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Including Genetic Variables in NTCP Models Where Are We? Where Are We Going?
Published in Tiziana Rancati, Claudio Fiorino, Modelling Radiotherapy Side Effects, 2019
Sarah L. Kerns, Suhong Yu, Catharine M. L. West
SNPs are single base pair variable sites that are relatively common – being generally present in at least 1% of the population. The average human genome contains approximately 10 million SNPs with a minor allele frequency of 1% or greater, thus there is great potential for such variation to impact disease risk. A minority of SNPs lie in exonic regions, and even fewer are non-synonymous SNPs that alter a protein’s amino acid sequence. Most SNP occur in non-coding introns and intergenic regions that may include regulatory elements such as splice sites, transcription factor binding sites, long range enhancers, or regions of epigenetic modification (Edwards et al. 2013). Over the past decade, results of large-scale genetic association studies have shown that most disease-associated SNPs are located in noncoding or regulatory regions (Maurano et al. 2012) rather than altering the amino acid sequence and function of a protein. Such disease-associated SNPs can alter the expression levels of genes nearby or far way, so-called expression quantitative trait loci (eQTL) (Nicolae et al. 2010). Perhaps not surprisingly, common SNPs tend to have only modest effects on disease susceptibility. Most individual SNPs only increase risk for disease by a few percent or explain a small fraction of the total variability in a quantitative trait.
Genetics of Psoriasis and Psoriatic Arthritis
Published in Siba P. Raychaudhuri, Smriti K. Raychaudhuri, Debasis Bagchi, Psoriasis and Psoriatic Arthritis, 2017
A third challenge is characterizing the functional relevance of the variants identified thus far. Only a small fraction of psoriasis susceptibility loci identified, including IL23R and CARD14, are located within coding regions, and thus may have rather straightforward causal effects on protein structure or function [93,201,202]. The remaining loci are located outside coding regions, such as in promoters, enhancers, or repressors, where they may function to alter the expression of nearby genes or even genes megabases away. Such expression quantitative trait loci (eQTL) can be identified by correlating SNPs with gene expression data generated from homogeneous and relevant cell types [142]. Bioinformatic tools such as the Encyclopedia of DNA Elements (ENCODE) database may also help to provide clues into the functions of these variants and allow us to generate hypotheses that can be experimentally tested using new techniques, such as chromosome conformation capture to identify interactions between far-apart genes, or CRISPR/Cas9 to confirm the causal nature of variants in cellular or animal models [142].
Genetics
Published in M. Alan Menter, Caitriona Ryan, Psoriasis, 2017
eQTLs in Ps: Expression quantitative trait loci (eQTL) analysis reveals SNPs that potentially affect gene expression (expression SNPs or eSNPs). Identified eSNPs in Ps include variants near TNIP1, IL23A, and IL12B.23TNIP1: A noncoding variant within or near the TNFAIP3 interacting protein 1 (TNIP1) gene is associated with Ps and as described earlier, this could be affecting its expression level. The region of association harbors both TNIP1 and ANXA6 genes. TNIP1 is involved in regulation of NF-κB signaling.41 It is upregulated in both atopic dermatitis (AD) and psoriatic involved skin compared with healthy skin. However, ANXA6, which encodes a calcium-dependent membrane and phospholipid binding protein, is upregulated in atopic skin compared with healthy skin but is downregulated in psoriatic versus healthy skin. It has been suggested that ANXA6 might differentiate AD from psoriatic skin through calcium-dependent effects in keratinocyte differentiation.42 There are a number of similar genes with opposing risk alleles at loci shared by AD and Ps.42 These loci include noncoding alterations in PRKR, the SPRR cluster of genes, and an antisense RNA of filaggrin (FLG-AS1). This is interpreted to be due to distinct genetic mechanisms with opposing effects in shared pathways influencing epidermal differentiation and the immune response.
Polygenic and Network-based studies in risk identification and demystification of cancer
Published in Expert Review of Molecular Diagnostics, 2022
Christopher El Hadi, Georges Ayoub, Yara Bachir, Michèle Haykal, Nadine Jalkh, Hampig Raphael Kourie
Elucidation of the contributing GVs and their functions underlying BC susceptibility has allowed better estimation of familial relative risk and thus improved the PRS. Post-GWAS analytical studies exploiting the minimally refined GWAS data have shown that some genomic features, including TFBSs, can be considered as susceptibility loci [86]. In silico evaluation of causal variants and subsequent molecular testing in in vitro model systems showed overlap between candidate causal variants and regulatory sequences, such as TFBSs and histone marks or open chromatin regions. In addition, eQTL studies are being performed to identify the genes they regulate as a result. In summary, better definition of genomic features for predicting causal variants and improved methods for incorporating external biological information into prediction models should improve the performance of PRS [87,88].
Genetics of endometriosis: a comprehensive review
Published in Gynecological Endocrinology, 2019
Danilo Deiana, Stefano Gessa, Michela Anardu, Angelos Daniilidis, Luigi Nappi, Maurizio N. D’Alterio, Alessandro Pontis, Stefano Angioni
Using various methods to directly modify the sequence and function of protein amino-acids would have provided more specific gene targets. The probable causal SNPs were found to be situated in sequences, not codifying but involved in regulatory functions of gene expressions, to such a degree that they can influence the development and progression of the illness [55]. Considering that the GWASs have identified different susceptible loci, the greatest challenges following up on these GWASs is to understand the functional consequences of these loci. Studying the association between genetic variations and gene expression offers a way to connect the variations of risk with the corresponding gene targets. The gene expression changes in different individuals and the levels of expression of determined genes are controlled by particular regulatory variants, called ‘expression quantitative traits loci’ (eQTL), which are able to influence the abundance of specific transcriptions [56].
Genetics of platelet traits in ischaemic stroke: focus on mean platelet volume and platelet count
Published in International Journal of Neuroscience, 2019
Kanika Vasudeva, Anjana Munshi
Multi-omic analysis for human platelets revealed the novel association of rs37667444 [MFN2] with PLT. Several techniques including functional, genetic and bioinformatics were employed to achieve this conclusion. This was the first study to corroborate the genetic mechanism underlying cardiovascular risk. Hundred and fifty-four healthy participants of European as well as African American ancestry were profiled for mRNA expression in platelets. Two pass cis-eQTL (expression quantitative trait loci) mapping and allele specific expression analysis narrowed down to unique 116 genes. On comparison of these findings with cardiovascular disease GWAS results, it was found that rs1474868 was associated with MFN2 expression levels, which has been implicated in myocardial infarction. Besides this, 39 new eQTL were found to be associated with stroke and other cardiovascular diseases. Mapping of rs1474868 revealed a novel unannotated human expressed exon region named as ‘Exone2b’. Exone2b is an alternative start site of MFN2, which is a platelet dominant isoform. This variant is nearly in perfect linkage disequilibrium with rs3766744. The vector containing rs3766744 upstream of luciferase gene confirmed the transcriptional activity of this variant. PLT was significantly associated with rs3766744 allele dosage [115].