REPEAT: Evolutionary and functional importance of simple repeats in the genome

About the project

More than 150 years since Darwin published his famous work 'The Origin Of Species' the causal relationship between the genotype (genome) and the phenotype (phenome) is still basically a mystery.

In particular, even though the role of natural selection in evolution is widely accepted, we do not understand how changes in the phenotype relate to genetic change and how this may cause adaptation and speciation under natural selection. However, what we do better understand, due to recent whole genome investigations using high throughput sequencing (HTS), is the dynamic nature of genome architectural changes. These include, gene copy numbers, inversions, transposable element dynamics and simple repeat variations.

Here we propose to investigate variations in simple trinucleotide repeats residing inside (coding) and in the vicinity (or in introns) of genes. We will relate such length variations to functional modulation of regulatory mechanisms affecting the phenotype. Specifically, we will test the hypothesis that hypervariable coding/regulatory repeats are promoting the ability of a species or population to adapt to a changing environment.

The project is cross-disciplinary and will utilize genomic, bioinformatics, statistics and experimental approaches. The goal is to understand how new mechanisms drives genomic architecture and divergence, taking into account fluctuations in the selection regimes. We aim to obtain new fundamental biological insights as well as novel bioinformatics, and statistical methodology.

Objectives

The primary objectives are to address

(i) how adaptational processes affect genome dynamics, and

(ii) to test if adaptation drives evolution (through selection) of length variants of amino
acid-coding simple repeats or regulatory associated simple repeats.

The goal is to understand how new mechanisms drives genomic architecture and divergence – taking into account fluctuations in the selection regimes.

We aim to obtain new fundamental biological insights as well as novel bioinformatics, and statistical methodology.