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31.1: Introduction

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    Differences in gene coding regions across different organisms do not completely explain the phenotypic variation we see. For example, although the phenotypic difference is high between humans and chimpanzees and low between different squirrel species, there is more genetic variation among the squirrel species [1]. These observations lead us to conclude that there must be more than just gene-coding variation that accounts for phenotypic variation; specifically, non-coding variation also influences how genes are expressed, and consequently influences the phenotype of an organism. In fact, previous research has shown that most genetic variation occurs in non-coding regions [2]. Furthermore, most expression patterns have been found to be heritable traits.

    Understanding how variation in non-coding regions affects co-regulating genes would allow us not only to understand but also control the expression of these and other related genes. This is especially relevant to the control of undesirable trait expressions like complex, polygenic diseases (Figure 31.1). In Mendelian disease, the majority of disease risk is predicted by coding variation, whereas in polygenic diseases the vast majority of causal variation is found outside of coding regions. This suggests that variation in the regulation of gene expression may play a greater role than genotypic variation in these polygenic diseases. Thus, the study of these trait associated variants is a step in the direction of understanding how genetic sequences both code for and control the expression of such diseases and their associated phenotypes.

    eQTLs (expression quantitative trait loci) encapsulate the idea of non-coding regions influencing mRNA expression introduced above: we can define an eQTL as a region of variants in a genome that are quanti- tatively correlated with the expression of another gene encoded by the organism. Usually, we will see that certain SNPs in certain non-coding regions will either enhance or disrupt the expression of a certain gene. The field of identifying, analyzing, and interpreting eQTLs in the genome has grown immensely over the last couple of years with hundreds of research papers being published.

    There are four main mechanisms for how eQTLs influence the expression of their associated genes:

    1. Altered transcription factor binding 2. Histone modifications
    3. Alternative splicing of mRNA
    4. miRNA silencing

    FAQ

    Q: What is the difference between an eQTL study and a GWAS?

    A: There are two fundamental differences. The first is in the nature of the phenotype being exam- ined. In an eQTL, the phenotype checked is usually on a lower level of biological abstraction (normalized gene expression levels) instead of a more higher-level, sometimes visible phenotype used in GWAS, such as ”black hair”). Secondly, in GWAS, usually because the phenotype be- ing correlated with various SNPs is a higher-level phenotype, we very rarely see tissue-specific GWAS. However, in eQTLs, the expression patterns of mRNA could vary greatly between tissue-types within the same individual, and eQTL studies for a specific tissue-type, such as neuron and glial cells, can be performed (Figure 31.2)


    31.1: Introduction is shared under a not declared license and was authored, remixed, and/or curated by LibreTexts.

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