Multiple Mechanisms Explain Genetic Effects at the CPED1-WNT16 Bone Mineral Density Locus

Gómez, Arianna; Addish, Sumaya; Alvarado, Kurtis; Boatemaa, Priscilla; Onyali, Anne C; Ramirez, Emily G; Rojas, Maria F; Rai, Jyoti; Reynolds, Kiana A; Tang, Weiliang; Kwon, Ronald Y.

doi:10.1007/s11914-023-00783-w

Cited by 3 publications

(2 citation statements)

References 49 publications

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“…In particular, genome-wide association studies (GWAS) have identified human chromosome region 7q31.31, also known as the CPED1-WNT16 locus, to be associated with BMD and fracture [2][3][4][5][6][7][8][9]. Of the five genes at this locus (WNT16, CPED1, ING3, FAM3C, and TSPAN12), WNT16 is the most studied and characterized [10]. In vivo mouse studies have shown that Wnt16 is necessary for bone mass and strength [5,6], in part by suppressing osteoclastogenesis [11] and promoting osteoblastogenesis [12] in cortical bone.…”

Section: Introductionmentioning

confidence: 99%

“…These two signals were significant even after accounting for genetic linkage. It has been postulated that the presence of multiple independent signals at the CPED1-WNT16 locus could reflect several causal variants that act on distinct genes [5,10]. Recently, Chesi et al performed high-resolution Capture C combined with ATACseq and found significant interactions between the CPED1 promoter and SNPs which served as proxies for the BMD sentinel SNP [16], evidence of the potential for CPED1 to function as a target gene at the CPED1-WNT16 locus, possibly in tandem with WNT16 [10].…”

Section: Introductionmentioning

confidence: 99%

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Loss ofcped1does not affect bone and lean mass in zebrafish

Alvarado,

Tang,

Watson

et al. 2024

Preprint

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Human genetic studies have nominated Cadherin-like and PC-esterase Domain-containing 1 (CPED1) as a candidate target gene mediating bone mineral density (BMD) and fracture risk heritability. Recent efforts to define the role ofCPED1in bone in mouse and human models have revealed complex alternative splicing and inconsistent results arising from gene targeting, making its function difficult to interpret. To better understand the role ofCPED1in adult bone mass and morphology, we turned to zebrafish, an emerging model for orthopaedic research. We analyzed two differentcped1mutant lines and performed deep phenotyping to characterize more than 200 measures of adult vertebral, craniofacial, and lean tissue morphology. We also examined alternative splicing of zebrafishcped1and gene expression in various cell/tissue types. Our studies fail to support an essential role ofcped1in adult zebrafish bone. Specifically, homozygous mutants for bothcped1mutant alleles, which are expected to result in loss-of-function and impact allcped1isoforms, exhibited no significant differences in the measures examined when compared to their respective wildtype controls, suggesting thatcped1does not significantly contribute to these traits. We identified sequence differences in critical residues of the catalytic triad between the zebrafish and mouse orthologs of CPED1, and discuss how these differences, as well as distinct alternative splicing, could underlie different functions ofCPED1orthologs in the two species. Our studies demonstrate thatcped1is not required for normal adult zebrafish bone mass, lean mass, or bone and lean mass morphology, adding to evidence that variants at 7q31.31 can act independently ofCPED1to influence BMD and fracture risk.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Loss ofcped1does not affect bone and lean mass in zebrafish

Alvarado,

Tang,

Watson

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

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Revealing chronic disease progression patterns using Gaussian process for stage inference

Wang,

Zhao,

Ross

et al. 2023

Journal of the American Medical Informatics Association

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Objective The early stages of chronic disease typically progress slowly, so symptoms are usually only noticed until the disease is advanced. Slow progression and heterogeneous manifestations make it challenging to model the transition from normal to disease status. As patient conditions are only observed at discrete timestamps with varying intervals, an incomplete understanding of disease progression and heterogeneity affects clinical practice and drug development. Materials and Methods We developed the Gaussian Process for Stage Inference (GPSI) approach to uncover chronic disease progression patterns and assess the dynamic contribution of clinical features. We tested the ability of the GPSI to reliably stratify synthetic and real-world data for osteoarthritis (OA) in the Osteoarthritis Initiative (OAI), bipolar disorder (BP) in the Adolescent Brain Cognitive Development Study (ABCD), and hepatocellular carcinoma (HCC) in the UTHealth and The Cancer Genome Atlas (TCGA). Results First, GPSI identified two subgroups of OA based on image features, where these subgroups corresponded to different genotypes, indicating the bone-remodeling and overweight-related pathways. Second, GPSI differentiated BP into two distinct developmental patterns and defined the contribution of specific brain region atrophy from early to advanced disease stages, demonstrating the ability of the GPSI to identify diagnostic subgroups. Third, HCC progression patterns were well reproduced in the two independent UTHealth and TCGA datasets. Conclusion Our study demonstrated that an unsupervised approach can disentangle temporal and phenotypic heterogeneity and identify population subgroups with common patterns of disease progression. Based on the differences in these features across stages, physicians can better tailor treatment plans and medications to individual patients.

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TransferGWAS of T1-weighted brain MRI data from UK Biobank

Rakowski,

Monti,

Lippert

2024

PLoS Genet

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Genome-wide association studies (GWAS) traditionally analyze single traits, e.g., disease diagnoses or biomarkers. Nowadays, large-scale cohorts such as UK Biobank (UKB) collect imaging data with sample sizes large enough to perform genetic association testing. Typical approaches to GWAS on high-dimensional modalities extract predefined features from the data, e.g., volumes of regions of interest. This limits the scope of such studies to predefined traits and can ignore novel patterns present in the data. TransferGWAS employs deep neural networks (DNNs) to extract low-dimensional representations of imaging data for GWAS, eliminating the need for predefined biomarkers. Here, we apply transferGWAS on brain MRI data from UKB. We encoded 36, 311 T1-weighted brain magnetic resonance imaging (MRI) scans using DNN models trained on MRI scans from the Alzheimer’s Disease Neuroimaging Initiative, and on natural images from the ImageNet dataset, and performed a multivariate GWAS on the resulting features. We identified 289 independent loci, associated among others with bone density, brain, or cardiovascular traits, and 11 regions having no previously reported associations. We fitted polygenic scores (PGS) of the deep features, which improved predictions of bone mineral density and several other traits in a multi-PGS setting, and computed genetic correlations with selected phenotypes, which pointed to novel links between diffusion MRI traits and type 2 diabetes. Overall, our findings provided evidence that features learned with DNN models can uncover additional heritable variability in the human brain beyond the predefined measures, and link them to a range of non-brain phenotypes.

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Multiple Mechanisms Explain Genetic Effects at the CPED1-WNT16 Bone Mineral Density Locus

Cited by 3 publications

References 49 publications

Loss ofcped1does not affect bone and lean mass in zebrafish

Loss ofcped1does not affect bone and lean mass in zebrafish

Revealing chronic disease progression patterns using Gaussian process for stage inference

TransferGWAS of T1-weighted brain MRI data from UK Biobank

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