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.

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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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