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Graphical Models in Genetics, Genomics, and Metagenomics
Published in Marloes Maathuis, Mathias Drton, Steffen Lauritzen, Martin Wainwright, Handbook of Graphical Models, 2018
Genome-wide association studies (GWAS) attempt to identify commonly occurring genetic variants that contribute to disease risk, and so far have identified thousands of SNPs that are associated with many human traits [5]. In its simplest form, GWAS analysis is formulated as a sequence of logistic regressions where the disease status from all individuals serve as the response and each genotyped SNP is the covariate. The resulting p-value for each SNP is then corrected for multiple comparisons using e.g. the Bonferroni adjustment. Although this standard approach has the power of identifying common SNPs with strong effects on phenotypes, it ignores the possible synergistic effects of genetic variants on disease phenotypes. Therefore network-assisted methods have been proposed to prioritize the GWASresults and to identify subnetwork of genes that are associated with phenotypes. The rationale of such network-based methods is that topologically related genetic variants are more likely to produce similar phenotypic effects.
Collecting and Making Sense of Big Data for Improved Health Care
Published in Ervin Sejdić, Tiago H. Falk, Signal Processing and Machine Learning for Biomedical Big Data, 2018
The KHP offers the opportunity, among many, to better understand the association between genes and addictive behavior such as smoking, alcoholism, and recreational drug use through genome-wide association studies (GWASs). A GWAS is an approach that involves rapidly scanning markers across the genomes of many people to find genetic variations associated with a particular disease [31]. Previous studies on the association between genes and smoking behavior have been challenged by the lack of accurate measurements on various facets of smoking such as initiation, intensity, and cessation. These facets may be affected by different biologic and environmental factors. For instance, there is strong evidence that a set of single-nucleotide polymorphisms (SNPs) located in the chromosome 15 cluster of virtually adjacent nicotinic receptor genes is a risk factor for heaviness of smoking as well as the strongest genetic risk for development of lung cancer [31]. An SNP is a variation in a single nucleotide that occurs at a specific position in the genome and can be used as a biomarker for certain disease conditions. However, current measurements such as the maximum level of smoking at a point in time provide only limited information to researchers.
Secure computation outsourcing of genome-wide association studies using homomorphic encryption
Published in Shin-ya Nishizaki, Masayuki Numao, Jaime Caro, Merlin Teodosia Suarez, Theory and Practice of Computation, 2019
Angelica Khryss Yvanne C. Ladisla, Richard Bryann L. Chua
Genome-wide association study (GWAS) is an approach that involves rapid scanning of genetic markers across the genomes of many people to find genetic variations associated with a particular phenotype (i.e. an individual’s observable trait) (Genome-Wide Association Studies, 2015). In GWAS, genetic markers are DNA sequences with known physical locations on chromosomes that exhibit polymorphism due to insertion, deletion, and/or substitution of nucleotides (Steen, 2015). Single nucleotide polymorphisms (SNP) are the most commonly examined markers in GWAS (Steen, 2015).
A Real-Time Data Clustering Scheme Using K-Medoids Based Optimal Neural Network Approach for Integrating Demographics and Diagnosis Codes
Published in IETE Journal of Research, 2021
S. Srijayanthi, T. Sethukarasi
The massive amount of data such as medical imaging databases, disease registries, electronic health records (EHR), and clinical trials are collected via healthcare organizations. The low-cost and larger-scale analyses are performed by the significance of healthcare data [1]. The presence of genome-wide association studies(GWAS) improves medical practice and research. Along with the recipient’s and donors’ health records, more than 350,000 transfusions databases and Scandinavian donations records were used. The limitations placed on the blood donors are regarded with a substantial impact on public health policies. According to the literature [2], the meaningful comorbidities are learned by using the database of EHR over 300,000 records in which various chronic disruptive pulmonary diseases are associated. This investigation contains the ability to enhance the clinical trial design, drug development, and COPD prognosis.
Discovery of genetic risk factors for disease
Published in Journal of the Royal Society of New Zealand, 2018
The lack of immediate translation is often seen as a limitation of the GWAS method. However, the difficulty in rapid translation of the GWAS results arises because we do not yet have comprehensive maps of cell specific regulatory elements in the genome. We are unable to take a set of SNPs with highly correlated signals, together with knowledge of the disease, and overlay the GWAS map on a map of regulatory sequences for the relevant tissue. This gap in our knowledge has prompted large scale genomics initiatives to provide these maps including projects such as ENCODE (Encode Project Consortium 2012), the Epigenome RoadMap (Roadmap Epigenomics Consortium et al. 2015) and GTEx (GTEx Consortium 2013). Levels of gene expression and epigenetic marks such as DNA methylation are subject to genetic control. The Genotype-Tissue Expression Project (GTEx) (GTEx Consortium 2013) is providing a publicly available resource to study the regulation and genetic control of gene expression in multiple tissues. Results from GWAS can be compared with the GTEx results to determine whether disease associated variants regulate the expression of any nearby genes.
A multivariate regression approach for identification of SNPs importance in prostate cancer
Published in Journal of Experimental & Theoretical Artificial Intelligence, 2019
Juan Enrique Sánchez Lasheras, Sergio Luis Suárez Gómez, Jesús Daniel Santos, Gemma Castaño-Vinyals, Beatriz Pérez-Gómez, Adonina Tardón
Genome-Wide Association Studies (GWAS) compare the DNA of the subjects with the phenotype of interest (cases) versus subjects without the phenotype (controls). Each individual provides a DNA sample, from which millions of genetic variants can be read using single nucleotide polymorphisms (SNPs). GWAS through the massive comparison of genotyping cases and controls will allow to discover new SNPs or metabolic pathways associated with PCa. Please note that machine learning methodologies has been employed in GWAS in PCa studies (González-Donquiles et al., 2017), in colorectal cancer (Álvarez Gutiérrez et al., 2017) and also in other pathologies such as Hodgkin lymphoma (Sud et al., 2017)