Abstract

Knowledge of the genetics related to growth is important for breeding chickens with the desired traits of rapid and efficient growth. A long-term selection experiment in the domestic chicken (Gallus gallus) has resulted in two highly divergent lines selected for juvenile body weight. These lines are a great genetic resource to identify the genetic basis of phenotypic variation mainly for growth traits. The aim of this thesis was to map the relationship between the genotype and phenotype and thereby reveal the genetic architecture of growth in the chicken. To this end, high-density genotyping and whole-genome resequencing of the lines were used to explore the genetics of the body weight difference between these lines. We further fine- mapped previously identified Quantitative Trait Loci (QTLs) for body weight and used bioinformatics approaches to identify the most promising candidate genes, mutations and biological pathways for growth for further functional evaluations. We also studied a previously mapped radial network of interacting QTLs to reveal potential biological interactions by analyzing biological pathways. In addition, we developed new computational genetic method and tools and used them to functionally explore specific genetic variants in selected regions in the genome. To functionally evaluate the effect of identified amino acid substitutions (AAS), we developed and implemented a bioinformatics method in a tool called PASE. The predictions of PASE, using physicochemical properties of amino acids, were consistent with other widely available homology-based tools. Our mapping strategy successfully fine-mapped the QTLs, and our bioinformatic strategies were efficient in identifying the candidate mutations and biological pathways for growth. The methods we developed could be applied to the genetic dissection of other complex traits.

Keywords

SNP; QTL; NGS; Candidate mutations; Association mapping; Imputation; Virginia chicken lines; Divergent selection; Functional characterization

Published in

Acta Universitatis Agriculturae Sueciae
2014, number: 2014:49
ISBN: 978-91-576-8046-4, eISBN: 978-91-576-8047-1
Publisher: Department of Clinical Sciences, Swedish University of Agricultural Sciences

SLU Authors

• Ahsan, Muhammad

  • Department of Clinical Sciences, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Bioinformatics (Computational Biology)
Genetics
Bioinformatics and Systems Biology


Permanent link to this page (URI)

https://res.slu.se/id/publ/55145