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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).
Nanomedicinal Genetic Manipulation: Promising Strategy to Treat Some Genetic Diseases
Published in Sarwar Beg, Mahfoozur Rahman, Md. Abul Barkat, Farhan J. Ahmad, Nanomedicine for the Treatment of Disease, 2019
Biswajit Mukherjee, Iman Ehsan, Debasmita Dutta, Moumita Dhara, Lopamudra Dutta, Soma Sengupta
Genome-wide association study (GWAS) is used widely to identify genetic factors that influence common and complex diseases. It is the method to study candidate genes in any disease. GWAS mainly focuses on single nucleotide polymorphisms (SNPs) and major diseases. With the help of GWAS, eight loci associated with hypertension are identified. The loci identified were CYP17A1, CYP1A2, FGF5, SH2B3, MTHFR, c10orf107, ZNF652, and PLCD3 (Newton-Cheh et al., 2009).
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)
Cadmium contamination in food crops: Risk assessment and control in smart age
Published in Critical Reviews in Environmental Science and Technology, 2023
Yan Huili, Zhang Hezifan, Hao Shuangnan, Wang Luyao, Xu Wenxiu, Ma Mi, Luo Yongming, He Zhenyan
With the development of sequencing technologies and strategies, high-density molecular markers covering the whole genome now can be acquired easily by large-scale re-sequencing. GWAS was born in response to this proper time and conditions. GWAS uses genotypic and phenotypic data of research population with high varieties to excavate natural variations existing in the population. By analyzing these data, researchers can discover SNPs which are significantly-linked to target phenotype. With these SNPs discovered, underlying genes and variations can also be mapped (Zhao, Yang, et al., 2018). Since the first application in Cd accumulation trait in 2018, about 20 researches in rice, wheat and maize have been carried out (Table 1). By searching through these natural variations in high-density SNP data acquired by GWAS, QTLs have been mapped to Cd-related genes or found to be involved into Cd accumulation process including Cd absorption, translocation and accumulation in grains. After further confirmation, several genes and their superior alleles have been excavated. For example, OsCd1 with its low-Cd superior alleles OsCd1V449 were identified from the GWAS using 127 rice cultivars (Yan et al., 2019). One major locus for maize grain Cd accumulation (qCd1), ZmHMA3, was also identified using GWAS (Baseggio et al., 2021). Except for confirmed genes, many other SNPs high-related to Cd accumulation with unknown function genes were also discovered by GWAS (Tang et al., 2021). Using GWAS in a population of 312 rice cultivars, Zhao et al. has discovered 14 QTLs related to Cd accumulation in rice grains (Zhao, Yang, et al., 2018). Some of candidate genes including OsLCD, OsNRAMP1, OsNRAMP5 and OsHMA3 were already identified to be related to Cd accumulation, but candidate genes of other 11 QTLs have not been revealed yet. Caused by allelic heterogeneity, distal noncoding regulatory elements and other complex linkage disequilibrium architecture, associated SNP might be located far from functional genes. Identifying the causal genes for association signals was still a challenge in several cases. Solutions to the issue depend on more comprehensive approaches, such as incorporating information from transcriptomic/proteomic variation or yeast complement systems to pinpoint causality of association signal and functional gene.Approaches for Natural Low-Cd Superior Variation Pyramiding