Functional interaction between Sequestosome-1/p62 and Autophagy-Linked FYVE-containing protein WDFY3 in human osteoclasts

Peer reviewedPostprin

Common Genetic Variants Explain the Majority of the Correlation Between Height and Intelligence : The Generation Scotland Study

Creative Commons Attribution LicensePeer reviewedPublisher PD

PADI4 genotype is not associated with rheumatoid arthritis in a large UK Caucasian population

BACKGROUND: Polymorphisms of the peptidylarginine deiminase type 4 (PADI4) gene confer susceptibility to rheumatoid arthritis (RA) in East Asian people. However, studies in European populations have produced conflicting results. This study explored the association of the PADI4 genotype with RA in a large UK Caucasian population. METHODS: The PADI4_94 (rs2240340) single nucleotide polymorphism (SNP) was directly genotyped in a cohort of unrelated UK Caucasian patients with RA (n=3732) and population controls (n=3039). Imputed data from the Wellcome Trust Case Control Consortium (WTCCC) was used to investigate the association of PADI4_94 with RA in an independent group of RA cases (n=1859) and controls (n=10 599). A further 56 SNPs spanning the PADI4 gene were investigated for association with RA using data from the WTCCC study. RESULTS: The PADI4_94 genotype was not associated with RA in either the present cohort or the WTCCC cohort. Combined analysis of all the cases of RA (n=5591) and controls (n=13 638) gave an overall OR of 1.01 (95% CI 0.96 to 1.05, p=0.72). No association with anti-CCP antibodies and no interaction with either shared epitope or PTPN22 was detected. No evidence for association with RA was identified for any of the PADI4 SNPs investigated. Meta-analysis of previously published studies and our data confirmed no significant association between the PADI4_94 genotype and RA in people of European descent (OR 1.06, 95% CI 0.99 to 1.13, p=0.12). CONCLUSION: In the largest study performed to date, the PADI4 genotype was not a significant risk factor for RA in people of European ancestry, in contrast to Asian populations

MICL controls inflammation in rheumatoid arthritis

Acknowledgments We thank G Milne, D MacCallum, S Hardison, G Wilson, C Wallace, S Hadebe and A Richmond for assistance; H. El-Gabalawy for tissues and the animal facility staff for the care of our animals. Flow cytometry was undertaken in the Iain Fraser Cytometry Centre, University of Aberdeen. Funding: GDB was funded by the Wellcome Trust and MRC (UK). AA and CDB are supported by the Arthritis Research UK Tissue Engineering Centre (grant 19429). This study makes use of data generated by the Wellcome Trust Case Control Consortium. A full list of the investigators who contributed to the generation of the data is available from http://www.wtccc.org.uk, and was funded by the Wellcome Trust (076113). MJGF was funded by The Arthritis Society and the Canadian Arthritis Network and J-ML by a scholarship from the Canadian Arthritis Network.Peer reviewedPublisher PD

Brief Report: Identification of BACH2 and RAD51B as Rheumatoid Arthritis Susceptibility Loci in a Meta-Analysis of Genome-Wide Data

Objective: A recent high-density fine-mapping (ImmunoChip) study of genetic associations in rheumatoid arthritis (RA) identified 14 risk loci with validated genome-wide significance, as well as a number of loci showing associations suggestive of significance (P = 5 × 10−5 < 5 × 10−8), but these have yet to be replicated. The aim of this study was to determine whether these potentially significant loci are involved in the pathogenesis of RA, and to explore whether any of the loci are associated with a specific RA serotype. Methods: A total of 16 single-nucleotide polymorphisms (SNPs) were selected for genotyping and association analyses in 2 independent validation cohorts, comprising 6,106 RA cases and 4,290 controls. A meta-analysis of the data from the original ImmunoChip discovery cohort and from both validation cohorts was carried out, for a combined total of 17,581 RA cases and 20,160 controls. In addition, stratified analysis of patient subsets, defined according to their anti–cyclic citrullinated peptide (anti-CCP) antibody status, was performed. Results: A significant association with RA risk (P < 0.05) was replicated for 6 of the SNPs assessed in the validation cohorts. All SNPs in the validation study had odds ratios (ORs) for RA susceptibility in the same direction as those in the ImmunoChip discovery study. One SNP, rs72928038, mapping to an intron of BACH2, achieved genome-wide significance in the meta-analysis (P = 1.2 × 10−8, OR 1.12), and a second SNP, rs911263, mapping to an intron of RAD51B, was significantly associated in the anti-CCP–positive RA subgroup (P = 4 × 10−8, OR 0.89), confirming that both are RA susceptibility loci. Conclusion: This study provides robust evidence for an association of RA susceptibility with genes involved in B cell differentiation (BACH2) and DNA repair (RAD51B). The finding that the RAD51B gene exhibited different associations based on serologic subtype adds to the expanding knowledge base in defining subgroups of RA

Genome sequencing with gene panel-based analysis for rare inherited conditions in a publicly funded healthcare system: implications for future testing

Acknowledgements This study would not be possible without the families, patients, clinicians, nurses, research scientists, laboratory staff, informaticians and the wider Scottish Genomes Partnership team to whom we give grateful thanks. This research was made possible through access to the data and findings generated by the 100,000 Genomes Project. The 100,000 Genomes Project is managed by Genomics England Limited (a wholly owned company of the Department of Health). The Scottish Genomes Partnership was funded by the Chief Scientist Office of the Scottish Government Health Directorates (SGP/1) and The Medical Research Council Whole Genome Sequencing for Health and Wealth Initiative (MC/PC/15080). The 100,000 Genomes Project is funded by the National Institute for Health Research and NHS England. The Wellcome Trust, Cancer Research UK and the Medical Research Council have also funded research infrastructure.Peer reviewedPublisher PD

Genome-wide association study of antidepressant treatment resistance in a population-based cohort using health service prescription data and meta-analysis with GENDEP

Antidepressants demonstrate modest response rates in the treatment of major depressive disorder (MDD). Despite previous genome-wide association studies (GWAS) of antidepressant treatment response, the underlying genetic factors are unknown. Using prescription data in a population and family-based cohort (Generation Scotland: Scottish Family Health Study; GS:SFHS), we sought to define a measure of (a) antidepressant treatment resistance and (b) stages of antidepressant resistance by inferring antidepressant switching as non-response to treatment. GWAS were conducted separately for antidepressant treatment resistance in GS:SFHS and the Genome-based Therapeutic Drugs for Depression (GENDEP) study and then meta-analysed (meta-analysis n = 4213, cases = 358). For stages of antidepressant resistance, a GWAS on GS:SFHS only was performed (n = 3452). Additionally, we conducted gene-set enrichment, polygenic risk scoring (PRS) and genetic correlation analysis. We did not identify any significant loci, genes or gene sets associated with antidepressant treatment resistance or stages of resistance. Significant positive genetic correlations of antidepressant treatment resistance and stages of resistance with neuroticism, psychological distress, schizotypy and mood disorder traits were identified. These findings suggest that larger sample sizes are needed to identify the genetic architecture of antidepressant treatment response, and that population-based observational studies may provide a tractable approach to achieving the necessary statistical power

Study of the common genetic background for rheumatoid arthritis and systemic lupus erythematosus

BACKGROUND: Evidence is beginning to emerge that there may be susceptibility loci for rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE) that are common to both diseases. OBJECTIVE: To investigate single nucleotide polymorphisms that have been reported to be associated with SLE in a UK cohort of patients with RA and controls. METHODS: 3962 patients with RA and 9275 controls were included in the study. Eleven SNPs mapping to confirmed SLE loci were investigated. These mapped to the TNFSF4, BANK1, TNIP1, PTTG1, UHRF1BP1, ATG5, JAZF1, BLK, KIAA1542, ITGAM and UBE2L3 loci. Genotype frequencies were compared between patients with RA and controls using the trend test. RESULTS: The SNPs mapping to the BLK and UBE2L3 loci showed significant evidence for association with RA. Two other SNPs, mapping to ATG5 and KIAA1542, showed nominal evidence for association with RA (p=0.02 and p=0.02, respectively) but these were not significant after applying a Bonferroni correction. Additionally, a significant global enrichment in carriage of SLE alleles in patients with RA compared with controls (p=9.1×10(-7)) was found. Meta-analysis of this and previous studies confirmed the association of the BLK and UBE2L3 gene with RA at genome-wide significance levels (p<5×10(-8)). Together, the authors estimate that the SLE and RA overlapping loci, excluding HLA-DRB1 alleles, identified so far explain ∼5.8% of the genetic susceptibility to RA as a whole. CONCLUSION: The findings confirm the association of the BLK and UBE2L3 loci with RA, thus adding to the list of loci showing overlap between RA and SLE

Quantifying the extent to which index event biases influence large genetic association studies

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this record.As genetic association studies increase in size to 100,000s of individuals, subtle biases may influence conclusions. One possible bias is "index event bias" (IEB) that appears due to the stratification by, or enrichment for, disease status when testing associations between genetic variants and a disease-associated trait. We aimed to test the extent to which IEB influences some known trait associations in a range of study designs and provide a statistical framework for assessing future associations. Analysing data from 113,203 non-diabetic UK Biobank participants, we observed three (near TCF7L2, CDKN2AB and CDKAL1) overestimated (BMI-decreasing) and one (near MTNR1B) underestimated (BMI-increasing) associations among 11 type 2 diabetes risk alleles (at P  500,000 if the prevalence of those diseases differs by > 10% from the background population. In conclusion, IEB may result in false positive or negative genetic associations in very large studies stratified or strongly enriched for/against disease cases.H.Y., A.R.W. and T.M.F. are supported by the European Research Council grant: 323195; SZ-245 50371-GLUCOSEGENES-FP7-IDEAS-ERC. S.E.J. is funded by the Medical Research Council (grant: MR/M005070/1). M.A.T., M.N.W. and A.M. are supported by the Wellcome Trust Institutional Strategic Support Award (WT097835MF). R.M.F. is a Sir Henry Dale Fellow (Wellcome Trust and Royal Society grant: 104150/Z/14/Z). R.B. is funded by the Wellcome Trust and Royal Society grant: 104150/Z/14/Z. J.T. is funded by a Diabetes Research and Wellness Foundation Fellowship. Z.K. received financial support from the Leenaards Foundation, the Swiss Institute of Bioinformatics and the Swiss National Science Foundation (31003A-143914) and SystemsX.ch (39). The work of M.P.B was supported by the National Heart, Lung, And Blood Institute of the National Institutes of Health under Award no. T32HL007779. Generation Scotland received core support from the Chief Scientist Office of the Scottish Government Health Directorates [CZD/16/6] and the Scottish Funding Council [HR03006]. E.R.P. holds a WT New investigator award 102820/Z/13/Z

Molecular genetic contributions to socioeconomic status and intelligence

Education, socioeconomic status, and intelligence are commonly used as predictors of health outcomes, social environment, and mortality. Education and socioeconomic status are typically viewed as environmental variables although both correlate with intelligence, which has a substantial genetic basis. Using data from 6815 unrelated subjects from the Generation Scotland study, we examined the genetic contributions to these variables and their genetic correlations. Subjects underwent genome-wide testing for common single nucleotide polymorphisms (SNPs). DNA-derived heritability estimates and genetic correlations were calculated using the ‘Genome-wide Complex Trait Analyses’ (GCTA) procedures. 21% of the variation in education, 18% of the variation in socioeconomic status, and 29% of the variation in general cognitive ability was explained by variation in common SNPs (SEs ~ 5%). The SNP-based genetic correlations of education and socioeconomic status with general intelligence were 0.95 (SE 0.13) and 0.26 (0.16), respectively. There are genetic contributions to intelligence and education with near-complete overlap between common additive SNP effects on these traits (genetic correlation ~ 1). Genetic influences on socioeconomic status are also associated with the genetic foundations of intelligence. The results are also compatible with substantial environmental contributions to socioeconomic status