Abstract

The genotype-phenotype link is a major research topic in the life sciences but remains highly complex to disentangle. Part of the complexity arises from the number of genes contributing to the observed phenotype. Despite the vast increase of molecular data, pinpointing the causal variant underlying a phenotype of interest is still challenging. In this study, we present an approach to map causal variation and molecular pathways underlying important phenotypes in pigs. We prioritize variation by utilizing and integrating predicted variant impact scores (pCADD), functional genomic information, and associated phenotypes in other mammalian species. We demonstrate the efficacy of our approach by reporting known and novel causal variants, of which many affect non-coding sequences. Our approach allows the disentangling of the biology behind important phenotypes by accelerating the discovery of novel causal variants and molecular mechanisms affecting important phenotypes in pigs. This information on molecular mechanisms could be applicable in other mammalian species, including humans.

Highlights

  • Closing the gap between genotype and phenotype is a major goal in the life sciences, but remains extremely challenging [28]

  • A combination of statistical fine-mapping methods and expression quantitative trait loci (QTL) studies are used to decrease the number of candidate genes and causal variants [8]

  • The analysis revealed 271 QTL regions with a genome-wide association significance threshold of -log10(P) > 6.0, and significant associations were observed for the majority of examined traits

Read more

Summary

Introduction

Closing the gap between genotype and phenotype is a major goal in the life sciences, but remains extremely challenging [28]. Genome-wide association studies (GWAS) have been instrumental in statistically linking genotypes and phenotypes. These studies, resulting in identification of quantitative trait loci (QTL), have resulted in better understanding of the genomic architecture of complex traits [65]. Functional annotation, facilitated by large consortium efforts including the Ency­ clopaedia of DNA Elements (ENCODE, [18]), can be applied to prioritize variants based on the likelihood of the variant(s) affecting gene expression. Despite this effort, identifying causal variants remains difficult, partly because of the fundamental complexity of phenotypegenotype relations, in which the environment plays an important role

Objectives
Methods
Results
Discussion
Conclusion
Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.