Prediction of potentially deleterious variants from whole-genome sequence data in pigs

Background

Identification of causative variants that contribute to traits is difficult, but with the availability of large samples of whole-genome sequenced animals and machine learning methods that predict function, it is becoming possible to describe the properties of potentially functional variants in aggregate and estimate the distribution of potentially deleterious variants in the genome.

Goal

This project aims to identify potentially functional, most likely deleterious, protein-coding variants in the pig, and describe their allele frequency distribution, genomic distribution and correlation with other variants.

Project description

The project will run MutPred2 to predict the effect of missense single nucleotide variants, and MutPred-LoF to predict the effect of loss-of-function variants in a large sample of publicly available whole-genome sequence data from pigs. The project will investigate the distribution along the genome, the allele frequency distribution of potentially functional variants compared to variants predicted not to be functional, and the correlation between potentially functional variants and neighbouring variants.

Specifications

Suitable for: Animal Science, Bioinformatics.