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TNFRSF6B and TNFRSF14 genes were recently associated with Crohn's disease and rheumatoid arthritis. TNFRSF14 is known as herpes virus entry mediator (HVEM), and herpes viruses have been involved in the aetiology of multiple sclerosis (MS). MS patients present human herpes virus 6 (HHV6) in active plaques and increased antibody responses to HHV6. We aimed to ascertain the role of these genes in MS susceptibility and to investigate the relationship of the gene encoding the widely expressed HVEM receptor with the active replication of HHV6 found in some MS patients. Genotyping of 1370 Spanish MS patients and 1715 ethnically matched controls was performed. HHV6A DNA levels (surrogate of active viral replication) were analysed in serum of MS patients during a 2-year follow-up. Both polymorphisms were associated with MS predisposition, with stronger effect in patients with HHV6 active replicationTNFRSF6B-rs4809330 A: P=0.028, OR1.13; TNFRSF14-rs6684865 A: overall P=0.0008, OR=1.2; and HHV6-positive patients vs controls: P=0.017, OR=1.69. © 2011 Macmillan Publishers Limited All rights reserved.

Multimarker transmission/disequilibrium tests (TDTs) are powerful association and linkage tests used to perform genome-wide filtering in the search for disease susceptibility loci. In contrast to case/control studies, they have a low rate of false positives for population stratification and admixture. However, the length of a region found in association with a disease is usually very large because of linkage disequilibrium (LD). Here, we define a multimarker proportional TDT (mTDT P ) designed to improve locus specificity in complex diseases that has good power compared to the most powerful multimarker TDTs. The test is a simple generalization of a multimarker TDT in which haplotype frequencies are used to weight the effect that each haplotype has on the whole measure. Two concepts underlie the features of the metric: the ‘common disease, common variant’ hypothesis and the decrease in LD with chromosomal distance. Because of this decrease, the frequency of haplotypes in strong LD with common disease variants decreases with increasing distance from the disease susceptibility locus. Thus, our haplotype proportional test has higher locus specificity than common multimarker TDTs that assume a uniform distribution of haplotype probabilities. Because of the common variant hypothesis, risk haplotypes at a given locus are relatively frequent and a metric that weights partial results for each haplotype by its frequency will be as powerful as the most powerful multimarker TDTs. Simulations and real data sets demonstrate that the test has good power compared with the best tests but has remarkably higher locus specificity, so that the association rate decreases at a higher rate with distance from a disease susceptibility or disease protective locus.

Background: Various approaches are being used to predict individual risk to polygenic diseases from data provided by genome-wide association studies. As there are substantial differences between the diseases investigated, the data sets used and the way they are tested, it is difficult to assess which models are more suitable for this task. Results: We compared different approaches for seven complex diseases provided by the Wellcome Trust Case Control Consortium (WTCCC) under a within-study validation approach. Risk models were inferred using a variety of learning machines and assumptions about the underlying genetic model, including a haplotype-based approach with different haplotype lengths and different thresholds in association levels to choose loci as part of the predictive model. In accordance with previous work, our results generally showed low accuracy considering disease heritability and population prevalence. However, the boosting algorithm returned a predictive area under the ROC curve (AUC) of 0.8805 for Type 1 diabetes (T1D) and 0.8087 for rheumatoid arthritis, both clearly over the AUC obtained by other approaches and over 0.75, which is the minimum required for a disease to be successfully tested on a sample at risk, which means that boosting is a promising approach. Its good performance seems to be related to its robustness to redundant data, as in the case of genome-wide data sets due to linkage disequilibrium. Conclusions: In view of our results, the boosting approach may be suitable for modeling individual predisposition to Type 1 diabetes and rheumatoid arthritis based on genome-wide data and should be considered for more in-depth research, This work was supported by the Spanish Secretary of Research, Development and Innovation [TIN2010-20900-C04-1]; the Spanish Health Institute Carlos III [PI13/02714]and [PI13/01527] and the Andalusian Research Program under project P08-TIC-03717 with the help of the European Regional Development Fund (ERDF)

Several variants in strong linkage disequilibrium (LD) at the SP140 locus have been associated with multiple sclerosis (MS), Crohn's disease (CD) and chronic lymphocytic leukemia (CLL). To determine the causal polymorphism, we have integrated high-density data sets of expression quantitative trait loci (eQTL), using GEUVADIS RNA sequences and 1000 Genomes genotypes, with MS-risk variants of the high-density Immunochip array performed by the International Multiple Sclerosis Genetic Consortium (IMSGC). The variants most associated with MS were also correlated with a decreased expression of the full-length RNA isoform of SP140 and an increase of an isoform lacking exon 7. By exon splicing assay, we have demonstrated that the rs28445040 variant was the causal factor for skipping of exon 7. Western blots of peripheral blood mononuclear cells from MS patients showed a significant allele-dependent reduction of the SP140 protein expression. To confirm the association of this functional variant with MS and to compare it with the best-associated variant previously reported by GWAS (rs10201872), a case-control study including 4384 MS patients and 3197 controls was performed. Both variants, in strong LD (r(2) = 0.93), were found similarly associated with MS [P-values, odds ratios: 1.9E-9, OR = 1.35 (1.22-1.49) and 4.9E-10, OR = 1.37 (1.24-1.51), respectively]. In conclusion, our data uncover the causal variant for the SP140 locus and the molecular mechanism associated with MS risk. In addition, this study and others previously reported strongly suggest that this functional variant may be shared with other immune-mediated diseases as CD and CLL.

It is already known that power in multimarker transmission/disequilibrium tests may improve with the number of markers as some associations may require several markers to be captured. However, a mechanism such as haplotype grouping must be used to avoid incremental complexity with the number of markers. 2G, a state-of-the-art transmission/disequilibrium test, implements this mechanism to its maximum extent by grouping haplotypes into only two groups, high and low-risk haplotypes, so that the test has only one degree of freedom regardless of the number of markers. The test checks whether those haplotypes more often transmitted from parents to offspring are truly high-risk haplotypes. In this paper we use haplotype similarity as prior knowledge to classify haplotypes as high or low risk ones and start with those haplotypes in which the prior will have lower impact i.e. those with the largest differences between transmission and non-transmission counts. If their counts are very different, the prior knowledge has little effect and haplotypes are classified as low or high risk as 2G does. We show a substantial gain in power achieved by this approach, in both simulation and real data sets.

Inferring haplotype phase is a subject of interest, and given the increasing number of GWAS and related applications, like genetic profiling, it is necessary for phasing algorithms to provide a good performance even when processing large DNA fragments. Most of studies focus on genomic regions of limited length, therefore, we propose to test the most common statistics with genetic regions of variable length. We have found that one of the most used, the switch error, is insufficient when considering long distances: it converges to a constant value which does not truly shows the quality of the inferred phase. Furthermore, the IGP (incorrect genotype percentage) is a much more precise measure of the quality of the algorithm. New phasing algorithms should not care only about the number of switches, because in some cases (classifiers to assess genetic risks, for example) is important to distinguish the haplotype of each parent to obtain better results.

The quantity and quality of genome-wide association studies for several diseases are constantly increasing. As a consequence, molecular biologists from different laboratories demand new visualization tools for them to explore results by view and formulate new conjectures to work on. Although nowadays most studies are not able to reconstruct individual haplotypes, the next generation sequencying technologies will allow to obtain individual haplotypes in most studies conducted in the next few years. As evolutionary analysis of the haplotypes can be an invaluable information to biomedical researchers to build hypotheses of genetic variation by considering haplotype evolution, we have build a web-based tool for biomedical researchers to build and visualize risk haplotype trees along a chromosome so that they can perform a visual online exploration of the genetic factors associated with complex diseases.

The human leukocyte antigen (HLA) DRB1*1501 has been consistently associated with multiple sclerosis (MS) in nearly all populations tested. This points to a specific antigen presentation as the pathogenic mechanism though this does not fully explain the disease association. The identification of expression quantitative trait loci (eQTL) for genes in the HLA locus poses the question of the role of gene expression in MS susceptibility. We analyzed the eQTLs in the HLA region with respect to MS-associated HLA-variants obtained from genome-wide association studies (GWAS). We found that the Tag of DRB1*1501, rs3135388 A allele, correlated with high expression of DRB1, DRB5 and DQB1 genes in a Caucasian population. In quantitative terms, the MS-risk AA genotype carriers of rs3135388 were associated with 15.7-, 5.2- and 8.3-fold higher expression of DQB1, DRB5 and DRB1, respectively, than the non-risk GG carriers. The haplotype analysis of expression-associated variants in a Spanish MS cohort revealed that high expression of DRB1 and DQB1 alone did not contribute to the disease. However, in Caucasian, Asian and African American populations, the DRB1*1501 allele was always highly expressed. In other immune related diseases such as type 1 diabetes, inflammatory bowel disease, ulcerative colitis, asthma and IgA deficiency, the best GWAS-associated HLA SNPs were also eQTLs for different HLA Class II genes. Our data suggest that the DR/DQ expression levels, together with specific structural properties of alleles, seem to be the causal effect in MS and in other immunopathologies rather than specific antigen presentation alone. © 2012 Alcina et al.

Tree-based transmission-disequilibrium tests are valuable tools to perform fine-mapping in the search of genetic factors for complex diseases, as they use evolutionary information to relate haplotypes affecting the disease. However, the number of different haplotype trees exponentially increases with the number of markers used, leading to spurious associations due to sample overfitting. If the usual Bonferroni correction is applied to avoid those spurious associations, true risk variants may also be missed. In this work we considered a different solution to avoid sample overfitting of haplotype trees. It consists of dividing the data set into at least two parts and using one of them to choose the haplotype tree which models the disease, and the other one to assess the statistical significance. As a practical example to evaluate the performance of our proposal, we modified the TreeDT algorithm and observed a significant improvement in reproducibility while reducing the type I errors.

To The Editor: Mannose binding lectin (MBL) is a pattern recognition molecule of the innate immune system. Single nucleotide polymorphisms (SNPs) in the proximal promoter (L/H and X/Y), the 5′UTR (...

Background: IL-2 receptor (IL2R) alpha is the specific component of the high affinity IL2R system involved in the immune response and in the control of autoimmunity. Methods and Results: Here we perform a replication and fine mapping of the IL2RA gene region analyzing 3 SNPs previously associated with multiple sclerosis (MS) and 5 SNPs associated with type 1 diabetes (T1D) in a collection of 798 MS patients and 927 matched Caucasian controls from the south of Spain. We observed association with MS in 6 of 8 SNPs. The rs1570538, at the 3′- UTR extreme of the gene, previously reported to have a weak association with MS, is replicated here (P = 0.032). The most associated T1D SNP (rs41295061) was not associated with MS in the present study. However, the rs35285258, belonging to another independent group of SNPs associated with T1D, showed the maximal association in this study but different risk allele. We replicated the association of only one (rs2104286) of the two IL2RA SNPs identified in the recently performed genome-wide association study of MS. Conclusions: These findings confirm and extend the association of this gene with MS and reveal a genetic heterogeneity of the associated polymorphisms and risk alleles between MS and T1D suggesting different immunopathological roles of IL2RA in these two diseases. © 2009 Antonio et al.

[Background] One of the challenges of the analysis of pooling-based genome wide association studies is to identify authentic associations among potentially thousands of false positive associations. [Results] We present a hierarchical and modular approach to the analysis of genome wide genotype data that incorporates quality control, linkage disequilibrium, physical distance and gene ontology to identify authentic associations among those found by statistical association tests. The method is developed for the allelic association analysis of pooled DNA samples, but it can be easily generalized to the analysis of individually genotyped samples. We evaluate the approach using data sets from diverse genome wide association studies including fetal hemoglobin levels in sickle cell anemia and a sample of centenarians and show that the approach is highly reproducible and allows for discovery at different levels of synthesis. [Conclusion] Results from the integration of Bayesian tests and other machine learning techniques with linkage disequilibrium data suggest that we do not need to use too stringent thresholds to reduce the number of false positive associations. This method yields increased power even with relatively small samples. In fact, our evaluation shows that the method can reach almost 70% sensitivity with samples of only 100 subjects., Supported by NHLBI grants R21 HL080463 (PS); R01 HL68970 (MHS); K-24, AG025727 (TP); K23 AG026754 (D.T.).

Modeling the complexity of sickle cell disease pathophysiology and severity is difficult. Using data from 3380 patients accounting for all common genotypes of sickle cell disease, Bayesian network modeling of 25 clinical events and laboratory tests was used to estimate sickle cell disease severity, which was represented as a score predicting the risk of death within 5 years. The reliability of the model was supported by analysis of 2 independent patient groups. In 1 group, the severity score was related to disease severity based on the opinion of expert clinicians. In the other group, the severity score was related to the presence and severity of pulmonary hypertension and the risk of death. Along with previously known risk factors for mortality, like renal insufficiency and leukocytosis, the network identified laboratory markers of the severity of hemolytic anemia and its associated clinical events as contributing risk factors. This model can be used to compute a personalized disease severity score allowing therapeutic decisions to be made according to the prognosis. The severity score could serve as an estimate of overall disease severity in genotype-phenotype association studies, and the model provides an additional method to study the complex pathophysiology of sickle cell disease.

Compared with expert systems for specific disease diagnosis, knowledge-based systems to assist decision making in triage usually try to cover a much wider domain but can use a smaller set of variables due to time restrictions, many of them subjective so that accurate models are difficult to build. In this paper, we first study criteria that most affect the performance of systems for triage assistance. Such criteria include whether principled approaches from machine learning can be used to increase accuracy and robustness and to represent uncertainty, whether data and model integration can be performed or whether temporal evolution can be modeled to implement retriage or represent medication responses. Following the most important criteria, we explore current systems and identify some missing features that, if added, may yield to more accurate triage systems.

[Background] The maximum likelihood estimator of D' – a standard measure of linkage disequilibrium – is biased toward disequilibrium, and the bias is particularly evident in small samples and rare haplotypes. [Results] This paper proposes a Bayesian estimation of D' to address this problem. The reduction of the bias is achieved by using a prior distribution on the pair-wise associations between single nucleotide polymorphisms (SNP)s that increases the likelihood of equilibrium with increasing physical distances between pairs of SNPs. We show how to compute the Bayesian estimate using a stochastic estimation based on MCMC methods, and also propose a numerical approximation to the Bayesian estimates that can be used to estimate patterns of LD in large datasets of SNPs. [Conclusion] Our Bayesian estimator of D' corrects the bias toward disequilibrium that affects the maximum likelihood estimator. A consequence of this feature is a more objective view about the extent of linkage disequilibrium in the human genome, and a more realistic number of tagging SNPs to fully exploit the power of genome wide association studies., Research supported by NIH/NHLBI grant R21 HL080463-01, NIH/NIDDK 1R01DK069646-01A1 and the Spanish research program [projects TIN2004-06204-C03-02 and TIN2005-02516].

Summary: BMapBuilder builds maps of pairwise linkage disequilibrium (LD) in either two or three dimensions. The optimized resolution allows for graphical display of LD for single nucleotide polymorphisms (SNPs) in a whole chromosome.Availability: The program is coded in Java, which runs on all relevant operating systems, including Windows, Mac and Unix/Linux, and is available fromContact: sebas@bu.eduSupplementary information: Maps displaying chomosome-wide LD are available at

The interaction of human experts with machine learning and data mining tools leads to improved results in decision-making support systems. In marketing decisions related to market segmentation, the use of only one technique does not guarantee an optimal solution, as such a solution may not even be achievable. In this paper, we analyse the market segmentation decisions in the performing arts through a combination of expert opinions and machine learning algorithms in order to obtain a consensual model that allows a better understanding of market preferences together with a deep knowledge about reliability in the obtained results. The results and data were applied to build a model of market segmentation of students based on their attendance in, attitudes towards, and intentions in attending opera and ballet performances.

A single point mutation in the β hemoglobin gene causes sickle cell disease (SCD), but patients have extremely variable phenotypes. Hemolysis-related complications include pulmonary hypertension (PHT), priapism, stroke and leg ulceration; blood viscosity and sickle vasoocclusion are associated with painful episodes, acute chest syndrome and osteonecrosis. Predicting who is at highest risk of death would be useful therapeutically and prognostically. Applying Bayesian network modeling that describes complex interactions among many variables by factorizing their joint probability distribution into modules, to data from 3380 SCD patients, we constructed a disease severity score (DSS: 0, least severe; 1, most severe), defining severity as risk of death within 5 years. A network of 24 variables described complex associations among clinical and laboratory complications of SCD. The analysis was validated in 140 patients whose SCD severity was assessed by expert clinicians and 210 adults where severity was also assessed by the echocardiographic diagnosis of PHT and death. Information about PHT allowed a comparison of the DSS with the tricuspid regurgitant jet velocity (TRJV), an objective marker of PHT and an independent risk factor for death. DSS and three indices of clinical severity (severity ranking of individuals by expert clinicians; objective measurement of the presence and severity of PHT; risk of prospective death) were correlated. Among living subjects, the median score was 0.57 in 135 patients without PHT, 0.64 in 40 patients with mild PHT and 0.86 in 15 patients with severe PHT. The difference in average score between living patients with and without PHT is significant. The same increasing trend was noticeable in the subjects who died during follow-up: 0.60 in subjects without PHT; 0.68 in subjects with mild PHT; 0.79 in subjects with severe PHT. The utility of the DSS is also supported by the ability to assign a score to subjects for whom the TRJV cannot be measured. Surprisingly, besides known risk factors like renal insufficiency and leukocytosis, we identified the intensity of hemolytic anemia and clinical events associated with hemolytic anemia as contributing to risk for death. Priapism, an excellent reflection of the hemolytic anemia-related complications of SCD, is associated with PHT and its association with death was unexpected. Laboratory variables predictive of disease severity included LDH and reticulocytes that reflect the intensity of hemolytic anemia. Elevated systolic blood pressure increased the odds of death by 3.4, consistent with hypertension as a marker of early death in SCD. Subjects with sickle cell anemia are at greatest risk compared with subjects with sickle cell anemia-α thalassemia and with subjects with HbSC disease. Our model suggests that the intensity of hemolytic anemia, estimated by LDH, reticulocyte count and AST, and shown previously to be associated with PHT, priapism, leg ulceration and possibly stroke, is an important contributor to death. This model can be used to compute a personalized measure of disease severity that might be useful for guiding therapeutic decisions and designing clinical trials.

The complex regulatory environment controlling HbF levels provides ample opportunity for modulating expression of this key modifier of sickle cell anemia (HbSS). Identifing genetic modulators by single gene or biochemical pathway-based candidate gene association studies is limited by the a priori hypothesis that they are responsible for modulation of HbF expression and thus, fail to capture the totality of possible modulators. In contrast, genome-wide association (GWA) studies using hundreds of thousands of SNPs across the entire genome can capture an unbiased assessment of gene association or linkage disequilibrium (LD) with a phenotype. A disadvantage of GWA is the cost when analyzing thousands of subjects. We developed a GWA screening strategy using pooled DNA samples followed by analysis using the Sentrix® HumanHap300 genotyping beadchip that includes over 317,000 tag SNPs, many derived from the Phase I of the HapMap project. These SNPs are within 10kb of a gene, or in regions that are evolutionary conserved, and incorporate the majority of the variation in regions of the genome with high LD by capturing approximately 80% of the loci genotyped in HapMap Phase I and II. DNA concentration for pooling was measured by RNAase-P assays and each pool was run in duplicate. Pools of approximately 60 DNA samples each were analyzed; pools included age, sex and β-globin gene haplotype-matched HbSS patients with the 1st and 4th quartiles of HbF levels. Raw data were processed to compute initial estimates of the allele frequencies. Estimates were normalized and SNPs with a minor allele frequency >0.15 were analyzed for allelic association using a Bayesian approach. Our analysis integrated the measure of association based on the allele frequencies, estimated from the pooled DNA samples, with the strength of LD between consecutive SNPs, information on minor allele frequencies derived from the HapMap project and an estimate of the variability between pools based on preliminary experimental data. By leveraging on this exogenous information we could reduce false positive associations without imposing unnecessarily stringent corrections for multiple comparisons that may lead to substantial loss of power. We identified more than 100 novel genes potentially involved with regulation of HbF and with roles in signal transduction, cell adhesion, and regulation of transcription. These genes are distributed unevenly among the whole genome, and chromosome 8 appears to be the most "enriched" for genes associated with regulation of HbF. Consistently with previous findings, our analysis confirms an involvement of genes in 6q22, 8q12 and 11p15.5 in the regulation of HbF. For further validation, we compared allele frequencies based on pooled DNA samples with those from individual genotyping of several SNPs. The high degree of agreement suggests that GWA based on pooled DNA samples can provide a cost-effective approach to the identification of the full array of genes responsible for HbF expression and can identify new candidates that must be further examined in follow-up genetic and biological studies.