Vittorio Perduca

Faster query answering in probabilistic databases using read-once functions

A boolean expression is in read-once form if each of its variables appears exactly once. When the variables denote independent events in a probability space, the probability of the event denoted by the whole expression in read-once form can be computed in polynomial time (whereas the general problem for arbitrary expressions is #P-complete). Known approaches to checking read-once property seem to require putting these expressions in disjunctive normal form. In this paper, we tell a better story for a large subclass of boolean event expressions: those that are generated by conjunctive queries without self-joins and on tuple-independent probabilistic databases. We first show that given a tuple-independent representation and the provenance graph of an SPJ query plan without self-joins, we can, without using the DNF of a result event expression, efficiently compute its co-occurrence graph. From this, the read-once form can already, if it exists, be computed efficiently using existing techniques. Our second and key contribution is a complete, efficient, and simple to implement algorithm for computing the read-once forms (whenever they exist) directly, using a new concept, that of co-table graph, which can be significantly smaller than the cooccurrence graph.

Alternative methods for H1 simulations in genome-wide association studies.

Objective: Assessing the statistical power to detect susceptibility variants plays a critical role in genome-wide association (GWA) studies both from the prospective and retrospective point of view. Power is empirically estimated by simulating phenotypes under a disease model H1. For this purpose, the gold standard consists in simulating genotypes given the phenotypes (e.g.Hapgen). We introduce here an alternative approach for simulating phenotypes under H1 that does not require generating new genotypes for each simulation. Methods: In order to simulate phenotypes with a fixed total number of cases and under a given disease model, we suggest 3 algorithms: (1) a simple rejection algorithm; (2) a numerical Markov chain Monte-Carlo (MCMC) approach, and (3) an exact and efficient backward sampling algorithm. In our study, we validated the 3 algorithms both on a simulated dataset and by comparing them with Hapgen on a more realistic dataset. For an application, we then conducted a simulation study on a 1000 Genomes Project dataset consisting of 629 individuals (314 cases) and 8,048 SNPs from chromosome X. We arbitrarily defined an additive disease model with two susceptibility SNPs and an epistatic effect. Results: The 3 algorithms are consistent, but backward sampling is dramatically faster than the other two. Our approach also gives consistent results with Hapgen. Using our application data, we showed that our limited design requires a biological a priori to limit the investigated region. We also proved that epistatic effects can play a significant role even when simple marker statistics (e.g. trend) are used. We finally showed that the overall performance of a GWA study strongly depends on the prevalence of the disease: the larger the prevalence, the better the power. Conclusions: Our approach is a valid alternative to Hapgen-type methods; it is not only dramatically faster but has 2 main advantages: (1) there is no need for sophisticated genotype models (e.g. haplotype frequencies, or recombination rates), and (2) the choice of the disease model is completely unconstrained (number of SNPs involved, gene-environment interactions, hybrid genetic models, etc.). Our 3 algorithms are available in an R package called ‘waffect’ (‘double-u affect’, for weighted affectations).

Identifying and correcting epigenetics measurements for systematic sources of variation.

BackgroundMethylation measures quantified by microarray techniques can be affected by systematic variation due to the technical processing of samples, which may compromise the accuracy of the measurement process and contribute to bias the estimate of the association under investigation. The quantification of the contribution of the systematic source of variation is challenging in datasets characterized by hundreds of thousands of features.In this study, we introduce a method previously developed for the analysis of metabolomics data to evaluate the performance of existing normalizing techniques to correct for unwanted variation. Illumina Infinium HumanMethylation450K was used to acquire methylation levels in over 421,000 CpG sites for 902 study participants of a case-control study on breast cancer nested within the EPIC cohort. The principal component partial R-square (PC-PR2) analysis was used to identify and quantify the variability attributable to potential systematic sources of variation. Three correcting techniques, namely ComBat, surrogate variables analysis (SVA) and a linear regression model to compute residuals were applied. The impact of each correcting method on the association between smoking status and DNA methylation levels was evaluated, and results were compared with findings from a large meta-analysis.ResultsA sizeable proportion of systematic variability due to variables expressing ‘batch’ and ‘sample position’ within ‘chip’ was identified, with values of the partial R2 statistics equal to 9.5 and 11.4% of total variation, respectively. After application of ComBat or the residuals’ methods, the contribution was 1.3 and 0.2%, respectively. The SVA technique resulted in a reduced variability due to ‘batch’ (1.3%) and ‘sample position’ (0.6%), and in a diminished variability attributable to ‘chip’ within a batch (0.9%). After ComBat or the residuals’ corrections, a larger number of significant sites (k = 600 and k = 427, respectively) were associated to smoking status than the SVA correction (k = 96).ConclusionsThe three correction methods removed systematic variation in DNA methylation data, as assessed by the PC-PR2, which lent itself as a useful tool to explore variability in large dimension data. SVA produced more conservative findings than ComBat in the association between smoking and DNA methylation.

Mutational and epigenetic signatures in cancer tissue linked to environmental exposures and lifestyle

Purpose of review In this article, we describe how recent advances in the study of mutational and epigenetic signatures in tumours provide new opportunities to understand the role of the environment and lifestyle in cancer development. Recent findings Cancer-related mutational events have been investigated for decades but only recently the wide availability of genomic sequences and epigenomic data from thousands of cancer genomes has made it possible to identify numerous distinct mutational and epigenetic signatures through the application of advanced mathematical models. Some of these signatures have been linked to endogenous factors such as defective DNA repair or the action of APOBEC cytidine deaminases and to exogenous factors such as tobacco smoke, ultraviolet light, aflatoxins, aristolochic acid and ionizing radiation. More recently, it has been shown that exposure to factors such as tobacco smoke may also leave marks in the DNA methylation profile of both normal and tumour tissue in target organs. Summary The analysis of mutational and epigenetic signatures is a novel and useful tool to study cancer. Their application to experimental studies and to studies with detailed data on environmental exposures and lifestyle is likely to improve our understanding of how the environment and lifestyle influence cancer development and its evolution.

KIM-1 as a blood-based marker for early detection of kidney cancer: a prospective nested case-control study

Purpose: Renal cell carcinoma (RCC) has the potential for cure with surgery when diagnosed at an early stage. Kidney injury molecule-1 (KIM-1) has been shown to be elevated in the plasma of RCC patients. We aimed to test whether plasma KIM-1 could represent a means of detecting RCC prior to clinical diagnosis. Experimental Design: KIM-1 concentrations were measured in prediagnostic plasma from 190 RCC cases and 190 controls nested within a population-based prospective cohort study. Cases had entered the cohort up to 5 years before diagnosis, and controls were matched on cases for date of birth, date at blood donation, sex, and country. We applied conditional logistic regression and flexible parametric survival models to evaluate the association between plasma KIM-1 concentrations and RCC risk and survival. Results: The incidence rate ratio (IRR) of RCC for a doubling in KIM-1 concentration was 1.71 [95% confidence interval (CI), 1.44–2.03, P = 4.1 × 10−23], corresponding to an IRR of 63.3 (95% CI, 16.2–246.9) comparing the 80th to the 20th percentiles of the KIM-1 distribution in this sample. Compared with a risk model including known risk factors of RCC (age, sex, country, body mass index, and tobacco smoking status), a risk model additionally including KIM-1 substantially improved discrimination between cases and controls (area under the receiver-operating characteristic curve of 0.8 compared with 0.7). High plasma KIM-1 concentrations were also associated with poorer survival (P = 0.0053). Conclusions: Plasma KIM-1 concentrations could predict RCC incidence up to 5 years prior to diagnosis and were associated with poorer survival. Clin Cancer Res; 24(22); 5594–601. ©2018 AACR.

Curve Selection For Predicting Breast Cancer Metastasis From Prospective Gene Expression In Blood

In this article we use gene expression measurements from blood samples to predict breast cancer metastasis. We compare several predictive models and propose a biologically motivated variable selection scheme. Curve selection is based on the assumption that gene expression intensity as a function of time should diverge between cases and controls: there should be a larger difference between case and control closer to diagnosis than years before. We obtain better predictions and more stable predictive signatures by using curve selection and show some evidence that metastasis can be detected in blood samples.We investigate whether there is information in gene expression levels in blood that predicts breast cancer metastasis. Our data comes from the NOWAC epidemiological cohort study where blood samples were provided at enrollment. This could be anywhere from years to weeks before any cancer diagnosis. When and if a cancer is diagnosed, it could be so in different ways: at a screening, between screenings, or in the clinic, outside of the screening program. To build predictive models we propose that variable selection should include followup time and stratify by detection method. We show by simulations that this improves the probability of selecting relevant predictor genes. We also demonstrate that it leads to improved predictions and more stable gene signatures in our data. There is some indication that blood gene expression levels hold predictive information about metastasis. With further development such information could be used for early detection of metastatic potential and as such aid in cancer treatment.

Appraising the causal relevance of DNA methylation for risk of lung cancer

Background DNA methylation changes in peripheral blood have recently been identified in relation to lung cancer risk. Some of these changes have been suggested to mediate part of the effect of smoking on lung cancer. However, limitations with conventional mediation analyses mean that the causal nature of these methylation changes has yet to be fully elucidated. Methods We first performed a meta-analysis of four epigenome-wide association studies (EWAS) of lung cancer (918 cases, 918 controls). Next, we conducted a two-sample Mendelian randomization analysis, using genetic instruments for methylation at CpG sites identified in the EWAS meta-analysis, and 29,863 cases and 55,586 controls from the TRICL-ILCCO lung cancer consortium, to appraise the possible causal role of methylation at these sites on lung cancer. Results 16 CpG sites were identified from the EWAS meta-analysis (FDR < 0.05), 14 of which we could identify genetic instruments for. Mendelian randomization provided little evidence that DNA methylation in peripheral blood at the 14 CpG sites play a causal role in lung cancer development (FDR>0.05), including for cg05575921-AHRR where methylation is strongly associated with both smoke exposure and lung cancer risk. Conclusions The results contrast with previous observational and mediation analysis, which have made strong claims regarding the causal role of DNA methylation. Thus, previous suggestions of a mediating role of methylation at sites identified in peripheral blood, such as cg05575921-AHRR, could be unfounded. However, this study does not preclude the possibility that differential DNA methylation at other sites is causally involved in lung cancer development, especially within lung tissue. Key Messages DNA methylation is a modifiable biomarker, giving it the potential to be targeted for intervention in many diseases, including lung cancer which is the most common cause of cancer-related death. This Mendelian randomization study attempted to evaluate whether there was a causal relationship, and thus potential for intervention, between DNA methylation measured in peripheral blood and lung cancer by assessing whether genetically altered DNA methylation levels impart differential lung cancer risks. Differential methylation at 14 CpG sites identified in epigenome-wide association analysis of lung cancer were assessed. Despite >99% power to detect the observational effect sizes, our Mendelian randomisation analysis gave little evidence that any of sites were causally linked to lung cancer. This is in stark contrast to previous analyses that suggested two CpG sites within the AHRR and F2RL3 locus, which were also observed in this analysis, mediate >30% of the effect of smoking on lung cancer. Overall findings suggest there is little or no role of differential methylation at the CpG sites identified within the blood in the development of lung cancer. Thus, targeting these sites for prevention of lung cancer is unlikely to yield effective treatments.