South America has a complex demographic history shaped by multiple migration and admixture events in pre- and post-colonial times. Settled over 14,000 years ago by Native Americans, South America has ...experienced migrations of European and African individuals, similar to other regions in the Americas. However, the timing and magnitude of these events resulted in markedly different patterns of admixture throughout Latin America. We use genome-wide SNP data for 437 admixed individuals from 5 countries (Colombia, Ecuador, Peru, Chile, and Argentina) to explore the population structure and demographic history of South American Latinos. We combined these data with population reference panels from Africa, Asia, Europe and the Americas to perform global ancestry analysis and infer the subcontinental origin of the European and Native American ancestry components of the admixed individuals. By applying ancestry-specific PCA analyses we find that most of the European ancestry in South American Latinos is from the Iberian Peninsula; however, many individuals trace their ancestry back to Italy, especially within Argentina. We find a strong gradient in the Native American ancestry component of South American Latinos associated with country of origin and the geography of local indigenous populations. For example, Native American genomic segments in Peruvians show greater affinities with Andean indigenous peoples like Quechua and Aymara, whereas Native American haplotypes from Colombians tend to cluster with Amazonian and coastal tribes from northern South America. Using ancestry tract length analysis we modeled post-colonial South American migration history as the youngest in Latin America during European colonization (9-14 generations ago), with an additional strong pulse of European migration occurring between 3 and 9 generations ago. These genetic footprints can impact our understanding of population-level differences in biomedical traits and, thus, inform future medical genetic studies in the region.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Systemic lupus erythematosus (SLE) is a genetically complex autoimmune disease characterized by loss of immune tolerance to nuclear and cell surface antigens. Previous genome-wide association studies ...(GWAS) had modest sample sizes, reducing their scope and reliability. Our study comprised 7,219 cases and 15,991 controls of European ancestry, constituting a new GWAS, a meta-analysis with a published GWAS and a replication study. We have mapped 43 susceptibility loci, including ten new associations. Assisted by dense genome coverage, imputation provided evidence for missense variants underpinning associations in eight genes. Other likely causal genes were established by examining associated alleles for cis-acting eQTL effects in a range of ex vivo immune cells. We found an over-representation (n = 16) of transcription factors among SLE susceptibility genes. This finding supports the view that aberrantly regulated gene expression networks in multiple cell types in both the innate and adaptive immune response contribute to the risk of developing SLE.
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IJS, NUK, SBMB, UL, UM, UPUK
Objective
The highly heterogeneous clinical presentation of systemic lupus erythematosus (SLE) is characterized by the unpredictable occurrence of disease flares and organ damage. Attempts to ...stratify lupus patients have been limited to classification based on clinical information, leading to unsuccessful clinical trials and controversial research results. This study was undertaken to develop and validate a robust method to stratify patients with lupus according to longitudinal disease activity and whole‐genome gene expression data in order to establish subgroups of patients who share disease progression mechanisms.
Methods
We used a cluster‐based approach to stratify SLE patients based on the correlation between disease activity scores and longitudinal gene expression information. Clustering robustness was evaluated by the bootstrap method, and the clusters were characterized in terms of clinical and functional features.
Results
We observed a clear partition into 3 different disease clusters in 2 independent sets of patients, one pediatric and one adult, which was not influenced by treatment, race, or other source of bias. Two of the clusters differentiated into a group showing a correlation between the percentage of neutrophils and disease activity progression and a group showing a correlation between the percentage of lymphocytes and disease activity progression. The third cluster, in which the percentage of neutrophils correlated to a lesser degree with disease activity, was functionally more heterogeneous. Patients in the neutrophil‐driven clusters had an increased risk of developing proliferative nephritis.
Conclusion
Our findings indicate that SLE patients can be stratified into 3 subgroups of patients who show different mechanisms of disease progression and are clinically differentiated. Our results have important implications for treatment options, the design of clinical trials, our understanding of the etiology of the disease, and the prediction of severe glomerulonephritis.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Genetic association studies (GAS) aims to evaluate the association between genetic variants and phenotypes. In the last few years, the number of this type of study has increased exponentially, but ...the results are not always reproducible due to experimental designs, low sample sizes and other methodological errors. In this field, meta-analysis techniques are becoming very popular tools to combine results across studies to increase statistical power and to resolve discrepancies in genetic association studies. A meta-analysis summarizes research findings, increases statistical power and enables the identification of genuine associations between genotypes and phenotypes. Meta-analysis techniques are increasingly used in GAS, but it is also increasing the amount of published meta-analysis containing different errors. Although there are several software packages that implement meta-analysis, none of them are specifically designed for genetic association studies and in most cases their use requires advanced programming or scripting expertise.
We have developed MetaGenyo, a web tool for meta-analysis in GAS. MetaGenyo implements a complete and comprehensive workflow that can be executed in an easy-to-use environment without programming knowledge. MetaGenyo has been developed to guide users through the main steps of a GAS meta-analysis, covering Hardy-Weinberg test, statistical association for different genetic models, analysis of heterogeneity, testing for publication bias, subgroup analysis and robustness testing of the results.
MetaGenyo is a useful tool to conduct comprehensive genetic association meta-analysis. The application is freely available at http://bioinfo.genyo.es/metagenyo/ .
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There is an urgent need for mental health promotion in nonclinical settings. Mindfulness-based programmes (MBPs) are being widely implemented to reduce stress, but a comprehensive evidence synthesis ...is lacking. We reviewed trials to assess whether MBPs promote mental health relative to no intervention or comparator interventions.
Following a detailed preregistered protocol (PROSPERO CRD42018105213) developed with public and professional stakeholders, 13 databases were searched to August 2020 for randomised controlled trials (RCTs) examining in-person, expert-defined MBPs in nonclinical settings. Two researchers independently selected, extracted, and appraised trials using the Cochrane Risk-of-Bias Tool 2.0. Primary outcomes were psychometrically validated anxiety, depression, psychological distress, and mental well-being questionnaires at 1 to 6 months after programme completion. Multiple testing was performed using p < 0.0125 (Bonferroni) for statistical significance. Secondary outcomes, meta-regression and sensitivity analyses were prespecified. Pairwise random-effects multivariate meta-analyses and prediction intervals (PIs) were calculated. A total of 11,605 participants in 136 trials were included (29 countries, 77% women, age range 18 to 73 years). Compared with no intervention, in most but not all scenarios MBPs improved average anxiety (8 trials; standardised mean difference (SMD) = -0.56; 95% confidence interval (CI) -0.80 to -0.33; p-value < 0.001; 95% PI -1.19 to 0.06), depression (14 trials; SMD = -0.53; 95% CI -0.72 to -0.34; p-value < 0.001; 95% PI -1.14 to 0.07), distress (27 trials; SMD = -0.45; 95% CI -0.58 to -0.31; p-value < 0.001; 95% PI -1.04 to 0.14), and well-being (9 trials; SMD = 0.33; 95% CI 0.11 to 0.54; p-value = 0.003; 95% PI -0.29 to 0.94). Compared with nonspecific active control conditions, in most but not all scenarios MBPs improved average depression (6 trials; SMD = -0.46; 95% CI -0.81 to -0.10; p-value = 0.012, 95% PI -1.57 to 0.66), with no statistically significant evidence for improving anxiety or distress and no reliable data on well-being. Compared with specific active control conditions, there is no statistically significant evidence of MBPs' superiority. Only effects on distress remained when higher-risk trials were excluded. USA-based trials reported smaller effects. MBPs targeted at higher-risk populations had larger effects than universal MBPs. The main limitation of this review is that confidence according to the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach is moderate to very low, mainly due to inconsistency and high risk of bias in many trials.
Compared with taking no action, MBPs of the included studies promote mental health in nonclinical settings, but given the heterogeneity between studies, the findings do not support generalisation of MBP effects across every setting. MBPs may have specific effects on some common mental health symptoms. Other preventative interventions may be equally effective. Implementation of MBPs in nonclinical settings should be partnered with thorough research to confirm findings and learn which settings are most likely to benefit.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Objective
Mutations in the ACP5 gene, which encodes tartrate‐resistant acid phosphatase (TRAP), cause the immuno‐osseous disorder spondyloenchondrodysplasia, which includes as disease features ...systemic lupus erythematosus (SLE) and a type I interferon (IFN) signature. Our aims were to identify TRAP substrates, determine the consequences of TRAP deficiency in immune cells, and assess whether ACP5 mutations are enriched in sporadic cases of SLE.
Methods
Interaction between TRAP and its binding partners was tested by a yeast 2‐hybrid screening, confocal microscopy, and immunoprecipitation/Western blotting. TRAP knockdown was performed using small interfering RNA. Phosphorylation of osteopontin (OPN) was analyzed by mass spectrometry. Nucleotide sequence analysis of ACP5 was performed by Sanger sequencing or next‐generation sequencing.
Results
TRAP and OPN colocalized and interacted in human macrophages and plasmacytoid dendritic cells (PDCs). TRAP dephosphorylated 3 serine residues on specific OPN peptides. TRAP knockdown resulted in increased OPN phosphorylation and increased nuclear translocation of IRF7 and P65, with resultant heightened expression of IFN‐stimulated genes and IL6 and TNF following Toll‐like receptor 9 stimulation. An excess of heterozygous ACP5 missense variants was observed in SLE compared to controls (P = 0.04), and transfection experiments revealed a significant reduction in TRAP activity in a number of variants.
Conclusion
Our findings indicate that TRAP and OPN colocalize and that OPN is a substrate for TRAP in human immune cells. TRAP deficiency in PDCs leads to increased IFNα production, providing at least a partial explanation for how ACP5 mutations cause lupus in the context of spondyloenchondrodysplasia. Detection of ACP5 missense variants in a lupus cohort suggests that impaired TRAP functioning may increase susceptibility to sporadic lupus.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
In cytometry analysis, a large number of markers is measured for thousands or millions of cells, resulting in high‐dimensional datasets. During the measurement of these samples, erroneous events can ...occur such as clogs, speed changes, slow uptake of the sample etc., which can influence the downstream analysis and can even lead to false discoveries. As these issues can be difficult to detect manually, an automated approach is recommended. In order to filter these erroneous events out, we created a novel quality control algorithm, Peak Extraction And Cleaning Oriented Quality Control (PeacoQC), that allows for automated cleaning of cytometry data. The algorithm will determine density peaks per channel on which it will remove low quality events based on their position in the isolation tree and on their mean absolute deviation distance to these density peaks. To evaluate PeacoQC's cleaning capability, it was compared to three other existing quality control algorithms (flowAI, flowClean and flowCut) on a wide variety of datasets. In comparison to the other algorithms, PeacoQC was able to filter out all different types of anomalies in flow, mass and spectral cytometry data, while the other methods struggled with at least one type. In the quantitative comparison, PeacoQC obtained the highest median balanced accuracy and a similar running time compared to the other algorithms while having a better scalability for large files. To ensure that the parameters chosen in the PeacoQC algorithm are robust, the cleaning tool was run on 16 public datasets. After inspection, only one sample was found where the parameters should be further optimized. The other 15 datasets were analyzed correctly indicating a robust parameter choice. Overall, we present a fast and accurate quality control algorithm that outperforms existing tools and ensures high‐quality data that can be used for further downstream analysis. An R implementation is available.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Objective
The biologic diagnosis of primary Sjögren disease (SjD) mainly relies on anti‐Ro60/SSA antibodies, whereas the significance of anti‐Ro52/TRIM21 antibodies currently remains unclear. The aim ...of this study was to characterize the clinical, serological, biologic, transcriptomic, and interferon profiles of patients with SjD according to their anti‐Ro52/TRIM21 antibody status.
Methods
Patients with SjD from the European PRECISESADS (n = 376) and the Brittany Diagnostic Suspicion of primitive Sjögren's Syndrome (DIApSS); (n = 146) cohorts were divided into four groups: double negative (Ro52−/Ro60−), isolated anti‐Ro52/TRIM21 positive (Ro52+), isolated anti‐Ro60/SSA positive (Ro60+), and double‐positive (Ro52+/Ro60+) patients. Clinical information; EULAR Sjögren Syndrome Disease Activity Index, a score representing systemic activity; and biologic markers associated with disease severity were evaluated. Transcriptome data obtained from whole blood by RNA sequencing and type I and II interferon signatures were analyzed for PRECISESADS patients.
Results
In the DIApSS cohort, Ro52+/Ro60+ patients showed significantly more parotidomegaly (33.3% vs 0%–11%) along with higher β2‐microglobulin (P = 0.0002), total immunoglobulin (P < 0.0001), and erythrocyte sedimentation rate levels (P = 0.002) as well as rheumatoid factor (RF) positivity (66.2% vs 20.8%–25%) compared to other groups. The PRECISESADS cohort corroborated these observations, with increased arthritis (P = 0.046), inflammation (P = 0.005), hypergammaglobulinemia (P < 0.0001), positive RF (P < 0.0001), leukopenia (P = 0.004), and lymphopenia (P = 0.009) in Ro52+/Ro60+ patients. Cumulative EULAR Sjögren Syndrome Disease Activity Index results further confirmed these disparities (P = 0.002). Transcriptome analysis linked anti‐Ro52/TRIM21 antibody positivity to interferon pathway activation as an underlying cause for these clinical correlations.
Conclusion
These results suggest that the combination of anti‐Ro52/TRIM21 and anti‐Ro60/SSA antibodies is associated with a clinical, biologic, and transcriptional profile linked to greater disease severity in SjD through the potentiation of the interferon pathway activation by anti‐Ro52/TRIM21 antibodies.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
In spite of the well-known clustering of multiple autoimmune disorders in families, analyses of specific shared genes and polymorphisms between systemic lupus erythematosus (SLE) and other autoimmune ...diseases (ADs) have been limited. Therefore, we comprehensively tested autoimmune variants for association with SLE, aiming to identify pleiotropic genetic associations between these diseases. We compiled a list of 446 non-Major Histocompatibility Complex (MHC) variants identified in genome-wide association studies (GWAS) of populations of European ancestry across 17 ADs. We then tested these variants in our combined Caucasian SLE cohorts of 1,500 cases and 5,706 controls. We tested a subset of these polymorphisms in an independent Caucasian replication cohort of 2,085 SLE cases and 2,854 controls, allowing the computation of a meta-analysis between all cohorts. We have uncovered novel shared SLE loci that passed multiple comparisons adjustment, including the VTCN1 (rs12046117, P = 2.02×10(-06)) region. We observed that the loci shared among the most ADs include IL23R, OLIG3/TNFAIP3, and IL2RA. Given the lack of a universal autoimmune risk locus outside of the MHC and variable specificities for different diseases, our data suggests partial pleiotropy among ADs. Hierarchical clustering of ADs suggested that the most genetically related ADs appear to be type 1 diabetes with rheumatoid arthritis and Crohn's disease with ulcerative colitis. These findings support a relatively distinct genetic susceptibility for SLE. For many of the shared GWAS autoimmune loci, we found no evidence for association with SLE, including IL23R. Also, several established SLE loci are apparently not associated with other ADs, including the ITGAM-ITGAX and TNFSF4 regions. This study represents the most comprehensive evaluation of shared autoimmune loci to date, supports a relatively distinct non-MHC genetic susceptibility for SLE, provides further evidence for previously and newly identified shared genes in SLE, and highlights the value of studies of potentially pleiotropic genes in autoimmune diseases.
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