Analysis of genetically independent phenotypes identifies shared genetic factors associated with chronic musculoskeletal pain conditions

Standard

Analysis of genetically independent phenotypes identifies shared genetic factors associated with chronic musculoskeletal pain conditions. / Tsepilov, Yakov A.; Freidin, Maxim B.; Shadrina, Alexandra S. et al.

In: Communications Biology, Vol. 3, No. 1, 329, 25.06.2020.

Research output: Contribution to journal › Article › peer-review

BibTeX

@article{4aaf986def0b4789b31c86b46c3b93e8,

title = "Analysis of genetically independent phenotypes identifies shared genetic factors associated with chronic musculoskeletal pain conditions",

abstract = "Chronic musculoskeletal pain affects all aspects of human life. However, mechanisms of its genetic control remain poorly understood. Genetic studies of pain are complicated by the high complexity and heterogeneity of pain phenotypes. Here, we apply principal component analysis to reduce phenotype heterogeneity of chronic musculoskeletal pain at four locations: the back, neck/shoulder, hip, and knee. Using matrices of genetic covariances, we constructed four genetically independent phenotypes (GIPs) with the leading GIP (GIP1) explaining 78.4% of the genetic variance of the analyzed conditions, and GIP2–4 explain progressively less. We identified and replicated five GIP1-associated loci and one GIP2-associated locus and prioritized the most likely causal genes. For GIP1, we showed enrichment with multiple nervous system-related terms and genetic correlations with anthropometric, sociodemographic, psychiatric/personality traits and osteoarthritis. We suggest that GIP1 represents a biopsychological component of chronic musculoskeletal pain, related to physiological and psychological aspects and reflecting pain perception and processing.",

keywords = "EXTRACELLULAR-MATRIX PROTEIN-1, OSTEOARTHRITIS SUSCEPTIBILITY, GDF5, METAANALYSIS, PREVALENCE, DEPRESSION, EXPRESSION, VARIANTS, GWAS, SNP",

author = "Tsepilov, {Yakov A.} and Freidin, {Maxim B.} and Shadrina, {Alexandra S.} and Sharapov, {Sodbo Z.} and Elgaeva, {Elizaveta E.} and Zundert, {Jan van} and Lennart Karssen and Pradeep Suri and Williams, {Frances M.K.} and Aulchenko, {Yurii S.}",

year = "2020",

month = jun,

day = "25",

doi = "10.1038/s42003-020-1051-9",

language = "English",

volume = "3",

journal = "Communications Biology",

issn = "2399-3642",

publisher = "Springer Nature",

number = "1",

}

RIS

TY - JOUR

T1 - Analysis of genetically independent phenotypes identifies shared genetic factors associated with chronic musculoskeletal pain conditions

AU - Tsepilov, Yakov A.

AU - Freidin, Maxim B.

AU - Shadrina, Alexandra S.

AU - Sharapov, Sodbo Z.

AU - Elgaeva, Elizaveta E.

AU - Zundert, Jan van

AU - Karssen, Lennart

AU - Suri, Pradeep

AU - Williams, Frances M.K.

AU - Aulchenko, Yurii S.

PY - 2020/6/25

Y1 - 2020/6/25

N2 - Chronic musculoskeletal pain affects all aspects of human life. However, mechanisms of its genetic control remain poorly understood. Genetic studies of pain are complicated by the high complexity and heterogeneity of pain phenotypes. Here, we apply principal component analysis to reduce phenotype heterogeneity of chronic musculoskeletal pain at four locations: the back, neck/shoulder, hip, and knee. Using matrices of genetic covariances, we constructed four genetically independent phenotypes (GIPs) with the leading GIP (GIP1) explaining 78.4% of the genetic variance of the analyzed conditions, and GIP2–4 explain progressively less. We identified and replicated five GIP1-associated loci and one GIP2-associated locus and prioritized the most likely causal genes. For GIP1, we showed enrichment with multiple nervous system-related terms and genetic correlations with anthropometric, sociodemographic, psychiatric/personality traits and osteoarthritis. We suggest that GIP1 represents a biopsychological component of chronic musculoskeletal pain, related to physiological and psychological aspects and reflecting pain perception and processing.

AB - Chronic musculoskeletal pain affects all aspects of human life. However, mechanisms of its genetic control remain poorly understood. Genetic studies of pain are complicated by the high complexity and heterogeneity of pain phenotypes. Here, we apply principal component analysis to reduce phenotype heterogeneity of chronic musculoskeletal pain at four locations: the back, neck/shoulder, hip, and knee. Using matrices of genetic covariances, we constructed four genetically independent phenotypes (GIPs) with the leading GIP (GIP1) explaining 78.4% of the genetic variance of the analyzed conditions, and GIP2–4 explain progressively less. We identified and replicated five GIP1-associated loci and one GIP2-associated locus and prioritized the most likely causal genes. For GIP1, we showed enrichment with multiple nervous system-related terms and genetic correlations with anthropometric, sociodemographic, psychiatric/personality traits and osteoarthritis. We suggest that GIP1 represents a biopsychological component of chronic musculoskeletal pain, related to physiological and psychological aspects and reflecting pain perception and processing.

KW - EXTRACELLULAR-MATRIX PROTEIN-1

KW - OSTEOARTHRITIS SUSCEPTIBILITY

KW - GDF5

KW - METAANALYSIS

KW - PREVALENCE

KW - DEPRESSION

KW - EXPRESSION

KW - VARIANTS

KW - GWAS

KW - SNP

UR - http://www.scopus.com/inward/record.url?scp=85087062025&partnerID=8YFLogxK

U2 - 10.1038/s42003-020-1051-9

DO - 10.1038/s42003-020-1051-9

M3 - Article

C2 - 32587327

AN - SCOPUS:85087062025

VL - 3

JO - Communications Biology

JF - Communications Biology

SN - 2399-3642

IS - 1

M1 - 329

ER -

ID: 24615306