An efficient algorithm to perform multiple testing in epistasis screening

in BMC Bioinformatics (2013), 14

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 ... [more ▼]

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 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 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. [less ▲]

Genomic Association Screening Methodology for High-Dimensional and Complex Data Structures: Detecting n-Order Interactions

Doctoral thesis (2012)

We developed a data-mining method, Model-Based Multifactor Dimensionality Reduction (MB-MDR) to detect epistatic interactions for different types of traits. MB-MDR enables the fast identification of gene ... [more ▼]

We developed a data-mining method, Model-Based Multifactor Dimensionality Reduction (MB-MDR) to detect epistatic interactions for different types of traits. MB-MDR enables the fast identification of gene-gene interactions among 1000nds of SNPs, without the need to make restrictive assumptions about the genetic modes of inheritance. This thesis primarily focused on applying Model-Based Multifactor Dimensionality Reduction for quantitative traits, its performance and application to a variety of data problems. We carried out several simulation studies to evaluate quantitative MB-MDR in terms of power and type I error, when data are noisy, non-normal or skewed and when important main effects are present. Firstly, we assessed the performance of MB-MDR in the presence of noisy data. The error sources considered were missing genotypes, genotyping error, phenotypic mixtures and genetic heterogeneity. Results from this study showed that MB-MDR is least affected by presence of small percentages of missing data and genotyping errors but much affected in the presence of phenotypic mixtures and genetic heterogeneity. This is in line with a similar study performed for binary traits. Although both Multifactor Dimensionality Reduction (MDR) and MB-MDR are data reduction techniques with a common basis, their ways of deriving significant interactions are substantially different. Nevertheless, effects on power of introducing error sources were quite similar. Irrespective of the trait under consideration, epistasis screening methodologies such as MB-MDR and MDR mainly suffer from the presence of phenotypic mixtures and genetic heterogeneity. Secondly, we extensively addressed the issue of adjusting for lower-order genetic effects during epistasis screening, using different adjustment strategies for SNPs in the functional SNP-SNP interaction pair, and/or for additional important SNPs. Since, in this thesis, we restrict attention to 2-locus interactions only, adjustment for lower-order effects always (and only) implies adjustment for main genetic effects. Unfortunately most data dimensionality reduction techniques based on MDR do not explicitly require that lower-order effects are included in the ‘model’ when investigating higher-order effects (a prerequisite for most traditional, especially regression-based, methods). However, epistasis results may be hampered by the presence of significant lower-order effects. Results from this study showed hugely increased type I errors when main effects were not taken into account or were not properly accounted for. We observed that additive coding (the most commonly used coding in practice) in main effects adjustment does not remove all of the potential main effects that deviate from additive genetic variance. In addition, also adjusting for main effects prior to MB-MDR (via a regression framework), whatever coding is adopted, does not control type I error in all scenarios. From this study, we concluded that correction for lower-order effects should preferentially be done via codominant coding, to reduce the chance of false positive epistasis findings. The recommended way of performing an MB-MDR epistasis screening is to always adjust the analysis for lower-order effects of the SNPs under investigation, “on-the-fly”. This correction avoids overcorrection for other SNPs, which are not part of the interacting SNP pair under study. Thirdly, we assessed the cumulative effect of trait deviations from normality and homoscedasticity on the overall performance of quantitative MB-MDR to detect 2-locus epistasis signals in the absence of main effects. Although MB-MDR itself is a non-parametric method, in the sense that no assumptions are made regarding genetic modes of inheritance, the data reduction part in MB-MDR relies on association tests. In particular, for quantitative traits, the default MB-MDR way is to use the Student’s t-test (steps 1 and 2 of MB-MDR). Also when correcting for lower-order effects during quantitative MB-MDR analysis, we intrinsically maneuver within a regression framework. Since the Student’s t-statistic is the square root of the ANOVA F-statistic. Hence, along these lines, for MB-MDR to give valid results, ANOVA assumptions have to be met. Therefore, we simulated data from normal and non-normal distributions, with constant and non-constant variances, and performed association tests via the student’s t-test as well as the unequal variance t-test, commonly known as the Welch’s t-test. At first somewhat surprising, the results of this study showed that MB-MDR maintains adequate type I errors, irrespective of data distribution or association test used. On the other hand, MB-MDR give rise to lower power results for non-normal data compared to normal data. With respect to the association tests used within MB-MDR, in most cases, Welch’s t-test led to lower power compared to student’s t-test. To maintain the balance between power and type I error, we concluded that when performing MB-MDR analysis with quantitative traits, one ideally first rank-transforms traits to normality and then applies MB-MDR modeling with Student’s t-test as choice of association test. Clearly, before embarking on using a method in practice, there is a need to extensively check the applicability of the method to the data at hand. This is a common practice in biostatistics, but often a forgotten standard operating procedure in genetic epidemiology, in particular in GWAI studies. In addition to the presentation of extensive simulation studies, we also presented some MB-MDR applications to real-life data problems. These analyses involved MB-MDR analyses on quantitative as well as binary complex disease traits, primarily in the context of asthma/allergy and Crohn’s disease. In two of the presented analyses, MB-MDR confirmed logistic regression and transmission disequilibrium test (TDT) results. Part of the aforementioned methodological developments was initiated on the basis of observations of MB-MDR behavior on real-life data. Both the practical and theoretical components of this thesis confirm our belief in the potential of MB-MDR as a promising and versatile tool for the identification of epistatic effects, irrespective of the design (family-based or unrelated individuals) and irrespective of the targeted disease trait (binary, continuous, censored, categorical, multivariate). A thorough characterization of the different faces of MB-MDR this versatility gives rise to is work in progress. [less ▲]

Application of mixed polygenic model to control for cryptic/genuine relatedness and population stratification.

Poster (2012, March 12)

In genome-wide association studies (GWAs), population stratification may cause inflated type I errors and overly-optimistic test results, when not properly corrected for. During the past decade, several ... [more ▼]

In genome-wide association studies (GWAs), population stratification may cause inflated type I errors and overly-optimistic test results, when not properly corrected for. During the past decade, several methods have been proposed for association testing in the presence of population stratification. Among these, principal components-based approaches are the most popular. Principal component analysis (PCA) allows data transformation to a new coordinate system such that the projection of the data along the first new coordinate (called the PC1) has the largest variance; the second PC has the second largest variance, and so on. In practice, two components are usually enough to adjust or to control for population stratification. They can easily be included in parametric association models as covariates. Despite the success of this strategy, there are still some caveats which need further attention. Among these are that principal component-based methods generally do not account for cryptic relatedness (kinship) between supposedly unrelated individuals, are not straightforwardly adapted to accommodate family-based designs or mixtures of families and unrelated individuals, and do not always take proper account of the trait under investigation. In this work, we present an easy-to-use alternative that addresses the aforementioned issues. For quantitative traits, we propose to first use the mixed polygenic model (possibly taking into account important non-genetic confounders as covariates), second to derive “polygenic” residuals from this model – hereby removing genomic kinship relationships, and third to consider these residuals as new traits in a classical genome-wide QTL analysis for “unrelated individuals”. The polygenic component of the aforementioned mixed polygenic model describes the contribution from multiple independently segregating genes, all having a small additive effect on the trait under investigation. Via an extensive simulation study, with various settings of population stratification and admixture, we show that this approach not only removes most of the “relatedness” between individuals (cryptic relatedness or known relatedness), but also removes most of the remaining substructures caused by population stratification or admixture. As a proof of concept, we demonstrate the efficiency of this robust method to control for population stratification on real-life genome-scale data from the SNP Health Association Resource (SHARe) Asthma Resource project (SHARP) (dbGaP accession number phs000166.v2.p1). We also provide leads to extend this method to dichotomous traits. [less ▲]

Safety And Cost Of Infliximab For The Treatment Of Belgian Pediatric Patients With Crohn’s Disease

in Acta Gastro-Enterologica Belgica (2012, February)

Polymorphisms in the lectin pathway genes as a possible cause of early chronic Pseudomonas aeruginosa colonization in cystic fibrosis patients.

in Human Immunology (2012)

Safety And Cost Of Infliximab For The Treatment Of Belgian Pediatric Patients With Crohn’s Disease

in Journal of Crohn’s and Colitis (2012)

Inference and comparison of different genetic stratification techniques

Conference (2012)

Pets in Infancy - Asthma or Allergy at School Age? Pooled Analysis of Individual Participant Data from 11 European Birth Cohorts

in PLoS ONE (2012)

Comparison of genetic association strategies in the presence of rare alleles

in BMC Proceedings (2011)

A robustness study to investigate the performance of parametric and non-parametric tests used in Model-Based Multifactor Dimensionality Reduction Epistasis Detection.

Poster (2011, September 19)

Model-Based Multifactor Dimensionality Reduction (MB-MDR) is data mining technique to identify gene-gene interactions among 1000nds of SNPs in a fast way, without making assumptions about the mode of ... [more ▼]

Model-Based Multifactor Dimensionality Reduction (MB-MDR) is data mining technique to identify gene-gene interactions among 1000nds of SNPs in a fast way, without making assumptions about the mode of genetic interactions. By construction, one of the implementations of MB-MDR involves testing one multi-locus genotype cell versus the remaining cells, hereby creating two imbalanced groups for trait distribution comparison. To date, for continuous traits, we have adopted a standard F-test to compare these groups. When normality assumption or homoscedasticity no longer hold, highly inflated results are to be expected. The power and type I error control of MB-MDR under these assumptions has been thoroughly investigated in Mahachie John et al [1]. The aim of this study is to assess, through simulations, the effects of ANOVA model violations on the performance of Model-Based Multifactor Dimensionality Reduction (MB-MDR). We quantify their effect on MB-MDR using default options, but at the same time introduce alternative options with increased performance. The better handling of imbalanced data using robust approaches [2] within a MB-MDR context is exemplified on real data for asthma-related phenotypes. 1. EJHG (2011), Early view 2. David Freedman, Statistical Models: Theory and Practice, Cambridge University Press (2000), ISBN 978-0521671057 [less ▲]