Genomic Prediction Accuracies for Growth and Carcass Traits in a Brangus Heifer Population

The predictive abilities and accuracies of genomic best linear unbiased prediction (GBLUP) and the Bayesian (BayesA, BayesB, BayesC and Lasso) genomic selection (GS) methods for economically important growth (birth, weaning, and yearling weights) and carcass (depth of rib fat, apercent intramuscular fat and longissimus muscle area) traits were characterized by estimating the linkage disequilibrium (LD) structure in Brangus heifers using single nucleotide polymorphisms (SNP) markers. Sharp declines in LD were observed as distance among SNP markers increased. The application of the GBLUP and the Bayesian methods to obtain the GEBV for growth and carcass traits within k-means and random clusters showed that k-means and random clustering had quite similar heritability estimates, but the Bayesian methods resulted in the lower estimates of heritability between 0.06 and 0.21 for growth and carcass traits compared with those between 0.21 and 0.35 from the GBLUP methodologies. Although the prediction ability of the GBLUP and the Bayesian methods were quite similar for growth and carcass traits, the Bayesian methods overestimated the accuracies of GEBV because of the lower estimates of heritability of growth and carcass traits. However, GBLUP resulted in accuracy of GEBV for growth and carcass traits that parallels previous reports.

PSVI-16 Comparison of univariate and multivariate machine learning applications for genomic predictions of growth and carcass traits from Brangus heifers

Data for growth and carcass traits were obtained from 738 Brangus heifers (3/8 Brahman-Bos indicus × 5/8 Angus-Bos taurus) registered with International Brangus Breeders Association. Phenotypes included body weights (BW, WW, and YW) collected at birth, ~205 and 365 d of age for growth traits and yearling ultrasound assessment of longissimus muscle area (LMA), percent intramuscular fat (IMF), and depth of rib fat (FAT) for carcass traits and were used to compare univariate and multivariate artificial neural networks (ANN) models with the learning algorithms (Levenberg–Marquardt (LM), Bayesian Regularization (BR) and Scaled Conjugate Gradient (SCG)) and transfer functions (tangent sigmoid and linear) using 1 to 20 neurons models based on the input from G genomic relationship matrix. Pearson correlation coefficients for evaluating model performances in testing datasets indicated that univariate ANN models resulted in better genomic prediction than the multivariate ANN models regardless of the learning algorithms and transfer functions for carcass traits. However, there was no clear difference between univariate and multivariate ANN models about genomic prediction for growth traits. In multivariate ANN models, the prediction performance of the combination of learning algorithms and transfer functions changed for growth and carcass traits and there were no superior multivariate ANN models with BR, LM and SCG learning algorithms and transfer functions. However, the correlation coefficients from univariate ANN-BR model indicated better genomic prediction than univariate ANN-LM and ANN-SCG models. The application of different transfer functions did not make any significant difference on the genomic prediction performance of ANN models with different learning algorithms. Results of this study suggest the use of univariate ANN models with BR learning algorithm for genomic prediction of growth and carcass traits.

Microsatellite-Based Genotyping, Analysis of Population Structure, Presence of Trichomonas vaginalis Virus (TVV) and Mycoplasma hominis in T. vaginalis Isolates from Southwest of Turkey

Background:

The present study aimed to determine genetic diversity of Trichomonas vaginalis (T. vaginalis) isolates with microsatellite markers in Turkey (Nov 2015 to 2016) and to create a web-based microsatellite typing (MT) approach for the global interpretation of the data. In addition, the endosymbiosis of Mycoplasma hominis (M. hominis) and T. vaginalis virus (TVV) in the isolates was also examined.

Methods:

The allele sizes for each locus were calculated and microsatellite types were determined according to the allele profiles. The population structure was examined with Bayesian clustering method. A website (http://mttype.adu.edu.tr) was created for collection and sharing of microsatellite data. Presence of TVV and M. hominis in T. vaginalis isolates were investigated with electrophoresis and PCR.

Results:

Of 630 vaginal samples T. vaginalis was detected in 30 (4.7%) and those were used for further analysis. The structure produced by a clustering algorithm revealed eight genetic groups. The typing of isolates according to microsatellites revealed 23 different microsatellite types. Three clones were determined among isolates (MT10 16.7%; MT18 10% and MT3 6.7%). The frequency of TVV and M. hominis was 16.6% (n=5) and 20% (n=6), respectively.

Conclusion:

Presence of three clones among 30 T. vaginalis isolates indicated that microsatellite-based genotyping was efficient to determine the clonal distribution of T. vaginalis isolates. Therefore, a promising tool might be developed further and adapted to the studies dealing with molecular epidemiology of T. vaginalis. Microsatellite data from forthcoming studies will be deposited and presented on the website. In addition, we also presented the frequency of two endosymbionts in T. vaginalis isolates for the first time in Turkey.

Comparative accuracies of genetic values predicted for economically important milk traits, genome-wide association, and linkage disequilibrium patterns of Canadian Holstein cows

Genomic selection methodologies and genome-wide association studies use powerful statistical procedures that correlate large amounts of high-density SNP genotypes and phenotypic data. Actual 305-d milk (MY), fat (FY), and protein (PY) yield data on 695 cows and 76,355 genotyping-by-sequencing-generated SNP marker genotypes from Canadian Holstein dairy cows were used to characterize linkage disequilibrium (LD) structure of Canadian Holstein cows. Also, the comparison of pedigree-based BLUP, genomic BLUP (GBLUP), and Bayesian (BayesB) statistical methods in the genomic selection methodologies and the comparison of Bayesian ridge regression and BayesB statistical methods in the genome-wide association studies were carried out for MY, FY, and PY. Results from LD analysis revealed that as marker distance decreases, LD increases through chromosomes. However, unexpected high peaks in LD were observed between marker pairs with larger marker distances on all chromosomes. The GBLUP and BayesB models resulted in similar heritability estimates through 10-fold cross-validation for MY and PY; however, the GBLUP model resulted in higher heritability estimates than BayesB model for FY. The predictive ability of GBLUP model was significantly lower than that of BayesB for MY, FY, and PY. Association analyses indicated that 28 high-effect markers and markers on Bos taurus autosome 14 located within 6 genes (DOP1B, TONSL, CPSF1, ADCK5, PARP10, and GRINA) associated significantly with FY.

PSVIII-20 Univariate Genomic Prediction with Different Heritability, QTL and SNP Panel Scenarios using Artificial Neural Network

A simulation study was carried out to determine the likely accuracy of genomic prediction from univariate artificial neural network model with 1 to 10 neurons (ANN-1–10) using the SNPs on the first chromosome of Brangus beef cattle for 50% genetically correlated two traits with heritabilities of 25% and 50% (T1h2=0.25 and T2h2=0.5) determined either by 50, 100, 250 or 500 QTL. After QTL were created by randomly selecting 50, 100, 250 and 500 SNPs from the 3361 SNPs of 719 animals, their effects were sampled from a bivariate normal distribution. The breeding value of animal i in each QTL scenario was generated as Σgijβ j where gij is the genotype of animal i at QTL j and the vector of β j has the effects of QTL j from a bivariate normal distribution for T1h2=0.25 and T2h2=0.5. Phenotypic values (Σgijβ j+ei) of animal i for traits were generated by adding residuals (ei) from a bivariate normal distribution to the Σgijβ j of animal i. Genomic predictions for T1h2=0.25 and T2h2=0.5 were carried out by univariate Feed Forward MultiLayer Perceptron ANN-1–10 neurons. Three sets of SNP panels were used for genomic prediction: only QTL genotypes (Panel1), all SNP markers, including the QTL (Panel2), and all SNP markers, excluding the QTL (Panel3). Correlations between phenotypes and predicted phenotypes from 10-fold cross validation for T1h2=0.25 and T2h2=0.5 were used to assess predictive ability of univariate ANN-1–10 neurons based on 4 QTL scenarios with 3 Panels of SNP panels. Table 1 shows that genomic predictions from the trait with high heritability can achieve higher correlation than those from the trait with low heritability. Panels including SNP markers perform better prediction than Panel1 and have a greater chance of including markers in LD with QTL and allow the possibility of predicting each QTL from collective action of several markers.

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PSVIII-21 Bivariate genomic prediction with different heritability, QTL and SNP panel scenarios using artificial neural network

A bivariate simulation study was carried out to compare the accuracies of genomic predictions from bivariate artificial neural network model with 1 to 10 neurons (ANN-1–10) using the SNPs on the first chromosome of Brangus beef cattle for 50% genetically correlated two traits with heritabilities of 25% and 50% (T1h2=0.25 and T2h2=0.5) determined either by 50, 100, 250 or 500 QTL. After QTL were created by randomly selecting 50, 100, 250 and 500 SNPs from the 3361 SNPs of 719 animals, their effects were sampled from a bivariate normal distribution. The breeding value of animal i in each QTL scenario was generated as Σgijβ j where gij is the genotype of animal i at QTL j and the vector of β j has the effects of QTL j from a bivariate normal distribution for T1h2=0.25 and T2h2=0.5. Phenotypic values (Σgijβ j+ei) of animal i for traits were generated by adding residuals (ei) from a bivariate normal distribution to the Σgijβ j of animal i. Genomic predictions for T1h2=0.25 and T2h2=0.5 were carried out by bivariate Feed Forward MultiLayer Perceptron ANN-1–10 neurons with three sets of SNP panels, only QTL genotypes (Panel1), all SNP markers, including the QTL (Panel2), and all SNP markers, excluding the QTL (Panel3). The correlations between phenotypes and predicted phenotypes from 10-fold cross validation for bivariate analysis of T1h2=0.25 and T2h2=0.5 were used to assess predictive ability of bivariate ANN-1–10 neurons based on 4 QTL scenarios with 3 Panels of SNP panels. Correlations for genomic predictions of T2h2=0.5 were higher than those from T2h2=0.25 for all QTL and Panel scenarios (Table 1). Panle2 including QTL and SNP performs better prediction than Panel1 and Panel3 in QTL100, QTL250 and QTL500 scenarios and the correlation from Panel3 including only SNP, which is more realistic, are similar to or higher than those from Panel1.

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Comparison of linear model and artificial neural network using antler beam diameter and length of white-tailed deer (Odocoileus virginianus) dataset

Evaluation of harvest data remains one of the most important sources of information in the development of strategies to manage regional populations of white-tailed deer. While descriptive statistics and simple linear models are utilized extensively, the use of artificial neural networks for this type of data analyses is unexplored. Linear model was compared to Artificial Neural Networks (ANN) models with Levenberg–Marquardt (L-M), Bayesian Regularization (BR) and Scaled Conjugate Gradient (SCG) learning algorithms, to evaluate the relative accuracy in predicting antler beam diameter and length using age and dressed body weight in white-tailed deer. Data utilized for this study were obtained from male animals harvested by hunters between 1977–2009 at the Berry College Wildlife Management Area. Metrics for evaluating model performance indicated that linear and ANN models resulted in close match and good agreement between predicted and observed values and thus good performance for all models. However, metrics values of Mean Absolute Error and Root Mean Squared Error for linear model and the ANN-BR model indicated smaller error and lower deviation relative to the mean values of antler beam diameter and length in comparison to other ANN models, demonstrating better agreement of the predicted and observed values of antler beam diameter and length. ANN-SCG model resulted in the highest error within the models. Overall, metrics for evaluating model performance from the ANN model with BR learning algorithm and linear model indicated better agreement of the predicted and observed values of antler beam diameter and length. Results of this study suggest the use of ANN generated results that are comparable to Linear Models of harvest data to aid in the development of strategies to manage white-tailed deer.