112l	Bmd40	3	85.38	36.4	10.9	rs3717575	95.29	−0.18	0.00	−0.26	0.00	−0.16	0.06	0.26
9930021J17Rik	Bmd40	3	85.46	33.4	46.0	rs3667802	87.20	−0.20	0.00	−0.26	0.00	−0.09	0.31	0.26
Nudt6	Bmd40	3	37.30	16.1	6.2	rs3724545	50.56	0.21	0.00	0.16	0.05	0.25	0.00	0.25
Gpatc4	Bmd40	3	87.85	33.4	23.4	rs3667802	87.20	−0.13	0.03	−0.25	0.00	−0.01	0.90	0.25
Snx27	Bmd40	3	94.30	33.4	7.7	rs3667802	87.20	0.21	0.00	0.18	0.03	0.22	0.01	0.22
Tloc1	Bmd40	3	30.69	15.0	4.9	rs3678630	47.98	0.22	0.00	0.19	0.02	0.20	0.02	0.22
Msr2	Bmd40	3	87.05	33.1	30.1	rs3667053	84.92	0.19	0.00	0.21	0.01	0.15	0.08	0.21
5830417I10Rik	Bmd40	3	88.62	33.4	27.0	rs3667802	87.20	0.16	0.00	0.21	0.01	0.11	0.18	0.21
Sucnr1	Bmd40	3	59.89	26.6	22.6	rs3714575	69.85	−0.16	0.01	−0.21	0.01	−0.07	0.42	0.21
Gpr89	Bmd40	3	96.67	37.0	26.8	rs3687989	96.76	0.18	0.00	0.20	0.01	0.17	0.04	0.20
Arfip1	Bmd40	3	84.30	33.1	10.9	rs3667053	84.92	0.19	0.00	0.17	0.04	0.20	0.02	0.20
Gm761	Bmd40	3	82.67	33.1	12.7	rs3715579	85.79	0.18	0.00	0.20	0.02	0.15	0.07	0.20
Tmod4	Bmd40	3	94.93	35.7	43.4	rs3661505	92.84	0.18	0.00	0.18	0.03	0.19	0.02	0.19
Siah2	Bmd40	3	58.48	26.9	6.6	rs3688679	74.12	0.16	0.01	0.11	0.17	0.18	0.03	0.18
Sfpq	Bmd7	4	126.70	53.6	44.6	rs3666537	126.15	0.36	0.00	0.26	0.00	0.45	0.00	0.45
Ccdc28b	Bmd7	4	129.30	56.4	74.3	rs3023007	129.23	−0.40	0.00	−0.37	0.00	−0.43	0.00	0.43
Map3k6	Bmd7	4	132.80	56.4	12.9	rs3023007	129.23	−0.41	0.00	−0.41	0.00	−0.38	0.00	0.41
Ahdc1	Bmd7	4	132.57	58.3	19.7	rs3714117	132.41	0.37	0.00	0.36	0.00	0.39	0.00	0.39
Pink1	Bmd7	4	137.87	61.3	42.9	rs3661463	137.18	−0.33	0.00	−0.30	0.00	−0.39	0.00	0.39
BC008163	Bmd7	4	132.46	58.3	16.1	rs3721169	132.48	0.31	0.00	0.27	0.00	0.38	0.00	0.38
BC039093	Bmd7	4	128.45	53.6	20.2	rs3666537	126.15	0.28	0.00	0.22	0.01	0.37	0.00	0.37
Extl1	Bmd7	4	133.91	56.4	7.6	rs3023007	129.23	−0.20	0.00	−0.33	0.00	−0.14	0.09	0.33
Otud3	Bmd7	4	138.45	61.3	9.8	rs3661463	137.18	−0.31	0.00	−0.31	0.00	−0.28	0.00	0.31
Tceb3	Bmd7	4	135.56	58.3	19.6	rs3714117	132.41	−0.19	0.00	−0.13	0.11	−0.30	0.00	0.30
Dhdds	Bmd7	4	133.52	58.3	15.2	rs3721169	132.48	−0.24	0.00	−0.30	0.00	−0.16	0.07	0.30
Hdac1	Bmd7	4	129.19	56.4	4.1	rs3023007	129.23	0.23	0.00	0.30	0.00	0.12	0.14	0.30
Psmb2	Bmd7	4	126.35	53.6	6.4	rs3666537	126.15	−0.25	0.00	−0.21	0.01	−0.30	0.00	0.30
Txlna	Bmd7	4	129.30	56.4	10.1	rs3023007	129.23	0.22	0.00	0.15	0.06	0.29	0.00	0.29
Cnksr1	Bmd7	4	133.78	58.3	26.1	rs3721169	132.48	0.20	0.00	0.15	0.06	0.29	0.00	0.29
Ccdc21	Bmd7	4	133.69	58.3	14.8	rs3721169	132.48	−0.17	0.00	−0.16	0.06	−0.28	0.00	0.28
Gpatc3	Bmd7	4	133.13	58.3	46.5	rs3721169	132.48	−0.23	0.00	−0.23	0.00	−0.28	0.00	0.28
Lsm10	Bmd7	4	125.77	56.4	6.1	rs3023007	129.23	0.21	0.00	0.24	0.00	0.17	0.04	0.24
Stx12	Bmd7	4	132.41	58.3	22.3	rs3721169	132.48	0.18	0.00	0.14	0.08	0.20	0.02	0.20
Siglech	Bmd41	7	63.02	34.2	5.7	rs3677657	81.77	0.24	0.00	0.48	0.00	0.16	0.06	0.48
Adm	Bmd41	7	117.77	40.1	7.2	rs3715384	101.95	−0.36	0.00	−0.37	0.00	−0.28	0.00	0.37
Zfp14	Bmd41	7	30.82	6.3	3.4	rs3662508	28.01	0.24	0.00	0.19	0.02	0.33	0.00	0.33
Picalm	Bmd41	7	97.28	34.2	7.8	rs3677657	81.77	−0.27	0.00	−0.32	0.00	−0.22	0.01	0.32
Tmem126a	Bmd41	7	97.60	40.1	3.5	rs3715384	101.95	−0.20	0.00	−0.31	0.00	−0.11	0.19	0.31
Pde2a	Bmd41	7	108.57	40.1	9.4	rs3715384	101.95	−0.29	0.00	−0.27	0.00	−0.30	0.00	0.30
Sv2b	Bmd41	7	82.26	34.2	3.5	rs3677657	81.77	−0.22	0.00	−0.27	0.00	−0.10	0.23	0.27
Chsy1	Bmd41	7	73.25	27.7	15.9	rs3662443	72.02	0.13	0.02	0.04	0.66	0.26	0.00	0.26
2610204K14Rik	Bmd41	7	88.91	34.2	12.7	rs3677657	81.77	−0.21	0.00	−0.25	0.00	−0.17	0.05	0.25
Ptov1	Bmd41	7	52.12	22.9	4.8	rs3690555	56.54	−0.18	0.00	−0.25	0.00	−0.07	0.41	0.25
Ngrn	Bmd41	7	87.41	36.5	27.0	rs3690268	89.63	0.20	0.00	0.25	0.00	0.13	0.13	0.25
Car11	Bmd41	7	52.96	20.5	39.0	rs3722844	53.20	0.22	0.00	0.25	0.00	0.22	0.01	0.25
Atg16l2	Bmd41	7	108.44	39.1	5.1	rs3672246	99.32	0.19	0.00	0.13	0.11	0.24	0.00	0.24
Slco3a1	Bmd41	7	81.42	34.2	15.2	rs3677657	81.77	0.16	0.01	0.23	0.00	0.09	0.28	0.23
Olfml1	Bmd41	7	114.71	40.2	17.2	rs3692018	103.94	0.21	0.00	0.23	0.00	0.16	0.05	0.23
Nipa1	Bmd41	7	63.23	26.9	5.6	rs3696018	64.36	0.16	0.01	0.10	0.20	0.23	0.01	0.23
2610511M17Rik	Bmd41	7	30.11	16.3	4.5	rs3671079	47.59	0.22	0.00	0.23	0.01	0.22	0.01	0.23
Nmb	Bmd41	7	88.05	36.5	14.4	rs3690268	89.63	−0.16	0.01	−0.22	0.01	−0.17	0.04	0.22
Lgals4	Bmd41	7	29.62	6.6	53.3	rs4226499	29.62	−0.20	0.00	−0.22	0.01	−0.15	0.08	0.22
Pop4	Bmd41	7	39.05	14.1	142.3	rs3716814	38.20	−0.19	0.00	−0.16	0.05	−0.22	0.01	0.22
Mcee	Bmd41	7	71.54	27.5	9.6	rs3680576	70.67	−0.13	0.03	−0.22	0.01	−0.02	0.79	0.22
Hddc3	Bmd41	7	87.49	36.5	165.7	rs3690268	89.63	0.17	0.00	0.21	0.01	0.14	0.10	0.21
Ech1	Bmd41	7	29.61	6.3	20.8	rs3662508	28.01	0.17	0.00	0.21	0.01	0.09	0.27	0.21
Rab30	Bmd41	7	99.89	37.6	15.1	rs3690467	92.62	0.17	0.00	0.21	0.01	0.09	0.28	0.21
Dlgh2	Bmd41	7	98.24	39.1	4.3	rs3672246	99.32	−0.12	0.05	−0.21	0.01	0.01	0.94	0.21
MMT00027957	Bmd41	7	61.91	22.9	4.6	rs3673884	56.73	−0.19	0.00	−0.21	0.01	−0.15	0.07	0.21
Mogat2	Bmd41	7	106.37	37.6	3.8	rs3671486	96.12	−0.19	0.00	−0.20	0.02	−0.19	0.02	0.20
Tpp1	Bmd41	7	112.89	38.0	3.6	rs3658777	96.76	0.15	0.01	0.14	0.08	0.20	0.02	0.20
2210412D01Rik	Bmd41	7	91.26	37.6	20.6	rs3671486	96.12	0.18	0.00	0.14	0.08	0.20	0.02	0.20
Hbxap	Bmd41	7	104.73	40.9	30.2	rs3660732	104.85	0.18	0.00	0.19	0.02	0.13	0.13	0.19
Ctsc	Bmd41	7	95.43	37.6	18.1	rs3680715	91.90	0.19	0.00	0.18	0.03	0.19	0.03	0.19
Rcn3	Bmd41	7	52.34	19.3	12.2	rs3689879	51.78	0.16	0.01	0.18	0.03	0.15	0.08	0.18
Ube3a	Bmd41	7	66.48	26.9	4.0	rs3696018	64.36	0.16	0.01	0.16	0.05	0.13	0.13	0.16
Bicc1	Bmd42	10	70.39	28.7	73.6	rs3690226	68.85	0.25	0.00	0.38	0.00	0.12	0.15	0.38
Tmem4	Bmd42	10	127.76	65.9	28.0	rs3676330	127.65	0.24	0.00	0.28	0.00	0.14	0.10	0.28
BC024063	Bmd42	10	81.21	29.3	5.8	rs3681069	70.57	0.22	0.00	0.25	0.00	0.14	0.10	0.25
A530089I17Rik	Bmd42	10	40.91	20.0	46.0	rs3679034	46.66	0.16	0.01	0.24	0.00	0.05	0.56	0.24
Dusp6	Bmd42	10	98.73	44.5	26.6	rs3704401	98.71	0.14	0.02	0.22	0.01	0.03	0.71	0.22
Timd4	Bmd11	11	46.62	22.6	19.9	rs3717281	45.74	0.30	0.00	0.43	0.00	0.21	0.01	0.43
Wnt9a	Bmd11	11	59.12	28.8	6.9	rs3712695	57.24	0.27	0.00	0.40	0.00	0.10	0.24	0.40
Zfp286	Bmd11	11	62.59	32.0	12.3	rs3714311	64.08	0.30	0.00	0.34	0.00	0.21	0.01	0.34
Rasd1	Bmd11	11	59.78	30.2	20.8	rs3692116	59.90	0.28	0.00	0.32	0.00	0.24	0.00	0.32
2310022M17Rik	Bmd11	11	31.57	14.8	35.1	rs3703534	32.01	−0.26	0.00	−0.32	0.00	−0.16	0.05	0.32
RP23.336J1.4	Bmd11	11	55.28	27.3	38.1	rs3684076	56.22	0.25	0.00	0.29	0.00	0.16	0.05	0.29
Larp1	Bmd11	11	57.82	30.2	7.4	rs3692116	59.90	−0.18	0.00	−0.28	0.00	−0.03	0.69	0.28
Adora2b	Bmd11	11	62.06	22.6	3.9	rs3717281	45.74	−0.22	0.00	−0.28	0.00	−0.10	0.24	0.28
Fancl	Bmd11	11	26.29	11.2	10.3	rs3711503	29.91	0.26	0.00	0.22	0.01	0.28	0.00	0.28
2810021J22Rik	Bmd11	11	58.68	28.8	5.4	rs3712695	57.24	0.17	0.00	0.25	0.00	0.10	0.22	0.25
3300001G02Rik	Bmd11	11	32.11	22.6	6.5	rs3717281	45.74	−0.16	0.01	−0.24	0.00	−0.07	0.42	0.24
Atpaf2	Bmd11	11	60.21	28.8	17.6	rs3712695	57.24	−0.22	0.00	−0.24	0.00	−0.20	0.02	0.24
Mrpl55	Bmd11	11	59.02	29.8	8.8	rs3683066	58.98	0.19	0.00	0.22	0.01	0.23	0.01	0.23
Nt5m	Bmd11	11	59.66	28.8	29.6	rs3712695	57.24	−0.14	0.02	−0.21	0.01	−0.07	0.42	0.21
Map2k3	Bmd11	11	60.75	28.8	12.0	rs3712695	57.24	−0.17	0.00	−0.19	0.02	−0.19	0.03	0.19
F2r	Bmd13	13	96.37	39.2	10.4	rs3694204	100.19	−0.20	0.00	−0.28	0.00	−0.20	0.02	0.28
Sfxn1	Bmd13	13	54.17	19.9	3.7	rs3721317	56.89	0.17	0.00	0.27	0.00	0.05	0.55	0.27
Ppwd1	Bmd13	13	105.00	41.7	89.9	rs3705003	104.86	0.21	0.00	0.17	0.04	0.26	0.00	0.26
Cox7c	Bmd13	13	86.18	30.4	5.4	rs3684993	82.62	−0.17	0.00	−0.24	0.00	−0.13	0.12	0.24
Cox7c	Bmd13	13	86.18	30.4	5.2	rs3684993	82.62	−0.17	0.00	−0.24	0.00	−0.13	0.12	0.24
Mctp1	Bmd13	13	76.72	27.6	5.3	rs3722699	76.42	0.18	0.00	0.15	0.06	0.23	0.01	0.23
Zfp367	Bmd13	13	64.23	27.6	6.5	rs3722699	76.42	−0.17	0.00	−0.17	0.04	−0.22	0.01	0.22
Tspan17	Bmd13	13	54.89	19.9	3.5	rs3721317	56.89	−0.20	0.00	−0.22	0.01	−0.10	0.24	0.22
Mmp14	Bmd14	14	55.05	19.7	34.9	rs3688833	55.38	0.25	0.00	0.09	0.27	0.44	0.00	0.44
Oxa1l	Bmd14	14	54.98	19.7	42.7	rs3707560	55.37	0.23	0.00	0.12	0.14	0.35	0.00	0.35
Entpd4	Bmd14	14	69.96	25.6	14.1	rs3724660	69.31	−0.20	0.00	−0.12	0.16	−0.31	0.00	0.31
Rab2b	Bmd14	14	52.88	19.3	27.7	rs3153444	54.70	0.22	0.00	0.14	0.08	0.31	0.00	0.31
Osgep	Bmd14	14	51.53	18.9	12.9	rs3653801	50.02	0.26	0.00	0.30	0.00	0.22	0.01	0.30
1190002H23Rik	Bmd14	14	79.69	33.5	5.2	rs3668114	86.83	−0.13	0.02	−0.07	0.43	−0.27	0.00	0.27
Stmn4	Bmd14	14	66.96	21.8	4.7	rs3691821	59.24	0.22	0.00	0.21	0.01	0.26	0.00	0.26
Supt16h	Bmd14	14	52.78	19.3	71.4	rs3153444	54.70	0.20	0.00	0.14	0.09	0.26	0.00	0.26
Hesx1	Bmd14	14	27.81	4.7	19.5	rs4230230	27.73	−0.21	0.00	−0.20	0.01	−0.25	0.00	0.25
Ear11	Bmd14	14	51.87	18.9	41.9	rs3653801	50.02	0.10	0.10	−0.03	0.71	0.24	0.00	0.24
3830406C13Rik	Bmd14	14	13.12	0.3	46.6	rs3663068	19.57	0.18	0.00	0.14	0.09	0.24	0.00	0.24
C130032J12Rik	Bmd14	14	47.92	17.9	69.9	rs3699072	48.11	0.14	0.02	0.06	0.50	0.24	0.00	0.24
Wnt5a	Bmd14	14	29.32	17.9	3.7	rs3699072	48.11	0.22	0.00	0.23	0.01	0.23	0.01	0.23
Slc7a7	Bmd14	14	54.99	19.3	22.3	rs3153444	54.70	0.12	0.04	0.03	0.70	0.23	0.01	0.23
Bnip3l	Bmd14	14	67.60	28.8	5.0	rs3722536	71.88	0.20	0.00	0.22	0.01	0.12	0.17	0.22
Ttc5	Bmd14	14	51.39	18.9	14.8	rs3653801	50.02	0.18	0.00	0.15	0.07	0.18	0.03	0.18
Ppic	Bmd43	18	53.57	38.4	4.4	rs3658163	68.82	−0.33	0.00	−0.37	0.00	−0.24	0.00	0.37
Mppe1	Bmd43	18	67.39	39.4	9.0	rs3669949	69.49	0.28	0.00	0.25	0.00	0.27	0.00	0.28
Smad4	Bmd43	18	73.80	41.4	70.4	rs3723567	73.76	−0.14	0.01	−0.06	0.49	−0.27	0.00	0.27
Hsd17b4	Bmd43	18	50.29	32.1	7.5	rs3675255	63.45	−0.14	0.01	−0.07	0.42	−0.25	0.00	0.25
Pmaip1	Bmd43	18	66.62	36.5	11.9	rs3716803	67.16	−0.12	0.04	−0.05	0.55	−0.22	0.01	0.22
Nedd4l	Bmd43	18	65.08	32.1	9.1	rs3724035	63.91	−0.17	0.00	−0.19	0.02	−0.12	0.14	0.19

We prioritized the 150 gene expression traits by ranking based on strength of correlation. We ranked using the absolute value of the maximum correlation among the three groups. Interestingly, of the top three prioritized genes at each locus, five (18.5%) have been previously shown to influence BMD and/or bone development including: twist homolog 2 (Twist2), ranked second within Bmd5(30); adrenomedullin (Adm), ranked second within Bmd41(31); Wingless-type MMTV integration site family, member 9A (Wnt9a), ranked second within Bmd11(32); matrix metalloproteinase 14 (Mmp14), ranked first within Bmd14(33,34); and MAD homolog 4 (Smad4), ranked third within Bmd43(35) (Table 3). To determine whether this represents a significant enrichment, we identified all transgenic and knockout strains with a bone mineralization defect by searching the “Phenotypes, Alleles & Disease Models” database at MGI (http://www.informatics.jax.org/searches/allele_form.shtml) using the search string “bone mineralization.” Of 7307 total strains, 220 (3.0%) were annotated as having differences in bone mineralization. This represents a 6-fold enrichment (3.0% versus 18.5%; Fisher's exact, p = 2.3 × 10⁻³) of known BMD genes at the top of our prioritized gene list.

Table 3.

Local eQTL LOD Scores and Correlations With BMD for the Three Genes Most Highly Correlated Within Each BMD QTL

Gene	QTL	Chr	Mbp	cM	LOD	r	p
Grem2	Bmd5	1	176.76	66.6	16.5	0.40	3.8 × 10−7
Twist2	Bmd5	1	93.70	43.6	11.7	0.35	1.2 × 10−5
Map4k4	Bmd5	1	39.96	15.7	3.6	−0.35	1.3 × 10−5
Mgst2	Bmd40	3	51.47	14.3	41.8	−0.37	4.3 × 10−6
Ttc14	Bmd40	3	33.70	16.4	8.7	0.29	3.0 × 10−4
3110057O12Rik	Bmd40	3	40.65	15.0	7.5	0.28	4.2 × 10−4
Sfpq	Bmd7	4	126.70	53.6	44.6	0.45	1.5 × 10−8
Ccdc28b	Bmd7	4	129.30	56.4	74.3	−0.43	7.7 × 10−8
Map3k6	Bmd7	4	132.80	56.4	12.9	−0.41	1.3 × 10−7
Siglech	Bmd41	7	63.02	34.2	5.7	0.48	3.3 × 10−10
Adm	Bmd41	7	117.77	40.1	7.2	−0.37	4.1 × 10−6
Zfp14	Bmd41	7	30.82	6.3	3.4	0.33	6.2 × 10−5
Timd4	Bmd11	11	46.62	22.6	19.9	0.43	3.2 × 10−8
Wnt9a	Bmd11	11	59.12	28.8	6.9	0.40	3.8 × 10−7
Zfp286	Bmd11	11	62.59	32.0	12.3	0.34	2.0 × 10−5
F2r	Bmd13	13	96.37	39.2	10.4	−0.28	6.2 × 10−4
Sfxn1	Bmd13	13	54.17	19.9	3.7	0.27	9.6 × 10−4
Ppwd1	Bmd13	13	105.00	41.7	89.9	0.26	2.3 × 10−3
Mmp14	Bmd14	14	55.05	19.7	34.9	0.44	5.0 × 10−8
Oxa1l	Bmd14	14	54.98	19.7	42.7	0.35	2.3 × 10−5
Entpd4	Bmd14	14	69.96	25.6	14.1	−0.31	2.0 × 10−4
Ppic	Bmd43	18	53.57	38.4	4.4	−0.37	4.3 × 10−6
Mppe1	Bmd43	18	67.39	39.4	9.0	0.28	1.6 × 10−6
Smad4	Bmd43	18	73.80	41.4	70.4	−0.27	1.1 × 10−3

Genes are ranked by strength of correlation.

cM, eQTL peak location in cM; LOD, eQTL peak LOD score; r, Pearson correlation coefficient between genes expression and BMD; p, correlation p value.

Predicting causal links between genes with local eQTLs and BMD

Genes with coincident local eQTLs are potentially key drivers of changes in BMD; however, eQTL coincidence and correlation alone are not enough to support causality. To more formally identify causal relationships between gene expression traits and BMD, we used NEO, a recently developed R function. NEO uses genetic markers to predict causal interactions between two traits, which in our case were gene expression and BMD.(13)

A NEO single marker analysis was performed for each gene with a local eQTL by anchoring the relationships using peak QTL markers. The analysis was performed using the whole cross and adjusting BMD for sex, cross direction, and weight. The analysis was also run in males for the male-biased QTLs (Bmd40, Bmd42, and Bmd11) and females for the three loci displaying suggestive evidence of being female-biased (Bmd5, Bmd14, and Bmd43) (Figs. 2 and 3). A total of 17 genes in the whole cross were predicted to be causal for BMD with single marker LEO.NB scores exceeding 1.0, indicating that, given the data, the causal model is at least 10 times more likely than any of the alternative models (Table 4). One additional gene, Wnt9a, whereas not significant in the whole cross, was significant in the male-specific NEO analysis. Of the five known bone genes mentioned above, Twist2, Mmp14, and Wnt9a were predicted as causal. Although not >1.0, Adm and Smad4 had positive single marker LEO.NB scores of 0.771 and 0.128, respectively (Supplemental Table 2).

Table 4.

Genes Predicted to be Causal With a NEO Single Marker LEO.NB Score ≥1.0

Gene	Chr	Mbp	Group	r	LEO.NB	RMSEA
Twist2	1	91.63	Whole cross	0.34	2.56	0.070
Zfp286	11	62.51	Whole cross	0.30	2.38	0.101
Timd4	11	46.59	Whole cross	0.30	2.32	0.106
Rasd1	11	59.69	Whole cross	0.28	2.20	0.101
Mmp14	14	48.95	Whole cross	0.25	2.20	0.018
Grem2	1	174.77	Whole cross	0.29	1.88	0.117
2310022M17Rik	11	31.56	Whole cross	−0.26	1.73	0.108
Oxa1l	14	48.88	Whole cross	0.23	1.70	0.000
Picalm	7	84.12	Whole cross	−0.27	1.69	0.142
3110009E18Rik	1	119.88	Whole cross	0.25	1.60	0.000
Osgep	14	46.01	Whole cross	0.26	1.58	0.087
Wnt9a	11	59.06	Males	0.40	1.56	0.105
Dusp12	1	170.81	Whole cross	0.26	1.53	0.130
Bicc1	10	70.98	Whole cross	0.25	1.53	0.000
RP23–336J1.4	11	55.22	Whole cross	0.25	1.38	0.057
Tmem4	10	128.06	Whole cross	0.24	1.33	0.000
Mppe1	18	67.45	Whole cross	0.28	1.20	0.004
Rab2b	14	47.36	Whole cross	0.22	1.13	0.056

r, Pearson correlation coefficient between a gene's expression and BMD in the specified group (whole cross or males only); LEO.NB, likelihood ratio of the causal model relative to the next best model (LEO.NB > 1.0 indicates the causal model fits the data at least 10 times better than either of the alternative models); RMSEA, root mean square error of approximation; RMSEA is a model fit index, and low values indicate good fit of the causal model.

Supplemental Table 2.

Full list of results for the NEO causlity predcition analysis. Genes ranked based on single marker LEO.NB causal score. r = partial Pearson correlation between gene and BMD (adjusted for sex, cross and weight in whole cross; weight and cross direction in the individual sexes); LEO.NB=causal score, is the −log10 of the P value for the causal model (m1) relative to the four other alternative models; neglogP = −log10 of the model fit P value, in structual equation modeling neglogP values close to 0 (P value close to 1) represent good model fit; m1 = causal model; m2 = reactive model; m3 = independent model; m4 and m5 = confounded models RMSEA = Root Mean Square Error of Approximation, is a model fit index, values close to 0 indicate good model fit

						neglogP					RMSEA
Gene	Chr	Mbp	r	LEO.NB	m1	m2	m3	m4	m5	m1	m2	m3	m4	m5
Whole cross
Twist2	1	91.63	0.34	2.56	0.92	8.30	6.47	3.49	10.60	0.07	0.34	0.29	0.20	0.39
Zfp286	11	62.51	0.30	2.38	1.35	8.27	4.65	3.73	10.50	0.10	0.34	0.24	0.21	0.38
Timd4	11	46.59	0.30	2.32	1.42	7.78	4.61	3.73	9.94	0.11	0.32	0.24	0.21	0.37
Rasd1	11	59.69	0.28	2.2	1.35	10.70	3.55	3.73	12.90	0.10	0.39	0.20	0.21	0.43
Mmp14	14	48.95	0.25	2.2	0.53	18.50	3.00	2.73	20.40	0.02	0.52	0.18	0.17	0.55
Grem2	1	174.77	0.29	1.88	1.61	5.21	4.72	3.49	6.98	0.12	0.26	0.24	0.20	0.31
2310022M17Rik	11	31.56	−0.26	1.73	1.45	10.60	3.18	3.73	12.70	0.11	0.39	0.19	0.21	0.43
Oxa1l	14	48.88	0.23	1.7	0.41	29.50	2.11	2.73	31.40	0.00	0.66	0.14	0.17	0.69
Picalm	7	84.12	−0.27	1.69	2.08	5.02	3.77	3.88	6.75	0.14	0.25	0.21	0.22	0.30
3110009E18Rik	1	119.88	0.25	1.6	0.36	42.90	1.96	3.49	45.60	0.00	0.81	0.14	0.20	0.83
Osgep	14	46.01	0.26	1.58	1.14	6.37	3.84	2.73	7.81	0.09	0.29	0.21	0.17	0.33
Dusp12	1	170.81	0.25	1.53	1.83	5.50	3.36	3.49	7.07	0.13	0.27	0.20	0.20	0.31
Bicc1	10	70.98	0.25	1.53	0.14	15.90	3.56	1.68	17.00	0.00	0.48	0.20	0.12	0.50
RP23.336J1.4	11	55.22	0.25	1.38	0.79	27.60	2.17	3.73	30.30	0.06	0.64	0.15	0.21	0.67
Tmem4	10	128.06	0.23	1.33	0.35	11.40	3.19	1.68	12.40	0.00	0.40	0.19	0.12	0.42
Mppe1	18	67.45	0.27	1.2	0.50	5.74	4.76	1.70	6.73	0.00	0.27	0.24	0.12	0.30
Rab2b	14	47.36	0.22	1.13	0.79	19.50	1.92	2.73	21.20	0.06	0.54	0.13	0.17	0.56
Hesx1	14	25.14	−0.21	0.859	1.27	9.28	2.13	2.73	10.60	0.10	0.36	0.15	0.17	0.39
Siglech	7	50.04	0.24	0.828	2.24	5.00	3.07	3.88	6.57	0.15	0.25	0.19	0.22	0.29
BC024063	10	81.94	0.21	0.785	0.89	3.65	2.94	1.68	4.34	0.07	0.21	0.18	0.12	0.23
Adm	7	104.48	−0.36	0.771	1.96	2.73	7.71	3.88	4.61	0.14	0.17	0.32	0.22	0.24
Thap4	1	93.53	0.23	0.721	1.68	9.02	2.40	3.49	10.70	0.12	0.35	0.16	0.20	0.39
Entpd4	14	63.87	−0.20	0.665	1.37	8.31	2.03	2.73	9.55	0.10	0.34	0.14	0.17	0.36
Nedd4l	18	65.26	−0.16	0.649	0.88	7.36	1.53	1.70	8.06	0.07	0.31	0.11	0.12	0.33
Zfp14	7	25.45	0.24	0.647	2.53	3.18	3.26	3.88	4.51	0.16	0.19	0.19	0.22	0.24
Supt16h	14	47.26	0.20	0.644	0.44	46.50	1.09	2.73	48.50	0.00	0.84	0.08	0.17	0.86
Ppwd1	13	100.42	0.20	0.63	0.63	35.00	1.26	2.83	36.90	0.04	0.73	0.10	0.18	0.75
A530089I17Rik	10	41.30	0.16	0.569	0.87	7.77	1.44	1.68	8.46	0.06	0.32	0.11	0.12	0.34
Ppfia4	1	134.15	−0.22	0.414	1.04	26.70	1.45	3.49	28.90	0.08	0.63	0.11	0.20	0.66
Smad4	18	73.87	−0.14	0.182	0.32	58.90	0.50	1.70	60.00	0.00	0.95	0.00	0.12	0.96
Dusp6	10	99.24	0.14	0.157	0.63	22.70	0.79	1.68	23.60	0.04	0.58	0.06	0.12	0.59
Dars2	1	160.94	−0.25	0.151	2.29	2.45	3.85	3.49	3.63	0.15	0.16	0.21	0.20	0.21
Hsd17b4	18	50.34	−0.14	0.142	1.04	6.13	1.18	1.70	6.70	0.08	0.28	0.09	0.12	0.30
Ppic	18	53.62	−0.33	0.0799	1.00	1.08	7.53	1.70	1.77	0.08	0.08	0.32	0.12	0.13
1110058L19Rik	1	24.24	−0.21	0.0704	2.42	3.08	2.49	3.49	4.13	0.16	0.19	0.16	0.20	0.22
Atpaf2	11	60.13	−0.22	0.0618	1.90	9.98	1.96	3.73	11.70	0.13	0.37	0.14	0.21	0.41
Fancl	11	26.28	0.26	0.038	2.46	2.50	3.93	3.73	3.77	0.16	0.16	0.22	0.21	0.21
3830406C13Rik	14	10.74	0.18	−0.0321	1.12	19.70	1.09	2.73	21.20	0.09	0.54	0.08	0.17	0.56
Car11	7	39.78	0.22	−0.033	1.28	28.00	1.24	3.88	30.40	0.10	0.65	0.09	0.22	0.67
Mgst2	3	51.29	−0.26	−0.0489	1.80	22.70	1.76	5.10	25.80	0.13	0.58	0.13	0.25	0.62
Ttc5	14	45.86	0.18	−0.0853	1.51	9.05	1.43	2.73	10.20	0.11	0.35	0.11	0.17	0.38
Ttc14	3	33.24	0.25	−0.0965	3.28	4.03	3.19	5.10	5.82	0.19	0.22	0.19	0.25	0.27
Stmn4	14	60.88	0.22	−0.209	1.96	1.75	3.16	2.73	2.52	0.14	0.13	0.19	0.17	0.16
Olfml1	7	101.52	0.21	−0.265	2.39	5.79	2.13	3.88	7.22	0.16	0.27	0.15	0.22	0.31
2610204K14Rik	7	75.57	−0.21	−0.27	2.18	8.24	1.91	3.88	9.85	0.15	0.34	0.13	0.22	0.37
Wnt5a	14	26.66	0.22	−0.27	1.98	1.71	3.11	2.73	2.47	0.14	0.12	0.19	0.17	0.16
Pmaip1	18	66.69	−0.12	−0.276	1.18	5.13	0.90	1.70	5.59	0.09	0.26	0.07	0.12	0.27
Zfp367	13	61.52	−0.17	−0.317	2.22	1.90	1.91	2.83	2.52	0.15	0.13	0.13	0.18	0.16
Dusp12	1	170.81	0.20	−0.352	2.23	5.40	1.88	3.49	6.60	0.15	0.26	0.13	0.20	0.30
3110057O12Rik	3	40.27	0.24	−0.553	3.26	4.83	2.70	5.10	6.64	0.19	0.25	0.17	0.25	0.30
Mrpl55	11	58.93	0.19	−0.701	2.70	3.26	2.00	3.73	4.28	0.17	0.19	0.14	0.21	0.23
Ngrn	7	74.06	0.20	−0.719	1.89	16.60	1.17	3.88	18.40	0.13	0.49	0.09	0.22	0.52
Lgals4	7	24.24	−0.20	−0.77	2.44	6.71	1.67	3.88	8.08	0.16	0.30	0.12	0.22	0.33
Atg16l2	7	95.40	0.20	−0.776	2.83	3.09	2.06	3.88	4.13	0.18	0.19	0.14	0.22	0.22
Glb1l	1	75.48	−0.18	−0.834	2.69	2.42	1.85	3.49	3.23	0.17	0.16	0.13	0.20	0.19
Tsn	1	118.05	0.18	−0.874	2.12	9.12	1.24	3.49	10.40	0.14	0.35	0.09	0.20	0.38
MMT00027957	7	39.68	−0.19	−0.934	2.99	2.29	2.06	3.88	3.19	0.18	0.15	0.14	0.22	0.19
E430029J22Rik	1	173.62	−0.17	−0.973	2.51	4.15	1.53	3.49	5.11	0.16	0.22	0.11	0.20	0.25
Wnt9a	11	59.06	0.27	−1.03	2.64	1.62	4.39	3.73	2.74	0.17	0.12	0.23	0.21	0.17
Ctsc	7	82.25	0.18	−1.09	2.67	5.12	1.58	3.88	6.28	0.17	0.25	0.12	0.22	0.29
Hbxap	7	91.63	0.18	−1.15	2.17	13.70	1.02	3.88	15.30	0.15	0.44	0.08	0.22	0.47
Adipor1	1	134.27	−0.17	−1.16	2.69	2.88	1.54	3.49	3.67	0.17	0.18	0.11	0.20	0.21
C130032J12Rik	14	42.38	0.14	−1.18	1.29	47.70	0.11	2.73	49.00	0.10	0.85	0.00	0.17	0.86
Ptov1	7	38.94	−0.18	−1.19	2.68	5.37	1.48	3.88	6.53	0.17	0.26	0.11	0.22	0.29
Ifi202b	1	173.90	0.17	−1.2	1.98	15.30	0.78	3.49	16.70	0.14	0.47	0.06	0.20	0.49
2210412D01Rik	7	77.96	0.18	−1.21	2.49	7.84	1.29	3.88	9.16	0.16	0.33	0.10	0.22	0.36
Larp1	11	57.74	−0.17	−1.21	2.50	6.67	1.29	3.73	7.85	0.16	0.30	0.10	0.21	0.33
Pde2a	7	95.56	−0.29	−1.25	2.72	1.47	5.13	3.88	2.68	0.17	0.11	0.26	0.22	0.17
Pop4	7	33.43	−0.19	−1.29	1.49	53.60	0.21	3.88	55.80	0.11	0.90	0.00	0.22	0.92
Bnip3l	14	61.52	0.19	−1.34	4.07	4.45	4.45	2.73	3.12	0.22	0.23	0.23	0.17	0.19
Usf1	1	171.34	0.16	−1.35	2.29	8.97	0.94	3.49	10.10	0.15	0.35	0.07	0.20	0.38
Map2k3	11	60.66	−0.17	−1.39	2.43	8.81	1.04	3.73	10.00	0.16	0.35	0.08	0.21	0.37
Slc7a7	14	48.89	0.12	−1.42	1.94	9.52	0.52	2.73	10.20	0.14	0.36	0.01	0.17	0.38
2810021J22Rik	11	58.59	0.16	−1.47	2.91	3.03	1.43	3.73	3.85	0.18	0.18	0.11	0.21	0.21
Tloc1	3	30.20	0.21	−1.49	3.88	2.50	2.40	5.10	3.74	0.22	0.16	0.16	0.25	0.21
Nudt6	3	36.87	0.21	−1.52	3.78	3.05	2.26	5.10	4.38	0.21	0.19	0.15	0.25	0.23
Sv2b	7	69.01	−0.22	−1.52	3.01	1.49	3.06	3.88	2.41	0.18	0.11	0.19	0.22	0.16
Ech1	7	24.23	0.17	−1.54	2.29	14.70	0.76	3.88	16.20	0.15	0.46	0.05	0.22	0.48
Slc39a10	1	47.10	0.15	−1.56	2.89	2.05	1.33	3.49	2.67	0.18	0.14	0.10	0.20	0.17
Sned1	1	93.12	0.15	−1.58	5.07	6.26	3.69	3.49	4.70	0.25	0.29	0.21	0.20	0.24
Adamts4	1	171.18	0.15	−1.59	2.48	6.90	0.89	3.49	7.86	0.16	0.30	0.07	0.20	0.33
Rab30	7	86.86	0.17	−1.61	2.56	9.14	0.95	3.88	10.40	0.17	0.36	0.07	0.22	0.38
Gas5	1	160.94	−0.15	−1.63	2.00	23.10	0.37	3.49	24.50	0.14	0.58	0.00	0.20	0.60
Rcn3	7	39.16	0.16	−1.64	2.83	5.19	1.18	3.88	6.20	0.18	0.26	0.09	0.22	0.29
Sept2	1	93.31	−0.15	−1.65	2.70	4.06	1.04	3.49	4.83	0.17	0.22	0.08	0.20	0.25
Nipa1	7	50.25	0.16	−1.65	2.93	4.07	1.28	3.88	5.00	0.18	0.22	0.10	0.22	0.25
Slco3a1	7	68.18	0.16	−1.67	2.55	9.82	0.88	3.88	11.10	0.16	0.37	0.07	0.22	0.39
Hddc3	7	74.14	0.17	−1.68	1.78	49.80	0.10	3.88	51.80	0.13	0.87	0.00	0.22	0.89
Cox7c	13	82.15	−0.17	−1.69	2.45	0.76	2.08	2.83	1.20	0.16	0.05	0.14	0.18	0.09
Snx27	3	93.99	0.21	−1.7	3.72	3.67	2.02	5.10	5.05	0.21	0.21	0.14	0.25	0.25
Ube3a	7	53.51	0.16	−1.73	2.98	3.79	1.25	3.88	4.67	0.18	0.21	0.10	0.22	0.24
Cox7c	13	82.15	−0.17	−1.73	2.46	0.73	2.11	2.83	1.17	0.16	0.05	0.14	0.18	0.09
Man1a2	3	99.99	0.23	−1.77	3.88	2.11	2.85	5.10	3.37	0.22	0.14	0.18	0.25	0.20
Nmb	7	74.70	−0.16	−1.77	2.54	11.00	0.76	3.88	12.30	0.16	0.39	0.05	0.22	0.42
Mctp1	13	72.70	0.18	−1.77	2.45	0.68	2.37	2.83	1.13	0.16	0.04	0.16	0.18	0.09
Adora2b	11	61.97	−0.21	−1.91	3.02	1.11	2.97	3.73	1.88	0.18	0.08	0.18	0.21	0.13
2610511M17Rik	7	24.73	0.22	−1.92	3.10	1.18	3.14	3.88	2.02	0.19	0.09	0.19	0.22	0.14
Uap1	1	170.08	0.12	−1.93	5.34	9.64	3.41	3.49	7.83	0.26	0.37	0.20	0.20	0.33
3300001G02Rik	11	32.10	−0.16	−1.93	3.21	1.28	1.60	3.73	1.85	0.19	0.10	0.12	0.21	0.13
F2r	13	91.79	−0.20	−2.02	3.80	2.78	4.15	2.83	1.78	0.21	0.17	0.22	0.18	0.13
9930021J17Rik	3	85.40	−0.20	−2.06	2.67	23.60	0.61	5.10	25.90	0.17	0.59	0.03	0.25	0.62
Ear11	14	46.35	0.10	−2.08	2.08	21.90	0.00	2.73	22.50	0.14	0.57	0.00	0.17	0.58
Arfip1	3	84.24	0.19	−2.12	3.47	7.08	1.34	5.10	8.66	0.20	0.31	0.10	0.25	0.34
Ngef	1	87.29	0.11	−2.14	4.45	4.52	2.31	3.49	3.56	0.23	0.24	0.15	0.20	0.20
1190002H23Rik	14	73.64	−0.13	−2.14	2.54	0.39	1.44	2.73	0.65	0.16	0.00	0.11	0.17	0.04
Mcee	7	58.17	−0.13	−2.24	6.11	11.00	3.88	3.88	8.79	0.28	0.39	0.22	0.22	0.35
Msr2	3	87.00	0.19	−2.32	3.07	15.40	0.75	5.10	17.30	0.19	0.47	0.05	0.25	0.50
Nt5m	11	59.57	−0.14	−2.32	2.47	23.10	0.15	3.73	24.30	0.16	0.58	0.00	0.21	0.60
Sfxn1	13	52.65	0.17	−2.37	2.62	0.25	2.34	2.83	0.57	0.17	0.00	0.16	0.18	0.03
Chsy1	7	59.99	0.13	−2.39	2.81	11.20	0.42	3.88	12.30	0.18	0.40	0.00	0.22	0.42
Sdpr	1	51.59	−0.10	−2.46	3.22	1.00	0.76	3.49	1.30	0.19	0.08	0.05	0.20	0.10
Fmo1	1	162.74	0.10	−2.46	3.05	2.58	0.60	3.49	3.02	0.19	0.17	0.03	0.20	0.18
Pet112l	3	85.32	−0.18	−2.55	4.01	3.29	1.46	5.10	4.39	0.22	0.19	0.11	0.25	0.23
Gm761	3	82.59	0.18	−2.57	3.42	10.40	0.85	5.10	12.00	0.20	0.38	0.06	0.25	0.41
Tmeff2	1	51.23	0.15	−2.64	4.20	2.32	2.66	3.49	1.56	0.23	0.15	0.17	0.20	0.12
Dlgh2	7	85.14	−0.12	−2.65	3.23	4.25	0.58	3.88	4.89	0.19	0.23	0.03	0.22	0.25
Gpr89	3	96.36	0.18	−2.69	3.27	15.10	0.58	5.10	16.90	0.19	0.47	0.03	0.25	0.50
Tmod4	3	94.61	0.18	−2.73	2.97	27.50	0.24	5.10	29.60	0.18	0.64	0.00	0.25	0.66
Mogat2	7	93.31	−0.19	−2.73	3.38	0.66	2.56	3.88	1.25	0.20	0.04	0.17	0.22	0.09
Serpine2	1	80.13	−0.08	−2.82	3.08	4.39	0.26	3.49	4.78	0.19	0.23	0.00	0.20	0.24
Pla2g4a	1	149.67	0.22	−2.86	4.83	3.35	5.20	3.49	1.97	0.25	0.20	0.26	0.20	0.14
Tspan17	13	53.40	−0.20	−2.88	2.91	0.25	3.44	2.83	0.03	0.18	0.00	0.20	0.18	0.00
Siah2	3	58.33	0.16	−3.01	3.93	5.38	0.93	5.10	6.52	0.22	0.26	0.07	0.25	0.29
5830417I10Rik	3	88.63	0.17	−3.08	3.31	21.50	0.23	5.10	23.20	0.20	0.56	0.00	0.25	0.59
Prkag3	1	75.04	−0.22	−3.15	3.25	0.10	3.63	3.49	0.52	0.19	0.00	0.21	0.20	0.01
Lancl1	1	67.29	0.04	−3.2	3.43	8.69	0.22	3.49	8.74	0.20	0.35	0.00	0.20	0.35
Sucnr1	3	59.76	−0.16	−3.24	3.60	14.10	0.36	5.10	15.50	0.21	0.45	0.00	0.25	0.47
Map4k4	1	40.20	−0.26	−3.71	4.27	1.50	6.02	3.49	0.57	0.23	0.11	0.28	0.20	0.03
Tmem126a	7	84.50	−0.20	−3.73	3.81	0.08	3.25	3.88	0.24	0.21	0.00	0.19	0.22	0.00
Gpatc4	3	87.79	−0.13	−3.83	4.03	11.50	0.19	5.10	12.50	0.22	0.40	0.00	0.25	0.42
Tpp1	7	99.86	0.15	−3.91	3.94	0.23	2.17	3.88	0.02	0.22	0.00	0.15	0.22	0.00
Ccdc28b	4	128.65	−0.40	−4.39	4.84	58.10	0.45	15.70	68.80	0.25	0.94	0.00	0.48	1.03
Ahdc1	4	132.03	0.37	−5.79	8.63	11.60	2.84	15.70	18.60	0.34	0.40	0.18	0.48	0.52
Map3k6	4	132.20	−0.40	−6.75	10.10	3.35	6.47	15.70	9.09	0.38	0.20	0.29	0.48	0.35
Sfpq	4	126.05	0.35	−6.8	7.53	29.70	0.73	15.70	37.80	0.32	0.67	0.05	0.48	0.76
Pink1	4	137.19	−0.33	−7.71	8.93	17.60	1.22	15.70	24.30	0.35	0.51	0.09	0.48	0.60
BC008163	4	131.86	0.31	−8.54	10.10	11.00	1.59	15.70	16.60	0.38	0.39	0.12	0.48	0.49
Otud3	4	137.78	−0.31	−8.64	11.80	3.40	3.16	15.70	7.42	0.41	0.20	0.19	0.48	0.32
BC039093	4	127.80	0.28	−9.88	10.70	12.60	0.86	15.70	17.60	0.39	0.42	0.06	0.48	0.51
Dhdds	4	132.93	−0.24	−11.6	12.30	7.38	0.79	15.70	10.80	0.42	0.31	0.06	0.48	0.39
Hdac1	4	128.54	0.22	−11.9	13.50	2.48	1.58	15.70	4.87	0.44	0.16	0.12	0.48	0.25
Psmb2	4	125.70	−0.24	−11.9	13.50	1.64	2.28	15.70	4.00	0.44	0.12	0.15	0.48	0.22
Gpatc3	4	132.54	−0.23	−12	12.20	21.90	0.24	15.70	25.40	0.42	0.57	0.00	0.48	0.61
Txlna	4	128.65	0.22	−12.1	12.80	6.32	0.73	15.70	9.29	0.43	0.29	0.05	0.48	0.36
Lsm10	4	125.12	0.21	−12.4	13.50	3.39	1.05	15.70	5.70	0.44	0.20	0.08	0.48	0.27
Extl1	4	133.32	−0.20	−12.7	13.50	3.98	0.80	15.70	6.25	0.44	0.22	0.06	0.48	0.29
Cnksr1	4	133.19	0.20	−13.1	13.20	13.70	0.11	15.70	16.20	0.43	0.44	0.00	0.48	0.49
Tceb3	4	134.89	−0.19	−13.2	13.50	16.00	0.34	15.70	18.20	0.44	0.48	0.00	0.48	0.51
Stx12	4	131.81	0.18	−13.3	13.90	18.90	0.66	15.70	20.60	0.45	0.53	0.04	0.48	0.55
Ccdc21	4	133.09	−0.17	−13.7	13.80	11.10	0.14	15.70	13.00	0.45	0.40	0.00	0.48	0.43
Males
Bicc1	10		0.38	2.48	0.32	10.80	3.66	2.80	12.90	0.00	0.54	0.29	0.24	0.60
Timd4	11		0.42	2.04	0.51	5.00	5.44	2.55	6.78	0.01	0.35	0.37	0.23	0.42
Rasd1	11		0.32	1.91	0.40	12.90	2.32	2.55	14.70	0.00	0.60	0.22	0.23	0.64
Wnt9a	11		0.40	1.56	0.99	2.63	5.03	2.55	4.08	0.11	0.23	0.35	0.23	0.31
Zfp286	11		0.34	1.54	1.01	3.95	3.29	2.55	5.36	0.11	0.30	0.27	0.23	0.37
2310022M17Rik	11		−0.32	1.49	1.06	4.16	2.87	2.55	5.52	0.11	0.31	0.25	0.23	0.37
RP23.336J1.4	11		0.28	1.03	0.60	13.10	1.64	2.55	14.80	0.05	0.60	0.17	0.23	0.64
Larp1	11		−0.28	0.76	1.49	2.59	2.25	2.55	3.61	0.15	0.23	0.21	0.23	0.29
Tmem4	10		0.28	0.595	1.27	5.72	1.86	2.80	7.13	0.13	0.38	0.18	0.24	0.43
2810021J22Rik	11		0.25	0.108	1.58	2.84	1.69	2.55	3.76	0.16	0.25	0.17	0.23	0.29
Atpaf2	11		−0.23	−0.118	1.37	5.40	1.26	2.55	6.47	0.14	0.37	0.13	0.23	0.41
BC024063	10		0.24	−0.188	1.87	2.36	1.68	2.80	3.27	0.18	0.22	0.17	0.24	0.27
Mgst2	3		−0.36	−0.295	2.20	9.99	1.91	5.73	13.40	0.21	0.52	0.19	0.38	0.61
Mrpl55	11		0.22	−0.315	1.54	4.12	1.23	2.55	5.06	0.16	0.31	0.13	0.23	0.35
A530089I17Rik	10		0.23	−0.385	1.77	3.44	1.38	2.80	4.43	0.18	0.28	0.14	0.24	0.33
Fancl	11		0.22	−0.39	1.84	1.86	1.45	2.55	2.56	0.18	0.18	0.15	0.23	0.23
Nt5m	11		−0.21	−0.635	1.23	11.70	0.60	2.55	12.90	0.13	0.57	0.05	0.23	0.60
Dusp6	10		0.22	−0.783	1.32	13.40	0.54	2.80	14.70	0.14	0.61	0.03	0.24	0.64
Map2k3	11		−0.18	−1.07	1.70	5.32	0.63	2.55	6.11	0.17	0.36	0.05	0.23	0.39
Adora2b	11		−0.27	−1.65	2.08	0.44	2.77	2.55	1.01	0.20	0.00	0.24	0.23	0.11
3300001G02Rik	11		−0.24	−1.73	2.17	0.44	2.09	2.55	0.92	0.21	0.00	0.20	0.23	0.10
Ttc14	3		0.29	−2.36	3.90	3.31	1.54	5.73	5.15	0.30	0.27	0.16	0.38	0.36
3110057O12Rik	3		0.28	−2.47	3.90	3.51	1.43	5.73	5.34	0.30	0.28	0.15	0.38	0.36
9930021J17Rik	3		−0.26	−3.05	3.46	10.80	0.41	5.73	13.00	0.28	0.54	0.00	0.38	0.60
Pet112l	3		−0.25	−3.12	4.49	1.88	1.37	5.73	3.18	0.33	0.18	0.14	0.38	0.27
Gpatc4	3		−0.24	−3.42	3.84	8.09	0.42	5.73	9.93	0.30	0.46	0.00	0.38	0.52
Man1a2	3		0.21	−3.9	4.60	2.86	0.70	5.73	4.02	0.33	0.25	0.07	0.38	0.31
Sucnr1	3		−0.20	−4.01	4.48	4.23	0.47	5.73	5.48	0.33	0.32	0.00	0.38	0.37
Msr2	3		0.21	−4.1	4.14	11.20	0.04	5.73	12.70	0.31	0.55	0.00	0.38	0.59
Gm761	3		0.20	−4.1	4.67	2.99	0.57	5.73	4.08	0.34	0.26	0.04	0.38	0.31
5830417I10Rik	3		0.21	−4.18	4.21	10.90	0.03	5.73	12.40	0.32	0.55	0.00	0.38	0.59
Gpr89	3		0.20	−4.18	4.40	6.44	0.23	5.73	7.75	0.33	0.41	0.00	0.38	0.45
Tloc1	3		0.18	−4.26	4.81	2.51	0.55	5.73	3.46	0.34	0.23	0.03	0.38	0.28
Tmod4	3		0.18	−4.35	4.80	16.40	0.45	5.73	17.30	0.34	0.68	0.00	0.38	0.70
Snx27	3		0.18	−4.4	4.70	4.21	0.30	5.73	5.24	0.34	0.32	0.00	0.38	0.36
Arfip1	3		0.17	−4.65	4.78	5.23	0.13	5.73	6.18	0.34	0.36	0.00	0.38	0.40
Nudt6	3		0.16	−4.69	4.98	2.80	0.30	5.73	3.57	0.35	0.24	0.00	0.38	0.29
Siah2	3		0.11	−5.26	5.29	3.82	0.03	5.73	4.27	0.36	0.30	0.00	0.38	0.32
Females
Mmp14	14		0.44	2.93	0.55	7.69	4.82	3.48	10.30	0.03	0.46	0.35	0.29	0.55
Oxa1l	14		0.35	1.44	0.60	15.50	2.03	3.48	18.00	0.05	0.68	0.20	0.29	0.74
Entpd4	14		−0.31	0.33	2.01	2.84	2.34	3.48	4.28	0.20	0.25	0.22	0.29	0.33
Rab2b	14		0.31	0.266	1.30	9.30	1.56	3.48	11.30	0.14	0.52	0.17	0.29	0.57
Smad4	18		−0.27	0.128	0.54	29.20	0.67	2.92	31.20	0.03	0.95	0.06	0.26	0.98
Mppe1	18		0.27	0.035	1.56	4.50	1.60	2.92	5.77	0.17	0.34	0.17	0.26	0.39
Hsd17b4	18		−0.25	−0.258	1.93	2.36	1.67	2.92	3.33	0.19	0.22	0.17	0.26	0.28
Twist2	1		0.31	−0.485	2.36	4.17	1.87	4.21	5.97	0.22	0.32	0.19	0.33	0.40
3110009E18Rik	1		0.31	−0.541	1.24	21.40	0.70	4.21	24.20	0.14	0.81	0.07	0.33	0.86
Hesx1	14		−0.25	−0.896	1.80	8.20	0.90	3.48	9.77	0.18	0.48	0.10	0.29	0.53
Supt16h	14		0.26	−0.924	1.34	20.00	0.42	3.48	21.90	0.15	0.78	0.00	0.29	0.82
Grem2	1		0.29	−0.966	2.89	2.14	1.92	4.21	3.48	0.26	0.21	0.19	0.33	0.29
Ear11	14		0.25	−1.15	1.55	16.60	0.41	3.48	18.40	0.16	0.71	0.00	0.29	0.75
Dusp12	1		0.28	−1.18	2.74	3.17	1.56	4.21	4.62	0.25	0.27	0.17	0.33	0.35
3830406C13Rik	14		0.24	−1.19	1.89	8.76	0.70	3.48	10.30	0.19	0.50	0.07	0.29	0.54
Slc7a7	14		0.24	−1.22	2.26	4.12	1.05	3.48	5.29	0.22	0.32	0.12	0.29	0.37
C130032J12Rik	14		0.24	−1.3	1.58	19.00	0.28	3.48	20.70	0.17	0.76	0.00	0.29	0.79
Ifi202b	1		0.27	−1.38	2.20	9.06	0.82	4.21	11.00	0.21	0.51	0.09	0.33	0.56
Lancl1	1		0.26	−1.48	2.38	7.24	0.90	4.21	8.99	0.23	0.45	0.10	0.33	0.51
Pmaip1	18		−0.22	−1.49	2.38	0.89	1.53	2.92	1.48	0.23	0.10	0.16	0.26	0.16
Osgep	14		0.22	−1.49	2.59	2.33	1.10	3.48	3.22	0.24	0.22	0.12	0.29	0.28
Dusp12	1		0.26	−1.59	2.77	3.89	1.18	4.21	5.31	0.25	0.31	0.13	0.33	0.37
Adamts4	1		0.24	−1.9	2.57	7.24	0.67	4.21	8.81	0.24	0.45	0.06	0.33	0.50
Dars2	1		−0.24	−1.98	3.32	1.34	1.52	4.21	2.29	0.28	0.15	0.16	0.33	0.22
Slc39a10	1		0.23	−2.01	3.12	2.52	1.11	4.21	3.62	0.27	0.23	0.12	0.33	0.30
Ttc5	14		0.18	−2.09	2.66	3.52	0.57	3.48	4.33	0.24	0.29	0.04	0.29	0.33
Stmn4	14		0.26	−2.15	2.84	0.69	2.09	3.48	1.44	0.25	0.07	0.21	0.29	0.15
Nedd4l	18		−0.12	−2.18	2.56	1.75	0.38	2.92	2.12	0.24	0.18	0.00	0.26	0.21
Serpine2	1		−0.22	−2.32	2.86	6.06	0.54	4.21	7.36	0.26	0.40	0.03	0.33	0.45
Usf1	1		0.22	−2.33	2.94	5.14	0.61	4.21	6.38	0.26	0.37	0.05	0.33	0.42
Ppfia4	1		−0.22	−2.49	2.70	11.20	0.20	4.21	12.60	0.25	0.57	0.00	0.33	0.61
Ppic	18		−0.24	−2.56	2.68	0.11	2.26	2.92	0.53	0.24	0.00	0.22	0.26	0.03
Bnip3l	14		0.12	−2.78	4.04	1.87	1.44	3.48	1.26	0.32	0.19	0.15	0.29	0.14
Wnt5a	14		0.23	−2.82	3.11	0.29	1.92	3.48	0.81	0.27	0.00	0.19	0.29	0.08
Thap4	1		0.18	−2.83	3.24	4.43	0.41	4.21	5.38	0.28	0.34	0.00	0.33	0.38
1190002H23Rik	14		−0.27	−3.01	3.14	0.13	2.57	3.48	0.68	0.27	0.00	0.24	0.29	0.06
Adipor1	1		−0.20	−3.06	3.70	0.65	1.25	4.21	1.25	0.30	0.06	0.14	0.33	0.14
Pla2g4a	1		0.14	−3.06	5.54	4.04	2.47	4.21	2.68	0.38	0.32	0.23	0.33	0.24
E430029J22Rik	1		−0.16	−3.11	3.42	3.94	0.31	4.21	4.73	0.29	0.31	0.00	0.33	0.35
Sned1	1		0.12	−3.26	5.46	4.27	2.19	4.21	3.00	0.38	0.33	0.21	0.33	0.26
Gas5	1		−0.15	−3.33	3.59	12.20	0.26	4.21	12.70	0.30	0.60	0.00	0.33	0.61
Sdpr	1		−0.22	−3.39	3.76	0.37	1.67	4.21	0.97	0.30	0.00	0.17	0.33	0.11
1110058L19Rik	1		−0.13	−3.41	3.81	1.35	0.40	4.21	1.78	0.31	0.15	0.00	0.33	0.18
Glb1l	1		−0.12	−3.59	3.86	1.44	0.28	4.21	1.82	0.31	0.16	0.00	0.33	0.19
Map4k4	1		−0.11	−3.69	4.73	1.63	1.35	4.21	1.04	0.35	0.17	0.15	0.33	0.11
Tsn	1		0.11	−3.81	3.81	4.03	0.00	4.21	4.42	0.31	0.32	0.00	0.33	0.34
Ngef	1		0.00	−3.89	4.56	3.68	0.67	4.21	3.32	0.34	0.30	0.06	0.33	0.28
Fmo1	1		−0.01	−3.93	4.44	2.10	0.50	4.21	1.86	0.34	0.21	0.01	0.33	0.19
Uap1	1		−0.02	−3.97	4.32	2.70	0.35	4.21	2.58	0.33	0.25	0.00	0.33	0.24
Sept2	1		−0.06	−4.02	4.08	2.21	0.07	4.21	2.34	0.32	0.21	0.00	0.33	0.22
Prkag3	1		−0.16	−4.12	4.22	0.15	1.27	4.21	0.11	0.33	0.00	0.14	0.33	0.00
Tmeff2	1		0.28	−4.48	4.50	0.59	3.52	4.21	0.03	0.34	0.05	0.29	0.33	0.00

Integrating linkage, high-density SNP maps, eQTL analysis, causality modeling, and association in MF1 mice identifies Wnt9a and Rasd1 as strong candidate causal genes for Bmd11

In the previous sections, we describe the use of linkage, global gene expression profiling, and causality modeling to identify BMD candidate genes. In this section, we illustrate the power of integrating these data with high-density SNP collections and association in outbred mice to identify candidate QTGs for Bmd11.

Bmd11, located on Chr 11, was the second most significant QTL in BXH F₂ mice, explaining 6% of the variance in BMD and mapping with a peak LOD score of 6.5 at 55.2 Mbp (Table 2; Fig. 1). Additionally, there was significant statistical evidence for male-biased expression of Bmd11 (Table 1) The 1.5 SI for Bmd11 extended from 27.7 to 64.1 Mbp.

To potentially fine-map Bmd11 and each of the other BMD QTLs, we performed an association analysis using outbred MF1 mice. First, we identified the 398 markers genotyped in MF1s that were located within the 1.5 SIs for each BMD QTL (Table 2). ANOVA was used to test the relationship between SNP genotype and whole body BMD. Although we report only the results of whole body, spinal and femoral BMD were also analyzed and gave very similar, albeit slightly less significant, associations (data not shown). Of the 398 SNPs, a group of three within Bmd11 (Chr 11) were the most significant, all with a nominal –logP of 3.74 (Fig. 4). Using a stringent permutation-based approach, the adjusted p values for the three Chr 11 SNPs were p = 0.06. The 95% bootstrap CI for the Chr 11 association was the 2.8-Mbp interval between rs3023265 at 57.6 Mbp and rs13481052 at 60.4 Mbp (Fig. 4). This reduction represents a 13-fold increase in resolution relative to the linkage results in F₂ mice. Importantly, the C3H-like haplotype across this region increased BMD in MF1 mice. This is concordant with C3H alleles at Bmd11 increasing BMD in F₂ mice (Table 1; Fig. 3), suggesting that the same variant(s) may be affecting BMD in both populations.

FIG. 4.

Chromosome 11 (Bmd11) SNPs are associated with whole body BMD in MF1 mice. Each panel contains the association results plotted as the negative log₁₀ of the ANOVA p value (−logP) for MF1 markers overlapping each of the nine BXH BMD QTL regions. Individual SNPs are represented by vertical bars. The solid horizontal lines represent the nominal p < 0.001 level of significance. The empiric p value for the most significant association on Chr 11 (determined by permutation) was p = 0.06. The 95% bootstrap CI for the Bmd11 association (2.8 Mbp) is highlighted by the dark horizontal line at the top of the Bmd11 (Chr 11) panel.

We also studied the genetic variation structure within Bmd11 using recently developed high-density SNP collections.(36) A total of 16,565 known SNPs, polymorphic between C57BL/6J and C3H/HeJ, were identified within the Bmd11 1.5 SI. Across the region, 43% of the 1456 25-kbp bins were nearly devoid of SNPs (zero or one SNP per 25 kbp), suggesting these regions are identical by descent between the two strains.(36) Interestingly, the most SNP dense region of Bmd11 overlapped the location of the most strongly associated SNPs in MF1 mice (Fig. 5). The mean SNP density of the 95% CI for the associated region was nearly 2.5-fold higher (28.1 SNPs per 25 kbp) than the mean of the entire Bmd11 region (11.4 SNPs per 25 kbp). These data suggest that the variation underlying Bmd11 is most likely within the region associated with BMD in MF1 mice.

FIG. 5.

An integrative genetics approach identified Wnt9a and Rasd1 as candidate QTGs for Bmd11. (Top) LOD score profile for Bmd11 in BXH F₂ mice. The 1.5 LOD-drop SI extended from 25.7 to 64.1 Mbp (defined by vertical lines). (Middle) Frequency of SNPs polymorphic between C57BL/6J and C3H/HeJ per 25 kbp bin across Bmd11. A total of 15 genes with prioritized local eQTL peaks were located within the Bmd11 SI interval (●) (Supplemental Table 1). (Bottom) Negative log₁₀ of the ANOVA p value (−logP) for each Bmd11 SNP in MF1 mice. The three most associated SNPs had empiric p values of p = 0.06. The 95% bootstrap CI for the Bmd11 association (2.8 Mbp) is highlighted by the dark horizontal line at the top of the Bmd11 (Chr 11) panel. Six of the genes with eQTL were predicted to be causal using NEO (●). Wnt9a and Rasd1 were the only genes located in the region, regulated by local eQTLs, and predicted to be causal. The x-axis for all panels is in base pairs.

In addition, the Bmd11 region contained a total of 15 genes correlated with BMD and regulated by local eQTL (Supplemental Table 1; Fig. 5). Of the 15, the expression data supported a causal relationship for 6 (Table 4). However, of the genes predicted as causal, only Wnt9a and Rasd1 were located within the 95% CI of the MF1 associated region (Fig. 5). Together these data suggest that expression differences in Wnt9a, Rasd1, or both underlie Bmd11.

DISCUSSION

The most difficult challenge in complex trait analysis is not the identification of QTLs but the transition from QTL to gene. Many strategies have been developed for QTL dissection; however, most are only marginally successful when used in isolation. In this study, we identified nine BMD QTLs covering roughly 300 cM of the mouse genome. These regions together contain between 4000 and 6000 “positional” candidates. To reduce this to a manageable number, we integrated linkage, genomic sequence, gene expression, causality modeling, and association to fine-map QTLs and identify a small number of “functional” candidates. Although these require further study, our approach also identified several genes known to influence bone development or mineralization, including Twist2, Mmp14, Wnt9a, Adm, and Smad4.

In our study, three QTLs displayed statistically significant interactions with sex and three others had suggestive sex effects. The only other previous linkage analysis for femoral BMD in BXH F₂ mice used only females, prohibiting the analysis of sex differences.(24) However, our results are in line with recent observations in humans(37–39) and other mouse crosses(40,41) showing that BMD is controlled in large part by sex-specific genetic variation. These data suggest that the BXH F₂ intercross is an excellent model to study gene × sex interactions affecting BMD.

In light of the apparent sex effects, we observed good concordance between our study and that of Beamer et al.(24) Of the 10 QTLs identified by Beamer et al.,(24) 5 (Bmd5, Bmd7, Bmd11, Bmd13, and Bmd14) were replicated in our study. Of the four novel QTLs, our discovery of two, Bmd40 and Bmd42, is most likely explained by their male-biased expression. The other two, Bmd41 and Bmd43, are likely because of differences in the measurement of volumetric versus areal BMD, the effect of a high-fat diet, or other environmental differences.

One potential limitation of our study is the use of adipose gene expression profiles to identify local eQTLs instead of bone tissue or primary osteoblasts/osteoclasts. Whereas it is likely that key expression differences were missed in the analysis, it is clear that many genes important in bone metabolism, and expressed in bone, are also expressed in adipose. Additionally, we have previously shown that the majority (63–88%) of genes with local eQTLs in one tissue will also vary in other tissues where the gene is expressed.(42) It is also possible that the expression in adipose of some of the putatively causal genes directly affects BMD.

The goal of our study was the identification, in an unbiased manner, of a small number of high-priority candidate BMD genes by integrating many complementary genetic and genomic datasets. This approach led to the identification of three categories of known (genes linked to bone development and/or density) and novel genes: (1) genes with prioritized coincident eQTLs; (2) genes with prioritized coincident eQTLs that were predicted to be causal; and (3) genes located in the MF1 associated/SNP dense region on Chr 11 with prioritized coincident local eQTLs that were predicted to be causal. These classifications will be used to prioritize the validation, using in vitro and in vivo approaches, of each candidate.

The exact origin of MF1 mice is unknown, but it is has been shown that MF1 haplotypes are similar to those of inbred strains.(18) Yalcin et al.(18) sequenced a 62-kbp region in 12 MF1 mice and found only four SNPs not shared among eight inbred strains previously sequenced across the same region.(43) Therefore, it is expected that a significant fraction of the same genetic variation, and QTLs, segregating between any two inbred strains are likely to be present in MF1 mice. Here we show that even a relatively small population (N = 97) of MF1s can be used to significantly reduce the genomic region harboring QTLs identified through linkage. Using MF1s, we were able to increase mapping resolution 13-fold for Bmd11. These data suggest that a genome-wide study of BMD using a larger and more densely genotyped outbred population consisting of both sexes would be informative.

Predicting causal relationships between a gene's expression and BMD is a potentially powerful filtering tool to distinguish between causal correlation and correlation caused by linkage or downstream reactivity. However, its use, especially to prioritize local eQTLs, could be prone to false predictions. False positives can arise because of strong linkage disequilibrium with the true causal gene and false negatives can arise because of complex relationships between the gene and trait (e.g., a case were the expression of the causal gene is several steps upstream of BMD). In addition, our analysis examined only genetically regulated transcript level changes. However, there are many other types of DNA perturbations, such as those affecting protein function, translational efficiency, or stability, that likely influence BMD and would be missed by our analysis. Additionally, our approach would miss variation in gene expression important to adult BMD that occurs much earlier in development.

Through the integration of multiple statistical techniques in two different mouse populations, we identified two candidates (Rasd1 and Wnt9a) that were correlated with BMD, predicted to be causal, and located on Chr 11 (Bmd11), in a region both linked and associated with BMD (Fig. 3). RAS, dexamethasone-induced 1 (Rasd1) is a Ras family GTPase protein shown to be critical for proper circadian rhythm.(44) Wnt9a is a member of the canonical WNT signaling pathway and has been shown to be involved in joint formation/maintenance and skeletogenesis.(32,45) The role of Rasd1 in BMD is unclear and will need to be validated. However, given its known role in skeletogenesis, it is likely that Wnt9a mediates at least a portion of the Bmd11 effect.

In summary, we showed the successful application of a truly “integrative” genetics approach to identify genes underlying changes in BMD. We used this strategy to identify many known and novel genes that are likely key drivers of the genetic differences in BMD between C57BL/6J and C3H/HeJ mice. Our strategy provides the framework for future integrative studies designed to identify factors contributing to complex traits.