Incorporation of Phenotypic Causal Networks into Genome-Wide Association Studies for Pregnancy Loss in Precocious Heifers Using Structural Equation Models with Mixed Effects

• Grant number: 25/16101-8
• Support Opportunities: Scholarships in Brazil - Master
• Start date: February 01, 2026
• End date: October 31, 2027
• Field of knowledge: Agronomical Sciences - Animal Husbandry - Genetics and Improvement of Domestic Animals
• Principal Investigator: Fernando Sebastián Baldi Rey
• Grantee: Meire Luiza Wirth
• Host Institution: Faculdade de Zootecnia e Engenharia de Alimentos (FZEA). Universidade de São Paulo (USP). Pirassununga , SP, Brazil

Abstract

Pregnancy loss is one of the main constraints on productivity in beef cattle systems, resulting in economic losses and compromising the reproductive efficiency of herds. Despite the increasing inclusion of reproductive traits in genetic improvement programs, knowledge regarding the genetic basis of pregnancy loss remains limited due to the complexity of interactions between genetic and phenotypic factors. In this context, the present project aims to investigate the genetic architecture of pregnancy loss in Nelore cattle through the modeling of causal networks among reproductive and productive traits using Structural Equation Models within the context of genome-wide association studies (SEM-GWAS). Data from 23,507 heifers belonging to the ANCP (Brazilian National Association of Breeders and Researchers) will be analyzed, of which 17,052 have available genotypes. The heifers were exposed to breeding between 10 and 14 months of age, with a second attempt at 24 months in case of failure to conceive. Pregnancy loss will be defined as a positive pregnancy diagnosis without a subsequent calving. The dataset also includes reproductive, growth, and carcass traits, which will be evaluated using multi-trait models (MTM). The methodology to be applied involves three main steps: (1) fitting multi-trait models to estimate (co)variance components; (2) inferring phenotypic causal networks using the Inductive Causation (IC) algorithm; and (3) decomposing SNP effects into direct and indirect components via SEM-GWAS. The analyses will be conducted using the BLUPF90 software suite and the R software. This project aims to integrate genomic, phenotypic, and causal information to enhance the understanding of the genetic architecture of pregnancy loss, providing support for more accurate selection strategies. (AU)