Société Française de Bioinformatique

Mots-Clés

génétique statistiques héritabilité

Description

M2 Internship in statistical Genetics : the unexplored part of human disease heritability
The genetic architecture of a human trait refers to the set of genetic variations, particularly Single Nucleotide Polymorphisms (SNPs), that contribute to the trait variation observed in a population as much as their effects. We propose a Master 2 internship focusing on complex genetic markers and heritability estimation, an exciting field that moves beyond traditional bi-allelic markers to uncover the hidden complexities of the genome. The genome-wide association studies, base on bi-allelic markers (SNPs), have been extremely prolific and helped unravelling the polygenic nature of complex diseases.
However, a large part of the genome remains unexplored, notably the VNTRs and other repeats but also regions of low complexity. It is possible that. These repeat variants are harder to assess through a statistical model. Even when it is possible to call them, a multiple allelic state will need the implementation of an adapted statistical test adapted to the multimodal nature of these risk factors. We are implementing a simple alternative procedure which consists in trimming and clustering the risk alleles and outputting the statistical significance of the better case-controls discriminant through permutation.
Alternatively, the k-mer technique offers a powerful approach to explore complex genomes by analyzing short DNA sequences without the need for full alignment or assembly. This method enables the detection of genomic patterns and associations in regions that are typically challenging to study, such as highly repetitive or ancient DNA. Our project aims to investigate the genomic regions that contribute to heritability but are not fully explained by simple binary variants. Using advanced statistical tools and computational model multi-allelic markers, and continuous genomic features. Your work will contribute to a deeper understanding of genetic architecture and its role in phenotypic variation and complex traits.
Objective :
• Apply calling pipeline to genotype the repeated markers genotype
• Apply trim and permute analysis to explore the power and robustness of the test.
• Apply k-mers counting algorithm (kmersGWAS)
• Implement and perform case controls analyses from k-mers count tables.
Requirements:
• A background in genetics, bioinformatics, or a related field.
• Experience with programming (e.g., Python, R) is a plus.
• Enthusiasm for tackling complex problems in genomics.
This internship is ideal for motivated students looking to advance their skills and make meaningful contributions to genetic research. Don’t miss the chance to be part of a dynamic team at the forefront of genomic science!
For more details or to apply, please contact us at Christian.dina@univ-nantes.fr