A molecular quantitative trait locus map for osteoarthritis

Steinberg, Julia; Southam, Lorraine; Roumeliotis, Theodoros I.; Clark, Matthew; Jayasuriya, Raveen L; Swift, Diane; Shah, Karan M.; Butterfield, Natalie C.; Brooks, Roger A.; McCaskie, Andrew W.; Bassett, J. H. Duncan; Williams, Graham R.; Choudhary, Jyoti; Wilkinson, J. Mark; Zeggini, Eleftheria

doi:10.1038/s41467-021-21593-7

Cited by 66 publications

(88 citation statements)

References 72 publications

(81 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Computational prediction of upstream drivers of the observed differential expression is challenging. There is risk of bias in their prediction due to the popularity of the regulators in the literature 35 and these prediction approaches, as in the in silico drug screen described 31 , use data derived primarily from non-joint cell types so only conserved regulatory mechanisms and drug responses are likely to be identified. A study of the transcriptional responses to the cytokines and growth factors in joint tissues would provide useful information for Transcriptomics of articular cartilage from mouse joints is technically challenging due to both contamination of dissected tissue with other cell types and the requirement for a long enzymatic digestion due to the ECM rich nature of the cartilage.…”

Section: Transcriptomics and Proteomicsmentioning

confidence: 99%

Osteoarthritis year in review: genetics, genomics, epigenetics

Young

Barter

Soul

2022

Osteoarthritis and Cartilage

View full text Add to dashboard Cite

Summary Objective In this review, we have highlighted the advances over the past year in genetics, genomics and epigenetics in the ﬁeld of osteoarthritis (OA). Methods A literature search of PubMed was performed using the criteria: “osteoarthritis” and one of the following terms “genetic(s), genomic(s), epigenetic(s), polymorphism, noncoding ribonucleic acid (RNA), microRNA, long noncoding RNA, lncRNA, circular RNA, RNA sequencing (RNA-seq), single cell sequencing, transcriptomics, or deoxyribonucleic acid (DNA) methylation between April 01, 2020 and April 30, 2021. Results In total we identiﬁed 765 unique publications, which eventually reduced to 380 of relevance to the field as judged by two assessors. Many of these studies included multiple search terms. We summarised advances relating to genetics, functional genetics, genomics and epigenetics, focusing on our personal key papers during the year. Conclusions This year few studies have identiﬁed new genetic variants contributing to OA susceptibility, but a focus has been on refining risk loci or their functional validation. The use of new technologies together with investigating the cross-talk between multiple tissue types, greater sample sizes and/or better patient classification (OA subtypes) will continue to increase our knowledge of disease mechanisms and progress towards understanding and treating OA.

show abstract

Section: Transcriptomics and Proteomicsmentioning

confidence: 99%

Osteoarthritis year in review: genetics, genomics, epigenetics

Young

Barter

Soul

2022

Osteoarthritis and Cartilage

View full text Add to dashboard Cite

show abstract

“…Notably, this study reported 36 genes with therapeutic potential for osteoarthritis, highlighting the downregulation of IL11 as a likely intervention point. The authors additionally stressed extracellular matrix (ECM) remodelling by chondrocytes in an inflammatory milieu as a fundamental molecular hallmark during cartilage degeneration [ 9 ▪ ]. The latter is also in agreement with the hypothesis that tissue crosstalk is a central aspect of osteoarthritis pathophysiology [ 10 ].…”

Section: Transcriptomicsmentioning

confidence: 99%

“…Integrating genetics with molecular profiles can identify molecular quantitative trait loci (molQTL). Steinberg et al [ 9 ▪ ] provided the first molQTL map in three osteoarthritis primary tissues: low-grade and high-grade cartilage, and synovium. This study identified 1891 genes targeted by an expression trait locus (eQTL) in at least one of these primary tissues.…”

Section: Transcriptomicsmentioning

confidence: 99%

“…In addition, the authors reported 172 genetic variants involved in differential regulation of gene expression between high-grade and low-grade cartilage (differential eQTLs) targeting 32 genes. Closer examinations of these genes revealed involvement in regulation of gene expression, nervous system development, response to stress, immune response, cell adhesion and catabolic processes [ 9 ▪ ].…”

Section: Transcriptomicsmentioning

confidence: 99%

See 1 more Smart Citation

Insights into the molecular landscape of osteoarthritis in human tissues

Katsoula

Kreitmaier

Zeggini

2021

Current Opinion in Rheumatology

Self Cite

View full text Add to dashboard Cite

Purpose of review To provide an overview of recent developments in the field of osteoarthritis research with a focus on insights gleaned from the application of different -omic technologies. Recent findings We searched for osteoarthritis-relevant studies focusing on transcriptomics, epigenomics, proteomics and metabolomics, published since November of 2019. Study designs showed a trend towards characterizing the genomic profile of osteoarthritis-relevant tissues with high resolution, for example either by using single-cell technologies or by considering several -omic levels and disease stages. Summary Multitissue interactions (cartilage–subchondral bone; cartilage–synovium) are prevalent in the pathophysiology of osteoarthritis, which is characterized by substantial matrix remodelling in an inflammatory milieu. Subtyping approaches using -omic technologies have contributed to the identification of at least two osteoarthritis endotypes. Studies using data integration approaches have provided molecular maps that are tissue-specific for osteoarthritis and pave the way for expanding these data integration approaches towards a more comprehensive view of disease aetiopathogenesis.

show abstract

“…We then relied on only p values and minor allele frequencies to calculate five posterior probabilities (i.e., PP0, PP1, PP2, PP3 and PP4) (Giambartolomei et al, 2014). Among these, large PP3 indicates that both the disease and the metabolite are associated, but with different causal variants; while large PP4 (>80%) supports both the disease and the metabolite are associated and share a single causal variant (Giambartolomei et al, 2014;Steinberg et al, 2021).…”

Section: Colocalization Analysis and Linkage Disequilibrium Score Regressionmentioning

confidence: 99%

Evaluating Causal Relationship Between Metabolites and Six Cardiovascular Diseases Based on GWAS Summary Statistics

et al. 2021

View full text Add to dashboard Cite

Cardiovascular diseases (CVDs) remain the main cause of morbidity and mortality worldwide. The pathological mechanism and underlying biological processes of these diseases with metabolites remain unclear. In this study, we conducted a two-sample Mendelian randomization (MR) analysis to evaluate the causal effect of metabolites on these diseases by making full use of the latest GWAS summary statistics for 486 metabolites and six major CVDs. Extensive sensitivity analyses were implemented to validate our MR results. We also conducted linkage disequilibrium score regression (LDSC) and colocalization analysis to investigate whether MR findings were driven by genetic similarity or hybridization between LD and disease-associated gene loci. We identified a total of 310 suggestive associations across all metabolites and CVDs, and finally obtained four significant associations, including bradykinin, des-arg(9) (odds ratio [OR] = 1.160, 95% confidence intervals [CIs]: 1.080–1.246, false discovery rate [FDR] = 0.022) on ischemic stroke, N-acetylglycine (OR = 0.946, 95%CIs: 0.920–0.973, FDR = 0.023), X-09026 (OR = 0.845, 95%CIs: 0.779–0.916, FDR = 0.021) and X-14473 (OR = 0.938, 95%CIs = 0.907–0.971, FDR = 0.040) on hypertension. Sensitivity analyses showed that these causal associations were robust, the LDSC and colocalization analyses demonstrated that the identified associations were unlikely confused by LD. Moreover, we identified 15 important metabolic pathways might be involved in the pathogenesis of CVDs. Overall, our work identifies several metabolites that have a causal relationship with CVDs, and improves our understanding of the pathogenesis and treatment strategies for these diseases.

show abstract

A molecular quantitative trait locus map for osteoarthritis

Cited by 66 publications

References 72 publications

Osteoarthritis year in review: genetics, genomics, epigenetics

Osteoarthritis year in review: genetics, genomics, epigenetics

Insights into the molecular landscape of osteoarthritis in human tissues

Evaluating Causal Relationship Between Metabolites and Six Cardiovascular Diseases Based on GWAS Summary Statistics

Contact Info

Product

Resources

About