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Updated: Sep 20, 2026

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)
Published on: August 21, 2016
National genomic evaluation for a novel functional longevity trait
Pedro Ramos1, Shogo Tsuruta2, Andre Garcia3
1Angus Genetics Inc, Saint Joseph, MO, USA. pramos@angus.org.
Background:
Cow longevity plays a major role in beef cattle industry profitability, reflecting the female's ability to remain productive in the herd. In this context, longitudinal models as random regression models (RRM) are the preferred method for genetic evaluation as they account for genetic and environmental effects over time. Traditionally, RRM longevity evaluations use binary phenotypes; however, redefining the phenotype as the number of calves could enhance variability, better identify cows with consistent reproductive performance, and penalize irregular calving patterns. To address these limitations, this study aimed to: (1) propose genomic evaluations for a novel functional longevity (FL) definition via RRM; (2) compare model fit using Legendre polynomials (LP) of varying orders; (3) evaluate the implication of FL genetic parameters for selection strategies.
Results:
The dataset included records from 2 million Angus cows and 1.6 million genotyped animals from the American and Canadian Angus Associations. FL was defined as the cumulative number of calvings, measured annually from 2 to 10 years of age. Genetic parameters were estimated with Gibbs sampling without genomic information, and genomic breeding values (GEBV) were estimated using RRM under single-step GBLUP with LP. Models were compared via the deviance information criterion and sire GEBV rankings across ages were evaluated using Spearman correlations and percentage of individuals in common (top 1%). The best fit was with a third-order LP and homogeneous residual variance. Heritabilities ranged from 0.04 to 0.12. Genetic correlations between ages were positive and high, with minimal fluctuations. GEBV rank correlations were consistent across ages, with Spearman correlations following the genetic correlations pattern. The percentage of commonly selected individuals between adjacent years was high. However, percentage decreased with increasing age gap, reaching 53% between ages 3 and 10.
Conclusions:
The new functional longevity definition is feasible for genomic evaluations and has a straightforward interpretation as breeding values represent the number of additional calves when comparing animals. FL heritability estimates indicate sufficient additive genetic variance to allow moderate response to selection. Based on genetic correlations and ranking comparisons, functional longevity at 6 years of age is the optimal age to maximize genetic progress in Angus cattle.
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