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Phenotypic causal inference using multi-omics data in Nelore cattle

Grant number: 17/18779-5
Support Opportunities:Scholarships abroad - Research Internship - Doctorate
Effective date (Start): March 01, 2018
Effective date (End): February 28, 2019
Field of knowledge:Agronomical Sciences - Animal Husbandry - Genetics and Improvement of Domestic Animals
Principal Investigator:Lucia Galvão de Albuquerque
Grantee:Tiago Bresolin
Supervisor: Guilherme Jordão de Magalhães Rosa
Host Institution: Faculdade de Ciências Agrárias e Veterinárias (FCAV). Universidade Estadual Paulista (UNESP). Campus de Jaboticabal. Jaboticabal , SP, Brazil
Research place: University of Wisconsin-Madison (UW-Madison), United States  


The availability of high-throughput SNP (single nucleotide polymorphism) arrays genotyping technology has allowed the identification of genome regions that underlie the phenotypic variability through genome-wide association studies (GWAS). For complex traits, a disadvantage of GWAS is their association with multiple variants of small effects, which explain only a small proportion of the genetic variation. To overcome this problem, genetical genomic studies have used the abundance of transcripts by messenger RNA (mRNA) profiling combined with genotype data to identify expression quantitative trait loci related to the phenotypic variability. However, these studies have summarized only the association among phenotype and common variants without causal direction between them. The aim of this study is to search for causal structures through the integration of phenotypic, genotypic and gene expression information for carcass and meat traits in Nelore cattle. Longissimus muscle area (LMA), backfat thickness (BF), Warner-Bratzler shear force (WBSF), marbling score (MB) and cooking losses (CL) traits will be used. Genotyping was performed using BovineHD BeadChip (Illumina®, Inc., San Diego, CA, USA) and GeneSeek ® Genomic Profiler Indicus HD - GGP75Ki (Neogen Corporation, Lincoln, NE, USA). The RNA sequencing was performed using HiSeq 2500 System (Illumina®, Inc., San Diego, CA, USA) to produce 2x100 base pairs paired-end reads. Gene-phenotype network will be inferred, in a multiple-step procedure, using causal structure learning approaches implemented in the bnlearn R package. (AU)

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Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
BRESOLIN, TIAGO; PASSAFARO, TIAGO LUCIANO; BRAZ, CAMILA URBANO; CARVALHO ALVES, ANDERSON ANTONIO; CARVALHEIRO, ROBERTO; LOYOLA CHARDULO, LUIZ ARTUR; DE MAGALHAES ROSA, GUILHERME JORDAO; DE ALBUQUERQUE, LUCIA GALVAO. Investigating potential causal relationships among carcass and meat quality traits using structural equation model in Nellore cattle. MEAT SCIENCE, v. 187, p. 7-pg., . (09/16118-5, 16/02366-0, 17/18779-5)

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