Assessment of osteoarthritis candidate genes in a meta-analysis of nine genome-wide association studies

OBJECTIVE: To assess candidate genes for association with osteoarthritis (OA) and identify promising genetic factors and, secondarily, to assess the candidate gene approach in OA. METHODS: A total of 199 candidate genes for association with OA were identified using Human Genome Epidemiology (HuGE) Navigator. All of their single-nucleotide polymorphisms (SNPs) with an allele frequency of >5% were assessed by fixed-effects meta-analysis of 9 genome-wide association studies (GWAS) that included 5,636 patients with knee OA and 16,972 control subjects and 4,349 patients with hip OA and 17,836 control subjects of European ancestry. An additional 5,921 individuals were genotyped for significantly associated SNPs in the meta-analysis. After correction for the number of independent tests, P values less than 1.58 × 10(-5) were considered significant. RESULTS: SNPs at only 2 of the 199 candidate genes (COL11A1 and VEGF) were associated with OA in the meta-analysis. Two SNPs in COL11A1 showed association with hip OA in the combined analysis: rs4907986 (P = 1.29 × 10(-5) , odds ratio [OR] 1.12, 95% confidence interval [95% CI] 1.06-1.17) and rs1241164 (P = 1.47 × 10(-5) , OR 0.82, 95% CI 0.74-0.89). The sex-stratified analysis also showed association of COL11A1 SNP rs4908291 in women (P = 1.29 × 10(-5) , OR 0.87, 95% CI 0.82-0.92); this SNP showed linkage disequilibrium with rs4907986. A single SNP of VEGF, rs833058, showed association with hip OA in men (P = 1.35 × 10(-5) , OR 0.85, 95% CI 0.79-0.91). After additional samples were genotyped, association at one of the COL11A1 signals was reinforced, whereas association at VEGF was slightly weakened. CONCLUSION: Two candidate genes, COL11A1 and VEGF, were significantly associated with OA in this focused meta-analysis. The remaining candidate genes were not associated

Very low frequency Syndromes

Dismorfología, Citogenética y Clínica: Resultados de estudios sobre los datos del ECEMCThe aim of this chapter is to summarize updated knowledge about the clinical characteristics, etiology, genetic and molecular aspects, as well as mechanisms involved in syndromes having very low frequency, in order to promote their better recognition. During the last five years, a total of 30 syndromes have been published in this chapter of the Boletín del ECEMC. This issue includes the following selected syndromes: Crouzon, Pfeiffer, Apert, Saethre-Chotzen, Carpenter and Muenke. All share craniosynostosis as the main clinical feature but also present with other birth defects, the most important being limb malformations, specially syndactyly and polydactyly. Over 100 syndromes with craniosynostosis have been described, usually involving multiple sutures, and several of them are associated with limb malformations. The clinical overlapping between those syndromes makes difficult to perform a neonatal diagnosis, based on their clinical findings. However, molecular genetic testing, specifically of the FRGR1-3 and TWIST1 genes, could help to establish the diagnosis of some of them. Early diagnosis is important for establishing the most suitable treatment for each patient, as well as to offer an accurate genetic counselling and the possibility of preimplantational and/or prenatal diagnosis.N

FlowCT for the analysis of large immunophenotypic datasets and biomarker discovery in cancer immunology

Large-scale immune monitoring is becoming routinely used in clinical trials to identify determinants of treatment responsiveness, particularly to immunotherapies. Flow cytometry remains one of the most versatile and high throughput approaches for single-cell analysis; however, manual interpretation of multidimensional data poses a challenge when attempting to capture full cellular diversity and provide reproducible results. We present FlowCT, a semi-automated workspace empowered to analyze large data sets. It includes pre-processing, normalization, multiple dimensionality reduction techniques, automated clustering, and predictive modeling tools. As a proof of concept, we used FlowCT to compare the T-cell compartment in bone marrow (BM) with peripheral blood (PB) from patients with smoldering multiple myeloma (SMM), identify minimally invasive immune biomarkers of progression from smoldering to active MM, define prognostic T-cell subsets in the BM of patients with active MM after treatment intensification, and assess the longitudinal effect of maintenance therapy in BM T cells. A total of 354 samples were analyzed and immune signatures predictive of malignant transformation were identified in 150 patients with SMM (hazard ratio [HR], 1.7; P < .001). We also determined progression-free survival (HR, 4.09; P < .0001) and overall survival (HR, 3.12; P = .047) in 100 patients with active MM. New data also emerged about stem cell memory T cells, the concordance between immune profiles in BM and PB, and the immunomodulatory effect of maintenance therapy. FlowCT is a new open-source computational approach that can be readily implemented by research laboratories to perform quality control, analyze high-dimensional data, unveil cellular diversity, and objectively identify biomarkers in large immune monitoring studies. These trials were registered at www.clinicaltrials.gov as #NCT01916252 and #NCT02406144.© 2022 by The American Society of Hematology. Licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0), permitting only noncommercial, nonderivative use with attribution. All other rights reserved