Genetic Insights into Head-to-Body Ratios Via Deep Learning-Based Image Segmentation and Implications for Common Diseases

Domain: biology
Published: 2025-12-24
Editorial outcome: minor_revision

Abstract
Head-to-body ratios (HBRs) are important anthropometric traits with direct relevance to human growth, development, and disease risk. However, the role of the proportions between head and body remains understudied, with the genetic basis of HBRs remaining largely unexplored. By applying deep learning models to 38,202 whole-body dual-energy X-ray absorptiometry images from the UK Biobank, we generated 10 distinct HBR phenotypes based on head (length/width) and various body dimensions. Our genome-wide association analyses identify 245 significant loci, with SNP-based heritability estimates ranging from 25% to 43%. Functional annotations show that genes prioritized for HBRs are enriched in chondrocytes in skeletal tissues and oligodendrocytes across multiple brain regions. Polygenic risk scores and mendelian randomization analyses further showed that HBRs are significantly associated with risks for cardiovascular, metabolic, musculoskeletal, and neuropsychiatric diseases, underscoring their potential value as health-related biomarkers. Evolutionary analyses show that HBR-associated variants are enriched in conserved genomic regions and human accelerated regions, particularly those influencing brain development. Overall, our study provides insights into the genetic architectures of HBRs, establishes their relevance to major human diseases, and offers evolutionary context for their biological significance.

Introduction
Mammals and birds have evolved a substantially larger brain for a given body size compared to other vertebrates1,2. Among these, humans exhibit an exceptional degree of brain enlargement relative to body size3,4. Head-to-body ratio (HBR) is an important morphological indicator for describing relative brain size and reflecting normal growth and development. It is also linked to certain genetic disorders and evolutionary adaptations. A larger HBR is often thought to reflect evolutionary selection for enhanced cognitive abilities5. Abnormal HBR is associated with neurological and physical impairments that impact quality of life. For example, autism spectrum disorder is linked to atypical rates of head growth relative to height during early childhood6,7, and excessive early head size growth maybe associated with an increased risk of cancer8. Therefore, understanding the factors influencing HBR could provide valuable insights into relevant traits and diseases. Most phenotypes are influenced by both environmental and genetic factors. While evidence from fossil records and global paleoclimatic reconstructions indicated that environmental factors account for only a limited degree of variation in body and brain size9, the extent to which genetic factors contribute to variation in HBR remains unclear and warrants further investigation.

The main elements of the HBR include head length, head width, height, shoulder width, trunk length, hip width, and leg length. However, except for height, these other elements are rarely measured in large sample sizes. While genome-wide association studies (GWAS) have successfully identified numerous loci associated with height10,11, the genetic basis of HBR remains largely understudied. Recently, deep-learning techniques to noninvasive medical imaging has been proven to be an effective approach for accurately and efficiently extracting anthropometric indicators. Furthermore, the combination of genetic, phenotypic, and imaging data by national biobanks facilitates the exploration for image-derived phenotypes (IDPs) with sufficiently large sample sizes. Several genetic studies have successfully applied computer vision to generate IDPs of the retina12, distribution of body fat13, skeletal measure14, pelvic form15, and heart structure16, linking significant loci to various disorders. Benefiting from these, we can avoid large-scale manual measurements and obtain skeletal measurements with greater accuracy, enabling a deeper investigation into the genetic architectures of HBR.

In this study, we applied computer vision methods to obtain height-adjusted measurements of human head length, head width, and body dimensions (including height, shoulder width, trunk length, hip width, and leg length) from biobank-scale whole-body dual-energy X-ray absorptiometry (DXA) images. We generated 10 ratio phenotypes between the head and the body measurements. We then performed genome-wide scans on these HBR phenotypes to provide a more comprehensive assessment of the genetic factors contributing to inter-individual variations in head-related body proportions. Functional annotations showed that the prioritized HBR genes are significantly enriched in somatotropes of the pituitary gland and oligodendrocytes in multiple brain regions. Specifically, we found that loci associated with HBR phenotypes are significantly enriched both in human accelerated regions and regulatory elements of differentially expressed genes between humans and great apes during development. Additionally, we evaluated the phenotypic correlations, genetic risks, and causal relationships between HBRs and common diseases, with a focus on cardiovascular, musculoskeletal, metabolic, and neuropsychiatric disorders. The overall design is shown in Fig. 1.

Results
HBRs computation from biobank-scale imaging data using deep-learning
We originally obtained 152,326 whole-body DXA images from UK Biobank (application number 46387). We developed a series of ResNet-15217 models to perform image quality control procedures, including selecting whole-body transparent images, removing cropping artifacts, excluding images with contrast abnormality, and eliminating non-frontal head view images. To enhance the processing capability of models, we processed the images with black and white backgrounds separately (detailed in the Methods). After quality control, a total of 48,410 images were remained (Fig. 2a). To facilitate subsequent GWAS analysis, we only kept the images derived from British white individuals with available genetic data. Finally, data from 38,202 individuals were remained. These individuals are aged between 46 and 86 years, reflecting adult morphology. We report baseline information about this analyzed cohort in Supplementary Data 1.

After data quality control, we manually annotated the head mask, which highlights the region of the head in the image, and six pixel-level landmarks (two shoulder joints, two hip joints and two ankle joints) on 400 images under the guidance from orthopedic doctors as training data. We applied computer vision architectures based on the U-Net framework18, using ResNet-15217 as the encoder, for head segmentation and landmarks estimation (Fig. 2b). Upon training, both head segmentation models for background images achieved a Dice loss below 0.0146 and an average intersection-over-union (IoU) score above 0.9744 on the test set. The six landmark models achieved Dice losses ranging from 0.0615 to 0.0857, and IoU scores ranging from 0.8479 to 0.8849. Each model achieved comparable performance on the validation and test datasets, demonstrating that the models were not overfitted and possessed good generalization capabilities (Supplementary Data 3). The detailed workflow for image processing and segmentation is shown in Supplementary Fig. 1.

After training and validating the deep-learning model on the 400 manually annotated images, we applied this model to segment the head and 6 landmarks on the remaining 48,410 whole-body DXA images. We defined height as the distance from the upper edge of the head to the ankle landmark. Head length was defined as the distance from the crown to the chin, while head width was measured as the maximum horizontal span across the skull, taken at the point where the head is widest. Shoulder width was defined as the distance between the two shoulder joints, and trunk length was defined as the vertical distance from the chin to the hip joint. Hip width was defined as the distance between the two hip joints, and leg length was defined as the vertical distance from the hip joint to the ankle joint. Using these measurements, we calculated 10 HBR phenotypes, namely the head length to body height ratio (LHR), head width to body height ratio (WHR), head length to shoulder width ratio (LSR), head width to shoulder width ratio (WSR), head length to trunk length ratio (LTR), head width to trunk length ratio (WTR), head length to hip width ratio (LHiR), head width to hip width ratio (WHiR), head length to leg length ratio (LLeR), head width to leg length ratio (WLeR), as shown in Fig. 2c and Supplementary Data 7.

Validation of HBRs estimates
To validate the robustness of the 10 HBR phenotypes, we first compared predictions of our models against values derived from manually annotated masks on a 40-image test set. This comparison yielded Pearson correlation coefficients all above 0.98 and normalized mean squared errors between 0.0202 and 0.0374 (Supplementary Fig. 3a, Supplementary Data 6). We further evaluated the reproducibility of our measurements through three tests. First, the correlation between torso length and leg length derived from the left and right limbs was 0.9250 and 0.9938, respectively, indicating high internal consistency (Supplementary Fig. 3b). Second, using repeat scans from 3632 individuals obtained at an average interval of two years, the test-retest correlation for HBRs ranged from 0.8435 to 0.9685 (Supplementary Fig. 3c). Third, to assess robustness to technical variations, we examined 500 samples with images containing two different background colors and found that the HBR correlations remained high, ranging from 0.8487 to 0.9728 (Supplementary Fig. 3d). We also downloaded the GWAS summary data of head width generated by Xu et al.15, the genetic correlation analysis result with our GWAS data for head width showed a relatively high genetic correlation with r=0.7705. These results demonstrate that the IDPs generated by our deep learning model are highly reproducible.

With the reliability of the HBR measures established, we next investigated their population-level characteristics. We observed that all the HBR values conform to the characteristics of a normal distribution (Supplementary Fig. 3e), and a moderate negative correlation between HBR phenotypes and height (r2 ranged from 0.0447 to 0.4974, Supplementary Fig. 3f).

Genome-wide association analyses on HBRs
We performed GWASs using imputed genotype data in UKB to identify variants associated with the 10 HBR phenotypes. After quality control (Supplementary Data 8), 38,202 individuals of white ancestry from British and 7,459,980 common biallelic single-nucleotide polymorphisms (SNPs) were included in our analyses.

Across the 10 HBR phenotypes, our GWAS identified a total of 7394 significant (p<5 × 10−8) SNPs located at 245 independent loci (Fig. 3a-b, Supplementary Fig. 4). After conditioning on all lead SNPs of independent loci discovered in a saturated GWAS for height11, 46 loci remained significant (Supplementary Data 10). The minimal deviation of univariate LD Score regression (LDSC)19 intercepts from 1.0 suggested that this inflation was attributed to polygenicity rather than to confounding (Fig. 3b). Based on the generated summary statistics for each HBR, the proportion of phenotype variance explained by SNPs for all HBR phenotypes ranged from 25.27% to 42.86%, indicating that HBRs are moderately heritable (Fig. 3c).

To investigate the extent of genetic overlap among the HBRs, we calculated the genetic correlation between each pair. All HBRs showed positive phenotypic correlations (r ranged from 0.1516 to 0.9436), and the genetic correlations between HBRs were also positive, ranging from 0.1968 to 0.9309 (Supplementary Fig. 5, Supplementary Data 11). We observed that phenotypes divided by trunk length and leg length were indeed highly correlated with those divided by height (r > 0.8). To be concise, the results for four HBRs based on trunk and leg length are only presented in the Supplementary Data 12–25.

To identify causal variants associated with HBRs, we conducted fine-mapping and identified 61 loci that had five or fewer causal variants within the 95% credible set, and two loci that had one causal variants (Supplementary Fig. 6, Supplementary Data 12). For example, FINEMAP20 nominated rs41271299 as the sole putative causal variant in the 6q22.3 locus associated with LLeR. Moreover, it remained significantly associated with LLeR in height-conditioned analyses, suggesting its association with LLeR is independent from height.

Gene prioritization of HBRs
We used four methods for gene prioritization: genomic annotation using ANNOVAR21, transcriptome-wide association study (TWAS22), GWAS colocalization analysis (COLOC23), and summary data-based mendelian randomization (SMR24) by integrating GWAS and eQTL data on the brain and whole blood from the GTEx project (V8). For TWAS and SMR, we retained genes that remained significant after multiple testing correction. For colocalization analysis, we set the threshold of posterior probability of hypothesis 4 (PPH4, indicating shared causal association between GWAS and eQTL) as 0.9. Finally, we identified 608 protein-coding genes associated with HBRs supported by at least one of the four analysis results (Fig. 4a and Supplementary Data 13).

Nearly half (285/608) of these prioritized genes are associated with more than one HBR, and more than 20% of these genes are linked to at least three HBRs. For example, WNT16 was found to be associated with WHR, WHiR, and WSR, as shown in Fig. 4b. WNT16 gene has been proposed to signal via the non-canonical pathway, influencing bone mineral density, cortical bone thickness, and bone strength25. Additionally, WNT16 had lead variants in the GWAS of brain structural connectome26, and includes GWAS signals associated with brain volume measurement27.

Biological insights from HBRs associations
To identify relevant biological processes or pathway for the prioritized genes, we performed gene set enrichment analysis for each HBR separately using data from the Gene Ontology (biological process category, GO-BP)28 and Kyoto Encyclopedia of Genes and Genomes (KEGG)29 database. This analysis revealed that the prioritized genes are enriched in 32 GO-BP terms (Fig. 4c), most of which are related to organ development, skeletal and connective tissue development, and growth hormone signaling (Supplementary Data 14). The prioritized gene sets are also enriched in 11 KEGG pathways directly associated with body development and metabolism (Fig. 4d), such as growth hormone synthesis, secretion, and action pathway30. These genes are also enriched in the cardiovascular JAK-STAT signaling pathway, whose dysregulation may lead to inflammation and cardiovascular diseases31 (Supplementary Data 15).

We further evaluated whether the prioritized genes are associated with skeletal or brain-related phenotypes in the mouse using the Mouse Genome Informatics (MGI) database32. The results indicate that, in addition to being broadly annotated with various phenotypes related to mouse body length, the HBR prioritized genes are also associated with six distinct brain-related phenotypes, including “abnormal forebrain development” and “abnormal neurocranium morphology” (Fig. 4e, Supplementary Data 16).

Cell types associated with HBRs
To identify relevant cells exhibiting excess expression across disease-associated genes implicated by GWAS, we performed single-cell disease-relevance score (scDRS)33 analysis using single-cell RNA sequencing data from 11 brain regions classified according to conventional brain structures34,35, as well as five human skeletal tissues (cranium, skull base, shoulder, hip, knee)36. The single-cell data comprised 3750 to 134,262 cells for brain-related datasets and 49,263 to 100,869 cells for skeletal-related datasets.

We identified 44 cell type-phenotype association pairs across brain regions and skeletal tissues (Supplementary Data 13). Notably, oligodendrocytes from five brain regions (cerebral cortex, cerebral nuclei, hippocampal formation, hypothalamus, and thalamic complex) were found to be associated with six HBRs (LHR, LHiR, LLeR, WHR, WHiR and WLeR). In addition, somatotropes in the pituitary tissue were also associated with WHR, and WSR. In skeletal tissues, WHR was associated with chondrocytes in all five skeletal tissues, while LSR was linked to early progenitors and endothelial cells in the shoulder. Furthermore, the two types of bone tissue cells were associated with corresponding body proportion phenotypes, specifically, LHiR was linked to chondrogenic and osteogenic cells in the hip, and WLeR was associated with chondrogenic cells in the knee (Fig. 5). These findings highlight the critical role of these cell types in the development of HBR phenotypes.

Evolutionary analysis
Both mammals and birds have evolved substantially larger brains relative to their body size compared to other vertebrates37, which might be related to the shared evolutionary characteristics between the two groups. Therefore, we investigated whether the variants associated with HBR phenotypes are enriched in genomic regions conserved between birds and mammals. We excluded genomic regions shared with reptiles and amphibians, focusing only on the genomic regions conserved between mammals and birds38 (Fig. 6a left). By comparing the distribution of HBR-associated variants to the matched randomly selected background SNPs, we found that HBR-associated variants are significantly enriched in the conserved genomic regions of mammals and birds, excluding those shared with reptiles and amphibians (p<0.002, Fig. 6a right, Supplementary Data 18). These results suggest that mammals and birds may have evolved larger brains independently, but through similar genetic mechanisms, implying that convergent evolution might be at play, where different lineages adopt similar genetic pathways to achieve a common phenotypic trait of an increased brain size relative to body size.

Considering relatively larger brain size in humans contributes to superior cognitive abilities compared to non-human primates39, we also investigated whether variants associated with HBR phenotypes overlapped with human accelerated regions (HARs) more than expected. HARs are genomic elements that are highly conserved across vertebrate and great ape evolution but exhibit significantly accelerated substitution rates in human40. The results showed that genetic signals from five HBR phenotypes (WHR, WSR, WTR, LHR, and WLeR), particularly those related with head width, are significantly enriched in HARs (FDR-adjusted p<0.05, Fig. 6b, Supplementary Data 19). Similarly, traits related to neurological and cognitive disorders (e.g., autism spectrum disorder and cognitive abilities) are also enriched in HARs, although not as significantly as the HBR phenotypes. In contrast, skeletal diseases such as joint pain and rheumatoid arthritis (RA) are not significantly enriched. These results suggest that genetic variants associated with HBR may be more closely linked to the evolution of human brain development.

We further examined the enrichment of genomic annotations reflecting divergence at various evolutionary timepoints from great apes to Homo sapiens. These annotations include regions that exhibit differences in epigenetic elements (such as enhancers and promoters) between humans and primates during early developmental stages41, as well as that acquired novel functions in the adult brain after the human divergence from rhesus macaques and chimpanzees42. Except for LSR, LTR, and WSR, most HBR phenotypes showed extensive enrichment in regulatory regions linked to human-ape differences during both fetal development and adulthood (Fig. 6c, Supplementary Data 20). This enrichment indicates that genetic variants associated with HBR phenotypes may result from evolutionary changes at critical developmental stages, influencing early growth and adult traits in humans compared to great apes. Moreover, we examined the enrichment of selection in the modern human lineage since diverging from the common ancestor with Neanderthals and Denisovans43.

The results showed that all head-width-related HBRs were significantly enriched (adjusted p<0.05) in depleted regions of archaic human genomes, whereas head-length-related HBRs were not (Fig. 6d, Supplementary Data 20). These results suggest that the further evolution of head-width-related HBRs after the divergence from great apes is associated with specific adaptations in modern humans, while head-length-related HBRs may have a weaker functional relationship with adaptive evolution. This disparity may be due to the more direct influence of head width on brain capacity compared to head length.

Phenotypic and genetic association of HBRs with common diseases
We mainly focus on diseases of the five major systems, including psychiatric44,45, neurological44,45, skeletal46, cardiovascular50,51, and metabolic diseases47,48, which may have potential relationships with HBRs according to published studies. We utilized the ICD-10 diagnoses from the UK Biobank and only common diseases with more than 1000 cases in the cohort were included in our analyses (Supplementary Data 21).

As shown in Fig. 7a and Supplementary Data 22, among the 133 significant association pairs, 78.95% exhibit negative correlations. For example, all 10 HBRs were negatively associated with atrial fibrillation (AF), and previous research corroborates this by demonstrating an association between AF and smaller brain volume49. We also observed that WHR was positively associated with ten diseases, such as hypertension (OR=1.09, adjusted p=1.07 × 10-8) and mononeuropathies of the upper limb (ULMN, OR=1.26, adjusted p=1.99 × 10-11). These results suggest that the HBR phenotypes are biologically meaningful and can reflect underlying health traits.

We next investigated whether the PRS generated from our GWAS results could predict the status of common diseases. To avoid overlap between the GWAS cohort and the tested individuals, data from 315,392 UKB participants of white British ancestry who did not have DXA image data were used for PRS analyses. PRs were generated using Bayesian regression with a continuous shrinkage prior50. Logistic regressions were then used to examine the relationship between the generated PRS and disease outcomes. As shown in Fig. 7b and Supplementary Data 23, after applying the Benjamini-Hochberg (BH) correction, most of the HBR phenotypes are associated with a trend toward lower disease risk (84.78%). This includes cardiovascular diseases such as AF, heart failure, and varicose veins, as well as metabolic or musculoskeletal disorders like type 2 diabetes and RA. However, eight HBR phenotypes are linked to an increased risk of developing ULMN, the plausibility of this association is supported by previous studies that have described connections between peripheral nerve conditions and brain structure51,52. These findings suggest that PRS effectively capture genetic risk factors of HBR associated with these diseases.

Mendelian randomization to assess the causal effects of HBRs on disease status
We further applied two-sample Mendelian randomization (MR) to investigate the causal effects of HBR phenotypes on 46 common diseases from five physiological systems. Our analysis identified a total of 46 significant causal associations (Fig. 7c, Supplementary Data 25). Consistent with the phenotype correlation and PRS analysis, the majority (75.76%) of these associations are protective. Specifically, the significant causal effects of HBR phenotypes on cardiovascular, metabolic, and musculoskeletal system diseases were generally protective. LHR, WHR, LSR, and WSR were consistently negatively associated with risks of AF, RA, and osteoarthritis. However, the effects of HBR phenotypes on psychiatric disorders were predominantly risk-increasing, with a notable exception being WHiR, which was negatively associated with the risk of schizophrenia (OR=0.95, 95% CI: 0.93 to 0.98, adjusted p=0.006). For example, WHR, WHiR, and LTR were risk factors for Alzheimer’s disease, and WSR was positively associated with the risk of bipolar disorder (OR=1.06, 95% CI: 1.02 to 1.09, adjusted p=0.002).

Methods
UKB participants and dataset
All analyses were performed using data from the UKB unless otherwise specified. The UKB is a large-scale, prospective, population-based cohort study that recruited 500,000 participants in the United Kingdom via mailed invitations starting in 200685. For this study, we analyzed data from 486,737 participants with available genetic data who had not withdrawn their consent as of August 20, 2020. Among these participants, approximately 80,000 had DXA imaging data released by the UK Biobank under bulk data field ID 20158, with 76,320 having accessible data as of November 13, 2023, along with baseline metadata including age, sex, and other relevant study variables.

The DXA images were acquired using an iDXA instrument (GE-Lunar, Madison, WI). For each participant, a series of up to 8 types images were captured: two whole-body images - one focusing on the skeleton and the other on adipose tissue, as well as images of the lumbar spine, the lateral spine from L4 to T4, each knee, and each hip. The bulk download provided 76,320 zip files, each corresponding to a specific patient identifier, commonly referred to as each EID of patient. Each zip file contains several DXA images of the patient, though not necessarily all, as described above.

Sample size selection for deep-learning models
Our sample size selection procedure was conducted as a two-stage process to ensure both statistical validity and practical utility. First, we employed different models to sequentially remove different categories of unqualified images (e.g., incomplete, abnormal contrast or resolution, and exhibited non-frontal head posture). The proportions of different types of unqualified images are different in the dataset. To ensure robust feature learning, we manually annotated varying sample sizes per model to include at least 50 instances of each unqualified category, following the guidance of previous studies86,87. Next, using the manually annotated data, we employed a progressive sampling strategy: iteratively expanding training sets until performance gains plateaued. Specifically, for each sample size, we performed 30 iterations of randomly splitting the dataset (80% training, 10% validation, 10% testing). In each iteration, we recorded the model’s loss and evaluation metric (AUC or IoU) on the test set of each task.

As shown in the Supplementary Fig. 2a and Supplementary Fig. 2b, the model performance (AUC↑ or IoU↑, Loss↓) improved significantly with more data initially but eventually plateaued, indicating that further sample increases yielded negligible gains. The final sample size for each model was chosen as the largest number within this stable performance plateau, ensuring we leveraged our annotated data efficiently. This resulted in the final sample sizes: 300 for body-class model, 500 for crop-class model, 800 for contrast-class and pose-class model, and 200 for all segmentation models

Classification to extract whole-body skeleton DXA images
We utilized Pydicom88 to extract metadata from the DICOM files, screened 152,326 “Total Body” images from 71,167 participants, converted the images into PNG and NPY formats, and excluded low-quality images with PNG file sizes of less than 40 KB. To extract the skeleton radiographs, we built a U-Net-based 2-classification model to classify two whole-body images.

We randomly selected 300 images for manual annotation, and randomly divided them into training, validation, and testing datasets in an 8:1:1 ratio (the same dataset partitioning strategy was used for all subsequent models). Using ResNet-15217 in the Python library Torchvision (v0.16.1)89 as encoder, using cross entropy as loss function, we trained for up to 10 epochs, and kept the best model on the validation set, which also had 100% accuracy on the test set. This classifier was run on all DXA images obtained from the UKB. After classification and removal of images, we were left with 76,153 whole-body skeleton DXA images (Supplementary Data 2).

Removal of poorly cropped images
After we determined the final set of whole-body DXA images, we performed additional quality control to remove images that were improperly cropped or had portions of the ankles or shoulders cut off. To do this, we created a binary classifier using Torchvision to differentiate between cropped and non-cropped images. We randomly selected 500 images for manual annotation. A CNN based on the ResNet-152 architecture was trained on this data for up to 30 epochs using cross-entropy as the loss function. The results had an area under the ROC curve (AUC) of 1.00 on validation data and testing data (Supplementary Data 3). Removal of all the cropped images resulted in a total of 69,025 whole-body images that we used for analysis.

Image standardization and background differentiation
The whole-body DXA images are varied in both pixel dimension and background, which will limit the performance of the segmentation model. In all the subsequent deep learning models, we trained two separate series of models, one for black background and one for white background. Broadly, the images comprised two main size specifications: black-background images were typically 681-811 by 272 pixels, while white-background images were generally 936-943 by 316-372 pixels. All images were padded to a standardized maximum height and width (960 × 384 pixels) using the methods described in ref. 14. After removing 15 images with abnormal background pixels, we retained 51,432 black background and 17,593 white background complete whole-body skeleton DXA images, respectively.

Removal of images with abnormal contrast
In the whole-body images with both backgrounds, we found that some images had too high contrast due to the UKB image processing, making it difficult to distinguish the details of the bones. To remove these images, we created a binary classifier using Torchvision for each background type. We randomly selected and manually annotated 800 images for each background. These images were trained for up to 30 epochs using a CNN based on the ResNet-152 architecture, with cross-entropy employed as the loss function. The results had an AUC of 1.00 on validation data and testing data of both backgrounds (Supplementary Data 3). After removing images with contrast anomalies, we retained 45,514 black background and 14,597 white background complete whole-body skeleton DXA images, respectively.

Classification of head pose
Since the head posture of the participants will affect the projected shape of the head when acquiring DXA images, we built a binary classification model to distinguish between images with direct gaze and non-direct gaze postures. We also randomly selected and manually annotated 800 images for each background. These images trained for up to 30 epochs using a CNN based on the ResNet-152 architecture, with cross-entropy employed as the loss function. The results show that the validation set AUC is 0.9732 and the test set AUC is 0.9683 in the black background images, and the validation set AUC is 0.9769 and the test set AUC is 1.00 in the white background images (Supplementary Data 3). After removing non-frontal images, we retained 36,319 black background and 12,091 white background complete whole-body skeleton DXA images, respectively. To ensure these filtering steps did not introduce selection bias, we then analyzed its effect on key phenotypes. Statistical analysis confirmed a negligible effect on both sex and age distributions (Tables S4 and S5). Although the Chi-squared tests for sex yielded significant p-values, the corresponding Cramér’s V values90 were all near or below 0.1. Likewise, Cohen’s d values91 from t-tests for age were all below 0.2, indicating a negligible impact.

Deep learning-based image segmentation models for identifying head and joint landmarks
To train our deep learning models, we manually annotated a total of 400 images, with 200 on a black background and 200 on a white background, under the guidance of the orthopedic doctors. We used 160 images of each type for training, and the rest were evenly split for validation and testing. The images that were chosen for this training dataset had an equal number of male and female individuals, were from the white British population group, and sampled equally across the age distribution of the UKB cohort.

We used the open-source annotation tool LabelMe92 in polygon mode to outline the head, and to label six body landmarks (left shoulder, right shoulder, left hip, right hip, left ankle, and right ankle). For masking each of these landmarks, the locations specified below were chosen because they were the easiest and most consistent to identify across all the images, which featured slightly different poses. The center of the head of the humerus was chosen to be masked for each shoulder landmarks. The center point of the tibial plateau was selected as the masking point for each hip joint landmark. The point where the ends of the tibia, fibula, and talus converge was chosen to be masked for each ankle landmarks. An example of the annotation of one image is shown in Fig. 2b with landmarks placed at each of the locations listed above.

We applied the U-Net architecture models with ResNet-152 encoders from the library Segmentation Models Pytorch (SMP, v0.3.3) to perform image segmentation on the head and the six joint landmarks. The default pre-trained weights provided by SMP were used. Dice loss was employed as the loss function, and training was conducted for up to 50 epochs, retaining the model with the best performance on the validation set for testing.

Obtaining head and body measurements and calculating HBRs
A major issue in integrating our analysis across different input pixel ratios was that these ratios corresponded to varying resolution scales, likely due to the differing distances at which the scanner was held above the patient. For instance, in one image, a pixel might represent 0.44 cm, while in another it could represent 0.46 cm. To address this scaling issue and standardize the images, we decided to regress the height directly measured on the image using the top of the head and the midpoint of the two ankle landmarks, which could be consistently identified across all image pixel ratios. We also incorporated overall height data extracted from the DICOM metainformation. Although the height measurements we used did not include the area below the ankles, they were relative measurements, and we utilized them to derive a scaling factor for the pixel ratio in each image to achieve standardization.

We calculated the head length and head width based on the bounding box around the head, and the shoulder width, trunk length, hip width, and leg length based on the center points of the landmarks. Individuals whose length measurements deviated from the mean by more than 3 standard deviations were excluded from the analysis. We calculated the ratios of head length and head width to the corresponding body measurements. A list of these HBRs can be found in Supplementary Data 7.

Participant and Genetic data quality control
For genome-wide association analyses, we filtered participants with correctly labeled whole-body DXA images (FID 20158) to include only Caucasian individuals (FID 22006) from the White British population, as determined by genetic PCA (FID 21000). We removed individuals who were outliers for heterozygosity or genotype missingness rates, as determined by UKB quality control of sample processing and DNA preparation for genotyping (FID 22027), individuals with missingness rates greater than 2% (FID 22005), and individuals with a kinship coefficient greater than 0.0442 (corresponding to at least one third-to fourth-degree relative). A total of 38,202 individuals remained (Supplementary Data 8).

Imputed genetic data for 486,737 individuals was downloaded from UKB for chromosomes 1 to 22 (FID 22828) and subsequently filtered to the quality-controlled subset using PLINK293. All duplicate SNPs were excluded (--rm-dup exclude-all) and only biallelic sites were retained (--snps-only just-acgt), with a maximum of 2 alleles (--max-alleles 2), a minor allele frequency of at least 1% (--maf 0.01), and genotype missingness no greater than 2% (--geno 0.02). A total of 7,459,980 SNPs remained in the final dataset.

GWAS and heritability analysis
Each HBR was used as a ratio phenotype in a GWAS performed with BOLT-LMM94. Covariates included the first 20 genetic principal components (FID 22009) provided by UKB, sex (FID 31), age (FID 21003), and “BACKGROUND”. Additionally, the DXA scanner serial number and the software version used for image processing were combined into a single categorical covariate with seven categories. Independent SNPs in each resulting GWAS were calculated using GCTA COJO24 (--cojo-slct) with a significance threshold of 5.0 × 10⁻⁸, and a window size of 1 Mbp for SNP heritability. Genetic correlations were calculated using LDSC. We then obtained all genome-wide significant, independent loci (n=7209) from Yengo et al.11, which were used in a conditional analysis with GCTA COJO for each of the HBR phenotypes.

Gene prioritization and functional annotation
For gene prioritization, we employed TWAS, COLOC, and SMR using eQTL data from GTEx project (V8)95. For each of the 10 HBRs, we utilized whole blood and 14 head-related and brain-related tissues (details in Supplementary Data 13). All tissues were pooled and corrected together, retaining genes that were significant in any tissue with an adjusted p-value after BH correction of less than 0.01. For COLOC, genes with a PPH4 > 0.9 were retained. We performed TWAS, SMR, and COLOC based on inferred cis-regulated gene expression using MR-JTI22, coloc23 and GCTA24 with default settings. The gene expression weights for TWAS and COLOC were downloaded from https://zenodo.org/records/3842289, and the eQTL summary data for SMR were downloaded from https://yanglab.westlake.edu.cn/software/smr/#DataResource. Pathway enrichment analysis was separately performed for each individual HBR using the R package clusterProfiler96. The resulting terms were adjusted for multiple comparisons using BH correction method, and terms with fewer than five hit genes were excluded. Relevant MGI phenotypes were manually selected from the mouse phenotype descriptions that included the terms “bone,” “skeletal,” “head,” and “brain.” Subsequently, we counted the number of genes annotated to these phenotypes within each HBR.

Associating cell types with HBR phenotypes
In this study, we analyzed the publicly available human embryonic skeletal snRNA-seq data36, human embryonic pituitary scRNA-seq dataset35 and adult human brain snRNA-seq dataset34, retaining the major tissues and cell types as defined in the original study. We used scDRS (v1.0.2) to link the scRNA-seq data with polygenic risk at a single-cell resolution, independent of cell type. For each type of HBR, polygenic scores were computed based on GWAS z-scores and scRNA-seq expression values. Cell-specific association p-values were determined by comparing the normalized disease scores to an empirical distribution of normalized scores generated from all control gene sets across all cells. We retained HBR phenotype–cell type associations with “assoc mcp” values less than 0.05 in each tissue.

Enrichment analysis for regions of evolutionary context
The conserved regions in the bird genome38 were converted to the hg19 reference genome using the UCSC liftOver tool, and the intersecting regions with mammalian conserved regions were identified. We extracted the intersection of alignments between five species of reptiles or amphibians and the human genome from the NCBI Comparative Genome Viewer. These genomic regions in reptiles were then marked within the shared conserved regions between birds and mammals. All other evolutionary genomic annotations were derived from Kun et al.14,97. Additionally, as a control for HAR enrichment, we selected the psychiatric disorders mentioned earlier and performed the same enrichment permutation experiment on the significant loci. We then used SNPsnap98 to assess the enrichment of each HBR-associated SNP based on MAF, the number of SNPs in LD under different LD thresholds, distance to the nearest gene, and gene density, using 1000 sets of null-matched SNPs for comparison (p<5 × 10−8). We extended the 500 bp upstream and downstream regions of both HBR SNPs and a random set of null-matched SNPs, respectively, to calculate their intersections with the annotation regions. The number of hits where the matched SNPs had equal or greater hits than the HBR SNPs was counted, and the ratio of this number to the number of random iterations was used as the p-value for the one-sided permutation test. The odds ratio was calculated as median value of the permutation tests. Finally, all p-values for HBRs and genomic annotations were adjusted for multiple comparisons using the BH correction method.

Phenotypic association of HBR with common diseases
We defined disease based on the annotation information in diagnoses of ICD-10 (Field 41270) in UKBB. In diagnoses of ICD10, diseases are categorized into different classes. We selected 46 common diseases from five categories: psychiatric, neurological, musculoskeletal system, circulatory system, and metabolic system. The selection was performed at the second hierarchical level of the ICD-10 structure (three-character codes), except for Pian. Only common diseases with more than 1000 cases in the cohort were included in our analyses. Patients received a “1” if a disease code appeared in their hospital records, and a “0” otherwise. The ICD-10 code and details for the diseases used in our study are shown in Supplementary Data 21. We counted the number of samples for each disease, used sex, age, imaging equipment, image background and first 10 principal components as covariates, performed logistic regression using the logistic regression method “Logit” of the statsmodels99 python library, calculated the exponent of the coefficients to get the odds ratio, and used the BH method to correct the p-value of the regressions.

Polygenic risk score analyses and logistic regression
We utilized PRS-CS50 to construct polygenic risk score (PRS) prediction models. PRS-CS is a command-line tool based on Python that uses GWAS summary statistics and external LD reference panels to infer the posterior effect sizes of SNPs under a continuous shrinkage prior. In our model, we incorporated 1,117,425 SNPs from HapMap3 and performed clumping using PLINK2-clump with parameters “r2=0.01” and “kb=250”. For the reference LD panel, we used the 1000 Genomes (1000G) data for the European population. The output from PRS-CS includes the chromosome, rsID, base position, A1, A2, and posterior effect size estimates for each SNP. We concatenated the output files for all chromosomes and then used the PLINK2 --score command to calculate the PRS for all genotyped non-imaged individuals of white British ancestry (who had also undergone genetic QC), with a sample size of 315,404 participants after excluding related individuals. We subsequently performed logistic regression for 46 manually selected diseases using the same method as for the phenotypic logistic regression, based on the polygenic scores.

Mendelian randomization
We collected summary statistics of GWAS for 46 diseases across five categories from several sources, including FinnGen (release R11)100, Million Veteran Program (MVP)101, the Psychiatric Genomics Consortium studies (PGC)102 and Social Science Genetic Association Consortium103. For diseases with multiple available summary statistics, the dataset with the largest effective sample size was selected for our analysis (Supplementary Data 21). We investigated the causal association between these diseases and HBRs using two-sample Mendelian randomization.

We first selected independent SNPs for each exposure (r2=0.001, window size=1 Mb, and p<5 × 10−8) using the clustering algorithm in PLINK2. The 1000G European data were used as a reference for LD estimation. For the IVs, three key assumptions must hold: 1) the selected IVs must be associated with the exposure (relevance assumption); 2) the selected IVs are not associated with potential confounders (independence assumption), and 3) the IVs affect the outcome only through their effect on the exposure (exclusion restriction assumption). We used RadialMR104 package to remove pleiotropic SNPs. The remaining SNPs were used to perform MR analysis. We used MR Steiger filtering to check whether the MR analysis estimates assessed the true causal direction105. We performed MR testing using the “mr_ivw” method with the TwoSampleMR106 R package, followed by BH correction for all MR test results.

For the significant MR results, we performed a further sensitivity analysis. First, we performed leave-one-out analysis to check whether the causal association was obviously driven by a single SNP (p-value<0.05 was regarded as an outlier). Second, we conducted MR-PRESSO107 to detect the presence of horizontal pleiotropy (p-value<0.05). Third, we executed MR-Egger regression to examine the potential bias of directional pleiotropy. The intercept in the Egger regression indicates the mean pleiotropic effect of all genetic variants, which is interpreted as evidence of directional pleiotropy when the value differs from zero (p-value<0.05). Cochran’s Q and Rucker’s Qʹ statistics were also calculated to check for the presence of heterogeneity for the Inverse-Variance Weighted and MR-Egger method, respectively.

Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability
The data from UK Biobank used in this study are available through a procedure described at: https://www.ukbiobank.ac.uk/enable-your-research. All public data sources are cited in the corresponding sections of the Methods. The GWAS summary statistics used in the MR analysis can be obtained from the URLs listed in Supplementary Data 21. The GWAS summary data generated in this study have been deposited in the Zenodo database under accession code 14835684 https://doi.org/10.5281/zenodo.17421944108 and GWAS Catalog database under accession IDs GCST90702447-GCST90702456 (https://www.ebi.ac.uk/gwas/).

Code availability
The codes used for quality control of the DXA images and for performing deep learning-based segmentation of the head and key landmarks are available at https://github.com/weishaoxia/head-body-ratio and archived at Zenodo https://doi.org/10.5281/zenodo.17422184109. We carried out all deep learning using the Python programming language (v3.9.23) with the PyTorch (v2.7.1+cu128) and cv2 (v4.12.0) libraries on NVIDIA RTX 5090 GPUs on the CentOS Linux system the CUDA (v12.8) toolkit.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Review of Shi et al, by Vagheesh M. Narasimhan. I sign all my reviews after Alexey Kondrashov signed his review of the first paper I ever published.\n\nThis paper uses new deep learning methodologies to understand the very interesting trait of head to body ratio. The main motivation for this is of course evolutionary, humans have developed much larger brains to given body size and therefore this phenotype is one that has changed considerably in recent human evolution. By using deep learning to identify the genetic basis of this phenotype the researchers are able to answer new questions that were previously not possible to be addressed. While the associations with disease are not very specific and don’t elucidate any pathways, the evolutionary analysis is quite interesting and provides a genetic support for the evolution of the human brain and helps to time evolutionary change using the enrichment in functional genomic annotations. I just have a few comments to help enrich the interpretability and readability of the results:\n\n1) When performing their GWAS, I wasn’t able to determine if they simply divided their phenotypes by height/shoulder width or did they control for height/shoulder width in their GWAS. These two approaches while quite similar are subtly different and controlling for these traits in the GWAS rather than simple division seems to result in less of an association with overall body size. We recently examined the differences between these approaches in Xu et al, Science, 2025 (appearing on the April 11th issue), and also reports GWAS of head width, and examines the relationship between head width and pelvic width.\n2) In the association between HBRs and disease, what was the hypothesis that was being tested? Why were these diseases chosen? Is there any relationship between diseases that affect particular parts of the body and the phenotypes that they are measuring? Without this, broadly showing that a phenotype is associated with health outcomes is not meaningful. The main trait being examined here is head length and width. Are these specifically associated with neuropsychiatric disorders or traits?\n3) Genetic examination of the human brain traits in the UKBiobank actually report several summary statistics of association with brain volume. As brain volume is likely correlated with HBRs could they perform a genetic correlation analysis between the traits? I think this would provide additional biological replication of their analysis.\n4) As body widths are highly correlated, and trunk lengths are highly correlated with height, why did the authors calculate so many of these ratios when a simple adjustment with one trait for overall length (say height) and one trait for overall width (shoulder width) may be sufficient? Is there any added value to doing this? Many of the correlations with diseases and evolutionary analysis also seem correlated so having so many traits examined seem redundant and could be moved to the supplement to make the main analysis much clearer. It is difficult to see trends in the associations when there are so many correlated traits.\n5) the github page says page not found.", "reviewer_2": "The authors used deep learning methods to identify head to body ratios from medical image and investigated the biological insights of the ratios using GWAS, PRS and other methods. It is an interesting papers. However, there are still several issues that should be addressed.\n\n1. Head-to-Body Ratios were estimated from medical image. It is the foundation of the study. It is critical to show the estimation is correct. Although the accuracy of the model was illustrated with Dice loss and IoU, there was no directly evaluation of the accuracy of the HBR estimation. High IoU may not mean high accuracy of HBR estimation. There are manually annotated images. The authors should include a supplementary figure to show the correlation between HBRs estimated from manually annotation and HBRs estimated with the model and a supplementary table of normalized mean squared error for different HBRs.\n2. There are about 76K whole-body skeleton DXA images and about 48K remained after filtering. Any bias during the filtering? Are there any phenotype (sex, age) difference before and after filtering?\n3. Deep learning is used several times in image processing. Each time, different size of samples were used to train the model. How was the sample size selected? Line 477, I think the 500 images should be randomly instead of manually selected to build the model.\n4. What's \"DS\" used in GWAS? Were related individuals excluded from the analysis?\n5. For gene prioritization, several methods were employed. Were the genes found in any method or the genes found in all methods were retained for the following analysis? Although HBRs are correlated, there are still some difference between them. Some may have opposite correlations with phenotype data. Pooling all genes together may reduce the power of pathway analysis. In Figure 4 C&D, there are 3 or less genes in a pathway, these pathways could be false positives.\n6. Why were birds used in evolutionary analysis instead of other animals?\n7. I assume the color indicates odds ratio in Figure 7A. According to the figure, there is a very weak association of PRS and those diseases (0.9-1.1). The two most significants are atrial fibrillation and Carpal Tunnel Syndrome. I did not see a strong connection between HBRs and those two diseases. How were the common diseases selected?\n\nI took a brief review of the code. The code includes a README file about how to set up the environment. However, there are few comments in the code about input data format and description of the functions."}, "author_response": "1 Response to reviewers’ comments:\n4 signed his review of the first paper I ever published.\n5\n7 body ratio. The main motivation for this is of course evolutionary, humans have developed much\n8 larger brains to given body size and therefore this phenotype is one that has changed considerably in\n9 recent human evolution. By using deep learning to identify the genetic basis of this phenotype the\n10 researchers are able to answer new questions that were previously not possible to be addressed.\n12 evolutionary analysis is quite interesting and provides a genetic support for the evolution of the\n13 human brain and helps to time evolutionary change using the enrichment in functional genomic\n14 annotations. I just have a few comments to help enrich the interpretability and readability of the\n15 results:\n16\n17 1. Comment: When performing their GWAS, I wasn’t able to determine if they simply divided\n18 their phenotypes by height/shoulder width or did they control for height/shoulder width in their\n20 traits in the GWAS rather than simple division seems to result in less of an association with overall\n21 body size. We recently examined the differences between these approaches in Xu et al, Science,\n22 2025 (appearing on the April 11th issue), and also reports GWAS of head width, and examines the\n23 relationship between head width and pelvic width.\n25\n27 investigating the overall head-to-body ratio (HBR) phenotype rather than focusing exclusively on\n28 head—we analyzed HBR by dividing our phenotype by height/shoulder width. This approach allows\n29 us to capture the proportional relationship between the head and the rest of the body. We have revised\n30 the Method section to clarify this point (Line 640). Our focus on HBR is motivated by several key\n31 considerations. Compared with metrics examining only on the head or the body, HBR provides a\n32 more comprehensive perspective. It is important not only as a key morphological trait in human\n33 evolutionary adaptation1,2, but also as a crucial indicator of growth and development, with established\n34 associations with a range of psychiatric3,4, neurological5,6, and skeletal disorders7. Furthermore, HBR\n35 offers insights into fundamental physiological processes, linking thermoregulation8 and energy\n36 metabolism9 to the risks of cardiovascular10,11 and metabolic diseases12,13.\n37\n39 two different analytical approaches: one adjusting for height/shoulder width as covariates, and the\n40 other using the ratio-based (divided) phenotype. As shown in Figure 1, although there are global\n41 genetic correlations between ratio-based phenotypes and covariate-adjustment phenotypes by LDSC,\n42 the overlapped GWAS significant loci were limited, indicating the divergence at the locus-specific\n43 level. The covariate-adjustment approach primarily focuses on the head, whereas the division method\n44 emphasizes the overall proportional phenotype. Given our interest in the overall proportional\n45 phenotype, we chose to retain the ratio-based method, which more directly reflects HBR. This\n46 approach is consistent with the methodology used in your study in 202315. We have added these results\n47 in Discussion section (Line 369) and supplementary Table S9.\n48\n50 validation of our head width measurements. We downloaded the GWAS summary data of head width\n51 generated by Xu et al., and performed genetic correlation analysis with our GWAS data for head\n52 width. The analysis result showed a relatively high genetic correlation with r = 0.77, supporting the\n53 reliability of our image-based phenotyping approach. We have added this result in Line 154.\n54\n55\n57 adjusted GWAS for 10 HBRs\n58\n59 2. Comment: In the association between HBRs and disease, what was the hypothesis that was being\n60 tested? Why were these diseases chosen? Is there any relationship between diseases that affect\n61 particular parts of the body and the phenotypes that they are measuring? Without this, broadly\n62 showing that a phenotype is associated with health outcomes is not meaningful. The main trait\n63 being examined here is head length and width. Are these specifically associated with\n64 neuropsychiatric disorders or traits?\n67 systems, including psychiatric, neurological, skeletal, cardiovascular, and metabolic diseases, which\n68 may have potential relationships with HBRs according to published studies. For example, as an\n69 important indicator of growth and development, abnormal HBR has been correlated with increased\n70 risk of macrocephaly7, autism spectrum disorder5,6 and schizophrenia3,4. In addition, HBR is linked\n71 to thermoregulation16 and energy metabolism17, and has been implicated in cardiovascular10,11 and\n72 metabolic diseases12,13. We utilized the ICD-10 diagnoses from the UK Biobank (Field ID: 41270)\n73 and only diseases with more than 1,000 cases in the cohort were included in our analyses. We have\n74 added the above statement in Line 311 and 705 to be clear.\n75\n77 the biological significance of the HBRs (Figure 7A-C in main text).\n78\n79 3. Comment: Genetic examination of the human brain traits in the UKBiobank actually report\n80 several summary statistics of association with brain volume. As brain volume is likely correlated\n81 with HBRs could they perform a genetic correlation analysis between the traits? I think this would\n82 provide additional biological replication of their analysis.\n84 summary data for brain volume: BV meta (n = 47,316; Jansen et al. 18), BV UKB (n = 17,062; Jansen\n85 et al. 18), and ICV (n = 79,174; Nawaz et al. 19), and performed genetic correlation analysis. As shown\n86 in Figure 2, the correlation r values for our HBRs and brain volume ranged from 0.2659 to 0.3954.\n88 phenotypes rather than head-specific traits. Therefore, GWAS summary data for head or brain traits\n89 only may not be suitable for the replication of our results and we didn’t use brain volume to replicate\n90 our findings.\n91\n93\n94 4. Comment: As body widths are highly correlated, and trunk lengths are highly correlated with\n95 height, why did the authors calculate so many of these ratios when a simple adjustment with one\n96 trait for overall length (say height) and one trait for overall width (shoulder width) may be\n97 sufficient? Is there any added value to doing this? Many of the correlations with diseases and\n98 evolutionary analysis also seem correlated so having so many traits examined seem redundant and\n99 could be moved to the supplement to make the main analysis much clearer. It is difficult to see\n100 trends in the associations when there are so many correlated traits.\n102 text can be streamlined to reduce redundancy and improve clarity. We performed phenotypic and\n103 genetic correlations for all the HBRs and observed that phenotypes divided by trunk length and leg\n104 length were indeed highly correlated with those divided by height (r > 0.8; Figure S5 in the\n105 supplement). To be concise, we have removed the results for four HBRs based on trunk length and\n106 leg length to the supplements to be clear. On the other hand, the correlation between hip width and\n107 shoulder width was moderate (r = 0.56). Phenotypic or genetic correlations between phenotypes\n108 derived from hip width and shoulder width were also moderate (r ranged from 0.4607 to 0.6357). In\n109 addition, over 88% of the prioritized genes for the two HBRs based on hip width were not found in\n110 the GWAS for HBRs based on shoulder width. Therefore, we kept the results derived from hip width\n111 in the main text. Finally, in the main text of the revised manuscript, 6 HBRs were remained. We also\n112 have added the above statement in Line 184. We believe that the results are now clearer. Thank you\n113 for your valuable suggestion!\n114\n115 5. Comment: the github page says page not found.\n117 its public release. The project was made public on GitHub (https://github.com/weishaoxia/head-\n118 body-ratio) in mid-April, and we have implemented updates and additional materials accordingly.\n119\n120\n122 1. Kappelman, J. The evolution of body mass and relative brain size in fossil hominids. Journal of human\n123 evolution30, 243-276 (1996).\n124 2. Grabowski, M. Bigger brains led to bigger bodies?: The correlated evolution of human brain and body size.\n126 3. Jones, G. H. & Lewis, J. E. Head circumference in elderly long-stay patients with schizophrenia. The British\n128 4. Ward, K. E., Friedman, L., Wise, A. & Schulz, S. C. Meta-analysis of brain and cranial size in schizophrenia.\n130 5. Lainhart, J. E. et al. Head circumference and height in autism: a study by the Collaborative Program of\n132 6. Chaste, P. et al. Adjusting head circumference for covariates in autism: clinical correlates of a highly heritable\n133 continuous trait. Biological Psychiatry74, 576-584 (2013).\n134 7. Williams, C. A., Dagli, A. & Battaglia, A. Genetic disorders associated with macrocephaly. American journal\n135 of medical genetics Part A146, 2023-2037 (2008).\n136 8. Will, M., Krapp, M., Stock, J. T. & Manica, A. Different environmental variables predict body and brain size\n137 evolution in Homo. Nat Commun12, 4116 (2021).\n138 9. Martin, R. D. Relative brain size and basal metabolic rate in terrestrial vertebrates. Nature293, 57-60 (1981).\n139 https://doi.org:10.1038/293057a0\n140 10. Barker, D., Godfrey, K., Osmond, C. & Bull, A. The relation of fetal length, ponderal index and head\n141 circumference to blood pressure and the risk of hypertension in adult life. Paediatric and perinatal\n142 epidemiology6, 35-44 (1992).\n143 11. Barker, D., Osmond, C., Simmonds, S. & Wield, G. The relation of small head circumference and thinness\n144 at birth to death from cardiovascular disease in adult life. British Medical Journal306, 422-426 (1993).\n145 12. Bucher, H., Prader, A. & Illig, R. Head circumference, height, bone age and weight in 103 children with\n146 congenital hypothyroidism before and during thyroid hormone replacement. Helvetica paediatrica\n147 acta40, 305-316 (1985).\n148 13. Renaud, D. L. Leukoencephalopathies associated with macrocephaly. Semin Neurol 32, 34-41 (2012).\n149 https://doi.org:10.1055/s-0032-1306384\n150 14. Xu, L. et al. The genetic architecture of and evolutionary constraints on the human pelvic form. Science\n151 388, eadq1521 (2025). https://doi.org:10.1126/science.adq1521\n152 15. Kun, E. et al. The genetic architecture and evolution of the human skeletal form. Science 381, eadf8009\n153 (2023). https://doi.org:10.1126/science.adf8009\n154 16. Naya, D. E., Naya, H. & Lessa, E. P. Brain size and thermoregulation during the evolution of the genus\n156 66-73 (2016).\n157 17. Kuzawa, C. W. et al. Metabolic costs and evolutionary implications of human brain development.\n159 18. Jansen, P. R. et al. Genome-wide meta-analysis of brain volume identifies genomic loci and genes shared\n160 with intelligence. Nat Commun11, 5606 (2020). https://doi.org:10.1038/s41467-020-19378-5\n161 19. Nawaz, M. S. et al. Thirty novel sequence variants impacting human intracranial volume. Brain Commun\n162 4, fcac271 (2022). https://doi.org:10.1093/braincomms/fcac271\nThe authors used deep learning methods to identify head to body ratios from medical image\nand investigated the biological insights of the ratios using GWAS, PRS and other methods. It\nis an interesting papers. However, there are still several issues that should be addressed.\n1. Comment: Head-to-Body Ratios were estimated from medical image. It is the foundation of\nthe study. It is critical to show the estimation is correct. Although the accuracy of the model\nwas illustrated with Dice loss and IoU, there was no directly evaluation of the accuracy of the\nHBR estimation. High IoU may not mean high accuracy of HBR estimation. There are manually\nannotated images. The authors should include a supplementary figure to show the correlation\nbetween HBRs estimated from manually annotation and HBRs estimated with the model and a\nsupplementary table of normalized mean squared error for different HBRs.\nResponse: Thank you for your constructive comments. We agree that a direct evaluation of the\nHBR estimation is critical.\nAs you suggested, we compared the ten HBRs derived from our model's predictions against\nthose calculated from the manually annotated masks for the 40 images in the test set. The results\nshowed that the Pearson correlation coefficients between model-predicted HBRs and manually\nannotated HBRs were all over 0.98, and the normalized mean squared errors were ranged from\n0.0202 to 0.0374 (Figure 1). These results support the high accuracy of HBR estimation. We\nhave added the above information in Line 143, Figure S3A and Table S6.\nFigure 1.Correlation coefficients (blue bars) and normalized mean squared errors (NMSE, red\nline) between model-predicted and manually annotated HBRs in the test set.\n2. Comment: There are about 76K whole-body skeleton DXA images and about 48K remained\nafter filtering. Any bias during the filtering? Are there any phenotype (sex, age) difference\nbefore and after filtering?\nResponse: Thank you for your comments. We filtered the dataset to exclude images that were\nincomplete (Crop), had abnormal contrast or resolution (Contrast), or exhibited non-frontal\nhead posture (Pose), as these issues could compromise the accuracy of downstream analysis.\nAs you suggested, to assess the differences in sex and age distributions before and after filtering,\nwe performed Chi-squared test and t-test. To ensure sample independence, the analyses were\nperformed by comparing the excluded versus included participants for each of the three filtering\nprocesses. Given our large sample size (N > 10,000), we were cautious of the “large-sample p-\nvalue” problem 1, wherein p-values are overly sensitive and indicate significance of negligible\npractical importance2. Accordingly, we supplemented significance testing with effect size\nmeasures, reporting Cramér’s V for the Chi-squared test3 and Cohen’s d for the t-test4. The\nconventional thresholds for small effects are Cramér’s V = 0.1 and Cohen’s d = 0.2,\nrespectively3,4.\nThe sex distribution for excluded or included samples in each filtering process is detailed in\nTable 1. While the Chi-squared tests yielded statistically significant p-values (as expected),\nthe Cramér’s V values were all near or below 0.1. This indicates that the filtering process had\na negligible effect on sex distribution. Similarly, for age distribution (Table 2), Cohen’s d\nvalues from the t-tests were all below 0.2, confirming that the filtering process also had a\nnegligible effect on age. We have added the above information in Line 565 in the manuscript\nand Table S4-S5.\nTable 1. Comparison of sex distribution between Included and Excluded participants with\ncorresponding chi-squared test results.\nIncluded Excluded Cramér's\np\nTotal Male Female Total Male Female V\nSkeletal 76,153 36,757 39,396\nCrop filtering 69,119 34,061 35,058 7,034 2,696 4,338 1.50e-68 0.063\nContrast filtering 60,111 29,490 30,621 16,042 7,267 8,775 2.74e-17 0.031\nPose filtering 48,410 20,800 27,610 27,743 15,957 11,786 <1e-308 0.140\nTable 2. Comparison of mean age between Included and Excluded participants with\ncorresponding t-test results.\nIncluded Excluded Cohen’s\nt p\nNum Mean Std Num Mean Std d\nSkeletal 76,153 65.513 9.617\nCrop filtering 69,119 65.578 9.633 7,034 64.881 9.434 -5.886 4.10e- -0.072\n46\n3. Comment: Deep learning is used several times in image processing. Each time, different\nsize of samples were used to train the model. How was the sample size selected? Line 477, I\nthink the 500 images should be randomly instead of manually selected to build the model.\nResponse:Thank you for your comments. For line 447, we are sorry for the ambiguous word\n“manually”. In fact, all 500 images we used were randomly selected to build the model. We\nhave revised this information in Line 523 to be clear.\nWe appreciate the opportunity to clarify our sample size selection procedure, which was\nconducted as a two-stage process to ensure both statistical validity and practical utility. First,\nwe employed different models to sequentially remove different categories of unqualified\nimages (e.g., incomplete, abnormal contrast or resolution, and exhibited non-frontal head\nposture). The proportions of different types of unqualified images are different in the dataset.\nTo ensure robust feature learning, we manually annotated varying sample sizes per model to\ninclude at least 50 instances of each unqualified category, following the methodology described\nin previous studies5,6. Next, using the manually annotated data, we employed a progressive\nsampling strategy: iteratively expanding training sets until performance gains plateaued.\nSpecifically, for each sample size, we performed 30 iterations of randomly splitting the dataset\n(80% training, 10% validation, 10% testing). In each iteration, we recorded the model's loss\nand evaluation metric (AUC or IoU) on the test set of each task.\nAs shown in the Figure 2 and Figure 3, the model performance (AUC↑ or IoU↑, Loss↓)\nimproved significantly with more data initially but eventually plateaued, indicating that further\nsample increases yielded negligible gains.The final sample size for each model was chosen as\nthe largest number within this stable performance plateau, ensuring we leveraged our annotated\ndata efficiently. This resulted in the final sample sizes: 300 for body-class model, 500 for crop-\nclass model, 800 for contrast-class and pose-class model, and 200 for all segmentation models.\nWe have added the above statement in Line 480 and Figure S2(A-B).\nFigure 2.Performance of classification models across different sample sizes. Solid lines represent\nmean test AUC and dashed lines represent mean test loss. Black curves correspond to the black\nbackground models, while light gray curves correspond to the white background models. Green\nopen circles indicate performance at the maximum sample size.\nFigure 3. Performance of segmentation models for different anatomical landmarks across different\nsample sizes. Solid lines represent mean test IoU and dashed lines represent mean test loss, both\nwith error bars representing the standard deviation across runs. Black curves correspond to the black\nbackground models, while light gray curves correspond to the white background models. Green\nopen circles indicate performance at the maximum sample size.\n4. Comment: What's \"DS\" used in GWAS? Were related individuals excluded from the analysis?\nResponse: Thank you for your comments. We apologize for the lack of clarity regarding the\n\"DS\" covariate. \"DS\" is an abbreviation for \"Device and Software\". This information was\nderived from the DICOM (Digital Imaging and Communications in Medicine) metadata of the\nimages, specifically from the \"Device Serial Number\" and \"Software Versions\" fields. In our\nanalysis, the DXA scanner's serial number and the software version used to process images\nwere combined into a single covariate, resulting in seven factor levels. We have now revised\nthe manuscript in Line 642 to clearly define this term to avoid any confusion.\nAs for the related individuals, in our initial analysis, we excluded related individuals based on\nthe pre-computed kinship data from the UK Biobank (Field ID: 22021). As stated in the original\nmanuscript on Line 579, we excluded 12 samples identified as \"individuals with more than nine\nthird-degree relatives or unknown kinship.\" Your comment prompted us to reconsider whether\nthis threshold was sufficiently stringent. To ensure the robustness of our findings, we performed\na more rigorous kinship analysis using KING in PLINK27,8. In refer to previous studies9,10, we\napplied a strict threshold, removing any individual with a kinship coefficient greater than\n0.0442 (corresponding to at least one third- to fourth-degree relative). This resulted in the\nexclusion of 1,016 individuals from the GWAS. Following this filtering, we ran the entire\nGWAS and all subsequent downstream analyses. We found that while the number of genome-\nwide significant SNPs decreased slightly (as shown in Table 3), the primary conclusions of our\nstudy remain unchanged. We have now revised the manuscript in Line 627 and all results are\nnow updated accordingly.\nTable 3. Number of Significant loci per GWAS for HBR Phenotypes — Original vs. Revised\nLHR LSR LTR LHiR LLeR WHR WSR WTR WHiR WLeR\nOriginal 21 11 8 25 36 39 21 21 33 65\nRevised 17 9 8 22 31 32 13 19 33 61\n5. Comment: For gene prioritization, several methods were employed. Were the genes found\nin any method or the genes found in all methods were retained for the following analysis?\nAlthough HBRs are correlated, there are still some difference between them. Some may have\nopposite correlations with phenotype data. Pooling all genes together may reduce the power of\npathway analysis. In Figure 4 C&D, there are 3 or less genes in a pathway, these pathways\ncould be false positives.\nResponse: Thank you for the comments. For gene prioritization, we retained genes that were\nidentified by any of the four methods employed (Closest Gene, TWAS, COLOC, and SMR).\nWe adopted this inclusive union strategy because each method captures different facets of a\ngene's potential link to the phenotype, detail as follows:\n Closest Gene mapping is a common annotation approach since genomic proximity often\nimplies functional relatedness;\n TWAS links genetically predicted gene expression to the trait11;\n COLOC provides strong evidence for a shared causal variant between GWAS and eQTL\nsignals12;\n SMR tests for a potential causal relationship from gene expression to the trait13.\nBy integrating the candidate genes from all four methods, we aimed to build a comprehensive\nlist for downstream functional analysis.\nAs for the pathway analysis, we apologize for our unclear description. The analysis was indeed\nperformed separately for each individual HBR phenotype. We agree with your assessment that\npathways supported by a very small number of hit genes could be false positives. In the revised\nmanuscript, only significant pathways with at least five hit genes were considered as enriched\npathways. After implementing this filter, we found that our primary conclusions remain\nunchanged. The significant pathways are still predominantly related to growth, development,\nand signal transduction. We have revised the method section in Line 662 and 665 to be clear,\nand we have updated the figures and text in the manuscript.\n6. Comment: Why were birds used in evolutionary analysis instead of other animals?\nResponse: Thank you for your comments. In evolutionary analysis, we used the conservation\nregion of birds because both birds and mammals have evolved a substantially larger brain for a\ngiven body size compared to other vertebrates14. This relative larger brain size might be related\nto the common evolutionary characteristics for both birds and mammals. For example, both\nbirds and mammals have relative higher cognitive function15. Specifically, birds with the most\ncomplex vocal learning abilities were also the best problem solvers and had the largest brains\nrelative to body size16. In addition, both birds and mammals could maintain a relatively stable\nbody temperature17, which may be associated with the role of HBR in thermoregulation.\nTherefore, we used birds in our evolutionary analysis. We have revised the statement in the\nMethod section (Line 258) and Discussion section (Line 418) accordingly to be clear.\n7. Comment: I assume the color indicates odds ratio in Figure 7A. According to the figure,\nthere is a very weak association of PRS and those diseases (0.9-1.1). The two most significants\nare atrial fibrillation and Carpal Tunnel Syndrome. I did not see a strong connection between\nHBRs and those two diseases. How were the common diseases selected?\nResponse: Thank you for your constructive comments.Yes, the magnitude of the associations\nbetween the PRS of HBRs and the diseases is relatively weak. This is primarily attributed to\nthe fact that the PRS analysis method typically generates small effect size. A PRS captures only\nthe genetic component of a physical phenotype. It therefore explains only a fraction of the total\nphenotypic variance. In our dataset, the variance in HBRs explained by the corresponding PRS\n( ) is, on average, 0.1778. Consequently, when this genetically-derived PRS is used as a proxy\nto predict a disease outcome, the resulting signal will naturally be weaker than the association\n𝑅²\nobserved when using the full HBR phenotype itself. This pattern is consistent with findings\nfrom other large-scale PRS studiesof physical phenotypes, in which the majority of significant\nORs are of a similar magnitude [e.g., Figure 4C in Ref18(0.9729-1.0469), and Figure 4B in\nRef19(0.9588-1.0569)]. In addition to PRS analysis, we further employed two-sample\nMendelian Randomization analysis and the results supported the causal association between\nHBR traits and those two diseases (Figure 7C). Therefore, we believed that the associations we\nobserved is reliable.\nIn the association analysis for HBRs on diseases, we mainly focus on diseases of the five major\nsystems, including psychiatric, neurological, skeletal, cardiovascular, and metabolic diseases,\naccording to previously published studies. For example, as an important indicator of growth\nand development, abnormal HBR has been correlated with increased risk of skeletal20 and\nneuropsychiatric disorders21,22. In addition, HBR is linked to thermoregulation23 and energy\nmetabolism24, and has been implicated in cardiovascular25,26 and metabolic diseases27,28. For the\nanalysis, we utilized second hierarchical level ICD-10 diagnoses from the UK Biobank (Field\nID: 41270), including only diseases with more than 1,000 cases in the cohort. Significant\nfindings included cardiovascular diseases like atrial fibrillation (AF) and neurological diseases\nlike carpal tunnel syndrome [mapped to its parent term, upper limb mononeuropathy (ULMN)].\nThese findings are supported by existing evidence of associations between AF and brain\nvolume29, and between peripheral nerve conditions and brain structure30,31. We have added the\nabove statement in Line 311 and 705 to be clear.\n8. Comment: I took a brief review of the code. The code includes a README file about how\nto set up the environment. However, there are few comments in the code about input data format\nand description of the functions.\nResponse: Thank you for your comment. We have updated the code in GitHub\n(https://github.com/weishaoxia/head-body-ratio) with detailed comments to clearly describe\nthe input data format and the functions.\nReference:\n1. Lantz, B. The large sample size fallacy. Scandinavian journal of caring sciences27, 487-492 (2013).\n2. Gómez-de-Mariscal, E. et al. Use of the p-values as a size-dependent function to address practical\ndifferences when analyzing large datasets. Sci Rep-Uk11, 20942 (2021).\n3. Kim, H.-Y. Statistical notes for clinical researchers: Chi-squared test and Fisher's exact test.\nRestorative dentistry & endodontics42, 152 (2017).\n4. Goulet-Pelletier, J.-C. & Cousineau, D. A review of effect sizes and their confidence intervals, Part I:\nThe Cohen’sd family. The Quantitative Methods for Psychology14, 242-265 (2018).\n5. Dong, Y., Pan, Y., Zhang, J. & Xu, W. in 2017 IEEE/ACM international conference on connected\nhealth: applications, systems and engineering technologies (CHASE). 51-57 (IEEE).\n6. Rajpurkar, P. et al. Deep learning for chest radiograph diagnosis: A retrospective comparison of the\nCheXNeXt algorithm to practicing radiologists. PLoS medicine15, e1002686 (2018).\n7. Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562,\n203-209 (2018).\n8. Manichaikul, A. et al. Robust relationship inference in genome-wide association studies.\nBioinformatics26, 2867-2873 (2010).\n9. Canela-Xandri, O., Rawlik, K. & Tenesa, A. An atlas of genetic associations in UK Biobank. Nature\ngenetics50, 1593-1599 (2018).\n10. Khera, A. V. et al. Genome-wide polygenic scores for common diseases identify individuals with risk\nequivalent to monogenic mutations. Nature genetics50, 1219-1224 (2018).\n11. Zhou, D. et al. A unified framework for joint-tissue transcriptome-wide association and Mendelian\nrandomization analysis. Nat Genet52, 1239-1246 (2020). https://doi.org:10.1038/s41588-020-\n0706-2\n12. Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies\nusing summary statistics. PLoS genetics10, e1004383 (2014).\n13. Yang, J., Lee, S. H., Goddard, M. E. & Visscher, P. M. GCTA: a tool for genome-wide complex trait\nanalysis. The American Journal of Human Genetics88, 76-82 (2011).\n14. Olkowicz, S. et al. Birds have primate-like numbers of neurons in the forebrain. Proceedings of the\nNational Academy of Sciences113, 7255-7260 (2016).\n15. Emery, N. J. & Clayton, N. S. The mentality of crows: convergent evolution of intelligence in corvids\nand apes. Science306, 1903-1907 (2004).\n16. Audet, J.-N., Couture, M. & Jarvis, E. D. Songbird species that display more-complex vocal learning\nare better problem-solvers and have larger brains. Science381, 1170-1175 (2023).\n17. Koteja, P. The evolution of concepts on the evolution of endothermy in birds and mammals.\nPhysiological and biochemical zoology77, 1043-1050 (2004).\n18. Xu, L. et al. The genetic architecture of and evolutionary constraints on the human pelvic form.\nScience388, eadq1521 (2025). https://doi.org:10.1126/science.adq1521\n19. Kun, E. et al. The genetic architecture and evolution of the human skeletal form. Science 381,\neadf8009 (2023). https://doi.org:10.1126/science.adf8009\n20. Williams, C. A., Dagli, A. & Battaglia, A. Genetic disorders associated with macrocephaly. American\njournal of medical genetics Part A146, 2023-2037 (2008).\n21. Chaste, P. et al. Adjusting head circumference for covariates in autism: clinical correlates of a highly\nheritable continuous trait. Biological Psychiatry74, 576-584 (2013).\n22. Ward, K. E., Friedman, L., Wise, A. & Schulz, S. C. Meta-analysis of brain and cranial size in\nschizophrenia. Schizophrenia research22, 197-213 (1996).\n23. Naya, D. E., Naya, H. & Lessa, E. P. Brain size and thermoregulation during the evolution of the\ngenus Homo. Comparative Biochemistry and Physiology Part A: Molecular & Integrative\nPhysiology191, 66-73 (2016).\n24. Kuzawa, C. W. et al. Metabolic costs and evolutionary implications of human brain development.\nProceedings of the National Academy of Sciences111, 13010-13015 (2014).\n25. Barker, D., Godfrey, K., Osmond, C. & Bull, A. The relation of fetal length, ponderal index and head\ncircumference to blood pressure and the risk of hypertension in adult life. Paediatric and\nperinatal epidemiology6, 35-44 (1992).\n26. Barker, D., Osmond, C., Simmonds, S. & Wield, G. The relation of small head circumference and\nthinness at birth to death from cardiovascular disease in adult life. British Medical Journal306,\n422-426 (1993).\n27. Bucher, H., Prader, A. & Illig, R. Head circumference, height, bone age and weight in 103 children\nwith congenital hypothyroidism before and during thyroid hormone replacement. Helvetica\npaediatrica acta40, 305-316 (1985).\n28. Renaud, D. L. Leukoencephalopathies associated with macrocephaly. Semin Neurol32, 34-41 (2012).\nhttps://doi.org:10.1055/s-0032-1306384\n29. Stefansdottir, H. et al. Atrial fibrillation is associated with reduced brain volume and cognitive\nfunction independent of cerebral infarcts. Stroke44, 1020-1025 (2013).\n30. Maeda, Y. et al. Functional deficits in carpal tunnel syndrome reflect reorganization of primary\nsomatosensory cortex. Brain137, 1741-1752 (2014). https://doi.org:10.1093/brain/awu096\n31. Maeda, Y. et al. Altered brain morphometry in carpal tunnel syndrome is associated with median\nnerve pathology. Neuroimage Clin 2, 313-319 (2013).\nhttps://doi.org:10.1016/j.nicl.2013.02.001"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have addressed all my comments adequately- Vagheesh M. Narasimhan", "reviewer_2": "The authors have addressed most of my comments except on. In the revised version:\n\"Additionally, the DXA scanner's serial number and the software version used to process images were combined into a\nsingle covariate, resulting in seven factor levels.\"\nA single covariate with seven factor leaves was used in the model. Is the covariate ordinal or not. If it is, how was the order\ndecided. If it is not, there should not be levels."}, "author_response": "Response to reviewers’ comments:\nReviewer #2 (Remarks to the Author):\nThe authors have addressed most of my comments except on. In the revised version:\n\"Additionally, the DXA scanner's serial number and the software version used to process\nimages were combined into a single covariate, resulting in seven factor levels.\"\nA single covariate with seven factor leaves was used in the model. Is the covariate ordinal or\nnot. If it is, how was the order decided. If it is not, there should not be levels\nResponse:Thank you for the comment.\nThe combined scanner-serial and software variable is a non-ordinal categorical variable and\ndoes not imply any order. We have updated the manuscript to clarify this, with the following\nsentence:\n“Additionally, the DXA scanner serial number and the software version used for image\nprocessing were combined into a single categorical covariate with seven categories.”\nWe sincerely appreciate your thorough review of our manuscript and the insightful\nrecommendations you provided for its improvement."}]

Decision letter