Big data and public health
Sridhar Venkatapuram, Alex Broadbent in The Routledge Handbook of Philosophy of Public Health, 2023
In a genome-wide association study (GWAS), the genomes of groups distinguished by some phenotypic feature, often the presence of a medical disorder, are compared to discover associations between genes and that feature. For example, a GWAS used data from the United Kingdom Biobank to identify 64 loci that may underlie coronary artery disease by comparing the genomes of individuals with versus without coronary artery disease (Van der Harst and Verweij 2018). Given the quantity of data required for a GWAS, algorithms are used for processing the information and ascertaining associations, which can pose problems for reliability. In addition, the algorithms used in GWASs to detect associations do not themselves provide any explanation regarding why or in what way genes and phenotypes are associated. The GWAS process itself does not give any hints at a mechanism of action for a gene’s role in a phenotype, but once associations are identified, they can be investigated further (Prosperi et al. 2018; Reimers et al. 2019; Turkheimer 2012).
Epidemiology of Asthma
Jonathan A. Bernstein, Mark L. Levy in Clinical Asthma, 2014
The prospective cohort study design follows groups of individuals who may not have manifested a disease or an outcome at the time of recruitment; it is often used to identify significant host and environmental influences on health and environmental exposures, and their effects on the expression of chronic disease later in life.14 Large numbers are, however, typically required in order to identify significant differences in the incidence in exposed and nonexposed groups. This requirement for large numbers of cases usually means that cohort studies are expensive to establish and maintain. Genome-wide association studies (GWAS), whereby large cohorts of patients are recruited to detect novel genes and markers associated with disease susceptibility, are a more recently employed study design. GWAS have elicited new lines of enquiry into the etiology and pathogenesis of asthma. Cross-sectional surveys, in which the data and the means of collection are specified in advance and the study population is clearly defined, can be straightforward and inexpensive and are attractive to scientists studying the epidemiology of asthma.15 Cross-sectional studies also allow exposures and disease status to be assessed simultaneously among individuals in a well-defined population. There can be a specific interval, such as a given calendar year during which a community-wide survey is conducted, or a fixed point in the course of events that varies in real time from person to person.16
Mapping and sequencing: From gene to genome
Peter S. Harper in The Evolution of Medical Genetics, 2019
Once we have considered these two important examples where genomic research is becoming genomic healthcare, we enter much more uncertain territory. This is especially the case for susceptibility to common disorders generally, the field where genome analysis has repeatedly been promoted as that which will most justify the use of genomic approaches to medicine. We saw earlier in this chapter how even extensive genome-wide association studies (GWAS) often failed to find strong and reproducible disease associations. While persistence in these and more sophisticated searches can be justified as a research approach to finding the biological basis for these frequent and burdensome disorders, using them in a clinical service setting for determining the susceptibility of individuals is an entirely different matter.
How to rekindle drug discovery process through integrative therapeutic targeting?
Published in Expert Opinion on Drug Discovery, 2018
Ashok Vaidya, Anuradha Roy, Rathnam Chaguturu
Genetics is often the route chosen for target identification and validation, a result of Genome-Wide Association Studies (GWAS) linking certain genetic variants or mutations to a disease condition or having a direct biomarker in the gene causing the disease. While we utilize the results of genome wide sequencing data and incorporate gene expression data within the rationale for pursuing drug discovery efforts of specific pathways and proteins, the use of genetics to aid in finding a cure is often left behind. Compensatory mutations that can rescue the original mutation is an area that can add another possible avenue to consider when searching for a disease-modifying drug. Can a drug against a protein in another pathway solve/prevent the condition caused by the original mutation? Cancer cells have a mutation which in part is responsible for their phenotype. When this mutation is combined with another mutation (which also by itself has no effect on the cell) the cell is doomed – hence being synthetic lethal. Finding compounds that cause this effect is a potential source of new drugs and a new mechanism of action for treating cancers. If two mutations can work together to provide lethality, could the reverse not also be true – synthetic health? This approach would seek non-related mutations which cure the phenotypic disease or at least slow it down. This strategy could also be used in drug–drug combination types of approaches and creation of chimeric compounds [2].
The role of epigenetics in the development of childhood asthma
Published in Expert Review of Clinical Immunology, 2019
Cancan Qi, Cheng-Jian Xu, Gerard H. Koppelman
The development of asthma results from the interaction of genetic factors with environmental exposures at different stages of development, in particular in early life [6]. The contribution of genetic factors to asthma risk has been demonstrated by twin studies, and the heritability was estimated between 50% and 90% [7]. The genome-wide association analysis (GWAS) has been widely used to identify genetic variants that contribute to disease. The first comprehensive GWAS of asthma reported by Moffatt et al. identified genetic variants at chromosome 17q21 regulating ORMDL3 expression that contribute to the risk of childhood asthma [8]. This study has been followed up by larger meta-analyses that collectively describe over 130 loci associated with asthma at genome-wide significance. For example, a recent meta-analysis of GWAS performed by the Trans-National Asthma Genetic Consortium(TAGC) consortium, which consisted of 23,948 asthma cases and 118,538 controls, identified 18 loci associated with asthma, but explained only 3.5% of the variance in asthma liability [9].
A clinical perspective on the expanding role of artificial intelligence in age-related macular degeneration
Published in Clinical and Experimental Optometry, 2022
Himeesh Kumar, Kai Lyn Goh, Robyn H Guymer, Zhichao Wu
AI models could also accelerate the discovery of novel disease pathways driving vision loss in AMD in large clinical and population-based studies, by automating the detection and phenotyping of AMD in an objective manner. For example, there are over 67,000 individuals, with and without AMD, in the United Kingdom Biobank that have undergone optical coherence tomography imaging, colour fundus photography, and genotyping.75 Automated image analyses, such as by using a deep learning model to detect reticular pseudodrusen,76 could be used to determine the presence of reticular pseudodrusen in those included in the United Kingdom Biobank. This information can subsequently be used to conduct a genome-wide association study, which could help uncover novel genetic associations and potential therapeutic pathways of this critical phenotype without requiring the laborious and costly clinical grading of such a large cohort.
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