The genetic architecture and evolution of the human skeletal form

Kun, Eucharist; Javan, Emily; Smith, Olivia S.; Gulamali, Faris F; J, de la Fuente; Flynn, Brianna I.; Vajrala, Kushal; Trutner, Zoe; Jayakumar, Prakash; Tucker‐Drob, Elliot M.; Sohail, Mashaal; Singh, Tarjinder; Narasimhan, Vagheesh M.

doi:10.1126/science.adf8009

Cited by 18 publications

(11 citation statements)

References 117 publications

(117 reference statements)

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“…S6A ). Interestingly, mice and humans lacking SCUBE develop with shorter bones and with abnormal craniofac i ial structures compared to wild-type individuals, consistent with the model that SCUBE also expands Hedgehog gradients in vivo 29–31 .…”

Section: Resultssupporting

confidence: 77%

Diffusion barriers imposed by tissue topology shape morphogen gradients

Schlissel,

Meziane,

Narducci

et al. 2024

Preprint

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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, 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.

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Section: Resultssupporting

confidence: 77%

Diffusion barriers imposed by tissue topology shape morphogen gradients

Schlissel,

Meziane,

Narducci

et al. 2024

Preprint

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“…We first restricted the dataset to individuals of white, British ancestry, applied standard variant and sample quality control (QC), and analyzed 12.1 million common biallelic SNPs with minor allele frequency greater than 0.1% 1 (“Genetic QC”). Next, as the bulk imaging data from the UKB consisted of DXA images that reflect scans of different body parts, we used a deep-learning approach 15 to subset the imaging dataset to only anterior-posterior (AP) view knee scans. We then removed individuals that had outlier image resolutions or poor quality DXA scans, and padded images to a standard size for processing (see “Image segmentation, phenotype measurement and quality control”).…”

Section: Resultsmentioning

confidence: 99%

“…Taking advantage of these developments in computer vision, recent genetic studies have successfully applied deep-learning methods to generate image-derived phenotypes (IDPs) of body fat distribution, heart structure, liver fat percentage, and brain morphology, and have linked them with genome-wide significant loci 10 – 14 . While some recent studies on musculoskeletal disease employ these novel phenotyping approaches 15 – 17 , neither these nor the studies on other traits have specifically investigated how generating quantitative IDPs that underlie binary disease status could be used to improve power for gene discovery at biobank scale.…”

Section: Introductionmentioning

confidence: 99%

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

Flynn,

Javan,

Lin

et al. 2023

npj Digit. Med.

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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.

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“…This feature allowed us to verify that DMR learns plausible nonlinearities for diabetes biomarkers. DMR's flexibility, facilitated by its PyTorch implementation, promises broad applicability, from trait embeddings derived from unsupervised models [27,28] to focused biomarker discovery, such as to define new anthropometric biomarkers [29] or blood clinical scores [30].…”

Section: Discussionmentioning

confidence: 99%

Genetics-driven Risk Predictions with Differentiable Mendelian Randomization

Sens,

Gräf,

Shilova

et al. 2024

Preprint

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Accurate predictive models of future disease onset are crucial for effective preventive healthcare, yet longitudinal datasets linking early risk factors to subsequent health outcomes are scarce. To address this challenge, we introduce Differentiable Mendelian Randomization (DMR), an extension of the classical Mendelian Randomization framework to learn risk predictors without longitudinal data. To do so, DMR leverages risk factors and genetic data from a healthy cohort, along with results from genome-wide association studies (GWAS) of diseases of interest. After training, the learned predictor can be used to assess risk for new patients solely based on risk factors. We validated DMR through comprehensive simulations and in future type 2 diabetes predictions in UK Biobank participants without diabetes, using follow-up onset labels for validation. Finally, we apply DMR to predict future Alzheimer′s onset from brain imaging biomarkers. Overall, with DMR we offer a new perspective in predictive modeling, showing it is possible to learn risk predictors leveraging genetics rather than longitudinal data.

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The genetic architecture and evolution of the human skeletal form

Cited by 18 publications

References 117 publications

Diffusion barriers imposed by tissue topology shape morphogen gradients

Diffusion barriers imposed by tissue topology shape morphogen gradients

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

Genetics-driven Risk Predictions with Differentiable Mendelian Randomization

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