Random Regression Model for Genetic Evaluation and Early Selection in the Iranian Holstein Population

The objective of this study was to use a model to predict breeding values for sires and cows at an early stage of the first lactation of cows and progeny groups in the Iranian Holstein population to enable the early selection of sires. An additional objective was to estimate genetic and phenotypic parameters associated with this model. The accuracy of predicted breeding values was investigated using cross-validation based on sequential genetic evaluations emulating yearly evaluation runs. The data consisted of 2,166,925 test-day records from 456,712 cows calving between 1990 and 2015. (Co)-variance components and breeding values were estimated using a random regression test-day model and the average information (AI) restricted maximum likelihood method (REML). Legendre polynomial functions of order three were chosen to fit the additive genetic and permanent environmental effects, and a homogeneous residual variance was assumed throughout lactation. The lowest heritability of daily milk yield was estimated to be just under 0.14 in early lactation, and the highest heritability of daily milk yield was estimated to be 0.18 in mid-lactation. Cross-validation showed a highly positive correlation of predicted breeding values between consecutive yearly evaluations for both cows and sires. Correlation between predicted breeding values based only on records of early lactation (5–90 days) and records including late lactation (181–305 days) were 0.77–0.87 for cows and 0.81–0.94 for sires. These results show that we can select sires according to their daughters’ early lactation information before they finish the first lactation. This can be used to decrease generation interval and to increase genetic gain in the Iranian Holstein population.

Estimation the Genetic Parameters for Milk Yield in Romanian Spotted, Simmental Type Cattle Breed

The aim of this study was to estimate the genetic parameters for test-day traits milk yield and the breeding value in Romanian Spotted, Simmental type cattle. Random regression test-day animal model was used to estimate the genetic parameters. The number of records were 2062 test-day from 302 cows. The data were obtained from Romanian Breeding Association Romanian Spotted, Simmental type. The heritability estimates values for milk yield ranged between 0.377 and 0.417. The heritability for fat test-day yield in our study was low the values ranged between 0.117 and 0.236 and for protein test-day yield was medium, the values ranged between 0.308 and 0.372. The breeding value for the best ten cows for milk yield ranged from 1946.57 to 3250.38 kg, for fat yield were between 64.92 and 98.86 kg and for protein ranged from 67.26 to 102.21 kg. The correlations between test-day milk yields ranging from 0.28 to 1. Genetic correlations between test day fat and protein yields were high.

Effect on weight of a homemade diet in women with overweight or obesity: A randomized controlled trial

The official dietetic guidelines for weight loss include the practice of “healthy eating”. However, such recommendations rarely take into account the cultural context. The aim of the present study was to measure the effect of recommending a traditional homemade diet (exemplified by typical meals consumed in Mexico) vs. recommending an iso-caloric healthy diet (represented by the eatwell plate) on the weight of Mexican women with overweight or obesity. Initially 159 women were randomly assigned to the homemade diet or the healthy diet and 30 women completed the intervention. The effect on weight of the recommended diet at 4, 8 and 12 weeks was determined by one-way analysis of variance and by random regression model. Participants on average reduced weight significantly throughout the intervention without statistical difference between the homemade diet and the healthy diet. This finding supports an anti-obesity strategy of recommending traditional diets in culturally recognised terms.

PSVII-27 Genetic analysis of disease resilience of nursery-to-finish pigs under a natural disease challenge model using reaction norms

Disease resilience is the ability of an animal to maintain performance across environments with different disease challenge loads (CL) and can be quantified using random regression reaction norm models that describe phenotype as a function of CL. Objectives of this study were to: 1) develop measures of CL using growth rate and clinical disease phenotypes under a natural disease challenge; 2) evaluate genetic variation in disease resilience. Data used were late nursery and finisher growth rates and clinical disease phenotypes, including medical treatment and mortality rates, and subjective health scores, collected on 50 batches of 60/75 crossbred (LRxY) barrows under a polymicrobial natural disease challenge. All pigs were genotyped using a 650K SNP panel. Different CL were derived from estimates of contemporary group effects and used as environmental covariates in reaction norm analyses of average daily gain (ADG) and treatment rate (TRT). The CL were compared based on model loglikelihoods and estimates of genetic variance, using both linear and cubic spline reaction norm models. Linear reaction norm models fitted the data significantly better than the standard genetic model and the cubic spline models fitted the data significantly better than the linear reaction norm model for most traits. CL based on early finisher ADG provided the best fit for nursery ADG, while CL based on clinical disease phenotypes was best for finisher ADG and TRT. With increasing CL, estimates of heritability for ADG initially decreased and then increased, while estimates of heritability for TRT generally increased with CL. Genetic correlations were low between ADG or TRT at high versus low CL but high for close CLs. Results can be used to select more resilient pigs across different CL levels, or high-performance animals at a given CL level, or a combination of these. Funded by Genome Canada, Genome Alberta, USDA-NIFA, and PigGenCanada.

PSXV-1 Genetic evaluation of longevity of cows culled due to fertility-related problems using random regression models and censored data

A recent study showed that longevity based on different culling reasons should be considered as different traits in genetic evaluations. However, it is still necessary to create a pipeline that avoid including/excluding animals culled for different reasons in every genetic evaluation run. This study aimed to: 1) perform a genetic evaluation of the longevity of cows culled due to fertility-related problems including records of animals culled for other reasons (i.e., age, structural problems, disease, and performance) as censored records; and, 2) identify the impact of censored data in the genetic parameters and breeding values estimated. Two longevity indicators were evaluated: traditional (TL; time from first calving to culling) and functional (FL; time period in which the cow was alive and also calving after its first calving) longevity. Both TL and FL were evaluated from 2 to 15 years-old, and codified as binary traits for each age (0 = culled and 1 = alive/calved). Both trait definitions were analyzed using a Bayesian random regression linear model. Animals culled for reasons other than fertility were either excluded from the data (standard) or had their records censored after the culling date reported in the dataset (censored). After the quality control, 154,419 and 450,124 animals had uncensored and censored records, respectively. Heritabilities estimated for TL over the ages ranged from 0.02 to 0.13 for standard, and from 0.01 to 0.12 for censored datasets. Heritabilities estimated for FL ranged from 0.01 to 0.14 (standard), and from 0.01 to 0.13 (censored). Average (SD) correlation of breeding values predicted over all ages, using the standard and censored datasets, was 0.77 (0.16) for TL, and 0.83 (0.11) for FL. Our findings suggest that including censored data in the analyses might impact the genomic evaluations and further work is need to determine the optimal predictive approach.

54 Genetics Reloaded: Large-scale Collection of Novel Phenotypes in Turkey

Effective application of genomic selection methodologies require genomic information, population-based pedigree data, and high-quality phenotypes. The implementation of such selection programs can increase the accuracy of breeding values, therefore improving the ability to estimate the genetic merit of livestock. In particular, traits with low heritability are amenable to genomic selection. The objective of this presentation is to outline improvement of traits such as livability, disease resistance, fertility, and other health and welfare traits in turkeys, which could considerably advance breeding programs. The aim of this study was to apply different methodologies (ssGBLUP, random regression approaches, etc.) to novel and conventional traits collected in commercial turkey lines (15,000 turkeys genotyped at 65K). This reference population was used to assess the increase in accuracy of selection using genomic information, which ranged from 0 to +0.3, depending on the trait. A further goal of the study was to monitor inbreeding within the different lines. A large number of high-quality phenotypes related to fertility, growth, production, and carcass composition were collected, as well as additional health and behaviour phenotypes related to livability. These traits are being developed for use in performance testing. Furthermore, meat quality (e.g. white striation, water holding capacity, pH, sheer force and colour, etc.) and total carcass composition phenotypes were analysed. With improved methodology, more detailed phenotypic information, and comprehensive data collection and integration, we present more accurate selection of parent stock for application in applied poultry breeding programs.

Covariance functions under B-spline polynomials to model Polled Nellore cattle growth

B-spline functions have been used in random regression models (RRM) to model animal weight from birth to adulthood because they are less vulnerable to common difficulties of other methods. However, its application to model growth traits of Polled Nellore cattle has been little studied. Therefore, this study aimed to evaluate polynomial functions of different orders and segment numbers to model effects associated with the Polled Nellore cattle growth curve. For this purpose, we used 15,148 weight records of 3,115 animals aged between 1 and 660 days and reared in northern Brazil and born between 1995 and 2010. Random effects were modeled using B-spline polynomials. As random effects, we considered the direct and maternal genetic additives, as well as direct and maternal permanent environments. As fixed effects were included contemporary group, cow age at calving (linear and quadratic) and fourth-order Legendre polynomials to represent average growth curve. The residue was modeled by considering seven age classes. The bestfitted model was the one that considered cubic B-spline functions with four knots for direct additive genetic effects and three knots for maternal genetic, animal permanent environment, and maternal permanent environment effects (C6555). Therefore, covariance functions under B-spline polynomials are efficient and can be used to model the growth curve of Polled Nellore cattle from birth to 660 days of age.