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Search for causal structures using graphical models in Nelore cattle

Grant number: 16/02366-0
Support type:Scholarships in Brazil - Doctorate
Effective date (Start): May 01, 2016
Effective date (End): July 31, 2019
Field of knowledge:Agronomical Sciences - Animal Husbandry
Principal Investigator:Lucia Galvão de Albuquerque
Grantee:Tiago Bresolin
Home Institution: Faculdade de Ciências Agrárias e Veterinárias (FCAV). Universidade Estadual Paulista (UNESP). Campus de Jaboticabal. Jaboticabal , SP, Brazil
Associated research grant:09/16118-5 - Genomic tools to genetic improvement of direct economic important traits in Nelore cattle, AP.TEM
Associated scholarship(s):17/18779-5 - Phenotypic causal inference using MULTI-OMICS data in Nelore cattle, BE.EP.DR

Abstract

One of the major challenges in the genetics and animal breeding area is to understand how is the expression phenotypes of economic importance. A step matter was given to the sequencing of the bovine genome which allowed to use the marker panels to identify regions of the genome responsible for the phenotype variation. To understand biological systems, researches has been developed using messenger RNA information, thus enabling potentially identify all the genes involved in the phenotype expression. However, many researches still need to be done to better understand how DNA, RNA, proteins and metabolites work together in the phenotype expression, as well as existing causal relationships between them. The objective of this project is to identify causal structures using phenotypic, genotypic and gene expression information for carcass and meat traits in Nelore cattle, trying to elucidate the form and extent of the link between gene activity and phenotypic variation. Phenotypic, genotypic and gene expression information of Nelore cattle will be use. The trait that will be studied are: ribeye area, fat thickness, marbling, meat tenderness, lipid content, instrumental meat color, cooking losses and myofibrillar fragmentation index. The search for causal structures will be carried out in multiple steps as follows: i) to explore the associations between phenotype and genotype to identify pQTL (phenotype Quantitative Trait Loci) for each trait; ii) to explore gene expression information to find eQTLs (expression quantitative trait loci) for each trait; iii) to identify pQTLs that are co-located in eQTLs found in the second step; and iv) to perform causal inference using phenotypes, pQTLs and pQTLs co-located in eQTL information through a graphical model. The search for significant pQTLs for each trait will be conducted through a genome-wide association study using haplotypes constructed from overlapping windows. To find significant eQTLs for each trait studied, TReCASE method will be used. Analyses using graphical models in order to find causal structures will be performed using the IAMB algorithm. This study may identify causal structures that can assist in understanding of the genetic and phenotypic expression for carcass and meat trait in Nelore cattle and them generate information that can be used for genetic improvement of the breed.