Analyzing the Effects of Genetic Variation in Noncoding Genomic Regions
| Autor: | Juan A. G. Ranea, James R. Perkins, Elena Rojano, Yasmina A. Mansur |
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| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
education.field_of_study Population Single-nucleotide polymorphism Genome-wide association study Computational biology Biology Genome Noncoding DNA 03 medical and health sciences 030104 developmental biology 0302 clinical medicine 030220 oncology & carcinogenesis Genetic variation DNA methylation education Genetic association |
| Popis: | The identification of genetic risk factors for complex diseases is a cornerstone of modern medical research. Multiple high-throughput techniques exist for this job, the most common of which is the use of genome-wide association studies (GWAS), which analyze DNA sequence variation within a population, seeking to associate specific variants with phenotypic traits. They typically analyze single-nucleotide polymorphisms (SNPs), defined as single-base changes in DNA that are present in a nonnegligible proportion of a population. SNPs can be located in coding and noncoding regions of the genome; in fact, GWAS in recent years have found a greater number of disease-associated SNPs in noncoding regions. This, alongside improvements in our understanding of noncoding genomic elements, has led to much interest in the effects of SNPs in noncoding DNA and their roles in a disease. They have been shown to affect intron usage and the function of cis-acting regulatory genomic elements such as promoter and enhancer regions. Huge advances have been achieved in the recent years in identifying the functional effects of such variants. They can alter DNA methylation patterns, histone modifications, transcription factor affinity, alternative splicing, and mRNA stability. In this chapter, we will review common techniques available for SNP discovery and the biological mechanisms that can be affected by noncoding SNPs and provide examples of how SNPs can affect two common complex diseases, cancer and asthma. Finally, we will compare and contrast the different tools available to annotate the genome with noncoding genomic element data and discuss methods and software that use this data to predict pathogenicity for a given SNP. |
| Databáze: | OpenAIRE |
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