Association study of MMP8 gene in osteoarthritis.

OBJECTIVES: Osteoarthritis (OA) is a joint disease common in the elderly. There is a prior functional evidence for different matrix metalloproteinases (MMPs), such as MMP8 and MMP9, having a role in the breakdown of cartilage extracellular matrix in OA. Thus, we analyzed whether the common genetic variants of MMP8 and MMP9 contribute to the risk of OA. MATERIALS AND METHODS: In total, 13 common tagging single-nucleotide polymorphisms (SNPs) were studied in a discovery knee OA cohort of 185 cases and 895 controls. For validation, two knee OA replication cohorts and two hand OA replication cohorts were studied (altogether 1369 OA cases, 4445 controls in the five cohorts). The χ(2) test for individual study cohorts and Cochran-Mantel-Haenszel test for combined meta-analysis were calculated using Plink. RESULTS: The rs1940475 SNP in MMP8 showed suggestive association in the discovery cohort (OR = 0.721, 95% CI 0.575-0.906; p = 0.005). Other knee and hand OA replication study cohorts showed similar trend for the predisposing allele without reaching statistical significance in independent replication cohorts nor in their meta-analysis (p > 0.05). Meta-analysis of all five hand and knee OA study cohorts yielded a p-value of 0.027 (OR = 0.904, 95% CI 0.826-0.989). CONCLUSIONS: Initial analysis of the MMP8 gene showed suggestive association between rs1940475 and knee OA, but the finding did not replicate in other study cohorts, even though the trend for predisposing allele was similar in all five cohorts. MMP-8 is a good biological candidate for OA, but our study did not find common variants with significant association in the gene.

Genetic distance as an alternative to physical distance for definition of gene units in association studies.

BACKGROUND: Some association studies, as the implemented in VEGAS, ALIGATOR, i-GSEA4GWAS, GSA-SNP and other software tools, use genes as the unit of analysis. These genes include the coding sequence plus flanking sequences. Polymorphisms in the flanking sequences are of interest because they involve cis-regulatory elements or they inform on untyped genetic variants trough linkage disequilibrium. Gene extensions have customarily been defined as ±50 Kb. This approach is not fully satisfactory because genetic relationships between neighbouring sequences are a function of genetic distances, which are only poorly replaced by physical distances. RESULTS: Standardized recombination rates (SRR) from the deCODE recombination map were used as units of genetic distances. We searched for a SRR producing flanking sequences near the ±50 Kb offset that has been common in previous studies. A SRR≥2 was selected because it led to gene extensions with median length=45.3 Kb and the simplicity of an integer value. As expected, boundaries of the genes defined with the ±50 Kb and with the SRR≥2 rules were rarely concordant. The impact of these differences was illustrated with the interpretation of top association signals from two large studies including many hits and their detailed analysis based in different criteria. The definition based in genetic distance was more concordant with the results of these studies than the based in physical distance. In the analysis of 18 top disease associated loci form the first study, the SRR≥2 genes led to a fully concordant interpretation in 17 loci; the ±50 Kb genes only in 6. Interpretation of the 43 putative functional genes of the second study based in the SRR≥2 definition only missed 4 of the genes, whereas the based in the ±50 Kb definition missed 10 genes. CONCLUSIONS: A gene definition based on genetic distance led to results more concordant with expert detailed analyses than the commonly used based in physical distance. The genome coordinates for each gene are provided to maintain a simple use of the new definitions.

A meta-analysis of genome-wide association studies identifies novel variants associated with osteoarthritis of the hip.

OBJECTIVES: Osteoarthritis (OA) is the most common form of arthritis with a clear genetic component. To identify novel loci associated with hip OA we performed a meta-analysis of genome-wide association studies (GWAS) on European subjects. METHODS: We performed a two-stage meta-analysis on more than 78,000 participants. In stage 1, we synthesised data from eight GWAS whereas data from 10 centres were used for 'in silico' or 'de novo' replication. Besides the main analysis, a stratified by sex analysis was performed to detect possible sex-specific signals. Meta-analysis was performed using inverse-variance fixed effects models. A random effects approach was also used. RESULTS: We accumulated 11,277 cases of radiographic and symptomatic hip OA. We prioritised eight single nucleotide polymorphism (SNPs) for follow-up in the discovery stage (4349 OA cases); five from the combined analysis, two male specific and one female specific. One locus, at 20q13, represented by rs6094710 (minor allele frequency (MAF) 4%) near the NCOA3 (nuclear receptor coactivator 3) gene, reached genome-wide significance level with p=7.9×10(-9) and OR=1.28 (95% CI 1.18 to 1.39) in the combined analysis of discovery (p=5.6×10(-8)) and follow-up studies (p=7.3×10(-4)). We showed that this gene is expressed in articular cartilage and its expression was significantly reduced in OA-affected cartilage. Moreover, two loci remained suggestive associated; rs5009270 at 7q31 (MAF 30%, p=9.9×10(-7), OR=1.10) and rs3757837 at 7p13 (MAF 6%, p=2.2×10(-6), OR=1.27 in male specific analysis). CONCLUSIONS: Novel genetic loci for hip OA were found in this meta-analysis of GWAS.

Genome-wide association study identifies 30 obsessive-compulsive disorder associated loci.

Obsessive-compulsive disorder (OCD) affects ~1% of the population and exhibits a high SNP-heritability, yet previous genome-wide association studies (GWAS) have provided limited information on the genetic etiology and underlying biological mechanisms of the disorder. We conducted a GWAS meta-analysis combining 53,660 OCD cases and 2,044,417 controls from 28 European-ancestry cohorts revealing 30 independent genome-wide significant SNPs and a SNP-based heritability of 6.7%. Separate GWAS for clinical, biobank, comorbid, and self-report sub-groups found no evidence of sample ascertainment impacting our results. Functional and positional QTL gene-based approaches identified 249 significant candidate risk genes for OCD, of which 25 were identified as putatively causal, highlighting WDR6, DALRD3, CTNND1 and genes in the MHC region. Tissue and single-cell enrichment analyses highlighted hippocampal and cortical excitatory neurons, along with D1- and D2-type dopamine receptor-containing medium spiny neurons, as playing a role in OCD risk. OCD displayed significant genetic correlations with 65 out of 112 examined phenotypes. Notably, it showed positive genetic correlations with all included psychiatric phenotypes, in particular anxiety, depression, anorexia nervosa, and Tourette syndrome, and negative correlations with a subset of the included autoimmune disorders, educational attainment, and body mass index.. This study marks a significant step toward unraveling its genetic landscape and advances understanding of OCD genetics, providing a foundation for future interventions to address this debilitating disorder.

UTMOST, a single and cross-tissue TWAS (Transcriptome Wide Association Study), reveals new ASD (Autism Spectrum Disorder) associated genes.

Autism spectrum disorders (ASD) is a complex neurodevelopmental disorder that may significantly impact on the affected individual's life. Common variation (SNPs) could explain about 50% of ASD heritability. Despite this fact and the large size of the last GWAS meta-analysis, it is believed that hundreds of risk genes in ASD have yet to be discovered. New tools, such as TWAS (Transcriptome Wide Association Studies) which integrate tissue expression and genetic data, are a great approach to identify new ASD susceptibility genes. The main goal of this study is to use UTMOST with the publicly available summary statistics from the largest ASD GWAS meta-analysis as genetic input. In addition, an in silico biological characterization for the novel associated loci was performed. Our results have shown the association of 4 genes at the brain level (CIPC, PINX1, NKX2-2, and PTPRE) and have highlighted the association of NKX2-2, MANBA, ERI1, and MITF at the gastrointestinal level. The gastrointestinal associations are quite relevant given the well-established but unexplored relationship between ASD and gastrointestinal symptoms. Cross-tissue analysis has shown the association of NKX2-2 and BLK. UTMOST-associated genes together with their in silico biological characterization seems to point to different biological mechanisms underlying ASD etiology. Thus, it would not be restricted to brain tissue and it will involve the participation of other body tissues such as the gastrointestinal.

Exploring the biological role of postzygotic and germinal de novo mutations in ASD.

De novo mutations (DNMs), including germinal and postzygotic mutations (PZMs), are a strong source of causality for Autism Spectrum Disorder (ASD). However, the biological processes involved behind them remain unexplored. Our aim was to detect DNMs (germinal and PZMs) in a Spanish ASD cohort (360 trios) and to explore their role across different biological hierarchies (gene, biological pathway, cell and brain areas) using bioinformatic approaches. For the majority of the analysis, a combined ASD cohort (N = 2171 trios) was created using previously published data by the Autism Sequencing Consortium (ASC). New plausible candidate genes for ASD such as FMR1 and NFIA were found. In addition, genes harboring PZMs were significantly enriched for miR-137 targets in comparison with germinal DNMs that were enriched in GO terms related to synaptic transmission. The expression pattern of genes with PZMs was restricted to early mid-fetal cortex. In contrast, the analysis of genes with germinal DNMs revealed a spatio-temporal window from early to mid-fetal development stages, with expression in the amygdala, cerebellum, cortex and striatum. These results provide evidence of the pathogenic role of PZMs and suggest the existence of distinct mechanisms between PZMs and germinal DNMs that are influencing ASD risk.

Psychiatric comorbidities in Asperger syndrome are related with polygenic overlap and differ from other Autism subtypes.

There is great phenotypic heterogeneity within autism spectrum disorders (ASD), which has led to question their classification into a single diagnostic category. The study of the common genetic variation in ASD has suggested a greater contribution of other psychiatric conditions in Asperger syndrome (AS) than in the rest of the DSM-IV ASD subtypes (Non_AS). Here, using available genetic data from previously performed genome-wide association studies (GWAS), we aimed to study the genetic overlap between five of the most related disorders (schizophrenia (SCZ), major depression disorder (MDD), attention deficit hyperactivity disorder (ADHD), obsessive-compulsive disorders (OCD) and anxiety (ANX)), and AS, comparing it with the overlap in Non_AS subtypes. A Spanish cohort of autism trios (N = 371) was exome sequenced as part of the Autism Sequencing Consortium (ASC) and 241 trios were extensively characterized to be diagnosed with AS following DSM-IV and Gillberg's criteria (N = 39) or not (N = 202). Following exome imputation, polygenic risk scores (PRS) were calculated for ASD, SCZ, ADHD, MDD, ANX, and OCD (from available summary data from Psychiatric Genomic Consortium (PGC) repository) in the Spanish trios' cohort. By using polygenic transmission disequilibrium test (pTDT), we reported that risk for SCZ (Pscz = 0.008, corrected-PSCZ = 0.0409), ADHD (PADHD = 0.021, corrected-PADHD = 0.0301), and MDD (PMDD = 0.039, corrected-PMDD = 0.0501) is over-transmitted to children with AS but not to Non_AS. Indeed, agnostic clustering procedure with deviation values from pTDT tests suggested two differentiated clusters of subjects, one of which is significantly enriched in AS (P = 0.025). Subsequent analysis with S-Predixcan, a recently developed software to predict gene expression from genotype data, revealed a clear pattern of correlation between cortical gene expression in ADHD and AS (P < 0.001) and a similar strong correlation pattern between MDD and AS, but also extendable to another non-brain tissue such as lung (P < 0.001). Altogether, these results support the idea of AS being qualitatively distinct from Non_AS autism and consistently evidence the genetic overlap between AS and ADHD, MDD, or SCZ.

Novel Gene-Based Analysis of ASD GWAS: Insight Into the Biological Role of Associated Genes.

Background: Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by its significant social impact and high heritability. The latest meta-analysis of ASD GWAS (genome-wide association studies) has revealed the association of several SNPs that were replicated in additional sets of independent samples. However, summary statistics from GWAS can be used to perform a gene-based analysis (GBA). GBA allows to combine all genetic information across the gene to create a single statistic (p-value for each gene). Thus, PASCAL (Pathway scoring algorithm), a novel GBA tool, has been applied to the summary statistics from the latest meta-analysis of ASD. GBA approach (testing the gene as a unit) provides an advantage to perform an accurate insight into the biological ASD mechanisms. Therefore, a gene-network analysis and an enrichment analysis for KEGG and GO terms were carried out. GENE2FUNC was used to create gene expression heatmaps and to carry out differential expression analysis (DEA) across GTEx v7 tissues and Brainspan data. dbMDEGA was employed to perform a DEG analysis between ASD and brain control samples for the associated genes and interactors. Results: PASCAL has identified the following loci associated with ASD: XRN2, NKX2-4, PLK1S1, KCNN2, NKX2-2, CRHR1-IT1, C8orf74 and LOC644172. While some of these genes were previously reported by MAGMA (XRN2, PLK1S1, and KCNN2), PASCAL has been useful to highlight additional genes. The biological characterization of the ASD-associated genes and their interactors have demonstrated the association of several GO and KEGG terms. Moreover, DEA analysis has revealed several up- and down-regulated clusters. In addition, many of the ASD-associated genes and their interactors have shown association with ASD expression datasets. Conclusions: This study identifies several associations at a gene level in ASD. Most of them were previously reported by MAGMA. This fact proves that PASCAL is an efficient GBA tool to extract additional information from previous GWAS. In addition, this study has characterized for the first time the biological role of the ASD-associated genes across brain regions, neurodevelopmental stages, and ASD gene-expression datasets.

Gene-based analysis of ADHD using PASCAL: a biological insight into the novel associated genes.

BACKGROUND: Attention-Deficit Hyperactivity Disorder (ADHD) is a complex neurodevelopmental disorder (NDD) which may significantly impact on the affected individual's life. ADHD is acknowledged to have a high heritability component (70-80%). Recently, a meta-analysis of GWAS (Genome Wide Association Studies) has demonstrated the association of several independent loci. Our main aim here, is to apply PASCAL (pathway scoring algorithm), a new gene-based analysis (GBA) method, to the summary statistics obtained in this meta-analysis. PASCAL will take into account the linkage disequilibrium (LD) across genomic regions in a different way than the most commonly employed GBA methods (MAGMA or VEGAS (Versatile Gene-based Association Study)). In addition to PASCAL analysis a gene network and an enrichment analysis for KEGG and GO terms were carried out. Moreover, GENE2FUNC tool was employed to create gene expression heatmaps and to carry out a (DEG) (Differentially Expressed Gene) analysis using GTEX v7 and BrainSpan data. RESULTS: PASCAL results have revealed the association of new loci with ADHD and it has also highlighted other genes previously reported by MAGMA analysis. PASCAL was able to discover new associations at a gene level for ADHD: FEZF1 (p-value: 2.2 × 10- 7) and FEZF1-AS1 (p-value: 4.58 × 10- 7). In addition, PASCAL has been able to highlight association of other genes that share the same LD block with some previously reported ADHD susceptibility genes. Gene network analysis has revealed several interactors with the associated ADHD genes and different GO and KEGG terms have been associated. In addition, GENE2FUNC has demonstrated the existence of several up and down regulated expression clusters when the associated genes and their interactors were considered. CONCLUSIONS: PASCAL has been revealed as an efficient tool to extract additional information from previous GWAS using their summary statistics. This study has identified novel ADHD associated genes that were not previously reported when other GBA methods were employed. Moreover, a biological insight into the biological function of the ADHD associated genes across brain regions and neurodevelopmental stages is provided.

De novo Mutations (DNMs) in Autism Spectrum Disorder (ASD): Pathway and Network Analysis.

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder (NDD) defined by impairments in social communication and social interactions, accompanied by repetitive behavior and restricted interests. ASD is characterized by its clinical and etiological heterogeneity, which makes it difficult to elucidate the neurobiological mechanisms underlying its pathogenesis. Recently, de novo mutations (DNMs) have been recognized as strong source of genetic causality. Here, we review different aspects of the DNMs associated with ASD, including their functional annotation and classification. In addition, we also focus on the most recent advances in this area, such as the detection of PZMs (post-zygotic mutations), and we outline the main bioinformatics tools commonly employed to study these. Some of these approaches available allow DNMs to be analyzed in the context of gene networks and pathways, helping to shed light on the biological processes underlying ASD. To end this review, a brief insight into the future perspectives for genetic studies into ASD will be provided.

Genetics of osteoarthritis.

Osteoarthritis (OA) is a complex disease caused by the interaction of multiple genetic and environmental factors. This review focuses on the studies that have contributed to the discovery of genetic susceptibility factors in OA. The most relevant associations discovered until now are discussed in detail: GDF-5, 7q22 locus, MCF2L, DOT1L, NCOA3 and also some important findings from the arcOGEN study. Moreover, the different approaches that can be used to minimize the specific problems of the study of OA genetics are discussed. These include the study of microsatellites, phenotype standardization and other methods such as meta-analysis of GWAS and gene-based analysis. It is expected that these new approaches contribute to finding new susceptibility genetic factors for OA.

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.

Association of a BMP5 microsatellite with knee osteoarthritis: case-control study.

INTRODUCTION: We aimed to explore the involvement of a multiallelic functional polymorphism in knee osteoarthritis (OA) susceptibility as a prototype of possible genetic factors escaping GWAS detection. METHODS: OA patients and controls from three European populations (Greece, Spain and the UK) adding up to 1003 patients (716 women, 287 men) that had undergone total knee joint replacement (TKR) due to severe primary OA and 1543 controls (758 women, 785 men) lacking clinical signs or symptoms of OA were genotyped for the D6S1276 microsatellite in intron 1 of BMP5. Genotype and mutiallelic trend tests were used to compare cases and controls. RESULTS: Significant association was found between the microsatellite and knee OA in women (P from 3.1 x10-4 to 4.1 x10-4 depending on the test), but not in men. Three of the alleles showed significant differences between patients and controls, one of them of increased risk and two of protection. The gender association and the allele direction of change were very concordant with those previously reported for hip OA. CONCLUSIONS: We have found association of knee OA in women with the D6S1276 functional microsatellite that modifies in cis the expression of BMP5 making this a sounder OA genetic factor and extending its involvement to other joints. This result also shows the interest of analysing other multiallelic polymorphisms.

Genetic risk load and age at symptom onset of knee osteoarthritis.

To test whether a higher genetic risk load for knee osteoarthritis (OA) is associated with an earlier age at symptom onset. Six polymorphisms in GDF5, PTGS2, 7q22 locus, DVWA, DIO3, and ASPN that have been associated with knee OA were analyzed in 255 patients that had undergone total knee replacement (TKR) because of primary OA and in 457 healthy controls. We looked for association between the number of risk alleles in each patient and his age at symptom onset with linear regression and t-tests between the upper and lower quartiles. There was not even a weak trend in the direction of a younger age at symptom onset in the patients carrying more risk alleles. Patients in the upper quartile of age at symptom onset (67.0 ± 2.8 years) carried the same number of OA risk alleles (5.4 ± 1.4 vs. 5.3 ± 1.0) than patients in the lower quartile (44.6 ± 5.5 years). We did not find any evidence in support of the hypothesis of an earlier knee OA symptom onset associated with higher genetic risk load as determined by the six loci. This result suggests that old age and genetic risk act as independent factors in the pathogenesis of OA. It also indicates that designing OA genetic studies with patients selected for early symptom onset will not provide any substantial power gain.

New sequence variants in HLA class II/III region associated with susceptibility to knee osteoarthritis identified by genome-wide association study.

Osteoarthritis (OA) is a common disease that has a definite genetic component. Only a few OA susceptibility genes that have definite functional evidence and replication of association have been reported, however. Through a genome-wide association study and a replication using a total of approximately 4,800 Japanese subjects, we identified two single nucleotide polymorphisms (SNPs) (rs7775228 and rs10947262) associated with susceptibility to knee OA. The two SNPs were in a region containing HLA class II/III genes and their association reached genome-wide significance (combined P = 2.43x10(-8) for rs7775228 and 6.73x10(-8) for rs10947262). Our results suggest that immunologic mechanism is implicated in the etiology of OA.