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The major histocompatibility complex (MHC) region is strongly associated with multiple sclerosis (MS) susceptibility. HLA-DRB1*15:01 has the strongest effect, and several other alleles have been reported at different levels of validation. Using SNP data fr

Background: Several thousand human genome epidemiology association studies are published every year investigating the relationship between common genetic variants and diverse phenotypes. Transparent reporting of study methods and results allows readers to better assess the validity of study findings. Here, we document reporting practices of human genome epidemiology studies.Methods: Articles were randomly selected from a continuously updated database of human genome epidemiology association studies to be representative of genetic epidemiology literature. The main analysis evaluated 315 articles published in 2001-2003. For a comparative update, we evaluated 28 more recent articles published in 2006, focusing on issues that were poorly reported in 2001-2003.Results: During both time periods, most studies comprised relatively small study populations and examined one or more genetic variants within a single gene. Articles were inconsistent in reporting the data needed to assess selection bias and the methods used to minimize misclassification (of the genotype, outcome, and environmental exposure) or to identify population stratification. Statistical power, the use of unrelated study participants, and the use of replicate samples were reported more often in articles published during 2006 when compared with the earlier sample.Conclusion: We conclude that many items needed to assess error and bias in human genome epidemiology association studies are not consistently reported. Although some improvements were seen over time, reporting guidelines and online supplemental material may help enhance the transparency of this literature. [ABSTRACT FROM AUTHOR]

Polygenic inheritance plays a pivotal role in driving multiple sclerosis susceptibility, an inflammatory demyelinating disease of the CNS. We developed polygenic risk scores (PRS) of multiple sclerosis and assessed associations with both disease status and severity in cohorts of European descent. The largest genome-wide association dataset for multiple sclerosis to date (n = 41 505) was leveraged to generate PRS scores, serving as an informative susceptibility marker, tested in two independent datasets, UK Biobank [area under the curve (AUC) = 0.73, 95% confidence interval (CI): 0.72-0.74, P = 6.41 × 10-146] and Kaiser Permanente in Northern California (KPNC, AUC = 0.8, 95% CI: 0.76-0.82, P = 1.5 × 10-53). Individuals within the top 10% of PRS were at higher than 5-fold increased risk in UK Biobank (95% CI: 4.7-6, P = 2.8 × 10-45) and 15-fold higher risk in KPNC (95% CI: 10.4-24, P = 3.7 × 10-11), relative to the median decile. The cumulative absolute risk of developing multiple sclerosis from age 20 onwards was significantly higher in genetically predisposed individuals according to PRS. Furthermore, inclusion of PRS in clinical risk models increased the risk discrimination by 13% to 26% over models based only on conventional risk factors in UK Biobank and KPNC, respectively. Stratifying disease risk by gene sets representative of curated cellular signalling cascades, nominated promising genetic candidate programmes for functional characterization. These pathways include inflammatory signalling mediation, response to viral infection, oxidative damage, RNA polymerase transcription, and epigenetic regulation of gene expression to be among significant contributors to multiple sclerosis susceptibility. This study also indicates that PRS is a useful measure for estimating susceptibility within related individuals in multicase families. We show a significant association of genetic predisposition with thalamic atrophy within 10 years of disease progression in the UCSF-EPIC cohort (P < 0.001), consistent with a partial overlap between the genetics of susceptibility and end-organ tissue injury. Mendelian randomization analysis suggested an effect of multiple sclerosis susceptibility on thalamic volume, which was further indicated to be through horizontal pleiotropy rather than a causal effect. In summary, this study indicates important, replicable associations of PRS with enhanced risk assessment and radiographic outcomes of tissue injury, potentially informing targeted screening and prevention strategies., (© The Author(s) 2022. Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

The Major Histocompatibility Complex (MHC) makes the largest genetic contribution to multiple sclerosis (MS) susceptibility, with 32 independent effects across the region explaining 20% of the heritability in European populations. Variation is high across populations with allele frequency differences and population-specific risk alleles identified. We sought to identify MHC-specific MS susceptibility variants and assess the effect of ancestral risk modification within 2652 Latinx and Hispanic individuals as well as 2435 Black and African American individuals. We have identified several novel susceptibility alleles which are rare in European populations including HLA-B*53:01, and we have utilized the differing linkage disequilibrium patterns inherent to these populations to identify an independent role for HLA-DRB1*15:01 and HLA-DQB1*06:02 on MS risk. We found a decrease in Native American ancestry in MS cases vs controls across the MHC, peaking near the previously identified MICB locus with a decrease of ~5.5% in Hispanics and ~0.4% in African Americans. We have identified several susceptibility variants, including within the MICB gene region, which show global ancestry risk modification and indicate ancestral differences which may be due in part to correlated environmental factors. We have also identified several susceptibility variants for which MS risk is modified by local ancestry and indicate true ancestral genetic differences; including HLA-DQB1*06:02 for which MS risk for European allele carriers is almost two times the risk for African allele carriers. These results validate the importance of investigating MS susceptibility at an ancestral level and offer insight into the epidemiology of MS phenotypic diversity., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2022 Beecham et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Lymph nodes (LNs) are the critical sites of immunity, and the stromal cells of LNs are crucial to their function. Our understanding of the stromal compartment of the LN has deepened recently with the characterization of nontraditional stromal cells. CD41 (integrin αIIb) is known to be expressed by platelets and hematolymphoid cells. We identified two distinct populations of CD41 + Lyve1 + and CD41 + Lyve1 - cells in the LNs. CD41 + Lyve1 - cells appear in the LN mostly at the later stages of the lives of mice. We identified CD41 + cells in human LNs as well. We demonstrated that murine CD41 + cells express mesodermal markers, such as Sca-1, CD105 and CD29, but lack platelet markers. We did not observe the presence of platelets around the HEVs or within proximity to fibroblastic reticular cells of the LN. Examination of thoracic duct lymph fluid showed the presence of CD41 + Lyve1 - cells, suggesting that these cells recirculate throughout the body. FTY720 reduced their trafficking to lymph fluid, suggesting that their egress is controlled by the S1P1 pathway. CD41 + Lyve1 - cells of the LNs were sensitive to radiation, suggestive of their replicative nature. Single cell RNA sequencing data showed that the CD41 + cell population in naïve mouse LNs expressed largely stromal cell markers. Further studies are required to examine more deeply the role of CD41 + cells in the function of LNs., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Dai, Uehara, Li, LaBarre, Banouni, Ichimura, Lee-Sundlov, Kasinath, Sullivan, Ni, Barone, Giannini, Bahmani, Sage, Patsopoulos, Tsokos, Bromberg, Hoffmeister, Jiang and Abdi.)

Background: Multiple sclerosis (MS) is an autoimmune condition of the central nervous system with a well-characterized genetic background. Prior analyses of MS genetics have identified broad enrichments across peripheral immune cells, yet the driver immune subsets are unclear., Results: We utilize chromatin accessibility data across hematopoietic cells to identify cell type-specific enrichments of MS genetic signals. We find that CD4 T and B cells are independently enriched for MS genetics and further refine the driver subsets to T h 17 and memory B cells, respectively. We replicate our findings in data from untreated and treated MS patients and find that immunomodulatory treatments suppress chromatin accessibility at driver cell types. Integration of statistical fine-mapping and chromatin interactions nominate numerous putative causal genes, illustrating complex interplay between shared and cell-specific genes., Conclusions: Overall, our study finds that open chromatin regions in CD4 T cells and B cells independently drive MS genetic signals. Our study highlights how careful integration of genetics and epigenetics can provide fine-scale insights into causal cell types and nominate new genes and pathways for disease., (© 2022. The Author(s).)

Epidemiological studies have suggested differences in the rate of multiple sclerosis (MS) in individuals of European ancestry compared to African ancestry, motivating genetic scans to identify variants that could contribute to such patterns. In a whole-genome scan in 899 African-American cases and 1155 African-American controls, we confirm that African-Americans who inherit segments of the genome of European ancestry at a chromosome 1 locus are at increased risk for MS [logarithm of odds (LOD) = 9.8], although the signal weakens when adding an additional 406 cases, reflecting heterogeneity in the two sets of cases [logarithm of odds (LOD) = 2.7]. The association in the 899 individuals can be fully explained by two variants previously associated with MS in European ancestry individuals. These variants tag a MS susceptibility haplotype associated with decreased CD58 gene expression (odds ratio of 1.37; frequency of 84% in Europeans and 22% in West Africans for the tagging variant) as well as another haplotype near the FCRL3 gene (odds ratio of 1.07; frequency of 49% in Europeans and 8% in West Africans). Controlling for all other genetic and environmental factors, the two variants predict a 1.44-fold higher rate of MS in European-Americans compared to African-Americans.

Nonhealing diabetic foot ulcers (DFUs) are characterized by low-grade chronic inflammation, both locally and systemically. We prospectively followed a group of patients who either healed or developed nonhealing chronic DFUs. Serum and forearm skin analysis, both at the protein expression and the transcriptomic level, indicated that increased expression of factors such as interferon-γ (IFN-γ), vascular endothelial growth factor, and soluble vascular cell adhesion molecule-1 were associated with DFU healing. Furthermore, foot skin single-cell RNA sequencing analysis showed multiple fibroblast cell clusters and increased inflammation in the dorsal skin of patients with diabetes mellitus (DM) and DFU specimens compared with control subjects. In addition, in myeloid cell DM and DFU upstream regulator analysis, we observed inhibition of interleukin-13 and IFN-γ and dysregulation of biological processes that included cell movement of monocytes, migration of dendritic cells, and chemotaxis of antigen-presenting cells pointing to an impaired migratory profile of immune cells in DM skin. The SLCO2A1 and CYP1A1 genes, which were upregulated at the forearm of nonhealers, were mainly expressed by the vascular endothelial cell cluster almost exclusively in DFU, indicating a potential important role in wound healing. These results from integrated protein and transcriptome analyses identified individual genes and pathways that can potentially be targeted for enhancing DFU healing., (© 2020 by the American Diabetes Association.)

In complex trait genetics, the ability to predict phenotype from genotype is the ultimate measure of our understanding of genetic architecture underlying the heritability of a trait. A complete understanding of the genetic basis of a trait should allow for predictive methods with accuracies approaching the trait's heritability. The highly polygenic nature of quantitative traits and most common phenotypes has motivated the development of statistical strategies focused on combining myriad individually non-significant genetic effects. Now that predictive accuracies are improving, there is a growing interest in the practical utility of such methods for predicting risk of common diseases responsive to early therapeutic intervention. However, existing methods require individual-level genotypes or depend on accurately specifying the genetic architecture underlying each disease to be predicted. Here, we propose a polygenic risk prediction method that does not require explicitly modeling any underlying genetic architecture. We start with summary statistics in the form of SNP effect sizes from a large GWAS cohort. We then remove the correlation structure across summary statistics arising due to linkage disequilibrium and apply a piecewise linear interpolation on conditional mean effects. In both simulated and real datasets, this new non-parametric shrinkage (NPS) method can reliably allow for linkage disequilibrium in summary statistics of 5 million dense genome-wide markers and consistently improves prediction accuracy. We show that NPS improves the identification of groups at high risk for breast cancer, type 2 diabetes, inflammatory bowel disease, and coronary heart disease, all of which have available early intervention or prevention treatments., (Copyright © 2020 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)

The study objective was to test the hypothesis that having histocompatible children increases the risk of rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE), possibly by contributing to the persistence of fetal cells acquired during pregnancy. We conducted a case control study using data from the UC San Francisco Mother Child Immunogenetic Study and studies at the Inova Translational Medicine Institute. We imputed human leukocyte antigen (HLA) alleles and minor histocompatibility antigens (mHags). We created a variable of exposure to histocompatible children. We estimated an average sequence similarity matching (SSM) score for each mother based on discordant mother-child alleles as a measure of histocompatibility. We used logistic regression models to estimate odds ratios (ORs) and 95% confidence intervals. A total of 138 RA, 117 SLE, and 913 control mothers were analyzed. Increased risk of RA was associated with having any child compatible at HLA-B (OR 1.9; 1.2-3.1), DPB1 (OR 1.8; 1.2-2.6) or DQB1 (OR 1.8; 1.2-2.7). Compatibility at mHag ZAPHIR was associated with reduced risk of SLE among mothers carrying the HLA-restriction allele B*07:02 (n = 262; OR 0.4; 0.2-0.8). Our findings support the hypothesis that mother-child histocompatibility is associated with risk of RA and SLE.

The neuroinflammatory response to traumatic brain injury (TBI) is critical to both neurotoxicity and neuroprotection, and has been proposed as a potentially modifiable driver of secondary injury in animal and human studies. Attempts to broadly target immune activation have been unsuccessful in improving outcomes, in part because the precise cellular and molecular mechanisms driving injury and outcome at acute, subacute, and chronic time points after TBI remain poorly defined. Microglia play a critical role in neuroinflammation and their persistent activation may contribute to long-term functional deficits. Activated microglia are characterized by morphological transformation and transcriptomic changes associated with specific inflammatory states. We analyzed the temporal course of changes in inflammatory genes of microglia isolated from injured brains at 2, 14, and 60 days after controlled cortical impact (CCI) in mice, a well-established model of focal cerebral contusion. We identified a time dependent, injury-associated change in the microglial gene expression profile toward a reduced ability to sense tissue damage, perform housekeeping, and maintain homeostasis in the early stages following CCI, with recovery and transition to a specialized inflammatory state over time. This later state starts at 14 days post-injury and is characterized by a biphasic pattern of IFNγ, IL-4, and IL-10 gene expression changes, with concurrent proinflammatory and anti-inflammatory gene changes. Our transcriptomic data sets are an important step to understand microglial role in TBI pathogenesis at the molecular level and identify common pathways that affect outcome. More studies to evaluate gene expression at the single cell level and focusing on subacute and chronic timepoint are warranted.

Despite major progress in defining the genetic basis of Mendelian disorders, the molecular etiology of many cases remains unknown. Patients with these undiagnosed disorders often have complex presentations and require treatment by multiple health care specialists. Here, we describe an integrated clinical diagnostic and research program using whole-exome and whole-genome sequencing (WES/WGS) for Mendelian disease gene discovery. This program employs specific case ascertainment parameters, a WES/WGS computational analysis pipeline that is optimized for Mendelian disease gene discovery with variant callers tuned to specific inheritance modes, an interdisciplinary crowdsourcing strategy for genomic sequence analysis, matchmaking for additional cases, and integration of the findings regarding gene causality with the clinical management plan. The interdisciplinary gene discovery team includes clinical, computational, and experimental biomedical specialists who interact to identify the genetic etiology of the disease, and when so warranted, to devise improved or novel treatments for affected patients. This program effectively integrates the clinical and research missions of an academic medical center and affords both diagnostic and therapeutic options for patients suffering from genetic disease. It may therefore be germane to other academic medical institutions engaged in implementing genomic medicine programs., Competing Interests: R.C.G. receives compensation for speaking or advisory services from AIA, Genome Medical, Helix, Invitae, Illumina, Prudential, and Roche. R.C.G., N.F., and S.R.S. are on the board of Veritas Genetics. C.A.M. receives compensation for advisory services from Personome and Recombine, and royalties for genetic testing from Partners HealthCare. The other authors declare no competing interests.

The contribution of genetic inheritance in multiple sclerosis was established early on. Although multiple sclerosis is not a Mendelian disease, its incidence and prevalence is higher in family members of affected individuals compared with the general population. Throughout the last decade, several small studies failed to identify any robust genetic associations besides the classic associations in the major histocompatibility complex region. During the past few years, genome-wide association studies (GWAS) have revolutionized the genetics of multiple sclerosis, uncovering more than 200 implicated genetic loci. Here, we describe these main findings and discuss the new avenues that these discoveries lay open., (Copyright © 2018 Cold Spring Harbor Laboratory Press; all rights reserved.)

Preterm birth (PTB), or the delivery prior to 37 weeks of gestation, is a significant cause of infant morbidity and mortality. Although twin studies estimate that maternal genetic contributions account for approximately 30% of the incidence of PTB, and other studies reported fetal gene polymorphism association, to date no consistent associations have been identified. In this study, we performed the largest reported genome-wide association study analysis on 1,349 cases of PTB and 12,595 ancestry-matched controls from the focusing on genomic fetal signals. We tested over 2 million single nucleotide polymorphisms (SNPs) for associations with PTB across five subpopulations: African (AFR), the Americas (AMR), European, South Asian, and East Asian. We identified only two intergenic loci associated with PTB at a genome-wide level of significance: rs17591250 (P = 4.55E-09) on chromosome 1 in the AFR population and rs1979081 (P = 3.72E-08) on chromosome 8 in the AMR group. We have queried several existing replication cohorts and found no support of these associations. We conclude that the fetal genetic contribution to PTB is unlikely due to single common genetic variant, but could be explained by interactions of multiple common variants, or of rare variants affected by environmental influences, all not detectable using a GWAS alone.

Objective: We investigated the association between 52 risk variants identified through genome-wide association studies and disease severity in multiple sclerosis (MS)., Methods: Ten unique MS case data sets were analyzed. The Multiple Sclerosis Severity Score (MSSS) was calculated using the Expanded Disability Status Scale at study entry and disease duration. MSSS was considered as a continuous variable and as 2 dichotomous variables (median and extreme ends; MSSS of ≤5 vs >5 and MSSS of <2.5 vs ≥7.5, respectively). Single nucleotide polymorphisms (SNPs) were examined individually and as both combined weighted genetic risk score (wGRS) and unweighted genetic risk score (GRS) for association with disease severity. Random-effects meta-analyses were conducted and adjusted for cohort, sex, age at onset, and HLA-DRB1*15:01., Results: A total of 7,125 MS cases were analyzed. The wGRS and GRS were not strongly associated with disease severity after accounting for cohort, sex, age at onset, and HLA-DRB1*15:01. After restricting analyses to cases with disease duration ≥10 years, associations were null (p value ≥0.05). No SNP was associated with disease severity after adjusting for multiple testing., Conclusions: The largest meta-analysis of established MS genetic risk variants and disease severity, to date, was performed. Results suggest that the investigated MS genetic risk variants are not associated with MSSS, even after controlling for potential confounders. Further research in large cohorts is needed to identify genetic determinants of disease severity using sensitive clinical and MRI measures, which are critical to understanding disease mechanisms and guiding development of effective treatments.

There is mounting evidence that complex human phenotypes are highly polygenic, with many loci harboring multiple causal variants, yet most genetic association studies examine each SNP in isolation. While this has led to the discovery of thousands of disease associations, discovered variants account for only a small fraction of disease heritability. Alternative multi-SNP methods have been proposed, but issues such as multiple-testing correction, sensitivity to genotyping error, and optimization for the underlying genetic architectures remain. Here we describe a local joint-testing procedure, complete with multiple-testing correction, that leverages a genetic phenomenon we call linkage masking wherein linkage disequilibrium between SNPs hides their signal under standard association methods. We show that local joint testing on the original Wellcome Trust Case Control Consortium (WTCCC) data set leads to the discovery of 22 associated loci, 5 more than the marginal approach. These loci were later found in follow-up studies containing thousands of additional individuals. We find that these loci significantly increase the heritability explained by genome-wide significant associations in the WTCCC data set. Furthermore, we show that local joint testing in a cis-expression QTL (eQTL) study of the gEUVADIS data set increases the number of genes containing significant eQTL by 10.7% over marginal analyses. Our multiple-hypothesis correction and joint-testing framework are available in a python software package called Jester, available at github.com/brielin/Jester., (Copyright © 2016 by the Genetics Society of America.)

Despite recent progress in the characterization of genetic loci associated with multiple sclerosis (MS) risk, the ubiquitous linkage disequilibrium operating across the genome has stalled efforts to distinguish causative variants from proxy single-nucleotide polymorphisms (SNPs). Here, we have identified through fine mapping and meta-analysis EVI5 as the most plausible disease risk gene within the 1p22.1 locus. We further show that an exonic SNP associated with risk induces changes in superficial hydrophobicity patterns of the coiled-coil domain of EVI5, which, in turns, affects the EVI5 interactome. Immunoprecipitation of wild-type and mutated EVI5 followed by mass spectrometry generated a roster of disease-specific interactors functionally linked to lipid metabolism. Among the exclusive binding partners of the risk variant, we describe the novel interaction with sphingosine 1-phosphate lyase (SGPL1)-a key enzyme for the creation of the sphingosine-1 phosphate gradient, which is relevant to the pathogenic process and therapeutic management of MS., (© The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Polygenic risk scores have shown great promise in predicting complex disease risk and will become more accurate as training sample sizes increase. The standard approach for calculating risk scores involves linkage disequilibrium (LD)-based marker pruning and applying a p value threshold to association statistics, but this discards information and can reduce predictive accuracy. We introduce LDpred, a method that infers the posterior mean effect size of each marker by using a prior on effect sizes and LD information from an external reference panel. Theory and simulations show that LDpred outperforms the approach of pruning followed by thresholding, particularly at large sample sizes. Accordingly, predicted R(2) increased from 20.1% to 25.3% in a large schizophrenia dataset and from 9.8% to 12.0% in a large multiple sclerosis dataset. A similar relative improvement in accuracy was observed for three additional large disease datasets and for non-European schizophrenia samples. The advantage of LDpred over existing methods will grow as sample sizes increase., (Copyright © 2015 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)

Genome-wide association studies have identified loci underlying human diseases, but the causal nucleotide changes and mechanisms remain largely unknown. Here we developed a fine-mapping algorithm to identify candidate causal variants for 21 autoimmune diseases from genotyping data. We integrated these predictions with transcription and cis-regulatory element annotations, derived by mapping RNA and chromatin in primary immune cells, including resting and stimulated CD4(+) T-cell subsets, regulatory T cells, CD8(+) T cells, B cells, and monocytes. We find that ∼90% of causal variants are non-coding, with ∼60% mapping to immune-cell enhancers, many of which gain histone acetylation and transcribe enhancer-associated RNA upon immune stimulation. Causal variants tend to occur near binding sites for master regulators of immune differentiation and stimulus-dependent gene activation, but only 10-20% directly alter recognizable transcription factor binding motifs. Rather, most non-coding risk variants, including those that alter gene expression, affect non-canonical sequence determinants not well-explained by current gene regulatory models.

The protein kinase C alpha (PRKCA) gene, encoding a Th17-cell-selective kinase, was repeatedly associated with multiple sclerosis (MS), but the underlying pathogenic mechanism remains unknown. We replicated the association in Italians (409 cases, 723 controls), identifying a protective signal in the PRKCA promoter (P = 0.033), and a risk haplotype in intron 3 (P = 7.7 × 10(-4); meta-analysis with previously published data: P = 4.01 × 10(-8)). Expression experiments demonstrated that the protective signal is associated with alleles conferring higher PRKCA expression levels, well fitting our observation that MS patients have significantly lower PRKCA mRNA levels in blood. The risk haplotype was shown to be driven by a GGTG ins/del polymorphism influencing the heterogeneous nuclear ribonucleoprotein H-dependent inclusion/skipping of a PRKCA alternative exon 3*. Indeed, exon 3* can be present in two different versions in PRKCA mRNAs (out-of-frame 61 bp or in-frame 66 bp long), and is preferentially included in transcripts generated through a premature polyadenylation event. The GGTG insertion downregulates 3* inclusion and shifts splicing towards the 66 bp isoform. Both events reduce the nonsense-mediated mRNA-decay-induced degradation of exon 3*-containing mRNAs. Since we demonstrated that the protein isoform produced through premature polyadenylation aberrantly localizes to the plasma membrane and/or in cytoplasmic clusters, dysregulated PRKCA 3* inclusion may represent an additional mechanism relevant to MS susceptibility., (© The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Background: Symptoms of attention deficit hyperactivity disorder (ADHD), diagnosed mainly in children, often persist into adulthood. Adults in this group have a high rate of other psychiatric problems and functional difficulties in a number of key areas such as academic achievement, interpersonal relationships, and employment. Although the usefulness of immediate-release methylphenidate in children has been extensively studied, studies in adults, which are few, demonstrate varying results., Objectives: To evaluate the efficacy and tolerability of immediate-release methylphenidate versus placebo in the treatment of adults with ADHD., Search Methods: We searched the following databases in November 2013: CENTRAL, Ovid MEDLINE, EMBASE, PsycINFO, Database of Abstracts of Reviews of Effects (DARE), and two trials registers. Biosis was searched in December 2013. We inspected references of all relevant papers to identify more studies and contacted authors of recently published trials., Selection Criteria: We included all randomized trials comparing immediate-release methylphenidate versus placebo in participants aged 18 years or older with ADHD. We excluded trials conducted on subpopulations of adults with ADHD such as adults with both ADHD and substance dependence., Data Collection and Analysis: Two review authors independently selected trials, extracted data, and assessed trial risk of bias. We contacted authors of trials to ask for additional and missing data. For dichotomous outcomes, we calculated risk ratios (RRs) and 95% confidence intervals (CIs). For continuous outcomes, we calculated mean differences (MDs) or standardized mean differences (SMDs) with 95% CIs., Main Results: Results from the 11 randomized controlled trials (474 participants, counting participants from cross-over studies as a single arm, and counting both arms from parallel studies) included in the review demonstrated improvement in core clinical ADHD symptoms of hyperactivity, impulsivity, and inattentiveness, and overall improvement. We were able to pool results from 10 studies, which included 466 participants.Most included studies were judged to have unclear risk of bias for most categories. However, as all studies were randomized, double-blind, and placebo-controlled and, in general, did not contain factors that significantly decreased the quality of the body of evidence, the quality of evidence was assessed as "high" for most outcomes according to the GRADE (Grades of Recommendation, Assessment, Development, and Evaluation) approach. For one outcome-inattentiveness-most information came from studies at unclear risk of bias, and so the quality of evidence for this outcome was judged as "moderate."Results are given as SMD for each of the core clinical symptoms of ADHD. In all cases, participant numbers were calculated by counting participants in a single arm from cross-over studies and in both arms from parallel studies. The SMD for the outcome of hyperactivity was -0.60 (95% CI -1.11 to -0.09, 6 studies, number of participants (n) = 245, high-quality evidence) in favor of immediate-release methylphenidate; the SMD for impulsivity was -0.62 (95% CI -1.08 to -0.17, 5 studies, n = 207, high-quality evidence) in favor of immediate-release methylphenidate; and the SMD for inattentiveness was -0.66 (95% CI -1.02 to -0.30, 7 studies, n = 391, moderate-quality evidence) in favor of immediate-release methylphenidate. Moderate to extreme statistical heterogeneity was detected for all outcomes. Subgroup analysis comparing high versus low doses did not indicate that higher doses of immediate-release methylphenidate were associated with greater efficacy.For overall change, the SMD was -0.72 (95% CI -1.12 to -0.32, 9 studies, n = 455, high-quality evidence) in favor of immediate-release methylphenidate.The effects of immediate-release methylphenidate on anxiety and depression as parameters of general changes in mental state were equivocal. Some trials reported reduction in depression and anxiety, others detailed no change, and still others described an increase in depressive and anxious symptoms.The most common adverse effect was loss of appetite, in some cases with weight loss. Although no study reported either of these effects as problematic or severe, the included studies were of short duration; thus clinical significance could not be properly assessed. Five studies reported changes in systolic or diastolic blood pressure, and three reported increases in heart rate. None of these results were judged to present cause for concern. No study reported clinically significant adverse effects-cardiovascular or other. Three studies did not mention adverse effects. We were unable to determine whether adverse effects were not discussed by study authors because none occurred, or because no data on adverse effects were collected., Authors' Conclusions: Data from randomized controlled trials suggest that immediate-release methylphenidate is efficacious for treating adults with ADHD with symptoms of hyperactivity, impulsivity, and inattentiveness, and for improving their overall clinical condition. Trial data suggest that adverse effects from immediate-release methylphenidate for adults with ADHD are not of serious clinical significance, although this conclusion may be limited, certainly in the case of weight loss, by the short duration of published studies.

Genome-wide association studies have been successful in identifying common variants that influence the susceptibility to complex diseases. From these studies, it has emerged that there is substantial overlap in susceptibility loci between diseases. In line with those findings, we hypothesized that shared genetic pathways may exist between multiple sclerosis (MS) and amyotrophic lateral sclerosis (ALS). While both diseases may have inflammatory and neurodegenerative features, epidemiological studies have indicated an increased co-occurrence within individuals and families. To this purpose, we combined genome-wide data from 4088 MS patients, 3762 ALS patients and 12 030 healthy control individuals in whom 5 440 446 single-nucleotide polymorphisms (SNPs) were successfully genotyped or imputed. We tested these SNPs for the excess association shared between MS and ALS and also explored whether polygenic models of SNPs below genome-wide significance could explain some of the observed trait variance between diseases. Genome-wide association meta-analysis of SNPs as well as polygenic analyses fails to provide evidence in favor of an overlap in genetic susceptibility between MS and ALS. Hence, our findings do not support a shared genetic background of common risk variants in MS and ALS.

Genome-wide association studies (GWASs) perform per-SNP association tests to identify variants involved in disease or trait susceptibility. However, such an approach is not powerful enough to unravel genes that are not individually contributing to the disease/trait, but that may have a role in interaction with other genes as a group. Pathway analysis is an alternative way to highlight such group of genes. Using SNP association P-values from eight multiple sclerosis (MS) GWAS data sets, we performed a candidate pathway analysis for MS susceptibility by considering genes interacting in the cell adhesion molecule (CAMs) biological pathway using Cytoscape software. This network is a strong candidate, as it is involved in the crossing of the blood-brain barrier by the T cells, an early event in MS pathophysiology, and is used as an efficient therapeutic target. We drew up a list of 76 genes belonging to the CAM network. We highlighted 64 networks enriched with CAM genes with low P-values. Filtering by a percentage of CAM genes up to 50% and rejecting enriched signals mainly driven by transcription factors, we highlighted five networks associated with MS susceptibility. One of them, constituted of ITGAL, ICAM1 and ICAM3 genes, could be of interest to develop novel therapeutic targets.

The major histocompatibility complex (MHC) region is strongly associated with multiple sclerosis (MS) susceptibility. HLA-DRB1*15:01 has the strongest effect, and several other alleles have been reported at different levels of validation. Using SNP data from genome-wide studies, we imputed and tested classical alleles and amino acid polymorphisms in 8 classical human leukocyte antigen (HLA) genes in 5,091 cases and 9,595 controls. We identified 11 statistically independent effects overall: 6 HLA-DRB1 and one DPB1 alleles in class II, one HLA-A and two B alleles in class I, and one signal in a region spanning from MICB to LST1. This genomic segment does not contain any HLA class I or II genes and provides robust evidence for the involvement of a non-HLA risk allele within the MHC. Interestingly, this region contains the TNF gene, the cognate ligand of the well-validated TNFRSF1A MS susceptibility gene. The classical HLA effects can be explained to some extent by polymorphic amino acid positions in the peptide-binding grooves. This study dissects the independent effects in the MHC, a critical region for MS susceptibility that harbors multiple risk alleles., Competing Interests: The authors have declared that no competing interests exist.

We have previously shown that rheumatoid arthritis (RA) risk alleles overlap between different ethnic groups. Here, we utilize a multiethnic approach to show that we can effectively discover RA risk alleles. Thirteen putatively associated SNPs that had not yet exceeded genome-wide significance (p < 5 × 10(-8)) in our previous RA genome-wide association study (GWAS) were analyzed in independent sample sets consisting of 4,366 cases and 17,765 controls of European, African American, and East Asian ancestry. Additionally, we conducted an overall association test across all 65,833 samples (a GWAS meta-analysis plus the replication samples). Of the 13 SNPs investigated, four were significantly below the study-wide Bonferroni corrected p value threshold (p < 0.0038) in the replication samples. Two SNPs (rs3890745 at the 1p36 locus [p = 2.3 × 10(-12)] and rs2872507 at the 17q12 locus [p = 1.7 × 10(-9)]) surpassed genome-wide significance in all 16,659 RA cases and 49,174 controls combined. We used available GWAS data to fine map these two loci in Europeans and East Asians, and we found that the same allele conferred risk in both ethnic groups. A series of bioinformatic analyses identified TNFRSF14-MMEL1 at the 1p36 locus and IKZF3-ORMDL3-GSDMB at the 17q12 locus as the genes most likely associated with RA. These findings demonstrate empirically that a multiethnic approach is an effective strategy for discovering RA risk loci, and they suggest that combining GWASs across ethnic groups represents an efficient strategy for gaining statistical power., (Copyright © 2012 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)

Multiple sclerosis is a common disease of the central nervous system in which the interplay between inflammatory and neurodegenerative processes typically results in intermittent neurological disturbance followed by progressive accumulation of disability. Epidemiological studies have shown that genetic factors are primarily responsible for the substantially increased frequency of the disease seen in the relatives of affected individuals, and systematic attempts to identify linkage in multiplex families have confirmed that variation within the major histocompatibility complex (MHC) exerts the greatest individual effect on risk. Modestly powered genome-wide association studies (GWAS) have enabled more than 20 additional risk loci to be identified and have shown that multiple variants exerting modest individual effects have a key role in disease susceptibility. Most of the genetic architecture underlying susceptibility to the disease remains to be defined and is anticipated to require the analysis of sample sizes that are beyond the numbers currently available to individual research groups. In a collaborative GWAS involving 9,772 cases of European descent collected by 23 research groups working in 15 different countries, we have replicated almost all of the previously suggested associations and identified at least a further 29 novel susceptibility loci. Within the MHC we have refined the identity of the HLA-DRB1 risk alleles and confirmed that variation in the HLA-A gene underlies the independent protective effect attributable to the class I region. Immunologically relevant genes are significantly overrepresented among those mapping close to the identified loci and particularly implicate T-helper-cell differentiation in the pathogenesis of multiple sclerosis.

Background: Meta-analysis is increasingly being employed as a screening procedure in large-scale association studies to select promising variants for follow-up studies. However, standard methods for meta-analysis require the assumption of an underlying genetic model, which is typically unknown a priori. This drawback can introduce model misspecifications, causing power to be suboptimal, or the evaluation of multiple genetic models, which augments the number of false-positive associations, ultimately leading to waste of resources with fruitless replication studies. We used simulated meta-analyses of large genetic association studies to investigate naïve strategies of genetic model specification to optimize screenings of genome-wide meta-analysis signals for further replication., Methods: Different methods, meta-analytical models and strategies were compared in terms of power and type-I error. Simulations were carried out for a binary trait in a wide range of true genetic models, genome-wide thresholds, minor allele frequencies (MAFs), odds ratios and between-study heterogeneity (τ²)., Results: Among the investigated strategies, a simple Bonferroni-corrected approach that fits both multiplicative and recessive models was found to be optimal in most examined scenarios, reducing the likelihood of false discoveries and enhancing power in scenarios with small MAFs either in the presence or in absence of heterogeneity. Nonetheless, this strategy is sensitive to τ² whenever the susceptibility allele is common (MAF ≥ 30%), resulting in an increased number of false-positive associations compared with an analysis that considers only the multiplicative model., Conclusion: Invoking a simple Bonferroni adjustment and testing for both multiplicative and recessive models is fast and an optimal strategy in large meta-analysis-based screenings. However, care must be taken when examined variants are common, where specification of a multiplicative model alone may be preferable.

Clinical trials have been the main tool used by the health sciences community to test and evaluate interventions, Trials can fall into two broad categories: pragmatic and explanatory. Pragmatic trials are designed to evaluate the effectiveness of interventions in real-life routine practice conditions, whereas explanatory trials aim to test whether an intervention works under optimal situations. Pragmatic trials produce results that can be generalized and applied in routine practice settings. Since most results from exploratory trials fail to be broadly generalizable, the "pragmatic design" has gained momentum. This review describes the concept of pragmatism, and explains in particular that there is a continuum between pragmatic and explanatory trials, rather than a dichotomy. Special focus is put on the limitations of the pragmatic trials, while recognizing the importance for and impact of this design on medical practice.

Genome-wide association studies (GWASs) have created a paradigm shift in discovering genetic associations for common diseases and phenotypes, but it is unclear whether the thousands of candidate genetic association studies performed in the pre-GWAS era had found any reliable associations for common diseases and phenotypes. We aimed to systematically evaluate whether loci proposed to harbor candidate associations before the advent of GWASs are replicated in GWASs. The GWAS data published through August, 2008 and included in the NHGRI catalog were screened and variants in candidate loci were selected on the basis of statistical significance (P<0.05) to create a list of independent, non-redundant associations. Altogether, 159 articles on GWASs were evaluated, 100 of which addressed past proposed candidate loci. A total of 291 independent, nominally significant (P<0.05) candidate gene associations were assembled after keeping only the SNP with lowest P-value for each locus and each phenotype; 108 of those had P<10(-3) for association and 41 had P<10(-7). A total of 22 of these 41 candidate gene associations pertained to binary phenotypes with a median odds ratio=2.91 (IQR: 1.82-4.6) and median minor allele frequency=0.17 (IQR: 0.12-0.29) in Caucasians; for comparison, 60 new associations of binary outcomes with P<10(-7) discovered in the same GWASs had much smaller effects (median odds ratio 1.30, IQR: 1.18-1.58) and modestly larger minor allele frequencies (median 0.27, IQR: 0.15-0.43). Overall, few of the numerous genetic associations proposed in the candidate gene era have been replicated in GWASs, but those that have been conclusively replicated have large genetic effects that should not be discarded.

Background: Images are important for conveying information, but there is no empirical evidence on whether imaging figures are properly selected and presented in the published medical literature. We therefore evaluated the selection and presentation of radiological imaging figures in major medical journals., Methodology/principal Findings: We analyzed articles published in 2005 in 12 major general and specialty medical journals that had radiological imaging figures. For each figure, we recorded information on selection, study population, provision of quantitative measurements, color scales and contrast use. Overall, 417 images from 212 articles were analyzed. Any comment/hint on image selection was made in 44 (11%) images (range 0-50% across the 12 journals) and another 37 (9%) (range 0-60%) showed both a normal and abnormal appearance. In 108 images (26%) (range 0-43%) it was unclear whether the image came from the presented study population. Eighty-three images (20%) (range 0-60%) had any quantitative or ordered categorical value on a measure of interest. Information on the distribution of the measure of interest in the study population was given in 59 cases. For 43 images (range 0-40%), a quantitative measurement was provided for the depicted case and the distribution of values in the study population was also available; in those 43 cases there was no over-representation of extreme than average cases (p = 0.37)., Significance: The selection and presentation of images in the medical literature is often insufficiently documented; quantitative data are sparse and difficult to place in context.

Genetic effects for common variants affecting complex disease risk are subtle. Single genome-wide association (GWA) studies are typically underpowered to detect these effects, and combination of several GWA data sets is needed to enhance discovery. The authors investigated the properties of the discovery process in simulated cumulative meta-analyses of GWA study-derived signals allowing for potential genetic model misspecification and between-study heterogeneity. Variants with null effects on average (but also between-data set heterogeneity) could yield false-positive associations with seemingly homogeneous effects. Random effects had higher than appropriate false-positive rates when there were few data sets. The log-additive model had the lowest false-positive rate. Under heterogeneity, random-effects meta-analyses of 2-10 data sets averaging 1,000 cases/1,000 controls each did not increase power, or the meta-analysis was even less powerful than a single study (power desert). Upward bias in effect estimates and underestimation of between-study heterogeneity were common. Fixed-effects calculations avoided power deserts and maximized discovery of association signals at the expense of much higher false-positive rates. Therefore, random- and fixed-effects models are preferable for different purposes (fixed effects for initial screenings, random effects for generalizability applications). These results may have broader implications for the design and interpretation of large-scale multiteam collaborative studies discovering common gene variants.

Background: Evidence for medical interventions sometimes derives from data that are no longer up to date. These data can influence the outcomes of meta-analyses, yet do not always reflect current clinical practice. We examined the age of the data used in meta-analyses contained within systematic reviews of medical interventions, and investigated whether authors consider the age of these data in their interpretations., Methods: From Issue 4, 2005, of the Cochrane Database of Systematic Reviews we randomly selected 10% of systematic reviews containing at least 1 meta-analysis. From this sample we extracted 1 meta-analysis per primary outcome. We calculated the number of years between the study's publication and 2005 (the year that the systematic review was published), as well as the number of years between the study's publication and the year of the literature search conducted in the study. We assessed whether authors discussed the implications of including less recent data, and, for systematic reviews containing meta-analyses of studies published before 1996, we calculated whether excluding the findings of those studies changed the significance of the outcomes. We repeated these calculations and assessments for 22 systematic reviews containing meta-analyses published in 6 high-impact general medical journals in 2005., Results: For 157 meta-analyses (n = 1149 trials) published in 2005, the median year of the most recent literature search was 2003 (interquartile range [IQR] 2002-04). Two-thirds of these meta-analyses (103/157, 66%) involved no trials published in the preceding 5 years (2001-05). Forty-seven meta-analyses (30%) included no trials published in the preceding 10 years (1996-2005). In another 16 (10%), the statistical significance of the outcomes would have been different had the studies been limited to those published between 1996 and 2005, although in some cases this change in significance would have been due to loss of power. Only 12 (8%) of the meta-analyses discussed the potential implications of including older studies. Among the 22 meta-analyses considered in high-impact general medical journals, 2 included no studies published in the 5 years prior to the reference year (2005), and 18 included at least 1 study published before 1996. Only 4 meta-analyses discussed the implications of including older studies., Interpretation: In most systematic reviews containing meta-analyses of evidence for health care interventions, very recent studies are rare. Researchers who conduct systematic reviews with meta-analyses, and clinicians who read the outcomes of these studies, should be made aware of the potential implications of including less recent data.

Background: Several approaches are available for evaluating heterogeneity in meta-analysis. Sensitivity analyses are often used, but these are often implemented in various non-standardized ways., Methods: We developed and implemented sequential and combinatorial algorithms that evaluate the change in between-study heterogeneity as one or more studies are excluded from the calculations. The algorithms exclude studies aiming to achieve either the maximum or the minimum final I(2) below a desired pre-set threshold. We applied these algorithms in databases of meta-analyses of binary outcome and >/=4 studies from Cochrane Database of Systematic Reviews (Issue 4, 2005, n = 1011) and meta-analyses of genetic associations (n = 50). Two I(2) thresholds were used (50% and 25%)., Results: Both algorithms have succeeded in achieving the pre-specified final I(2) thresholds. Differences in the number of excluded studies varied from 0% to 6% depending on the database and the heterogeneity threshold, while it was common to exclude different specific studies. Among meta-analyses with initial I(2) > 50%, in the large majority [19 (90.5%) and 208 (85.9%) in genetic and Cochrane meta-analyses, respectively] exclusion of one or two studies sufficed to decrease I(2) < 50%. Similarly, among meta-analyses with initial I(2) > 25%, in most cases [16 (57.1%) and 382 (81.3%), respectively) exclusion of one or two studies sufficed to decrease heterogeneity even <25%. The number of excluded studies correlated modestly with initial estimated I(2) (correlation coefficients 0.52-0.68 depending on algorithm used)., Conclusions: The proposed algorithms can be routinely applied in meta-analyses as standardized sensitivity analyses for heterogeneity. Caution is needed evaluating post hoc which specific studies are responsible for the heterogeneity.