Principals about principal components in statistical genetics.

Principal components (PCs) are widely used in statistics and refer to a relatively small number of uncorrelated variables derived from an initial pool of variables, while explaining as much of the total variance as possible. Also in statistical genetics, principal component analysis (PCA) is a popular technique. To achieve optimal results, a thorough understanding about the different implementations of PCA is required and their impact on study results, compared to alternative approaches. In this review, we focus on the possibilities, limitations and role of PCs in ancestry prediction, genome-wide association studies, rare variants analyses, imputation strategies, meta-analysis and epistasis detection. We also describe several variations of classic PCA that deserve increased attention in statistical genetics applications.

A roadmap to multifactor dimensionality reduction methods.

Complex diseases are defined to be determined by multiple genetic and environmental factors alone as well as in interactions. To analyze interactions in genetic data, many statistical methods have been suggested, with most of them relying on statistical regression models. Given the known limitations of classical methods, approaches from the machine-learning community have also become attractive. From this latter family, a fast-growing collection of methods emerged that are based on the Multifactor Dimensionality Reduction (MDR) approach. Since its first introduction, MDR has enjoyed great popularity in applications and has been extended and modified multiple times. Based on a literature search, we here provide a systematic and comprehensive overview of these suggested methods. The methods are described in detail, and the availability of implementations is listed. Most recent approaches offer to deal with large-scale data sets and rare variants, which is why we expect these methods to even gain in popularity.

Association of IL33-IL-1 receptor-like 1 (IL1RL1) pathway polymorphisms with wheezing phenotypes and asthma in childhood.

BACKGROUND: Genome-wide association studies identified IL33 and IL-1 receptor-like 1 (IL1RL1)/IL18R1 as asthma susceptibility loci. IL33 and IL1RL1 constitute a single ligand-receptor pathway. OBJECTIVE: In 2 birth cohorts, the Prevalence and Incidence of Asthma and Mite Allergy (PIAMA) study and Avon Longitudinal Study of Parents and Children (ALSPAC), we analyzed associations of longitudinal wheezing phenotypes and asthma with single nucleotide polymorphisms (SNPs) of 8 genes encoding IL-33, IL1RL1, its coreceptor IL1RAcP, its adaptors myeloid differentiation primary response gene 88 (MyD88) and Toll-IL-11 receptor domain containing adaptor protein (TIRAP), and the downstream IL-1 receptor-associated kinase 1, IL-1 receptor-associated kinase 4, and TNF receptor-associated factor 6 (TRAF6). Furthermore, we investigated whether SNPs in this pathway show replicable evidence of gene-gene interaction. METHODS: Ninety-four SNPs were investigated in 2007 children in the PIAMA study and 7247 children in ALSPAC. Associations with wheezing phenotypes and asthma at 8 years of age were analyzed in each cohort and subsequently meta-analyzed. Gene-gene interactions were assessed through model-based multifactor dimensionality reduction in the PIAMA study, and gene-gene interactions of 10 SNP pairs were further evaluated. RESULTS: Intermediate-onset wheeze was associated with SNPs in several genes in the IL33-IL1RL1 pathway after applying multiple testing correction in the meta-analysis: 2 IL33 SNPs (rs4742170 and rs7037276), 1 IL-1 receptor accessory protein (IL1RAP) SNP (rs10513854), and 1 TRAF6 SNP (rs5030411). Late-onset wheeze was associated with 2 IL1RL1 SNPs (rs10208293 and rs13424006), and persistent wheeze was associated with 1 IL33 SNP (rs1342326) and 1 IL1RAP SNP (rs9290936). IL33 and IL1RL1 SNPs were nominally associated with asthma. Three SNP pairs showed interaction for asthma in the PIAMA study but not in ALSPAC. CONCLUSIONS: IL33-IL1RL1 pathway polymorphisms are associated with asthma and specific wheezing phenotypes; that is, most SNPs are associated with intermediate-onset wheeze, a phenotype closely associated with sensitization. We speculate that IL33-IL1RL1 pathway polymorphisms affect development of wheeze and subsequent asthma through sensitization in early childhood.

Genetic and microbial factors modulating the ubiquitin proteasome system in inflammatory bowel disease.

OBJECTIVE: Altered microbiota composition, changes in immune responses and impaired intestinal barrier functions are observed in IBD. Most of these features are controlled by proteases and their inhibitors to maintain gut homeostasis. Unrestrained or excessive proteolysis can lead to pathological gastrointestinal conditions. The aim was to validate the identified protease IBD candidates from a previously performed systematic review through a genetic association study and functional follow-up. DESIGN: We performed a genetic association study in a large multicentre cohort of patients with Crohn's disease (CD) and UC from five European IBD referral centres in a total of 2320 CD patients, 2112 UC patients and 1796 healthy controls. Subsequently, we did an extensive functional assessment of the candidate genes to explore their causality in IBD pathogenesis. RESULTS: Ten single nucleotide polymorphisms (SNPs) in four genes were significantly associated with CD: CYLD, USP40, APEH and USP3. CYLD was the most significant gene with the intronically located rs12324931 the strongest associated SNP (p(FDR)=1.74e-17, OR=2.24 (1.83 to 2.74)). Five SNPs in four genes were significantly associated with UC: USP40, APEH, DAG1 and USP3. CYLD, as well as some of the other associated genes, is part of the ubiquitin proteasome system (UPS). We therefore determined if the IBD-associated adherent-invasive Escherichia coli (AIEC) can modulate the UPS functioning. Infection of intestinal epithelial cells with the AIEC LF82 reference strain modulated the UPS turnover by reducing poly-ubiquitin conjugate accumulation, increasing 26S proteasome activities and decreasing protein levels of the NF-κB regulator CYLD. This resulted in IκB-α degradation and NF-κB activation. This activity was very important for the pathogenicity of AIEC since decreased CYLD resulted in increased ability of AIEC LF82 to replicate intracellularly. CONCLUSIONS: Our results reveal the UPS, and CYLD specifically, as an important contributor to IBD pathogenesis, which is favoured by both genetic and microbial factors.

An efficient algorithm to perform multiple testing in epistasis screening.

BACKGROUND: Research in epistasis or gene-gene interaction detection for human complex traits has grown over the last few years. It has been marked by promising methodological developments, improved translation efforts of statistical epistasis to biological epistasis and attempts to integrate different omics information sources into the epistasis screening to enhance power. The quest for gene-gene interactions poses severe multiple-testing problems. In this context, the maxT algorithm is one technique to control the false-positive rate. However, the memory needed by this algorithm rises linearly with the amount of hypothesis tests. Gene-gene interaction studies will require a memory proportional to the squared number of SNPs. A genome-wide epistasis search would therefore require terabytes of memory. Hence, cache problems are likely to occur, increasing the computation time. In this work we present a new version of maxT, requiring an amount of memory independent from the number of genetic effects to be investigated. This algorithm was implemented in C++ in our epistasis screening software MBMDR-3.0.3. We evaluate the new implementation in terms of memory efficiency and speed using simulated data. The software is illustrated on real-life data for Crohn's disease. RESULTS: In the case of a binary (affected/unaffected) trait, the parallel workflow of MBMDR-3.0.3 analyzes all gene-gene interactions with a dataset of 100,000 SNPs typed on 1000 individuals within 4 days and 9 hours, using 999 permutations of the trait to assess statistical significance, on a cluster composed of 10 blades, containing each four Quad-Core AMD Opteron(tm) Processor 2352 2.1 GHz. In the case of a continuous trait, a similar run takes 9 days. Our program found 14 SNP-SNP interactions with a multiple-testing corrected p-value of less than 0.05 on real-life Crohn's disease (CD) data. CONCLUSIONS: Our software is the first implementation of the MB-MDR methodology able to solve large-scale SNP-SNP interactions problems within a few days, without using much memory, while adequately controlling the type I error rates. A new implementation to reach genome-wide epistasis screening is under construction. In the context of Crohn's disease, MBMDR-3.0.3 could identify epistasis involving regions that are well known in the field and could be explained from a biological point of view. This demonstrates the power of our software to find relevant phenotype-genotype higher-order associations.

Model-based multifactor dimensionality reduction for detecting epistasis in case-control data in the presence of noise.

Analyzing the combined effects of genes and/or environmental factors on the development of complex diseases is a great challenge from both the statistical and computational perspective, even using a relatively small number of genetic and nongenetic exposures. Several data-mining methods have been proposed for interaction analysis, among them, the Multifactor Dimensionality Reduction Method (MDR) has proven its utility in a variety of theoretical and practical settings. Model-Based Multifactor Dimensionality Reduction (MB-MDR), a relatively new MDR-based technique that is able to unify the best of both nonparametric and parametric worlds, was developed to address some of the remaining concerns that go along with an MDR analysis. These include the restriction to univariate, dichotomous traits, the absence of flexible ways to adjust for lower order effects and important confounders, and the difficulty in highlighting epistatic effects when too many multilocus genotype cells are pooled into two new genotype groups. We investigate the empirical power of MB-MDR to detect gene-gene interactions in the absence of any noise and in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity. Power is generally higher for MB-MDR than for MDR, in particular in the presence of genetic heterogeneity, phenocopy, or low minor allele frequencies.

Levels of C-reactive protein are associated with response to infliximab therapy in patients with Crohn's disease.

BACKGROUND & AIMS: Infliximab is an antibody against tumor necrosis factor-α that is used to treat patients with moderate to severe Crohn's disease (CD). C-reactive protein (CRP) is a marker used to identify and follow individuals with CD. We analyzed changes in levels of CRP in a large cohort of patients with CD undergoing treatment with infliximab. METHODS: Serial levels of CRP were analyzed in 718 CD patients. Blood was collected before each infusion; a total of 8845 CRP levels were available for analysis. The correlations between CRP levels and need for dose adjustment, outcomes, and mucosal healing (based on endoscopic analysis of 253 patients) were evaluated. Therapy adjustment was considered successful if therapy continued without need for change. Subgroup analysis was performed by using data from 268 patients who received 8 weeks of maintenance therapy. RESULTS: More patients with high baseline levels of CRP responded to infliximab than patients with normal levels (90.8% vs 82.6%; P = .014). Early normalization of CRP levels correlated with sustained long-term response (P < .001). CRP levels remained significantly higher among patients who lost their response to infliximab, compared with those with a sustained response (P = .001). At time of loss of response, CRP levels were significantly increased (median, 11.2 mg/L) and did not return to baseline levels (median, 18.2 mg/L; P = .039). CRP correlated with mucosal healing (P = .033). CONCLUSIONS: CRP is a good marker of disease activity in patients treated with infliximab. Increased levels of CRP indicate mucosal inflammation and a likelihood of clinical relapse.

Performance of model-based multifactor dimensionality reduction methods for epistasis detection by controlling population structure.

BACKGROUND: In genome-wide association studies the extent and impact of confounding due to population structure have been well recognized. Inadequate handling of such confounding is likely to lead to spurious associations, hampering replication, and the identification of causal variants. Several strategies have been developed for protecting associations against confounding, the most popular one is based on Principal Component Analysis. In contrast, the extent and impact of confounding due to population structure in gene-gene interaction association epistasis studies are much less investigated and understood. In particular, the role of nonlinear genetic population substructure in epistasis detection is largely under-investigated, especially outside a regression framework. METHODS: To identify causal variants in synergy, to improve interpretability and replicability of epistasis results, we introduce three strategies based on a model-based multifactor dimensionality reduction approach for structured populations, namely MBMDR-PC, MBMDR-PG, and MBMDR-GC. RESULTS: Simulation results comparing the performance of various approaches show that in the presence of population structure MBMDR-PC and MBMDR-PG consistently better control type I error rate at the nominal level than MBMDR-GC. Moreover, our proposed three methods of population structure correction outperform MDR-SP in terms of statistical power. CONCLUSION: We demonstrate through extensive simulation studies the effect of various degrees of genetic population structure and relatedness on epistasis detection and propose appropriate remedial measures based on linear and nonlinear sample genetic similarity.

Fast MR arthrography using VIBE sequences to evaluate the rotator cuff.

PURPOSE: The purpose of this paper was to evaluate if short volumetric interpolated breath-hold examination (VIBE) sequences can be used as a substitute for T1-weighted with fat saturation (T1-FS) sequences when performing magnetic resonance (MR) arthrography to diagnose rotator cuff tears. MATERIALS AND METHODS: Eighty-two patients underwent direct MR arthrography of the shoulder joint using VIBE (acquisition time of 13 s) and T1-FS (acquisition time of 5 min) sequences in the axial and paracoronal plane on a 1.0-T MR unit. Two radiologists scored rotator cuff tendons on VIBE and T1-FS images separately as normal, small/large partial thickness and full thickness tears with or without geyser sign. T1-FS sequences were considered the gold standard. Surgical correlation was available in a small sample. RESULTS: Sensitivity, specificity, and positive and negative predictive values of VIBE were greater than 92% for large articular-sided partial thickness and full thickness tears. For detecting fraying and articular-sided small partial thickness tears, these parameters were 55%, 94%, 94%, and 57%, respectively. The simple kappa value was 0.76, and the weighted kappa value was 0.86 for agreement between T1-FS and VIBE scores. All large partial and full thickness tears at surgery were correctly diagnosed using VIBE or T1-FS MR images. CONCLUSION: Fast MR arthrography of the shoulder joint using VIBE sequences showed good concordance with the classically used T1-FS sequences for the appearance of the rotator cuff, in particular for large articular-sided partial thickness tears and for full thickness tears. Due to its very short acquisition time, VIBE may be especially useful when performing MR arthrography in claustrophobic patients or patients with a painful shoulder.

Confounding of linkage disequilibrium patterns in large scale DNA based gene-gene interaction studies.

BACKGROUND: In Genome-Wide Association Studies (GWAS), the concept of linkage disequilibrium is important as it allows identifying genetic markers that tag the actual causal variants. In Genome-Wide Association Interaction Studies (GWAIS), similar principles hold for pairs of causal variants. However, Linkage Disequilibrium (LD) may also interfere with the detection of genuine epistasis signals in that there may be complete confounding between Gametic Phase Disequilibrium (GPD) and interaction. GPD may involve unlinked genetic markers, even residing on different chromosomes. Often GPD is eliminated in GWAIS, via feature selection schemes or so-called pruning algorithms, to obtain unconfounded epistasis results. However, little is known about the optimal degree of GPD/LD-pruning that gives a balance between false positive control and sufficient power of epistasis detection statistics. Here, we focus on Model-Based Multifactor Dimensionality Reduction as one large-scale epistasis detection tool. Its performance has been thoroughly investigated in terms of false positive control and power, under a variety of scenarios involving different trait types and study designs, as well as error-free and noisy data, but never with respect to multicollinear SNPs. RESULTS: Using real-life human LD patterns from a homogeneous subpopulation of British ancestry, we investigated the impact of LD-pruning on the statistical sensitivity of MB-MDR. We considered three different non-fully penetrant epistasis models with varying effect sizes. There is a clear advantage in pre-analysis pruning using sliding windows at r 2 of 0.75 or lower, but using a threshold of 0.20 has a detrimental effect on the power to detect a functional interactive SNP pair (power < 25%). Signal sensitivity, directly using LD-block information to determine whether an epistasis signal is present or not, benefits from LD-pruning as well (average power across scenarios: 87%), but is largely hampered by functional loci residing at the boundaries of an LD-block. CONCLUSIONS: Our results confirm that LD patterns and the position of causal variants in LD blocks do have an impact on epistasis detection, and that pruning strategies and LD-blocks definitions combined need careful attention, if we wish to maximize the power of large-scale epistasis screenings.

Genome-wide association interaction analysis for Alzheimer's disease.

We propose a minimal protocol for exhaustive genome-wide association interaction analysis that involves screening for epistasis over large-scale genomic data combining strengths of different methods and statistical tools. The different steps of this protocol are illustrated on a real-life data application for Alzheimer's disease (AD) (2259 patients and 6017 controls from France). Particularly, in the exhaustive genome-wide epistasis screening we identified AD-associated interacting SNPs-pair from chromosome 6q11.1 (rs6455128, the KHDRBS2 gene) and 13q12.11 (rs7989332, the CRYL1 gene) (p = 0.006, corrected for multiple testing). A replication analysis in the independent AD cohort from Germany (555 patients and 824 controls) confirmed the discovered epistasis signal (p = 0.036). This signal was also supported by a meta-analysis approach in 5 independent AD cohorts that was applied in the context of epistasis for the first time. Transcriptome analysis revealed negative correlation between expression levels of KHDRBS2 and CRYL1 in both the temporal cortex (ß = -0.19, p = 0.0006) and cerebellum (ß = -0.23, p < 0.0001) brain regions. This is the first time a replicable epistasis associated with AD was identified using a hypothesis free screening approach.

A robustness study of parametric and non-parametric tests in model-based multifactor dimensionality reduction for epistasis detection.

BACKGROUND: Applying a statistical method implies identifying underlying (model) assumptions and checking their validity in the particular context. One of these contexts is association modeling for epistasis detection. Here, depending on the technique used, violation of model assumptions may result in increased type I error, power loss, or biased parameter estimates. Remedial measures for violated underlying conditions or assumptions include data transformation or selecting a more relaxed modeling or testing strategy. Model-Based Multifactor Dimensionality Reduction (MB-MDR) for epistasis detection relies on association testing between a trait and a factor consisting of multilocus genotype information. For quantitative traits, the framework is essentially Analysis of Variance (ANOVA) that decomposes the variability in the trait amongst the different factors. In this study, we assess through simulations, the cumulative effect of deviations from normality and homoscedasticity on the overall performance of quantitative Model-Based Multifactor Dimensionality Reduction (MB-MDR) to detect 2-locus epistasis signals in the absence of main effects. METHODOLOGY: Our simulation study focuses on pure epistasis models with varying degrees of genetic influence on a quantitative trait. Conditional on a multilocus genotype, we consider quantitative trait distributions that are normal, chi-square or Student's t with constant or non-constant phenotypic variances. All data are analyzed with MB-MDR using the built-in Student's t-test for association, as well as a novel MB-MDR implementation based on Welch's t-test. Traits are either left untransformed or are transformed into new traits via logarithmic, standardization or rank-based transformations, prior to MB-MDR modeling. RESULTS: Our simulation results show that MB-MDR controls type I error and false positive rates irrespective of the association test considered. Empirically-based MB-MDR power estimates for MB-MDR with Welch's t-tests are generally lower than those for MB-MDR with Student's t-tests. Trait transformations involving ranks tend to lead to increased power compared to the other considered data transformations. CONCLUSIONS: When performing MB-MDR screening for gene-gene interactions with quantitative traits, we recommend to first rank-transform traits to normality and then to apply MB-MDR modeling with Student's t-tests as internal tests for association.

Molecular reclassification of Crohn's disease: a cautionary note on population stratification.

Complex human diseases commonly differ in their phenotypic characteristics, e.g., Crohn's disease (CD) patients are heterogeneous with regard to disease location and disease extent. The genetic susceptibility to Crohn's disease is widely acknowledged and has been demonstrated by identification of over 100 CD associated genetic loci. However, relating CD subphenotypes to disease susceptible loci has proven to be a difficult task. In this paper we discuss the use of cluster analysis on genetic markers to identify genetic-based subgroups while taking into account possible confounding by population stratification. We show that it is highly relevant to consider the confounding nature of population stratification in order to avoid that detected clusters are strongly related to population groups instead of disease-specific groups. Therefore, we explain the use of principal components to correct for population stratification while clustering affected individuals into genetic-based subgroups. The principal components are obtained using 30 ancestry informative markers (AIM), and the first two PCs are determined to discriminate between continental origins of the affected individuals. Genotypes on 51 CD associated single nucleotide polymorphisms (SNPs) are used to perform latent class analysis, hierarchical and Partitioning Around Medoids (PAM) cluster analysis within a sample of affected individuals with and without the use of principal components to adjust for population stratification. It is seen that without correction for population stratification clusters seem to be influenced by population stratification while with correction clusters are unrelated to continental origin of individuals.

Lower-order effects adjustment in quantitative traits model-based multifactor dimensionality reduction.

Identifying gene-gene interactions or gene-environment interactions in studies of human complex diseases remains a big challenge in genetic epidemiology. An additional challenge, often forgotten, is to account for important lower-order genetic effects. These may hamper the identification of genuine epistasis. If lower-order genetic effects contribute to the genetic variance of a trait, identified statistical interactions may simply be due to a signal boost of these effects. In this study, we restrict attention to quantitative traits and bi-allelic SNPs as genetic markers. Moreover, our interaction study focuses on 2-way SNP-SNP interactions. Via simulations, we assess the performance of different corrective measures for lower-order genetic effects in Model-Based Multifactor Dimensionality Reduction epistasis detection, using additive and co-dominant coding schemes. Performance is evaluated in terms of power and familywise error rate. Our simulations indicate that empirical power estimates are reduced with correction of lower-order effects, likewise familywise error rates. Easy-to-use automatic SNP selection procedures, SNP selection based on "top" findings, or SNP selection based on p-value criterion for interesting main effects result in reduced power but also almost zero false positive rates. Always accounting for main effects in the SNP-SNP pair under investigation during Model-Based Multifactor Dimensionality Reduction analysis adequately controls false positive epistasis findings. This is particularly true when adopting a co-dominant corrective coding scheme. In conclusion, automatic search procedures to identify lower-order effects to correct for during epistasis screening should be avoided. The same is true for procedures that adjust for lower-order effects prior to Model-Based Multifactor Dimensionality Reduction and involve using residuals as the new trait. We advocate using "on-the-fly" lower-order effects adjusting when screening for SNP-SNP interactions using Model-Based Multifactor Dimensionality Reduction analysis.

Does pet ownership in infancy lead to asthma or allergy at school age? Pooled analysis of individual participant data from 11 European birth cohorts.

OBJECTIVE: To examine the associations between pet keeping in early childhood and asthma and allergies in children aged 6-10 years. DESIGN: Pooled analysis of individual participant data of 11 prospective European birth cohorts that recruited a total of over 22,000 children in the 1990s. EXPOSURE DEFINITION: Ownership of only cats, dogs, birds, rodents, or cats/dogs combined during the first 2 years of life. OUTCOME DEFINITION: Current asthma (primary outcome), allergic asthma, allergic rhinitis and allergic sensitization during 6-10 years of age. DATA SYNTHESIS: Three-step approach: (i) Common definition of outcome and exposure variables across cohorts; (ii) calculation of adjusted effect estimates for each cohort; (iii) pooling of effect estimates by using random effects meta-analysis models. RESULTS: We found no association between furry and feathered pet keeping early in life and asthma in school age. For example, the odds ratio for asthma comparing cat ownership with "no pets" (10 studies, 11489 participants) was 1.00 (95% confidence interval 0.78 to 1.28) (I(2) = 9%; p = 0.36). The odds ratio for asthma comparing dog ownership with "no pets" (9 studies, 11433 participants) was 0.77 (0.58 to 1.03) (I(2) = 0%, p = 0.89). Owning both cat(s) and dog(s) compared to "no pets" resulted in an odds ratio of 1.04 (0.59 to 1.84) (I(2) = 33%, p = 0.18). Similarly, for allergic asthma and for allergic rhinitis we did not find associations regarding any type of pet ownership early in life. However, we found some evidence for an association between ownership of furry pets during the first 2 years of life and reduced likelihood of becoming sensitized to aero-allergens. CONCLUSIONS: Pet ownership in early life did not appear to either increase or reduce the risk of asthma or allergic rhinitis symptoms in children aged 6-10. Advice from health care practitioners to avoid or to specifically acquire pets for primary prevention of asthma or allergic rhinitis in children should not be given.

Model-Based Multifactor Dimensionality Reduction to detect epistasis for quantitative traits in the presence of error-free and noisy data.

Detecting gene-gene interactions or epistasis in studies of human complex diseases is a big challenge in the area of epidemiology. To address this problem, several methods have been developed, mainly in the context of data dimensionality reduction. One of these methods, Model-Based Multifactor Dimensionality Reduction, has so far mainly been applied to case-control studies. In this study, we evaluate the power of Model-Based Multifactor Dimensionality Reduction for quantitative traits to detect gene-gene interactions (epistasis) in the presence of error-free and noisy data. Considered sources of error are genotyping errors, missing genotypes, phenotypic mixtures and genetic heterogeneity. Our simulation study encompasses a variety of settings with varying minor allele frequencies and genetic variance for different epistasis models. On each simulated data, we have performed Model-Based Multifactor Dimensionality Reduction in two ways: with and without adjustment for main effects of (known) functional SNPs. In line with binary trait counterparts, our simulations show that the power is lowest in the presence of phenotypic mixtures or genetic heterogeneity compared to scenarios with missing genotypes or genotyping errors. In addition, empirical power estimates reduce even further with main effects corrections, but at the same time, false-positive percentages are reduced as well. In conclusion, phenotypic mixtures and genetic heterogeneity remain challenging for epistasis detection, and careful thought must be given to the way important lower-order effects are accounted for in the analysis.

Comparison of genetic association strategies in the presence of rare alleles.

In the quest for the missing heritability of most complex diseases, rare variants have received increased attention. Advances in large-scale sequencing have led to a shift from the common disease/common variant hypothesis to the common disease/rare variant hypothesis or have at least reopened the debate about the relevance and importance of rare variants for gene discoveries. The investigation of modeling and testing approaches to identify significant disease/rare variant associations is in full motion. New methods to better deal with parameter estimation instabilities, convergence problems, or multiple testing corrections in the presence of rare variants or effect modifiers of rare variants are in their infancy. Using a recently developed semiparametric strategy to detect causal variants, we investigate the performance of the model-based multifactor dimensionality reduction (MB-MDR) technique in terms of power and family-wise error rate (FWER) control in the presence of rare variants, using population-based and family-based data (FAM-MDR). We compare family-based results obtained from MB-MDR analyses to screening findings from a quantitative trait Pedigree-based association test (PBAT). Population-based data were further examined using penalized regression models. We restrict attention to all available single-nucleotide polymorphisms on chromosome 4 and consider Q1 as the outcome of interest. The considered family-based methods identified marker C4S4935 in the VEGFC gene with estimated power not exceeding 0.35 (FAM-MDR), when FWER was kept under control. The considered population-based methods gave rise to highly inflated FWERs (up to 90% for PBAT screening).

Molecular reclassification of Crohn's disease by cluster analysis of genetic variants.

BACKGROUND: Crohn's Disease (CD) has a heterogeneous presentation, and is typically classified according to extent and location of disease. The genetic susceptibility to CD is well known and genome-wide association scans (GWAS) and meta-analysis thereof have identified over 30 susceptibility loci. Except for the association between ileal CD and NOD2 mutations, efforts in trying to link CD genetics to clinical subphenotypes have not been very successful. We hypothesized that the large number of confirmed genetic variants enables (better) classification of CD patients. METHODOLOGY/PRINCIPAL FINDINGS: To look for genetic-based subgroups, genotyping results of 46 SNPs identified from CD GWAS were analyzed by Latent Class Analysis (LCA) in CD patients and in healthy controls. Six genetic-based subgroups were identified in CD patients, which were significantly different from the five subgroups found in healthy controls. The identified CD-specific clusters are therefore likely to contribute to disease behavior. We then looked at whether we could relate the genetic-based subgroups to the currently used clinical parameters. Although modest differences in prevalence of disease location and behavior could be observed among the CD clusters, Random Forest analysis showed that patients could not be allocated to one of the 6 genetic-based subgroups based on the typically used clinical parameters alone. This points to a poor relationship between the genetic-based subgroups and the used clinical subphenotypes. CONCLUSIONS/SIGNIFICANCE: This approach serves as a first step to reclassify Crohn's disease. The used technique can be applied to other common complex diseases as well, and will help to complete patient characterization, in order to evolve towards personalized medicine.