ArticleBMC genomics2026
Genetic marker analysis of RAPGEF6 gene and covariance structure modelling for egg.
Article in BMC genomics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundEgg production sustainability is economically critical for layer farms. The Marandi chicken, an indigenous breed from northwestern Iran (Azerbaijan region), possesses significant potential for efficient egg production, yet its genetic potential remains underexplored. This study aimed to investigate the association between single nucleotide polymorphisms (SNPs) in the RAPGEF6 gene and productive and reproductive traits in the Marandi chicken population.
methodsPerformance and reproductive data were collected from 153 Marandi hens. Genotyping was performed using PCR-RFLP. Data were analyzed using two complementary approaches in SAS: a repeated-measure model (with eight covariance structures) for repeated egg weight measurements, and Generalized Linear Models (GLM) for the remaining growth and performance traits. Genotype frequencies for the studied SNPs were determined. Post-hoc statistical power analysis was conducted using GLMPOWER in SAS to evaluate the ability of the current sample size to detect observed effect sizes.
resultsA significant association between one SNP and Maturity Weight (P = 0.014) was found in the present work, while no significant associations were found for the other 16 traits (P > 0.05). However, post-hoc power analysis revealed that the study had only 5% power to detect this effect, indicating that this finding is statistically unstable. The ARMA (1,1) model demonstrated good performance in accounting for the repeated egg weight measurements.
conclusionsPolymorphisms in the RAPGEF6 gene may influence growth traits in Marandi chickens, offering insights into the genetic underpinnings of performance. However, given the single-SNP design, the small sample size, and the severely limited statistical power (5%), all findings should be interpreted as exploratory and preliminary, warranting validation in independent, well-powered populations. No recommendations for marker-assisted selection can be made based on the current data. The optimal ARMA (1,1) covariance structure enhanced repeated-record analysis precision for egg-laying performance, facilitating a more consistent and reliable assessment of egg production patterns.
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