G-protein-coupled receptors and asthma endophenotypes: the cysteinyl leukotriene system in perspective

Genetic variation in specific G-protein coupled receptors (GPCRs) is associated with a spectrum of respiratory disease predispositions and drug response phenotypes. Although certain GPCR gene variants can be disease-causing through the expression of inactive, overactive, or constitutively active receptor proteins, many more GPCR gene variants confer risk for potentially deleterious endophenotypes. Endophenotypes are traits, such as bronchiole hyperactivity, atopy, and aspirin intolerant asthma, which have a strong genetic component and are risk factors for a variety of more complex outcomes that may include disease states. GPCR genes implicated in asthma endophenotypes include variants of the cysteinyl leukotriene receptors (CYSLTR1 and CYSLTR2), and prostaglandin D2 receptors (PTGDR and CRTH2), thromboxane A2 receptor (TBXA2R), beta2-adrenergic receptor (ADRB2), chemokine receptor 5 (CCR5), and the G protein-coupled receptor associated with asthma (GPRA). This review of the contribution of variability in these genes places the contribution of the cysteinyl leukotriene system to respiratory endophenotypes in perspective. The genetic variant(s) of receptors that are associated with endophenotypes are discussed in the context of the extent to which they contribute to a disease phenotype or altered drug efficacy.     

Can Genetic Analysis of Putative Blood Alzheimer’s Disease Biomarkers Lead to Identification of Susceptibility Loci?

Although 24 Alzheimer’s disease (AD) risk loci have been reliably identified, a large portion of the predicted heritability for AD remains unexplained. It is expected that additional loci of small effect will be identified with an increased sample size. However, the cost of a significant increase in Case-Control sample size is prohibitive. The current study tests whether exploring the genetic basis of endophenotypes, in this case based on putative blood biomarkers for AD, can accelerate the identification of susceptibility loci using modest sample sizes. Each endophenotype was used as the outcome variable in an independent GWAS. Endophenotypes were based on circulating concentrations of proteins that contributed significantly to a published blood-based predictive algorithm for AD. Endophenotypes included Monocyte Chemoattractant Protein 1 (MCP1), Vascular Cell Adhesion Molecule 1 (VCAM1), Pancreatic Polypeptide (PP), Beta2 Microglobulin (B2M), Factor VII (F7), Adiponectin (ADN) and Tenascin C (TN-C). Across the seven endophenotypes, 47 SNPs were associated with outcome with a p-value ≤1x10^-7. Each signal was further characterized with respect to known genetic loci associated with AD. Signals for several endophenotypes were observed in the vicinity of CR1, MS4A6A/MS4A4E, PICALM, CLU, and PTK2B. The strongest signal was observed in association with Factor VII levels and was located within the F7 gene. Additional signals were observed in MAP3K13, ZNF320, ATP9B and TREM1. Conditional regression analyses suggested that the SNPs contributed to variation in protein concentration independent of AD status. The identification of two putatively novel AD loci (in the Factor VII and ATP9B genes), which have not been located in previous studies despite massive sample sizes, highlights the benefits of an endophenotypic approach for resolving the genetic basis for complex diseases. The coincidence of several of the endophenotypic signals with known AD loci may point to novel genetic interactions and should be further investigated.     

Increased 90-kDa ribosomal S6 kinase (Rsk) activity is protective against mutant huntingtin toxicity

Uncovering the underlying genetic component of any disease is key to the understanding of its pathophysiology and may open new avenues for development of therapeutic strategies and biomarkers. In the past several years, there has been an explosion of genome-wide association studies (GWAS) resulting in the discovery of novel candidate genes conferring risk for complex diseases, including neurodegenerative diseases. Despite this success, there still remains a substantial genetic component for many complex traits and conditions that is unexplained by the GWAS findings. Additionally, in many cases, the mechanism of action of the newly discovered disease risk variants is not inherently obvious. Furthermore, a genetic region with multiple genes may be identified via GWAS, making it difficult to discern the true disease risk gene. Several alternative approaches are proposed to overcome these potential shortcomings of GWAS, including the use of quantitative, biologically relevant phenotypes. Gene expression levels represent an important class of endophenotypes. Genetic linkage and association studies that utilize gene expression levels as endophenotypes determined that the expression levels of many genes are under genetic influence. This led to the postulate that there may exist many genetic variants that confer disease risk via modifying gene expression levels. Results from the handful of genetic studies which assess gene expression level endophenotypes in conjunction with disease risk suggest that this combined phenotype approach may both increase the power for gene discovery and lead to an enhanced understanding of their mode of action. This review summarizes the evidence in support of gene expression levels as promising endophenotypes in the discovery and characterization of novel candidate genes for complex diseases, which may also represent a novel approach in the genetic studies of Alzheimer's and other neurodegenerative diseases.     

Construction of the model for the Genetic Analysis Workshop 14 simulated data: genotype-phenotype relationships,gene interaction,linkage, association,disequilibrium, and ascertainment effects for a complex phenotype

The Genetic Analysis Workshop 14 simulated dataset was designed 1) To test the ability to find genes related to a complex disease (such as alcoholism). Such a disease may be given a variety of definitions by different investigators, have associated endophenotypes that are common in the general population, and is likely to be not one disease but a heterogeneous collection of clinically similar, but genetically distinct, entities. 2) To observe the effect on genetic analysis and gene discovery of a complex set of gene x gene interactions. 3) To allow comparison of microsatellite vs. large-scale single-nucleotide polymorphism (SNP) data. 4) To allow testing of association to identify the disease gene and the effect of moderate marker x marker linkage disequilibrium. 5) To observe the effect of different ascertainment/disease definition schemes on the analysis. Data was distributed in two forms. Data distributed to participants contained about 1,000 SNPs and 400 microsatellite markers. Internet-obtainable data consisted of a finer 10,000 SNP map, which also contained data on controls. While disease characteristics and parameters were constant, four "studies" used varying ascertainment schemes based on differing beliefs about disease characteristics. One of the studies contained multiplex two- and three-generation pedigrees with at least four affected members. The simulated disease was a psychiatric condition with many associated behaviors (endophenotypes), almost all of which were genetic in origin. The underlying disease model contained four major genes and two modifier genes. The four major genes interacted with each other to produce three different phenotypes, which were themselves heterogeneous. The population parameters were calibrated so that the major genes could be discovered by linkage analysis in most datasets. The association evidence was more difficult to calibrate but was designed to find statistically significant association in 50% of datasets. We also simulated some marker x marker linkage disequilibrium around some of the genes and also in areas without disease genes. We tried two different methods to simulate the linkage disequilibrium.     

Whole-genome association studies on alcoholism comparing different phenotypes using single-nucleotide polymorphisms and microsatellites

Alcoholism is a complex disease. As with other common diseases, genetic variants underlying alcoholism have been illusive, possibly due to the small effect from each individual susceptible variant, gene x environment and gene x gene interactions and complications in phenotype definition. We conducted association tests, the family-based association tests (FBAT) and the backward haplotype transmission association (BHTA), on the Collaborative Study of the Genetics of Alcoholism (COGA) data provided by Genetic Analysis Workshop (GAW) 14. Efron's local false discovery rate method was applied to control the proportion of false discoveries. For FBAT, we compared the results based on different types of genetic markers (single-nucleotide polymorphisms (SNPs) versus microsatellites) and different phenotype definitions (clinical diagnoses versus electrophysiological phenotypes). Significant association results were found only between SNPs and clinical diagnoses. In contrast, significant results were found only between microsatellites and electrophysiological phenotypes. In addition, we obtained the association results for SNPs and microsatellites using COGA diagnosis as phenotype based on BHTA. In this case, the results for SNPs and microsatellites are more consistent. Compared to FBAT, more significant markers are detected with BHTA.     

Personality endophenotypes for bipolar affective disorder: a family-based genetic association analysis

Genetic analyses of complex conditions such as bipolar disorder (BD) may be facilitated by the use of intermediate phenotypes. Various personality traits are overrepresented in people with BD and their unaffected relatives, and may constitute genetically transmitted risk factors or endophenotypes of the illness. In this study, we administered a battery of seven different personality questionnaires comprising 19 subscales to 31 Caucasian BD families (n = 241). Ten of these personality traits showed significant evidence of heritability and were therefore selected as candidate endophenotypes. In addition, a principal components analysis produced two heritable components (negative affect and appetitive drive), which accounted for a considerable proportion of the variance (29% + 12%) and were also used in the analysis. A family-based quantitative association study was carried out using the orthogonal model from the quantitative transmission disequilibrium tests (QTDT) program. Monte Carlo permutations (M = 500), which allow for non-normal data and provide a global P value, corrected for multiple testing, were used to calculate empirical P values for the within-family component of association. The 3' untranslated region repeat polymorphism of the dopamine transporter gene (SLC6A3) was associated with self-directedness (P < 0.0001) and negative affect (P = 0.010). The short allele of the serotonin transporter gene (SLC6A4) promoter polymorphism showed a trend toward association with higher harm avoidance (P = 0.016) and negative affect (P = 0.028). The catechol-o-methyltransferase val158met polymorphism was weakly associated with the personality traits, 'Spirituality' (P = 0.040) and irritable temperament (P = 0.022). Furthermore, the met allele of the brain-derived neurotrophic factor val66met polymorphism was associated with higher hyperthymic temperament scores. We raise the possibility that the 10R allele of the SLC6A3 repeat polymorphism and the short allele of the SLC6A4 promoter variant constitute risk factors for irritable-aggressive and anxious-dysthymic subtypes of BD, respectively.     

Testing and estimating gene-environment interactions in family-based association studies

Summary .   We propose robust and efficient tests and estimators for gene–environment/gene–drug interactions in family-based association studies in which haplotypes, dichotomous/quantitative phenotypes, and complex exposure/treatment variables are analyzed. Using causal inference methodology, we show that the tests and estimators are robust against unmeasured confounding due to population admixture and stratification, provided that Mendel's law of segregation holds and that the considered exposure/treatment variable is not affected by the candidate gene under study. We illustrate the practical relevance of our approach by an application to a chronic obstructive pulmonary disease study. The data analysis suggests a gene–environment interaction between a single nucleotide polymorphism in the Serpine2 gene and smoking status/pack-years of smoking. Simulation studies show that the proposed methodology is sufficiently powered for realistic sample sizes and that it provides valid tests and effect size estimators in the presence of admixture and stratification.     

Genomics and genome-wide association studies: an integrative approach to expression QTL mapping

Expression QTL mapping by integrating genome-wide gene expression and genotype data is a promising approach to identifying functional genetic variation, but is hampered by the large number of multiple comparisons inherent in such studies. A novel approach to addressing multiple testing problems in genome-wide family-based association studies is screening candidate markers using heritability or conditional power. We apply these methods in settings in which microarray gene expression data are used as phenotypes, screening for SNPs near the expressed genes. We perform association analyses for phenotypes using a univariate approach. We also perform simulations on trios with large numbers of causal SNPs to determine the optimal number of markers to use in a screen. We demonstrate that our family-based screening approach performs well in the analysis of integrative genomic datasets and that screening using either heritability or conditional power produces similar, though not identical, results.     

Potential cognitive endophenotypes in multigenerational families: segregating ADHD from a genetic isolate

Endophenotypes are neurobiological markers cosegregating and associated with illness. These biomarkers represent a promising strategy to dissect ADHD biological causes. This study was aimed at contrasting the genetics of neuropsychological tasks for intelligence, attention, memory, visual-motor skills, and executive function in children from multigenerational and extended pedigrees that cluster ADHD in a genetic isolate. In a sample of 288 children and adolescents, 194 (67.4%) ADHD affected and 94 (32.6%) unaffected, a battery of neuropsychological tests was utilized to assess the association between genetic transmission and the ADHD phenotype. We found significant differences between affected and unaffected children in the WISC block design, PIQ and FSIQ, continuous vigilance, and visual-motor skills, and these variables exhibited a significant heritability. Given the association between these neuropsychological variables and ADHD, and also the high genetic component underlying their transmission in the studied pedigrees, we suggest that these variables be considered as potential cognitive endophenotypes suitable as quantitative trait loci (QTLs) in future studies of linkage and association.     

Accounting for linkage in family-based tests of association with missing parental genotypes

In studies of complex diseases, a common paradigm is to conduct association analysis at markers in regions identified by linkage analysis, to attempt to narrow the region of interest. Family-based tests for association based on parental transmissions to affected offspring are often used in fine-mapping studies. However, for diseases with late onset, parental genotypes are often missing. Without parental genotypes, family-based tests either compare allele frequencies in affected individuals with those in their unaffected siblings or use siblings to infer missing parental genotypes. An example of the latter approach is the score test implemented in the computer program TRANSMIT. The inference of missing parental genotypes in TRANSMIT assumes that transmissions from parents to affected siblings are independent, which is appropriate when there is no linkage. However, using computer simulations, we show that, when the marker and disease locus are linked and the data set consists of families with multiple affected siblings, this assumption leads to a bias in the score statistic under the null hypothesis of no association between the marker and disease alleles. This bias leads to an inflated type I error rate for the score test in regions of linkage. We present a novel test for association in the presence of linkage (APL) that correctly infers missing parental genotypes in regions of linkage by estimating identity-by-descent parameters, to adjust for correlation between parental transmissions to affected siblings. In simulated data, we demonstrate the validity of the APL test under the null hypothesis of no association and show that the test can be more powerful than the pedigree disequilibrium test and family-based association test. As an example, we compare the performance of the tests in a candidate-gene study in families with Parkinson disease.     

Mapping disease genes: family-based association studies.

With recent rapid advances in mapping of the human genome, including highly polymorphic and closely linked markers, studies of marker associations with disease are increasingly relevant for mapping disease genes. The use of nuclear-family data in association studies was initially developed to avoid possible ethnic mismatching between patients and randomly ascertained controls. The parental marker alleles not transmitted to an affected child or never transmitted to an affected sib pair form the so-called AFBAC (affected family-based controls) population. In this paper, the theoretical foundation of the AFBAC method is proved for any single-locus model of disease and for any nuclear family-based ascertainment scheme. In a random-mating population, when there is a marker association with disease, the AFBAC population provides an unbiased estimate of the overall population (control) marker alleles when the recombination fraction (theta) between the marker and disease genes is sufficiently small that it can be taken as zero (theta = 0). With population stratification, only marker associations present in the subpopulations will be detected with family-based analyses. Of more importance, however, is the fact that, when theta not equal to 0, differences between transmitted parental (patient) marker allele frequencies and non- or never-transmitted parental marker allele frequencies (implying a marker association with disease) can only be observed for marker genes linked to a disease gene (theta < 1/2). Thus, associations of unlinked marker loci with disease at the population level, caused by population stratification, migration, or admixture, are eliminated. This validates the use of family-based association tests as an appropriate strategy for mapping disease genes.     

A Gaussian copula approach for the analysis of secondary phenotypes in case-control genetic association studies

In many case-control genetic association studies, a set of correlated secondary phenotypes that may share common genetic factors with disease status are collected. Examination of these secondary phenotypes can yield valuable insights about the disease etiology and supplement the main studies. However, due to unequal sampling probabilities between cases and controls, standard regression analysis that assesses the effect of SNPs (single nucleotide polymorphisms) on secondary phenotypes using cases only, controls only, or combined samples of cases and controls can yield inflated type I error rates when the test SNP is associated with the disease. To solve this issue, we propose a Gaussian copula-based approach that efficiently models the dependence between disease status and secondary phenotypes. Through simulations, we show that our method yields correct type I error rates for the analysis of secondary phenotypes under a wide range of situations. To illustrate the effectiveness of our method in the analysis of real data, we applied our method to a genome-wide association study on high-density lipoprotein cholesterol (HDL-C), where "cases" are defined as individuals with extremely high HDL-C level and "controls" are defined as those with low HDL-C level. We treated 4 quantitative traits with varying degrees of correlation with HDL-C as secondary phenotypes and tested for association with SNPs in LIPG, a gene that is well known to be associated with HDL-C. We show that when the correlation between the primary and secondary phenotypes is >0.2, the P values from case-control combined unadjusted analysis are much more significant than methods that aim to correct for ascertainment bias. Our results suggest that to avoid false-positive associations, it is important to appropriately model secondary phenotypes in case-control genetic association studies.     

The SERPINE2 gene is associated with chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) is a complex human disease likely influenced by multiple genes, cigarette smoking, and gene-by-smoking interactions, but only severe alpha 1-antitrypsin deficiency is a proven genetic risk factor for COPD. Prior linkage analyses in the Boston Early-Onset COPD Study have demonstrated significant linkage to a key intermediate phenotype of COPD on chromosome 2q. We integrated results from murine lung development and human COPD gene-expression microarray studies with human COPD linkage results on chromosome 2q to prioritize candidate-gene selection, thus identifying SERPINE2 as a positional candidate susceptibility gene for COPD. Immunohistochemistry demonstrated expression of serpine2 protein in mouse and human adult lung tissue. In family-based association testing of 127 severe, early-onset COPD pedigrees from the Boston Early-Onset COPD Study, we observed significant association with COPD phenotypes and 18 single-nucleotide polymorphisms (SNPs) in the SERPINE2 gene. Association of five of these SNPs with COPD was replicated in a case-control analysis, with cases from the National Emphysema Treatment Trial and controls from the Normative Aging Study. Family-based and case-control haplotype analyses supported similar regions of association within the SERPINE2 gene. When significantly associated SNPs in these haplotypic regions were included as covariates in linkage models, LOD score attenuation was observed most markedly in a smokers-only linkage model (LOD 4.41, attenuated to 1.74). After the integration of murine and human microarray data to inform candidate-gene selection, we observed significant family-based association and independent replication of association in a case-control study, suggesting that SERPINE2 is a COPD-susceptibility gene and is likely influenced by gene-by-smoking interaction.     

A Powerful Nonparametric Statistical Framework for Family-Based Association Analyses

Family-based study design is commonly used in genetic research. It has many ideal features, including being robust to population stratification (PS). With the advance of high-throughput technologies and ever-decreasing genotyping cost, it has become common for family studies to examine a large number of variants for their associations with disease phenotypes. The yield from the analysis of these family-based genetic data can be enhanced by adopting computationally efficient and powerful statistical methods. We propose a general framework of a family-based U-statistic, referred to as family-U, for family-based association studies. Unlike existing parametric-based methods, the proposed method makes no assumption of the underlying disease models and can be applied to various phenotypes (e.g., binary and quantitative phenotypes) and pedigree structures (e.g., nuclear families and extended pedigrees). By using only within-family information, it can offer robust protection against PS. In the absence of PS, it can also utilize additional information (i.e., between-family information) for power improvement. Through simulations, we demonstrated that family-U attained higher power over a commonly used method, family-based association tests, under various disease scenarios. We further illustrated the new method with an application to large-scale family data from the Framingham Heart Study. By utilizing additional information (i.e., between-family information), family-U confirmed a previous association of CHRNA5 with nicotine dependence.     

Psychiatric endophenotypes and the development of valid animal models

Endophenotypes are quantifiable components in the genes-to-behaviors pathways, distinct from psychiatric symptoms, which make genetic and biological studies of etiologies for disease categories more manageable. The endophenotype concept has emerged as a strategic tool in neuropsychiatric research. This emergence is due to many factors, including the modest reproducibility of results from studies directed toward etiologies and appreciation for the complex relationships between genes and behavior. Disease heterogeneity is often guaranteed, rather than simplified, through the current diagnostic system; inherent benefits of endophenotypes include more specific disease concepts and process definitions. Endophenotypes can be neurophysiological, biochemical, endocrine, neuroanatomical, cognitive or neuropsychological. Heritability and stability (state independence) represent key components of any useful endophenotype. Importantly, they characterize an approach that reduces the complexity of symptoms and multifaceted behaviors, resulting in units of analysis that are more amenable to being modeled in animals. We discuss the benefits of more direct interpretation of clinical endophenotypes by basic behavioral scientists. With the advent of important findings regarding the genes that predispose to psychiatric illness, we are at an important crossroads where, without anthropomorphizing, animal models may provide homologous components of psychiatric illness, rather than simply equating to similar (loosely analogized) behaviors, validators of the efficacy of current medications or models of symptoms. We conclude that there exists a need for increased collaboration between clinicians and basic scientists, the result of which should be to improve diagnosis, classification and treatment on one end and to increase the construct relevance of model organisms on the other.     

Using the noninformative families in family-based association tests: a powerful new testing strategy

For genetic association studies with multiple phenotypes, we propose a new strategy for multiple testing with family-based association tests (FBATs). The strategy increases the power by both using all available family data and reducing the number of hypotheses tested while being robust against population admixture and stratification. By use of conditional power calculations, the approach screens all possible null hypotheses without biasing the nominal significance level, and it identifies the subset of phenotypes that has optimal power when tested for association by either univariate or multivariate FBATs. An application of our strategy to an asthma study shows the practical relevance of the proposed methodology. In simulation studies, we compare our testing strategy with standard methodology for family studies. Furthermore, the proposed principle of using all data without biasing the nominal significance in an analysis prior to the computation of the test statistic has broad and powerful applications in many areas of family-based association studies.     

The ITGAV rs3738919-C allele is associated with rheumatoid arthritis in the European Caucasian population: a family-based study

The integrin alpha(v)beta3, whose alpha(v) subunit is encoded by the ITGAV gene, plays a key role in angiogenesis. Hyperangiogenesis is involved in rheumatoid arthritis (RA) and the ITGAV gene is located in 2q31, one of the suggested RA susceptibility loci. Our aim was to test the ITGAV gene for association and linkage to RA in a family-based study from the European Caucasian population. Two single nucleotide polymorphisms were genotyped by PCR-restriction fragment length polymorphism in 100 French Caucasian RA trio families (one RA patient and both parents), 100 other French families and 265 European families available for replication. The genetic analyses for association and linkage were performed using the comparison of allelic frequencies (affected family-based controls), the transmission disequilibrium test, and the genotype relative risk.We observed a significant RA association for the C allele of rs3738919 in the first sample (affected family-based controls, RA index cases 66.5% versus controls 56.7%; P = 0.04). The second sample showed the same trend, and the third sample again showed a significant RA association. When all sets were combined, the association was confirmed (affected family-based controls, RA index cases 64.6% versus controls 58.1%; P = 0.005). The rs3738919-C allele was also linked to RA (transmission disequilibrium test, 56.5% versus 50% of transmission; P = 0.009) and the C-allele-containing genotype was more frequent in RA index cases than in controls (RA index cases 372 versus controls 339; P = 0.002, odds ratio = 1.94, 95% confidence interval = 1.3-2.9). The rs3738919-C allele of the ITGAV gene is associated with RA in the European Caucasian population, suggesting ITGAV as a new minor RA susceptibility gene.     

A penalized structural equation modeling method accounting for secondary phenotypes for variable selection on genetically regulated expression from PrediXcan for Alzheimer's disease

As the global burden of mental illness is estimated to become a severe issue in the near future, it demands the development of more effective treatments. Most psychiatric diseases are moderately to highly heritable and believed to involve many genes. Development of new treatment options demands more knowledge on the molecular basis of psychiatric diseases. Toward this end, we propose to develop new statistical methods with improved sensitivity and accuracy to identify disease‐related genes specialized for psychiatric diseases. The qualitative psychiatric diagnoses such as case control often suffer from high rates of misdiagnosis and oversimplify the disease phenotypes. Our proposed method utilizes endophenotypes, the quantitative traits hypothesized to underlie disease syndromes, to better characterize the heterogeneous phenotypes of psychiatric diseases. We employ the structural equation modeling using the liability‐index model to link multiple genetically regulated expressions from PrediXcan and the manifest variables including endophenotypes and case‐control status. The proposed method can be considered as a general method for multivariate regression, which is particularly helpful for psychiatric diseases. We derive penalized retrospective likelihood estimators to deal with the typical small sample size issue. Simulation results demonstrate the advantages of the proposed method and the real data analysis of Alzheimer's disease illustrates the practical utility of the techniques. Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative database.     

Localization and identification of human quantitative trait loci: king harvest has surely come

The scientific process of localization and subsequent identification of genes influencing risk of common diseases is still in its infancy. Initial localization of disease-related loci has traditionally been performed using family-based linkage methods to scan the genome. Early pronouncements of the failure of this approach for common diseases were premature and based on comparing suboptimal linkage designs with overly optimistic and empirically unproven association-based designs. On the contrary, substantial recent progress in the positional cloning of genes influencing such complex phenotypes suggests that modern approaches based around a family-based linkage paradigm will be successful. In particular, the rapidly growing emphasis on the analysis of the genetic basis of quantitative correlates of disease risk represents a novel and promising approach in which initial localization is performed using linkage and subsequent identification utilizes association approaches in positional candidate genes.     

Association of polymorphisms in the apolipoprotein E region with susceptibility to and progression of multiple sclerosis

Multiple sclerosis (MS) is a chronic inflammatory disorder of the central nervous system, with a complex etiology that includes a strong genetic component. The contribution of the major histocompatibility complex (MHC) has been established in numerous genetic linkage and association studies. In addition to the MHC, the chromosome 19q13 region surrounding the apolipoprotein E (APOE) gene has shown consistent evidence of involvement in MS when family-based analyses were conducted. Furthermore, several clinical reports have suggested that the APOE-4 allele may be associated with more-severe disease and faster progression of disability. To thoroughly examine the role of APOE in MS, we genotyped its functional alleles, as well as seven single-nucleotide polymorphisms (SNPs) located primarily within 13 kb of APOE, in a data set of 398 families. Using family-based association analysis, we found statistically significant evidence that an SNP haplotype near APOE is associated with MS susceptibility (P=.005). An analysis of disease progression in 614 patients with MS from 379 families indicated that APOE-4 carriers are more likely to be affected with severe disease (P=.03), whereas a higher proportion of APOE-2 carriers exhibit a mild disease course (P=.02).