130 Utilization of Nutrack to Assess Variance Components and Heritability of Activity Traits

2021 ◽  
Vol 99 (Supplement_1) ◽  
pp. 17-17
Author(s):  
Lexi M Ostrand ◽  
Melanie D Trenhaile-Grannemann ◽  
Garrett See ◽  
Ty B Schmidt ◽  
Eric Psota ◽  
...  
Keyword(s):  
Animal Model ◽  
Mixed Models ◽  
Variance Components ◽  
Average Speed ◽  
Genetic Variances ◽  
Sow Longevity ◽  
Selection Decisions ◽  
Recorded Data ◽  
Residual Variances ◽  
And Behavior

Abstract Overall activity and behavior are integral components of sows remaining productive in the herd. This investigation studied overall activity of group housed replacement gilts and the heritability of various activity traits. Beginning around 20 wk of age, video recorded data of approximately 75 gilts/group for a total of 2,378 gilts over 32 groups was collected for 7 consecutive d using the NUtrack System, which tracks distance travelled (m), avg speed (m/s), angle rotated (degrees), and time standing (s), sitting (s), eating (s), and laying (s). The recorded phenotypes were standardized to the distribution observed within a pen for each group. The final values used for analysis were the average daily standardized values. Data were analyzed using mixed models (RStudio V 1.2.5033) including effects of sire, dam, dam’s sire and dam, dam’s grandsire and granddam, farrowing group, barn, pen, and on-test date. Sire had an effect on every activity trait P < 0.001), and dam had an effect on average speed (P < 0.001). The dam’s sire had an effect on all activity traits (P < 0.001) and the dam’s grandsire had an effect on average speed (P < 0.001). Heritabilities and variance components of activity traits were estimated in ASReml 4 using an animal model with a two-generation pedigree. Genetic variances are 0.17 +/- 0.029, 0.19 +/- 0.034, and 0.11 +/- 0.024, residual variances are 0.37 +/- 0.023, 0.41 +/- 0.027, and 0.41 +/- 0.022, phenotypic variances are 0.54 +/- 0.018, 0.60 +/- 0.020, and 0.52 +/- 0.016, and heritabilities are 0.32 +/- 0.048, 0.32 +/- 0.049, and 0.21 +/- 0.044 for average speed, distance, and lie respectively. NUtrack offers potential to aid in selection decisions. Given the results presented herein, continued investigation into these activity traits and their association with sow longevity is warranted.

scholarly journals PSVIII-22 Utilization of NUtrack to assess sire influence on pig activity and activity effects on weight

2020 ◽  
Vol 98 (Supplement_3) ◽  
pp. 222-222
Author(s):  
Lexi Ostrand ◽  
Garrett See ◽  
Melanie D Trenhaile-Grannemann ◽  
Ty Schmidt ◽  
Eric Psota ◽  
...  
Keyword(s):  
Fixed Effects ◽  
Mixed Model ◽  
Pearson Correlation ◽  
Analysis Data ◽  
Activity Data ◽  
Structural Conformation ◽  
Sow Longevity ◽  
Recorded Data ◽  
And Behavior ◽  
The Impact

Abstract Structural conformation and behavior are integral components in sows remaining productive in the herd. This initial investigation studied the impact of activity data on weight and the impact of sires on overall activity of group housed replacement gilts. Beginning at 19 wk of age, video recorded data of approximately 75 gilts/wk for a total of 230 gilts over 3 wk was collected for 7 consecutive d using the NUtrack system, which tracks distance travelled (m), time standing (s), eating (s), and laying (s), and angle rotated (degrees). Any d that logged for less than 24 hr were dropped from analysis. Data were analyzed using mixed models (SAS V 9.4) including random effects of dam, barn, pen, and on-test date, and fixed effects including total laying time, angle rotated, distance travelled, time standing, and sire. For 20 wk wt, the mixed model included distance, laying, angle, and standing. Sire had an effect on distance traveled (P < 0.05). The following traits also had an effect on distance: angle (P < 0.01), laying (P < 0.001), standing (P < 0.001), sitting (P < 0.01), and eating (P < 0.01). Pearson correlation coefficient showed a positive correlation between wt and laying (0.26), and negative correlations between wt and distance (-0.34), standing (-0.28) and angle (-0.42). The regression of distance on 20 wk wt yielded a regression coefficient of -0.027 (P < 0.05) m/d per lb and the model explained 11.85% of the variation in 20 wk wt. NUtrack can be utilized to track activities and distance travelled of swine in group pens. Given the results presented herein, continued investigation into the heritability of these activity traits and their association with sow longevity is warranted.

scholarly journals A novel metric of reliability in pressure pain threshold measurement

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Bernard Liew ◽  
Ho Yin Lee ◽  
David Rügamer ◽  
Alessandro Marco De Nunzio ◽  
Nicola R. Heneghan ◽  
...  
Keyword(s):  
Mixed Models ◽  
Variance Components ◽  
Pain Threshold ◽  
Intraclass Correlation ◽  
Pressure Pain Threshold ◽  
Total Variance ◽  
Lumbar Region ◽  
Pressure Pain ◽  
Pressure Algometer ◽  
Testing Protocols

AbstractThe inter-session Intraclass Correlation Coefficient (ICC) is a commonly investigated and clinically important metric of reliability for pressure pain threshold (PPT) measurement. However, current investigations do not account for inter-repetition variability when calculating inter-session ICC, even though a PPT measurement taken at different sessions must also imply different repetitions. The primary aim was to evaluate and report a novel metric of reliability in PPT measurement: the inter-session-repetition ICC. One rater recorded ten repetitions of PPT measurement over the lumbar region bilaterally at two sessions in twenty healthy adults using a pressure algometer. Variance components were computed using linear mixed-models and used to construct ICCs; most notably inter-session ICC and inter-session-repetition ICC. At 70.1% of the total variance, the source of greatest variability was between subjects ($${\sigma }_{subj}^{2}$$ σ subj 2 = 222.28 N2), whereas the source of least variability (1.5% total variance) was between sessions ($${\sigma }_{sess}^{2}$$ σ sess 2 = 4.83 N2). Derived inter-session and inter-session-repetition ICCs were 0.88 (95%CI: 0.77 to 0.94) and 0.73 (95%CI: 0.53 to 0.84) respectively. Inter-session-repetition ICC provides a more conservative estimate of reliability than inter-session ICC, with the magnitude of difference being clinically meaningful. Quantifying individual sources of variability enables ICC construction to be reflective of individual testing protocols.

Estimation of variance components in normal linear mixed models with additivity

2015 ◽  
Author(s):  
Dário Ferreira ◽  
Sandra S. Ferreira ◽  
Célia Nunes ◽  
João T. Mexia
Keyword(s):  
Mixed Models ◽  
Variance Components ◽  
Linear Mixed Models ◽  
Estimation Of Variance

scholarly journals Estimation of variance components of milk, fat, and protein yields of Tunisian Holstein dairy cattle using Bayesian and REML methods

2016 ◽  
Vol 59 (2) ◽  
pp. 243-248 ◽  
Author(s):  
Hafedh Ben Zaabza ◽  
Abderrahmen Ben Gara ◽  
Hedi Hammami ◽  
Mohamed Amine Ferchichi ◽  
Boulbaba Rekik
Keyword(s):  
Dairy Cattle ◽  
Bayesian Methods ◽  
Milk Fat ◽  
Variance Components ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Bayesian Procedure ◽  
Milk Traits ◽  
Residual Variances ◽  
Estimation Of Variance

Abstract. A multi-trait repeatability animal model under restricted maximum likelihood (REML) and Bayesian methods was used to estimate genetic parameters of milk, fat, and protein yields in Tunisian Holstein cows. The estimates of heritability for milk, fat, and protein yields from the REML procedure were 0.21 ± 0.05, 0.159 ± 0.04, and 0.158 ± 0.04, respectively. The corresponding results from the Bayesian procedure were 0.273 ± 0.02, 0.198 ± 0.01, and 0.187 ± 0.01. Heritability estimates tended to be larger via the Bayesian than those obtained by the REML method. Genetic and permanent environmental variances estimated by REML were smaller than those obtained by the Bayesian analysis. Inversely, REML estimates of the residual variances were larger than Bayesian estimates. Genetic and permanent correlation estimates were on the other hand comparable by both REML and Bayesian methods with permanent environmental being larger than genetic correlations. Results from this study confirm previous reports on genetic parameters for milk traits in Tunisian Holsteins and suggest that a multi-trait approach can be an alternative for implementing a routine genetic evaluation of the Tunisian dairy cattle population.

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