Evidence for polygenic susceptibility to multiple sclerosis--the shape of things to come

Laboratory Diagnosis of Intrathecal Synthesis of Immunoglobulins: A Review about the Contribution of OCBs and K-index

The diagnosis of MS relies on a combination of imaging, clinical examinations, and biological analyses, including blood and cerebrospinal fluid (CSF) assessments. G-Oligoclonal bands (OCBs) are considered a "gold standard" for MS diagnosis due to their high sensitivity and specificity. Recent advancements have involved the introduced of kappa free light chain (k-FLC) assay into cerebrospinal fluid (CSF) and serum (S), along with the albumin quotient, leading to the development of a novel biomarker known as the "K-index" or "k-FLC index". The use of the K-index has been recommended to decrease costs, increase laboratory efficiency, and to skip potential subjective operator-dependent risk that could happen during the identification of OCBs profiles. This review aims to provide a comprehensive overview and analysis of recent scientific articles, focusing on updated methods for MS diagnosis with an emphasis on the utility of the K-index. Numerous studies indicate that the K-index demonstrates high sensitivity and specificity, often comparable to or surpassing the diagnostic accuracy of OCBs evaluation. The integration of the measure of the K-index with OCBs assessment emerges as a more precise method for MS diagnosis. This combined approach not only enhances diagnostic accuracy, but also offers a more efficient and cost-effective alternative.

Ultrasound-Sensitive Intelligent Nanosystems: A Promising Strategy for the Treatment of Neurological Diseases

Neurological diseases are a major global health challenge, affecting hundreds of millions of people worldwide. Ultrasound therapy plays an irreplaceable role in the treatment of neurological diseases due to its noninvasive, highly focused, and strong tissue penetration capabilities. However, the complexity of brain and nervous system and the safety risks associated with prolonged exposure to ultrasound therapy severely limit the applicability of ultrasound therapy. Ultrasound-sensitive intelligent nanosystems (USINs) are a novel therapeutic strategy for neurological diseases that bring greater spatiotemporal controllability and improve safety to overcome these challenges. This review provides a detailed overview of therapeutic strategies and clinical advances of ultrasound in neurological diseases, focusing on the potential of USINs-based ultrasound in the treatment of neurological diseases. Based on the physical and chemical effects induced by ultrasound, rational design of USINs is a prerequisite for improving the efficacy of ultrasound therapy. Recent developments of ultrasound-sensitive nanocarriers and nanoagents are systemically reviewed. Finally, the challenges and developing prospects of USINs are discussed in depth, with a view to providing useful insights and guidance for efficient ultrasound treatment of neurological diseases.

Potential application of elastic nets for shared polygenicity detection with adapted threshold selection

Current research suggests that hundreds to thousands of single nucleotide polymorphisms (SNPs) with small to modest effect sizes contribute to the genetic basis of many disorders, a phenomenon labeled as polygenicity. Additionally, many such disorders demonstrate polygenic overlap, in which risk alleles are shared at associated genetic loci. A simple strategy to detect polygenic overlap between two phenotypes is based on rank-ordering the univariate p-values from two genome-wide association studies (GWASs). Although high-dimensional variable selection strategies such as Lasso and elastic nets have been utilized in other GWAS analysis settings, they are yet to be utilized for detecting shared polygenicity. In this paper, we illustrate how elastic nets, with polygenic scores as the dependent variable and with appropriate adaptation in selecting the penalty parameter, may be utilized for detecting a subset of SNPs involved in shared polygenicity. We provide theory to better understand our approaches, and illustrate their utility using synthetic datasets. Results from extensive simulations are presented comparing the elastic net approaches with the rank ordering approach, in various scenarios. Results from simulations studies exhibit one of the elastic net approaches to be superior when the correlations among the SNPs are high. Finally, we apply the methods on two real datasets to illustrate further the capabilities, limitations and differences among the methods.

Polygenic risk score association with multiple sclerosis susceptibility and phenotype in Europeans

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.

Statistical Methods for Disease Risk Prediction with Genotype Data

Single-nucleotide polymorphism (SNP) is the basic unit to understand the heritability of complex traits. One attractive application of the susceptible SNPs is to construct prediction models for assessing disease risk. Here, we introduce prediction methods for human traits using SNPs data, including the polygenic risk score (PRS), linear mixed models (LMMs), penalized regressions, and methods for controlling population stratification.

Multi-context genetic modeling of transcriptional regulation resolves novel disease loci

A majority of the variants identified in genome-wide association studies fall in non-coding regions of the genome, indicating their mechanism of impact is mediated via gene expression. Leveraging this hypothesis, transcriptome-wide association studies (TWAS) have assisted in both the interpretation and discovery of additional genes associated with complex traits. However, existing methods for conducting TWAS do not take full advantage of the intra-individual correlation inherently present in multi-context expression studies and do not properly adjust for multiple testing across contexts. We introduce CONTENT-a computationally efficient method with proper cross-context false discovery correction that leverages correlation structure across contexts to improve power and generate context-specific and context-shared components of expression. We apply CONTENT to bulk multi-tissue and single-cell RNA-seq data sets and show that CONTENT leads to a 42% (bulk) and 110% (single cell) increase in the number of genetically predicted genes relative to previous approaches. We find the context-specific component of expression comprises 30% of heritability in tissue-level bulk data and 75% in single-cell data, consistent with cell-type heterogeneity in bulk tissue. In the context of TWAS, CONTENT increases the number of locus-phenotype associations discovered by over 51% relative to previous methods across 22 complex traits.

Genome-wide analysis of schizophrenia and multiple sclerosis identifies shared genomic loci with mixed direction of effects

Common genetic variants identified in genome-wide association studies (GWAS) show varying degrees of genetic pleiotropy across complex human disorders. Clinical studies of schizophrenia (SCZ) suggest that in addition to neuropsychiatric symptoms, patients with SCZ also show variable immune dysregulation. Epidemiological studies of multiple sclerosis (MS), an autoimmune, neurodegenerative disorder of the central nervous system, suggest that in addition to the manifestation of neuroinflammatory complications, patients with MS may also show co-occurring neuropsychiatric symptoms with disease progression. In this study, we analyzed the largest available GWAS datasets for SCZ (N = 161,405) and MS (N = 41,505) using Gaussian causal mixture modeling (MiXeR) and conditional/conjunctional false discovery rate (condFDR) frameworks to explore and quantify the shared genetic architecture of these two complex disorders at common variant level. Despite detecting only a negligible genetic correlation (rG = 0.057), we observe polygenic overlap between SCZ and MS, and a substantial genetic enrichment in SCZ conditional on associations with MS, and vice versa. By leveraging this cross-disorder enrichment, we identified 36 loci jointly associated with SCZ and MS at conjunctional FDR < 0.05 with mixed direction of effects. Follow-up functional analysis of the shared loci implicates candidate genes and biological processes involved in immune response and B-cell receptor signaling pathways. In conclusion, this study demonstrates the presence of polygenic overlap between SCZ and MS in the absence of a genetic correlation and provides new insights into the shared genetic architecture of these two disorders at the common variant level.

Genetic variants associated with sepsis

BACKGROUND

The variable presentations and different phenotypes of sepsis suggest that risk of sepsis comes from many genes each having a small effect. The cumulative effect can be used to create individual risk profile. The purpose of this study was to create a polygenic risk score and determine the genetic variants associated with sepsis.

METHODS

We sequenced ~14 million single nucleotide polymorphisms with a minimac imputation quality R2>0.3 and minor allele frequency >10-6 in patients with Sepsis-2 or Sepsis-3. Genome-wide association was performed using Firth bias-corrected logistic regression. Semi-parsimonious logistic regression was used to create polygenic risk scores and reduced regression to determine the genetic variants independently associated with sepsis.

FINDINGS

2261 patients had sepsis and 13,068 control patients did not. The polygenic risk scores had good discrimination: c-statistic = 0.752 ± 0.005 for Sepsis-2 and 0.752 ± 0.007 for Sepsis-3. We found 772 genetic variants associated with Sepsis-2 and 442 with Sepsis-3, p<0.01. After multivariate adjustment, 100 variants on 85 genes were associated with Sepsis-2 and 69 variants in 54 genes with Sepsis-3. Twenty-five variants were present in both the Sepsis-2 and Sepsis-3 groups out of 32 genes that were present in both groups. The other 7 genes had different variants present. Most variants had small effect sizes.

CONCLUSIONS

Sepsis-2 and Sepsis-3 have both separate and shared genetic variants. Most genetic variants have small effects sizes, but cumulatively, the polygenic risk scores have good discrimination.

Predicting Multiple Sclerosis: Challenges and Opportunities

Determining effective means of preventing Multiple Sclerosis (MS) relies on testing preventive strategies in trial populations. However, because of the low incidence of MS, demonstrating that a preventive measure has benefit requires either very large trial populations or an enriched population with a higher disease incidence. Risk scores which incorporate genetic and environmental data could be used, in principle, to identify high-risk individuals for enrolment in preventive trials. Here we discuss the concepts of developing predictive scores for identifying individuals at high risk of MS. We discuss the empirical efforts to do so using real cohorts, and some of the challenges-both theoretical and practical-limiting this work. We argue that such scores could offer a means of risk stratification for preventive trial design, but are unlikely to ever constitute a clinically-helpful approach to predicting MS for an individual.

Genetic prediction of complex traits with polygenic scores: a statistical review

Accurate genetic prediction of complex traits can facilitate disease screening, improve early intervention, and aid in the development of personalized medicine. Genetic prediction of complex traits requires the development of statistical methods that can properly model polygenic architecture and construct a polygenic score (PGS). We present a comprehensive review of 46 methods for PGS construction. We connect the majority of these methods through a multiple linear regression framework which can be instrumental for understanding their prediction performance for traits with distinct genetic architectures. We discuss the practical considerations of PGS analysis as well as challenges and future directions of PGS method development. We hope our review serves as a useful reference both for statistical geneticists who develop PGS methods and for data analysts who perform PGS analysis.

An Analysis of Factors Affecting the Severity of Cycling Crashes Using Binary Regression Model

The increasing use of bicycles rises the interest in investigating the safety aspects of daily commuting. In this investigation, more than 14,000 cyclists’ injuries were analyzed to determine the relationship between severity, road infrastructure characteristics, and surface conditions using binary regression. Minor and major severity categories were distinguished. A binary equation consists of 28 factors is extracted. It has been found that each factor related to roadway characteristics has its negative and positive impacts on cyclist severity such as traffic control, location type, topography, and roadway divisions. Regarding the road surface components, good, paved, and marked roads are associated with a higher probability of major injuries due to the expected greater frequencies of cyclists on roads with good conditions. In conclusion, probabilities of major injuries are higher in urban areas, higher speed limits, signalized intersections, inclined topographies, one-way roads, and during the daytime which require more attention and better considerations.

Gene-Environment Interactions in Multiple Sclerosis: A UK Biobank Study

Objective

We sought to determine whether genetic risk modifies the effect of environmental risk factors for multiple sclerosis (MS). To test this hypothesis, we tested for statistical interaction between polygenic risk scores (PRS) capturing genetic susceptibility to MS and environmental risk factors for MS in UK Biobank.

Methods

People with MS were identified within UK Biobank using ICD-10–coded MS or self-report. Associations between environmental risk factors and MS risk were quantified with a case-control design using multivariable logistic regression. PRS were derived using the clumping-and-thresholding approach with external weights from the largest genome-wide association study of MS. Separate scores were created including major histocompatibility complex (MHC) (PRS_MHC) and excluding (PRS_non-MHC) the MHC locus. The best-performing PRS were identified in 30% of the cohort and validated in the remaining 70%. Interaction between environmental and genetic risk factors was quantified using the attributable proportion due to interaction (AP) and multiplicative interaction.

Results

Data were available for 2,250 people with MS and 486,000 controls. Childhood obesity, earlier age at menarche, and smoking were associated with MS. The optimal PRS were strongly associated with MS in the validation cohort (PRS_MHC: Nagelkerke's pseudo-R² 0.033, p = 3.92 × 10⁻¹¹¹; PRS_non-MHC: Nagelkerke's pseudo-R² 0.013, p = 3.73 × 10⁻⁴³). There was strong evidence of interaction between polygenic risk for MS and childhood obesity (PRS_MHC: AP = 0.17, 95% CI 0.06–0.25, p = 0.004; PRS_non-MHC: AP = 0.17, 95% CI 0.06–0.27, p = 0.006).

Conclusions

This study provides novel evidence for an interaction between childhood obesity and a high burden of autosomal genetic risk. These findings may have significant implications for our understanding of MS biology and inform targeted prevention strategies.

Gene-based association analysis reveals involvement of LAMA5 and cell adhesion pathways in nicotine dependence in African- and European-American samples

Nicotine dependence (ND) is a chronic brain disorder that causes heavy social and economic burdens. Although many susceptibility genetic loci have been reported, they can explain only approximately 5%-10% of the genetic variance for the disease. To further explore the genetic etiology of ND, we genotyped 242 764 SNPs using an exome chip from both European-American (N = 1572) and African-American (N = 3371) samples. Gene-based association analysis revealed 29 genes associated significantly with ND. Of the genes in the AA sample, six (i.e., PKD1L2, LAMA5, MUC16, MROH5, ATP8B1, and FREM1) were replicated in the EA sample with p values ranging from 0.0031 to 0.0346. Subsequently, gene enrichment analysis revealed that cell adhesion-related pathways were significantly associated with ND in both the AA and EA samples. Considering that LAMA5 is the most significant gene in cell adhesion-related pathways, we did in vitro functional analysis of this gene, which showed that nicotine significantly suppressed its mRNA expression in HEK293T cells (p < 0.001). Further, our cell migration experiment showed that the migration rate was significantly different in wild-type and LAMA5-knockout (LAMA5-KO)-HEK293T cells. Importantly, nicotine-induced cell migration was abolished in LAMA5-KO cells. Taken together, these findings indicate that LAMA5, as well as cell adhesion-related pathways, play an important role in the etiology of smoking addiction, which warrants further investigation.

Bile acid metabolism is altered in multiple sclerosis and supplementation ameliorates neuroinflammation

Multiple sclerosis (MS) is an inflammatory demyelinating disorder of the CNS. Bile acids are cholesterol metabolites that can signal through receptors on cells throughout the body, including in the CNS and the immune system. Whether bile acid metabolism is abnormal in MS is unknown. Using global and targeted metabolomic profiling, we identified lower levels of circulating bile acid metabolites in multiple cohorts of adult and pediatric patients with MS compared with controls. In white matter lesions from MS brain tissue, we noted the presence of bile acid receptors on immune and glial cells. To mechanistically examine the implications of lower levels of bile acids in MS, we studied the in vitro effects of an endogenous bile acid, tauroursodeoxycholic acid (TUDCA), on astrocyte and microglial polarization. TUDCA prevented neurotoxic (A1) polarization of astrocytes and proinflammatory polarization of microglia in a dose-dependent manner. TUDCA supplementation in experimental autoimmune encephalomyelitis reduced the severity of disease through its effects on G protein-coupled bile acid receptor 1 (GPBAR1). We demonstrate that bile acid metabolism was altered in MS and that bile acid supplementation prevented polarization of astrocytes and microglia to neurotoxic phenotypes and ameliorated neuropathology in an animal model of MS. These findings identify dysregulated bile acid metabolism as a potential therapeutic target in MS.

Neuromyelitis optica is an HLA associated disease different from Multiple Sclerosis: a systematic review with meta-analysis

Neuromyelitis Optica and Multiple Sclerosis are idiopathic inflammatory demyelinating diseases of the central nervous system that currently are considered distinct autoimmune diseases, so differences in genetic susceptibility would be expected. This study aimed to investigate the HLA association with Neuromyelitis Optica by a systematic review with meta-analysis. The STROBE instrument guided research paper assessments. Thirteen papers published between 2009 and 2020 were eligible. 568 Neuromyelitis Optica patients, 41.4% Asians, 32.4% Latin Americans and 26.2% Europeans were analyzed. Only alleles of the DRB1 locus were genotyped in all studies. Neuromyelitis Optica patients have 2.46 more chances of having the DRB1*03 allelic group than controls. Ethnicity can influence genetic susceptibility. The main HLA association with Neuromyelitis Optica was the DRB1*03:01 allele in Western populations and with the DPB1*05:01 allele in Asia. Differences in the Multiple Sclerosis and Neuromyelitis Optica genetic susceptibility was confirmed in Afro descendants. The DRB1*03 allelic group associated with Neuromyelitis Optica has also been described in other systemic autoimmune diseases.

Polygenic Score Models for Alzheimer's Disease: From Research to Clinical Applications

The high prevalence of Alzheimer's disease (AD) among the elderly population and its lack of effective treatments make this disease a critical threat to human health. Recent epidemiological and genetics studies have revealed the polygenic nature of the disease, which is possibly explainable by a polygenic score model that considers multiple genetic risks. Here, we systemically review the rationale and methods used to construct polygenic score models for studying AD. We also discuss the associations of polygenic risk scores (PRSs) with clinical outcomes, brain imaging findings, and biochemical biomarkers from both the brain and peripheral system. Finally, we discuss the possibility of incorporating polygenic score models into research and clinical practice along with potential challenges.

A review of studies examining the association between genetic biomarkers (short tandem repeats and single-nucleotide polymorphisms) and risk of prostate cancer: the need for valid predictive biomarkers

Prostate cancer (PCa) is a challenging polygenic disease because the genes that cause PCa remain largely elusive and are affected by several causal factors. Consequently, research continuously strives to identify a genetic marker which could be used as an indicator to predict the most vulnerable (i.e., predisposed) segments of the population to the disease or for the gene which may be directly responsible for PCa. To enhance the genetic etiology of PCa, this research sought to discover the key studies conducted in this field using data from the main journal publication search engines, as it was hoped that this could shed light on the main research findings from these studies, which in turn could assist in determining these genes or markers. From the research highlighted, the studies primarily used two kinds of markers: short tandem repeats or single-nucleotide polymorphisms. These markers were found to be quite prevalent in all the chromosomes within the research carried out. It also became apparent that the studies differed in both quantity and quality, as well as being conducted in a variety of societies. Links were also determined between the degree and strength of the relationship between these markers and the occurrence of the disease. From the studies identified, most recommended a larger and more diverse survey for the parameters which had not been studied before, as well as an increase in the size of the community (i.e., the population) being studied. This is an indication that work in this field is far from complete, and thus, current research remains committed toward finding genetic markers that can be used clinically for the diagnosis and screening of patients with PCa.

Association of multiple sclerosis with vitiligo: a systematic review and meta-analysis

Polyautoimmunity implicates that some autoimmune diseases share common etiopathogenesis. Some studies have reported an association between multiple sclerosis (MS) and vitiligo; meanwhile, other studies have failed to confirm this association. We performed a systemic review and meta-analysis to examine the association of MS with vitiligo. We searched the MEDLINE and Embase databases on March 8, 2020 for relevant case–control, cross-sectional, and cohort studies. The Newcastle–Ottawa Scale was used to evaluate the risk of bias of the included studies. Where applicable, we performed a meta-analysis to calculate the pooled odds ratio (OR) for case–control/cross-sectional studies and risk ratio for cohort studies with 95% confidence interval (CI). Our search identified 285 citations after removing duplicates. Six case–control studies with 12,930 study subjects met our inclusion criteria. Our meta-analysis found no significant association of MS with prevalent vitiligo (pooled OR 1.33; 95% CI 0.80‒2.22). Analysis of the pooled data failed to display any increase of prevalent vitiligo in MS patients compared with controls. Ethnic and genetic factors may play an important role for sporadically observed associations between MS and vitiligo. Future studies of this association should therefore consider stratification by ethnic or genetic factors.

Polygenic approaches to detect gene-environment interactions when external information is unavailable

The exploration of 'gene-environment interactions' (G × E) is important for disease prediction and prevention. The scientific community usually uses external information to construct a genetic risk score (GRS), and then tests the interaction between this GRS and an environmental factor (E). However, external genome-wide association studies (GWAS) are not always available, especially for non-Caucasian ethnicity. Although GRS is an analysis tool to detect G × E in GWAS, its performance remains unclear when there is no external information. Our 'adaptive combination of Bayes factors method' (ADABF) can aggregate G × E signals and test the significance of G × E by a polygenic test. We here explore a powerful polygenic approach for G × E when external information is unavailable, by comparing our ADABF with the GRS based on marginal effects of SNPs (GRS-M) and GRS based on SNP × E interactions (GRS-I). ADABF is the most powerful method in the absence of SNP main effects, whereas GRS-M is generally the best test when single-nucleotide polymorphisms main effects exist. GRS-I is the least powerful test due to its data-splitting strategy. Furthermore, we apply these methods to Taiwan Biobank data. ADABF and GRS-M identified gene × alcohol and gene × smoking interactions on blood pressure (BP). BP-increasing alleles elevate more BP in drinkers (smokers) than in nondrinkers (nonsmokers). This work provides guidance to choose a polygenic approach to detect G × E when external information is unavailable.

Quantifying the Predictive Accuracy of a Polygenic Risk Score for Predicting Incident Cancer Cases : Application to the CARTaGENE Cohort

With the increasing use of polygenic risk scores (PRS) there is a need for adapted methods to evaluate the predictivity of these tools. In this work, we propose a new pseudo-R² criterion to evaluate PRS predictive accuracy for time-to-event data. This new criterion is related to the score statistic derived under a two-component mixture model. It evaluates the effect of the PRS on both the propensity to experience the event and on the dynamic of the event among the susceptible subjects. Simulation results show that our index has good properties. We compared our index to other implemented pseudo-R² for survival data. Along with our index, two other indices have comparable good behavior when the PRS has a non-null propensity effect, and our index is the only one to detect when the PRS has only a dynamic effect. We evaluated the 5-year predictivity of an 18-single-nucleotide-polymorphism PRS for incident breast cancer cases on the CARTaGENE cohort using several pseudo-R² indices. We report that our index, which summarizes both a propensity and a dynamic effect, had the highest predictive accuracy. In conclusion, our proposed pseudo-R² is easy to implement and well suited to evaluate PRS for predicting incident events in cohort studies.

Association of small-fiber polyneuropathy with three previously unassociated rare missense SCN9A variants

BACKGROUND

Small fiber polyneuropathy (SFN) involves ectopic firing and degeneration of small-diameter, somatic/autonomic peripheral axons. Causes include diabetes, inflammation and rare pathogenic mutations, including in SCN9-11 genes that encode small fiber sodium channels.

AIMS

The aim of this study is to associate a new phenotype-immunotherapy-responsive SFN-with rare amino acid-substituting SCN9A variants and present potential explanations.

METHODS

A retrospective chart review of two Caucasians with skin biopsy confirmed SFN and rare SCN9A single nucleotide polymorphisms not previously reported in neuropathy.

RESULTS

A 47-year-old with 4 years of disabling widespread neuropathic pain and exertional intolerance had nerve- and skin biopsy-confirmed SFN, with blood tests revealing only high-titer antinuclear antibodies and low complement C4 consistent with B cell dysimmunity. Six years of intravenous immunoglobulin (IVIg) therapy markedly improved sensory and autonomic symptoms and normalized his neurite density. After whole exome sequencing revealed a potentially pathogenic SCN9A-A3734G variant, sodium channel blockers were tried. Herpes zoster left a 32-year-old with disabling exertional intolerance ("chronic fatigue syndrome"), postural syncope and tachycardia, arm and leg paresthesias, reduced sweating, and distal hairloss. Screening revealed antinuclear and potassium channel autoantibodies, so prednisone and then IVIg were prescribed with great benefit. During 4 years of immunotherapy, his symptoms and function improved, and all abnormal biomarkers (autonomic testing and skin biopsies) normalized. Whole exome sequencing then revealed two nearby compound heterozygous SCN9A variants that were computer-predicted to be deleterious.

CONCLUSIONS

These cases newly associate three novel amino acid-substituting SCN9A variants with immunotherapy-responsive neuropathy. Only larger studies can determine whether these are contributory or coincidental, but they associate new variants with moderate or high likelihood of pathogenicity with a new highly related phenotype.

Pharmacological enrichment of polygenic risk for precision medicine in complex disorders

Individuals with complex disorders typically have a heritable burden of common variation that can be expressed as a polygenic risk score (PRS). While PRS has some predictive utility, it lacks the molecular specificity to be directly informative for clinical interventions. We therefore sought to develop a framework to quantify an individual’s common variant enrichment in clinically actionable systems responsive to existing drugs. This was achieved with a metric designated the pharmagenic enrichment score (PES), which we demonstrate for individual SNP profiles in a cohort of cases with schizophrenia. A large proportion of these had elevated PES in one or more of eight clinically actionable gene-sets enriched with schizophrenia associated common variation. Notable candidates targeting these pathways included vitamins, antioxidants, insulin modulating agents, and cholinergic drugs. Interestingly, elevated PES was also observed in individuals with otherwise low common variant burden. The biological saliency of PES profiles were observed directly through their impact on gene expression in a subset of the cohort with matched transcriptomic data, supporting our assertion that this gene-set orientated approach could integrate an individual’s common variant risk to inform personalised interventions, including drug repositioning, for complex disorders such as schizophrenia.

Novel targets for drug discovery in celiac disease

Celiac disease is a lifelong, immunological disorder induced by dietary protein-gluten, in a genetically susceptible populations, resulting in different clinical manifestations, the release of antibodies, and damage to the intestinal mucosa. The only recommended therapy for the disease is to strictly follow a gluten-free diet (GFD), which is difficult to comply with. A GFD is found to be ineffective in some active Celiac disease cases. Therefore, there is an unmet need for an alternative nondietary therapeutic approach. The review focuses on the novel drug targets for Celiac disease.

A flexible and parallelizable approach to genome-wide polygenic risk scores

The heritability of most complex traits is driven by variants throughout the genome. Consequently, polygenic risk scores, which combine information on multiple variants genome-wide, have demonstrated improved accuracy in genetic risk prediction. We present a new two-step approach to constructing genome-wide polygenic risk scores from meta-GWAS summary statistics. Local linkage disequilibrium (LD) is adjusted for in Step 1, followed by, uniquely, long-range LD in Step 2. Our algorithm is highly parallelizable since block-wise analyses in Step 1 can be distributed across a high-performance computing cluster, and flexible, since sparsity and heritability are estimated within each block. Inference is obtained through a formal Bayesian variable selection framework, meaning final risk predictions are averaged over competing models. We compared our method to two alternative approaches: LDPred and lassosum using all seven traits in the Welcome Trust Case Control Consortium as well as meta-GWAS summaries for type 1 diabetes (T1D), coronary artery disease, and schizophrenia. Performance was generally similar across methods, although our framework provided more accurate predictions for T1D, for which there are multiple heterogeneous signals in regions of both short- and long-range LD. With sufficient compute resources, our method also allows the fastest runtimes.

Opioid Addiction, Genetic Susceptibility, and Medical Treatments: A Review

Opioid addiction is a chronic and complex disease characterized by relapse and remission. In the past decade, the opioid epidemic or opioid crisis in the United States has raised public awareness. Methadone, buprenorphine, and naloxone have proven their effectiveness in treating addicted individuals, and each of them has different effects on different opioid receptors. Classic and molecular genetic research has provided valuable information and revealed the possible mechanism of individual differences in vulnerability for opioid addiction. The polygenic risk score based on the results of a genome-wide association study (GWAS) may be a promising tool to evaluate the association between phenotypes and genetic markers across the entire genome. A novel gene editing approach, clustered, regularly-interspaced short palindromic repeats (CRISPR), has been widely used in basic research and potentially applied to human therapeutics such as mental illness; many applications against addiction based on CRISPR are currently under research, and some are successful in animal studies. In this article, we summarized the biological mechanisms of opioid addiction and medical treatments, and we reviewed articles about the genetics of opioid addiction, the promising approach to predict the risk of opioid addiction, and a novel gene editing approach. Further research on medical treatments based on individual vulnerability is needed.

Using game theory and decision decomposition to effectively discern and characterise bi-locus diseases

In order to gain insight into oligogenic disorders, understanding those involving bi-locus variant combinations appears to be key. In prior work, we showed that features at multiple biological scales can already be used to discriminate among two types, i.e. disorders involving true digenic and modifier combinations. The current study expands this machine learning work towards dual molecular diagnosis cases, providing a classifier able to effectively distinguish between these three types. To reach this goal and gain an in-depth understanding of the decision process, game theory and tree decomposition techniques are applied to random forest predictors to investigate the relevance of feature combinations in the prediction. A machine learning model with high discrimination capabilities was developed, effectively differentiating the three classes in a biologically meaningful manner. Combining prediction interpretation and statistical analysis, we propose a biologically meaningful characterization of each class relying on specific feature strengths. Figuring out how biological characteristics shift samples towards one of three classes provides clinically relevant insight into the underlying biological processes as well as the disease itself.

Multiple sclerosis genetics

A broad scientific consensus has emerged linking multiple sclerosis (MS) risk to multiple independent and interacting DNA variants that are relatively frequent in the population and act in concert with environmental exposures. The multifactorial, polygenic model of heritability provided the rationale and impetus to pursue genome-wide association studies (GWAS), which have been highly successful in uncovering genetic variants influencing susceptibility. Over 200 loci have been firmly associated with MS susceptibility. The main association signal genome-wide maps to the major histocompatibility complex ( MHC) gene cluster in chromosome 6p21. This association has been observed across all populations studied. However, a significant proportion of MS heritability remains unexplained. Decoding the genetics of MS represents a long-standing and important research goal in this disease, as the demonstration of even modest functional genomic effects on risk or the course of MS is likely to reveal fundamental disease mechanisms and possibly yield new therapeutic opportunities.

Investigating multiple sclerosis genetic susceptibility on the founder population of east-central Sardinia via association and linkage analysis of immune-related loci

BACKGROUND

A wealth of single-nucleotide polymorphisms (SNPs) responsible for multiple sclerosis (MS) susceptibility have been identified; however, they explain only a fraction of MS heritability.

OBJECTIVES

We contributed to discovery of new MS susceptibility SNPs by studying a founder population with high MS prevalence.

METHODS

We analyzed ImmunoChip data from 15 multiplex families and 94 unrelated controls from the Nuoro Province, Sardinia, Italy. We tested each SNP for both association and linkage with MS, the linkage being explored in terms of identity-by-descent (IBD) sharing excess and using gene dropping to compute a corresponding empirical p-value. By targeting regions that are both associated and in linkage with MS, we increase chances of identifying interesting genomic regions.

RESULTS

We identified 486 MS-associated (p < 1 × 10-4) and 18,426 MS-linked (p < 0.05) SNPs. A total of 111 loci were both linked and associated with MS, 18 of them pointing to 14 non-major histocompatibility complex (MHC) genes, and 93 of them located in the MHC region.

CONCLUSION

We discovered new suggestive signals and confirmed some previously identified ones. We believe this to represent a significant step toward an understanding of the genetic basis of MS.

[Familial multiple sclerosis in Tomsk region]

AIM

Multiple sclerosis (MS) has a multifactorial etiology. To explore a role of genetic factors in the pathogenesis of MS, the authors studied familial cases of MS.

MATERIAL AND METHODS

For an analysis of familial cases, a database of 320 MS patients registered in Tomsk region since 1980 till 2014 was used. The following items for each patient were recorded: disease onset, manifestation of disease, duration of first remission, rate of progression of disease.

RESULTS AND CONCLUSION

Nine families with several members with MS were identified. In 2014, the frequency of familial cases in the MS population of Tomsk region was 4.7%. The prevalence of familial MS was 1.4 cases per 100.000 of people. The younger age at disease onset and higher rate of disease progression measured with the EDSS in parent-child pairs were identified. The most families with several members with MS were characterized by clinical polymorphism of onset, duration and rate of disease progression.

Common DNA Variants Accurately Rank an Individual of Extreme Height

Polygenic scores (or genetic risk scores) quantify the aggregate of small effects from many common genetic loci that have been associated with a trait through genome-wide association. Polygenic scores were first used successfully in schizophrenia and have since been applied to multiple phenotypes including multiple sclerosis, rheumatoid arthritis, and height. Because human height is an easily-measured and complex polygenic trait, polygenic height scores provide exciting insights into the predictability of aggregate common variant effect on the phenotype. Shawn Bradley is an extremely tall former professional basketball player from Brigham Young University and the National Basketball Association (NBA), measuring 2.29 meters (7′6^″, 99.99999th percentile for height) tall, with no known medical conditions. Here, we present a case where a rare combination of common SNPs in one individual results in an extremely high polygenic height score that is correlated with an extreme phenotype. While polygenic scores are not clinically significant in the average case, our findings suggest that for extreme phenotypes, polygenic scores may be more successful for the prediction of individuals.

Using Full Genomic Information to Predict Disease: Breaking Down the Barriers Between Complex and Mendelian Diseases

While sequence-based genetic tests have long been available for specific loci, especially for Mendelian disease, the rapidly falling costs of genome-wide genotyping arrays, whole-exome sequencing, and whole-genome sequencing are moving us toward a future where full genomic information might inform the prognosis and treatment of a variety of diseases, including complex disease. Similarly, the availability of large populations with full genomic information has enabled new insights about the etiology and genetic architecture of complex disease. Insights from the latest generation of genomic studies suggest that our categorization of diseases as complex may conceal a wide spectrum of genetic architectures and causal mechanisms that ranges from Mendelian forms of complex disease to complex regulatory structures underlying Mendelian disease. Here, we review these insights, along with advances in the prediction of disease risk and outcomes from full genomic information.

Metabolic defects in multiple sclerosis

Brain injuries in multiple sclerosis (MS) involve immunopathological, structural and metabolic defects on myelin sheath, oligodendrocytes (OLs), axons and neurons suggesting that different cellular mechanisms ultimately result in the formation of MS plaques, demyelination, inflammation and brain damage. Bioenergetics, oxygen and ion metabolism dominate the metabolic and biochemical pathways that maintain neuronal viability and impulse transmission which directly or indirectly point to mitochondrial integrity and adenosine triphosphate (ATP) availability indicating the involvement of mitochondria in the pathogenesis of MS. Loss of myelin proteins including myelin basic protein (MBP), proteolipid protein (PLP), myelin associated glycoprotein (MAG), myelin oligodendrocyte glycoproetin (MOG), 2, 3,-cyclic nucleotide phosphodiestarase (CNPase); microglia and microphage activation, oligodendrocyte apoptosis as well as expression of inducible nitric oxide synthase (i-NOS) and myeloperoxidase activities have been implicated in a subset of Balo's type and relapsing remitting MS (RRMS) lesions indicating the involvement of metabolic defects and oxidative stress in MS. Here, we provide an insighting review of defects in cellular metabolism including energy, oxygen and metal metabolism in MS as well as the relevance of animal models of MS in understanding the molecular, biochemical and cellular mechanisms of MS pathogenesis. Additionally, we also discussed the potential for mitochondrial targets and antioxidant protection for therapeutic benefits in MS.

Pharmacogenetics of glatiramer acetate therapy for multiple sclerosis: the impact of genome-wide association studies identified disease risk loci

AIM

Association analysis of genome-wide association studies (GWAS) identified multiple sclerosis (MS) risk genetic variants with glatiramer acetate (GA) treatment efficacy.

PATIENTS & METHODS

SNPs in 17 GWAS-identified immune response loci were analyzed in 296 Russian MS patients as possible markers of optimal GA treatment response for at least 2 years.

RESULTS

Alleles/genotypes of EOMES, CLEC16A, IL22RA2, PVT1 and HLA-DRB1 were associated by themselves with event-free phenotype during GA treatment for at least 2 years (p _f  = 0.032 - 0.00092). The biallelic combinations including EOMES, CLEC16A, IL22RA2, PVT1, TYK2, CD6, IL7RA and IRF8 genes were associated with response to GA with increased significance level (p _f  = 0.0060 - 1.1 × 10^-5). The epistasic interactions or additive effects were observed between the components of the identified biallelic combinations.

CONCLUSION

We pinpointed the involvement of several GWAS-identified MS risk loci in GA therapy efficacy. These findings may be aggregated to predict the optimal GA response in MS patients.

Fourteen sequence variants that associate with multiple sclerosis discovered by meta-analysis informed by genetic correlations

A meta-analysis of publicly available summary statistics on multiple sclerosis combined with three Nordic multiple sclerosis cohorts (21,079 cases, 371,198 controls) revealed seven sequence variants associating with multiple sclerosis, not reported previously. Using polygenic risk scores based on public summary statistics of variants outside the major histocompatibility complex region we quantified genetic overlap between common autoimmune diseases in Icelanders and identified disease clusters characterized by autoantibody presence/absence. As multiple sclerosis-polygenic risk scores captures the risk of primary biliary cirrhosis and vice versa (P = 1.6 × 10⁻⁷, 4.3 × 10⁻⁹) we used primary biliary cirrhosis as a proxy-phenotype for multiple sclerosis, the idea being that variants conferring risk of primary biliary cirrhosis have a prior probability of conferring risk of multiple sclerosis. We tested 255 variants forming the primary biliary cirrhosis-polygenic risk score and found seven multiple sclerosis-associating variants not correlated with any previously established multiple sclerosis variants. Most of the variants discovered are close to or within immune-related genes. One is a low-frequency missense variant in TYK2, another is a missense variant in MTHFR that reduces the function of the encoded enzyme affecting methionine metabolism, reported to be dysregulated in multiple sclerosis brain.

Combining studies and comparing across diseases turned up 14 novel gene variants linked to multiple sclerosis (MS). A team led by Kári Stefánsson and Ingileif Jónsdóttir from deCODE genetics in Reykjavík, Iceland, amalgamated data from a large international study of MS with three smaller ones from Sweden, Norway and Iceland. They conducted a meta-analysis on the combined data set — which encompassed around 21,000 MS patients and 372,000 population controls — and uncovered seven new genetic risk variants linked to MS. The researchers then compared the genetic overlap between various autoimmune diseases in the Icelandic cohort, and documented a close relationship between MS and primary biliary cirrhosis (PBC). They looked more closely at variants linked to PBC, and found that seven also increased the risk for MS, bringing the tally of novel gene variants up to fourteen.

A pharmacogenetic signature of high response to Copaxone in late-phase clinical-trial cohorts of multiple sclerosis

Background

Copaxone is an efficacious and safe therapy that has demonstrated clinical benefit for over two decades in patients with relapsing forms of multiple sclerosis (MS). On an individual level, patients show variability in their response to Copaxone, with some achieving significantly higher response levels. The involvement of genes (e.g., HLA-DRB1*1501) with high inter-individual variability in Copaxone’s mechanism of action (MoA) suggests the potential contribution of genetics to treatment response. This study aimed to identify genetic variants associated with Copaxone response in patient cohorts from late-phase clinical trials.

Methods

Single nucleotide polymorphisms (SNPs) associated with high and low levels of response to Copaxone were identified using genome-wide SNP data in a discovery cohort of 580 patients from two phase III clinical trials of Copaxone. Multivariable Bayesian modeling on the resulting SNPs in an expanded discovery cohort with 1171 patients identified a multi-SNP signature of Copaxone response. This signature was examined in 941 Copaxone-treated MS patients from seven independent late-phase trials of Copaxone and assessed for specificity to Copaxone in 310 Avonex-treated and 311 placebo-treated patients, also from late-phase trials.

Results

A four-SNP signature consisting of rs80191572 (in UVRAG), rs28724893 (in HLA-DQB2), rs1789084 (in MBP), and rs139890339 (in ZAK(CDCA7)) was identified as significantly associated with Copaxone response. Copaxone-treated signature-positive patients had a greater reduction in annualized relapse rate (ARR) compared to signature-negative patients in both discovery and independent cohorts, an effect not observed in Avonex-treated patients. Additionally, signature-positive placebo-treated cohorts did not show a reduction in ARR, demonstrating the predictive as opposed to prognostic nature of the signature. A 10% subset of patients, delineated by the signature, showed marked improvements across multiple clinical parameters, including ARR, MRI measures, and higher proportion with no evidence of disease activity (NEDA).

Conclusions

This study is the largest pharmacogenetic study in MS reported to date. Gene regions underlying the four-SNP signature have been linked with pathways associated with either Copaxone’s MoA or the pathophysiology of MS. The pronounced association of the four-SNP signature with clinical improvements in a ~10% subset of the MS patient population demonstrates the complex interplay of immune mechanisms and the individualized nature of response to Copaxone.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-017-0436-y) contains supplementary material, which is available to authorized users.

Human Demographic History Impacts Genetic Risk Prediction across Diverse Populations

The vast majority of genome-wide association studies (GWASs) are performed in Europeans, and their transferability to other populations is dependent on many factors (e.g., linkage disequilibrium, allele frequencies, genetic architecture). As medical genomics studies become increasingly large and diverse, gaining insights into population history and consequently the transferability of disease risk measurement is critical. Here, we disentangle recent population history in the widely used 1000 Genomes Project reference panel, with an emphasis on populations underrepresented in medical studies. To examine the transferability of single-ancestry GWASs, we used published summary statistics to calculate polygenic risk scores for eight well-studied phenotypes. We identify directional inconsistencies in all scores; for example, height is predicted to decrease with genetic distance from Europeans, despite robust anthropological evidence that West Africans are as tall as Europeans on average. To gain deeper quantitative insights into GWAS transferability, we developed a complex trait coalescent-based simulation framework considering effects of polygenicity, causal allele frequency divergence, and heritability. As expected, correlations between true and inferred risk are typically highest in the population from which summary statistics were derived. We demonstrate that scores inferred from European GWASs are biased by genetic drift in other populations even when choosing the same causal variants and that biases in any direction are possible and unpredictable. This work cautions that summarizing findings from large-scale GWASs may have limited portability to other populations using standard approaches and highlights the need for generalized risk prediction methods and the inclusion of more diverse individuals in medical genomics.

Human Demographic History Impacts Genetic Risk Prediction across Diverse Populations

The vast majority of genome-wide association studies (GWASs) are performed in Europeans, and their transferability to other populations is dependent on many factors (e.g., linkage disequilibrium, allele frequencies, genetic architecture). As medical genomics studies become increasingly large and diverse, gaining insights into population history and consequently the transferability of disease risk measurement is critical. Here, we disentangle recent population history in the widely used 1000 Genomes Project reference panel, with an emphasis on populations underrepresented in medical studies. To examine the transferability of single-ancestry GWASs, we used published summary statistics to calculate polygenic risk scores for eight well-studied phenotypes. We identify directional inconsistencies in all scores; for example, height is predicted to decrease with genetic distance from Europeans, despite robust anthropological evidence that West Africans are as tall as Europeans on average. To gain deeper quantitative insights into GWAS transferability, we developed a complex trait coalescent-based simulation framework considering effects of polygenicity, causal allele frequency divergence, and heritability. As expected, correlations between true and inferred risk are typically highest in the population from which summary statistics were derived. We demonstrate that scores inferred from European GWASs are biased by genetic drift in other populations even when choosing the same causal variants and that biases in any direction are possible and unpredictable. This work cautions that summarizing findings from large-scale GWASs may have limited portability to other populations using standard approaches and highlights the need for generalized risk prediction methods and the inclusion of more diverse individuals in medical genomics.

Secondary phenotype analysis in ascertained family designs: application to the Leiden longevity study

The case-control design is often used to test associations between the case-control status and genetic variants. In addition to this primary phenotype, a number of additional traits, known as secondary phenotypes, are routinely recorded, and typically, associations between genetic factors and these secondary traits are studied too. Analysing secondary phenotypes in case-control studies may lead to biased genetic effect estimates, especially when the marker tested is associated with the primary phenotype and when the primary and secondary phenotypes tested are correlated. Several methods have been proposed in the literature to overcome the problem, but they are limited to case-control studies and not directly applicable to more complex designs, such as the multiple-cases family studies. A proper secondary phenotype analysis, in this case, is complicated by the within families correlations on top of the biased sampling design. We propose a novel approach to accommodate the ascertainment process while explicitly modelling the familial relationships. Our approach pairs existing methods for mixed-effects models with the retrospective likelihood framework and uses a multivariate probit model to capture the association between the mixed type primary and secondary phenotypes. To examine the efficiency and bias of the estimates, we performed simulations under several scenarios for the association between the primary phenotype, secondary phenotype and genetic markers. We will illustrate the method by analysing the association between triglyceride levels and glucose (secondary phenotypes) and genetic markers from the Leiden Longevity Study, a multiple-cases family study that investigates longevity. © 2017 The Authors. Statistics in Medicine Published by JohnWiley & Sons Ltd.

Polygenic risk assessment reveals pleiotropy between sarcoidosis and inflammatory disorders in the context of genetic ancestry

Sarcoidosis is a complex disease of unknown etiology characterized by the presence of granulomatous inflammation. Though various immune system pathways have been implicated in disease, the relationship between the genetic determinants of sarcoidosis and other inflammatory disorders has not been characterized. Herein, we examined the degree of genetic pleiotropy common to sarcoidosis and other inflammatory disorders to identify shared pathways and disease systems pertinent to sarcoidosis onset. To achieve this, we quantify the association of common variant polygenic risk scores from nine complex inflammatory disorders with sarcoidosis risk. Enrichment analyses of genes implicated in pleiotropic associations were further used to elucidate candidate pathways. In European-Americans, we identify significant pleiotropy between risk of sarcoidosis and risk of asthma (R²=2.03%; p=8.89×10⁻⁹), celiac disease (R²=2.03%; p=8.21×10⁻⁹), primary biliary cirrhosis (R²=2.43%; p=2.01×10⁻¹⁰), and rheumatoid arthritis (R²=4.32%; p=2.50×10⁻¹⁷). These associations validate in African Americans only after accounting for the proportion of genome-wide European ancestry, where we demonstrate similar effects of polygenic risk for African-Americans with the highest levels of European ancestry. Variants and genes implicated in European-American pleiotropic associations were enriched for pathways involving interleukin-12, interleukin-27, and cell adhesion molecules, corroborating the hypothesized immunopathogenesis of disease.

Does the Gut Microbiota Influence Immunity and Inflammation in Multiple Sclerosis Pathophysiology?

Aim. Evaluation of the impact of gut microflora on the pathophysiology of MS. Results. The etiopathogenesis of MS is not fully known. Gut microbiota may be of a great importance in the pathogenesis of MS, since recent findings suggest that substitutions of certain microbial population in the gut can lead to proinflammatory state, which can lead to MS in humans. In contrast, other commensal bacteria and their antigenic products may protect against inflammation within the central nervous system. The type of intestinal flora is affected by antibiotics, stress, or diet. The effects on MS through the intestinal microflora can also be achieved by antibiotic therapy and Lactobacillus. EAE, as an animal model of MS, indicates a strong influence of the gut microbiota on the immune system and shows that disturbances in gut physiology may contribute to the development of MS. Conclusions. The relationship between the central nervous system, the immune system, and the gut microbiota relates to the influence of microorganisms in the development of MS. A possible interaction between gut microbiota and the immune system can be perceived through regulation by the endocannabinoid system. It may offer an opportunity to understand the interaction comprised in the gut-immune-brain axis.

Correlation of geographic distributions of haptoglobin alleles with prevalence of multiple sclerosis (MS) - a narrative literature review

We have proposed that the myelin damage observed in multiple sclerosis (MS) may be partly mediated through the long-term release and degradation of extracellular hemoglobin (Hb) and the products of its oxidative degradation [Cellular and Molecular Life Sciences, 71, 1789-1798, 2014]. The protein haptoglobin (Hpt) binds extracellular Hb as a first line of defense, and can serve as a vascular antioxidant. Humans have two different Hpt alleles: Hpt1 and Hpt2, giving either homozygous Hpt1-1 or Hpt2-2 phenotypes, or a heterozygous Hpt1-2 phenotype. We questioned whether those geographic regions with higher frequency of the Hpt2 allele (conversely, lower frequency of Hpt1 allele) would correlate with an increased incidence of MS, because different Hpt phenotypes will have variable anti-oxidative potentials in protecting myelin from damage inflicted by extracellular Hb and its degradation products. To test this hypothesis, we undertook a systematic analysis of the literature on reported geographic distributions of Hpt alleles to compare them with data reported in the World Health Organization Atlas of worldwide MS prevalence. We found the frequency of the Hpt1 allele to be low in European and North American countries with a high prevalence of MS, consistent with our hypothesis. However, this correlation was not observed in China and India, countries with the lowest Hpt1 frequencies, yet low reported prevalence of MS. Nevertheless, this work shows the need for continued refinement of geographic patterns of MS prevalence, including data on ethnic or racial origin, and for new clinical studies to probe the observed correlation and evaluate Hpt phenotype as a predictor of disease variability and progression, severity, and/or comorbidity with cardiovascular disorders.

Genetic susceptibility to multiple sclerosis: Brain structure and cognitive function in the general population

BACKGROUND

Multiple sclerosis (MS) affects brain structure and cognitive function and has a heritable component. Over a 100 common genetic risk variants have been identified, but most carriers do not develop MS. For other neurodegenerative diseases, risk variants have effects outside patient populations, but this remains uninvestigated for MS.

OBJECTIVES

To study the effect of MS-associated genetic variants on brain structure and cognitive function in the general population.

METHODS

We studied middle-aged and elderly individuals (mean age = 65.7 years) from the population-based Rotterdam Study. We determined 107 MS variants and additionally created a risk score combining all variants. Magnetic resonance imaging ( N = 4710) was performed to obtain measures of brain macrostructure, white matter microstructure, and gray matter voxel-based morphometry. A cognitive test battery ( N = 7556) was used to test a variety of cognitive domains.

RESULTS

The MS risk score was associated with smaller gray matter volume over the whole brain (β_standardized = -0.016; p = 0.044), but region-specific analyses did not survive multiple testing correction. Similarly, no significant associations with brain structure were observed for individual variants. For cognition, rs2283792 was significantly associated with poorer memory (β = -0.064; p = 3.4 × 10^-5).

CONCLUSION

Increased genetic susceptibility to MS may affect brain structure and cognition in persons without disease, pointing to a "hidden burden" of MS.

PoCos: Population Covering Locus Sets for Risk Assessment in Complex Diseases

Susceptibility loci identified by GWAS generally account for a limited fraction of heritability. Predictive models based on identified loci also have modest success in risk assessment and therefore are of limited practical use. Many methods have been developed to overcome these limitations by incorporating prior biological knowledge. However, most of the information utilized by these methods is at the level of genes, limiting analyses to variants that are in or proximate to coding regions. We propose a new method that integrates protein protein interaction (PPI) as well as expression quantitative trait loci (eQTL) data to identify sets of functionally related loci that are collectively associated with a trait of interest. We call such sets of loci “population covering locus sets” (PoCos). The contributions of the proposed approach are three-fold: 1) We consider all possible genotype models for each locus, thereby enabling identification of combinatorial relationships between multiple loci. 2) We develop a framework for the integration of PPI and eQTL into a heterogenous network model, enabling efficient identification of functionally related variants that are associated with the disease. 3) We develop a novel method to integrate the genotypes of multiple loci in a PoCo into a representative genotype to be used in risk assessment. We test the proposed framework in the context of risk assessment for seven complex diseases, type 1 diabetes (T1D), type 2 diabetes (T2D), psoriasis (PS), bipolar disorder (BD), coronary artery disease (CAD), hypertension (HT), and multiple sclerosis (MS). Our results show that the proposed method significantly outperforms individual variant based risk assessment models as well as the state-of-the-art polygenic score. We also show that incorporation of eQTL data improves the performance of identified POCOs in risk assessment. We also assess the biological relevance of PoCos for three diseases that have similar biological mechanisms and identify novel candidate genes. The resulting software is publicly available at http://compbio.case.edu/pocos/.

Several studies try to predict the individual disease risk using genetic data obtained from genome wide association studies (GWAS). Earlier studies only focus on individual genetic variants. However, studies on disease mechanisms suggest the aggregation of genomic variants may contribute to diseases. For this reason, researchers commonly use prior biological knowledge to identify genetic variants that are functionally related. However, these approaches are often limited to variants that are in the coding regions of genes. However, several risk variants are in the regulatory region. Here, we incorporate known regulatory and functional interactions to find sets of genetic variants which are informative features for risk assessment. Our result on seven complex diseases show that our method outperforms individual variant based risk assessment models, as well as other methods that integrate multiple genetic variants.

Analysis of Heritability Using Genome-Wide Data

Most analyses of genome-wide association data consider each variant independently without considering or adjusting for the genetic background present in the rest of the genome. New approaches to genome analysis use representations of genomic sharing to better account for confounding factors like population stratification or to directly approximate heritability through the estimated sharing of individuals in a dataset. These approaches use mixed linear models, which relate genotypic sharing to phenotypic sharing, and rely on the efficient computation of genetic sharing among individuals in a dataset. This unit describes the principles and practical application of mixed models for the analysis of genome-wide association study data. © 2016 by John Wiley & Sons, Inc.

Genetics and neuropsychology: A merger whose time has come

Genetics and neuropsychology have historically been 2 rather distant and unrelated fields. With the very rapid advances that have been taking place in genetics, research and treatment of disorders of cognition in the 21st century are likely to be increasingly informed by individual differences in genetics and epigenetics. Although neuropsychologists are not expected to become geneticists, it is our view that increased training in genetics should become more central to training in neuropsychology. This relationship should not be unidirectional. Here we note ways in which an understanding of genetics and epigenetics can inform neuropsychology. On the other hand, given the complexity of cognitive phenotypes, neuropsychology can also play a valuable role in informing and refining genetic studies. Greater integration of the 2 should advance both fields.

Epstein-Barr virus in multiple sclerosis

BACKGROUND/OBJECTIVES

Multiple sclerosis (MS) is an inflammatory disorder of the central nervous system. Many diseases are associated with Epstein-Barr virus (EBV) infection, such as infectious mononucleosis and many types of malignancies, and it is thought to be related to some diseases of autoimmune origin, such as rheumatoid arthritis, systemic lupus erythematosis, and others. The present study aimed to assess EBV in patients with MS.

PATIENTS AND METHODS

This case-control study was conducted from October 2012 to September 2013 on 75 MS patients and non-MS controls. Both were tested quantitatively for immunoglobulin G (IgG) antibodies against Epstein-Barr nuclear antigen-1 (EBNA1) and viral capsid antigen (VCA) using the enzyme linked immunosorbent assay technique.

RESULTS

Seventy MS patients (93.3%) were positive for EBNA1 IgG compared with 68 controls (90.7%). In MS patients, the mean EBNA1 IgG serum level was 310.91 (±131.05) U/ml; meanwhile, among controls the mean serum EBNA IgG level was 177.81 (±104.98) U/ml.All patients with MS were positive for VCA IgG, whereas only 60 (80.0%) controls were positive. In the MS group, the VCA IgG mean level was 302.19 (±152.11) U/ml compared with 167.94 (±111.79) U/ml in controls. The differences in the serum levels of both markers between the two groups were statistically significant (P<0.001).

CONCLUSION AND RECOMMENDATIONS

EBV proved to have a unique immunological pattern in MS patients when compared with non-MS controls. Further studies for more confirmation of the relation between EBV and MS on a large scale are recommended.

Emerging Concepts on the Gut Microbiome and Multiple Sclerosis

Microbiota of the human body perform fundamental tasks that contribute to normal development, health, and homeostasis and are intimately associated with numerous organ systems, including the gut. Microbes begin gut inhabitance immediately following birth and promote proper gut epithelial construction and function, metabolism and nutrition, and immune system development. Inappropriate immune recognition of self-tissue can lead to autoimmune disease, including conditions such as multiple sclerosis (MS), in which the immune system recognizes and attacks central nervous system tissue. Preclinical studies have demonstrated a requirement of gut microbiota for neuroinflammatory autoimmune disease in animal models, and a growing number of clinical investigations are finding associations between MS status and the composition of the gut microbiota. In this review, we examine current undertakings into better understanding the role of gut bacteria and their phages in MS development, review associations of the gut microbiota makeup and MS, and discuss potential mechanisms by which the gut microbiota may be manipulated for therapeutic benefit.