Epilepsy in childhood and school performance: a nation-wide cohort study.

Childhood epilepsy has been linked to poor academic performance, but large-scale studies are lacking. In this nation-wide study of school-aged children, we examined the association between childhood epilepsy and school performance in standardized tests according to phenotypic and treatment-related characteristics. We performed a matched register-based cohort study of children born in Denmark (1997-2009) who participated in the Danish National School Test Programme between 2010 and 2019. We used population and health registers to identify children with epilepsy and a randomly sampled sex- and age-matched reference cohort without epilepsy (ratio 1:10). Norm-based test scores from language and mathematics reflecting performance as a percentile of the nation-wide distribution of scores (scale 1-100) were used to assess academic performance. Adjusted differences in mean standardized scores between children with and without epilepsy were estimated using linear regression models. Among 582 840 children participating in the School Test Programme, we identified 4659 (0.8%) children with epilepsy (52.8% males) and 46 590 matched reference children. Median age at epilepsy onset was 7.5 years (interquartile range: 4.0-10.6). Childhood epilepsy was associated with poorer school performance overall (mean score = 48.2 versus references = 56.7; adjusted difference = -6.7, 95% CI: -7.4 to -6.0), and worse performance was found in all epilepsy subgroups, including in 3534 children with uncomplicated epilepsy (i.e. no other pre-existing neurologic or intellectual disabilities and no identified possible cause for epilepsy; adjusted difference = -6.0, 95% CI: -6.8 to -5.2). No major variation by sex, age or subject was observed, but larger score differences were seen in children using antiseizure medication at time of testing (e.g. valproate monotherapy, adjusted difference = -9.3, 95% CI: -11.5 to -7.0 and lamotrigine monotherapy, adjusted difference = -13.1, 95% CI: -15.0 to -11.3) and in children with psychiatric comorbidity, especially epilepsy with comorbid intellectual disability (adjusted difference = -27.0, 95% CI: -30.0 to -23.9) and epilepsy with comorbid attention deficit/hyperactivity disorder (adjusted difference = -15.7, 95% CI: -19.0 to -12.4). Children with epilepsy scored significantly lower than their unaffected siblings (adjusted difference = -6.2, 95% CI: -7.1 to -5.4). In conclusion, childhood epilepsy was associated with impaired academic performance throughout schooling, which suggest that there is a widespread need for educational support of children with epilepsy, even when the child has no other comorbidities and when the epilepsy appears well-managed.

Leveraging pleiotropy to discover and interpret GWAS results for sleep-associated traits.

Genetic association studies of many heritable traits resulting from physiological testing often have modest sample sizes due to the cost and burden of the required phenotyping. This reduces statistical power and limits discovery of multiple genetic associations. We present a strategy to leverage pleiotropy between traits to both discover new loci and to provide mechanistic hypotheses of the underlying pathophysiology. Specifically, we combine a colocalization test with a locus-level test of pleiotropy. In simulations, we show that this approach is highly selective for identifying true pleiotropy driven by the same causative variant, thereby improves the chance to replicate the associations in underpowered validation cohorts and leads to higher interpretability. Here, as an exemplar, we use Obstructive Sleep Apnea (OSA), a common disorder diagnosed using overnight multi-channel physiological testing. We leverage pleiotropy with relevant cellular and cardio-metabolic phenotypes and gene expression traits to map new risk loci in an underpowered OSA GWAS. We identify several pleiotropic loci harboring suggestive associations to OSA and genome-wide significant associations to other traits, and show that their OSA association replicates in independent cohorts of diverse ancestries. By investigating pleiotropic loci, our strategy allows proposing new hypotheses about OSA pathobiology across many physiological layers. For example, we identify and replicate the pleiotropy across the plateletcrit, OSA and an eQTL of DNA primase subunit 1 (PRIM1) in immune cells. We find suggestive links between OSA, a measure of lung function (FEV1/FVC), and an eQTL of matrix metallopeptidase 15 (MMP15) in lung tissue. We also link a previously known genome-wide significant peak for OSA in the hexokinase 1 (HK1) locus to hematocrit and other red blood cell related traits. Thus, the analysis of pleiotropic associations has the potential to assemble diverse phenotypes into a chain of mechanistic hypotheses that provide insight into the pathogenesis of complex human diseases.

Seasonal Variation and Risk of Febrile Seizures: A Danish Nationwide Cohort Study.

INTRODUCTION: Onset of febrile seizures varies with calendar season. However, it has not previously been assessed, how season of birth interacts with age and peak risk of febrile seizures, and whether season of birth correlates with the cumulative risk of febrile seizures at 5 years of age (i.e., when children are no longer of risk of febrile seizures). METHODS: We identified all singleton children born in Denmark between 1977 and 2011 who were alive at 3 months of age (N = 2,103,232). We used the Danish Civil Registration System to identify age and sex of the children and the Danish National Patient Register to identify children hospitalized with febrile seizures from 3 months to 5 years of age. Follow-up ended on December 31, 2016, when all children had reached 5 years of age. RESULTS: The relative risk of admission with a first febrile seizure varied with calendar month; in February (a winter month in Denmark), the risk was more than doubled (hazard ratio: 2.10 [95% confidence interval [CI]: 2.03-2.18]) compared with August (a summer month in Denmark). The age-specific incidence of a first febrile seizure by birth month identified the highest peak incidence of a first febrile seizure among children born in November (reaching a peak incidence of 350 first admissions with a febrile seizure per 100,000 person months at age 16 months) as compared to children born in July (reaching a peak incidence of 200 first admissions with a febrile seizure per 100,000 person months at age 16 months). However, the cumulative incidence of any admission with febrile seizures before 5 years was not correlated with season of birth (3.69% [95% CI: 3.64-3.74%] for winter births, 3.57% [95% CI: 3.52-3.62%] for spring births, 3.55% [95% CI: 3.50-3.59%] for summer births, and 3.64% [95% CI: 3.59-3.69%] for fall births). DISCUSSION/CONCLUSION: The study found a significant seasonal variation in onset of the first febrile seizure and in the age-specific peak incidence of febrile seizures. However, there was no correlation between season of birth and cumulative incidence of febrile seizures at 5 years of age suggesting that children who are predisposed to febrile seizures will eventually go on to experience a febrile seizure regardless of season of birth.

Birth characteristics and risk of febrile seizures.

OBJECTIVE: Febrile seizure is a common childhood disorder that affects 2-5% of all children, and is associated with later development of epilepsy and psychiatric disorders. This study determines how the incidence of febrile seizures correlates with birth characteristics, age, sex and brain development. METHODS: This is a cohort study of all children born Denmark between 1977 and 2011 who were alive at 3 months of age (N = 2,103,232). The Danish National Patient Register was used to identify children with febrile seizures up to 5 years of age. Follow-up ended on 31 December 2016 when all cohort members had potentially reached 5 years of age. RESULTS: In total, 75,593 (3.59%, 95% CI: 3.57-3.62%) were diagnosed with febrile seizures. Incidence peaked at 16.7 months of age (median: 16.7 months, interquartile range: 12.5-24.0). The 5-year cumulative incidence of febrile seizures increased with decreasing birth weight (<1500 g; 5.42% (95% CI: 4.98-5.88% vs. 3,000-4,000 g; 3.53% (95% CI: 3.50-3.56%)) and with decreasing gestational age at birth (31-32 weeks; 5.90% (95% CI: 5.40-6.44%) vs. 39-40 weeks; 3.56% (95% CI: 3.53-3.60)). Lower gestational age at birth was associated with higher age at onset of a first febrile seizure; an association that essentially disappeared when correcting for age from conception. CONCLUSIONS: The risk of febrile seizures increased with decreasing birth weight and gestational age at birth. The association between low gestational age at birth and age at first febrile seizure suggests that onset of febrile seizures is associated with the stage of brain development.

ImmuneRegulation: a web-based tool for identifying human immune regulatory elements.

Humans vary considerably both in their baseline and activated immune phenotypes. We developed a user-friendly open-access web portal, ImmuneRegulation, that enables users to interactively explore immune regulatory elements that drive cell-type or cohort-specific gene expression levels. ImmuneRegulation currently provides the largest centrally integrated resource on human transcriptome regulation across whole blood and blood cell types, including (i) ∼43,000 genotyped individuals with associated gene expression data from ∼51,000 experiments, yielding genetic variant-gene expression associations on ∼220 million eQTLs; (ii) 14 million transcription factor (TF)-binding region hits extracted from 1945 ChIP-seq studies; and (iii) the latest GWAS catalog with 67,230 published variant-trait associations. Users can interactively explore associations between queried gene(s) and their regulators (cis-eQTLs, trans-eQTLs or TFs) across multiple cohorts and studies. These regulators may explain genotype-dependent gene expression variations and be critical in selecting the ideal cohorts or cell types for follow-up studies or in developing predictive models. Overall, ImmuneRegulation significantly lowers the barriers between complex immune regulation data and researchers who want rapid, intuitive and high-quality access to the effects of regulatory elements on gene expression in multiple studies to empower investigators in translating these rich data into biological insights and clinical applications, and is freely available at https://immuneregulation.mssm.edu.

Integrative Genetic and Epigenetic Analysis Uncovers Regulatory Mechanisms of Autoimmune Disease.

Genome-wide association studies in autoimmune and inflammatory diseases (AID) have uncovered hundreds of loci mediating risk. These associations are preferentially located in non-coding DNA regions and in particular in tissue-specific DNase I hypersensitivity sites (DHSs). While these analyses clearly demonstrate the overall enrichment of disease risk alleles on gene regulatory regions, they are not designed to identify individual regulatory regions mediating risk or the genes under their control, and thus uncover the specific molecular events driving disease risk. To do so we have departed from standard practice by identifying regulatory regions which replicate across samples and connect them to the genes they control through robust re-analysis of public data. We find significant evidence of regulatory potential in 78/301 (26%) risk loci across nine autoimmune and inflammatory diseases, and we find that individual genes are targeted by these effects in 53/78 (68%) of these. Thus, we are able to generate testable mechanistic hypotheses of the molecular changes that drive disease risk.

Large-Scale trans-eQTLs Affect Hundreds of Transcripts and Mediate Patterns of Transcriptional Co-regulation.

Efforts to decipher the causal relationships between differences in gene regulation and corresponding differences in phenotype have been stymied by several basic technical challenges. Although detecting local, cis-eQTLs is now routine, trans-eQTLs, which are distant from the genes of origin, are far more difficult to find because millions of SNPs must currently be compared to thousands of transcripts. Here, we demonstrate an alternative approach: we looked for SNPs associated with the expression of many genes simultaneously and found that hundreds of trans-eQTLs each affect hundreds of transcripts in lymphoblastoid cell lines across three African populations. These trans-eQTLs target the same genes across the three populations and show the same direction of effect. We discovered that target transcripts of a high-confidence set of trans-eQTLs encode proteins that interact more frequently than expected by chance, are bound by the same transcription factors, and are enriched for pathway annotations indicative of roles in basic cell homeostasis. We thus demonstrate that our approach can uncover trans-acting transcriptional control circuits that affect co-regulated groups of genes: a key to understanding how cellular pathways and processes are orchestrated.

Novel determinants of mammalian primary microRNA processing revealed by systematic evaluation of hairpin-containing transcripts and human genetic variation.

Mature microRNAs (miRNAs) are processed from hairpin-containing primary miRNAs (pri-miRNAs). However, rules that distinguish pri-miRNAs from other hairpin-containing transcripts in the genome are incompletely understood. By developing a computational pipeline to systematically evaluate 30 structural and sequence features of mammalian RNA hairpins, we report several new rules that are preferentially utilized in miRNA hairpins and govern efficient pri-miRNA processing. We propose that a hairpin stem length of 36 ± 3 nt is optimal for pri-miRNA processing. We identify two bulge-depleted regions on the miRNA stem, located ∼16-21 nt and ∼28-32 nt from the base of the stem, that are less tolerant of unpaired bases. We further show that the CNNC primary sequence motif selectively enhances the processing of optimal-length hairpins. We predict that a small but significant fraction of human single-nucleotide polymorphisms (SNPs) alter pri-miRNA processing, and confirm several predictions experimentally including a disease-causing mutation. Our study enhances the rules governing mammalian pri-miRNA processing and suggests a diverse impact of human genetic variation on miRNA biogenesis.

Pleiotropy in complex traits: challenges and strategies.

Genome-wide association studies have identified many variants that each affects multiple traits, particularly across autoimmune diseases, cancers and neuropsychiatric disorders, suggesting that pleiotropic effects on human complex traits may be widespread. However, systematic detection of such effects is challenging and requires new methodologies and frameworks for interpreting cross-phenotype results. In this Review, we discuss the evidence for pleiotropy in contemporary genetic mapping studies, new and established analytical approaches to identifying pleiotropic effects, sources of spurious cross-phenotype effects and study design considerations. We also outline the molecular and clinical implications of such findings and discuss future directions of research.

Network Analysis of Genome-Wide Selective Constraint Reveals a Gene Network Active in Early Fetal Brain Intolerant of Mutation.

Using robust, integrated analysis of multiple genomic datasets, we show that genes depleted for non-synonymous de novo mutations form a subnetwork of 72 members under strong selective constraint. We further show this subnetwork is preferentially expressed in the early development of the human hippocampus and is enriched for genes mutated in neurological Mendelian disorders. We thus conclude that carefully orchestrated developmental processes are under strong constraint in early brain development, and perturbations caused by mutation have adverse outcomes subject to strong purifying selection. Our findings demonstrate that selective forces can act on groups of genes involved in the same process, supporting the notion that purifying selection can act coordinately on multiple genes. Our approach provides a statistically robust, interpretable way to identify the tissues and developmental times where groups of disease genes are active.

Changes in T-cell subsets identify responders to FcR-nonbinding anti-CD3 mAb (teplizumab) in patients with type 1 diabetes.

The mechanisms whereby immune therapies affect progression of type 1 diabetes (T1D) are not well understood. Teplizumab, an FcR nonbinding anti-CD3 mAb, has shown efficacy in multiple randomized clinical trials. We previously reported an increase in the frequency of circulating CD8(+) central memory (CD8CM) T cells in clinical responders, but the generalizability of this finding and the molecular effects of teplizumab on these T cells have not been evaluated. We analyzed data from two randomized clinical studies of teplizumab in patients with new- and recent-onset T1D. At the conclusion of therapy, clinical responders showed a significant reduction in circulating CD4(+) effector memory T cells. Afterward, there was an increase in the frequency and absolute number of CD8CM T cells. In vitro, teplizumab expanded CD8CM T cells by proliferation and conversion of non-CM T cells. Nanostring analysis of gene expression of CD8CM T cells from responders and nonresponders versus placebo-treated control subjects identified decreases in expression of genes associated with immune activation and increases in expression of genes associated with T-cell differentiation and regulation. We conclude that CD8CM T cells with decreased activation and regulatory gene expression are associated with clinical responses to teplizumab in patients with T1D.

Genetic variants near TNFAIP3 on 6q23 are associated with systemic lupus erythematosus.

Systemic lupus erythematosus (SLE) is an autoimmune disease influenced by genetic and environmental factors. We carried out a genome-wide association scan and replication study and found an association between SLE and a variant in TNFAIP3 (rs5029939, meta-analysis P = 2.89 x 10(-12), OR = 2.29). We also found evidence of two independent signals near TNFAIP3 associated with SLE, including one previously associated with rheumatoid arthritis (RA). These results establish that variants near TNFAIP3 contribute to differential risk of SLE and RA.

Weight loss after gastric bypass is associated with a variant at 15q26.1.

The amount of weight loss attained after Roux-en-Y gastric bypass (RYGB) surgery follows a wide and normal distribution, and recent evidence indicates that this weight loss is due to physiological, rather than mechanical, mechanisms. To identify potential genetic factors associated with weight loss after RYGB, we performed a genome-wide association study (GWAS) of 693 individuals undergoing RYGB and then replicated this analysis in an independent population of 327 individuals undergoing RYGB. We found that a 15q26.1 locus near ST8SIA2 and SLCO3A1 was significantly associated with weight loss after RYGB. Expression of ST8SIA2 in omental fat of these individuals at baseline was significantly associated with weight loss after RYGB. Gene expression analysis in RYGB and weight-matched, sham-operated (WMS) mice revealed that expression of St8sia2 and Slco3a1 was significantly altered in metabolically active tissues in RYGB-treated compared to WMS mice. These findings provide strong evidence for specific genetic influences on weight loss after RYGB and underscore the biological nature of the response to RYGB.

Progress and challenges for treating Type 1 diabetes.

It has been more than 30 years since the initial trials of Cyclosporin A to treat patients with new onset Type 1 diabetes (T1D). Since that time, there have been insights into genetic predisposition to the disease, the failures of immune tolerance, and mechanisms that cause the immune mediated ß cell destruction. The genetic loci associated affect lymphocyte development and tolerance mechanisms. Discoveries related to the roles of specific immune responses gene such as the major histocompatibility complex, PTPN22, CTLA-4, IL-2RA, as well as the mechanisms of antigen presentation in the thymus have suggested ways in which autoreactivity may follow changes in the functions of these genes that are associated with risk. Antigens that are recognized by the immune system in patients with T1D have been identified. With this information, insights into the novel cellular mechanisms leading to the initiation and orchestration of ß cell killing have been developed such as the presentation of unique antigens within the islets. Clinical trials have been performed, some of which have shown efficacy in improving ß cell function but none have been able to permanently prevent loss of insulin secretion. The reasons for the lack of long term success are not clear but may include the heterogeneity of the immune response and in individual responses to immune therapies, recurrence of autoimmunity after the initial effects of the therapies, or even intrinsic mechanisms of ß cell death that proceeds independently of immune attack after initiation of the disease. In this review, we cover developments that have led to new therapeutics and characteristics of patients who may show the most benefits from therapies. We also identify areas of incomplete understanding that might be addressed to develop more effective therapeutic strategies.

Immune-mediated disease genetics: the shared basis of pathogenesis.

Recent genetic studies in multiple autoimmune and inflammatory diseases have identified hundreds of genomic loci harboring risk variants. These variants are shared between diseases at unexpectedly high rates, providing a molecular basis for the shared pathogenesis of immune-mediated disease. If properly used, these results could allow us to identify specific pathways underlying disease; explain disease heterogeneity by grouping patients by molecular causes rather than overall symptomatology; and develop more rational approaches to diagnosis and therapy targeting these molecular defects. Here we review the current state of play in the genetics of immune-mediated disease, evidence for this sharing and how this new knowledge can lead to medically actionable discoveries of pathobiology.

Unraveling multiple MHC gene associations with systemic lupus erythematosus: model choice indicates a role for HLA alleles and non-HLA genes in Europeans.

We have performed a meta-analysis of the major-histocompatibility-complex (MHC) region in systemic lupus erythematosus (SLE) to determine the association with both SNPs and classical human-leukocyte-antigen (HLA) alleles. More specifically, we combined results from six studies and well-known out-of-study control data sets, providing us with 3,701 independent SLE cases and 12,110 independent controls of European ancestry. This study used genotypes for 7,199 SNPs within the MHC region and for classical HLA alleles (typed and imputed). Our results from conditional analysis and model choice with the use of the Bayesian information criterion show that the best model for SLE association includes both classical loci (HLA-DRB1(∗)03:01, HLA-DRB1(∗)08:01, and HLA-DQA1(∗)01:02) and two SNPs, rs8192591 (in class III and upstream of NOTCH4) and rs2246618 (MICB in class I). Our approach was to perform a stepwise search from multiple baseline models deduced from a priori evidence on HLA-DRB1 lupus-associated alleles, a stepwise regression on SNPs alone, and a stepwise regression on HLA alleles. With this approach, we were able to identify a model that was an overwhelmingly better fit to the data than one identified by simple stepwise regression either on SNPs alone (Bayes factor [BF] > 50) or on classical HLA alleles alone (BF > 1,000).

Proteins encoded in genomic regions associated with immune-mediated disease physically interact and suggest underlying biology.

Genome-wide association studies (GWAS) have defined over 150 genomic regions unequivocally containing variation predisposing to immune-mediated disease. Inferring disease biology from these observations, however, hinges on our ability to discover the molecular processes being perturbed by these risk variants. It has previously been observed that different genes harboring causal mutations for the same Mendelian disease often physically interact. We sought to evaluate the degree to which this is true of genes within strongly associated loci in complex disease. Using sets of loci defined in rheumatoid arthritis (RA) and Crohn's disease (CD) GWAS, we build protein-protein interaction (PPI) networks for genes within associated loci and find abundant physical interactions between protein products of associated genes. We apply multiple permutation approaches to show that these networks are more densely connected than chance expectation. To confirm biological relevance, we show that the components of the networks tend to be expressed in similar tissues relevant to the phenotypes in question, suggesting the network indicates common underlying processes perturbed by risk loci. Furthermore, we show that the RA and CD networks have predictive power by demonstrating that proteins in these networks, not encoded in the confirmed list of disease associated loci, are significantly enriched for association to the phenotypes in question in extended GWAS analysis. Finally, we test our method in 3 non-immune traits to assess its applicability to complex traits in general. We find that genes in loci associated to height and lipid levels assemble into significantly connected networks but did not detect excess connectivity among Type 2 Diabetes (T2D) loci beyond chance. Taken together, our results constitute evidence that, for many of the complex diseases studied here, common genetic associations implicate regions encoding proteins that physically interact in a preferential manner, in line with observations in Mendelian disease.

Pervasive sharing of genetic effects in autoimmune disease.

Genome-wide association (GWA) studies have identified numerous, replicable, genetic associations between common single nucleotide polymorphisms (SNPs) and risk of common autoimmune and inflammatory (immune-mediated) diseases, some of which are shared between two diseases. Along with epidemiological and clinical evidence, this suggests that some genetic risk factors may be shared across diseases-as is the case with alleles in the Major Histocompatibility Locus. In this work we evaluate the extent of this sharing for 107 immune disease-risk SNPs in seven diseases: celiac disease, Crohn's disease, multiple sclerosis, psoriasis, rheumatoid arthritis, systemic lupus erythematosus, and type 1 diabetes. We have developed a novel statistic for Cross Phenotype Meta-Analysis (CPMA) which detects association of a SNP to multiple, but not necessarily all, phenotypes. With it, we find evidence that 47/107 (44%) immune-mediated disease risk SNPs are associated to multiple-but not all-immune-mediated diseases (SNP-wise P(CPMA)<0.01). We also show that distinct groups of interacting proteins are encoded near SNPs which predispose to the same subsets of diseases; we propose these as the mechanistic basis of shared disease risk. We are thus able to leverage genetic data across diseases to construct biological hypotheses about the underlying mechanism of pathogenesis.

Genetic mapping across autoimmune diseases reveals shared associations and mechanisms.

Autoimmune and inflammatory diseases are polygenic disorders of the immune system. Many genomic loci harbor risk alleles for several diseases, but the limited resolution of genetic mapping prevents determining whether the same allele is responsible, indicating a shared underlying mechanism. Here, using a collection of 129,058 cases and controls across 6 diseases, we show that ~40% of overlapping associations are due to the same allele. We improve fine-mapping resolution for shared alleles twofold by combining cases and controls across diseases, allowing us to identify more expression quantitative trait loci driven by the shared alleles. The patterns indicate widespread sharing of pathogenic mechanisms but not a single global autoimmune mechanism. Our approach can be applied to any set of traits and is particularly valuable as sample collections become depleted.

Genome-wide detection and characterization of positive selection in human populations.

With the advent of dense maps of human genetic variation, it is now possible to detect positive natural selection across the human genome. Here we report an analysis of over 3 million polymorphisms from the International HapMap Project Phase 2 (HapMap2). We used 'long-range haplotype' methods, which were developed to identify alleles segregating in a population that have undergone recent selection, and we also developed new methods that are based on cross-population comparisons to discover alleles that have swept to near-fixation within a population. The analysis reveals more than 300 strong candidate regions. Focusing on the strongest 22 regions, we develop a heuristic for scrutinizing these regions to identify candidate targets of selection. In a complementary analysis, we identify 26 non-synonymous, coding, single nucleotide polymorphisms showing regional evidence of positive selection. Examination of these candidates highlights three cases in which two genes in a common biological process have apparently undergone positive selection in the same population:LARGE and DMD, both related to infection by the Lassa virus, in West Africa;SLC24A5 and SLC45A2, both involved in skin pigmentation, in Europe; and EDAR and EDA2R, both involved in development of hair follicles, in Asia.