Polymorphisms in genes of melatonin biosynthesis and signaling support the light-at-night hypothesis for breast cancer.

Light-at-night triggers the decline of pineal gland melatonin biosynthesis and secretion and is an IARC-classified probable breast-cancer risk factor. We applied a large-scale molecular epidemiology approach to shed light on the putative role of melatonin in breast cancer. We investigated associations between breast-cancer risk and polymorphisms at genes of melatonin biosynthesis/signaling using a study population of 44,405 women from the Breast Cancer Association Consortium (22,992 cases, 21,413 population-based controls). Genotype data of 97 candidate single nucleotide polymorphisms (SNPs) at 18 defined gene regions were investigated for breast-cancer risk effects. We calculated adjusted odds ratios (ORs) and 95% confidence intervals (CI) by logistic regression for the main-effect analysis as well as stratified analyses by estrogen- and progesterone-receptor (ER, PR) status. SNP-SNP interactions were analyzed via a two-step procedure based on logic regression. The Bayesian false-discovery probability (BFDP) was used for all analyses to account for multiple testing. Noteworthy associations (BFDP < 0.8) included 10 linked SNPs in tryptophan hydroxylase 2 (TPH2) (e.g. rs1386492: OR = 1.07, 95% CI 1.02-1.12), and a SNP in the mitogen-activated protein kinase 8 (MAPK8) (rs10857561: OR = 1.11, 95% CI 1.04-1.18). The SNP-SNP interaction analysis revealed noteworthy interaction terms with TPH2- and MAPK-related SNPs (e.g. rs1386483R ∧ rs1473473D ∧ rs3729931D: OR = 1.20, 95% CI 1.09-1.32). In line with the light-at-night hypothesis that links shift work with elevated breast-cancer risks our results point to SNPs in TPH2 and MAPK-genes that may impact the intricate network of circadian regulation.

Influence of breast cancer risk factors on proliferation and DNA damage in human breast glandular tissues: role of intracellular estrogen levels, oxidative stress and estrogen biotransformation.

Breast cancer etiology is associated with both proliferation and DNA damage induced by estrogens. Breast cancer risk factors (BCRF) such as body mass index (BMI), smoking, and intake of estrogen-active drugs were recently shown to influence intratissue estrogen levels. Thus, the aim of the present study was to investigate the influence of BCRF on estrogen-induced proliferation and DNA damage in 41 well-characterized breast glandular tissues derived from women without breast cancer. Influence of intramammary estrogen levels and BCRF on estrogen receptor (ESR) activation, ESR-related proliferation (indicated by levels of marker transcripts), oxidative stress (indicated by levels of GCLC transcript and oxidative derivatives of cholesterol), and levels of transcripts encoding enzymes involved in estrogen biotransformation was identified by multiple linear regression models. Metabolic fluxes to adducts of estrogens with DNA (E-DNA) were assessed by a metabolic network model (MNM) which was validated by comparison of calculated fluxes with data on methoxylated and glucuronidated estrogens determined by GC- and UHPLC-MS/MS. Intratissue estrogen levels significantly influenced ESR activation and fluxes to E-DNA within the MNM. Likewise, all BCRF directly and/or indirectly influenced ESR activation, proliferation, and key flux constraints influencing E-DNA (i.e., levels of estrogens, CYP1B1, SULT1A1, SULT1A2, and GSTP1). However, no unambiguous total effect of BCRF on proliferation became apparent. Furthermore, BMI was the only BCRF to indeed influence fluxes to E-DNA (via congruent adverse influence on levels of estrogens, CYP1B1 and SULT1A2).

Combining heterogeneous subgroups with graph-structured variable selection priors for Cox regression.

BACKGROUND: Important objectives in cancer research are the prediction of a patient's risk based on molecular measurements such as gene expression data and the identification of new prognostic biomarkers (e.g. genes). In clinical practice, this is often challenging because patient cohorts are typically small and can be heterogeneous. In classical subgroup analysis, a separate prediction model is fitted using only the data of one specific cohort. However, this can lead to a loss of power when the sample size is small. Simple pooling of all cohorts, on the other hand, can lead to biased results, especially when the cohorts are heterogeneous. RESULTS: We propose a new Bayesian approach suitable for continuous molecular measurements and survival outcome that identifies the important predictors and provides a separate risk prediction model for each cohort. It allows sharing information between cohorts to increase power by assuming a graph linking predictors within and across different cohorts. The graph helps to identify pathways of functionally related genes and genes that are simultaneously prognostic in different cohorts. CONCLUSIONS: Results demonstrate that our proposed approach is superior to the standard approaches in terms of prediction performance and increased power in variable selection when the sample size is small.

Interval estimation of the overall treatment effect in random-effects meta-analyses: Recommendations from a simulation study comparing frequentist, Bayesian, and bootstrap methods.

There exists a variety of interval estimators for the overall treatment effect in a random-effects meta-analysis. A recent literature review summarizing existing methods suggested that in most situations, the Hartung-Knapp/Sidik-Jonkman (HKSJ) method was preferable. However, a quantitative comparison of those methods in a common simulation study is still lacking. Thus, we conduct such a simulation study for continuous and binary outcomes, focusing on the medical field for application. Based on the literature review and some new theoretical considerations, a practicable number of interval estimators is selected for this comparison: the classical normal-approximation interval using the DerSimonian-Laird heterogeneity estimator, the HKSJ interval using either the Paule-Mandel or the Sidik-Jonkman heterogeneity estimator, the Skovgaard higher-order profile likelihood interval, a parametric bootstrap interval, and a Bayesian interval using different priors. We evaluate the performance measures (coverage and interval length) at specific points in the parameter space, that is, not averaging over a prior distribution. In this sense, our study is conducted from a frequentist point of view. We confirm the main finding of the literature review, the general recommendation of the HKSJ method (here with the Sidik-Jonkman heterogeneity estimator). For meta-analyses including only two studies, the high length of the HKSJ interval limits its practical usage. In this case, the Bayesian interval using a weakly informative prior for the heterogeneity may help. Our recommendations are illustrated using a real-world meta-analysis dealing with the efficacy of an intramyocardial bone marrow stem cell transplantation during coronary artery bypass grafting.

Influence of breast cancer risk factors and intramammary biotransformation on estrogen homeostasis in the human breast.

Understanding intramammary estrogen homeostasis constitutes the basis of understanding the role of lifestyle factors in breast cancer etiology. Thus, the aim of the present study was to identify variables influencing levels of the estrogens present in normal breast glandular and adipose tissues (GLT and ADT, i.e., 17ß-estradiol, estrone, estrone-3-sulfate, and 2-methoxy-estrone) by multiple linear regression models. Explanatory variables (exVARs) considered were (a) levels of metabolic precursors as well as levels of transcripts encoding proteins involved in estrogen (biotrans)formation, (b) data on breast cancer risk factors (i.e., body mass index, BMI, intake of estrogen-active drugs, and smoking) collected by questionnaire, and (c) tissue characteristics (i.e., mass percentage of oil, oil%, and lobule type of the GLT). Levels of estrogens in GLT and ADT were influenced by both extramammary production (menopausal status, intake of estrogen-active drugs, and BMI) thus showing that variables known to affect levels of circulating estrogens influence estrogen levels in breast tissues as well for the first time. Moreover, intratissue (biotrans)formation (by aromatase, hydroxysteroid-17beta-dehydrogenase 2, and beta-glucuronidase) influenced intratissue estrogen levels, as well. Distinct differences were observed between the exVARs exhibiting significant influence on (a) levels of specific estrogens and (b) the same dependent variables in GLT and ADT. Since oil% and lobule type of GLT influenced levels of some estrogens, these variables may be included in tissue characterization to prevent sample bias. In conclusion, evidence for the intracrine activity of the human breast supports biotransformation-based strategies for breast cancer prevention. The susceptibility of estrogen homeostasis to systemic and tissue-specific modulation renders both beneficial and adverse effects of further variables associated with lifestyle and the environment possible.

Qualitative and quantitative differences in estrogen biotransformation in human breast glandular and adipose tissues: implications for studies using mammary biospecimens.

Because of its assumed role in breast cancer etiology, estrogen biotransformation (and interaction of compounds therewith) has been investigated in human biospecimens for decades. However, little attention has been paid to the well-known fact that large inter-individual variations exist in the proportion of breast glandular (GLT) and adipose (ADT) tissues and less to adequate tissue characterization. To assess the relevance of this, the present study compares estrogen biotransformation in GLT and ADT. GLT and ADT were isolated from 47 reduction mammoplasty specimens derived from women without breast cancer and were characterized histologically and by their percentages of oil. Levels of 12 unconjugated and five conjugated estrogens were analyzed by GC- and UHPLC-MS/MS, respectively, and levels of 27 transcripts encoding proteins involved in estrogen biotransformation by Taqman® probe-based PCR. Unexpectedly, one-third of specimens provided neat GLT only after cryosection. Whereas 17ß-estradiol, estrone, and estrone-3-sulfate were detected in both tissues, estrone-3-glucuronide and 2-methoxy-estrone were detected predominately in GLT and ADT, respectively. Estrogen levels as well as ratios 17ß-estradiol/estrone and estrone-3-sulfate/estrone differed significantly between GLT and ADT, yet less than between individuals. Furthermore, estrogen levels in GLT and ADT correlated significantly with each other. In contrast, levels of most transcripts encoding enzymes involved in biotransformation differed more than between individuals and did not correlate between ADT and GLT. Thus, mixed breast tissues (and plasma) will not provide meaningful information on local estrogen biotransformation (and interaction of compounds therewith) whereas relative changes in 17ß-estradiol levels may be investigated in the more abundant ADT.

Evaluation of the DNA damage in lymphocytes, sperm and buccal cells of workers under environmental and occupational boron exposure conditions.

Industrial production and use of boron compounds have increased during the last decades, especially for the manufacture of borosilicate glass, fiberglass, metal alloys and flame retardants. This study was conducted in two districts of Balikesir; Bandirma and Bigadic, which geographically belong to the Marmara Region of Turkey. Bandirma is the production and exportation zone for the produced boric acid and some borates and Bigadic has the largest B deposits in Turkey. 102 male workers who were occupationally exposed to boron from Bandirma and 110 workers who were occupationally and environmentally exposed to boron from Bigadic participated to our study. In this study the DNA damage in the sperm, blood and buccal cells of 212 males was evaluated by comet and micronucleus assays. No significant increase in the DNA damage in blood, sperm and buccal cells was observed in the residents exposed to boron both occupationally and environmentally (p = 0.861) for Comet test in the sperm samples, p = 0.116 for Comet test in the lymphocyte samples, p = 0.042 for micronucleus (MN) test, p = 0.955 for binucleated cells (BN), p = 1.486 for condensed chromatin (CC), p = 0.455 for karyorrhectic cells (KHC), p = 0.541 for karyolitic cells (KLY), p = 1.057 for pyknotic cells (PHC), p = 0.331 for nuclear bud (NBUD)). No correlations were seen between blood boron levels and tail intensity values of the sperm samples, lymphocyte samples, frequencies of MN, BN, KHC, KYL, PHC and NBUD. The results of this study came to the same conclusions of the previous studies that boron does not induce DNA damage even under extreme exposure conditions.

Identification of interactions of binary variables associated with survival time using survivalFS.

Many medical studies aim to identify factors associated with a time to an event such as survival time or time to relapse. Often, in particular, when binary variables are considered in such studies, interactions of these variables might be the actual relevant factors for predicting, e.g., the time to recurrence of a disease. Testing all possible interactions is often not possible, so that procedures such as logic regression are required that avoid such an exhaustive search. In this article, we present an ensemble method based on logic regression that can cope with the instability of the regression models generated by logic regression. This procedure called survivalFS also provides measures for quantifying the importance of the interactions forming the logic regression models on the time to an event and for the assessment of the individual variables that take the multivariate data structure into account. In this context, we introduce a new performance measure, which is an adaptation of Harrel's concordance index. The performance of survivalFS and the proposed importance measures is evaluated in a simulation study as well as in an application to genotype data from a urinary bladder cancer study. Furthermore, we compare the performance of survivalFS and its importance measures for the individual variables with the variable importance measure used in random survival forests, a popular procedure for the analysis of survival data. These applications show that survivalFS is able to identify interactions associated with time to an event and to outperform random survival forests.

Integrative analysis of genome-wide gene copy number changes and gene expression in non-small cell lung cancer.

Non-small cell lung cancer (NSCLC) represents a genomically unstable cancer type with extensive copy number aberrations. The relationship of gene copy number alterations and subsequent mRNA levels has only fragmentarily been described. The aim of this study was to conduct a genome-wide analysis of gene copy number gains and corresponding gene expression levels in a clinically well annotated NSCLC patient cohort (n = 190) and their association with survival. While more than half of all analyzed gene copy number-gene expression pairs showed statistically significant correlations (10,296 of 18,756 genes), high correlations, with a correlation coefficient >0.7, were obtained only in a subset of 301 genes (1.6%), including KRAS, EGFR and MDM2. Higher correlation coefficients were associated with higher copy number and expression levels. Strong correlations were frequently based on few tumors with high copy number gains and correspondingly increased mRNA expression. Among the highly correlating genes, GO groups associated with posttranslational protein modifications were particularly frequent, including ubiquitination and neddylation. In a meta-analysis including 1,779 patients we found that survival associated genes were overrepresented among highly correlating genes (61 of the 301 highly correlating genes, FDR adjusted p<0.05). Among them are the chaperone CCT2, the core complex protein NUP107 and the ubiquitination and neddylation associated protein CAND1. In conclusion, in a comprehensive analysis we described a distinct set of highly correlating genes. These genes were found to be overrepresented among survival-associated genes based on gene expression in a large collection of publicly available datasets.

Identification and replication of the interplay of four genetic high-risk variants for urinary bladder cancer.

Little is known whether genetic variants identified in genome-wide association studies interact to increase bladder cancer risk. Recently, we identified two- and three-variant combinations associated with a particular increase of bladder cancer risk in a urinary bladder cancer case-control series (Leibniz Research Centre for Working Environment and Human Factors at TU Dortmund (IfADo), 1501 cases, 1565 controls). In an independent case-control series (Nijmegen Bladder Cancer Study, NBCS, 1468 cases, 1720 controls) we confirmed these two- and three-variant combinations. Pooled analysis of the two studies as discovery group (IfADo-NBCS) resulted in sufficient statistical power to test up to four-variant combinations by a logistic regression approach. The New England and Spanish Bladder Cancer Studies (2080 cases and 2167 controls) were used as a replication series. Twelve previously identified risk variants were considered. The strongest four-variant combination was obtained in never smokers. The combination of rs1014971[AA] near apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like 3A (APOBEC3A) and chromobox homolog 6 (CBX6), solute carrier family 1s4 (urea transporter), member 1 (Kidd blood group) (SLC14A1) exon single nucleotide polymorphism (SNP) rs1058396[AG, GG], UDP glucuronosyltransferase 1 family, polypeptide A complex locus (UGT1A) intron SNP rs11892031[AA] and rs8102137[CC, CT] near cyclin E1 (CCNE1) resulted in an unadjusted odds ratio (OR) of 2.59 (95% CI = 1.93-3.47; P = 1.87 × 10-10), while the individual variant ORs ranged only between 1.11 and 1.30. The combination replicated in the New England and Spanish Bladder Cancer Studies (ORunadjusted = 1.60, 95% CI = 1.10-2.33; P = 0.013). The four-variant combination is relatively frequent, with 25% in never smoking cases and 11% in never smoking controls (total study group: 19% cases, 14% controls). In conclusion, we show that four high-risk variants can statistically interact to confer increased bladder cancer risk particularly in never smokers.

Integration of Multiple Genomic Data Sources in a Bayesian Cox Model for Variable Selection and Prediction.

Bayesian variable selection becomes more and more important in statistical analyses, in particular when performing variable selection in high dimensions. For survival time models and in the presence of genomic data, the state of the art is still quite unexploited. One of the more recent approaches suggests a Bayesian semiparametric proportional hazards model for right censored time-to-event data. We extend this model to directly include variable selection, based on a stochastic search procedure within a Markov chain Monte Carlo sampler for inference. This equips us with an intuitive and flexible approach and provides a way for integrating additional data sources and further extensions. We make use of the possibility of implementing parallel tempering to help improve the mixing of the Markov chains. In our examples, we use this Bayesian approach to integrate copy number variation data into a gene-expression-based survival prediction model. This is achieved by formulating an informed prior based on copy number variation. We perform a simulation study to investigate the model's behavior and prediction performance in different situations before applying it to a dataset of glioblastoma patients and evaluating the biological relevance of the findings.

Applicability of the Global Lung Initiative 2012 Reference Values for Spirometry for Longitudinal Data of Elderly Women.

BACKGROUND AND OBJECTIVES: Lung function depends nonlinearly on age and height, so that the use of age and height specific reference values is required. The widely used age and height specific GLI (Global Lung Initiative) z-scores derived from cross-sectional data, however, have not been proven for validity in an elderly population or for longitudinal data. Therefore, we aimed to test their validity in a population of elderly women followed prospectively for more than 20 years. METHODS: We used spirometric data (forced expiratory volume in 1 second (FEV1) and forced vital capacity (FVC) and FEV1/FVC) from the SALIA cohort of German women (baseline: 1985-1994 (aged 55 years), follow-up: 2008/2009 and 2012/2013). We calculated GLI-z-scores for baseline and follow-up examination separately (cross-sectional evaluation) and individual differences in z-scores between baseline and follow-up (longitudinal evaluation) for healthy never-smoking women. RESULTS: GLI reference values for FEV1, FVC and FEV1/FVC were cross-sectionally and longitudinally equivalent with our SALIA data. The mean change in z-scores between baseline and follow-up was 0.33 for FEV1, 0.38 for FVC and -0.10 for FEV1/FVC. CONCLUSIONS: In conclusion, GLI z-scores fit cross-sectionally and longitudinally with FEV1, FVC and FEV1/FVC measured in women from Germany which indicates that they can be used in longitudinal association analyses.

Uncovering distinct protein-network topologies in heterogeneous cell populations.

BACKGROUND: Cell biology research is fundamentally limited by the number of intracellular components, particularly proteins, that can be co-measured in the same cell. Therefore, cell-to-cell heterogeneity in unmeasured proteins can lead to completely different observed relations between the same measured proteins. Attempts to infer such relations in a heterogeneous cell population can yield uninformative average relations if only one underlying biochemical network is assumed. To address this, we developed a method that recursively couples an iterative unmixing process with a Bayesian analysis of each unmixed subpopulation. RESULTS: Our approach enables to identify the number of distinct cell subpopulations, unmix their corresponding observations and resolve the network structure of each subpopulation. Using simulations of the MAPK pathway upon EGF and NGF stimulations we assess the performance of the method. We demonstrate that the presented method can identify better than clustering approaches the number of subpopulations within a mixture of observations, thus resolving correctly the statistical relations between the proteins. CONCLUSIONS: Coupling the unmixing of multiplexed observations with the inference of statistical relations between the measured parameters is essential for the success of both of these processes. Here we present a conceptual and algorithmic solution to achieve such coupling and hence to analyze data obtained from a natural mixture of cell populations. As the technologies and necessity for multiplexed measurements are rising in the systems biology era, this work addresses an important current challenge in the analysis of the derived data.

Improvements in algorithms for phenotype inference: the NAT2 example.

Numerous studies have analyzed the impact of N-acetyltransferase 2 (NAT2) polymorphisms on drug efficacy, side effects as well as cancer risk. Here, we present the state of the art of deriving haplotypes from polymorphisms and discuss the available software. PHASE v2.1 is currently considered a gold standard for NAT2 haplotype assignment. In vitro studies have shown that some slow acetylation genotypes confer reduced protein stability. This has been observed particularly for G191A, T341C and G590A. Substantial ethnic variations of the acetylation status have been described. Probably, upcoming agriculture and the resulting change in diet caused a selection pressure for slow acetylation. In recent years much research has been done to reduce the complexity of NAT2 genotyping. Deriving the haplotype from seven SNPs is still considered a gold standard. However, meanwhile several studies have shown that a two-SNP combination, C282T and T341C, results in a similarly good distinction in Caucasians. However, attempts to further reduce complexity to only one 'tagging SNP' (rs1495741) may lead to wrong predictions where phenotypically slow acetylators were genotyped as intermediate or rapid. Numerous studies have shown that slow NAT2 haplotypes are associated with increased urinary bladder cancer risk and increased risk of anti-tuberculosis drug-induced hepatotoxicity. A drawback of the current practice of solely discriminating slow, intermediate and rapid genotypes for phenotype inference is limited resolution of differences between slow acetylators. Future developments to differentiate between slow and ultra-slow genotypes may further improve individualized drug dosing and epidemiological studies of cancer risk.

Comparison of Bayesian methods for flexible modeling of spatial risk surfaces in disease mapping.

Bayesian hierarchical models usually model the risk surface on the same arbitrary geographical units for all data sources. Poisson/gamma random field models overcome this restriction as the underlying risk surface can be specified independently to the resolution of the data. Moreover, covariates may be considered as either excess or relative risk factors. We compare the performance of the Poisson/gamma random field model to the Markov random field (MRF)-based ecologic regression model and the Bayesian Detection of Clusters and Discontinuities (BDCD) model, in both a simulation study and a real data example. We find the BDCD model to have advantages in situations dominated by abruptly changing risk while the Poisson/gamma random field model convinces by its flexibility in the estimation of random field structures and by its flexibility incorporating covariates. The MRF-based ecologic regression model is inferior. WinBUGS code for Poisson/gamma random field models is provided.

Refinement of the prediction of N-acetyltransferase 2 (NAT2) phenotypes with respect to enzyme activity and urinary bladder cancer risk.

Polymorphisms of N-acetyltransferase 2 (NAT2) are well known to modify urinary bladder cancer risk as well as efficacy and toxicity of pharmaceuticals via reduction in the enzyme's acetylation capacity. Nevertheless, the discussion about optimal NAT2 phenotype prediction, particularly differentiation between different degrees of slow acetylation, is still controversial. Therefore, we investigated the impact of single nucleotide polymorphisms and their haplotypes on slow acetylation in vivo and on bladder cancer risk. For this purpose, we used a study cohort of 1,712 bladder cancer cases and 2,020 controls genotyped for NAT2 by RFLP-PCR and for the tagSNP rs1495741 by TaqMan(®) assay. A subgroup of 344 individuals was phenotyped by the caffeine test in vivo. We identified an 'ultra-slow' acetylator phenotype based on combined *6A/*6A, *6A/*7B and *7B/*7B genotypes containing the homozygous minor alleles of C282T (rs1041983, *6A, *7B) and G590A (rs1799930, *6A). 'Ultra-slow' acetylators have significantly about 32 and 46 % lower activities of caffeine metabolism compared with other slow acetylators and with the *5B/*5B genotypes, respectively (P < 0.01, both). The 'ultra-slow' genotype showed an association with bladder cancer risk in the univariate analysis (OR = 1.31, P = 0.012) and a trend adjusted for age, gender and smoking habits (OR = 1.22, P = 0.082). In contrast, slow acetylators in general were not associated with bladder cancer risk, neither in the univariate (OR = 1.02, P = 0.78) nor in the adjusted (OR = 0.98, P = 0.77) analysis. In conclusion, this study suggests that NAT2 phenotype prediction should be refined by consideration of an 'ultra-slow' acetylation genotype.

TCL1A and ATM are co-expressed in chronic lymphocytic leukemia cells without deletion of 11q.

Chronic lymphocytic leukemia is characterized by the accumulation of B cells that are resistant to apoptosis. This resistance is induced by pro-survival stimuli from the microenvironment. TCL1 and ATM are central to the pathogenesis of the disease and associated with more aggressive disease. Their protein products have recently been shown to physically interact in leukemic cells and to impact on NF-κB signaling, which is a key regulator of apoptosis. In the present study we show that TCL1 and ATM are significantly co-expressed and up-regulated in malignant cells compared to non-malignant B cells, and that expression of TCL1 is partially deregulated by aberrant DNA-methylation. In addition, complex external stimuli induce essentially similar TCL1 and ATM time-course kinetics. In line with a coordinative regulation of NF-κB signaling by TCL1, its knockdown induced apoptosis in primary leukemia cells. These findings suggest that both genes functionally cooperate to modulate similar apoptosis-related cellular pathways.

Cluster-localized sparse logistic regression for SNP data.

The task of analyzing high-dimensional single nucleotide polymorphism (SNP) data in a case-control design using multivariable techniques has only recently been tackled. While many available approaches investigate only main effects in a high-dimensional setting, we propose a more flexible technique, cluster-localized regression (CLR), based on localized logistic regression models, that allows different SNPs to have an effect for different groups of individuals. Separate multivariable regression models are fitted for the different groups of individuals by incorporating weights into componentwise boosting, which provides simultaneous variable selection, hence sparse fits. For model fitting, these groups of individuals are identified using a clustering approach, where each group may be defined via different SNPs. This allows for representing complex interaction patterns, such as compositional epistasis, that might not be detected by a single main effects model. In a simulation study, the CLR approach results in improved prediction performance, compared to the main effects approach, and identification of important SNPs in several scenarios. Improved prediction performance is also obtained for an application example considering urinary bladder cancer. Some of the identified SNPs are predictive for all individuals, while others are only relevant for a specific group. Together with the sets of SNPs that define the groups, potential interaction patterns are uncovered.

Optimal strategies for sequential validation of significant features from high-dimensional genomic data.

High-dimensional genomic studies play a key role in identifying critical features that are significantly associated with a phenotypic outcome. The two most important examples are the detection of (1) differentially expressed genes from genome-wide gene expression studies and (2) single-nucleotide polymorphisms (SNPs) from genome-wide association studies. Such experiments are often associated with high noise levels, and the validity of statistical conclusions suffers from low sample size compared to large number of features. The corresponding multiple testing problem calls for the identification of optimal strategies for controlling the numbers of false discoveries and false nondiscoveries. In addition, a frequent validation problem is that features identified as important in one study are often less so in another study. Adjustment for multiple testing in both studies separately increases the risk of missing the crucial features even further. These problems can be addressed by sequential validation strategies, where only significant features identified in one study enter as candidates in the next study. The quality associated with different studies, for example, in terms of noise levels, may vary considerably. By performing simulation studies it is possible to demonstrate that the optimal order for this stepwise procedure is to sort experimental studies according to their quality in descending order. The impact of the method for multiple testing adjustment (Bonferroni-Holm, FDR) was also analyzed. Finally, the sequential validation strategy was applied to three large breast cancer studies with gene expression measurements, confirming the crucial impact of the order of the validation steps in a real-world application.