Identification of New Genes and Loci Associated With Bone Mineral Density Based on Mendelian Randomization

Liu, Yijun; Jin, Guang; Wang, Xue; Dong, Ying; Ding, Fupeng

doi:10.3389/fgene.2021.728563

Cited by 5 publications

(7 citation statements)

References 45 publications

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“…These findings of osteoporosis as a potential sex-specific predictor of AD, with shared relationships through MS4A6A , is an unknown and unexpected result identified from single-hypothesis-driven follow-up from our prediction models. Prior studies have established the MS4A gene cluster as a risk for AD, with one study identifying the cluster based on Mendelian randomization 77 , and another that identified a stronger female-specific effect size for MS4A6A 78 . Some studies investigating the role of the MS4A family suggest mechanisms that involve immune function, particularly among microglia 79 .…”

Section: Discussionmentioning

confidence: 99%

Leveraging electronic health records and knowledge networks for Alzheimer’s disease prediction and sex-specific biological insights

Tang,

Rankin,

Cerono

et al. 2024

Nat Aging

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Identification of Alzheimer’s disease (AD) onset risk can facilitate interventions before irreversible disease progression. We demonstrate that electronic health records from the University of California, San Francisco, followed by knowledge networks (for example, SPOKE) allow for (1) prediction of AD onset and (2) prioritization of biological hypotheses, and (3) contextualization of sex dimorphism. We trained random forest models and predicted AD onset on a cohort of 749 individuals with AD and 250,545 controls with a mean area under the receiver operating characteristic of 0.72 (7 years prior) to 0.81 (1 day prior). We further harnessed matched cohort models to identify conditions with predictive power before AD onset. Knowledge networks highlight shared genes between multiple top predictors and AD (for example, APOE, ACTB, IL6 and INS). Genetic colocalization analysis supports AD association with hyperlipidemia at the APOE locus, as well as a stronger female AD association with osteoporosis at a locus near MS4A6A. We therefore show how clinical data can be utilized for early AD prediction and identification of personalized biological hypotheses.

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Section: Discussionmentioning

confidence: 99%

Leveraging electronic health records and knowledge networks for Alzheimer’s disease prediction and sex-specific biological insights

Tang,

Rankin,

Cerono

et al. 2024

Nat Aging

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“…A bone mineral density GWAS analysis of female patients shows p-value association with AD GWAS around the MS4A family locus, and this is further supported by MS4A6A eQTL colocalization with both Alzheimer and female HBMD. Prior studies have established the MS4A gene cluster as a risk for AD, with one study identifying the cluster based on mendelian randomization 60 , and another that identifies a stronger female-specific effect size for MS4A6A 61 . Some studies investigating the role of the MS4A family suggest mechanisms that involve immune function, particularly among microglia 62 .…”

Section: Discussionmentioning

confidence: 99%

Leveraging Electronic Medical Records and Knowledge Networks to Predict Disease Onset and Gain Biological Insight Into Alzheimer’s Disease

Tang

Rankin

Cerono

et al. 2023

Preprint

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Early identification of Alzheimer's Disease (AD) risk can aid in interventions before disease progression. We demonstrate that electronic health records (EHRs) combined with heterogeneous knowledge networks (e.g., SPOKE) allow for (1) prediction of AD onset and (2) generation of biological hypotheses linking phenotypes with AD. We trained random forest models that predict AD onset with mean AUROC of 0.72 (-7 years) to .81 (-1 day). Top identified conditions from matched cohort trained models include phenotypes with importance across time, early in time, or closer to AD onset. SPOKE networks highlight shared genes between top predictors and AD (e.g., APOE, IL6, TNF, and INS). Survival analysis of top predictors (hyperlipidemia and osteoporosis) in external EHRs validates an increased risk of AD. Genetic colocalization confirms hyperlipidemia and AD association at the APOE locus, and AD with osteoporosis colocalize at a locus close to MS4A6A with a stronger female association.

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“…Pathway analysis demonstrates the complex function of candidate genes and reinforces the need for functional studies to clarify causality. More recently, summary data-based Mendelian randomization (SMR) analysis was implemented to investigate new genes and loci associated with BMD [ 18 ]. SMR performs an aggregation of the GWAS SNPs with data from expression quantitative trait loci (eQTL).…”

Section: Large Gwas and Wgs Have Identified Multiple Loci Associated ...mentioning

confidence: 99%

“…SMR performs an aggregation of the GWAS SNPs with data from expression quantitative trait loci (eQTL). The authors identified 12,477 SNPs that regulated 564 genes, which are associated with BMD [ 18 ]. Still, the function for many of these genes is unknown, especially whether it is relevant to bone homeostasis.…”

Section: Large Gwas and Wgs Have Identified Multiple Loci Associated ...mentioning

confidence: 99%

Functional Validation of Osteoporosis Genetic Findings Using Small Fish Models

Kague

Karasik

2022

Genes

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The advancement of human genomics has revolutionized our understanding of the genetic architecture of many skeletal diseases, including osteoporosis. However, interpreting results from human association studies remains a challenge, since index variants often reside in non-coding regions of the genome and do not possess an obvious regulatory function. To bridge the gap between genetic association and causality, a systematic functional investigation is necessary, such as the one offered by animal models. These models enable us to identify causal mechanisms, clarify the underlying biology, and apply interventions. Over the past several decades, small teleost fishes, mostly zebrafish and medaka, have emerged as powerful systems for modeling the genetics of human diseases. Due to their amenability to genetic intervention and the highly conserved genetic and physiological features, fish have become indispensable for skeletal genomic studies. The goal of this review is to summarize the evidence supporting the utility of Zebrafish (Danio rerio) for accelerating our understanding of human skeletal genomics and outlining the remaining gaps in knowledge. We provide an overview of zebrafish skeletal morphophysiology and gene homology, shedding light on the advantages of human skeletal genomic exploration and validation. Knowledge of the biology underlying osteoporosis through animal models will lead to the translation into new, better and more effective therapeutic approaches.

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Identification of New Genes and Loci Associated With Bone Mineral Density Based on Mendelian Randomization

Cited by 5 publications

References 45 publications

Leveraging electronic health records and knowledge networks for Alzheimer’s disease prediction and sex-specific biological insights

Leveraging electronic health records and knowledge networks for Alzheimer’s disease prediction and sex-specific biological insights

Leveraging Electronic Medical Records and Knowledge Networks to Predict Disease Onset and Gain Biological Insight Into Alzheimer’s Disease

Functional Validation of Osteoporosis Genetic Findings Using Small Fish Models

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