The genetic architecture and evolution of the human skeletal form

Lrig3-deficient mice exhibit strain-specific alterations in liver fat accumulation, intestinal morphology, and middle ear inflammation

The transmembrane protein leucine-rich repeats and immunoglobulin-like domains 3 (LRIG3) regulates fat metabolism and bone morphogenetic protein (BMP) signaling. Lrig3-deficient mice exhibit impaired development of the snout and the inner ear lateral canal, neural defects, and cardiac hypertrophy in adulthood. However, no thorough and unbiased analysis of the physiological functions of Lrig3 has previously been performed. To address this knowledge gap, we performed histopathological examination of 42 tissues and organs from 1-year-old female C57BL/6JBomTac and 129S1-U mice with different Lrig3 genotypes. Among the scored pathologies, three were significantly associated with Lrig3 genotype: spontaneous macrovesicular hepatocellular degeneration (hepatocellular steatosis) was less prevalent in Lrig3-deficient C57BL/6JBomTac mice, whereas dilated or flaccid ileum and otitis media were more common in Lrig3-deficient 129S1-U mice. To further investigate hepatic steatosis phenotypes, 8-week-old C57BL/6JBomTac mice of both sexes and different Lrig3 genotypes were subjected to consuming a high-fat diet (HFD) for 8 weeks. The HFD regimen led to relatively few cases of hepatocellular steatosis, with no significant differences among the genotypes; however, female Lrig3-deficient mice presented reduced microvesicular hepatocellular degeneration compared with their wild-type littermates. This study revealed that Lrig3 regulates liver fat accumulation, intestinal morphology, and middle ear inflammation in a mouse strain-dependent manner.

The genetic architecture of and evolutionary constraints on the human pelvic form

Human pelvic evolution following the human-chimpanzee divergence is thought to result in an obstetrical dilemma, a mismatch between large infant brains and narrowed female birth canals, but empirical evidence has been equivocal. By using deep learning on 31,115 dual-energy x-ray absorptiometry scans from UK Biobank, we identified 180 loci associated with seven highly heritable pelvic phenotypes. Birth canal phenotypes showed sex-specific genetic architecture, aligning with reproductive function. Larger birth canals were linked to slower walking pace and reduced back pain but increased hip osteoarthritis risk, whereas narrower birth canals were associated with reduced pelvic floor disorder risk but increased obstructed labor risk. Lastly, genetic correlation between birth canal and head widths provides evidence of coevolution between the human pelvis and brain, partially mitigating the dilemma.

Pitfalls in performing genome-wide association studies on ratio traits

Genome-wide association studies (GWASs) are often performed on ratios composed of a numerator trait divided by a denominator trait. Examples include body mass index (BMI) and the waist-to-hip ratio, among many others. Explicitly or implicitly, the goal of forming the ratio is typically to adjust for an association between the numerator and denominator. While forming ratios may be clinically expedient, there are several important issues with performing GWAS on ratios. Forming a ratio does not "adjust" for the denominator in the sense of conditioning on it, and it is unclear whether associations with ratios are attributable to the numerator, the denominator, or both. Here we demonstrate that associations arising in ratio GWAS can be entirely denominator driven, implying that at least some associations uncovered by ratio GWAS may be due solely to a putative adjustment variable. In a survey of 10 common ratio traits, we find that the ratio model disagrees with the adjusted model (performing GWAS on the numerator while conditioning on the denominator) at around 1/3 of loci. Using BMI as an example, we show that variants detected by only the ratio model are more strongly associated with the denominator (height), while variants detected by only the adjusted model are more strongly associated with the numerator (weight). Although the adjusted model provides effect sizes with a clearer interpretation, it is susceptible to collider bias. We propose and validate a simple method of correcting for the genetic component of collider bias via leave-one-chromosome-out polygenic scoring.

Using deep-learning based segmentation to enable spatial evaluation of knee osteoarthritis (SEKO) in rodent models

OBJECTIVE

In preclinical models of osteoarthritis (OA), histology is commonly used to evaluate joint remodeling. The current study introduces a deep learning driven histological analysis pipeline for the spatial evaluation of knee osteoarthritis (SEKO) focused on quantifying and visualizing joint remodeling in the medial compartment of rodent knees.

METHODS

The SEKO pipeline contains both segmentation and visualization tools. For segmentation, two separate convolutional neural network architectures, HRNet and U-Net, were considered for identifying multiple regions of interest. Following segmentation, SEKO calculates multiple morphometric and location dependent measures to summarize joint-level changes. Additionally, SEKO generates probabilistic heat maps for visualization of the spatial aspects of joint remodeling.

RESULTS

SEKO incorporated the U-NET architecture - due to its higher prediction accuracy - and identified similar cartilage loss changes that were reported using by-hand segmentation in prior work. Additionally, SEKO enabled the detection of changes in subchondral bone area and location dependent bone remodeling. SEKO also enabled visualization of spatial changes in cartilage thinning and bone remodeling using probabilistic heat maps.

CONCLUSION

The SEKO pipeline offers the potential for objective comparison of OA progression and therapeutic interventions through visualization of spatial and morphometric changes. SEKO is provided as an open-source tool for the OA research community, facilitating collaborative research efforts and comprehensive analysis of knee joint histology.

Optimized phenotyping of complex morphological traits: enhancing discovery of common and rare genetic variants

Genotype-phenotype (G-P) analyses for complex morphological traits typically utilize simple, predetermined anatomical measures or features derived via unsupervised dimension reduction techniques (e.g. principal component analysis (PCA) or eigen-shapes). Despite the popularity of these approaches, they do not necessarily reveal axes of phenotypic variation that are genetically relevant. Therefore, we introduce a framework to optimize phenotyping for G-P analyses, such as genome-wide association studies (GWAS) of common variants or rare variant association studies (RVAS) of rare variants. Our strategy is two-fold: (i) we construct a multidimensional feature space spanning a wide range of phenotypic variation, and (ii) within this feature space, we use an optimization algorithm to search for directions or feature combinations that are genetically enriched. To test our approach, we examine human facial shape in the context of GWAS and RVAS. In GWAS, we optimize for phenotypes exhibiting high heritability, estimated from either family data or genomic relatedness measured in unrelated individuals. In RVAS, we optimize for the skewness of phenotype distributions, aiming to detect commingled distributions that suggest single or few genomic loci with major effects. We compare our approach with eigen-shapes as baseline in GWAS involving 8246 individuals of European ancestry and in gene-based tests of rare variants with a subset of 1906 individuals. After applying linkage disequilibrium score regression to our GWAS results, heritability-enriched phenotypes yielded the highest SNP heritability, followed by eigen-shapes, while commingling-based traits displayed the lowest SNP heritability. Heritability-enriched phenotypes also exhibited higher discovery rates, identifying the same number of independent genomic loci as eigen-shapes with a smaller effective number of traits. For RVAS, commingling-based traits resulted in more genes passing the exome-wide significance threshold than eigen-shapes, while heritability-enriched phenotypes lead to only a few associations. Overall, our results demonstrate that optimized phenotyping allows for the extraction of genetically relevant traits that can specifically enhance discovery efforts of common and rare variants, as evidenced by their increased power in facial GWAS and RVAS.

Tracing human trait evolution through integrative genomics and temporal annotations

Understanding the evolution of human traits is a fundamental yet challenging question. In a recent Cell Genomics article, Kun et al.1 integrate large-scale genomic and phenotypic data, including deep-learning-derived imaging phenotypes, with temporal annotations to estimate the timing of evolutionary changes that led to differences in traits between modern humans and primates or hominin ancestors.

Multi-omic spatial effects on high-resolution AI-derived retinal thickness

Retinal thickness is a marker of retinal health and more broadly, is seen as a promising biomarker for many systemic diseases. Retinal thickness measurements are procured from optical coherence tomography (OCT) as part of routine clinical eyecare. We processed the UK Biobank OCT images using a convolutional neural network to produce fine-scale retinal thickness measurements across > 29,000 points in the macula, the part of the retina responsible for human central vision. The macula is disproportionately affected by high disease burden retinal disorders such as age-related macular degeneration and diabetic retinopathy, which both involve metabolic dysregulation. Analysis of common genomic variants, metabolomic, blood and immune biomarkers, disease PheCodes and genetic scores across a fine-scale macular thickness grid, reveals multiple novel genetic loci including four on the X chromosome; retinal thinning associated with many systemic disorders including multiple sclerosis; and multiple associations to correlated metabolites that cluster spatially in the retina. We highlight parafoveal thickness to be particularly susceptible to systemic insults. These results demonstrate the gains in discovery power and resolution achievable with AI-leveraged analysis. Results are accessible using a bespoke web interface that gives full control to pursue findings.

A Litmus Test for Confounding in Polygenic Scores

Polygenic scores (PGSs) are being rapidly adopted for trait prediction in the clinic and beyond. PGSs are often thought of as capturing the direct genetic effect of one's genotype on their phenotype. However, because PGSs are constructed from population-level associations, they are influenced by factors other than direct genetic effects, including stratification, assortative mating, and dynastic effects ("SAD effects"). Our interpretation and application of PGSs may hinge on the relative impact of SAD effects, since they may often be environmentally or culturally mediated. We developed a method that estimates the proportion of variance in a PGS (in a given sample) that is driven by direct effects, SAD effects, and their covariance. We leverage a comparison of a PGS of interest based on a standard GWAS with a PGS based on a sibling GWAS-which is largely immune to SAD effects-to quantify the relative contribution of each type of effect to variance in the PGS of interest. Our method, Partitioning Genetic Scores Using Siblings (PGSUS, pron. "Pegasus"), breaks down variance components further by axes of genetic ancestry, allowing for a nuanced interpretation of SAD effects. In particular, PGSUS can detect stratification along major axes of ancestry as well as SAD variance that is "isotropic" with respect to axes of ancestry. Applying PGSUS, we found evidence of stratification in PGSs constructed using large meta-analyses of height and educational attainment as well as in a range of PGSs constructed using the UK Biobank. In some instances, a given PGS appears to be stratified along a major axis of ancestry in one prediction sample but not in another (for example, in comparisons of prediction in samples from different countries, or in ancient DNA vs. contemporary samples). Finally, we show that different approaches for adjustment for population structure in GWASs have distinct advantages with respect to mitigation of ancestry-axis-specific and isotropic SAD variance in PGS. Our study illustrates how family-based designs can be combined with standard population-based designs to guide the interpretation and application of genomic predictors.

Archaic hominin admixture and its consequences for modern humans

As anatomically modern humans dispersed out of Africa, they encountered and mated with now extinct hominins, including Neanderthals and Denisovans. It is now well established that all non-African individuals derive approximately 2% of their genome from Neanderthal ancestors and individuals of Melanesian and Australian aboriginal ancestry inherited an additional 2%-5% of their genomes from Denisovan ancestors. Attention has started to shift from documenting amounts of archaic admixture and identifying introgressed segments to understanding their molecular, phenotypic, and evolutionary consequences and refining models of human history. Here, we review recent insights into admixture between modern and archaic humans, emphasizing methodological innovations and the functional and phenotypic effects Neanderthal and Denisovan sequences have in contemporary individuals.

The trait-specific timing of accelerated genomic change in the human lineage

Humans exhibit distinct characteristics compared to our primate and ancient hominin ancestors. To investigate genomic bursts in the evolution of these traits, we use two complementary approaches to examine enrichment among genome-wide association study loci spanning diseases and AI-based image-derived brain, heart, and skeletal tissue phenotypes with genomic regions reflecting four evolutionary divergence points. These regions cover epigenetic differences among humans and rhesus macaques, human accelerated regions (HARs), ancient selective sweeps, and Neanderthal-introgressed alleles. Skeletal traits such as pelvic width and limb proportions showed enrichment in evolutionary annotations that mirror morphological changes in the primate fossil record. Additionally, we observe enrichment of loci associated with the longitudinal fasciculus in human-gained epigenetic elements since macaques, the visual cortex in HARs, and the thalamus proper in Neanderthal-introgressed alleles, implying that associated cognitive functions such as language processing, decision-making, sensory signaling, and motor control are enriched at different evolutionary depths.

Osteoarthritis

Osteoarthritis is a heterogeneous disorder that is increasingly prevalent largely due to aging and obesity, resulting in a major disease burden worldwide. Knowledge about the underlying aetiology has improved, with increased understanding of the role of genetic factors, the microbiome, and existence of different pain mechanisms. However, this knowledge has not yet been translated into new treatment options. New evidence has questioned the efficacy of recommended treatments, such as therapeutic exercise programmes and the focus on weight loss, but managing obesity and maintaining activity remain important for the prevention and management of osteoarthritis. Approaches should consider individual and cultural preferences and resource availability to increase patient and community engagement, and optimise outcomes worldwide. Most of the focus has been on established osteoarthritis where management is primarily directed at relieving symptoms. The search for the much needed effective treatments that improve both symptoms and structure, often referred to as disease-modifying osteoarthritic drugs, is ongoing. Promising data indicate that targeting inflammation is effective in hand osteoarthritis.

Modulating vertebrate physiology by genomic fine-tuning of GPCR functions

G protein-coupled receptors (GPCRs) play a crucial role as membrane receptors, facilitating the communication of eukaryotic species with their environment and regulating cellular and organ interactions. Consequently, GPCRs hold immense potential in contributing to adaptation to ecological niches and responding to environmental shifts. Comparative analyses of vertebrate genomes reveal patterns of GPCR gene loss, expansion, and signatures of selection. Integrating these genomic data with insights from functional analyses of gene variants enables the interpretation of genotype-phenotype correlations. This review underscores the involvement of GPCRs in adaptive processes, presenting numerous examples of how alterations in GPCR functionality influence vertebrate physiology or, conversely, how environmental changes impact GPCR functions. The findings demonstrate that modifications in GPCR function contribute to adapting to aquatic, arid, and nocturnal habitats, influencing camouflage strategies, and specializing in particular dietary preferences. Furthermore, the adaptability of GPCR functions provides an effective mechanism in facilitating past, recent, or ongoing adaptations in animal domestication and human evolution and should be considered in therapeutic strategies and drug development.

Valid inference for machine learning-assisted genome-wide association studies

Machine learning (ML) has become increasingly popular in almost all scientific disciplines, including human genetics. Owing to challenges related to sample collection and precise phenotyping, ML-assisted genome-wide association study (GWAS), which uses sophisticated ML techniques to impute phenotypes and then performs GWAS on the imputed outcomes, have become increasingly common in complex trait genetics research. However, the validity of ML-assisted GWAS associations has not been carefully evaluated. Here, we report pervasive risks for false-positive associations in ML-assisted GWAS and introduce Post-Prediction GWAS (POP-GWAS), a statistical framework that redesigns GWAS on ML-imputed outcomes. POP-GWAS ensures valid and powerful statistical inference irrespective of imputation quality and choice of algorithm, requiring only GWAS summary statistics as input. We employed POP-GWAS to perform a GWAS of bone mineral density derived from dual-energy X-ray absorptiometry imaging at 14 skeletal sites, identifying 89 new loci and revealing skeletal site-specific genetic architecture. Our framework offers a robust analytic solution for future ML-assisted GWAS.

Massively parallel approaches for characterizing noncoding functional variation in human evolution

The genetic differences underlying unique phenotypes in humans compared to our closest primate relatives have long remained a mystery. Similarly, the genetic basis of adaptations between human groups during our expansion across the globe is poorly characterized. Uncovering the downstream phenotypic consequences of these genetic variants has been difficult, as a substantial portion lies in noncoding regions, such as cis-regulatory elements (CREs). Here, we review recent high-throughput approaches to measure the functions of CREs and the impact of variation within them. CRISPR screens can directly perturb CREs in the genome to understand downstream impacts on gene expression and phenotypes, while massively parallel reporter assays can decipher the regulatory impact of sequence variants. Machine learning has begun to be able to predict regulatory function from sequence alone, further scaling our ability to characterize genome function. Applying these tools across diverse phenotypes, model systems, and ancestries is beginning to revolutionize our understanding of noncoding variation underlying human evolution.

Body shape from birth to adulthood is associated with skeletal development: A Mendelian randomization study

BACKGROUND

Observational studies have shown that childhood obesity is associated with adult bone health but yield inconsistent results. We aimed to explore the potential causal association between body shape and skeletal development.

METHODS

We used two-sample Mendelian randomization (MR) to estimate causal relationships between body shape from birth to adulthood and skeletal phenotypes, with exposures including placental weight, birth weight, childhood obesity, BMI, lean mass, fat mass, waist circumference, and hip circumference. Independent genetic instruments associated with the exposures at the genome-wide significance level (P < 5 × 10-8) were selected from corresponding large-scale genome-wide association studies. The inverse-variance weighted analysis was chosen as the primary method, and complementary MR analyses included the weighted median, MR-Egger, weighted mode, and simple mode.

RESULTS

The MR analysis shows strong evidence that childhood (β = -1.29 × 10-3, P = 8.61 × 10-5) and adulthood BMI (β = -1.28 × 10-3, P = 1.45 × 10-10) were associated with humerus length. Tibiofemoral angle was negatively associated with childhood BMI (β = -3.60 × 10-1, P = 3.00 × 10-5) and adolescent BMI (β = -3.62 × 10-1, P = 2.68 × 10-3). In addition, genetically predicted levels of appendicular lean mass (β = 1.16 × 10-3, P = 1.49 × 10-13), whole body fat mass (β = 1.66 × 10-3, P = 1.35 × 10-9), waist circumference (β = 1.72 × 10-3, P = 6.93 × 10-8) and hip circumference (β =1.28 × 10-3, P = 4.34 × 10-6) were all associated with tibia length. However, we found no causal association between placental weight, birth weight and bone length/width.

CONCLUSIONS

This large-scale MR analysis explores changes in growth patterns in the length/width of major bone sites, highlighting the important role of childhood body shape in bone development and providing insights into factors that may drive bone maturation.

Advances in skeletal genomics research across tissues and cells

Phenotypic variation within the skeleton has biological, behavioral, and biomedical functional implications for individuals and species. Thus, it is critical to understand how genomic, environmental, and mediating regulatory factors combine and interact to drive skeletal trait development and evolution. Recent research efforts to clarify these mechanisms have been made possible by expanded collections of genomic and phenotypic data from in vivo skeletal tissues, as well as the development of relevant in vitro skeletal cell culture systems. This review outlines this current work and recommends that continued exploration of this complexity should include an increased focus on how interactions between genomic and physiologically relevant contexts contribute to skeletal trait variation at population and evolutionary scales.

Diffusion barriers imposed by tissue topology shape Hedgehog morphogen gradients

Animals use a small number of morphogens to pattern tissues, but it is unclear how evolution modulates morphogen signaling range to match tissues of varying sizes. Here, we used single-molecule imaging in reconstituted morphogen gradients and in tissue explants to determine that Hedgehog diffused extracellularly as a monomer, and rapidly transitioned between membrane-confined and -unconfined states. Unexpectedly, the vertebrate-specific protein SCUBE1 expanded Hedgehog gradients by accelerating the transition rates between states without affecting the relative abundance of molecules in each state. This observation could not be explained under existing models of morphogen diffusion. Instead, we developed a topology-limited diffusion model in which cell-cell gaps create diffusion barriers, which morphogens can only overcome by passing through a membrane-unconfined state. Under this model, SCUBE1 promoted Hedgehog secretion and diffusion by allowing it to transiently overcome diffusion barriers. This multiscale understanding of morphogen gradient formation unified prior models and identified knobs that nature can use to tune morphogen gradient sizes across tissues and organisms.

Diffusion barriers imposed by tissue topology shape morphogen gradients

Animals use a small number of morphogens to pattern tissues, but it is unclear how evolution modulates morphogen signaling range to match tissues of varying sizes. Here, we used single molecule imaging in reconstituted morphogen gradients and in tissue explants to determine that Hedgehog diffused extra-cellularly as a monomer, and rapidly transitioned between membrane-confined and -unconfined states. Unexpectedly, the vertebrate-specific protein SCUBE1 expanded Hedgehog gradients by accelerating the transition rates between states without affecting the relative abundance of molecules in each state. This observation could not be explained under existing models of morphogen diffusion. Instead, we developed a topology-limited diffusion model in which cell-cell gaps create diffusion barriers, and morphogens can only overcome the barrier by passing through a membrane-unconfined state. Under this model, SCUBE1 promotes Hedgehog secretion and diffusion by allowing it to transiently overcome diffusion barriers. This multiscale understanding of morphogen gradient formation unified prior models and discovered novel knobs that nature can use to tune morphogen gradient sizes across tissues and organisms.

Am I big boned? Bone length scaled reference data for HRpQCT measures of the radial and tibial diaphysis in White adults

Cross-sectional size of a long bone shaft influences its mechanical properties. We recently used high-resolution peripheral quantitative computed tomography (HRpQCT) to create reference data for size measures of the radial and tibial diaphyses. However, data did not take into account the impact of bone length. Human bone exhibits relatively isometric allometry whereby cross-sectional area increases proportionally with bone length. The consequence is that taller than average individuals will generally have larger z-scores for bone size outcomes when length is not considered. The goal of the current work was to develop a means of determining whether an individual's cross-sectional bone size is suitable for their bone length. HRpQCT scans performed at 30 % of bone length proximal from the distal end of the radius and tibia were acquired from 1034 White females (age = 18.0 to 85.3 y) and 392 White males (age = 18.4 to 83.6 y). Positive relationships were confirmed between bone length and cross-sectional areas and estimated mechanical properties. Scaling factors were calculated and used to scale HRpQCT outcomes to bone length. Centile curves were generated for both raw and bone length scaled HRpQCT data using the LMS approach. Excel-based calculators are provided to facilitate calculation of z-scores for both raw and bone length scaled HRpQCT outcomes. The raw z-scores indicate the magnitude that an individual's HRpQCT outcomes differ relative to expected sex- and age-specific values, with the scaled z-scores also considering bone length. The latter enables it to be determined whether an individual or population of interest has normal sized bones for their length, which may have implications for injury risk. In addition to providing a means of expressing HRpQCT bone size outcomes relative to bone length, the current study also provides centile curves for outcomes previously without reference data, including tissue mineral density and moments of inertia.

Deep learning based phenotyping of medical images improves power for gene discovery of complex disease

Electronic health records are often incomplete, reducing the power of genetic association studies. For some diseases, such as knee osteoarthritis where the routine course of diagnosis involves an X-ray, image-based phenotyping offers an alternate and unbiased way to ascertain disease cases. We investigated this by training a deep-learning model to ascertain knee osteoarthritis cases from knee DXA scans that achieved clinician-level performance. Using our model, we identified 1931 (178%) more cases than currently diagnosed in the health record. Individuals diagnosed as cases by our model had higher rates of self-reported knee pain, for longer durations and with increased severity compared to control individuals. We trained another deep-learning model to measure the knee joint space width, a quantitative phenotype linked to knee osteoarthritis severity. In performing genetic association analysis, we found that use of a quantitative measure improved the number of genome-wide significant loci we discovered by an order of magnitude compared with our binary model of cases and controls despite the two phenotypes being highly genetically correlated. In addition we discovered associations between our quantitative measure of knee osteoarthritis and increased risk of adult fractures- a leading cause of injury-related death in older individuals-, illustrating the capability of image-based phenotyping to reveal epidemiological associations not captured in the electronic health record. For diseases with radiographic diagnosis, our results demonstrate the potential for using deep learning to phenotype at biobank scale, improving power for both genetic and epidemiological association analysis.