He), genome divergence (FST), and allele frequency changes between bulks (DeltaAF). We report on the genome wide patterns of variation resulting from selection and highlight specific genomic features associated with resistance. Expected heterozygosity (He) showed an increased level of fixation in the resistant bulk, indicating a greater selection pressure was applied. In total, 1,311 biallelic loci were detected as significant FST outliers (p < 0.01) in comparisons between the resistant and susceptible bulks. These loci were detected in 206 regions along the chromosomes and contained 275 genes. We estimated changes in allele frequency between bulks resulting from selection for resistance by leveraging the allele frequencies of an unselected bulk. DeltaAF was a more stringent test of selection and recovered 186 significant loci, representing 32 genes, all of which were also detected using FST. Estimates of population genetic parameters and statistical significance were visualized with respect to the EL10.2 physical map and produced a candidate gene list that was enriched for function in cell wall metabolism and plant disease resistance, including pathogen perception, signal transduction, and pathogen response. Specific variation associated with these genes was also reported and represents genetic markers for validation and prediction of resistance to Rhizoctonia. Select and sequence experiments offer a means to characterize the genetic base of sugar beet, inform selection within breeding programs, and prioritize candidate variation for functional studies.Understanding the genetic basis of polygenic traits is a major challenge in agricultural species, especially in non-model systems. Select and sequence (SnS) experiments carried out within existing breeding programs provide a means to simultaneously identify the genomic background of a trait while improving the mean phenotype for a population. Using pooled whole genome sequencing (WGS) of selected and unselected bulks derived from a synthetic outcrossing sugar beet population EL57 (PI 663212), which segregates for seedling rhizoctonia resistance, we identified a putative genomic background involved in conditioning a resistance phenotype. Population genomic parameters were estimated to measure fixation ( Beta vulgaris) is a long-standing challenge. The USDA sugar beet germplasm is enriched for important traits such as resistance to disease and adaptation to local production regions. The pedigrees of this material suggest many of these traits can be traced back to wide hybridizations between sugar beet and wild beet (Beta maritima) (The characterization of resistance sources for the genetic improvement of beet (aritima) . Beet isaritima) . Commercaritima) . The usearitima)  and is aaritima) .Advances in genomic resources, population genomic methods and experimental design have improved the process of gene discovery in agricultural species. Reference genomes provide an anchor to link genetic variation with coding sequences underlying phenotypic variation . AccuratPhenotypic variation in beets is often measured at the level of the population due to difficulties inbreeding and fixing variation in single plants, fostering comparisons between populations rather than comparisons between individuals. Pooled sequencing of populations is an effective way to capture and compare causal genetic variation in beet, either by partitioning variation according to phenotypes within a segregating population, or by comparing different populations. The utility of pooled data has been demonstrated  and can Rhizoctonia solani Kühn is a soil borne pathogen which can cause seedling dampening off and crown and root rot, both of which can severely impact sugar yield for growers , inform future experiments, and provide genetic solutions to long standing challenges faced by sugar beet producers.This research is focused on understanding plant host resistance to seedling Rhizoctonia by sequencing bulks of phenotypically distinct individuals derived from a synthetic outcrossing population, EL57 (PI 663212). Using WGS, existing reference genomes, and selection for resistance we highlight a genomic background associated with seedling Rhizoctonia resistance. In identifying the genetic determinants underlying resistance we show how these methods can be used to characterize polygenic traits in beet  introgressed from C869 (PI 628754) or C869 CMS (PI 628755). 21% of the parents were derived from C869 CMS and thus have the S-cytoplasm. Male sterility, both nuclear male sterility from C869 and CMS, was used to capture pollen from open-pollinated increases of a wide variety of pollinators from 1997 through 2007. Traits expected to be segregating in the population include Aphanomyces seedling disease and Cercospora leaf spot resistances contributed by sugar beet germplasms SP7622 , USH20 , and SP85303 , Rhizoctonia resistance derived from EL51 , curly top and rhizomania resistance selections from C931 (PI 636340) and EL0204 (PI 655951) , a series of Aphanomyces resistant or salt-tolerant germination breeding lines and selections  , a series of 17 nematode resistant breeding lines from the Salinas, CA USDA-ARS breeding program , and a mixture of released and unreleased breeding lines derived from high sucrose, smooth-root selections .EL57 was planted in the SVREC seedling Rhizoctonia nursery on May 15, 2017, as a large selection block of 161 plots and inoculated with Rhizoctonia isolate RG2-2 on June 6. Seven plots of EL57 were not inoculated as a control. Approximately 8 weeks later leaves were harvested from non-inoculated and inoculated plots, representing three bulks. Resistant and susceptible bulks were chosen from the inoculated plots, and an unselected bulk was taken from non-inoculated plots. Leaf material from 25 plants was harvested and pooled for each of the three bulks. Pooled leaf material was homogenized, and DNA was extracted using the Macherey-Nagel NucleoSpin Plant II Genomic DNA extraction kit . One microgram of DNA for each population was submitted to Admera Health, LLC, where NGS libraries were constructed using TruSeq bar-code adapters. The sequencing reactions were carried out on the Illumina Hi-Seq 2500 in a 2 × 150 bp paired-end format with a target coverage of 80x relative to the predicted 758 Mb genome size of beet . Post-seB. vulgaris reference genome assembly EL10.2 . The variable p was designated as the reference allele of the EL10.2 reference genome and q as the alternate state. The degree of fixation was estimated at all biallelic sites using the expected heterozygosity (He) or 2pq. Global levels of fixation with respect to selection were calculated as the average of He across all sites. FST was used to calculate differentiation between the resistant and susceptible populations at each locus (Eq. 1). The parameter FST24 was estimated by calculating FST within a sliding window of 25 biallelic variant sites, or 12 variant sites flanking a single biallelic locus across the genome. Significant regions along chromosomes were determined by loci that showed a significant FST value at a given locus. Significance thresholds were defined by P-values < 0.01, calculated using the empirical distribution of all FST values.A python program was used to count alleles for all biallelic variants within the three populations. Allele frequency was estimated for the resistant, susceptible, and unselected EL57 populations. Population genetic parameters were estimated using the allele frequency within each population such that  relative to the total population (t), where p is the allele frequency of allele (p).Equation 1: shows FDelta allele frequency was calculated by using a series of Boolean operators to determine the loci which pass allele frequency thresholds in selected populations relative to the unselected population. This provided a null distribution from which to derive the genomic locations of large changes in allele frequency with respect to selection for resistance.Equation 2: determines sites where the relative change in allele frequency between resistant and susceptible bulks is > 0.8 and the allele frequency change between susceptible and unselected bulks is low, >0.15.ST24 along chromosomes with a density plot of all significant FST values. Python was used for the manipulation of data sets and estimation of population genetic parameters, while R libraries were used for plotting the final data matrixes. DeltaAF was also plotted across the genome, highlighting only those regions where divergence in allele frequency between the resistant and susceptible bulk was high and the susceptible and unselected population was low. Regions along the chromosome of high significance were determined by investigating a significant locus and searching within a 50 kb window upstream to determine the size and significance of a region with respect to selection. All regions with significant FST or DeltaAF were visualized using R. The density and distribution of variation was also considered by plotting data relative to the physical map provided by the EL10.2 genome assembly along with EL10 gene models and annotations. SNPeff . Determination of putative gene loci involved in resistance were based on all previous analysis as well as significant homology to functional validated candidates in other species. Markers were derived for use in predicting seedling rhizoctonia resistance by extracting significant variation from .vcf files.A combination of statistical analysis and genome resources were leveraged to identify targets within or adjacent to significant regions across the genome  and post-inoculation at the end of the season (October 17) were 4.9 plants/plot (SD = 1.74), or approximately 77% plant death. This suggested that the disease nursery provided a strong selection pressure for resistance to Rhizoctonia solani infection and an opportunity to identify a genetic basis for this important trait. Three bulks were sampled for WGS each representing 25 plants . Susceptible individuals (S bulk) were selected with respect to leaf symptoms and confirmed as showing root symptoms as well. Resistant individuals (R bulk) showed no visual leaf or root symptoms  were sown, inoculated with symptoms . An unseHe) showed the degree of fixation resulting from selection for a resistance phenotype. A reduction in He was observed between the unselected (0.304) and the resistant bulk (0.298). He for the susceptible bulk (0.305) was closer to the unselected bulk . Biallelic variation accounted for 2,812,301 loci (86.93%) of the total variation and multi-allelic variation accounted for 249,045 (7.70%). Biallelic and multi allelic variation was further categorized by type, including insertions, deletions, single nucleotide polymorphisms (SNP), multi nucleotide polymorphisms (mnp) and complex substitutions . The biated bulk . This isted bulk .ST and DeltaAF. FST estimated the apportionment of variation in allele frequency between bulks. The empirical distribution of FST allowed us to assign significance values  to all biallelic loci. At significance levels of p< 0.05, p< 0.01, and p< 0.001. FST values were equal to 0.22, 0.84, and 0.91 respectively. In total, 1,311 loci were detected with significant FST (p < 0.01). Since FST shows divergence at a single site, it can be hard to interpret if the divergence is the result of genetic drift or selection. To address this issue, FST was also calculated in a sliding window (FST24) which considered 24 adjacent variant sites as a single entity. This could reduce noise from genetic drift under the assumption that if selection was acting on a site, linkage disequilibrium would cause adjacent sites to diverge along with the causal variant. As expected, the FST24 analysis reduced the number of significant regions associated with divergence between the resistant and susceptible bulks  and looking for physical clusters of significant FST values within 50 kb of a given locus. This produced a list of regions along the chromosome which were the most diverged between the resistant and susceptible bulks  within 50 kb of another significant variant site. The average size of a significant region was 27,624 bp, with a range from 3 to 130,093 bp in length. The complete table with FST values is presented in ST.Significant genomic regions were defined by taking all 1311 Fle bulks . In totaST and DeltaAF values were extracted by using a significance threshold and determining their physical position relative to gene boundaries (5′ URT and 3′ UTR) or the upstream promoter sequences, defined as 3 kb flanking the gene. In total, 1,316 loci with high FST were associated with 206 regions, containing 311 genes. These could be further subdivided into variants associated with the gene sequences (275)   and thoses (275) . In totaes (275)  and 10 wes (275) . All of ST and DeltaAF) with custom visualization tools to inspect regions of significance with respect to the EL10.2 physical map  and changes in allele frequency (DeltaAF) resulting from selection and identified candidate genomic variation underlying Rhizoctonia resistance. We generated a list of putative candidate genes by visualizing the population genetic data with respect to the EL10.2 physical map. The candidate gene list was enriched for genes associated with pathogen defense and cell—wall biosynthesis, both of which are plausible components of rhizoctonia resistance. Additional rounds of selection within the EL57 base population or the advancement of generations in the presence of divergent selection could further resolve causal genetic variation and validate the genetic basis of Rhizoctonia resistance in beet.Identifying the genetic basis of quantitative traits is a longstanding challenge in crop improvement. In this report, we used a select and sequence (SnS) approach to identify contrasting genetic variants between resistant and susceptible bulks drawn from a synthetic breeding population segregating for resistance to seedling rhizoctonia infection. Using pooled sequencing, we estimated population genetic parameters to investigate fixation (He), genome divergence (FHe) estimated using all biallelic sites showed a greater level of fixation in the resistant bulk, suggesting that the genetic background that conditions resistance to Rhizoctonia could be selected and identified within a highly heterozygous population. The fact that only a few resistance sources have been identified even with considerable effort suggests genome informed approaches may be key to characterizing the source under study here as well as identifying new sources for Rhizoctonia resistance in other diverse populations.SnS experiments using segregating populations provide a system to study the underlying genetics of polygenic traits as part of ongoing selection activities within a breeding program. Here we show how pooled sequencing could be used for discovery of key genetic variation when applied to polygenic traits within a population with an extremely broad genetic base. In this case, Rhizoctonia resistance stored within the synthetic EL57 population was derived from EL51, which can be traced to FC701 as the likely source . SignifiST and DeltaAF are complementary statistics that were both able to identify the effects of selection across the genome. DeltaAF was a more stringent statistic in terms of the number of significant loci and putative genes detected, as evidenced by all significant DeltaAF loci appearing as a subset of significant FST loci. It has been shown that generating a null distribution of allele frequency in an unselected population is important for separating signal from noise when detecting selection  and resistant (EL51) varieties . ResistaIn conclusion, this research provides a genomic perspective to seedling Rhizoctonia resistance in beets, a complex polygenic trait with agricultural importance. We think it is a useful exercise to develop methods and generate lists of candidate genes involved with important traits in order to validate results and prioritize candidate variation for functional studies. A better understanding of the limitations of these experiments and our ability to detect significant variation is warranted. The detection of PAV in pooled data is perhaps the most visible limitation of this experiment. If PAV is causal and not represented in the genomic data then we rely on linkage, which is not a strength of pooled sequencing designs. Future experiments should address the ability of pooled assemblies to represent the genomes of populations under investigation with respect to PAV, and whether PAV frequency can be measured. Starting with the best possible catalog of variants for population genetic parameters represents the highest degree of resolution for the identification of causal variation. Select and sequence experiments have the potential to explore the genetic base of beet through the identification of alleles in wild material as well as characterize existing germplasm for agriculturally important traits. Using this approach, beet breeding programs can simultaneously generate markers and improve the genetic base of populations using phenotypic selection.PRJNA563463). The EL10.2 genome assembly (https://genomevolution.org/coge/GenomeInfo.pl?gid=57232). All variant files (.vcf) and files used for visualization are available at (Data Dryad - https://doi.org/10.5061/dryad.3j9kd51kg). All code is available at (https://github.com/BetaGenomeNinja/EL57). This includes bash scripts, Jupyter-note books for python code development, python scripts for data manipulation and pop gen estimators and R code for plotting and visualization of data.The original contributions presented in the study are publicly available. This data can be found here: EL57 Illumina reads for the resistance, susceptible and unselected populations were deposited to NCBI under BioProject (JM developed the synthetic EL57 population. PG, JM, and AF conceived and designed the experiments and edited reviewed and approved the final manuscript. JM and PG performed the experiments. PG and AF analyzed the data. PG wrote the draft manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Cardiometabolic diseases are an increasing global health burden. While socioeconomic, environmental, behavioural, and genetic risk factors have been identified, a better understanding of the underlying mechanisms is required to develop more effective interventions. Magnetic resonance imaging (MRI) has been used to assess organ health, but biobank-scale studies are still in their infancy. Using over 38,000 abdominal MRI scans in the UK Biobank, we used deep learning to quantify volume, fat, and iron in seven organs and tissues, and demonstrate that imaging-derived phenotypes reflect health status. We show that these traits have a substantial heritable component (8–44%) and identify 93 independent genome-wide significant associations, including four associations with liver traits that have not previously been reported. Our work demonstrates the tractability of deep learning to systematically quantify health parameters from high-throughput MRI across a range of organs and tissues, and use the largest-ever study of its kind to generate new insights into the genetic architecture of these traits. 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) . In paraThe 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 , with soTo 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  which indicated that liver volume is at its smallest between 12 and 2 pm, attributed to changes in hydration and glycogen content . We alsoTo assess which IDPs are associated with health-related outcomes, we defined a set of diseases based on inpatient hospital episode statistics , and assessed the association between each IDP and disease diagnoses . AlthougLiver volume was significantly associated with chronic liver disease and cirrhosis  as well as T2D  and hypertension . Kidney volume was associated with chronic kidney disease (CKD) . Interestingly, pancreas volume was associated more strongly with Type 1 diabetes (T1D) , than T2D . In contrast pancreatic fat showed a small association with T2D  and not with T1D (p=0.241). Lung volume was most strongly associated with tobacco use  and disorders relating to chronic airway obstruction (COPD) , with larger lung volume corresponding to a greater likelihood of respiratory disease diagnosis. Spleen volume was associated with myeloproliferative disease , especially chronic lymphocytic leukaemia . Liver fat was associated with T2D . Liver iron was associated with T2D  and iron deficiency anaemia  VAT was associated with a wide range of cardiometabolic outcomes including hypertension , T2D , and lipid metabolism disorders , while ASAT was only associated with cholelithiasis and cholecystitis . This association remained statistically significant, after adjusting for VAT, counter to reports that only VAT is predictive of gallstones . OverallTo 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  and sex hormone binding globulin (SHBG) . Kidney volume was associated with serum cystatin C , serum creatinine , consistent with observations that smaller kidneys function less effectively . PancreaLiver fat was associated with multiple liver function biomarkers including triglycerides , SHBG  alanine aminotransferase , and gamma glutamyltransferase . 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 , we founWe 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 . This isTo 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 . UtilisiFor each IDP, we estimated SNP-heritability using the BOLT-REML model . Heritabg = 0.782, p=4.60e-137) between ASAT and VAT. There was substantial genetic correlation between VAT and liver fat  and between VAT and pancreas fat . We found a negative genetic correlation between pancreas volume and fat , and between pancreas volume and iron .To understand the extent to which genetic variation explains the correlation between traits, we used bivariate LD score regression  to estimg = 0.94, p=3.2e-143) and between VAT and conventional surrogate markers such as waist circumference . The strongest genetic correlation with lung volume was with FVC , 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 , kidney volume and serum creatinine , and liver iron and erythrocyte distribution width .To identify traits with a shared genetic basis, we estimated genetic correlation between IDPs and 282 complex traits with a heritable component . A total of 650 IDP-trait pairs showed evidence of nonzero genetic correlation; 347 of these involved with measures of size or body composition . We founIn order to identify tissues or cell types contributing to the heritability of each trait, we used stratified LD score regression  Materia–5.For each locus containing at least one variant exceeding the study-wide significance threshold, we used GCTA COJO  to identPPP1R3B. PPP1R3B is expressed in liver and skeletal muscle, and promotes hepatic glycogen biosynthesis , lies on chromosome 8, 175 kb from the nearest protein-coding gene, ynthesis . Althougynthesis ; in our GCKR . This signal colocalised with T2D, hypercholesterolaemia and hyperlipidaemia, gout and gallstones, as well as other lipid and cardiovascular traits in the UKBB. This locus has previously been associated with NAFLD  have previously been associated with NAFLD .Of the eight study-wide independent signals associated with liver fat, three  colocalises with hyperlipidaemia and atherosclerosis and has been linked to lipid levels in previous studies, and SNPs in this gene have an established role in the development of NAFLD (TM6SF2 (lead SNP rs188247550) is also associated with hyperlipidaemia and has previously been linked to alcohol-induced cirrhosis  colocalises with cholesterol, LDL-cholesterol, and HDL-cholesterol levels, with the risk allele for higher fat associated with higher LDL-cholesterol. While this variant has not previously been associated with liver fat, missense and protein truncating variants in MARC1 have been associated with protection from all-cause cirrhosis, and also associated with liver fat and circulating lipids . This signal colocalises aspartate aminotransferase (AST), and HDL cholesterol levels in serum. GPAM knockout mice have reduced adiposity and its inhibition reduces food intake and increases insulin sensitivity in diet-induced obesity (We found an association between intronic and  obesity . Our datMTTP with 67 variants in the 95% credible set was associated with liver fat. Candidate gene studies have linked missense mutations in MTTP to NAFLD (A region overlapping to to NAFLD . Rare noto NAFLD . Inhibitto NAFLD .HFE (rs1800562 and rs1799945) and TMPRSS6 . SLC40A1 encodes ferroportin, a protein essential for iron homeostasis  colocalised with diabetic-related traits. This is consistent with our findings that T1D was associated with smaller pancreatic volume.We identified 11 study-wide significant associations with pancreatic volume. None were coding or colocalised with the expression of protein-coding genes. Two signals  for pancreatic fat; rs507666, which tags the A1 allele, lies in the 95% credible set at this locus. This signal colocalises with lipid and cardiovascular traits and outcomes, and is consistent with previous reports that blood group A is associated with lipid levels, cardiovascular outcomes (The top association for pancreatic fat (lead SNP rs10422861) was intronic to tabolism . There woutcomes  and incroutcomes .CBFA2T3 in the pancreas. rs7405380 lies in a promoter flanking region which is active in pancreatic tissue (ensemble regulatory region ENSR00000546057). CBFA2T3 belongs to a family of ubiquitously expressed transcriptional repressors, highly expressed in the pancreas, about which little is known. A recent study identified Cbfa2t3 as a target of Hes1, which plays a critical role in regulating pancreatic development (An association with pancreatic fat (lead SNP rs7405380) colocalises with the expression of elopment . This SNFAF1 and CDKN2C (lead SNP rs775103516), and five other loci. In contrast to liver iron, where we identified strong signals at regions associated with ferroportin and hepcidin loci, we found no study-wide significant associations with pancreatic iron.We identified signals at a locus on chromosome 1 containing A locus on chromosome 2 was associated with average kidney volume. This signal colocalises with biomarkers of kidney function  and a SNP in the 95% credible set, rs807624, has previously been reported as associated with Wilms tumor , a pediaPDILT/UMOD locus (lead SNP rs77924615), that colocalises with hypertension, cystatin C, creatine, and kidney and urinary calculus in the UKBB. This locus has previously been associated with hypertension as well as estimated glomerular filtration rate (eGFR) and CKD  was not study-wide significant. We identified three additional signals associated with VAT volume. rs559407214 (nearest gene CEBPA) is independent of the nearby pancreatic fat signal. rs73221948 lies 150 kb from the nearest protein coding gene. This signal colocalises with triglyceride levels and HDL levels. This has previously been reported  at the well-known reported , in addireported  Finally,reported .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  may reflect the predicted organ atrophy associated with ageing, likely underpinned by mechanism(s) similar to those reported for brain and skeletal muscle . IndividThe liver plays a pivotal role in the regulation of iron homeostasis, with iron excess to requirements stored in hepatocytes . EpidemiEctopic fat accumulation showed a more complex relationship with ageing. Although pancreatic fat increased with age for both men and women , liver fMost 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 , and lunThe strong association between VAT and development of metabolic dysfunction is well established , and conPNPLA3, 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.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 , 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 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 . AdditioIn 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.All abdominal scans were performed using a Siemens Aera 1.5T scanner (Syngo MR D13) . We analysed four distinct groups of acquisitions: (1) the Dixon protocol with six separate series covering 1.1 m of the participants (neck-to-knees), (2) a high-resolution T1-weighted (T1w) 3D acquisition of the pancreas volume, (3a) a single-slice multi-echo acquisition sequence for liver fat and iron, and (3b) a single-slice multi-echo acquisition sequence for pancreas fat and iron. Additional details of the MRI protocol may be found elsewhere . The prohttp://www.ukbiobank.ac.uk/ethics/), and these ethical regulations cover the work in this study. Written informed consent was obtained from all participants.The UK Biobank has approval from the North West Multi-centre Research Ethics Committee (MREC) to obtain and disseminate data and samples from the participants . The Dixon data were assembled into a single 3D volume for each participant using an automated fat-water swap detection and correction procedure. No additional preprocessing was necessary for the T1w 3D data for the pancreas. Proton density fat fraction (PDFF) and R2* were estimated from the single-slice multi-echo data for the liver and pancreas . The R2*mitk.org). All annotations were visually inspected at multiple stages by experienced analysts before use in modelling.For each organ, we defined a standard operating procedure and provided training to a team of radiographers, utilising MITK, a free open-source software system for development of interactive medical image processing software . This is the first time that segmentations for multiple major organs and compartments have been published on the UKBB dataset. Comparisons across datasets are also difficult because evaluation would be confounded by the specifics of how individuals are chosen, the conventions of annotation, and specifics of data acquisition or processing.We re-purposed an updated 3D iteration of the U-net architecture  based onTwo structures, the ‘body cavity’ and ‘abdominal cavity’, were segmented using neural-network based methods from the Dixon segmentation to estimate ASAT and VAT. For estimation of VAT, the abdominal cavity was used to isolate only tissue in the abdomen and pelvis. The fat channel was thresholded, small holes filled, and segmentations of abdominal organs  were removed to produce the final mask of VAT. For ASAT estimation, the body cavity was used to exclude all tissue internal to the body. A bounding box was computed based on the abdominal cavity, where the upper and lower bounds in the superior-inferior (z) direction were used to define the limits of the ASAT compartment.To automatically segment livers on 2D liver acquisitions, we trained one 2D U-net model with standard data augmentations for IDEAL, and another model for GRE. During inference, we ensured high specificity, at the cost of recall, by ablating the foreground mask by 25%. We made this trade-off because it is critical to include only liver tissue in the downstream analysis. In addition we removed voxels with R2* values outside the physiological range  . Final vWe performed pancreas 3D segmentation on the high-resolution T1w 3D acquisition based on a recent iteration of the U-net architecture used in label-free microscopy , using 1All statistical analyses were performed using R version 3.6.0.We compared the values extracted in our study with those from previous studies, available from the following UK Biobank fields:VAT (Field 22407) and ASAT (Field 22408) Liver fat (22400) and liver iron (22402) For fitting linear models, we used the R function 'lm'. For fitting smoothing splines, we used the 'splines' package. To determine whether a coefficient was statistically significant in a set of models, we adjusted the p-values for each coefficient using Bonferroni correction. We compared models with and without scan time using ANOVA.We looked for systematic differences between scanning centre, and trends by scan date . BecauseWe used the R package PheWAS  to combiWe defined two Bonferroni-adjusted p-values: a single-trait value of 6.63e-5, and a study-wide value of 6.03e-6. As many of the diagnoses are correlated, we expect this threshold to be conservative.We used the R package PHESANT  to generWe defined two Bonferroni-adjusted p-values: a single-trait value of 2.75e-5, and a study-wide value of 2.49e-6. As many traits are correlated, we expect this threshold to be conservative.We follow the methods described in a previous study .https://www.ncbi.nlm.nih.gov/grc/human/regions/MHC), and (ii) restricted to HapMap3 SNPs . QC was performed in PLINK v.1.90 using the following criteria: removal of samples with discordant sex , sample-level missingness <0.02, European genetic ancestry as defined by the UK Biobank . Outcome variables were inverse normal transformed and regressed on gene carrier status, adjusted for genetic sex, age, age2, the first 10 principal components of genetic ancestry, scaled scan date, scaled scan time, and study centre as fixed effects and genetic relatedness as a random effects term.Exome sequencing variant calls from the raw FE variant calling pipeline  were dow Biobank . Variant Biobank . A total Biobank . A filteWe estimated the heritability of each trait using restricted maximum likelihood as implemented in BOLT version 2.3.2 .https://data.broadinstitute.org/alkesgroup/LDSCORE). Following the original developers of this method  as used in We computed genetic correlation between traits using bivariate LDSC .https://www.ncbi.nlm.nih.gov/grc/human/regions/MHC). For each locus, we considered variants with genome-wide evidence of association (Pjoint <10–8) to be conditionally independent. We annotated each independent signal with the nearest known protein-coding gene using the OpenTargets genetics resource (May 2019 version).We performed approximate conditional analysis using genome-wide complex trait analysis (GCTA) , conside2 for case control studies  sorted variants by increasing Bayes factors , (ii) included variants until the cumulative sum of the posterior probabilities was ≥ 1−c, where c corresponds to the credible set cutoff of 0.95.We calculated the posterior probability, πj, that the j th variant is driving the association, given l variants in the region, by:http://www.nealelab.is/uk-biobank (Version 2). For disease phenotypes, we additionally downloaded summary statistics computed using SAIGE  to seek evidence for colocalisation with expression in one of 49 tissues.To identify other traits potentially sharing the same underlying causal variant, we downloaded a catalog of summary statistics using the UK Biobank cohort from ng SAIGE  from httWe performed colocalisation analysis using the coloc R package  using deIn addition to the colocalisation analysis with UK Biobank traits, order to identify GWAS signals tagged by any of our associations from previous studies (not including the UK Biobank traits described above), we queried the Open Targets Genetics Resource , versionhttps://github.com/marcsous/pdff (MATLAB code to estimate the PDFF is available from Dr Mark Bydder at ous/pdff .https://github.com/recoh/pipeline (swh:1:rev:13dc77941cb2919417108637eade6c8448374229). Fitted models and code to apply the models is available from https://github.com/calico/ukbb-mri-sseg/ (swh:1:rev:4acdad6bf5e6cd08436d91ac6d4a494cf1365d98).Code to preprocess the imaging data is available from  In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Acceptance summary:The authors advance the understanding of abdominal organ related diseases by utilizing MRI scans, genetic information, and clinically defined trait resources from the large UK Biobank using advanced statistical methods. Ample discussion and comparison of how their results relate to known findings from existing literature is given.Decision letter after peer review:eLife. Your article has been reviewed by 2 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Matthias Barton as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Constantinos Parisinos (Reviewer #2).Thank you for submitting your article "Genetic architecture of 11 organ traits derived from abdominal MRI using deep learning" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission. Details are provided below and include recommendations relating to the introduction, readability and discussion to further improve the paper for the readers.While you are very welcome to submit a revision, there is an alternative you may want to consider. It is possible this work would be more powerful as multiple papers. Say the following three papers, (1) How you built the IDPs, (2) IDP vs. clinically defined phenotype analyses, (3) IDP vs. genetic analyses. Or maybe (1) How you built the IDPs and the IDP vs. clinically defined phenotype analyses, (2) IDP vs. genetic analyses. That might help you get your main points across more clearly, as the key takeaways are difficult right now, given all the results.Essential revisions:1) IntroductionConsider starting the introduction differently, rather than with a description of the UKB project. What the problem with big data is and why the development of deep learning models for image analysis is essential for this day and age. An introduction based on the way the argument is presented in the abstract would set the story better.2) This is a general readability comment.There are a lot of results and methods to digest here. The Discussion section did not fully synthesize nor address all the results given. Recommend that either the Discussion section needs to be extended, or you should be more discriminatory in what is included in the Results section.3) MethodsWe would also recommend shortening some of the methods, but giving full details of them in the appendix. This would allow the reader to get a broad view of what is happening for the methods, but not get bogged down in all the details that are included right now. For example, giving a broad overview of how the IDPs were created in the Methods section in the text, but moving all the nitty gritty details to the appendix.4) ResultsA lot of interesting associations were found. Would there be any way to summarize the ones that may be clinically significant in the near future?5) Since the exome analysis did not find any significant results, you could say that you considered an exome analysis in your methods/Results section, but move all the details and results for it to the appendix.6) "MRI has become the gold standard for clinical research"Too generic, MRI not the golden standard for many things, please rephrase something along the lines of "MRI is a safe, non-invasive method that can be used to accurately measure multiple phenotypes including…"7) Was the calculated kinship matrix used in the GWAS the same as the one used in the exome-wide association study? How was the kinship matrix calculated for the GWAS ? You say you only included participants recorded as "Caucasian" for GWAS. Did you check this using PCA?8) Did you use a set of LD pruned SNPs to calculate genomic control from the GWAS results? More attention should be given to understanding/justifying why you might have these inflated lambda values from your GWAS results. I also find it curious that many of your phenotypes have exactly the same lambda values. What do the QQplots of the GWAS p-values  look like ?9) For the ICD code and raw data trait regression analyses, more detail should be given about how you ensured model stability. Having only 20 cases for your response variable in logistic regression will not give you stable results with the number of covariates you've included. What did the case/control ratio look like in your significant findings? Were a lot of your significant findings from models that had only a small number of cases? There was no mention of how many cases were needed in the "Other traits" logistic models. Did you use the "at least 20 cases" rule there too? I would expect that some of your clinical phenotypes were very skewed or zero inflated. Did you check/account for this?10) What are the levels for the covariate Ethnicity used in your clinical trait regression analyses? Investigating how ethnicity was related to any of your clinical traits could lead to interesting discussion, e.g., were any clinical trait associations driven by the non-white subsample. This could allow for some discussion of differing disease burden through a diversity lens, even though your genetic cohort was white British.11) Table 1 should be in the Results section Table 1 comments- The pancreas volume X % Female cell has % in it, while the others do not. Why does the 38,881 not match the 38,683 stated in the above paragraph? Are the entries for age, BMI, and height giving mean (SD)?12) The last part of the Figure 1 legend is for E, F, and G, not just E.13) A comment for this sentence, "Interestingly, pancreas volume was associated more strongly with Type 1 diabetes (T1D) , than T2D , while pancreatic fat showed a small association with T2D  and not with T1D (p=0.241)." The p-value for Type 1 diabetes should be e-21, not e21. Also, are the first two p-values actually meaningfully different from each other?14) Figure 2 – top right figure; some labelling is unclear.15) It would be interesting to denote what is a novel association and what is a previously existing association in the Figure 3C Manhattan plot. Figure 3 – manhattan plot peaks are not labelled (e.g. with lead snp/gene) – this may be more useful16) Could you tie in your heritability estimates for the 11 IDPs into the discussion about age-related impacts on organ function . You mention "probably reflecting genetic and environmental exposures," do your heritability estimates align with this?17) More detail should be given about what you mean by "raw data" in the Other traits methods section. Is that text notes from the clinician?18) There are some x-axis readability issues for your Figure S1.B plots.19) Can you grey out the non-significant dots in Figures S8-10 ?Reviewer #1:This manuscript aims to better understand the underlying mechanisms and drivers of abdominal organ related complex diseases. The authors utilize the large amount of magnetic resonance imaging (MRI), genetic, and clinically defined trait information available in the UK Biobank to better understand complex diseases related to the liver, pancreas, kidneys, spleen, and lungs. The authors take advantage of the previously underutilized MRI scans available in UK Biobank specific to abdominal organs. Their consideration of multiple data types within UK Biobank cohort is timely given the current demand for rigorous analyses of the rapidly growing electronic health record and biobank databases.Since most other researchers using the MRI UK Biobank data have studied cardiac and brain related traits, this manuscript gives insights to the previously understudied images of abdominal organs. The authors' detailed description of how they produced the image derived phenotypes (IDP) used in these analyses, using deep learning and the MRI scans, will be valuable to any researcher interested in using medical imaging data. The authors are the first to extend the procedure used to characterize IDPs from MRI scans to the large UK Biobank cohort. Researchers can apply to access these IDPs or the authors' code for creating them, which will progress the use of medical imaging data in future analyses in the scientific community.The methods performed here utilize many different types of data available within the UK Biobank. Care was taken to extract clinically defined phenotypes from the data records using ICD codes and raw data fields, although some details and choices for the models used to relate these clinically defined traits to the IDP traits is lacking. The authors' approach of scanning the different data types in a pairwise fashion  is not cutting edge, as it does not integrate these multiple distinct data types together during analyses, but ultimately meets their goals of gaining a better understanding of significant associations with the complex IDPs.The genetic analyses were conducted using only individuals of white British ancestry due to the small sample sizes of non-white populations in the UK Biobank cohort. Thus, the results from the genetic analyses may not be applicable to non-white populations. Additionally, since the clinically defined traits vs. IDP analyses were conducted using all samples, discussion relating these results to the genetic vs. IDP results is difficult, as the potential of the clinically defined trait vs. IDP associations being driven by the non-white subsample was not investigated. These issues simply serve as a reminder for the need to value diversity in the genetics community, as they could have been avoided with better recruitment efforts by genetic cohorts.The authors support their conclusions using both their results and previously cited literature, although it is sometimes difficult to synthesize their conclusions because of the large number of results presented and analyses performed. While the authors did not have an external data set to replicate their findings, good discussion is provided to give context and support their findings using previous studies and existing literature. The analyses and given results achieve their goals of better understanding abdominal organ related diseases and of better using the MRI scans available in the UK Biobank.Reviewer #2:This is an important and very well written paper where the authors have developed deep learning algorithms to automate extractions of certain measures/phenotypes from MRI scans. They show that these measurements are associated with health and disease, and can be used with genetic data to gain new insights into biology.StrengthsStrengths include a large sample size from an unselected cohort, with genetics, blood tests and clinical outcomes available to investigate associations.WeaknessesThere is no validation cohort for the genetic analysis.GWAS on White British Ancestry only.The cohort in UKB who have undergone MR imaging is slightly healthier than the overall cohort due to some of the exclusion criteria, this should be commented on.Although recruitment into the UK Biobank study finished in2010, scanning began in 2014. The median follow-up period from scanning is 2.5 years, limiting power to evaluate the prognostic value of IDPs, or to evaluate whether they are a cause or consequence of the disease state.ImpactThe work is likely to significantly impact big data/ imaging research, since it is now possible to automate phenotype extraction using deep learning pipelines, compared to the much more labour intensive method of manual extraction. Essential revisions:1) IntroductionConsider starting the introduction differently, rather than with a description of the UKB project. What the problem with big data is and why the development of deep learning models for image analysis is essential for this day and age. An introduction based on the way the argument is presented in the abstract would set the story better.Thank you for this feedback. We have reframed the Introduction to introduce on the one hand, the role of MRI imaging in understanding the basis of disease, and on the other the recent explosion of biobank-scale research. We agree that this better contextualizes this work which brings together these two disparate fields.2) This is a general readability comment.There are a lot of results and methods to digest here. The Discussion section did not fully synthesize nor address all the results given. Recommend that either the Discussion section needs to be extended, or you should be more discriminatory in what is included in the Results section.While mindful of the length, we have expanded the discussion to:1. Better emphasize to the deep learning innovations required to execute this work;2. Place the genetics results in the context of previous studies.3) MethodsWe would also recommend shortening some of the methods, but giving full details of them in the appendix. This would allow the reader to get a broad view of what is happening for the methods, but not get bogged down in all the details that are included right now. For example, giving a broad overview of how the IDPs were created in the Methods section in the text, but moving all the nitty gritty details to the appendix.Thank you for your comments, we agree that reducing the detail within the paper would improve readability particularly for a broad audience. We have therefore added an Appendix (Appendix 1) with the full details of the methods, and substantially abbreviated the main body of the text.4) ResultsA lot of interesting associations were found. Would there be any way to summarize the ones that may be clinically significant in the near future?We agree with this point, with so many interesting associations, determining which have the most meaning is important. We have expanded the last paragraph of the Discussion highlighting what we perceive to be the key clinically relevant findings of the study and their potential importance for personalised medicine. We have also expanded part of the discussion to emphasize relevance for particular disease areas where this type of imaging has to date not been routinely in the clinic.5) Since the exome analysis did not find any significant results, you could say that you considered an exome analysis in your methods/Results section, but move all the details and results for it to the appendix.Thank you for this recommendation. while some of the exome analysis has been suggestive of interesting results, as yet nothing has demonstrated significance. We have therefore as suggested moved the details relating to the exome analysis to the Supplemental sections.6) "MRI has become the gold standard for clinical research"Too generic, MRI not the golden standard for many things, please rephrase something along the lines of "MRI is a safe, non-invasive method that can be used to accurately measure multiple phenotypes including…"We thank the reviewers for their comments and agree this was an overgeneralization on our part. We have now clarified our wording in the introduction of the manuscript to reflect this.7) Was the calculated kinship matrix used in the GWAS the same as the one used in the exome-wide association study? How was the kinship matrix calculated for the GWAS ? You say you only included participants recorded as "Caucasian" for GWAS. Did you check this using PCA?We have added additional details on the inclusion criteria, identification of Caucasian participants, and the calculation of the kinship matrix to the section “Genome-wide association study” (p. 27).8) Did you use a set of LD pruned SNPs to calculate genomic control from the GWAS results? More attention should be given to understanding/justifying why you might have these inflated lambda values from your GWAS results. I also find it curious that many of your phenotypes have exactly the same lambda values. What do the QQplots of the GWAS p-values  look like ?We have replaced the unpruned genomic control estimates from the original text/tables with an estimate from the same set of LD pruned, genotyped SNPs described above . The different phenotypes still have very similar lambda values; we have added more significant figures to the updated ST4. We have added QQ plots .9) For the ICD code and raw data trait regression analyses, more detail should be given about how you ensured model stability. Having only 20 cases for your response variable in logistic regression will not give you stable results with the number of covariates you've included. What did the case/control ratio look like in your significant findings? Were a lot of your significant findings from models that had only a small number of cases? There was no mention of how many cases were needed in the "Other traits" logistic models. Did you use the "at least 20 cases" rule there too? I would expect that some of your clinical phenotypes were very skewed or zero inflated. Did you check/account for this?The number of cases/controls is given in Supplementary File 1d; we have added this information to Supplementary File 1e, and also logged when a logistic model was used in the ‘model’ column. While we have not conducted any formal statistical test, we did not observe any particular enrichment in traits with very small numbers of cases and the main findings were typically based on >100 cases.10) What are the levels for the covariate Ethnicity used in your clinical trait regression analyses? Investigating how ethnicity was related to any of your clinical traits could lead to interesting discussion, e.g., were any clinical trait associations driven by the non-white subsample. This could allow for some discussion of differing disease burden through a diversity lens, even though your genetic cohort was white British.We agree with the reviewer that ethnicity is an important covariate in this analysis and have provided more detail regarding this factor. We have added clarification that we used self-reported ethnicity from the UK Biobank. We agree that this is an interesting potential avenue for future research, especially given the observed heterogeneity in anthropometric and metabolic traits between people of different ancestries. Mindful of the length of the paper, we leave this as a potential avenue for future research.11) Table 1 should be in the Results section Table 1 comments- The pancreas volume X % Female cell has % in it, while the others do not. Why does the 38,881 not match the 38,683 stated in the above paragraph? Are the entries for age, BMI, and height giving mean (SD)?We have clarified the legend of Table 1, removed the extraneous % sign, and moved it to the start of the Results section. We have provided an additional table (Supplementary File 1b) which gives more context on the discrepancy between the number of scans/participants from different stages of the processing pipeline (these were not used consistently and we agree this was confusing).12) The last part of the Figure 1 legend is for E, F, and G, not just E.Thank you for highlighting the issue with the figure caption, we have now corrected this in the text.13) A comment for this sentence, "Interestingly, pancreas volume was associated more strongly with Type 1 diabetes (T1D) , than T2D , while pancreatic fat showed a small association with T2D  and not with T1D (p=0.241)." The p-value for Type 1 diabetes should be e-21, not e21. Also, are the first two p-values actually meaningfully different from each other?We have corrected the typo. While the p-values cannot meaningfully be compared due to the differences in sample size, we have added the (approximate) 95% confidence intervals for the respective coefficients of T2D and T1D, and noted that they do not overlap.14) Figure 2 – top right figure; some labelling is unclear.We have abbreviated some of the overplotted text to make this plot more legible.15) It would be interesting to denote what is a novel association and what is a previously existing association in the Figure 3C Manhattan plot. Figure 3 – manhattan plot peaks are not labelled (e.g. with lead snp/gene) – this may be more usefulWe have added labels. Since these traits (except liver fat and iron) have not been studied before, we labelled all the traits.16) Could you tie in your heritability estimates for the 11 IDPs into the discussion about age-related impacts on organ function . You mention "probably reflecting genetic and environmental exposures," do your heritability estimates align with this?Thank you for raising this comment. The heritability estimates of the different organs have overlapping confidence intervals. However, we are not sure that this would support or refute the genetic or environmental origin of changes in organ size over time. Rather than speculate on this further (given the length of the paper), we have removed this unclear statement, and added a sentence that large-scale longitudinal datasets will be required to interrogate the genetic/environmental basis of changes in organ volume more systematically.17) More detail should be given about what you mean by "raw data" in the Other traits methods section. Is that text notes from the clinician?raw data’ with the phrase ‘unprocessed image data’.Thank you for this comment, we were referring to the original MRI images obtained from the scanner before any processing had taken place. We have clarified this by replacing the term ‘18) There are some x-axis readability issues for your Figure S1.B plots.We have improved the layout of this figure to make it more readable.19) Can you grey out the non-significant dots in Figures S8-10 ?This is a great suggestion; we have done this and agree that it improves readability significantly.


Many sexually selected traits exhibit phenotypic plasticity. Despite a growing appreciation for the ecological context in which sexual selection occurs, and for the role of plasticity in shaping traits associated with local adaptation and divergence, there is an important gap in knowledge about the onset and duration of plasticity in sexual trait expression. Integrating this temporal dimension of plasticity into models of sexual selection informs our understanding of the information conveyed by sexual traits and our predictions related to trait evolution, and is critical in this time of unprecedented and rapid environmental change. We conducted a systematic review of 869 studies to ask how trait modalities  relate to the onset and duration of plasticity in vertebrate sexual signals. We show that this literature is dominated by studies of coloration in birds and fish, and most studies take place during the breeding season. Where possible, we integrate results across studies to link physiology of specific trait modalities with the life stage  during which plasticity occurs in well‐studied traits. Limitations of our review included a lack of replication in our dataset, which precluded formal analysis. We argue that the timing of trait plasticity, in addition to environmental context, is critical for determining whether and how various communication signals are associated with ecological context, because plasticity may be ongoing or occur at only one point in an individual's lifetime, and determining a fixed trajectory of trait expression. We advocate for careful consideration of the onset and duration of plasticity when analyzing how environmental variation affects sexual trait expression and associated evolutionary outcomes. Despite a growing appreciation for the ecological context in which sexual selection occurs, there is an important gap in knowledge about the onset and duration of plasticity in sexual trait expression. We conducted a systematic review of 869 studies to ask how trait modalities  relate to the onset and duration of plasticity in vertebrate sexual signals. We argue that the timing of trait plasticity, in addition to environmental context, is critical for determining whether and how various communication signals are associated with ecological context, because plasticity may be ongoing or occur at only one point in an individual's lifetime, and determining a fixed trajectory of trait expression. For example, chemical signals can change with relatively short‐term changes in diet or stress  and whether different trait modalities  have predictable patterns in the onset and duration of sensitive periods. Our review yielded the following insights: (1) Timing of trait sensitivity to environmental influences has rarely been emphasized in sexual selection research, (2) a temporal understanding of plasticity can integrate results of different studies, which provide seemingly conflicting evidence about trait–environment associations, and (3) the onset and duration of sensitive periods in sexual signals has important implications for sexual trait expression and evolution.2We compiled relevant papers by searching Web of Science on August 27, 2020, with the keywords “taxon, trait, sexual selection, and condition‐depend*” and on March 30, 2021, with the keywords “taxon, trait, sexual selection, and plasticity” where taxon was one of five vertebrate groups: amphibian, bird, fish, mammal, or reptile; and trait was one of five trait modalities: acoustic, behavioral, chemical, morphological, or visual. Using all possible taxon–trait keyword combinations resulted in 50 unique searches. Due to the diverse terminology and study systems used by researchers studying sexual selection, our search is not exhaustive. Rather, we intended to capture a representative sample of vertebrate research in sexually selected traits over the last few decades. We chose to use the term “condition dependence” as well as “plasticity” in our searches as this is the most widely used terminology across years and taxonomic groups to study trait plasticity in the context of sexual selection.We systematically screened all titles and abstracts and, if necessary, methods and results to exclude papers that: were on invertebrates, only considered traits other than sexually selected pre‐mating traits , did not contain data at the level of the individual, were not empirical , or did not manipulate or measure an environmental variable Figure . We chosWe thoroughly read and scored all selected papers with particular attention to life stage, study design , types of environmental variables, types of traits, and the quality of evidence that the trait of interest is sexually selected. For a detailed list of scoring criteria and notes, see Table To answer our driving questions about the state of the literature in the context of the timing of sexual trait plasticity, we summarized the distribution of papers among years, taxa, traits, environmental variables, experimental designs, and life stages. We also summarized the number of traits or environmental variables studied by these papers. To gain a deeper understanding of the findings of this body of literature, we identified traits in our database that (1) were studied across multiple life stages, (2) had good evidence for being sexually selected, that is, were correlated with mating or reproductive success, and (3) were measured in a relatively consistent way such that a synthesis across studies was possible. For each of the papers on these well‐studied traits, we scored the trait–environment association as positive, negative, or not significant. We then synthesized the results to gain insight about the onset and duration of sensitive periods in that trait's development. Unfortunately, a meta‐analysis of sensitive periods across trait modalities was not possible due to insufficient replication across life stages within taxa and the diversity of analysis techniques  used to study the effects of environment on trait expression.3Our searches returned 869 unique papers Figure . Of thesVisual signal traits were the most commonly studied and chemical traits the least‐well represented among selected papers Figure . NutritiAll of our selected papers framed the trait(s) in question as a sexual signal, reproductive trait, or similar. However, the evidence given for sexual selection  was often weak or nonexistent. Over a third of the traits in selected papers were not clearly linked with mate choice or reproductive success . In these cases, the authors either did not provide citations for statements describing a trait as sexually selected or provided indirect evidence . About half of the papers did cite evidence of the trait being linked with mate choice and/or reproductive success (99/203 traits or 49%), and a few exceptional papers directly tested the link with mate choice and/or reproductive success, as well as trait plasticity , zebra finches , three‐spined sticklebacks , house sparrows , and great tits . Together, these species represented over a third of the papers in our dataset. We identified four traits that met our criteria for further synthesis: song characteristics in zebra finches, sigmoid displays in guppies, orange coloration in guppies, and black badge size in house sparrows  studied in multiple life stages, (2) sexually selected, and (3) measured consistently across studies). Below, we report what environment–trait associations have been found at different life stages and in response to different environmental variables for these traits. Furthermore, we integrate the results of different studies to highlight potential patterns of developmental, seasonal, or continuous plasticity in sexual trait development.The most well‐represented species among selected papers were guppies  helps explain when results of these studies agree or disagree with one another. The key is that inferences about trait plasticity from each study are limited to the time point in which the trait was analyzed and the environmental variable under consideration. When dietary manipulations took place during the juvenile stage, no association was found between carotenoid supplementation Grether,  or food 3.1.2In addition to coloration, male guppies use a ritualized courtship behavior termed the “sigmoid display.” This display is thought to show off a male's color patterns and display rates are linked with increased reproductive success Houde, .Our selected papers included 11 papers that analyzed plasticity of sigmoid display rates at different life stages Table , althougThese studies provide mixed evidence for the effect of light levels during courtship (breeding) on display rates. Where some studies found no evidence of a correlation , and across studies, food quantity was associated with increased display rates. Consistent with previous work that has established the continuous plasticity of behavioral traits Table , there i3.1.3House sparrows Figure  are an eIncluded in our review were six studies that analyzed the environmental sensitivity of badge size at different life stages Table : Two of In contrast, badge size increased with aggressive social conditions during molt  believed to underlie trait expression, and when those mechanisms are expected to be influenced by changes in environmental variables and induce change in trait expression.Gather information about life stages separately. This can be done by (1) focusing on one life stage for a particular study, or (2) for longitudinal studies, by ensuring that repeated measurements are taken during the transition from one life stage to another, and for experimental studies, by swapping a subset of the control and treatment groups at the transition from one life stage to another. This allows researchers to discern the life stage during which a sensitive period occurs.When using models of sexual selection as a framework for studying trait expression, evaluate the quality of evidence that the trait is linked with reproductive success. A trait linked with variables including territory quality and dominance, for example., is not necessarily sexually selected.Include explicit information, such as a diagram of experimental design  at other time points.Despite the well‐supported existence of sensitive periods in plastic traits, and the potentially important ways in which these sensitive periods could affect mate choice Box  and diveMost traits in our dataset were only studied in one life stage across multiple studies. Presumably, researchers are selecting relevant life stages to study trait expression, but this is rarely made clear. Publication bias toward positive results could also explain the absence of papers studying environmental variation at life stages that do not influence trait expression. In addition, our dataset may have left out relevant papers. We are well aware that there is a rich and diverse set of literature that was not captured in our search, despite our efforts to use search terms that have been used in a variety of systems and over decades.4.1The time at which trait expression is sensitive to the environment varies among traits. Some traits are plastic only during early development , some traits are plastic at particular, recurrent times , and yet other traits remain plastic throughout life  may affect the expression, cost, or honesty of trait, creating new selection on preferences ; Data curation (lead); Formal analysis (lead); Funding acquisition ; Methodology ; Resources ; Visualization (lead); Writing‐original draft (lead); Writing‐review & editing (lead). Rebecca J. Safran: Conceptualization (supporting); Data curation (supporting); Formal analysis (supporting); Funding acquisition ; Methodology ; Resources ; Writing‐original draft (supporting); Writing‐review & editing (supporting).Table S1‐S2Click here for additional data file.


The root system is critical for the survival of nearly all land plants and a key target for improving abiotic stress tolerance, nutrient accumulation, and yield in crop species. Although many methods of root phenotyping exist, within field studies, one of the most popular methods is the extraction and measurement of the upper portion of the root system, known as the root crown, followed by trait quantification based on manual measurements or 2D imaging. However, 2D techniques are inherently limited by the information available from single points of view. Here, we used X-ray computed tomography to generate highly accurate 3D models of maize root crowns and created computational pipelines capable of measuring 71 features from each sample. This approach improves estimates of the genetic contribution to root system architecture and is refined enough to detect various changes in global root system architecture over developmental time as well as more subtle changes in root distributions as a result of environmental differences. We demonstrate that root pulling force, a high-throughput method of root extraction that provides an estimate of root mass, is associated with multiple 3D traits from our pipeline. Our combined methodology can therefore be used to calibrate and interpret root pulling force measurements across a range of experimental contexts or scaled up as a stand-alone approach in large genetic studies of root system architecture. In maize, the entirety of primary, seminal, lateral, crown, and brace roots together form a complex architecture which controls the plant's ability to effectively acquire water, scavenge nutrients, and resist lodging . As a rePSTOL1 [DRO1 [DRO1 homolog [B. napus doubled haploid mapping population, RPF was positively associated with later flowering time and subsequently lower grain yield [In its simplest form, root phenotyping of crop species such as maize or rice can be performed by manual measurement of a limited set of amenable traits, such as root mass, length, width, or the growing angle, either in soil or soil-free conditions. Currently known genes controlling quantitative root system architecture traits in rice were identified using such measurements, including PSTOL1 , DRO1 [1L1 [DRO1 , and a r homolog . In fiel homolog –15. Hist homolog –21. Whilin yield , while iin yield , suggestMore intricate phenotyping of root system architecture can be performed upon two-dimensional images of either field-excavated root crowns, or young gel-media grown root systems, followed by analysis with specialized software –31. SuchAs a result, interest and capacity towards three-dimensional root phenotyping have been increasing, driven in part by technical advances and interdisciplinary approaches . For exaHere, we integrate two protocols, first sampling via RPF and washing mature, lignified, field-grown maize root crowns, followed by imaging via XRT and trait quantification for over 290 roots across multiple field seasons. By imaging the roots absent of soil or other media, scanning and segmentation times were significantly reduced such that replication across two environments and/or two time points was possible. We extracted up to 71 3D features for each root crown sample, including up to 65 traits with significant variation between genotypes, as well as root shape or distributional traits, which showed differences between experimental contexts. The median broad-sense heritability across all traits ranged from 0.23 to 0.56, depending on the germplasm and conditions. Finally, we examined covariance between 3D traits and RPF values to identify correlations between high-resolution phenomics and high-throughput field data. This study therefore demonstrates how XRT can provide insights into the root architectural attributes that influence RPF, which can then be used together for more informative studies in the mapping and breeding of root traits, including in multienvironment analyses.Genomes 2 Fields (G2F) germplasm (https://www.genomes2fields.org/) were planted in May 2017 in a split-plot design with full irrigation or limited irrigation (drought) treatments, with two field replicates per treatment for a total of 1060 plots. Prior to planting, the field was fertilized with nitrogen at 65 lbs per acre.All plants were grown at the Colorado State University Agricultural Research Development and Education Center in Fort Collins, CO, USA , in 2017 and 2018. 260 genotypes from the The Shoot Apical Meristem (SAM) diversity panel  along wiIn both the G2F and SAM experiments, each plot consisted of two 12-foot rows with 30-inch spacing between rows and 9-inch spacing between plants within rows, corresponding to a planting density of 62000 plants/ha. The irrigated treatments received approximately 1 inch of water per week, while the drought treatments were irrigated until well established (approx. 5 weeks after planting) and then received only natural precipitation , except at the root harvesting when it also received irrigation to homogenize the root harvesting process.The protocol used for root pulling and harvesting was similar to that in . BrieflyRoots from the G2F 2017 experiment were also imaged in 2D Figure  using a N = 107 and 187, respectively), root crowns were clamped at the stem with a small vise and imaged using a North Star X5000 X-ray system  , generating 1800 radiographs per sample at 10 fps (100 ms integration time). To provide an internal calibration of the image geometry, a fixed standard  was imaged with each sample batch. The radiographs were then reconstructed using efX-CT (North Star Imaging) and exported as an unadjusted RAW volume, resulting in a voxel size of 109–113 μm depending on the sample batch.For 3D phenotyping of samples from the selected G2F and SAM populations  . Dependiraw2img. The slices were then thresholded, binarized, and skeletonized using the custom scripts batch-segmentation, which performs basic thresholding of the roots from air in the 3D volume, and batch-skeleton, which calls the skeletonization and feature extraction pipeline previously developed and described in [For each sample, the RAW volume was converted to 2D slices using the custom Python script ribed in , 39, andribed in . These 1ribed in . Fractalribed in , was estribed in . Densityribed in .batch-skeleton are available in Table https://github.com/Topp-Roots-Lab/3d-root-crown-analysis-pipeline/A list and basic description of root features measured using All downstream  analysis was performed in the R statistical computing environment. Initially, principal component analysis using all 71 3D root traits was used to identify large outliers, leading to the removal of 2 samples in the G2F 2017 data and 3 samples in the SAM 2018 data. Additionally, for all univariate analyses, outliers within each trait were identified and omitted if they were beyond the 1st quartile minus 1.5 ∗ interquartile-range or the 3rd quartile plus 1.5 ∗ interquartile-range.car package [p values from car were adjusted using the Benjamini-Hochberg method to account for multiple testing and control the false discovery rate to an adjusted p value < 0.05. Individual two-sample comparisons as seen in boxplots were performed using Mann-Whitney U tests. Correlations between root traits were calculated using Spearman's correlation coefficient. Linear regressions were performed using the lm function in R.After univariate outlier removal, analysis of variance (ANOVA) was performed for each trait using the  package . Subsequlme4 package [Yijk ~ Gi + Ej + (G∗E)ij + eijk, where Y is the phenotypic value, Gi is the ith genotype, Ej is the jth environment, (G∗E)ij is the interaction between the ith genotype and the jth environment, and eijk is the residual error of the kth sample from the ith genotype and jth environment. Broad-sense heritability was calculated using the equation H2 = σG/ where σG is the estimated phenotypic variance due to genotype, σGxE is the estimated phenotypic variance due to genotype x environment, σresidual is the residual variance, e is the number of environments, and re is the average number of biological replicates per genotype across both environments [Variance components were estimated by using the  package  to fit tronments . This heprcomp function. For PCA-LDA, PCA was performed upon each genotype subset, and the number of principal components required to explain 90% of the trait variance was used as inputs into the LDA function from the MASS package [randomForest [caret [mtry and ntree parameters found using a grid search approach between every combination of mtry between 1 and 20 and ntree values of 500, 1000, 2500, and 5000. Parameters giving the best accuracy were kept, as calculated by 10-fold cross-validation repeated 3 times. From the final random forest models, the proximity matrix was calculated and nonmetric multidimensional scaling was used to visualize the distances between samples.Principal component analysis (PCA) of the 3D root data within the G2M and SAM experiments was performed using the base R  package . The ranomForest  and caret [caret  R packagIn each of two field seasons, maize genotypes (30 from the G2F panel and 20 from the SAM panel) were grown under two different irrigation treatments, providing two environments in terms of soil moisture. At the designated time point(s) for sampling (see Methods), root crowns were excavated by root pulling force. The root crowns, which maintain their overall 3D structure due to lignification, were washed clean and subsequently imaged using a Northstar X5000 X-ray computed tomography system . In totaH2) in the G2F experiment for each 3D and 2D trait, as well as for RPF  and 2D measurements . The traits with the highest heritability, however, were 3D-derived maximum root count (H2 = 0.76) and average root radius (H2 = 0.74), illustrating where 3D root phenotyping is particularly adept. Among 2D traits, high heritability did not necessarily result in high association with RPF, although some traits such as 2D area had a strong positive association , fractal dimension top view (mean H2 = 0.81), maximum root count (mean H2 = 0.80), convex hull volume (mean H2 = 0.79), and surface area (mean H2 = 0.79) were among the most heritable traits, although the individual performance of these traits fluctuated depending on the time point. However, the average heritability across all 3D traits was only slightly higher at first time point (0.510) than at second time point (0.505), suggesting that heritability of most root traits is relatively static over this time span. One interesting exception to this is RPF itself, which had a H2 value of only 0.59 at the first time point, but significantly increased to a H2 value of 0.85 by the second time point. This indicates that RPF measurements taken later in the plant life cycle may be more informative and reliable for the purposes of distinguishing genotypic differences in maize root system architecture, as well as for breeding. Overall, however, traits with higher average heritability across time points tended to also have a greater correlation in measurements between time points  was calculated separately within each time point Figure . In genes Figure .Focusing on 3D root traits and RPF, we subsequently wanted to examine whether genotype and environment effects were significant factors on a trait-by-trait basis . Using aIn the SAM experiment, the situation was somewhat reversed: in support of the overall higher trait heritability, a remarkable 65 root traits had a significant effect from genotype, but only 17 traits had a significant effect from environment, while 37 traits had a significant effect from time point . Traits Root pulling force has been used historically and recently as a proxy for root mass and root volume. Nevertheless, to have additional and more detailed information on the architectural changes that RPF measures would increase its utility as a field assay. We first calculated correlations between RPF and 3D root phenotype across all measured samples, irrespective of genotype and environment, or time point in the case of the SAM data. In both experiments, RPF was most correlated with root volume, fractal dimension, surface area, total root length, root crown width, number of bifurcating clusters, and number of root tips. Figures . Traits Associations between root pulling force and root system architecture traits are most useful if they are not only significantly correlated but also exhibit a close linear relationship. Regression analysis between RPF and positively correlated 3D architecture traits (as observed in the G2F experiment), such as fractal dimension and surface area, showed a reasonably good fit Figures . In contTo explore the degree to which trends across multiple traits may be associated with RPF, we performed principal component analysis (PCA) from the G2F and SAM data using the 3D-based root phenotypes alone Figure . In the We next applied supervised multivariate classification methods to determine which traits were most closely associated with differences in genotype, environment, or time Table . Becauseth overall in the G2F set and 19th overall in the SAM set. Interestingly, traits related to overall root size such as volume or surface area did not seem to be important factors overall in discriminating between genotypes, suggesting that these metrics, although intuitive and undoubtedly important in other contexts, are by themselves insufficient to distinguish between multiple and often subtly distinct genotypes, highlighting the need for the more comprehensive phenotyping described here to fully capture quantitative differences in root development.We examined what traits were most important towards PCA-LDA classification across all genotype combinations Figure . In bothTo evaluate the overall effect of the environment  and time , we performed random forest classification to distinguish between the two possible levels of each variable upon root system architecture. For these classifications, we included all genotypes, which increased the sample size for each model. Using 10-fold cross-validation, the best model parameters resulted in a classification accuracy of 81.0%, indicating that while the environment had an effect which was detectable using classification techniques, the contrasting irrigation regimes were not so dramatic as to result in a shift in root system architecture obvious across every sample . NeverthFor classifying roots based on time point in the SAM data, using 10-fold cross-validation, the best model parameters resulted in a random forest classification accuracy of 78.6% , which wOverall, multiple analytical approaches corroborate the conclusion that distinct sets of root traits are relevant depending on the germplasm, environment, and developmental time stage, reinforcing the relevance of high-dimensional root phenotyping. This also demonstrates not only the complexity of root system architectures, but its propensity to change under different contexts and genetic influences, and its ability to adapt to various conditions.We have shown that using X-ray computed tomography, changes in 3D root architecture between different treatments and conditions in the field can be measured in a biologically interpretable way, while also with high precision and detail. For example, soil moisture conditions affect the solidity of the root system, particularly in the midportion of the crown. In contrast, changes over time influence not only the overall size of the root crown but also solidity in the upper and lower portions of the crown. Interestingly, in both contexts, the depth  of the root crown also was a distinguishing feature. This is likely specific to root crowns excavated using the root pulling method, as under standard shovel excavation, the root depth would be more arbitrary unless carefully controlled for in the experimental design and execution. By using root pulling, however, the depth is a function of the root system and the soil conditions, as these determine where the root crown breaks and therefore encapsulates some information. Finally, in many instances, the 3D architectural measurements were easily sufficient to distinguish different maize genotypes, primarily using an entirely different set of traits including specific root length, median/maximum root count, and average root radius.Arabidopsis grown on media plates, or root crowns excavated from the field. While relatively straightforward and convenient, 2D imaging does not represent true root system architecture in its natural form and therefore may omit important information. More recently, optical imaging platforms have been developed to perform 3D imaging of plants growing in gel media [To date, 2D imaging has been by far the most popular form of quantifying root system architecture, whether in el media , 39, 50.el media . Once thel media .Still, the significant overhead associated with 3D imaging and analysis of roots will be a limitation to many researchers for the foreseeable future. We also addressed this by making explicit comparisons between high information content 3D phenotypes and root pulling force, which is accessible and can be scaled to high throughput levels. RPF measurements had several significant positive correlations with 3D architecture traits including volume, surface area, total root length, number of roots, and fraction dimension. Indeed, fractal dimension was a surprisingly powerful trait, not only being highly correlated with RPF but also having high heritability and contributing significantly to differentiating root system architecture over time. This is additional evidence that fractal dimension of root systems , 63–66 iHowever, there remain sufficient sources of variance among features such that future improvements could strengthen the relationship between the above traits and RPF values. For example, we performed root pulling manually by hand, but a field robot or other form of mechanical assistance may result in more consistent RPF measurements . FurtherA better understanding of the exact nature and the potential interactions between the many root traits investigated here, such as exactly how fractal dimension and root volume together affect RPF, as well as the addition of more traits that could theoretically be calculated from 3D imaging (such as those relating to topology), may lead to additional insight into the relationship between root system architecture and RPF. As the interplay between these traits and above-ground performance such as yield and shoot biomass can be weak or inconsistent due to pleiotropy, linkage, and a gap in the understanding of root-shoot interactions, fully resolving how these traits interact would be beneficial for isolating desirable phenotypes and increasing selection efficiency within breeding applications. This would also be useful in multienvironment or stress-related studies, which typically require high sample sizes that the RPF method is well-suited for. Indeed, our study suggests that many more field-scale studies, utilizing wide-ranging conditions and germplasms, will be needed to fully characterize and understand quantitative root system architecture and genotype-environment interactions in diverse plant species such as maize.