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<p>Random regression models (RRM) have been recognised as suitable for analysis of longitudinal data like growth. Growth in Sandyno sheep, a dual purpose (meat and wool) synthetic breed evolved from Nilagiri breed of South India by crossing with Merino and Rambouillet sheep was modeled using RRM to estimate genetic parameters and results obtained were compared with values already estimated using conventional methods. A mixed RRM withcontemporary group of year and season of birth, sex, litter size and age of dam at lambing, as fixed effects and direct additive, maternal genetic, maternal permanent environmental and individual permanent environmental factors, as random effect was used in different orders of fit. Age at recording of weight was included as the control variable. The RRM with fourth order for direct genetic effectand heterogenous error variance (9 classes) was found to have the best fit. The pattern of variation over ages was similar for direct and maternal genetic variance. Phenotypic and individual permanent environmental variance showed similar increasing trend with age until 2 years.The heritability estimates obtained through RRM for 3 month (3&#xa0;W), 6 month (6&#xa0;W), 9 month (9&#xa0;W), 12 month (12&#xa0;W), 18 month (18&#xa0;W) and 24 month (24&#xa0;W) body weights were 0.211,0.397,0.457,0.432,0.242 and 0.172, respectively. The values for 3&#xa0;W and 18&#xa0;W were similar to those obtained through univariate analysis, while other estimates were on the higher side. The trend of estimates over ages was similar between RRM and conventional methods. Values of direct and maternal genetic correlations were positive and high.This finding of high positive genetic correlation among body weight at different ages was also evident from the trend of Eigen functions estimated from genetic (co)variance matrix.The trajectories for first and second Eigen functions, which account for more than 98.5 per cent genetic variation showed a uniform trend with positive values and slight increase with age. Selection on these factors will improve weight at all ages, with more response at later ages after 9 months of age. The estimates of heritability obtained were precise for ages up to 24 months and values indicate good scope for improvement through selection. The ability of RRM to model growth by virtue of accounting for covariance between measurements and GXE interactions makes it suitable for evaluation of growth performance.</p>

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Random regressions to model growth in Sandyno sheep, a synthetic breed developed for the Nilgiri hills of South India

  • 期刊论文

  • 发表日期：

    2026年3月14日

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  • R. Venkataramanan,
  • V. Arthy,
  • S. Mohamedrilwan,
  • N. Prema

摘要

Random regression models (RRM) have been recognised as suitable for analysis of longitudinal data like growth. Growth in Sandyno sheep, a dual purpose (meat and wool) synthetic breed evolved from Nilagiri breed of South India by crossing with Merino and Rambouillet sheep was modeled using RRM to estimate genetic parameters and results obtained were compared with values already estimated using conventional methods. A mixed RRM withcontemporary group of year and season of birth, sex, litter size and age of dam at lambing, as fixed effects and direct additive, maternal genetic, maternal permanent environmental and individual permanent environmental factors, as random effect was used in different orders of fit. Age at recording of weight was included as the control variable. The RRM with fourth order for direct genetic effectand heterogenous error variance (9 classes) was found to have the best fit. The pattern of variation over ages was similar for direct and maternal genetic variance. Phenotypic and individual permanent environmental variance showed similar increasing trend with age until 2 years.The heritability estimates obtained through RRM for 3 month (3 W), 6 month (6 W), 9 month (9 W), 12 month (12 W), 18 month (18 W) and 24 month (24 W) body weights were 0.211,0.397,0.457,0.432,0.242 and 0.172, respectively. The values for 3 W and 18 W were similar to those obtained through univariate analysis, while other estimates were on the higher side. The trend of estimates over ages was similar between RRM and conventional methods. Values of direct and maternal genetic correlations were positive and high.This finding of high positive genetic correlation among body weight at different ages was also evident from the trend of Eigen functions estimated from genetic (co)variance matrix.The trajectories for first and second Eigen functions, which account for more than 98.5 per cent genetic variation showed a uniform trend with positive values and slight increase with age. Selection on these factors will improve weight at all ages, with more response at later ages after 9 months of age. The estimates of heritability obtained were precise for ages up to 24 months and values indicate good scope for improvement through selection. The ability of RRM to model growth by virtue of accounting for covariance between measurements and GXE interactions makes it suitable for evaluation of growth performance.