Prediction of potentially functional variants from whole-genome sequence data in laying hens

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. We have previously identified genomic regions associated with body mass in a genome-wide association study, and we are now searching for candidate variants that may explain them.

Goal

This project aims to identify potentially functional, most likely deleterious, protein-coding variants in laying hens, and describe their allele and genotype frequencies, their genomic distribution and identify candidate causative variants for body mass.

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 whole-genome sequence data from crossbred laying hens. The project will investigate use these predictions to identify candidate causative variants for body mass associations.

Specifications

Suitable for: Animal Science, Bioinformatics.