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| Author(s): |
Tiago Bresolin
Total Authors: 1
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| Document type: | Doctoral Thesis |
| Press: | Jaboticabal. 2019-10-14. |
| Institution: | Universidade Estadual Paulista (Unesp). Faculdade de Ciências Agrárias e Veterinárias. Jaboticabal |
| Defense date: | 2019-07-18 |
| Advisor: | Lucia Galvão de Albuquerque; Roberto Cavalheiro |
| Abstract | |
Quantitative traits and genotypes information have been collected for each animal and used to identify genome regions related to phenotypes variation. However, these investigations are, usually, performed based on correlation or association statistical tests, which do not imply in causation. In order to fully explore these information, powerful causal inference methods have been developed to estimate causal effects among the variables under study. Despite significant progress in this field infer causal effect among random variables remains a challenge and some few studies have explored causal relationships in quantitative genetics and animal breeding. In this context, two studies were performed with the following objectives: 1) Search for the causal relationship among carcass yield and meat quality traits using a structural equation model (SEM), under linear mixed model context in Nelore cattle, and 2) Reconstruct gene-phenotype networks and perform causal network analysis through the integrating of phenotypic, genotypic, and transcriptomic data in Nelore cattle. For the first study, a total of 4,479 animals with phenotypic information for hot carcass weight (HCW), longissimus muscle area (LMA), backfat thickness (BF), Warner-Bratzler shear force (WBSF), and marbling score (MB) traits were used. Animals were genotyped using BovineHD BeadChip and GeneSeek Genomic Profiler Indicus HD - GGP75Ki. For causal inference using SEM a multistep procedure methodology was used as follow: a) a standard multi-trait model for studied traits was fitted to access the posterior residual (co)variances, b) the Inductive Causation (IC) algorithm was used to infer causal structures between traits using the posterior residual (co)variances, and c) from the selected causal structure retrieved by the IC algorithm the SEM was fitted. Applying 95 %, 90 % and 85 % highest posterior density (HPD) the same graph was detected by the IC algorithm with undirected links between BF with HCW and MB. Extra link between WBSF and HCW and the direction between BF and HWC were identified using narrow HPD interval (80 %), whereas the link between BF and MB remained undirected. Two different SEM were fitted based on the causal network retrieved by the IC algorithm with either arrow BF → MB or BF ← MB. The most feasible SEM comprise the following links between traits: WBSF → LMA, WBSF → HCW, HCW → LMA, BF → HCW, and BF → MB, with structural coefficients posterior means equal -0.29, 0.43, 0.10, 1.92, and 0.03, respectively. The final SEM revealed some interesting relationships among the traits, and the causal effects suggest that interventions on WBSF and BF would direct affect HCW and LMA. For the second study, a total of 4,599 animals with phenotypic (LMA, BF, and WBSF) and genotypic (as previously described) information were used. RNA sequencing (RNA-Seq) for 80 Nelore cattle muscle tissue samples was carried out by Illumina HiSeq 2500 System to produce 2x100 base pairs paired-end reads using muscle tissue sample. Gene-phenotype networks and causal network analysis were performed using a three-step approach as follow: a) genome scan analyses to identify the association between genotypic and phenotypic data (pQTL – phenotype quantitative trait loci mapping), and between genotypic and gene expression data (eQTL – expression quantitative trait loci mapping). The markers effects estimated in every single pQTL mapping for the phenotypes studied (LMA, BF, and WBSF) were used to perform a multi-trait analysis. b) significant regions from both QTL mapping (multi-trait and eQTL) were co-localized, and c) network reconstruction using causal structural learning algorithm incorporating LMA, BF, WBSF eQTL and gene expression traits was performed. From the multi-trait analysis, 14 genome regions were significant across LMA, BF, and WBSF and 19 cis-eQTL were overlapping five of the genome regions. Based on the cis-eQTL position (the most significant in each genome region), thirty-two nearby genes were identified. Integrating phenotypes, genotypes and gene expression data the inferred network indicated that the rs137704711, located in chromosome 20, affected the three phenotypes (LMA, BF, and WBSF), and the rs133894950, located in chromosome 16, affected BF through the expression of several genes located in different chromosomes. The causal inferences performed using different methodologies were able to identify important causal relationships among the variables under study. (AU) | |
| FAPESP's process: | 16/02366-0 - SEARCH FOR CAUSAL STRUCTURES USING GRAPHICAL MODELS IN NELORE CATTLE |
| Grantee: | Tiago Bresolin |
| Support Opportunities: | Scholarships in Brazil - Doctorate |