1. Genetics and Genomics
  2. Medicine

Estimating bone marrow adiposity from head MRI and identifying its genetic architecture

  1. Tobias Kaufmann  Is a corresponding author
  2. Pål Marius Bjørnstad
  3. Martin Falck
  4. Stener Nerland
  5. Kevin O'Connell
  6. Oleksandr Frei
  7. Ole A Andreassen
  8. Lars T Westlye
  9. Srdjan Djurovic
  10. Timothy Hughes  Is a corresponding author
  1. Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Germany
  2. German Center for Mental Health (DZPG), Partner Site Tübingen, Germany
  3. Norwegian Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Norway
  4. Department of Medical Genetics, Oslo University Hospital, Norway
  5. Department of Psychiatric Research, Diakonhjemmet Hospital, Norway
  6. Department of Pharmacy, University of Oslo, Norway
  7. KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Norway
  8. Department of Psychology, University of Oslo, Norway
https://doi.org/10.7554/eLife.101499.3
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Cite this article
  1. Tobias Kaufmann
  2. Pål Marius Bjørnstad
  3. Martin Falck
  4. Stener Nerland
  5. Kevin O'Connell
  6. Oleksandr Frei
  7. Ole A Andreassen
  8. Lars T Westlye
  9. Srdjan Djurovic
  10. Timothy Hughes
(2026)
Estimating bone marrow adiposity from head MRI and identifying its genetic architecture
eLife 13:RP101499.
https://doi.org/10.7554/eLife.101499.3
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19 figures, 10 tables and 1 additional file

Figures

Figure 1
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Overview of the artificial neural network training, application, and validation.

(a) The data used for training the network were generated by simulating 336 different head types (with an example simulation for one head type provided here), where a head type is defined by mean bone thickness, mean BM intensity, and mean body fat thickness. There are 5000 datapoints per head each with 50 intensities per datapoint. OT: outer table, IT: inner table, BM: bone marrow. (b) We evaluated the performance of the neural network using two metrics: (1) the overlap between the predicted and the true BM location (defined by the simulation), and (2) the ratio between the predicted signal intensity of the BM and the true intensity of the BM. (c) Illustration of the process of extracting the intensity arrays from the MRI scan, locating the BM, and computing its intensity. (d) The algorithm was validated on a real dataset consisting of 30 individuals that were scanned 10 times each at 3-day intervals. For each scan, we computed the intensity of the outer bone (blue) and of the BM (yellow), which displayed high concordance across scans of the same individuals. The quality control measures correctly identified scans not producing reliable measures (13 of 300, 11 of which fail due to too few datapoints). (e) Application of the algorithm to a cohort of monozygotic and same-sex dizygotic twins. Correlation in BMA between twin pairs in coloured boxes.

Figure 2
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The calvarial BMA phenotype.

(a) BM intensity distribution in males (blue) and females (yellow). (b) BMA age trajectories for males and females. Males in blue and females in yellow (never hormone replacement therapy (HRT)) and orange (ever HRT). The effects of age and HRT on female BMA levels were both statistically significant (p-value <2.0E−16). (c) Sex-dimorphic relationship between BMA and calvarial bone mineral density. (d) Relationship between BMA and BMI. Fitted lines in (b–d) were computed using a general additive model, and shading represents 95% confidence intervals.

Figure 3
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BMA genome-wide significant loci.

(a) QQ-plot of p-values for the male, female, and joint discovery GWAS. (b) Comparison of male (outer), female (middle), and joint (inner) discovery GWAS significant loci. (c) Manhattan plot of the 41 genome-wide significant loci in the joint discovery GWAS named after the gene closest to the top lead single-nucleotide polymorphism (SNP) (bold). Other genes in each locus for which genome-wide significant SNPs were eQTLs are listed below the closest gene. Three loci were closely adjacent to another more significant locus and are marked ‘+’. SNPs with p < 0.05 not plotted.

Figure 4
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Gene expression in mesenchymal lineage cells of mouse BM.

(a) The differentiation of BM mesenchymal cells. Early (EMP), intermediate (IMP), and late mesenchymal progenitors (LMP). Lineage committed progenitors (LCP). Marrow adipogenic lineage precursor (MALP). Lipid-laden adipocyte (LiLA). Osteoblast (OB). Osteocyte (Ocy). (b) A clustering of the gene expression profiles across cell types. Gene names are for mouse genes (these broadly match human gene names). Of the 40 genes which are closest to the lead single-nucleotide polymorphism (SNP) for each locus, 28 had expression in the scRNAseq data (bold). LiLA expression data was not available in this dataset.

Appendix 1—figure 1
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Convergence of the artificial neural network before 300 epochs.
Appendix 1—figure 2
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Types of errors in BM localization for a datapoint.

The bone marrow is typically more intense than the neighbouring bone, so only errors of type 1 usually result in overestimates of BM intensity for the given datapoint. All other error types (2, 3, and 4) typically result in an underestimate of BM intensity as they incorporate less intense bone layers. This explains why the BM intensity ratio (predicted/true) averaged over all datapoints of a calvarium is centred on 0.95 and not 1 (see also S3).

Appendix 1—figure 3
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Model performance on 336 simulated head types (6 bone thicknesses, 7 BMA intensities, 8 body fat thicknesses).

Top panel: heads coloured by mean bone thickness. Middle panel: heads coloured by BM intensities. Bottom panel: heads coloured by body fat thickness.

Appendix 1—figure 4
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Examples of neural network performance on seven different UK Biobank scans with different levels of subcutaneous fat thickness, bone marrow adiposity, and bone thickness.

Outer table (OT), bone marrow (BM), inner table (IT). Predicted boundaries of BM (white lines) and of outer and inner tables (green lines). Adjacent datapoints (x-axis) in this plot are not necessarily adjacent on the calvarium.

Appendix 1—figure 5
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Distribution of number of datapoints per scan (for 41,648 scans).

Scans with fewer than 5000 datapoints are likely affected by poor image quality and cannot be used to reliably measure bone marrow intensity.

Appendix 1—figure 6
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Outer bone intensity mean and standard deviation for 39,414 scans.
Appendix 1—figure 7
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BMA map of the calvarium.

BMA map of the calvarium with nose pointing north (left) and first layer of bone marrow plotted against bone marrow intensity for all datapoints in the same scan (right). Top figure for a calvarium with high levels of adiposity, but where no datapoints have a Mahalanobis distance greater than 25. Bottom figure for a calvarium with relatively low levels of adiposity, where the vast majority of points do have a Mahalanobis distance lower than 25 (light red) but where some datapoints have a Mahalanobis distance greater than 25 (light blue). In the corresponding BMA map one can observe that these datapoints with higher intensity are all located on the sagittal suture (which is not high intensity) and correspond to datapoints in which the NN has failed to correctly localize the bone marrow layers.

Appendix 1—figure 8
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Additional quality control filters.

A combination of a maximal value for the standard deviation of outer bone intensity and of a minimum value for the fraction of datapoints passing the Mahalanobis distance cutoff (see previous figure) enables additional QC of correct bone marrow localization.

Appendix 1—figure 9
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Impact of intensity normalization procedure on test–retest validity.

In our proposed pipeline, T1 image intensities were rescaled to assume values between 0 and 255 as part of the recon-all processing stream of FreeSurfer. We compared this normalization approach to five additional normalization procedures that have been extensively used in the image processing literature. Specifically, we performed normalization with Kernel Density Estimation (KDE), WhiteStripe (WS), Gaussian Mixture Model (GMM), Fuzzy C-Means (FCM), and Z-score normalization (ZS). Details on these methods and their implementation are given in Reinhold et al., 2019. Using test–retest data from the HNU sample, we derived estimates of BMA for each subject and scan (30 individuals, 10 repeat scans) based on each of the five additional normalization approaches. The comparison between normalization strategies shows that several of the normalization strategies yield strong test–retest reliability, yet our approach is on par with, or better than, the other normalization strategies.

Appendix 1—figure 10
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Validation against a quantitative measure of BMA.

Data from the MICA-MICs cohort allowed us to compare our BMA estimates to BMA estimates derived from microstructurally sensitive quantitative T1 images acquired with a MP2RAGE MRI sequence. We derived quantitative estimates of BMA from the quantitative T1 images, as well as semi-quantitative estimates based on T1-weighted images after FreeSurfer normalization (our approach) and five additional normalization approaches (KDE, WS, GMM, FCM, and ZS; see also Appendix 1—figure 9). Panel (a) illustrates the resulting calvarial maps for an example subject, and panel (b) depicts the statistical results. Our BMA estimation approach shows high correlations with estimates derived from T1-weighted images normalized with KDE, GMM, FCM, and ZS, whereas correlations between WS normalization and the other methods are lower. Most importantly, we find significant correlations between the original BMA estimation approach and fully quantitative BMA estimates. The negative correlation here is expected since T1 relaxation times are inversely correlated with fat. These significant correlations corroborate the semi-quantitative nature of our approach. We note that the correlation between quantitative and semi-quantitative approaches, while significant (p = 0.0017), seems moderate (our approach and KDE normalization performing best with r = –0.44). However, it is important to emphasize that the age distribution of the MICA-MICs sample is very narrow (29.54 ± 5.62 years) and therefore there is little age-related variance in the derived BMA estimates. We would therefore expect even higher correlations between semi-quantitative and quantitative approaches in datasets with more variation in bone marrow adiposity, such as data covering a wider age span or life events such as menopause.

Appendix 1—figure 11
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Illustration of the semi-quantitativeness of another measure obtained from T1-weighted MRI.

To further illustrate that semi-quantitative approaches from T1-weighted scans can provide plausible measures, we have additionally included an analysis of another calvarial bone measure, specifically the outer-bone intensity. Bone mineral density measures the mineral content of bone in g/cm2 and correlates negatively with cortical bone signal intensity in T1-weighted images. We can therefore obtain a second independent estimate of the quantitativeness of signal intensity from the comparison of mean outer-bone signal intensity with calvarial bone mineral density (measured by DEXA scan). We estimate a correlation of –0.715 (CI [-0.72,–0.71]) between these variables in the 29,093 individuals that had both MRI scans and calvarial bone mineral density measurements. This correlation is most likely an accurate estimate of the quantitativeness of the signal intensity as there is a wide distribution of bone mineral density in the 29,093 individuals used for this estimate.

Appendix 1—figure 12
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Significant difference in bone marrow adiposity between patients with osteoporosis and control individuals without a diagnosis within the ICD-10 M category.

We identified a set of N = 220 individuals with an ICD-10 diagnosis of osteoporosis (ICD-10 codes M80-82) in the UK Biobank dataset and compared this group with N=30,127 controls, excluding individuals with any other diagnosis within the M category. In the models adjusted for age, sex, and scanner, BMA was significantly higher in the osteoporosis group (t = 9.3; p < 2e−16). Among females, the effect was similar also when controlling for HRT use (t = 7.3; p = 3e−13). This analysis shows that our proposed BMA measure allows for robust comparison between subjects and provides another replication of a well-established biological relationship, that is, BMA increase in osteoporosis.

Appendix 1—figure 13
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The WNT16 locus in the joint discovery sample.
Appendix 1—figure 14
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The ESR1 locus in the male and female discovery samples.
Author response image 1
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Tables

Table 1
Genetic correlation and overlap of BMA with other traits.

rg: genetic correlation. Q-values are FDR corrected p-values. Genetic overlap: number of causal variants that overlap (i.e. are shared) between BMA and the second trait. Total number of estimated causal variants for a trait (i.e. explaining 90% of single-nucleotide polymorphism (SNP) heritability) is the sum of those unique to trait and overlap (large total is indicative of high polygenicity). NA*: The publicly available summary statistics of the BMI GWAS contained only 3.3 M SNPs; it was thus not possible to apply the MiXeR method to this dataset. Sex-specific genetic correlations, standard errors for causal variant partitioning, and Akaike information criterion values (Appendix 1—table 9). Bold highlighting for: genetic correlation p < 0.01, overlap fraction >0.5, correlation of effect sizes within overlapping >0.5 or<–0.5.

TraitGenetic correlation (LDSR)Genetic overlap (MiXeR)
All subjectsPartitioning of causal variants
Mean nb per partition		Overlap as fraction of all BMA causal variantsCorr. of effect sizes within overlap (SE)Akaike information criterion
rgpQUnique to BMAOverlapUnique to traitBest vs minBest vs max
Bone mineral density–0.411.5E−221.8E−21349714710.99–0.95 (0.05)13.362.47
Body mass index–0.209.3E−155.6E−14NA*NA*NA*NA*NA*NA*NA*
Waist-to-hip ratio–0.101.2E−022.60E−026343633000.87–0.48 (0.16)5.751.30
Blood pressure systolic–0.026.5E−016.8E−012547539210.95–0.25 (0.05)45.314.85
Blood pressure diastolic–0.033.4E−014.1E−012747335410.95–0.21 (0.04)58.372.77
Coronary artery disease–0.103.6E−027.2E−0227522511400.45–0.40 (0.11)8.5324.01
Type-2 diabetes–0.076.5E−021.1E−013611397520.28–0.69 (0.16)8.7577.44
Insomnia–0.052.1E−012.8E−0124825288680.50–0.45 (0.32)2.493.57
Multiple sclerosis–0.026.8E−016.8E−0147426860.050.45 (0.14)5.8242.60
Parkinson’s disease–0.151.7E−034.1E−03432688440.14–0.75 (0.14)1.5541.25
Alzheimer’s disease–0.139.2E−021.4E−0148515520.03–0.61 (0.24)10.9037.84
General cognitive ability0.091.1E−033.3E−0321728310,8020.570.93 (0.12)1.478.73
Educational attainment0.109.9E−054.0E−0416633412,8550.670.90 (0.11)2.419.45
Appendix 1—table 1
Twin-based heritability estimates using the NACE model fitted in R using the twinlm() function of the mets package.

NACE: normal (N) additive genetic effects (A), common environment (C), the unique environment (E). Female pairs aged 26–36 and male pairs aged 22–34. The Falconer method produced similar heritability estimates.

NACEACE
Males (114 MZ and 60 DZ twins)0.910.000.09
Females (160 MZ and 96 DZ twins)0.600.220.18
Appendix 1—table 2
OLS regression analysis of the determinants of BMD (in males and females).

Two models are shown for females, one including BMA (left) and one excluding BMA (right). Age and HRT have statistically significant effects on BMD when BMA is excluded. The coefficient on HRT drops by a factor of 20 and loses statistical significance when BMA is included in the regression, and R2 of the regression increases by a factor of 4, thus indicating that the effect of HRT on BMD is largely mediated by HRT’s effect on BMA. The number of samples is somewhat reduced because BMD data was not available for all samples with BMA data.

BMD explanatory variablesMalesFemalesFemales
Coeff.p-valueCoeff.p-valueCoeff.p-value
Intercept2.873<2e−163.161<2e−163.028<2e−16
BMA–0.006<2e−16–0.012<2e−16Excluded
Age–0.0025.66E−15–0.004<2e−16–0.012<2e−16
Scanner_10.086<2e−160.063<2e−160.0481.07E−08
Scanner_20.0160.007560.0030.612–0.0050.464
EverHRTNot relevant0.0020.750.0341.16E−09
R-squared0.094<2e−160.303<2e−160.071<2e−16
N14,26214,83114,831
Appendix 1—table 3
Characteristics of the discovery and replication samples.
Discovery is white British
Replication is cross-ethnic
AgeMin1st QMedianMean3rd QMaxStandard deviation
Discovery45.1358.5164.9464.3370.2482.067.51
Replication44.5756.6962.9362.8968.8781.077.706
SexMaleFemale HRTFemale no HRTTotal
Discovery16,140637310,52933,042
Replication233887417464958
ScannerUKBB_11027UKBB_11025UKBB_11026Total
Discovery851420,652387633,042
Replication794289812664958
Appendix 1—table 4
LDSR estimates of h2SNP and genomic inflation.
Discovery GWASesReplication GWAS
MaleFemaleBoth sexesBoth sexes
StatisticErrorStatisticErrorStatisticErrorStatisticError
Total observed scale h20.42590.05190.23260.03760.31480.03290.28590.1002
Lambda GC1.10821.08331.15870.9809
Mean chi21.14071.11481.24570.989
Intercept1.00310.00851.0370.00751.03820.00930.96020.006
Ratio0.02230.06020.32250.06570.15540.0379NA(mean chi2 < 1)
Appendix 1—table 5
Discovery (both sexes) genome–wide significant loci and lead SNP for each locus (third column), with effect size, standard error, and p-value for these SNPs in the male only, female only, and replication (non-white ethnicity, both sexes) samples.

In grey, lead SNPs that are GWS in males but not in females. In pink, lead SNPs with opposite effect sign in the discovery (male + female) vs the cross-ethnic replication (male + female). 92.7% of the lead SNPs of the discovery sample showed the same effect direction in the replication sample.

FemaleMaleMale and femaleReplication
Locus IDSNPCHRBPA1A2Nearest geneBETASEp-valueBETASEp-valueBETASEp-valueBETASEp-value
1rs6679981122697860AGWNT4–1.370.181.79E−14–1.350.191.95E–12–1.360.132.76E–25–0.530.331.10E–01
2rs6760396271954468AGDYSF0.660.155.44E–060.850.164.49E–080.750.111.27E–12–0.070.288.03E–01
3rs11675489285498783GATCF7L10.80.149.15E–090.710.151.98E–060.750.11.21E–130.240.273.70E–01
4rs10188985285941074AGGNLY–0.460.141.05E–03–0.690.154.81E–06–0.570.12.72E–08–0.20.274.58E–01
5rs134278802101783867CTTBC1D8–0.740.156.84E–07–0.470.163.46E–03–0.610.112.75E–08–0.540.296.23E–02
6rs134151012166564362CTCSRNP30.530.141.03E–040.610.153.45E–050.570.11.37E–080.070.268.06E–01
7rs124722172242126109CTANO70.50.144.55E–040.690.157.52E–060.590.11.59E–080.530.285.76E–02
8rs450575941003022TCFGFRL1–0.720.151.35E–06–0.960.161.75E–09–0.840.111.55E–14–0.590.294.55E–02
9rs1833517555784364GACTC-236F12.41.070.251.97E–051.010.271.42E–041.040.181.18E–081.570.432.68E–04
10rs12211905674457833GTCD109–0.310.142.67E–02–0.970.151.18E–10–0.630.17.57E–10–0.520.275.46E–02
11rs131945086127144683CTCENPW0.580.161.91E–040.940.172.08E–080.760.113.62E–110.980.333.01E–03
12rs48700456151908047AGCCDC1700.910.151.70E–091.160.161.22E–121.030.111.53E–200.490.276.84E–02
13rs10272752738118313TCSFRP4–0.870.152.17E–08–1.160.161.65E–12–1.010.112.94E–19–1.360.35.66E–06
14rs12704872796334616CTSHFM10.460.149.07E–040.660.159.04E–060.560.13.93E–080.380.271.50E–01
15rs45446698799332948GTCYP3A71.960.333.50E–091.240.366.46E–041.610.254.93E–111.920.771.33E–02
16rs37793817120966790GAWNT16–2.860.152.08E–75–2.980.175.43E–70–2.920.112.61E–143–2.590.34.28E–18
17rs33006089090902GAPPP1R3B0.420.142.18E–030.80.155.21E–080.610.11.55E–090.610.262.01E–02
18rs17744726811411823AGBLK0.420.156.10E–030.970.162.62E–090.690.116.19E–100.270.293.54E–01
19rs73229843822501409CGBIN3–1.530.411.63E–04–2.50.441.56E–08-20.32.49E–11–1.920.862.64E–02
20rs12836888108416308CTANGPT10.420.143.00E–030.770.153.13E–070.590.19.58E–090.270.273.16E–01
21rs119958248120012700CGCOLEC10–0.90.147.84E–11–0.560.151.79E–04–0.730.15.30E–13–1.060.277.92E–05
22rs117474778924200738TCIZUMO31.570.394.80E–051.640.431.13E–041.610.292.13E–08–1.150.781.43E–01
23rs5146599136142203CAOBP2B0.770.151.78E–070.650.164.18E–050.710.114.86E–110.390.281.56E–01
24rs49943329137117194TCRXRA0.830.144.56E–090.440.153.64E–030.640.16.65E–100.740.277.01E–03
25rs108264091028449221TGMPP7–0.810.141.98E–08–0.670.151.03E–05–0.740.11.53E–12–0.090.267.18E–01
26rs174763641071094504CTHK1–0.460.223.77E–02–1.420.241.97E–09–0.930.168.41E–09–0.240.496.24E–01
27rs14635971081139141GAZCCHC24–0.440.141.37E–03–0.730.151.10E–06–0.580.11.06E–08–0.730.276.26E–03
28rs79452111127472777TCLGR4–0.640.143.72E–06–0.870.155.84E–09–0.750.11.39E–13–0.420.261.06E–01
29rs107692391147130540GCC11orf49–0.540.152.24E–04–0.770.161.08E–06–0.650.111.32E–09–0.210.284.51E–01
30rs13520751169457293TCCCND1–0.410.142.76E–03–0.840.151.54E–08–0.620.17.96E–10–0.120.266.58E–01
31rs6190906811128342957GAETS10.540.193.64E–031.020.25.58E–070.770.142.13E–0800.399.95E–01
32rs123115041228478771GACCDC910.490.144.46E–041.220.156.98E–160.850.11.87E–161.210.278.43E–06
33rs108759061249385679TCDDN–0.560.163.74E–04–0.790.174.17E–06–0.670.127.45E–09–0.860.34.51E–03
34rs1388188781343148546GCTNFSF11–2.150.436.33E–07–1.640.464.17E–04–1.90.321.91E–091.020.993.03E–01
35rs95437351375213211ACAL355390.1–0.630.141.32E–05–0.550.164.05E–04–0.590.112.48E–08–0.50.287.32E–02
36rs7626431454422767TGBMP40.710.141.93E–070.460.152.07E–030.590.15.28E–090.180.275.06E–01
37rs24140981551537806TCCYP19A10.30.143.27E–020.920.151.94E–090.610.17.01E–090.460.289.99E–02
38rs1187043917833790TGNXN–0.940.144.78E–11–1.160.153.28E–14–1.050.11.19E–23–0.90.281.39E–03
39rs1126987471747948829GATAC4–2.010.318.43E–11–0.920.357.57E–03–1.50.239.05E–11–2.050.713.77E–03
40rs37901612010640090TAJAG1(SNP overlaps JAG1)0.950.144.63E–120.880.152.19E–090.920.18.12E–201.050.267.52E–05
41rs349523182011177055AGJAG1 (SNP 536 kbp from JAG1)1.580.326.10E–071.840.345.47E–081.710.231.63E–130.840.662.07E–01
Appendix 1—table 6
Comparison of p-values between the ’both sexes’ discovery sample and the male, female, and replication (both sexes) samples.
p < 5e−8p < 5e−2
MaleFemaleReplicationMaleFemaleReplication
Based on all discovery SNPs that are p < 5e−842.0%31.3%3.1%100.0%99.5%62.2%
Based on one lead SNP for each of the 41 loci41.5%29.3%2.4%100.0%100.0%39.0%
Appendix 1—table 7
Human genes for which expression data was located in the Zhong dataset.

Closest indicates that the gene is closest to the lead SNP in a locus. Genes in the table not marked closest are genes for which SNPs in the locus are strong eQTLs.

Human geneMouse geneClosest
ANGPT1Angpt1Closest
BIN3Bin3Closest
BLKBlkClosest
BMP4Bmp4Closest
CCDC170Ccdc170Closest
CCDC91Ccdc91Closest
CCND1Ccnd1Closest
CD109Cd109Closest
CENPWCenpwClosest
CPED1Cped1
CSRNP3Csrnp3Closest
CYP26B1Cyp26b1
DLX5Dlx5
DLX6Dlx6
DYSFDysfClosest
ESR1Esr1
ETS1Ets1Closest
FAM167AFam167a
FAM3CFam3c
FGFRL1Fgfrl1Closest
GALNT3Galnt3
HK1Hk1Closest
JAG1Jag1Closest
LGR4Lgr4Closest
LRP4Lrp4
MPP7Mpp7Closest
NXNNxnClosest
ORAOV1Oraov1
PASKPask
PPIFPpif
PPP1R3BPpp1r3bClosest
RXRARxraClosest
SFRP4Sfrp4Closest
SHFM1Shfm1Closest
SURF1Surf1
TBC1D8Tbc1d8Closest
TCF7L1Tcf7l1Closest
TNFRSF11BTnfrsf11b
TNFSF11Tnfsf11Closest
WNT16Wnt16Closest
WNT4Wnt4Closest
ZCCHC24Zcchc24Closest
Appendix 1—table 8
Human genes for which expression data was not located in the Zhong dataset.
Human geneMouse geneClosestReason for failure to locate expression in mouse scRNAseq data
ANO7Ano7ClosestGene close to zero expression in the Zhong dataset so it is considered artifact noise.
c11orf491110051M20RikClosestNo expression detected.
COLEC10Colec10ClosestNo expression detected. GTEx RNA-seq data show zero expression in bone marrow.
AL355390.1N/AClosestHuman-specific uncategorized lncRNA.
CTC-236F12.4N/AClosestHuman-specific uncategorized lncRNA.
CYP19A1Cyp19a1ClosestNo expression detected.
CYP3A7Cyp3a13ClosestGene close to zero expression in the Zhong dataset so it is considered artifact noise.
DDNDdnClosestGene close to zero expression in the Zhong dataset so it is considered artifact noise.
GNLYN/AClosestHuman, chimpanzee, cow, and dog. No ortholog in mouse.
IZUMO3Izumo3ClosestNo expression detected.
OBP2BObp2aClosestNo expression detected.
TAC4Tac4ClosestNo expression detected.
Appendix 1—table 9
Akaike information criterion values for all MiXeR models.

Positive best_vs_min_overlap AIC estimates indicate that MiXeR can accurately distinguish the reported overlap from the minimum possible overlap allowed. Positive best_vs_max_overlap AIC estimates indicate that MiXeR can accurately distinguish the reported overlap from the maximum possible overlap allowed.

TraitGenetic correlation (LDSR)Genetic overlap (MiXeR)Akaike information criterion
MaleFemaleAll subjectsPartitioning of causal variants
Mean nb per partition (SE)		Overlap as fraction of all BMA causal variantsCorr. of effect sizes within overlap (SE)
rgprgprgpQUnique to BMAOverlapUnique to traitBest vs minBest vs max
Bone mineral density–0.361.4E–14–0.472.6E–17–0.411.5E–222.0E–213 (3)497 (58)1471 (56)0.99–0.95 (0.05)13.362.47
Body mass index–0.183.9E–09–0.245.1E–09–0.209.3E–156.1E–14NA*NA*NA*NA*NA*NA*NA*
Waist-to-hip ratio–0.0925.0E–02–0.1036.0E–02–0.101.2E–022.60E–0263 (74)436 (93)3300 (466)0.87–0.48 (0.16)5.751.30
Blood pressure systolic–0.027.0E–01–0.035.5E–01–0.026.5E–016.8E–0125 (22)475 (63)3921 (78)0.95–0.25 (0.05)45.314.85
Blood pressure diastolic–0.034.6E–01–0.052.5E–01–0.033.4E–014.0E–0127 (31)473 (69)3541 (90)0.95–0.21 (0.04)58.372.77
Coronary artery disease–0.097.6E–02–0.101.2E–01–0.103.6E–026.6E–02275 (48)225 (57)1140 (95)0.45–0.40 (0.11)8.5324.01
TypE–2 diabetes–0.093.3E–02–0.044.3E–01–0.076.5E–021.1E–01361 (67)139 (31)752 (49)0.28–0.69 (0.16)8.7577.44
Insomnia–0.043.5E–01–0.054.2E–01–0.052.1E–012.7E–01248 (144)252 (161)8868 (614)0.50–0.45 (0.32)2.493.57
Multiple sclerosis–0.017.6E–01–0.026.9E–01–0.026.8E–016.8E–01474 (59)26 (8)86 (8)0.050.45 (0.14)5.8242.60
Parkinson’s disease–0.192.0E–04–0.101.5E–01–0.151.7E–034.4E–03432 (58)68 (15)844 (126)0.14–0.75 (0.14)1.5541.25
Alzheimer’s disease–0.064.3E–01–0.225.3E–02–0.139.2E–021.3E–01485 (60)15 (8)52 (10)0.03–0.61 (0.24)10.9037.84
General cognitive ability0.073.2E–020.123.7E–030.091.1E–033.6E–03217 (70)283 (51)10,802 (358)0.570.93 (0.12)1.478.73
Educational attainment0.106.0E–040.101.1E–020.109.9E–054.3E–04166 (60)334 (34)12,855 (285)0.670.90 (0.11)2.419.45

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  1. Tobias Kaufmann
  2. Pål Marius Bjørnstad
  3. Martin Falck
  4. Stener Nerland
  5. Kevin O'Connell
  6. Oleksandr Frei
  7. Ole A Andreassen
  8. Lars T Westlye
  9. Srdjan Djurovic
  10. Timothy Hughes
(2026)
Estimating bone marrow adiposity from head MRI and identifying its genetic architecture
eLife 13:RP101499.
https://doi.org/10.7554/eLife.101499.3