Genome-wide association studies pathway-based meta-analysis for residual feed intake in beef cattle.

Genome-wide association studies (GWASes) have been performed to search for genomic regions associated with residual feed intake (RFI); however inconsistent results have been obtained. A meta-analysis may improve these results by decreasing the false-positive rate. Additionally, pathway analysis is a powerful tool that complements GWASes, as it enables identification of gene sets involved in the same pathway that explain the studied phenotype. Because there are no reports on GWAS pathways-based meta-analyses for RFI in beef cattle, we used several GWAS results to search for significant pathways that may explain the genetic mechanism underlying this trait. We used an efficient permutation hypothesis test that takes into account the linkage disequilibrium patterns between SNPs and the functional feasibility of the identified genes over the whole genome. One significant pathway (valine, leucine and isoleucine degradation) related to RFI was found. The three genes in this pathway-methylcrotonoyl-CoA carboxylase 1 (MCCC1), aldehyde oxidase 1 (AOX1) and propionyl-CoA carboxylase alpha subunit (PCCA)-were found in three different studies. This same pathway was also reported in a transcriptome analysis from two cattle populations divergently selected for high and low RFI. We conclude that a GWAS pathway-based meta-analysis can be an appropriate method to uncover biological insights into RFI by combining useful information from different studies.

Seleção genômica ampla para curvas de crescimento / Genome Wide Selection for growth curves

Foi proposta uma metodologia para avaliação genética de curvas de crescimento considerando-se informações de marcadores SNPs (Single Nucleotide Polymorphisms). Em um primeiro passo foram ajustados modelos de crescimento não lineares (logístico) aos dados de peso-idade de cada animal, e em um segundo passo as estimativas dos parâmetros de tais modelos foram consideradas como fenótipos em um modelo de regressão (LASSO Bayesiano - BL) cujas covariáveis foram os genótipos dos marcadores SNPs. Este enfoque possibilitou estimar os valores genéticos genômicos (GBV) para peso em qualquer tempo da trajetória de crescimento, refletindo na confecção de curvas de crescimento genômicas, as quais permitiram a identificação de grupos de indivíduos geneticamente superiores em relação à eficiência de crescimento. Os dados simulados utilizados neste estudo foram constituídos de 2000 indivíduos (1000 na população de treinamento e 1000 na população de validação) contendo 453 marcadores SNPs distribuídos sobre cinco cromossomos. Os resultados indicaram a alta eficiência do método BL em predizer GBVs da população de validação com base na população de treinamento (coeficientes de correlação variaram entre 0,79 e 0,93), bem como a alta eficiência na detecção de QTLs, uma vez que os marcadores com maiores efeitos estimados encontravam-se em posições dos cromossomos próximas àquelas nas quais se encontravam os verdadeiros QTLs postulados na simulação.

A methodology was proposed for the genetic evaluation of growth curves considering SNP (Single Nucleotide Polymorphisms) markers. At the first step, nonlinear regression growth models (Logistic) were fitted to the weight-age of each animal, and on second step the parameter estimates of the Logistic model were used as phenotype in a regression model (Bayesian LASSO - BL) which covariates were given by SNP genotypes. This approach allows the estimation of GBV (Genomic Breeding Values) for weight at either time of growth trajectory, allowing also the production of genomic growth curves, which selected groups of individuals with larger growth efficiency. The simulated data set was constituted of 2,000 individuals (being 1,000 in the training and 1,000 in the validation population) each one with 453 SNP markers distributed along 5 chromosomes. The results indicated high efficiency of the BL method to predict GBV in the validation population using information from the training population (correlation coefficients varying between 0.79 and 0.93). The BL also presented high efficiency to detect QTL, once the most expressive estimated SNP effects were located at positions closed to true QTL position fixed in the simulation.