Browsing by Subject "GWAS"

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    Differential privacy with bounded priors: Reconciling utility and privacy in genome-wide association studies
    (ACM, 2015-10) Tramèr, F.; Huang, Z.; Hubaux J.-P.; Ayday, Erman
    Differential privacy (DP) has become widely accepted as a rigorous definition of data privacy, with stronger privacy guarantees than traditional statistical methods. However, recent studies have shown that for reasonable privacy budgets, differential privacy significantly affects the expected utility. Many alternative privacy notions which aim at relaxing DP have since been proposed, with the hope of providing a better tradeoff between privacy and utility. At CCS'13, Li et al. introduced the membership privacy framework, wherein they aim at protecting against set membership disclosure by adversaries whose prior knowledge is captured by a family of probability distributions. In the context of this framework, we investigate a relaxation of DP, by considering prior distributions that capture more reasonable amounts of background knowledge. We show that for different privacy budgets, DP can be used to achieve membership privacy for various adversarial settings, thus leading to an interesting tradeoff between privacy guarantees and utility. We re-evaluate methods for releasing differentially private χ2-statistics in genome-wide association studies and show that we can achieve a higher utility than in previous works, while still guaranteeing membership privacy in a relevant adversarial setting. © 2015 ACM.
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    Diverse SNP selection for epistasis test prioritization
    (2019-08) Çaylak, Gizem
    Genome-wide association studies explain a fraction of the underlying heritability of genetic diseases. Epistatic interactions between two or more loci help closing the gap and identifying those complex interactions provides a promising road to a better understanding of complex traits. Unfortunately, sheer number of loci combinations to consider and hypotheses to test prohibit the process both computationally and statistically. This is true even if only pairs of loci are considered. Epistasis prioritization algorithms have proven useful for reducing the computational burden and limiting the number of tests to perform. While current methods aim at avoiding linkage disequilibrium and covering the case cohort, none aims at diversifying the topological layout of the selected SNPs which can detect complementary variants. In this thesis, a two stage pipeline to prioritize epistasis test is proposed. In the first step, a submodular set function is optimized to select a diverse set of SNPs that span the underlying genome to (i) avoid linkage disequilibrium and (ii) pair SNPs that relate to complementary function. In the second step, selected SNPs are used as seeds to a fast epistasis detection algorithm. The algorithm is compared with the state-of-the-art method LinDen on three datasets retrieved from Wellcome Trust Case Control Consortium: type two diabates, hypertension and bipolar disorder. The results show that the pipeline drastically reduces the number of tests to perform while the number of statistically significant epistatic pairs discovered increases.
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    Effects of psychosis-associated genetic markers on brain volumetry: A systematic review of replicated findings and an independent validation
    (Cambridge University Press, 2022-09-28) Ribeiro, Nuno Vouga; Tavares, Vânia; Bramon, Elvira; Toulopoulou, Timothea; Valli, Isabel; Shergill, Sukhi; Murray, Robin; Prata, Diana
    Background. Given psychotic illnesses’ high heritability and associations with brain structure, numerous neuroimaging-genetics findings have been reported in the last two decades. However, few findings have been replicated. In the present independent sample we aimed to replicate any psychosis-implicated SNPs (single nucleotide polymorphisms), which had previously shown at least two main effects on brain volume. Methods. A systematic review for SNPs showing a replicated effect on brain volume yielded 25 studies implicating seven SNPs in five genes. Their effect was then tested in 113 subjects with either schizophrenia, bipolar disorder, ‘at risk mental state’ or healthy state, for whole-brain and region-of-interest (ROI) associations with grey and white matter volume changes, using voxel-based morphometry. Results. We found FWER-corrected (Family-wise error rate) (i.e. statistically significant) associations of: (1) CACNA1C-rs769087-A with larger bilateral hippocampus and thalamus white matter, across the whole brain; and (2) CACNA1C-rs769087-A with larger superior frontal gyrus, as ROI. Higher replication concordance with existing literature was found, in decreasing order, for: (1) CACNA1C-rs769087-A, with larger dorsolateral-prefrontal/superior frontal gyrus and hippocampi (both with anatomical and directional concordance); (2) ZNF804Ars11681373-A, with smaller angular gyrus grey matter and rectus gyri white matter (both with anatomical and directional concordance); and (3) BDNF-rs6265-T with superior frontal and middle cingulate gyri volume change (with anatomical and allelic concordance). Conclusions. Most literature findings were not herein replicated. Nevertheless, high degree/ likelihood of replication was found for two genome-wide association studies- and one candidate-implicated SNPs, supporting their involvement in psychosis and brain structure. © The Author(s), 2022. Published by Cambridge University Press.
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    SPADIS: An algorithm for selecting predictive and diverse SNPs in GWAS
    (IEEE, 2021) Yılmaz, Serhan; Taştan, Ö.; Çiçek, A. Ercüment
    Phenotypic heritability of complex traits and diseases is seldom explained by individual genetic variants identified in genome-wide association studies (GWAS). Many methods have been developed to select a subset of variant loci, which are associated with or predictive of the phenotype. Selecting connected SNPs on SNP-SNP networks have been proven successful in finding biologically interpretable and predictive SNPs. However, we argue that the connectedness constraint favors selecting redundant features that affect similar biological processes and therefore does not necessarily yield better predictive performance. In this paper, we propose a novel method called SPADIS that favors the selection of remotely located SNPs in order to account for their complementary effects in explaining a phenotype. SPADIS selects a diverse set of loci on a SNP-SNP network. This is achieved by maximizing a submodular set function with a greedy algorithm that ensures a constant factor approximation to the optimal solution. We compare SPADIS to the state-of-the-art method SConES, on a dataset of Arabidopsis Thaliana with continuous flowering time phenotypes. SPADIS has better average phenotype prediction performance in 15 out of 17 phenotypes when the same number of SNPs are selected and provides consistent improvements across multiple networks and settings on average. Moreover, it identifies more candidate genes and runs faster.
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    Spadis: selecting predictive and diverse SNPS in GWAS
    (2018-05) Yılmaz, Serhan
    Phenotypic heritability of complex traits and diseases is seldom explained by individual genetic variants identi ed in genome-wide association studies (GWAS). Many methods have been developed to select a subset of variant loci, which are associated with or predictive of the phenotype. Selecting connected Single Nucleotide Polymorphisms (SNPs) on SNP-SNP networks has been proven successful in nding biologically interpretable and predictive SNPs. However, we argue that the connectedness constraint favors selecting redundant features that a ect similar biological processes and therefore does not necessarily yield better predictive performance. To this end, we propose a novel method called SPADIS that favors the selection of remotely located SNPs in order to account for their complementary e ects in explaining a phenotype. SPADIS selects a diverse set of loci on a SNP-SNP network. This is achieved by maximizing a submodular set function with a greedy algorithm that ensures a constant factor (1 − 1=e) approximation to the optimal solution. We compare SPADIS to the state-of-the-art method SConES, on a dataset of Arabidopsis Thaliana with continuous owering time phenotypes. SPADIS has better average phenotype prediction performance in 15 out of 17 phenotypes when the same number of SNPs are selected and provides consistent improvements across multiple networks and settings on average. Moreover, it identi es more candidate genes and runs faster. We also investigate the use of Hi-C data to construct SNP-SNP network in the context of SNP selection problem for the rst time, which yields improvements in regression performance across all methods.