A genetic correlation scan identifies blood proteins associated with bone mineral density

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Abstract

Background

Osteoporosis is a common metabolic bone disease that is characterized by low bone mass. However, limited efforts have been made to explore the functional relevance of the blood proteome to bone mineral density across different life stages.

Methods

Using genome-wide association study summary data of the blood proteome and two independent studies of bone mineral density, we conducted a genetic correlation scan of bone mineral density and the blood proteome. Linkage disequilibrium score regression analysis was conducted to assess genetic correlations between each of the 3283 plasma proteins and bone mineral density.

Results

Linkage disequilibrium score regression identified 18 plasma proteins showing genetic correlation signals with bone mineral density in the TB-BMD cohort, such as MYOM2 (coefficient = 0.3755, P value = 0.0328) among subjects aged 0 ~ 15, POSTN (coefficient = − 0.5694, P value = 0.0192) among subjects aged 30 ~ 45 and PARK7 (coefficient = − 0.3613, P value = 0.0052) among subjects aged over 60.

Conclusions

Our results identified multiple plasma proteins associated with bone mineral density and provided novel clues for revealing the functional relevance of plasma proteins to bone mineral density.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12891-022-05453-z.

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Most cited references 46

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An Atlas of Genetic Correlations across Human Diseases and Traits

Brendan Bulik-Sullivan, Hilary Finucane, Verneri Anttila … (2015)

Identifying genetic correlations between complex traits and diseases can provide useful etiological insights and help prioritize likely causal relationships. The major challenges preventing estimation of genetic correlation from genome-wide association study (GWAS) data with current methods are the lack of availability of individual genotype data and widespread sample overlap among meta-analyses. We circumvent these difficulties by introducing a technique – cross-trait LD Score regression – for estimating genetic correlation that requires only GWAS summary statistics and is not biased by sample overlap. We use this method to estimate 276 genetic correlations among 24 traits. The results include genetic correlations between anorexia nervosa and schizophrenia, anorexia and obesity and associations between educational attainment and several diseases. These results highlight the power of genome-wide analyses, since there currently are no significantly associated SNPs for anorexia nervosa and only three for educational attainment.

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Genomic atlas of the human plasma proteome

Bram P. Prins, Sheri Wilcox, Erik S Zimmerman … (2018)

Although plasma proteins have important roles in biological processes and are the direct targets of many drugs, the genetic factors that control inter-individual variation in plasma protein levels are not well understood. Here we characterize the genetic architecture of the human plasma proteome in healthy blood donors from the INTERVAL study. We identify 1,927 genetic associations with 1,478 proteins, a fourfold increase on existing knowledge, including trans associations for 1,104 proteins. To understand the consequences of perturbations in plasma protein levels, we apply an integrated approach that links genetic variation with biological pathway, disease, and drug databases. We show that protein quantitative trait loci overlap with gene expression quantitative trait loci, as well as with disease-associated loci, and find evidence that protein biomarkers have causal roles in disease using Mendelian randomization analysis. By linking genetic factors to diseases via specific proteins, our analyses highlight potential therapeutic targets, opportunities for matching existing drugs with new disease indications, and potential safety concerns for drugs under development.

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Is Open Access

LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis

Jie Zheng, A. Erzurumluoglu, Benjamin L Elsworth … (2016)

Abstract Motivation LD score regression is a reliable and efficient method of using genome-wide association study (GWAS) summary-level results data to estimate the SNP heritability of complex traits and diseases, partition this heritability into functional categories, and estimate the genetic correlation between different phenotypes. Because the method relies on summary level results data, LD score regression is computationally tractable even for very large sample sizes. However, publicly available GWAS summary-level data are typically stored in different databases and have different formats, making it difficult to apply LD score regression to estimate genetic correlations across many different traits simultaneously. Results In this manuscript, we describe LD Hub - a centralized database of summary-level GWAS results for 173 diseases/traits from different publicly available resources/consortia and a web interface that automates the LD score regression analysis pipeline. To demonstrate functionality and validate our software, we replicated previously reported LD score regression analyses of 49 traits/diseases using LD Hub; and estimated SNP heritability and the genetic correlation across the different phenotypes. We also present new results obtained by uploading a recent atopic dermatitis GWAS meta-analysis to examine the genetic correlation between the condition and other potentially related traits. In response to the growing availability of publicly accessible GWAS summary-level results data, our database and the accompanying web interface will ensure maximal uptake of the LD score regression methodology, provide a useful database for the public dissemination of GWAS results, and provide a method for easily screening hundreds of traits for overlapping genetic aetiologies. Availability and Implementation The web interface and instructions for using LD Hub are available at http://ldsc.broadinstitute.org/ Supplementary information Supplementary data are available at Bioinformatics online.

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Author and article information

Contributors

Bin Shen: shenbin_1971@163.com

Journal

Journal ID (nlm-ta): BMC Musculoskelet Disord

Journal ID (iso-abbrev): BMC Musculoskelet Disord

Title: BMC Musculoskeletal Disorders

Publisher: BioMed Central (London )

ISSN (Electronic): 1471-2474

Publication date (Electronic): 3 June 2022

Publication date PMC-release: 3 June 2022

Publication date Collection: 2022

Volume: 23

Electronic Location Identifier: 530

Affiliations

GRID grid.412901.f, ISNI 0000 0004 1770 1022, Orthopedic Research Institute, Department of Orthopedics, , Sichuan University West China Hospital, ; 37# Guoxue Road, Chengdu, 610041 Sichuan Province PR China

Article

Publisher ID: 5453

DOI: 10.1186/s12891-022-05453-z

PMC ID: 9164489

SO-VID: c5d13a36-9040-491e-830f-154cddcbfaa5

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 14 February 2022

Date accepted : 17 May 2022

Funding

Funded by: National Natural Science Foundation of China

Award ID: 81802210

Award ID: 81974347

Funded by: FundRef http://dx.doi.org/10.13039/501100004829, Department of Science and Technology of Sichuan Province;

Award ID: 2021YFS0122

Custom metadata

ScienceOpen disciplines: Orthopedics

Keywords: bone mineral density,linkage disequilibrium score regression,human plasma protein,genome-wide association study,osteoporosis,blood proteome

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ScienceOpen disciplines: Orthopedics

Keywords: bone mineral density, linkage disequilibrium score regression, human plasma protein, genome-wide association study, osteoporosis, blood proteome

A genetic correlation scan identifies blood proteins associated with bone mineral density

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Most cited references 46

An Atlas of Genetic Correlations across Human Diseases and Traits

Genomic atlas of the human plasma proteome

LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis

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