HUMVAR

"Human variation: causes, patterns and consequences"

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 Obiettivo del progetto (Objective)

'Mutations are the source of genetic variation in natural populations, provide material for molecular evolution and, importantly, cause human genetic diseases. Yet the mechanisms of mutagenesis are, to date, not completely understood. Low mutation frequencies limit direct observations in wet-lab experiments. However, hundreds of resequenced human genomes (including cancerous samples) and hundreds of completely sequenced genomes of other species are accumulating at a growing pace. Thus, bioinformatic analyses of mutations are becoming feasible. It is known that mutation rates fluctuate greatly from locus to locus in mammalian genomes, and that the rates of some mutation types co-vary regionally. The causes of this variation and co-variation remain largely unexplored, and deciphering them computationally is expected to unravel the intricacies of mutagenesis. Compared with wet-lab experiments, computational analyses enable us to study mutations in their native genomic environment and on a whole-genome scale. Here we propose to utilize human resequencing data in conjunction with completely sequenced mammalian genomes in order to study regional (primarily intrachromosomal) variation and co-variation in rates of different mutation types. Additionally, we will perform comparisons between cancerous and non-cancerous mutations, highlighting the unique features of mutations associated with cancer. Several statistical multivariate and multi-scale techniques will be utilized. The proposed research will advance our understanding of mutagenesis, including the unique aspects of mutagenesis during cancer. Additionally, it will provide information vital for improving models of the evolutionary process, alignment algorithms, and algorithms for the prediction of functional elements. The tools developed here are expected to bridge interdisciplinary differences in concepts and data between biology and statistics, as well as between bioinformatics and experimental biochemistry.'

Introduzione (Teaser)

European scientists wished to address the process of mutagenesis and how distinct patterns are influenced by their genomic localisation. Given the low mutation rates of wet-lab experiments, the HUMVAR study followed a bioinformatics approach on hundreds of sequenced human and mammalian genomes.

Descrizione progetto (Article)

Mutations are alterations in the sequence of the DNA that occur in the form of base additions, deletions or substitutions. Although mutations are the source of genetic variation and evolution in natural populations, they can cause human genetic diseases.

The plethora of sequencing data available from various studies on normal as well as cancerous samples urged scientists of the EU-funded 'Human variation: Causes, patterns and consequences' (HUMVAR) project to investigate the mutagenesis process. To this end, they performed bioinformatics analysis of hundreds of resequenced human genomes. This method facilitated the studying of mutations in their native genomic environment and on a whole-genome scale.

A key objective was to understand why mutation rates fluctuate among different genetic loci and to evaluate if this is a chromosome region-specific phenomenon. For this purpose, the genome was categorised into segments, each characterised by a specific mutation profile. These segments were then examined for the presence of certain genomic landscape features and the contributions of different biochemical processes to mutagenesis evaluated.

Results showed that mutations that accumulated in neutrally evolving regions of the human genome over millions of years shared similar profiles with recently segregated mutations. Additionally, mutation rates were found to dictate the genomic localisation of genes and non-coding functional marks. Comparative bioinformatics analysis with other mammalian genomes enabled scientists to observe the evolutionary history of primate chromosomes.

The HUMVAR research project also developed tools for genomic research and statistical analysis that could be used in future studies. By deciphering the association between elevated mutation rates and the presence of particular genomic features, the outcome of the work advanced our understanding of the overall process of mutagenesis.