MRI is often regarded as the gold standard for the measurement of body composition in clinical research, with measurements of visceral adipose tissue (VAT), liver, and pancreatic fat content having an enormous impact on our understanding of conditions such as type-2 diabetes (T2D) and nonalcoholic fatty liver disease (NAFLD) (Thomas et al., 2013). In parallel to these developments, biobank-scale genome-wide association studies and epidemiological studies have elucidated the genetic basis of many complex traits, and shed light on their role in disease. The recent augmentation of the UK Biobank study with an imaging protocol has opened up many new avenues of research. In this work, we develop automated methods to quantify abdominal organ traits, characterise their genetic architecture, and explore their relationship to risk factors and disease outcomes.

The MRI protocol in the UKBB includes multiple tissues and organs with the potential for a wide variety of clinically relevant variables. However, genetic studies utilising the UKBB MRI-derived features have focused mainly on brain and cardiac traits (Elliott et al., 2018; Miller et al., 2016; Pirruccello et al., 2020), with some limited studies focussed on liver iron (n = 8,289) and MRI-based corrected T1 (n = 14,440) (Parisinos et al., 2020; Wilman et al., 2019). Thus, the full potential of the UKBB abdominal MRI data has not been realised, in part due to the lack of suitable automated methods to extract the variety and depth of relevant features from multiple organs in very large cohorts.

To address this issue, we trained models using deep learning on expert manual annotations, following preprocessing and quality control, to automatically segment key organs from the UKBB MRI data (Table 1 and Materials and methods). Additionally, we quantified fat and iron content where suitable acquisitions were available (Figure 1—figure supplement 1a, and Materials and methods). In total, we defined 11 Image Derived Phenotypes (IDPs): volume of the liver, pancreas, kidneys, spleen, lungs, VAT, and abdominal subcutaneous adipose tissue (ASAT), and fat and iron content of the liver and pancreas. By linking these traits to measures of risk factors, genetic variation, and disease outcomes, we are able to better characterise their role in disease risk.

Table 1 characterises the study population compared to the entire imaging cohort. We were able to successfully extract IPDs from >99% of available scans for each modality (Table 1 and Supplementary file 1b).

Characterisation of IDPs in the UK biobank population

Previous studies have derived measures of VAT and ASAT, liver fat and iron in the UK Biobank from a subset of the scanned participants (McKay et al., 2018; West et al., 2016; Wilman et al., 2017). Our IDPs show a correlation of 0.87 (liver iron) to 1.0 (fat volume) (Materials and methods; Figure 1—figure supplement 1). The distribution of each organ-specific measure in the scanned population is summarised in Figure 1E,F and G and Table 2.

Table 2

Trait	Organ	Combined	Female	Male	# Study-wide significant GWAS hits
Volume (L)	VAT	3.92 (2.3)	2.78 (1.6)	5.14 (2.3)	3
	ASAT	8.16 (4.1)	9.57 (4.3)	6.64 (3.2)	1
	Lungs	2.67 (0.73)	2.32 (0.53)	3.03 (0.75)	5
	Spleen	0.17 (0.072)	0.14 (0.054)	0.2 (0.078)	29
	Kidney	0.14 (0.03)	0.12 (0.023)	0.16 (0.028)	9
	Pancreas	0.06 (0.018)	0.06 (0.016)	0.06 (0.019)	11
	Liver	1.38 (0.3)	1.28 (0.25)	1.49 (0.3)	11
Fat (%)	Pancreas	10.41 (7.9)	8.34 (6.7)	12.6 (8.5)	8
	Liver	5.06 (5)	4.43 (4.7)	5.73 (5.2)	11
Iron (mg/g)	Pancreas	0.77 (0.097)	0.8 (0.1)	0.75 (0.084)	0
	Liver	1.22 (0.26)	1.2 (0.24)	1.24 (0.28)	6*

1. *Due to complex LD structure in this region, we were not able to finemap the HFE locus. We count two signals at this locus (rs1800562 and rs1799945).

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All IDPs, except liver fat, showed a statistically significant association with age after adjusting for imaging centre and date (Figure 1B), although the magnitudes of the changes are generally small (e.g. −8.8 ml or −0.03 s.d./year for liver volume, −27.7 ml or −0.0067 s.d./year for ASAT, and 24.3 ml or 0.011 s.d./year for VAT). Liver, pancreas, kidney, spleen, and ASAT volumes decreased, while VAT and lung volumes increased with age. Liver and pancreatic iron and pancreatic fat increase slightly with age. Several IDPs (volumes of liver, kidney, lung, and pancreas, as well as liver fat and iron) showed statistically significant evidence of heterogeneity in age-related changes between men and women. We found excess liver iron (>1.8 mg/g) in 3.22% of men and 1.75% of women.

To explore diurnal variation, we investigated correlation between the imaging timestamp and IDPs. We find a decrease in liver volume during the day, with volume at 12 noon being on average 112 ml smaller than volume at 8 am, and a return to almost the original volume by 8 pm. This has previously been suggested in small ultrasound studies (n = 8) which indicated that liver volume is at its smallest between 12 and 2 pm, attributed to changes in hydration and glycogen content (Leung et al., 1986). We also observe smaller, but still statistically significant, associations between time of day and liver and pancreas iron, as well as ASAT, VAT, kidney, and lung volume. Although these changes appear to be physiological in nature, we are currently unable to rule out other potential sources of confounding, however unlikely (for example, different groups of participants being more likely to attend the scanning appointment at different times of day).

IDPs are associated with organ-specific disease outcomes

To assess which IDPs are associated with health-related outcomes, we defined a set of diseases based on inpatient hospital episode statistics (Materials and methods), and assessed the association between each IDP and disease diagnoses (Figure 2 and Supplementary file 1c). Although we were not able to evaluate cause and effect, we found evidence that IDPs reflect organ function and health from the association with disease outcomes.

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Figure 2—source data 1

Source data for Figure 2 and Figure 2—figure supplement 1–5, phenome-wide association study across all IDPs.

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Liver volume was significantly associated with chronic liver disease and cirrhosis (p=4.5e-06, beta = 0.389) as well as T2D (p=1.3e-92, beta = 0.73) and hypertension (p=3.9e-17, beta = 0.18). Kidney volume was associated with chronic kidney disease (CKD) (p=8.0e-23, beta = −1.0). Interestingly, pancreas volume was associated more strongly with Type 1 diabetes (T1D) (p=4.9e-21, beta = −0.77, approximate 95% confidence interval [−0.93,–0.608]), than T2D (p=1.1e-17, beta = −0.27, approximate 95% confidence interval [−0.332,–0.208]). In contrast pancreatic fat showed a small association with T2D (beta = 0.181, p=1.16e-07) and not with T1D (p=0.241). Lung volume was most strongly associated with tobacco use (p=1.8e-46, beta = 0.50) and disorders relating to chronic airway obstruction (COPD) (p=3.6e-35, beta = 0.61), with larger lung volume corresponding to a greater likelihood of respiratory disease diagnosis. Spleen volume was associated with myeloproliferative disease (p=2.2e-33, beta = 0.74), especially chronic lymphocytic leukaemia (p=9.9e-24, beta = 0.78). Liver fat was associated with T2D (p=1.4e-34, beta = 0.29). Liver iron was associated with T2D (p=3.1e-19, beta = −0.43) and iron deficiency anaemia (p=5.3e-12, beta = −0.44) VAT was associated with a wide range of cardiometabolic outcomes including hypertension (p=1e-49, beta = 0.39), T2D (p=8.1e-44, beta = 0.69), and lipid metabolism disorders (p=1.9e-33, beta = 0.42), while ASAT was only associated with cholelithiasis and cholecystitis (p=1.3e-08, beta = 0.38). This association remained statistically significant, after adjusting for VAT, counter to reports that only VAT is predictive of gallstones (Radmard et al., 2015). Overall, this supports the key role of VAT and liver fat in the development of metabolic syndrome.

IDPs are associated with organ-specific biomarkers, physiological measures, and behavioural traits

To further explore the extent to which our IDPs reflect organ health, we assessed correlation between the IDPs and 87 biomarkers from blood, serum, and urine, chosen to reflect a range of health conditions (Materials and methods, Figure 2—figure supplement 1, Supplementary file 1d). We also investigated associations between IDPs and 352 lifestyle and exposure factors, 844 self-reported medical history factors, 500 physical and anthropometric measures, and 769 self-reported diet and exercise measures (Figure 2—figure supplements 3–5, Supplementary file 1d).

Across multiple abdominal organs, we observed strong correlations between IDPs and biomarkers reflective of organ function. For example, liver volume was associated with triglycerides (p=1.19e-242, beta = 0.247) and sex hormone binding globulin (SHBG) (p=3.43e-210, beta = −0.216). Kidney volume was associated with serum cystatin C (p<1e-300, beta = −0.534), serum creatinine (p<1e-300, beta = −0.48), consistent with observations that smaller kidneys function less effectively (Jovanović et al., 2013). Pancreas volume was associated with glycated haemoglobin (HbA1c) (p=8.49e-28, beta = −0.0601), but the association with glucose was not statistically significant after Bonferroni correction (p=8.13e-05). Spleen volume was associated with multiple haematological measurements, including reticulocyte count (p<1e-300, beta = 0.25), mean sphered cell volume (p<1e-300, beta = −0.323), and platelet distribution width (p<1e-300, beta = 0.277).

Liver fat was associated with multiple liver function biomarkers including triglycerides (p=7.66e-219, beta-0.177), SHBG (p=4.75e-189, beta = −0.156) alanine aminotransferase (p<1e-300, beta = 0.226), and gamma glutamyltransferase (p=1.63e-194, beta = 0.162). Consistent with disease outcomes, which showed a correlation between hepatic iron, but not pancreatic iron, with iron deficiency anaemia, liver iron levels were correlated with measures of iron in the blood (e.g. mean corpuscular haemoglobin (MCH), p=1.71e-240, beta = 0.174), while pancreatic iron did not show any such association (MCH p=0.218).

Consistent with previous reports (Harrison-Findik, 2007), we found that liver iron was associated with lower alcohol consumption (p=3e-116, beta = −0.247) and higher intake of red meat (beef intake p=1.61e-61, beta = 0.168; lamb/mutton intake p=7.13e-56, beta = 0.165). Liver iron was also associated with suppressed T2* derived from neuroimaging in the same UKBB cohort (Elliott et al., 2018), particularly in the putamen (left: p=1.53e-68, beta = −0.138; right: p=1.01e-69, beta = −0.14). There were no such associations for pancreatic iron (left p=0.223; right p=0.194). Additionally, we found that liver fat was associated with lower birth weight (p=1.76e-30, beta = −0.0849) and comparative body size at age 10 (p=4.79e-76, beta = −0.22). Low birth weight has previously been associated with severity of pediatric non-alcoholic steatohepatitis (NASH) (Bugianesi et al., 2017), abnormal fat distribution (Parkinson et al., 2020), and liver fat levels in adults born prematurely (Thomas et al., 2011).

We found strong associations between increased lung volume and smoking status, tobacco smoking, COPD and lung disorders, wheeze, diagnosis of asthma and treatment for asthma, a decreased lung capacity as well as forced vital capacity (FVC) and forced expiratory volume in 1 s (FEV1)/FVC ratio (Figure 2—figure supplement 5). This is perhaps surprising in light of the age-related decreases in FEV1 and FVC; however, it has been shown that lung volume increases with both age and as a consequence of obstructive pulmonary diseases (Lutfi, 2017). Although lung volume estimated via MRI is not a widely used clinical measure, our data suggests it may be a biomarker of ageing-related respiratory complications.

Genetic architecture of abdominal IDPs

To explore the genetic architecture of the IDPs, we performed a genome-wide association study (GWAS) for each IDP of 9 million single-nucleotide polymorphisms (SNPs) in the approximately 30,000 individuals of white British ancestry (Bycroft et al., 2018; Materials and methods). We verified that the test statistics showed no overall inflation compared to the expectation by examining the intercept of linkage disequilibrium (LD) score regression (LDSC) (Bulik-Sullivan et al., 2015b; Supplementary file 1e). Utilising a generalised linear mixed model framework and SKAT-O test implemented in SAIGE-GENE (Zhou et al., 2020), we performed gene-based exome-wide association studies in the 11,134 participants with IDP and exome sequencing data. Test statistics were well calibrated and we found no study-wide significant associations (Figure 3—figure supplement 1). The number of individuals included in the analysis for each IDP is given in Table 1, together with the number of study-wide significant independent signals for each IDP.

Organ volume, fat, and iron are heritable

For each IDP, we estimated SNP-heritability using the BOLT-REML model (Loh et al., 2015a; Materials and methods). All IDPs showed a significant heritable component, indicating that genetic variation contributes substantially to the variation between individuals (Figure 3A). Heritability is largely unaffected by the inclusion of height and BMI as additional covariates, indicating that it is not a function of overall body size.

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Genetic correlation between abdominal IDPs

To understand the extent to which genetic variation explains the correlation between traits, we used bivariate LD score regression (Bulik-Sullivan et al., 2015a) to estimate the genetic correlation between all 55 IDP pairs, with and without including height and BMI as covariates (Materials and methods). After Bonferroni correction, we found a statistically significant non-zero genetic correlation between 22 of the 55 unadjusted IDP-pairs traits (Figure 3B and Supplementary file 1f), the strongest (r_g = 0.782, p=4.60e-137) between ASAT and VAT. There was substantial genetic correlation between VAT and liver fat (r_g = 0.58, p=3.7e-38) and between VAT and pancreas fat (r_g = 0.569, p=2.79e-16). We found a negative genetic correlation between pancreas volume and fat (r_g = −0.45, p=2.1e-06), and between pancreas volume and iron (r_g = −0.5, p=5.2e-05).

IDPs share a genetic basis with other physiological traits

To identify traits with a shared genetic basis, we estimated genetic correlation between IDPs and 282 complex traits with a heritable component (Materials and methods). A total of 650 IDP-trait pairs showed evidence of nonzero genetic correlation; 347 of these involved with measures of size or body composition (Supplementary file 1g and Figure 3—figure supplement 2). We found substantial genetic correlation between ASAT volume and other measures of body fat, such as whole-body fat mass (r_g = 0.94, p=3.2e-143) and between VAT and conventional surrogate markers such as waist circumference (r_g = 0.75, p=1.6e-109). The strongest genetic correlation with lung volume was with FVC (r_g = 0.7, p=3.1e-71), with FEV and height also significant. We also found more modest genetic correlation between organ volumes and biochemical measures, such as liver fat and ALT (r_g = 0.5, p=4.5e-23), kidney volume and serum creatinine (r_g = −0.4, p=3.9e-22), and liver iron and erythrocyte distribution width (r_g = −0.33, p=2.1e-14).

Heritability is enriched in organ-specific cell types

In order to identify tissues or cell types contributing to the heritability of each trait, we used stratified LD score regression (Finucane et al., 2015) (Materials and methods). Liver fat showed evidence for enrichment in hepatocytes (p=4.20e-6) and liver tissue (p=2.2e-5), and pancreatic fat showed evidence for enrichment in pancreas tissue (smallest p=9.74e-5). Spleen volume showed enrichment in spleen cells (p=7.39e-10) and immune cell types including T cells, B cells, and natural killer cells, and neutrophils. VAT, ASAT, and lung volumes did not show evidence of significant heritability enrichment in any tissue or cell types (Figure 3—figure supplements 3–5).

We have developed a pipeline to systematically quantify organ and tissue parameters from MRI scans of over 38,000 participants in the UKBB imaging cohort, producing the largest sample size to date of abdominal imaging-derived phenotypes (IDPs). The training of our segmentation pipeline incorporated a broad range of data augmentation options, including smooth 3D geometric warps, to achieve better data efficiency. This enabled us to achieve good segmentation performance (Jaccard index >0.8) with a limited training dataset size of ~100 images. Since manual annotation of 3D images is a labor-intensive process, automating this process has removed a substantial barrier to large-scale studies of clinical images, and in turn facilitated new insights. The semantic segmentation models are robust to several sources of visual heterogeneity arising from deformable tissues and joints, and thus facilitate high-throughput analysis of MRI data.

The observed age-related decrease in organ volume (liver, pancreas, kidney, spleen) may reflect the predicted organ atrophy associated with ageing, likely underpinned by mechanism(s) similar to those reported for brain and skeletal muscle (Mitchell et al., 2012; Svennerholm et al., 1997). Individual organs exhibited distinct patterns of atrophy, with liver and pancreas exhibiting the largest reduction. The increase in VAT (but not ASAT) and lung volume with age may point at the overriding impact of environmental factors upon these tissues. Given that VAT and ASAT are exposed to similar exogenous factors, we hypothesise that the plasticity capacity of their adipocytes (hypertrophy and hyperplasia), and therefore tissue lipolysis and inflammation, ectopic fat deposition and insulin sensitivity, are differentially affected by the ageing process (Mancuso and Bouchard, 2019). Future studies which incorporate large-scale longitudinal imaging data will enable detailed interrogation of these changes between individuals.

The liver plays a pivotal role in the regulation of iron homeostasis, with iron excess to requirements stored in hepatocytes (Anderson and Shah, 2013). Epidemiologic studies utilising indirect methods based on serum markers (i.e. the ratio of serum transferrin receptor to serum ferritin) describe an age-related increase in total body iron, declining at a very late age (Cook et al., 2003). However, studies with direct measurements, although far more limited in scope and size, point towards a linear relationship with age (Kühn et al., 2017; McKay et al., 2018; Nomura et al., 1988; Schwenzer et al., 2008), similar to that observed in our study. The discrepancy between total and organ-specific changes with age may relate to the complex relationship between liver iron storage and circulating iron, which is known to be compromised by age related organ dysfunction and the inflammasome (Anderson and Shah, 2013). Similar patterns for pancreatic iron were observed (Schwenzer et al., 2008), again reflecting the overall iron homeostasis in the body.

Ectopic fat accumulation showed a more complex relationship with ageing. Although pancreatic fat increased with age for both men and women (Schwenzer et al., 2008), liver fat increased only up to approximately 60 years of age before plateauing in women and decreasing in men (Kühn et al., 2017; Nomura et al., 1988). Previous studies have suggested a linear relationship (Thomas et al., 2012; Wilman et al., 2017), but this may reflect the paucity of older participants (>60 years) in those cohorts, thus lacking the power to detect the true effects of age on liver fat. Both liver fat and iron were associated with T2D, consistent with previous studies (McKay et al., 2018). No association was observed between pancreatic fat or iron content with either T1D or T2D, despite the observed association between pancreas volume and T1D. This is surprising given its proposed causal role assigned to this fat depot in T2D (Taylor, 2013). Interestingly, although both liver and pancreas volume decreased with age, pancreatic fat did not, in agreement with previous observations (Majumder et al., 2017). Additionally, there was considerably greater diurnal variation in liver volume compared with the pancreas. These observations add credence to the growing evidence of disparate mechanisms for the accumulation of fat in these organs (Hellerstein, 1999). Furthermore, given the observed diurnal variation in organ volume, fat and iron content, coupled to the known effects of feeding on the circadian clock on organ function (Kalhan and Ghosh, 2015), scheduling of MRI measurements of participants may be an important consideration in longitudinal studies.

Most organ volumes were associated with disease, highlighting the potential medical relevance of abdominal MRI-derived parameters. Associations with potential clinical relevance included kidney volume with chronic kidney disease (Grantham et al., 2006), and lung volumes with chronic obstructive pulmonary disease, bronchitis, and respiratory disease. Liver volume was associated with chronic liver disease (Lin et al., 1998) and cirrhosis (Hagan et al., 2014) as well as diabetes and hypertension. Although there is a strong correlation between liver volume and liver fat, liver volume is not generally measured in relation to metabolic disease. Whilst spleen volumes can be enlarged in response to a whole host of diseases such as infection, haematological, congestive, inflammatory, and neoplastic (Pozo et al., 2009), we found spleen volume to be most strongly associated with leukaemia. Although organ volume is not a widely-used measure for disease diagnosis, spleen volume is a useful metric for predicting outcome and response to treatment (Shimomura et al., 2018), and a robust automated measure of this IDP could be a powerful auxiliary clinical tool. Indeed, the associations with deep-learning derived organ and tissue parameters may become increasingly medically relevant in the future, as machine intelligence becomes more widely adopted as a component of clinical care.

The strong association between VAT and development of metabolic dysfunction is well established (Lee et al., 2018), and confirmed herein on a much larger cohort. No association between ASAT and disease, apart from incidence of gallstones, were observed. The overall role of subcutaneous fat in disease development is still debated. Viewed as benign or neutral in terms of risk of metabolic disease (Kuk et al., 2006), especially subcutaneous fat around the hips, ASAT does appear to be associated with components of the metabolic syndrome, though not after correcting for VAT or waist circumference (Elffers et al., 2017; Irlbeck et al., 2010). It has been suggested that subdivisions of ASAT may convey different risks, with superficial ASAT conferring little or no risk compared to deeper layers (Kelley et al., 2000). These conflicting results may reflect different approaches to ASAT and VAT measurement (MRI vs indirect assessment), size and make-up of study cohorts. Future studies within the UKBB and other biobanks will allow these relationships to be explored in more depth.

Through GWAS, we identify a substantial heritable component to organ volume, fat and iron content, both before and after adjusting for body size. We demonstrate heritability enrichment in relevant tissues and cell types (hepatocytes for liver fat, and pancreas for pancreatic fat), suggesting that there may be specific mechanisms underpinning organ morphology and function that warrant further investigation. For the traits that have been studied before in other cohorts, we replicate known associations such as the PNPLA3, TM6SF2, and APOE loci with liver fat, and of the HFE and TMPRSS6 loci with liver iron. In addition, we identify several novel associations that may suggest mechanisms for further study, including an association between GPAM and liver fat, PPP1R3B and liver volume (but not liver fat), CB2FAT3 and pancreatic fat, and SLC40A1 and liver iron. Colocalisation analysis with gene expression in specific tissues implicated CBFA2T3 in changes of pancreatic fat. We found little overlap between the significant loci for VAT, ASAT, liver fat, and pancreatic fat, highlighting the need to develop more refined definitions of adiposity to better understand the role it plays in disease risk. Our gene-based burden test for rare exome variants was limited by the smaller sample size available for this study. However, the substantial heritable component suggests that the planned studies involving up to 100,000 scanned individuals, including whole exome and whole genome sequence data, will yield many further insights into the genetic basis of organ form, and its relationship to function.

This study has some limitations. Although recruitment into the UK Biobank study finished in 2010, scanning began in 2014. The median follow-up period from scanning is 2.5 years, limiting our power to evaluate the prognostic value of IDPs, or to evaluate whether they are a cause or consequence of the disease state. Since medical records will continue to be collected prospectively, we will be able to assess this more systematically in future studies. Our genetic studies were limited to participants of white British ancestry. While this did not greatly affect power due to the demographics of the imaging cohort, future imaging studies which incorporate greater diversity of ancestry and environmental exposure will facilitate fine-mapping as well as potentially elucidate new mechanisms (Wojcik et al., 2019). Additionally, we did not explore in detail the relationship between either ancestry or self-reported ethnicity, because of the limited sample size in the imaging cohort of non-White-British participants. Future studies with other cohorts could explore this question. Finally, while this study focussed on tractable measures derived from segmentation, we expect that future studies will allow us to define more sophisticated traits derived from organ segmentations and will give deeper insight into the relationship between organ form and function.

In conclusion, by systematically quantifying 11 IDPs covering several organs in the largest abdominal imaging cohort to date, we have associated organ parameters with environmental exposures, quantitative biomarkers, and clinical outcomes. In addition, we have characterised the genetic basis of these imaging-derived phenotypes to recapitulate previously identified associations with clinical endpoints, as well as uncover novel associations that may reflect new aspects of disease etiology or organ physiology. These findings could ultimately give insight into causes of complex disease, and potentially lead to new non-invasive diagnostic techniques. Moreover, the observations relating pancreatic volume to type-1 diabetes and liver volume with chronic liver disease along with gender differences, genetic susceptibility and volumetric changes related to diurnal variation will be important factors to consider for the growing field of personalised medicine. Deep-learning models trained on imaging data thus enhance our understanding of abdominal organ health and disease, and may guide strategies for personalised medicine or pave the way for new treatments in the future.

Summary statistics from all genome-wide association studies described in this paper are available from the NHGRI-EBI GWAS Catalog, accession numbers GCST90016666-GCST90016676, URL http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90016001-GCST90017000/GCST90016676/. All underlying data, and derived quantities, are available by application from the UK Biobank at http://www.ukbiobank.ac.uk.

1. 1. Cule M
   2. Liu Y
   3. Basty N
   4. Whitcher B
   5. Bell JD
   6. Sorokin EP
   7. van Bruggen N
   8. Thomas EL

   (2021) NHGRI-EBI GWAS Catalog

   ID GCST90016666-GCST90016676. Genetic architecture of 11 organ traits derived from abdominal MRI using deep learning.

   http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90016001-GCST90017000/GCST90016676/

1. 1. The Genotype-Tissue Expression (GTEx) Project

   (2016) GTEx v7

   ID GTEx_Analysis_v7_eQTL_all_associations. GTEx v7.

   https://storage.googleapis.com/gtex_analysis_v7/single_tissue_eqtl_data/GTEx_Analysis_v7_eQTL_all_associations.tar.gz
2. 1. Abbott L
   2. Bryant S
   3. Churchhouse C
   4. Ganna A
   5. Howrigan D
   6. Palmer D
   7. Neale B
   8. Walters R
   9. Carey C
   10. The Hail team
   11. Anttila V
   12. Aragam K
   13. Baumann A
   14. Cole J
   15. Daly MJ
   16. Damian R
   17. Haas M
   18. Hirschhorn J
   19. Jones E
   20. Munshi R
   21. Rivas M
   22. Vedantam S

   (2018) Neale lab

   ID GWAS v2. Neale lab GWAS v2.

   http://www.nealelab.is/uk-biobank/
3. 1. Carvalho-Silva D
   2. Pierleoni A
   3. Pignatelli M
   4. Ong CK
   5. Fumis L
   6. Karamanis N
   7. Carmona M
   8. Faulconbridge A
   9. Hercules A
   10. McAuley E
   11. Miranda A
   12. Peat G
   13. Spitzer M
   14. Barrett J
   15. Hulcoop DG
   16. Papa E
   17. Koscielny G
   18. Dunham I

   (2018) OpenTargets Genetics

   ID 190505. OpenTargets Genetics.

   https://genetics-docs.opentargets.org/data-access/data-download
4. 1. Zhou W
   2. Nielsen JB
   3. Fritsche LG
   4. Dey R
   5. Gabrielsen ME
   6. Wolford BN
   7. LeFaive J
   8. VandeHaar P
   9. Gagliano SA
   10. Gifford A
   11. Bastarache LA
   12. Wei WQ
   13. Denny JC
   14. Lin M
   15. Hveem K
   16. Kang HM
   17. Abecasis GR
   18. Willer CJ
   19. Lee S

   (2018) SAIGE UK Biobank GWAS

   ID UKBB_SAIGE_HRC. Data from: Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies.

   ftp://share.sph.umich.edu/UKBB_SAIGE_HRC/

1. 1. Altshuler DM
   2. Gibbs RA
   3. Peltonen L
   4. Altshuler DM
   5. Gibbs RA
   6. Peltonen L
   7. Dermitzakis E
   8. Schaffner SF
   9. Yu F
   10. Peltonen L
   11. Dermitzakis E
   12. Bonnen PE
   13. Altshuler DM
   14. Gibbs RA
   15. de Bakker PI
   16. Deloukas P
   17. Gabriel SB
   18. Gwilliam R
   19. Hunt S
   20. Inouye M
   21. Jia X
   22. Palotie A
   23. Parkin M
   24. Whittaker P
   25. Yu F
   26. Chang K
   27. Hawes A
   28. Lewis LR
   29. Ren Y
   30. Wheeler D
   31. Gibbs RA
   32. Muzny DM
   33. Barnes C
   34. Darvishi K
   35. Hurles M
   36. Korn JM
   37. Kristiansson K
   38. Lee C
   39. McCarrol SA
   40. Nemesh J
   41. Dermitzakis E
   42. Keinan A
   43. Montgomery SB
   44. Pollack S
   45. Price AL
   46. Soranzo N
   47. Bonnen PE
   48. Gibbs RA
   49. Gonzaga-Jauregui C
   50. Keinan A
   51. Price AL
   52. Yu F
   53. Anttila V
   54. Brodeur W
   55. Daly MJ
   56. Leslie S
   57. McVean G
   58. Moutsianas L
   59. Nguyen H
   60. Schaffner SF
   61. Zhang Q
   62. Ghori MJ
   63. McGinnis R
   64. McLaren W
   65. Pollack S
   66. Price AL
   67. Schaffner SF
   68. Takeuchi F
   69. Grossman SR
   70. Shlyakhter I
   71. Hostetter EB
   72. Sabeti PC
   73. Adebamowo CA
   74. Foster MW
   75. Gordon DR
   76. Licinio J
   77. Manca MC
   78. Marshall PA
   79. Matsuda I
   80. Ngare D
   81. Wang VO
   82. Reddy D
   83. Rotimi CN
   84. Royal CD
   85. Sharp RR
   86. Zeng C
   87. Brooks LD
   88. McEwen JE
   89. International HapMap 3 Consortium

   (2010) Integrating common and rare genetic variation in diverse human populations

   Nature 467:52–58.

   https://doi.org/10.1038/nature09298

   • PubMed
   • Google Scholar
2. 1. Anderson ER
   2. Shah YM

   (2013) Iron homeostasis in the liver

   Comprehensive Physiology 3:315–330.

   https://doi.org/10.1002/cphy.c120016

   • PubMed
   • Google Scholar
3. Conference

   1. Basty N
   2. Liu Y
   3. Cule M
   4. Thomas EL
   5. Bell JD
   6. Whitcher B

   (2020) Automated measurement of pancreatic fat and iron concentration using Multi-Echo and T1-Weighted MRI data

   2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI). pp. 345–348.

   https://doi.org/10.1109/ISBI45749.2020.9098650

   • Google Scholar
4. 1. Buch S
   2. Stickel F
   3. Trépo E
   4. Way M
   5. Herrmann A
   6. Nischalke HD
   7. Brosch M
   8. Rosendahl J
   9. Berg T
   10. Ridinger M
   11. Rietschel M
   12. McQuillin A
   13. Frank J
   14. Kiefer F
   15. Schreiber S
   16. Lieb W
   17. Soyka M
   18. Semmo N
   19. Aigner E
   20. Datz C
   21. Schmelz R
   22. Brückner S
   23. Zeissig S
   24. Stephan AM
   25. Wodarz N
   26. Devière J
   27. Clumeck N
   28. Sarrazin C
   29. Lammert F
   30. Gustot T
   31. Deltenre P
   32. Völzke H
   33. Lerch MM
   34. Mayerle J
   35. Eyer F
   36. Schafmayer C
   37. Cichon S
   38. Nöthen MM
   39. Nothnagel M
   40. Ellinghaus D
   41. Huse K
   42. Franke A
   43. Zopf S
   44. Hellerbrand C
   45. Moreno C
   46. Franchimont D
   47. Morgan MY
   48. Hampe J

   (2015) A genome-wide association study confirms PNPLA3 and identifies TM6SF2 and MBOAT7 as risk loci for alcohol-related cirrhosis

   Nature Genetics 47:1443–1448.

   https://doi.org/10.1038/ng.3417

   • PubMed
   • Google Scholar
5. 1. Bugianesi E
   2. Bizzarri C
   3. Rosso C
   4. Mosca A
   5. Panera N
   6. Veraldi S
   7. Dotta A
   8. Giannone G
   9. Raponi M
   10. Cappa M
   11. Alisi A
   12. Nobili V

   (2017) Low birthweight increases the likelihood of severe steatosis in pediatric Non-Alcoholic fatty liver disease

   American Journal of Gastroenterology 112:1277–1286.

   https://doi.org/10.1038/ajg.2017.140

   • PubMed
   • Google Scholar
6. 1. Bulik-Sullivan B
   2. Finucane HK
   3. Anttila V
   4. Gusev A
   5. Day FR
   6. Loh PR
   7. Duncan L
   8. Perry JR
   9. Patterson N
   10. Robinson EB
   11. Daly MJ
   12. Price AL
   13. Neale BM
   14. ReproGen Consortium
   15. Psychiatric Genomics Consortium
   16. Genetic Consortium for Anorexia Nervosa of the Wellcome Trust Case Control Consortium 3

   (2015a) An atlas of genetic correlations across human diseases and traits

   Nature Genetics 47:1236–1241.

   https://doi.org/10.1038/ng.3406

   • PubMed
   • Google Scholar
7. 1. Bulik-Sullivan BK
   2. Loh PR
   3. Finucane HK
   4. Ripke S
   5. Yang J
   6. Patterson N
   7. Daly MJ
   8. Price AL
   9. Neale BM
   10. Schizophrenia Working Group of the Psychiatric Genomics Consortium

   (2015b) LD score regression distinguishes confounding from polygenicity in genome-wide association studies

   Nature Genetics 47:291–295.

   https://doi.org/10.1038/ng.3211

   • PubMed
   • Google Scholar
8. 1. Buniello A
   2. MacArthur JAL
   3. Cerezo M
   4. Harris LW
   5. Hayhurst J
   6. Malangone C
   7. McMahon A
   8. Morales J
   9. Mountjoy E
   10. Sollis E
   11. Suveges D
   12. Vrousgou O
   13. Whetzel PL
   14. Amode R
   15. Guillen JA
   16. Riat HS
   17. Trevanion SJ
   18. Hall P
   19. Junkins H
   20. Flicek P
   21. Burdett T
   22. Hindorff LA
   23. Cunningham F
   24. Parkinson H

   (2019) The NHGRI-EBI GWAS catalog of published genome-wide association studies, targeted arrays and summary statistics 2019

   Nucleic Acids Research 47:D1005–D1012.

   https://doi.org/10.1093/nar/gky1120

   • PubMed
   • Google Scholar
9. 1. Bycroft C
   2. Freeman C
   3. Petkova D
   4. Band G
   5. Elliott LT
   6. Sharp K
   7. Motyer A
   8. Vukcevic D
   9. Delaneau O
   10. O'Connell J
   11. Cortes A
   12. Welsh S
   13. Young A
   14. Effingham M
   15. McVean G
   16. Leslie S
   17. Allen N
   18. Donnelly P
   19. Marchini J

   (2018) The UK biobank resource with deep phenotyping and genomic data

   Nature 562:203–209.

   https://doi.org/10.1038/s41586-018-0579-z

   • PubMed
   • Google Scholar
10. Software

    1. Bydder M

    (2020a) pdff

    GitHub.

    https://github.com/marcsous/pdff
11. 1. Bydder M
    2. Ghodrati V
    3. Gao Y
    4. Robson MD
    5. Yang Y
    6. Hu P

    (2020b) Constraints in estimating the proton density fat fraction

    Magnetic Resonance Imaging 66:1–8.

    https://doi.org/10.1016/j.mri.2019.11.009

    • PubMed
    • Google Scholar
12. 1. Carroll RJ
    2. Bastarache L
    3. Denny JC

    (2014) R PheWAS: data analysis and plotting tools for phenome-wide association studies in the R environment

    Bioinformatics 30:2375–2376.

    https://doi.org/10.1093/bioinformatics/btu197

    • PubMed
    • Google Scholar
13. 1. Carvalho-Silva D
    2. Pierleoni A
    3. Pignatelli M
    4. Ong C
    5. Fumis L
    6. Karamanis N
    7. Carmona M
    8. Faulconbridge A
    9. Hercules A
    10. McAuley E
    11. Miranda A
    12. Peat G
    13. Spitzer M
    14. Barrett J
    15. Hulcoop DG
    16. Papa E
    17. Koscielny G
    18. Dunham I

    (2019) Open targets platform: new developments and updates two years on

    Nucleic Acids Research 47:D1056–D1065.

    https://doi.org/10.1093/nar/gky1133

    • PubMed
    • Google Scholar
14. 1. Cook JD
    2. Flowers CH
    3. Skikne BS

    (2003) The quantitative assessment of body iron

    Blood 101:3359–3363.

    https://doi.org/10.1182/blood-2002-10-3071

    • PubMed
    • Google Scholar
15. 1. Cuchel M
    2. Bloedon LT
    3. Szapary PO
    4. Kolansky DM
    5. Wolfe ML
    6. Sarkis A
    7. Millar JS
    8. Ikewaki K
    9. Siegelman ES
    10. Gregg RE
    11. Rader DJ

    (2007) Inhibition of microsomal triglyceride transfer protein in familial hypercholesterolemia

    New England Journal of Medicine 356:148–156.

    https://doi.org/10.1056/NEJMoa061189

    • PubMed
    • Google Scholar
16. Preprint

    1. de Lichtenberg KH
    2. Seymour PA
    3. Jørgensen MC
    4. Kim Y-H
    5. Grapin-Botton A
    6. Magnuson MA
    7. Nakic N
    8. Ferrer J
    9. Serup P

    (2018) Notch controls multiple pancreatic cell fate regulators through direct Hes1-mediated repression

    bioRxiv.

    https://doi.org/10.1101/336305

    • Google Scholar
17. 1. Donovan A
    2. Lima CA
    3. Pinkus JL
    4. Pinkus GS
    5. Zon LI
    6. Robine S
    7. Andrews NC

    (2005) The iron exporter ferroportin/Slc40a1 is essential for iron homeostasis

    Cell Metabolism 1:191–200.

    https://doi.org/10.1016/j.cmet.2005.01.003

    • PubMed
    • Google Scholar
18. 1. Elffers TW
    2. de Mutsert R
    3. Lamb HJ
    4. de Roos A
    5. Willems van Dijk K
    6. Rosendaal FR
    7. Jukema JW
    8. Trompet S

    (2017) Body fat distribution, in particular visceral fat, is associated with cardiometabolic risk factors in obese women

    PLOS ONE 12:e0185403.

    https://doi.org/10.1371/journal.pone.0185403

    • PubMed
    • Google Scholar
19. 1. Elliott LT
    2. Sharp K
    3. Alfaro-Almagro F
    4. Shi S
    5. Miller KL
    6. Douaud G
    7. Marchini J
    8. Smith SM

    (2018) Genome-wide association studies of brain imaging phenotypes in UK biobank

    Nature 562:210–216.

    https://doi.org/10.1038/s41586-018-0571-7

    • PubMed
    • Google Scholar
20. 1. Emdin CA
    2. Haas ME
    3. Khera AV
    4. Aragam K
    5. Chaffin M
    6. Klarin D
    7. Hindy G
    8. Jiang L
    9. Wei WQ
    10. Feng Q
    11. Karjalainen J
    12. Havulinna A
    13. Kiiskinen T
    14. Bick A
    15. Ardissino D
    16. Wilson JG
    17. Schunkert H
    18. McPherson R
    19. Watkins H
    20. Elosua R
    21. Bown MJ
    22. Samani NJ
    23. Baber U
    24. Erdmann J
    25. Gupta N
    26. Danesh J
    27. Saleheen D
    28. Chang KM
    29. Vujkovic M
    30. Voight B
    31. Damrauer S
    32. Lynch J
    33. Kaplan D
    34. Serper M
    35. Tsao P
    36. Mercader J
    37. Hanis C
    38. Daly M
    39. Denny J
    40. Gabriel S
    41. Kathiresan S
    42. Million Veteran Program

    (2020) A missense variant in mitochondrial amidoxime reducing component 1 gene and protection against liver disease

    PLOS Genetics 16:e1008629.

    https://doi.org/10.1371/journal.pgen.1008629

    • PubMed
    • Google Scholar
21. 1. ENCODE Project Consortium

    (2012) An integrated encyclopedia of DNA elements in the human genome

    Nature 489:57–74.

    https://doi.org/10.1038/nature11247

    • PubMed
    • Google Scholar
22. 1. Fehrmann RS
    2. Karjalainen JM
    3. Krajewska M
    4. Westra HJ
    5. Maloney D
    6. Simeonov A
    7. Pers TH
    8. Hirschhorn JN
    9. Jansen RC
    10. Schultes EA
    11. van Haagen HH
    12. de Vries EG
    13. te Meerman GJ
    14. Wijmenga C
    15. van Vugt MA
    16. Franke L

    (2015) Gene expression analysis identifies global gene dosage sensitivity in Cancer

    Nature Genetics 47:115–125.

    https://doi.org/10.1038/ng.3173

    • PubMed
    • Google Scholar
23. 1. Finucane HK
    2. Bulik-Sullivan B
    3. Gusev A
    4. Trynka G
    5. Reshef Y
    6. Loh PR
    7. Anttila V
    8. Xu H
    9. Zang C
    10. Farh K
    11. Ripke S
    12. Day FR
    13. Purcell S
    14. Stahl E
    15. Lindstrom S
    16. Perry JR
    17. Okada Y
    18. Raychaudhuri S
    19. Daly MJ
    20. Patterson N
    21. Neale BM
    22. Price AL
    23. ReproGen Consortium
    24. Schizophrenia Working Group of the Psychiatric Genomics Consortium
    25. RACI Consortium

    (2015) Partitioning heritability by functional annotation using genome-wide association summary statistics

    Nature Genetics 47:1228–1235.

    https://doi.org/10.1038/ng.3404

    • PubMed
    • Google Scholar
24. 1. Finucane HK
    2. Reshef YA
    3. Anttila V
    4. Slowikowski K
    5. Gusev A
    6. Byrnes A
    7. Gazal S
    8. Loh PR
    9. Lareau C
    10. Shoresh N
    11. Genovese G
    12. Saunders A
    13. Macosko E
    14. Pollack S
    15. Perry JRB
    16. Buenrostro JD
    17. Bernstein BE
    18. Raychaudhuri S
    19. McCarroll S
    20. Neale BM
    21. Price AL
    22. Brainstorm Consortium

    (2018) Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types

    Nature Genetics 50:621–629.

    https://doi.org/10.1038/s41588-018-0081-4

    • PubMed
    • Google Scholar
25. 1. Giambartolomei C
    2. Vukcevic D
    3. Schadt EE
    4. Franke L
    5. Hingorani AD
    6. Wallace C
    7. Plagnol V

    (2014) Bayesian test for colocalisation between pairs of genetic association studies using summary statistics

    PLOS Genetics 10:e1004383.

    https://doi.org/10.1371/journal.pgen.1004383

    • PubMed
    • Google Scholar
26. 1. Grantham JJ
    2. Torres VE
    3. Chapman AB
    4. Guay-Woodford LM
    5. Bae KT
    6. King BF
    7. Wetzel LH
    8. Baumgarten DA
    9. Kenney PJ
    10. Harris PC
    11. Klahr S
    12. Bennett WM
    13. Hirschman GN
    14. Meyers CM
    15. Zhang X
    16. Zhu F
    17. Miller JP
    18. CRISP Investigators

    (2006) Volume progression in polycystic kidney disease

    New England Journal of Medicine 354:2122–2130.

    https://doi.org/10.1056/NEJMoa054341

    • PubMed
    • Google Scholar
27. 1. GTEx Consortium
    2. Laboratory, Data Analysis &Coordinating Center (LDACC)—Analysis Working Group
    3. Statistical Methods groups—Analysis Working Group
    4. Enhancing GTEx (eGTEx) groups
    5. NIH Common Fund
    6. NIH/NCI
    7. NIH/NHGRI
    8. NIH/NIMH
    9. NIH/NIDA
    10. Biospecimen Collection Source Site—NDRI
    11. Biospecimen Collection Source Site—RPCI
    12. Biospecimen Core Resource—VARI
    13. Brain Bank Repository—University of Miami Brain Endowment Bank
    14. Leidos Biomedical—Project Management
    15. ELSI Study
    16. Genome Browser Data Integration &Visualization—EBI
    17. Genome Browser Data Integration &Visualization—UCSC Genomics Institute, University of California Santa Cruz
    18. Lead analysts:
    19. Laboratory, Data Analysis &Coordinating Center (LDACC):
    20. NIH program management:
    21. Biospecimen collection:
    22. Pathology:
    23. eQTL manuscript working group:
    24. Battle A
    25. Brown CD
    26. Engelhardt BE
    27. Montgomery SB

    (2017) Genetic effects on gene expression across human tissues

    Nature 550:204–213.

    https://doi.org/10.1038/nature24277

    • PubMed
    • Google Scholar
28. 1. Guo H
    2. Fortune MD
    3. Burren OS
    4. Schofield E
    5. Todd JA
    6. Wallace C

    (2015) Integration of disease association and eQTL data using a Bayesian colocalisation approach highlights six candidate causal genes in immune-mediated diseases

    Human Molecular Genetics 24:3305–3313.

    https://doi.org/10.1093/hmg/ddv077

    • PubMed
    • Google Scholar
29. 1. Hagan MT
    2. Sayuk GS
    3. Lisker-Melman M
    4. Korenblat KM
    5. Kerr TA
    6. Chapman WC
    7. Crippin JS

    (2014) Liver volume in the cirrhotic patient: does size matter?

    Digestive Diseases and Sciences 59:886–891.

    https://doi.org/10.1007/s10620-014-3038-1

    • PubMed
    • Google Scholar
30. 1. Harrison-Findik DD

    (2007) Role of alcohol in the regulation of iron metabolism

    World Journal of Gastroenterology 13:4925–4930.

    https://doi.org/10.3748/wjg.v13.i37.4925

    • Google Scholar
31. 1. Hellerstein MK

    (1999) De novo lipogenesis in humans: metabolic and regulatory aspects

    European Journal of Clinical Nutrition 53 Suppl 1:s53–s65.

    https://doi.org/10.1038/sj.ejcn.1600744

    • PubMed
    • Google Scholar
32. 1. Heng TS
    2. Painter MW
    3. Immunological Genome Project Consortium

    (2008) The immunological genome project: networks of gene expression in immune cells

    Nature Immunology 9:1091–1094.

    https://doi.org/10.1038/ni1008-1091

    • PubMed
    • Google Scholar
33. 1. Hsiao PJ
    2. Lee MY
    3. Wang YT
    4. Jiang HJ
    5. Lin PC
    6. Yang YH
    7. Kuo KK

    (2015) MTTP-297H polymorphism reduced serum cholesterol but increased risk of non-alcoholic fatty liver disease-a cross-sectional study

    BMC Medical Genetics 16:93.

    https://doi.org/10.1186/s12881-015-0242-6

    • PubMed
    • Google Scholar
34. 1. Irlbeck T
    2. Massaro JM
    3. Bamberg F
    4. O'Donnell CJ
    5. Hoffmann U
    6. Fox CS

    (2010) Association between single-slice measurements of visceral and abdominal subcutaneous adipose tissue with volumetric measurements: the framingham heart study

    International Journal of Obesity 34:781–787.

    https://doi.org/10.1038/ijo.2009.279

    • Google Scholar
35. 1. Jovanović D
    2. Gasic B
    3. Pavlovic S
    4. Naumovic R

    (2013) Correlation of kidney size with kidney function and anthropometric parameters in healthy subjects and patients with chronic kidney diseases

    Renal Failure 35:896–900.

    https://doi.org/10.3109/0886022X.2013.794683

    • PubMed
    • Google Scholar
36. 1. Kalhan SC
    2. Ghosh A

    (2015) Dietary iron, circadian clock, and hepatic gluconeogenesis

    Diabetes 64:1091–1093.

    https://doi.org/10.2337/db14-1697

    • PubMed
    • Google Scholar
37. 1. Karczewski KJ
    2. Francioli LC
    3. Tiao G
    4. Cummings BB
    5. Alföldi J
    6. Wang Q
    7. Collins RL
    8. Laricchia KM
    9. Ganna A
    10. Birnbaum DP
    11. Gauthier LD
    12. Brand H
    13. Solomonson M
    14. Watts NA
    15. Rhodes D
    16. Singer-Berk M
    17. England EM
    18. Seaby EG
    19. Kosmicki JA
    20. Walters RK
    21. Tashman K
    22. Farjoun Y
    23. Banks E
    24. Poterba T
    25. Wang A
    26. Seed C
    27. Whiffin N
    28. Chong JX
    29. Samocha KE
    30. Pierce-Hoffman E
    31. Zappala Z
    32. O'Donnell-Luria AH
    33. Minikel EV
    34. Weisburd B
    35. Lek M
    36. Ware JS
    37. Vittal C
    38. Armean IM
    39. Bergelson L
    40. Cibulskis K
    41. Connolly KM
    42. Covarrubias M
    43. Donnelly S
    44. Ferriera S
    45. Gabriel S
    46. Gentry J
    47. Gupta N
    48. Jeandet T
    49. Kaplan D
    50. Llanwarne C
    51. Munshi R
    52. Novod S
    53. Petrillo N
    54. Roazen D
    55. Ruano-Rubio V
    56. Saltzman A
    57. Schleicher M
    58. Soto J
    59. Tibbetts K
    60. Tolonen C
    61. Wade G
    62. Talkowski ME
    63. Neale BM
    64. Daly MJ
    65. MacArthur DG
    66. Genome Aggregation Database Consortium

    (2020) The mutational constraint spectrum quantified from variation in 141,456 humans

    Nature 581:434–443.

    https://doi.org/10.1038/s41586-020-2308-7

    • PubMed
    • Google Scholar
38. 1. Kawaguchi T
    2. Shima T
    3. Mizuno M
    4. Mitsumoto Y
    5. Umemura A
    6. Kanbara Y
    7. Tanaka S
    8. Sumida Y
    9. Yasui K
    10. Takahashi M
    11. Matsuo K
    12. Itoh Y
    13. Tokushige K
    14. Hashimoto E
    15. Kiyosawa K
    16. Kawaguchi M
    17. Itoh H
    18. Uto H
    19. Komorizono Y
    20. Shirabe K
    21. Takami S
    22. Takamura T
    23. Kawanaka M
    24. Yamada R
    25. Matsuda F
    26. Okanoue T

    (2018) Risk estimation model for nonalcoholic fatty liver disease in the japanese using multiple genetic markers

    PLOS ONE 13:e0185490.

    https://doi.org/10.1371/journal.pone.0185490

    • PubMed
    • Google Scholar
39. 1. Kelley DE
    2. Thaete FL
    3. Troost F
    4. Huwe T
    5. Goodpaster BH

    (2000) Subdivisions of subcutaneous abdominal adipose tissue and insulin resistance

    American Journal of Physiology-Endocrinology and Metabolism 278:E941–E948.

    https://doi.org/10.1152/ajpendo.2000.278.5.E941

    • PubMed
    • Google Scholar
40. 1. Kichaev G
    2. Bhatia G
    3. Loh PR
    4. Gazal S
    5. Burch K
    6. Freund MK
    7. Schoech A
    8. Pasaniuc B
    9. Price AL

    (2019) Leveraging polygenic functional enrichment to improve GWAS power

    The American Journal of Human Genetics 104:65–75.

    https://doi.org/10.1016/j.ajhg.2018.11.008

    • PubMed
    • Google Scholar
41. 1. Kitchener RL
    2. Grunden AM

    (2012) Prolidase function in proline metabolism and its medical and biotechnological applications

    Journal of Applied Microbiology 113:233–247.

    https://doi.org/10.1111/j.1365-2672.2012.05310.x

    • PubMed
    • Google Scholar
42. 1. Kozlitina J
    2. Smagris E
    3. Stender S
    4. Nordestgaard BG
    5. Zhou HH
    6. Tybjærg-Hansen A
    7. Vogt TF
    8. Hobbs HH
    9. Cohen JC

    (2014) Exome-wide association study identifies a TM6SF2 variant that confers susceptibility to nonalcoholic fatty liver disease

    Nature Genetics 46:352–356.

    https://doi.org/10.1038/ng.2901

    • PubMed
    • Google Scholar
43. 1. Kuhajda FP
    2. Aja S
    3. Tu Y
    4. Han WF
    5. Medghalchi SM
    6. El Meskini R
    7. Landree LE
    8. Peterson JM
    9. Daniels K
    10. Wong K
    11. Wydysh EA
    12. Townsend CA
    13. Ronnett GV

    (2011) Pharmacological glycerol-3-phosphate acyltransferase inhibition decreases food intake and adiposity and increases insulin sensitivity in diet-induced obesity

    American Journal of Physiology-Regulatory, Integrative and Comparative Physiology 301:R116–R130.

    https://doi.org/10.1152/ajpregu.00147.2011

    • PubMed
    • Google Scholar
44. 1. Kühn JP
    2. Meffert P
    3. Heske C
    4. Kromrey ML
    5. Schmidt CO
    6. Mensel B
    7. Völzke H
    8. Lerch MM
    9. Hernando D
    10. Mayerle J
    11. Reeder SB

    (2017) Prevalence of fatty liver disease and hepatic iron overload in a northeastern german population by using quantitative MR imaging

    Radiology 284:706–716.

    https://doi.org/10.1148/radiol.2017161228

    • PubMed
    • Google Scholar
45. 1. Kuk JL
    2. Katzmarzyk PT
    3. Nichaman MZ
    4. Church TS
    5. Blair SN
    6. Ross R

    (2006) Visceral fat is an independent predictor of all-cause mortality in men

    Obesity 14:336–341.

    https://doi.org/10.1038/oby.2006.43

    • PubMed
    • Google Scholar
46. 1. Kundaje A
    2. Meuleman W
    3. Ernst J
    4. Bilenky M
    5. Yen A
    6. Heravi-Moussavi A
    7. Kheradpour P
    8. Zhang Z
    9. Wang J
    10. Ziller MJ
    11. Amin V
    12. Whitaker JW
    13. Schultz MD
    14. Ward LD
    15. Sarkar A
    16. Quon G
    17. Sandstrom RS
    18. Eaton ML
    19. Wu YC
    20. Pfenning AR
    21. Wang X
    22. Claussnitzer M
    23. Liu Y
    24. Coarfa C
    25. Harris RA
    26. Shoresh N
    27. Epstein CB
    28. Gjoneska E
    29. Leung D
    30. Xie W
    31. Hawkins RD
    32. Lister R
    33. Hong C
    34. Gascard P
    35. Mungall AJ
    36. Moore R
    37. Chuah E
    38. Tam A
    39. Canfield TK
    40. Hansen RS
    41. Kaul R
    42. Sabo PJ
    43. Bansal MS
    44. Carles A
    45. Dixon JR
    46. Farh KH
    47. Feizi S
    48. Karlic R
    49. Kim AR
    50. Kulkarni A
    51. Li D
    52. Lowdon R
    53. Elliott G
    54. Mercer TR
    55. Neph SJ
    56. Onuchic V
    57. Polak P
    58. Rajagopal N
    59. Ray P
    60. Sallari RC
    61. Siebenthall KT
    62. Sinnott-Armstrong NA
    63. Stevens M
    64. Thurman RE
    65. Wu J
    66. Zhang B
    67. Zhou X
    68. Beaudet AE
    69. Boyer LA
    70. De Jager PL
    71. Farnham PJ
    72. Fisher SJ
    73. Haussler D
    74. Jones SJ
    75. Li W
    76. Marra MA
    77. McManus MT
    78. Sunyaev S
    79. Thomson JA
    80. Tlsty TD
    81. Tsai LH
    82. Wang W
    83. Waterland RA
    84. Zhang MQ
    85. Chadwick LH
    86. Bernstein BE
    87. Costello JF
    88. Ecker JR
    89. Hirst M
    90. Meissner A
    91. Milosavljevic A
    92. Ren B
    93. Stamatoyannopoulos JA
    94. Wang T
    95. Kellis M
    96. Roadmap Epigenomics Consortium

    (2015) Integrative analysis of 111 reference human epigenomes

    Nature 518:317–330.

    https://doi.org/10.1038/nature14248

    • PubMed
    • Google Scholar
47. 1. Lee JJ
    2. Pedley A
    3. Hoffmann U
    4. Massaro JM
    5. Levy D
    6. Long MT

    (2018) Visceral and intrahepatic fat are associated with cardiometabolic risk factors above other ectopic fat depots: the framingham heart study

    The American Journal of Medicine 131:684–692.

    https://doi.org/10.1016/j.amjmed.2018.02.002

    • PubMed
    • Google Scholar
48. 1. Leung NW
    2. Farrant P
    3. Peters TJ

    (1986) Liver volume measurement by ultrasonography in normal subjects and alcoholic patients

    Journal of Hepatology 2:157–164.

    https://doi.org/10.1016/S0168-8278(86)80074-5

    • PubMed
    • Google Scholar
49. 1. Li J
    2. Lin H
    3. Liu T
    4. Zhang Z
    5. Prince MR
    6. Gillen K
    7. Yan X
    8. Song Q
    9. Hua T
    10. Zhao X
    11. Zhang M
    12. Zhao Y
    13. Li G
    14. Tang G
    15. Yang G
    16. Brittenham GM
    17. Wang Y

    (2018) Quantitative susceptibility mapping (QSM) minimizes interference from cellular pathology in R2* estimation of liver iron concentration

    Journal of Magnetic Resonance Imaging 48:1069–1079.

    https://doi.org/10.1002/jmri.26019

    • Google Scholar
50. 1. Lin XZ
    2. Sun YN
    3. Liu YH
    4. Sheu BS
    5. Cheng BN
    6. Chen CY
    7. Tsai HM
    8. Shen CL

    (1998)

    Liver volume in patients with or without chronic liver diseases

    Hepato-Gastroenterology 45:1069–1074.

    • PubMed
    • Google Scholar
51. 1. Littlejohns TJ
    2. Holliday J
    3. Gibson LM
    4. Garratt S
    5. Oesingmann N
    6. Alfaro-Almagro F
    7. Bell JD
    8. Boultwood C
    9. Collins R
    10. Conroy MC
    11. Crabtree N
    12. Doherty N
    13. Frangi AF
    14. Harvey NC
    15. Leeson P
    16. Miller KL
    17. Neubauer S
    18. Petersen SE
    19. Sellors J
    20. Sheard S
    21. Smith SM
    22. Sudlow CLM
    23. Matthews PM
    24. Allen NE

    (2020) The UK biobank imaging enhancement of 100,000 participants: rationale, data collection, management and future directions

    Nature Communications 11:2624.

    https://doi.org/10.1038/s41467-020-15948-9

    • PubMed
    • Google Scholar
52. 1. Liu Q
    2. Liu SS
    3. Zhao ZZ
    4. Zhao BT
    5. Du SX
    6. Jin WW
    7. Xin YN

    (2019) TRIB1 rs17321515 gene polymorphism increases the risk of coronary heart disease in general population and non-alcoholic fatty liver disease patients in chinese han population

    Lipids in Health and Disease 18:165.

    https://doi.org/10.1186/s12944-019-1108-2

    • PubMed
    • Google Scholar
53. Software

    1. Liu Y

    (2021) ukbb-mri-sseg, version swh:1:rev:4acdad6bf5e6cd08436d91ac6d4a494cf1365d98

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:31c02628581a4187643f52821dbb2c0712adc89a;origin=https://github.com/calico/ukbb-mri-sseg/;visit=swh:1:snp:171132f095f211a38e034ecdc12eda62061df8e1;anchor=swh:1:rev:4acdad6bf5e6cd08436d91ac6d4a494cf1365d98
54. 1. Loh PR
    2. Bhatia G
    3. Gusev A
    4. Finucane HK
    5. Bulik-Sullivan BK
    6. Pollack SJ
    7. de Candia TR
    8. Lee SH
    9. Wray NR
    10. Kendler KS
    11. O'Donovan MC
    12. Neale BM
    13. Patterson N
    14. Price AL
    15. Schizophrenia Working Group of Psychiatric Genomics Consortium

    (2015a) Contrasting genetic architectures of schizophrenia and other complex diseases using fast variance-components analysis

    Nature Genetics 47:1385–1392.

    https://doi.org/10.1038/ng.3431

    • PubMed
    • Google Scholar
55. 1. Loh PR
    2. Tucker G
    3. Bulik-Sullivan BK
    4. Vilhjálmsson BJ
    5. Finucane HK
    6. Salem RM
    7. Chasman DI
    8. Ridker PM
    9. Neale BM
    10. Berger B
    11. Patterson N
    12. Price AL

    (2015b) Efficient bayesian mixed-model analysis increases association power in large cohorts

    Nature Genetics 47:284–290.

    https://doi.org/10.1038/ng.3190

    • PubMed
    • Google Scholar
56. Website

    1. Loh P-R

    (2018) BOLT-LMM v2. 3.2 user manual

    Accessed May 2, 2019.

    https://data.broadinstitute.org/alkesgroup/BOLT-LMM/downloads/old/BOLT-LMM_v2.3.3_manual.pdf
57. 1. Lutfi MF

    (2017) The physiological basis and clinical significance of lung volume measurements

    Multidisciplinary Respiratory Medicine 12:3.

    https://doi.org/10.1186/s40248-017-0084-5

    • PubMed
    • Google Scholar
58. 1. Majumder S
    2. Philip NA
    3. Takahashi N
    4. Levy MJ
    5. Singh VP
    6. Chari ST

    (2017) Fatty pancreas: should we be concerned?

    Pancreas 46:1251–1258.

    https://doi.org/10.1097/MPA.0000000000000941

    • PubMed
    • Google Scholar
59. 1. Maller JB
    2. McVean G
    3. Byrnes J
    4. Vukcevic D
    5. Palin K
    6. Su Z
    7. Howson JM
    8. Auton A
    9. Myers S
    10. Morris A
    11. Pirinen M
    12. Brown MA
    13. Burton PR
    14. Caulfield MJ
    15. Compston A
    16. Farrall M
    17. Hall AS
    18. Hattersley AT
    19. Hill AV
    20. Mathew CG
    21. Pembrey M
    22. Satsangi J
    23. Stratton MR
    24. Worthington J
    25. Craddock N
    26. Hurles M
    27. Ouwehand W
    28. Parkes M
    29. Rahman N
    30. Duncanson A
    31. Todd JA
    32. Kwiatkowski DP
    33. Samani NJ
    34. Gough SC
    35. McCarthy MI
    36. Deloukas P
    37. Donnelly P
    38. Wellcome Trust Case Control Consortium

    (2012) Bayesian refinement of association signals for 14 loci in 3 common diseases

    Nature Genetics 44:1294–1301.

    https://doi.org/10.1038/ng.2435

    • PubMed
    • Google Scholar
60. 1. Mancuso P
    2. Bouchard B

    (2019) The impact of aging on adipose function and adipokine synthesis

    Frontiers in Endocrinology 10:137.

    https://doi.org/10.3389/fendo.2019.00137

    • PubMed
    • Google Scholar
61. 1. Mayr R
    2. Griffiths WJ
    3. Hermann M
    4. McFarlane I
    5. Halsall DJ
    6. Finkenstedt A
    7. Douds A
    8. Davies SE
    9. Janecke AR
    10. Vogel W
    11. Cox TM
    12. Zoller H

    (2011) Identification of mutations in SLC40A1 that affect ferroportin function and phenotype of human ferroportin iron overload

    Gastroenterology 140:2056–2063.

    https://doi.org/10.1053/j.gastro.2011.02.064

    • PubMed
    • Google Scholar
62. 1. McKay A
    2. Wilman HR
    3. Dennis A
    4. Kelly M
    5. Gyngell ML
    6. Neubauer S
    7. Bell JD
    8. Banerjee R
    9. Thomas EL

    (2018) Measurement of liver iron by magnetic resonance imaging in the UK biobank population

    PLOS ONE 13:e0209340.

    https://doi.org/10.1371/journal.pone.0209340

    • PubMed
    • Google Scholar
63. 1. Mehta MB
    2. Shewale SV
    3. Sequeira RN
    4. Millar JS
    5. Hand NJ
    6. Rader DJ

    (2017) Hepatic protein phosphatase 1 regulatory subunit 3B (Ppp1r3b) promotes hepatic glycogen synthesis and thereby regulates fasting energy homeostasis

    Journal of Biological Chemistry 292:10444–10454.

    https://doi.org/10.1074/jbc.M116.766329

    • PubMed
    • Google Scholar
64. 1. Millard LAC
    2. Davies NM
    3. Gaunt TR
    4. Davey Smith G
    5. Tilling K

    (2018) Software application profile: phesant: a tool for performing automated phenome scans in UK biobank

    International Journal of Epidemiology 47:29–35.

    https://doi.org/10.1093/ije/dyx204

    • PubMed
    • Google Scholar
65. 1. Miller KL
    2. Alfaro-Almagro F
    3. Bangerter NK
    4. Thomas DL
    5. Yacoub E
    6. Xu J
    7. Bartsch AJ
    8. Jbabdi S
    9. Sotiropoulos SN
    10. Andersson JL
    11. Griffanti L
    12. Douaud G
    13. Okell TW
    14. Weale P
    15. Dragonu I
    16. Garratt S
    17. Hudson S
    18. Collins R
    19. Jenkinson M
    20. Matthews PM
    21. Smith SM

    (2016) Multimodal population brain imaging in the UK biobank prospective epidemiological study

    Nature Neuroscience 19:1523–1536.

    https://doi.org/10.1038/nn.4393

    • PubMed
    • Google Scholar
66. Conference

    1. Milletari F
    2. Navab N
    3. Ahmadi SA

    (2016)

    V-net: fully convolutional neural networks for volumetric medical image segmentation

    In 2016 Fourth International Conference on 3D Vision (3DV). pp. 565–571.

    • Google Scholar
67. 1. Mitchell WK
    2. Williams J
    3. Atherton P
    4. Larvin M
    5. Lund J
    6. Narici M

    (2012) Sarcopenia, Dynapenia, and the impact of advancing age on human skeletal muscle size and strength; a quantitative review

    Frontiers in Physiology 3:260.

    https://doi.org/10.3389/fphys.2012.00260

    • PubMed
    • Google Scholar
68. 1. Nomura H
    2. Kashiwagi S
    3. Hayashi J
    4. Kajiyama W
    5. Tani S
    6. Goto M

    (1988) Prevalence of fatty liver in a general population of Okinawa, Japan

    Japanese Journal of Medicine 27:142–149.

    https://doi.org/10.2169/internalmedicine1962.27.142

    • PubMed
    • Google Scholar
69. 1. Otsu N

    (1979) A threshold selection method from Gray-Level histograms

    IEEE Transactions on Systems, Man, and Cybernetics 9:62–66.

    https://doi.org/10.1109/TSMC.1979.4310076

    • Google Scholar
70. 1. Ounkomol C
    2. Seshamani S
    3. Maleckar MM
    4. Collman F
    5. Johnson GR

    (2018) Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy

    Nature Methods 15:917–920.

    https://doi.org/10.1038/s41592-018-0111-2

    • PubMed
    • Google Scholar
71. 1. Parisinos CA
    2. Wilman HR
    3. Thomas EL
    4. Kelly M
    5. Nicholls RC
    6. McGonigle J
    7. Neubauer S
    8. Hingorani AD
    9. Patel RS
    10. Hemingway H
    11. Bell JD
    12. Banerjee R
    13. Yaghootkar H

    (2020) Genome-wide and mendelian randomisation studies of liver MRI yield insights into the pathogenesis of steatohepatitis

    Journal of Hepatology 73:241–251.

    https://doi.org/10.1016/j.jhep.2020.03.032

    • PubMed
    • Google Scholar
72. 1. Parkinson JRC
    2. Emsley R
    3. Adkins JLT
    4. Longford N
    5. Ozanne SE
    6. Holmes E
    7. Modi N

    (2020) Clinical and molecular evidence of accelerated ageing following very preterm birth

    Pediatric Research 87:1005–1010.

    https://doi.org/10.1038/s41390-019-0709-9

    • PubMed
    • Google Scholar
73. 1. Partin JS
    2. Partin JC
    3. Schubert WK
    4. McAdams AJ

    (1974) Liver ultrastructure in Abetalipoproteinemia: evolution of micronodular cirrhosis

    Gastroenterology 67:107–118.

    https://doi.org/10.1016/S0016-5085(19)32931-2

    • PubMed
    • Google Scholar
74. 1. Pers TH
    2. Karjalainen JM
    3. Chan Y
    4. Westra HJ
    5. Wood AR
    6. Yang J
    7. Lui JC
    8. Vedantam S
    9. Gustafsson S
    10. Esko T
    11. Frayling T
    12. Speliotes EK
    13. Boehnke M
    14. Raychaudhuri S
    15. Fehrmann RS
    16. Hirschhorn JN
    17. Franke L
    18. Genetic Investigation of ANthropometric Traits (GIANT) Consortium

    (2015) Biological interpretation of genome-wide association studies using predicted gene functions

    Nature Communications 6:5890.

    https://doi.org/10.1038/ncomms6890

    • PubMed
    • Google Scholar
75. 1. Pirruccello JP
    2. Bick A
    3. Wang M
    4. Chaffin M
    5. Friedman S
    6. Yao J
    7. Guo X
    8. Venkatesh BA
    9. Taylor KD
    10. Post WS
    11. Rich S
    12. Lima JAC
    13. Rotter JI
    14. Philippakis A
    15. Lubitz SA
    16. Ellinor PT
    17. Khera AV
    18. Kathiresan S
    19. Aragam KG

    (2020) Analysis of cardiac magnetic resonance imaging in 36,000 individuals yields genetic insights into dilated cardiomyopathy

    Nature Communications 11:2254.

    https://doi.org/10.1038/s41467-020-15823-7

    • PubMed
    • Google Scholar
76. 1. Pozo AL
    2. Godfrey EM
    3. Bowles KM

    (2009) Splenomegaly: investigation, diagnosis and management

    Blood Reviews 23:105–111.

    https://doi.org/10.1016/j.blre.2008.10.001

    • PubMed
    • Google Scholar
77. 1. Raabe M
    2. Flynn LM
    3. Zlot CH
    4. Wong JS
    5. Véniant MM
    6. Hamilton RL
    7. Young SG

    (1998) Knockout of the abetalipoproteinemia gene in mice: reduced lipoprotein secretion in heterozygotes and embryonic lethality in homozygotes

    PNAS 95:8686–8691.

    https://doi.org/10.1073/pnas.95.15.8686

    • PubMed
    • Google Scholar
78. 1. Radmard AR
    2. Merat S
    3. Kooraki S
    4. Ashraf M
    5. Keshtkar A
    6. Sharafkhah M
    7. Jafari E
    8. Malekzadeh R
    9. Poustchi H

    (2015) Gallstone disease and obesity: a population-based study on abdominal fat distribution and gender differences

    Annals of Hepatology 14:702–709.

    https://doi.org/10.1016/S1665-2681(19)30765-3

    • PubMed
    • Google Scholar
79. 1. Reeder SB
    2. Pineda AR
    3. Wen Z
    4. Shimakawa A
    5. Yu H
    6. Brittain JH
    7. Gold GE
    8. Beaulieu CH
    9. Pelc NJ

    (2005) Iterative decomposition of water and fat with Echo asymmetry and least-squares estimation (IDEAL): Application with fast spin-echo imaging

    Magnetic Resonance in Medicine 54:636–644.

    https://doi.org/10.1002/mrm.20624

    • Google Scholar
80. 1. Regier AA
    2. Farjoun Y
    3. Larson DE
    4. Krasheninina O
    5. Kang HM
    6. Howrigan DP
    7. Chen BJ
    8. Kher M
    9. Banks E
    10. Ames DC
    11. English AC
    12. Li H
    13. Xing J
    14. Zhang Y
    15. Matise T
    16. Abecasis GR
    17. Salerno W
    18. Zody MC
    19. Neale BM
    20. Hall IM

    (2018) Functional equivalence of genome sequencing analysis pipelines enables harmonized variant calling across human genetics projects

    Nature Communications 9:4038.

    https://doi.org/10.1038/s41467-018-06159-4

    • PubMed
    • Google Scholar
81. 1. Richardson TG
    2. Sanderson E
    3. Palmer TM
    4. Ala-Korpela M
    5. Ference BA
    6. Davey Smith G
    7. Holmes MV

    (2020) Evaluating the relationship between circulating lipoprotein lipids and apolipoproteins with risk of coronary heart disease: a multivariable mendelian randomisation analysis

    PLOS Medicine 17:e1003062.

    https://doi.org/10.1371/journal.pmed.1003062

    • PubMed
    • Google Scholar
82. 1. Romeo S
    2. Kozlitina J
    3. Xing C
    4. Pertsemlidis A
    5. Cox D
    6. Pennacchio LA
    7. Boerwinkle E
    8. Cohen JC
    9. Hobbs HH

    (2008) Genetic variation in PNPLA3 confers susceptibility to nonalcoholic fatty liver disease

    Nature Genetics 40:1461–1465.

    https://doi.org/10.1038/ng.257

    • PubMed
    • Google Scholar
83. Conference

    1. Ronneberger O
    2. Fischer P
    3. Brox T

    (2015)

    U-net: convolutional networks for biomedical image segmentation

    International Conference on Medical Image Computing and Computer-Assisted Intervention.

    • Google Scholar
84. 1. Schwenzer NF
    2. Machann J
    3. Haap MM
    4. Martirosian P
    5. Schraml C
    6. Liebig G
    7. Stefan N
    8. Häring HU
    9. Claussen CD
    10. Fritsche A
    11. Schick F

    (2008) T2* relaxometry in liver, pancreas, and spleen in a healthy cohort of one hundred twenty-nine subjects-correlation with age, gender, and serum ferritin

    Investigative Radiology 43:854–860.

    https://doi.org/10.1097/RLI.0b013e3181862413

    • PubMed
    • Google Scholar
85. Preprint

    1. Sethi A
    2. Taylor L
    3. Ruby G
    4. Venkataraman J
    5. Cule M
    6. Melamud E

    (2020) Calcification of abdominal aorta is an underappreciated cardiovascular disease risk factor

    medRxiv.

    https://doi.org/10.1101/2020.05.07.20094706

    • Google Scholar
86. 1. Shimomura Y
    2. Hara M
    3. Katoh D
    4. Hashimoto H
    5. Ishikawa T

    (2018) Enlarged spleen is associated with low neutrophil and platelet engraftment rates and poor survival after allogeneic stem cell transplantation in patients with acute myeloid leukemia and myelodysplastic syndrome

    Annals of Hematology 97:1049–1056.

    https://doi.org/10.1007/s00277-018-3278-9

    • PubMed
    • Google Scholar
87. 1. Speliotes EK
    2. Yerges-Armstrong LM
    3. Wu J
    4. Hernaez R
    5. Kim LJ
    6. Palmer CD
    7. Gudnason V
    8. Eiriksdottir G
    9. Garcia ME
    10. Launer LJ
    11. Nalls MA
    12. Clark JM
    13. Mitchell BD
    14. Shuldiner AR
    15. Butler JL
    16. Tomas M
    17. Hoffmann U
    18. Hwang SJ
    19. Massaro JM
    20. O'Donnell CJ
    21. Sahani DV
    22. Salomaa V
    23. Schadt EE
    24. Schwartz SM
    25. Siscovick DS
    26. Voight BF
    27. Carr JJ
    28. Feitosa MF
    29. Harris TB
    30. Fox CS
    31. Smith AV
    32. Kao WH
    33. Hirschhorn JN
    34. Borecki IB
    35. NASH CRN
    36. GIANT Consortium
    37. MAGIC Investigators
    38. GOLD Consortium

    (2011) Genome-wide association analysis identifies variants associated with nonalcoholic fatty liver disease that have distinct effects on metabolic traits

    PLOS Genetics 7:e1001324.

    https://doi.org/10.1371/journal.pgen.1001324

    • PubMed
    • Google Scholar
88. 1. Stender S
    2. Smagris E
    3. Lauridsen BK
    4. Kofoed KF
    5. Nordestgaard BG
    6. Tybjaerg-Hansen A
    7. Pennacchio LA
    8. Dickel DE
    9. Cohen JC
    10. Hobbs HH

    (2018) Relationship between genetic variation at PPP1R3B and levels of liver glycogen and triglyceride

    Hepatology 67:2182–2195.

    https://doi.org/10.1002/hep.29751

    • PubMed
    • Google Scholar
89. 1. Storey JD
    2. Tibshirani R

    (2003) Statistical significance for genomewide studies

    PNAS 100:9440–9445.

    https://doi.org/10.1073/pnas.1530509100

    • PubMed
    • Google Scholar
90. 1. Svennerholm L
    2. Boström K
    3. Jungbjer B

    (1997) Changes in weight and compositions of major membrane components of human brain during the span of adult human life of swedes

    Acta Neuropathologica 94:345–352.

    https://doi.org/10.1007/s004010050717

    • PubMed
    • Google Scholar
91. 1. Taylor R

    (2008) Pathogenesis of type 2 diabetes: tracing the reverse route from cure to cause

    Diabetologia 51:1781–1789.

    https://doi.org/10.1007/s00125-008-1116-7

    • PubMed
    • Google Scholar
92. 1. Taylor R

    (2013) Type 2 diabetes: etiology and reversibility

    Diabetes Care 36:1047–1055.

    https://doi.org/10.2337/dc12-1805

    • PubMed
    • Google Scholar
93. 1. Thomas EL
    2. Parkinson JR
    3. Hyde MJ
    4. Yap IK
    5. Holmes E
    6. Doré CJ
    7. Bell JD
    8. Modi N

    (2011) Aberrant adiposity and ectopic lipid deposition characterize the adult phenotype of the preterm infant

    Pediatric Research 70:507–512.

    https://doi.org/10.1203/PDR.0b013e31822d7860

    • PubMed
    • Google Scholar
94. 1. Thomas EL
    2. Parkinson JR
    3. Frost GS
    4. Goldstone AP
    5. Doré CJ
    6. McCarthy JP
    7. Collins AL
    8. Fitzpatrick JA
    9. Durighel G
    10. Taylor-Robinson SD
    11. Bell JD

    (2012) The missing risk: mri and MRS phenotyping of abdominal adiposity and ectopic fat

    Obesity 20:76–87.

    https://doi.org/10.1038/oby.2011.142

    • PubMed
    • Google Scholar
95. Book

    1. Thomas EL
    2. Fitzpatrick J
    3. Frost GS
    4. Bell JD

    (2013)

    Metabolic syndrome, overweight and fatty liver

    In: Berdanier C. D, Dwyer J. T, Heber D, editors. Handbook of Nutrition and Food (3rd edn). CRC Press. pp. 763–768.

    • Google Scholar
96. 1. Turnbull C
    2. Perdeaux ER
    3. Pernet D
    4. Naranjo A
    5. Renwick A
    6. Seal S
    7. Munoz-Xicola RM
    8. Hanks S
    9. Slade I
    10. Zachariou A
    11. Warren-Perry M
    12. Ruark E
    13. Gerrard M
    14. Hale J
    15. Hewitt M
    16. Kohler J
    17. Lane S
    18. Levitt G
    19. Madi M
    20. Morland B
    21. Neefjes V
    22. Nicholson J
    23. Picton S
    24. Pizer B
    25. Ronghe M
    26. Stevens M
    27. Traunecker H
    28. Stiller CA
    29. Pritchard-Jones K
    30. Dome J
    31. Grundy P
    32. Rahman N

    (2012) A genome-wide association study identifies susceptibility loci for Wilms tumor

    Nature Genetics 44:681–684.

    https://doi.org/10.1038/ng.2251

    • PubMed
    • Google Scholar
97. 1. Tustison NJ
    2. Avants BB
    3. Cook PA
    4. Zheng Y
    5. Egan A
    6. Yushkevich PA
    7. Gee JC

    (2010) N4ITK: improved N3 Bias correction

    IEEE Transactions on Medical Imaging 29:1310–1320.

    https://doi.org/10.1109/TMI.2010.2046908

    • PubMed
    • Google Scholar
98. 1. Wakefield J

    (2007) A Bayesian measure of the probability of false discovery in genetic epidemiology studies

    The American Journal of Human Genetics 81:208–227.

    https://doi.org/10.1086/519024

    • PubMed
    • Google Scholar
99. 1. West J
    2. Dahlqvist Leinhard O
    3. Romu T
    4. Collins R
    5. Garratt S
    6. Bell JD
    7. Borga M
    8. Thomas L

    (2016) Feasibility of MR-Based body composition analysis in large scale population studies

    PLOS ONE 11:e0163332.

    https://doi.org/10.1371/journal.pone.0163332

    • PubMed
    • Google Scholar
100. Software

     1. Whitcher B
     2. Basty N

     (2021) Image Processing and Quality Control for Abdominal MRI in the UK Biobank, version swh:1:rev:13dc77941cb2919417108637eade6c8448374229

     Software Heritage.

     https://archive.softwareheritage.org/swh:1:dir:94ad50212b537bde4f6d2ed12fb81eae3e817164;origin=https://github.com/recoh/pipeline;visit=swh:1:snp:c2d43d8c628ba81d5b2ac522d09f3b50c032bc60;anchor=swh:1:rev:13dc77941cb2919417108637eade6c8448374229
101. 1. Wilman HR
     2. Kelly M
     3. Garratt S
     4. Matthews PM
     5. Milanesi M
     6. Herlihy A
     7. Gyngell M
     8. Neubauer S
     9. Bell JD
     10. Banerjee R
     11. Thomas EL

     (2017) Characterisation of liver fat in the UK biobank cohort

     PLOS ONE 12:e0172921.

     https://doi.org/10.1371/journal.pone.0172921

     • PubMed
     • Google Scholar
102. 1. Wilman HR
     2. Parisinos CA
     3. Atabaki-Pasdar N
     4. Kelly M
     5. Thomas EL
     6. Neubauer S
     7. Mahajan A
     8. Hingorani AD
     9. Patel RS
     10. Hemingway H
     11. Franks PW
     12. Bell JD
     13. Banerjee R
     14. Yaghootkar H
     15. IMI DIRECT Consortium

     (2019) Genetic studies of abdominal MRI data identify genes regulating hepcidin as major determinants of liver iron concentration

     Journal of Hepatology 71:594–602.

     https://doi.org/10.1016/j.jhep.2019.05.032

     • PubMed
     • Google Scholar
103. 1. Wojcik GL
     2. Graff M
     3. Nishimura KK
     4. Tao R
     5. Haessler J
     6. Gignoux CR
     7. Highland HM
     8. Patel YM
     9. Sorokin EP
     10. Avery CL
     11. Belbin GM
     12. Bien SA
     13. Cheng I
     14. Cullina S
     15. Hodonsky CJ
     16. Hu Y
     17. Huckins LM
     18. Jeff J
     19. Justice AE
     20. Kocarnik JM
     21. Lim U
     22. Lin BM
     23. Lu Y
     24. Nelson SC
     25. Park SL
     26. Poisner H
     27. Preuss MH
     28. Richard MA
     29. Schurmann C
     30. Setiawan VW
     31. Sockell A
     32. Vahi K
     33. Verbanck M
     34. Vishnu A
     35. Walker RW
     36. Young KL
     37. Zubair N
     38. Acuña-Alonso V
     39. Ambite JL
     40. Barnes KC
     41. Boerwinkle E
     42. Bottinger EP
     43. Bustamante CD
     44. Caberto C
     45. Canizales-Quinteros S
     46. Conomos MP
     47. Deelman E
     48. Do R
     49. Doheny K
     50. Fernández-Rhodes L
     51. Fornage M
     52. Hailu B
     53. Heiss G
     54. Henn BM
     55. Hindorff LA
     56. Jackson RD
     57. Laurie CA
     58. Laurie CC
     59. Li Y
     60. Lin DY
     61. Moreno-Estrada A
     62. Nadkarni G
     63. Norman PJ
     64. Pooler LC
     65. Reiner AP
     66. Romm J
     67. Sabatti C
     68. Sandoval K
     69. Sheng X
     70. Stahl EA
     71. Stram DO
     72. Thornton TA
     73. Wassel CL
     74. Wilkens LR
     75. Winkler CA
     76. Yoneyama S
     77. Buyske S
     78. Haiman CA
     79. Kooperberg C
     80. Le Marchand L
     81. Loos RJF
     82. Matise TC
     83. North KE
     84. Peters U
     85. Kenny EE
     86. Carlson CS

     (2019) Genetic analyses of diverse populations improves discovery for complex traits

     Nature 570:514–518.

     https://doi.org/10.1038/s41586-019-1310-4

     • PubMed
     • Google Scholar
104. 1. Wood JC
     2. Enriquez C
     3. Ghugre N
     4. Tyzka JM
     5. Carson S
     6. Nelson MD
     7. Coates TD

     (2005) MRI R2 and R2* mapping accurately estimates hepatic iron concentration in transfusion-dependent thalassemia and sickle cell disease patients

     Blood 106:1460–1465.

     https://doi.org/10.1182/blood-2004-10-3982

     • PubMed
     • Google Scholar
105. 1. Wuttke M
     2. Li Y
     3. Li M
     4. Sieber KB
     5. Feitosa MF
     6. Gorski M
     7. Tin A
     8. Wang L
     9. Chu AY
     10. Hoppmann A
     11. Kirsten H
     12. Giri A
     13. Chai JF
     14. Sveinbjornsson G
     15. Tayo BO
     16. Nutile T
     17. Fuchsberger C
     18. Marten J
     19. Cocca M
     20. Ghasemi S
     21. Xu Y
     22. Horn K
     23. Noce D
     24. van der Most PJ
     25. Sedaghat S
     26. Yu Z
     27. Akiyama M
     28. Afaq S
     29. Ahluwalia TS
     30. Almgren P
     31. Amin N
     32. Ärnlöv J
     33. Bakker SJL
     34. Bansal N
     35. Baptista D
     36. Bergmann S
     37. Biggs ML
     38. Biino G
     39. Boehnke M
     40. Boerwinkle E
     41. Boissel M
     42. Bottinger EP
     43. Boutin TS
     44. Brenner H
     45. Brumat M
     46. Burkhardt R
     47. Butterworth AS
     48. Campana E
     49. Campbell A
     50. Campbell H
     51. Canouil M
     52. Carroll RJ
     53. Catamo E
     54. Chambers JC
     55. Chee ML
     56. Chee ML
     57. Chen X
     58. Cheng CY
     59. Cheng Y
     60. Christensen K
     61. Cifkova R
     62. Ciullo M
     63. Concas MP
     64. Cook JP
     65. Coresh J
     66. Corre T
     67. Sala CF
     68. Cusi D
     69. Danesh J
     70. Daw EW
     71. de Borst MH
     72. De Grandi A
     73. de Mutsert R
     74. de Vries APJ
     75. Degenhardt F
     76. Delgado G
     77. Demirkan A
     78. Di Angelantonio E
     79. Dittrich K
     80. Divers J
     81. Dorajoo R
     82. Eckardt KU
     83. Ehret G
     84. Elliott P
     85. Endlich K
     86. Evans MK
     87. Felix JF
     88. Foo VHX
     89. Franco OH
     90. Franke A
     91. Freedman BI
     92. Freitag-Wolf S
     93. Friedlander Y
     94. Froguel P
     95. Gansevoort RT
     96. Gao H
     97. Gasparini P
     98. Gaziano JM
     99. Giedraitis V
     100. Gieger C
     101. Girotto G
     102. Giulianini F
     103. Gögele M
     104. Gordon SD
     105. Gudbjartsson DF
     106. Gudnason V
     107. Haller T
     108. Hamet P
     109. Harris TB
     110. Hartman CA
     111. Hayward C
     112. Hellwege JN
     113. Heng CK
     114. Hicks AA
     115. Hofer E
     116. Huang W
     117. Hutri-Kähönen N
     118. Hwang SJ
     119. Ikram MA
     120. Indridason OS
     121. Ingelsson E
     122. Ising M
     123. Jaddoe VWV
     124. Jakobsdottir J
     125. Jonas JB
     126. Joshi PK
     127. Josyula NS
     128. Jung B
     129. Kähönen M
     130. Kamatani Y
     131. Kammerer CM
     132. Kanai M
     133. Kastarinen M
     134. Kerr SM
     135. Khor CC
     136. Kiess W
     137. Kleber ME
     138. Koenig W
     139. Kooner JS
     140. Körner A
     141. Kovacs P
     142. Kraja AT
     143. Krajcoviechova A
     144. Kramer H
     145. Krämer BK
     146. Kronenberg F
     147. Kubo M
     148. Kühnel B
     149. Kuokkanen M
     150. Kuusisto J
     151. La Bianca M
     152. Laakso M
     153. Lange LA
     154. Langefeld CD
     155. Lee JJ
     156. Lehne B
     157. Lehtimäki T
     158. Lieb W
     159. Lim SC
     160. Lind L
     161. Lindgren CM
     162. Liu J
     163. Liu J
     164. Loeffler M
     165. Loos RJF
     166. Lucae S
     167. Lukas MA
     168. Lyytikäinen LP
     169. Mägi R
     170. Magnusson PKE
     171. Mahajan A
     172. Martin NG
     173. Martins J
     174. März W
     175. Mascalzoni D
     176. Matsuda K
     177. Meisinger C
     178. Meitinger T
     179. Melander O
     180. Metspalu A
     181. Mikaelsdottir EK
     182. Milaneschi Y
     183. Miliku K
     184. Mishra PP
     185. Mohlke KL
     186. Mononen N
     187. Montgomery GW
     188. Mook-Kanamori DO
     189. Mychaleckyj JC
     190. Nadkarni GN
     191. Nalls MA
     192. Nauck M
     193. Nikus K
     194. Ning B
     195. Nolte IM
     196. Noordam R
     197. O'Connell J
     198. O'Donoghue ML
     199. Olafsson I
     200. Oldehinkel AJ
     201. Orho-Melander M
     202. Ouwehand WH
     203. Padmanabhan S
     204. Palmer ND
     205. Palsson R
     206. Penninx B
     207. Perls T
     208. Perola M
     209. Pirastu M
     210. Pirastu N
     211. Pistis G
     212. Podgornaia AI
     213. Polasek O
     214. Ponte B
     215. Porteous DJ
     216. Poulain T
     217. Pramstaller PP
     218. Preuss MH
     219. Prins BP
     220. Province MA
     221. Rabelink TJ
     222. Raffield LM
     223. Raitakari OT
     224. Reilly DF
     225. Rettig R
     226. Rheinberger M
     227. Rice KM
     228. Ridker PM
     229. Rivadeneira F
     230. Rizzi F
     231. Roberts DJ
     232. Robino A
     233. Rossing P
     234. Rudan I
     235. Rueedi R
     236. Ruggiero D
     237. Ryan KA
     238. Saba Y
     239. Sabanayagam C
     240. Salomaa V
     241. Salvi E
     242. Saum KU
     243. Schmidt H
     244. Schmidt R
     245. Schöttker B
     246. Schulz CA
     247. Schupf N
     248. Shaffer CM
     249. Shi Y
     250. Smith AV
     251. Smith BH
     252. Soranzo N
     253. Spracklen CN
     254. Strauch K
     255. Stringham HM
     256. Stumvoll M
     257. Svensson PO
     258. Szymczak S
     259. Tai ES
     260. Tajuddin SM
     261. Tan NYQ
     262. Taylor KD
     263. Teren A
     264. Tham YC
     265. Thiery J
     266. Thio CHL
     267. Thomsen H
     268. Thorleifsson G
     269. Toniolo D
     270. Tönjes A
     271. Tremblay J
     272. Tzoulaki I
     273. Uitterlinden AG
     274. Vaccargiu S
     275. van Dam RM
     276. van der Harst P
     277. van Duijn CM
     278. Velez Edward DR
     279. Verweij N
     280. Vogelezang S
     281. Völker U
     282. Vollenweider P
     283. Waeber G
     284. Waldenberger M
     285. Wallentin L
     286. Wang YX
     287. Wang C
     288. Waterworth DM
     289. Bin Wei W
     290. White H
     291. Whitfield JB
     292. Wild SH
     293. Wilson JF
     294. Wojczynski MK
     295. Wong C
     296. Wong TY
     297. Xu L
     298. Yang Q
     299. Yasuda M
     300. Yerges-Armstrong LM
     301. Zhang W
     302. Zonderman AB
     303. Rotter JI
     304. Bochud M
     305. Psaty BM
     306. Vitart V
     307. Wilson JG
     308. Dehghan A
     309. Parsa A
     310. Chasman DI
     311. Ho K
     312. Morris AP
     313. Devuyst O
     314. Akilesh S
     315. Pendergrass SA
     316. Sim X
     317. Böger CA
     318. Okada Y
     319. Edwards TL
     320. Snieder H
     321. Stefansson K
     322. Hung AM
     323. Heid IM
     324. Scholz M
     325. Teumer A
     326. Köttgen A
     327. Pattaro C
     328. Lifelines Cohort Study
     329. V. A. Million Veteran Program

     (2019) A catalog of genetic loci associated with kidney function from analyses of a million individuals

     Nature Genetics 51:957–972.

     https://doi.org/10.1038/s41588-019-0407-x

     • PubMed
     • Google Scholar
106. 1. Yang J
     2. Ferreira T
     3. Morris AP
     4. Medland SE
     5. Madden PA
     6. Heath AC
     7. Martin NG
     8. Montgomery GW
     9. Weedon MN
     10. Loos RJ
     11. Frayling TM
     12. McCarthy MI
     13. Hirschhorn JN
     14. Goddard ME
     15. Visscher PM
     16. Genetic Investigation of ANthropometric Traits (GIANT) Consortium
     17. DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium

     (2012) Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits

     Nature Genetics 44:369–375.

     https://doi.org/10.1038/ng.2213

     • PubMed
     • Google Scholar
107. 1. Yu H
     2. Shimakawa A
     3. McKenzie CA
     4. Brodsky E
     5. Brittain JH
     6. Reeder SB

     (2008) Multiecho water-fat separation and simultaneous R 2* estimation with multifrequency fat spectrum modeling

     Magnetic Resonance in Medicine 60:1122–1134.

     https://doi.org/10.1002/mrm.21737

     • Google Scholar
108. 1. Zhang H
     2. Mooney CJ
     3. Reilly MP

     (2012) ABO blood groups and cardiovascular diseases

     International Journal of Vascular Medicine 2012:641917.

     https://doi.org/10.1155/2012/641917

     • PubMed
     • Google Scholar
109. 1. Zhang BL
     2. He N
     3. Huang YB
     4. Song FJ
     5. Chen KX

     (2014) ABO blood groups and risk of Cancer: a systematic review and meta-analysis

     Asian Pacific Journal of Cancer Prevention 15:4643–4650.

     https://doi.org/10.7314/APJCP.2014.15.11.4643

     • PubMed
     • Google Scholar
110. Conference

     1. Zhang Y
     2. Yang Q

     (2021) A survey on Multi-Task learning

     IEEE Transactions on Knowledge and Data Engineering.

     https://doi.org/10.1109/TKDE.2021.3070203

     • Google Scholar
111. 1. Zhou W
     2. Nielsen JB
     3. Fritsche LG
     4. Dey R
     5. Gabrielsen ME
     6. Wolford BN
     7. LeFaive J
     8. VandeHaar P
     9. Gagliano SA
     10. Gifford A
     11. Bastarache LA
     12. Wei WQ
     13. Denny JC
     14. Lin M
     15. Hveem K
     16. Kang HM
     17. Abecasis GR
     18. Willer CJ
     19. Lee S

     (2018) Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies

     Nature Genetics 50:1335–1341.

     https://doi.org/10.1038/s41588-018-0184-y

     • PubMed
     • Google Scholar
112. 1. Zhou W
     2. Zhao Z
     3. Nielsen JB
     4. Fritsche LG
     5. LeFaive J
     6. Gagliano Taliun SA
     7. Bi W
     8. Gabrielsen ME
     9. Daly MJ
     10. Neale BM
     11. Hveem K
     12. Abecasis GR
     13. Willer CJ
     14. Lee S

     (2020) Scalable generalized linear mixed model for region-based association tests in large biobanks and cohorts

     Nature Genetics 52:634–639.

     https://doi.org/10.1038/s41588-020-0621-6

     • PubMed
     • Google Scholar
113. 1. Zhu Z
     2. Guo Y
     3. Shi H
     4. Liu C-L
     5. Panganiban RA
     6. Chung W
     7. O'Connor LJ
     8. Himes BE
     9. Gazal S
     10. Hasegawa K
     11. Camargo CA
     12. Qi L
     13. Moffatt MF
     14. Hu FB
     15. Lu Q
     16. Cookson WOC
     17. Liang L

     (2020) Shared genetic and experimental links between obesity-related traits and asthma subtypes in UK biobank

     Journal of Allergy and Clinical Immunology 145:537–549.

     https://doi.org/10.1016/j.jaci.2019.09.035

     • Google Scholar

Author details

1. Yi Liu

   Calico Life Sciences LLC, South San Francisco, United States

   Contribution

   Software, Formal analysis, Investigation, Visualization, Methodology, Writing - original draft

   Competing interests

   Employee, Calico Life Sciences LLC. This work was funded by Calico Life Sciences LLC.

   "This ORCID iD identifies the author of this article:" 0000-0003-2745-6940

2. Nicolas Basty

   Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom

   Contribution

   Data curation, Software, Formal analysis, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1330-0913

3. Brandon Whitcher

   Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom

   Contribution

   Data curation, Software, Formal analysis, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-6452-2399

4. Jimmy D Bell

   Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Investigation, Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3804-1281

5. Elena P Sorokin

   Calico Life Sciences LLC, South San Francisco, United States

   Contribution

   Software, Formal analysis, Visualization, Methodology, Writing - original draft, Writing - review and editing

   Competing interests

   Employee, Calico Life Sciences LLC.This work was funded by Calico Life Sciences LLC.

   "This ORCID iD identifies the author of this article:" 0000-0001-8957-8869

6. Nick van Bruggen

   Calico Life Sciences LLC, South San Francisco, United States

   Contribution

   Conceptualization, Resources, Supervision, Writing - review and editing

   Competing interests

   Employee, Calico Life Sciences LLC. This work was funded by Calico Life Sciences LLC.

7. E Louise Thomas

   Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom

   Contribution

   Conceptualization, Data curation, Supervision, Investigation, Methodology, Writing - original draft, Project administration, Writing - review and editing

   Contributed equally with

   Madeleine Cule

   For correspondence

   l.thomas3@westminster.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4235-4694

8. Madeleine Cule

   Calico Life Sciences LLC, South San Francisco, United States

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

   Contributed equally with

   E Louise Thomas

   For correspondence

   cule@calicolabs.com

   Competing interests

   Employee, Calico Life Sciences LLC. This work was funded by Calico Life Sciences LLC.

   "This ORCID iD identifies the author of this article:" 0000-0002-7400-5643

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