Chloé-Agathe Azencott

The Evaluation of Tools Used to Predict the Impact of Missense Variants Is Hindered by Two Types of Circularity

Prioritizing missense variants for further experimental investigation is a key challenge in current sequencing studies for exploring complex and Mendelian diseases. A large number of in silico tools have been employed for the task of pathogenicity prediction, including PolyPhen‐2, SIFT, FatHMM, MutationTaster‐2, MutationAssessor, Combined Annotation Dependent Depletion, LRT, phyloP, and GERP++, as well as optimized methods of combining tool scores, such as Condel and Logit. Due to the wealth of these methods, an important practical question to answer is which of these tools generalize best, that is, correctly predict the pathogenic character of new variants. We here demonstrate in a study of 10 tools on five datasets that such a comparative evaluation of these tools is hindered by two types of circularity: they arise due to (1) the same variants or (2) different variants from the same protein occurring both in the datasets used for training and for evaluation of these tools, which may lead to overly optimistic results. We show that comparative evaluations of predictors that do not address these types of circularity may erroneously conclude that circularity confounded tools are most accurate among all tools, and may even outperform optimized combinations of tools.

Efficient network-guided multi-locus association mapping with graph cuts

Motivation: As an increasing number of genome-wide association studies reveal the limitations of the attempt to explain phenotypic heritability by single genetic loci, there is a recent focus on associating complex phenotypes with sets of genetic loci. Although several methods for multi-locus mapping have been proposed, it is often unclear how to relate the detected loci to the growing knowledge about gene pathways and networks. The few methods that take biological pathways or networks into account are either restricted to investigating a limited number of predetermined sets of loci or do not scale to genome-wide settings. Results: We present SConES, a new efficient method to discover sets of genetic loci that are maximally associated with a phenotype while being connected in an underlying network. Our approach is based on a minimum cut reformulation of the problem of selecting features under sparsity and connectivity constraints, which can be solved exactly and rapidly. SConES outperforms state-of-the-art competitors in terms of runtime, scales to hundreds of thousands of genetic loci and exhibits higher power in detecting causal SNPs in simulation studies than other methods. On flowering time phenotypes and genotypes from Arabidopsis thaliana, SConES detects loci that enable accurate phenotype prediction and that are supported by the literature. Availability: Code is available at http://webdav.tuebingen.mpg.de/u/karsten/Forschung/scones/. Contact: chloe-agathe.azencott@tuebingen.mpg.de Supplementary information: Supplementary data are available at Bioinformatics online.

GLIDE: GPU-Based Linear Regression for Detection of Epistasis

Due to recent advances in genotyping technologies, mapping phenotypes to single loci in the genome has become a standard technique in statistical genetics. However, one-locus mapping fails to explain much of the phenotypic variance in complex traits. Here, we present GLIDE, which maps phenotypes to pairs of genetic loci and systematically searches for the epistatic interactions expected to reveal part of this missing heritability. GLIDE makes use of the computational power of consumer-grade graphics cards to detect such interactions via linear regression. This enabled us to conduct a systematic two-locus mapping study on seven disease data sets from the Wellcome Trust Case Control Consortium and on in-house hippocampal volume data in 6 h per data set, while current single CPU-based approaches require more than a year’s time to complete the same task.

Multi-Task Feature Selection on Multiple Networks via Maximum Flows

We propose a new formulation of multi-task feature selection coupled with multiple network regularizers, and show that the problem can be exactly and efficiently solved by maximum flow algorithms. This method contributes to one of the central topics in data mining: How to exploit structural information in multivariate data analysis, which has numerous applications, such as gene regulatory and social network analysis. On simulated data, we show that the proposed method leads to higher accuracy in discovering causal features by solving multiple tasks simultaneously using networks over features. Moreover, we apply the method to multi-locus association mapping with Arabidopsis thaliana genotypes and flowering time phenotypes, and demonstrate its ability to recover more known phenotype-related genes than other state-of-the-art methods.

The inconvenience of data of convenience: Computational research beyond post-mortem analyses

The inconvenience of data of convenience: computational research beyond post-mortem analyses

Machine learning and genomics: precision medicine versus patient privacy

Machine learning can have a major societal impact in computational biology applications. In particular, it plays a central role in the development of precision medicine, whereby treatment is tailored to the clinical or genetic features of the patient. However, these advances require collecting and sharing among researchers large amounts of genomic data, which generates much concern about privacy. Researchers, study participants and governing bodies should be aware of the ways in which the privacy of participants might be compromised, as well as of the large body of research on technical solutions to these issues. We review how breaches in patient privacy can occur, present recent developments in computational data protection and discuss how they can be combined with legal and ethical perspectives to provide secure frameworks for genomic data sharing. This article is part of a discussion meeting issue ‘The growing ubiquity of algorithms in society: implications, impacts and innovations’.

Network-Guided Biomarker Discovery

Identifying measurable genetic indicators (or biomarkers) of a specific condition of a biological system is a key element of precision medicine. Indeed it allows to tailor diagnostic, prognostic and treatment choice to individual characteristics of a patient. In machine learning terms, biomarker discovery can be framed as a feature selection problem on whole-genome data sets. However, classical feature selection methods are usually underpowered to process these data sets, which contain orders of magnitude more features than samples. This can be addressed by making the assumption that genetic features that are linked on a biological network are more likely to work jointly towards explaining the phenotype of interest. We review here three families of methods for feature selection that integrate prior knowledge in the form of networks.