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Analysis of Gene-Environment Interactions
Published in Ørnulf Borgan, Norman E. Breslow, Nilanjan Chatterjee, Mitchell H. Gail, Alastair Scott, Christopher J. Wild, Handbook of Statistical Methods for Case-Control Studies, 2018
where and if ; that is, one becomes affected by the disease if the value of a latent trait is above a certain clinical threshold C. This is also known as a liability threshold (LT) model. In statistical genetics, this model has been widely used for estimation of heritability partly because of computational convenience. The scale of a liability threshold model is similar to a logistic scale; hence, modeling gene-environment interaction using this model may not contribute additional insights compared to a logistic model (Han et al., 2015). There are some other nonstandard models discussed in the literature (Clayton, 2012).
Diabetic retinopathy progression associated with haplotypes of two VEGFA SNPs rs2010963 and rs699947
Published in Egyptian Journal of Basic and Applied Sciences, 2023
Haider Ali Alnaji, Rabab Omran, Aizhar H. Hasan, Mohammed Qasim Al Nuwaini
The findings suggest a moderate LD in two SNPs (rs2010963 and rs699947). The rs699947, as mentioned early in the study, it was observed to have no significant association with DR; however, the C allele shared with the G allele of rs2010963; both alleles are minor alleles (mutant) and contributed significantly >2 folds to DR progression. Thus, it is valuable to measure the LD, which confers a piece of more information if statistical genetics fails to find an association. LD occur when nonrandom gametic alleles are associated at various loci in a population [37]. The haplotype has an effect that differs from genotypes on disease, and the locus is still of clinical importance even though there is no link between genotype and clinical condition [38]. The VEGF gene SNPs located at the promoter region and 5’ UTR and
Long non-coding RNAs and their potential impact on diagnosis, prognosis, and therapy in prostate cancer: racial, ethnic, and geographical considerations
Published in Expert Review of Molecular Diagnostics, 2021
Rebecca Morgan, Willian Abraham da Silveira, Ryan Christopher Kelly, Ian Overton, Emma H. Allott, Gary Hardiman
Given the evidence highlighting the potential roles of lncRNAs in PC tumorigenesis, there is an urgent need to understand the functional consequences of lncRNA differences between African and Northern European PC patients. One of the pressing challenges for integrative computational biology and statistical genetics in racial disparities research is predicting genotype-to-phenotypes. The ability to identify the key drivers among a multitude of interacting molecules is challenging. Additional obstacles include the rapid growth of data, the and unavailability of data through issues with incompleteness, inaccuracies, heterogeneity, and data silos [144,145]. LncRNAs may be prioritized according to their impact upon gene regulatory networks through graph theoretic and systems biology approaches [146,147]. Understanding the biological underpinnings of racial differences in PC will ultimately provide an opportunity to help eliminate them by identifying possible PC diagnostic and prognostic biomarkers as well as potential therapeutic targets for men of African descent.
The evolution in our understanding of the genetics of rheumatoid arthritis and the impact on novel drug discovery
Published in Expert Opinion on Drug Discovery, 2020
Filip Machaj, Jakub Rosik, Bartosz Szostak, Andrzej Pawlik
Understanding the genetics of RA and the impact of gene function on RA pathogenesis is essential in the process of drug discovery. Genome-wide association studies (GWASs) were the first studies to search for disease-associated genetic variants. GWASs have analyzed the associations of disease risk with several genes and identified a number of disease risk genes. These results have led to a better understanding of the disease pathophysiology and the detection of pathways that could be therapeutic targets. Progress in the technologies used to examine the human genome, e.g. the development of next-generation sequencers and high-density SNP microarrays, have improved the potential of the search for disease-associated genes that may become targets for therapy and have contributed to novel drug discovery. Statistical genetics is very important for the understanding of the roles of several genes in disease pathogenesis. Together, GWASs and statistical genetics enable the identification of disease risk genes, help to explain the roles of these genes in disease pathogenesis and aid the identification of pathways that may be therapeutic targets for RA.