scholarly journals 208 Crossbred evaluations using single-step genomic BLUP and algorithm for proven and young with different sources of data

2019 ◽  
Vol 97 (Supplement_3) ◽  
pp. 41-42 ◽  
Author(s):  
Ivan Pocrnic ◽  
Daniela Lourenco ◽  
Ching-Yi Chen ◽  
William O Herring ◽  
Ignacy Misztal
Keyword(s):  
Animal Model ◽  
Predictive Ability ◽  
Single Step ◽  
Eigenvalue Decomposition ◽  
Genomic Information ◽  
Training Set ◽  
Pure Lines ◽  
Chromosome Segments ◽  
Different Sources ◽  
Different Levels

Abstract Genomic selection is routinely applied to many purebred farm species but can be extended to predictions across purebreds as well as for crossbreds. This is useful for swine and poultry, for which selection in nucleus herds is typically performed on purebreds, whereas the commercial products are crossbreds. Single-step genomic BLUP (ssGBLUP) is a widely applied method that can use algorithm for proven and young (APY), that allows for greater computing efficiency by exploiting the theory of limited dimensionality of genomic information and chromosome segments (Me). This study investigates the predictivity as a proxy for accuracy across and within two purebred pig lines and their crosses, under the application of APY in ssGBLUP setup, and different levels of Me overlapping across populations. The data consisted of approximately 210k phenotypic records for two traits and more than 720k animals in pedigree. Genotypes for 43k SNP were available for 46k animals, from which 26k and 16k belong to purebreds, and 4k to crossbreds. The models included bivariate animal model with three lines evaluated as one joint line, and for each trait individually a three-trait animal model with each line treated as a separate trait. Both models provided the same predictivity across and within the lines. Using either of the pure lines data as a training set resulted in a similar predictivity for the crossbreeds. Across-line predictive ability was limited to less than half of the maximum predictivity for each line. For crossbreds, APY performed equivalently to direct inverse when the number of core animals was equal to the number of eigenvalues explaining 98–99% of the variance of G including all lines. Predictivity across the lines is achievable because of the shared Me between them. The number of those shared segments can be obtained via eigenvalue decomposition of genomic information available for each line.

scholarly journals Applying the Metafounders Approach for Genomic Evaluation in a Multibreed Beef Cattle Population

2020 ◽  
Vol 11 ◽  
Author(s):  
Vinícius Silva Junqueira ◽  
Paulo Sávio Lopes ◽  
Daniela Lourenco ◽  
Fabyano Fonseca e Silva ◽  
Fernando Flores Cardoso
Keyword(s):  
Beef Cattle ◽  
Variance Components ◽  
Genetic Relationships ◽  
A Priori ◽  
Predictive Ability ◽  
Single Step ◽  
Pedigree Information ◽  
Phenotypic Variance ◽  
Genomic Information ◽  
The Impact

Pedigree information is incomplete by nature and commonly not well-established because many of the genetic ties are not known a priori or can be wrong. The genomic era brought new opportunities to assess relationships between individuals. However, when pedigree and genomic information are used simultaneously, which is the case of single-step genomic BLUP (ssGBLUP), defining the genetic base is still a challenge. One alternative to overcome this challenge is to use metafounders, which are pseudo-individuals that describe the genetic relationship between the base population individuals. The purpose of this study was to evaluate the impact of metafounders on the estimation of breeding values for tick resistance under ssGBLUP for a multibreed population composed by Hereford, Braford, and Zebu animals. Three different scenarios were studied: pedigree-based model (BLUP), ssGBLUP, and ssGBLUP with metafounders (ssGBLUPm). In ssGBLUPm, a total of four different metafounders based on breed of origin (i.e., Hereford, Braford, Zebu, and unknown) were included for the animals with missing parents. The relationship coefficient between metafounders was in average 0.54 (ranging from 0.34 to 0.96) suggesting an overlap between ancestor populations. The estimates of metafounder relationships indicate that Hereford and Zebu breeds have a possible common ancestral relationship. Inbreeding coefficients calculated following the metafounder approach had less negative values, suggesting that ancestral populations were large enough and that gametes inherited from the historical population were not identical. Variance components were estimated based on ssGBLUPm, ssGBLUP, and BLUP, but the values from ssGBLUPm were scaled to provide a fair comparison with estimates from the other two models. In general, additive, residual, and phenotypic variance components in the Hereford population were smaller than in Braford across different models. The addition of genomic information increased heritability for Hereford, possibly because of improved genetic relationships. As expected, genomic models had greater predictive ability, with an additional gain for ssGBLUPm over ssGBLUP. The increase in predictive ability was greater for Herefords. Our results show the potential of using metafounders to increase accuracy of GEBV, and therefore, the rate of genetic gain in beef cattle populations with partial levels of missing pedigree information.

scholarly journals 213 Genomic selection of carcass quality traits in crossbred pigs using a reference population

2019 ◽  
Vol 97 (Supplement_3) ◽  
pp. 41-41
Author(s):  
Matteo Bergamaschi ◽  
Christian Maltecca ◽  
Clint Schwab ◽  
Justin Fix ◽  
Francesco Tiezzi
Keyword(s):  
Fixed Effects ◽  
Carcass Traits ◽  
Predictive Ability ◽  
Reference Population ◽  
Single Step ◽  
Carcass Quality ◽  
Quality Traits ◽  
Single Gender ◽  
Training Set ◽  
Crossbred Pigs

Abstract The objective of this work was to evaluate the predictive ability of different models applied to carcass traits in crossbred pigs. The pigs were divided in 2 finishing flows: A=36,110 and B=95,041 animals, and were progeny of 386 sires (almost entirely genotyped with the 60k SNP chip). In flow A, individuals were housed into single-sire single-gender pens, and split-marketing on a pen basis was applied. In flow B, individuals were kept in standard commercial conditions and split-marketing on an individuals basis was applied. A dataset containing individual records of three carcass traits: back-fat (BF), loin depth (LD), and carcass daily gain (CACG) was used. Data from flow A were divided into training and validation sets on the basis of contemporary groups (8 in training and 1 in testing). Variance components and solutions were obtained using the BLUPF90 suite of programs. Models included fixed effects (dam line, sow parity, sex, cross fostering, and contemporary group) and random effects (additive genetic, batch, litter, and residual). Models tested were univariate vs multivariate and pedigree vs single-step. The addition of flow B records to the training set was evaluated, by including or excluding these records. Heritabilities were 0.68±0.023 for BF, 0.47±0.018 for LD, and 0.55±0.023 for CACG. CACG gain was correlated with BF (0.43±0.029) and LD (0.39±0.03). Low genetic correlation was found between BF and LD (0.17±0.034). Prediction accuracies were 0.39±0.05, 0.17±0.06, and 0.13±0.03 for BF, LD, and CACG respectively. The mean accuracy of BF, LD, and CG increased (~6%) when records from flow B were included in the training set, whereas the increase of accuracy between models (univariate vs multivariate) was not significant. The inclusion of sire genotypes did not improve prediction accuracy significantly. Based on these results, the prediction of carcass quality traits in crossbred pigs is possible.

49 Predicting Breeding Values of Purebred Pigs for Crossbred Performance Using Crossbred Phenotypes and Genotypes

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 23-23
Author(s):  
Natalia Leite ◽  
Ching-Yi Chen ◽  
Willian O Herring ◽  
Shogo Tsuruta ◽  
Daniela Lourenco
Keyword(s):  
Quality Control ◽  
Animal Model ◽  
Average Daily Gain ◽  
Snp Markers ◽  
Single Step ◽  
Low Density ◽  
Genomic Information ◽  
Breeding Values ◽  
Partial Data ◽  
Daily Gain

Abstract Phenotyping a large number of crossbred progeny for the evaluation of purebred animals can be expensive. As genotyping with low-density panels is becoming cheaper, we aimed to evaluate the tradeoff between having different percentages of genotypes and phenotypes for crossbred progeny of candidate boars. We used the linear regression (LR) method to investigate changes in accuracy, bias, and inflation of breeding values for crossbred traits in purebred boars. A total of 304,582 purebred and 147,474 crossbred animals were phenotyped for average daily gain (ADG) and backfat thickness (BF), out of which 46,691 purebred and 13,117 crossbred animals were genotyped. Genomic information consisted of imputed genotypes for 40,247 SNP markers after quality control. A four-trait animal model under single-step GBLUP was used that included phenotypes recorded in purebred and crossbred animals as correlated traits. The LR statistics were calculated based on breeding values of young purebred sires from complete and partial data. The first complete data included genotypes for purebreds and phenotypes for purebreds and crossbreds, whereas the second included also genotypes for crossbreds. The partial data included phenotypes on 50% or none of the progeny of validation sires, with or without genotypes for crossbred animals. When 50% of the progeny has phenotypes, adding genotypes for crossbred progeny marginally increased accuracy of ADG (0.77 vs 0.78) for 47 boars with more than 150 progeny with phenotypes. No increase was observed for BF. A small increase in bias and inflation by adding crossbred genotypes was observed for ADG but not for BF. When no phenotypes were available for crossbred progeny, accuracy for both traits was lower but improved with crossbred genotypes for ADG (0.61 vs 0.64) for boars with more than 150 progeny. The tradeoff between phenotypes and genotypes should be further investigated in larger datasets with more validation boars.

scholarly journals Evaluation of factors that predict the success rate of trial of labor after the cesarean section

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yang Mi ◽  
Pengfei Qu ◽  
Na Guo ◽  
Ruimiao Bai ◽  
Jiayi Gao ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Cesarean Section ◽  
Receiver Operating Characteristic Curve ◽  
Success Rate ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Predictive Ability ◽  
Training Set ◽  
History Of ◽  
Operating Characteristic Curve

Abstract Background For most women who have had a previous cesarean section, vaginal birth after cesarean section (VBAC) is a reasonable and safe choice, but which will increase the risk of adverse outcomes such as uterine rupture. In order to reduce the risk, we evaluated the factors that may affect VBAC and and established a model for predicting the success rate of trial of the labor after cesarean section (TOLAC). Methods All patients who gave birth at Northwest Women’s and Children’s Hospital from January 2016 to December 2018, had a history of cesarean section and voluntarily chose the TOLAC were recruited. Among them, 80% of the population was randomly assigned to the training set, while the remaining 20% were assigned to the external validation set. In the training set, univariate and multivariate logistic regression models were used to identify indicators related to successful TOLAC. A nomogram was constructed based on the results of multiple logistic regression analysis, and the selected variables included in the nomogram were used to predict the probability of successfully obtaining TOLAC. The area under the receiver operating characteristic curve was used to judge the predictive ability of the model. Results A total of 778 pregnant women were included in this study. Among them, 595 (76.48%) successfully underwent TOLAC, whereas 183 (23.52%) failed and switched to cesarean section. In multi-factor logistic regression, parity = 1, pre-pregnancy BMI < 24 kg/m2, cervical score ≥ 5, a history of previous vaginal delivery and neonatal birthweight < 3300 g were associated with the success of TOLAC. The area under the receiver operating characteristic curve in the prediction and validation models was 0.815 (95% CI: 0.762–0.854) and 0.730 (95% CI: 0.652–0.808), respectively, indicating that the nomogram prediction model had medium discriminative power. Conclusion The TOLAC was useful to reducing the cesarean section rate. Being primiparous, not overweight or obese, having a cervical score ≥ 5, a history of previous vaginal delivery or neonatal birthweight < 3300 g were protective indicators. In this study, the validated model had an approving predictive ability.

scholarly journals Emerging issues in genomic selection

2021 ◽  
Author(s):  
I Misztal ◽  
I Aguilar ◽  
D Lourenco ◽  
L Ma ◽  
J Steibel ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Sparse Matrices ◽  
Large Datasets ◽  
Single Step ◽  
Genomic Information ◽  
Long Term Effects ◽  
Additive Variance ◽  
Recent Developments ◽  
Emerging Issues ◽  
Using Data

Abstract Genomic selection is now practiced successfully across many species. However, many questions remain such as long-term effects, estimations of genomic parameters, robustness of GWAS with small and large datasets, and stability of genomic predictions. This study summarizes presentations from at the 2020 ASAS symposium. The focus of many studies until now is on linkage disequilibrium (LD) between two loci. Ignoring higher level equilibrium may lead to phantom dominance and epistasis. The Bulmer effect leads to a reduction of the additive variance; however, selection for increased recombination rate can release anew genetic variance. With genomic information, estimates of genetic parameters may be biased by genomic preselection, but costs of estimation can increase drastically due to the dense form of the genomic information. To make computation of estimates feasible, genotypes could be retained only for the most important animals, and methods of estimation should use algorithms that can recognize dense blocks in sparse matrices. GWAS studies using small genomic datasets frequently find many marker-trait associations whereas studies using much bigger datasets find only a few. Most current tools use very simple models for GWAS, possibly causing artifacts. These models are adequate for large datasets where pseudo-phenotypes such as deregressed proofs indirectly account for important effects for traits of interest. Artifacts arising in GWAS with small datasets can be minimized by using data from all animals (whether genotyped or not), realistic models, and methods that account for population structure. Recent developments permit computation of p-values from GBLUP, where models can be arbitrarily complex but restricted to genotyped animals only, and to single-step GBLUP that also uses phenotypes from ungenotyped animals. Stability was an important part of nongenomic evaluations, where genetic predictions were stable in the absence of new data even with low prediction accuracies. Unfortunately, genomic evaluations for such animals change because all animals with genotypes are connected. A top ranked animal can easily drop in the next evaluation, causing a crisis of confidence in genomic evaluations. While correlations between consecutive genomic evaluations are high, outliers can have differences as high as one SD. A solution to fluctuating genomic evaluations is to base selection decisions on groups of animals. While many issues in genomic selection have been solved, many new issues that require additional research continue to surface.

scholarly journals Comparison of models for missing pedigree in single-step genomic prediction

2021 ◽  
Vol 99 (2) ◽  
Author(s):  
Yutaka Masuda ◽  
Shogo Tsuruta ◽  
Matias Bermann ◽  
Heather L Bradford ◽  
Ignacy Misztal
Keyword(s):  
Density Function ◽  
Single Step ◽  
Joint Density ◽  
Pedigree Information ◽  
Relationship Matrix ◽  
Genomic Information ◽  
Breeding Values ◽  
Joint Density Function ◽  
Information Updates ◽  
Genetic Trends

Abstract Pedigree information is often missing for some animals in a breeding program. Unknown-parent groups (UPGs) are assigned to the missing parents to avoid biased genetic evaluations. Although the use of UPGs is well established for the pedigree model, it is unclear how UPGs are integrated into the inverse of the unified relationship matrix (H-inverse) required for single-step genomic best linear unbiased prediction. A generalization of the UPG model is the metafounder (MF) model. The objectives of this study were to derive 3 H-inverses and to compare genetic trends among models with UPG and MF H-inverses using a simulated purebred population. All inverses were derived using the joint density function of the random breeding values and genetic groups. The breeding values of genotyped animals (u2) were assumed to be adjusted for UPG effects (g) using matrix Q2 as u2∗=u2+Q2g before incorporating genomic information. The Quaas–Pollak-transformed (QP) H-inverse was derived using a joint density function of u2∗ and g updated with genomic information and assuming nonzero cov(u2∗,g′). The modified QP (altered) H-inverse also assumes that the genomic information updates u2∗ and g, but cov(u2∗,g′)=0. The UPG-encapsulated (EUPG) H-inverse assumed genomic information updates the distribution of u2∗. The EUPG H-inverse had the same structure as the MF H-inverse. Fifty percent of the genotyped females in the simulation had a missing dam, and missing parents were replaced with UPGs by generation. The simulation study indicated that u2∗ and g in models using the QP and altered H-inverses may be inseparable leading to potential biases in genetic trends. Models using the EUPG and MF H-inverses showed no genetic trend biases. These 2 H-inverses yielded the same genomic EBV (GEBV). The predictive ability and inflation of GEBVs from young genotyped animals were nearly identical among models using the QP, altered, EUPG, and MF H-inverses. Although the choice of H-inverse in real applications with enough data may not result in biased genetic trends, the EUPG and MF H-inverses are to be preferred because of theoretical justification and possibility to reduce biases.

scholarly journals Preoperative Predicting the WHO/ISUP Nuclear Grade of Clear Cell Renal Cell Carcinoma by Computed Tomography-Based Radiomics Features

2020 ◽  
Vol 11 (1) ◽  
pp. 8
Author(s):  
Claudia-Gabriela Moldovanu ◽  
Bianca Boca ◽  
Andrei Lebovici ◽  
Attila Tamas-Szora ◽  
Diana Sorina Feier ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Renal Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Predictive Ability ◽  
Nuclear Grade ◽  
Training Set ◽  
Cell Renal Cell Carcinoma ◽  
Validation Set ◽  
Sensitivity Specificity

Nuclear grade is important for treatment selection and prognosis in patients with clear cell renal cell carcinoma (ccRCC). This study aimed to determine the ability of preoperative four-phase multiphasic multidetector computed tomography (MDCT)-based radiomics features to predict the WHO/ISUP nuclear grade. In all 102 patients with histologically confirmed ccRCC, the training set (n = 62) and validation set (n = 40) were randomly assigned. In both datasets, patients were categorized according to the WHO/ISUP grading system into low-grade ccRCC (grades 1 and 2) and high-grade ccRCC (grades 3 and 4). The feature selection process consisted of three steps, including least absolute shrinkage and selection operator (LASSO) regression analysis, and the radiomics scores were developed using 48 radiomics features (10 in the unenhanced phase, 17 in the corticomedullary (CM) phase, 14 in the nephrographic (NP) phase, and 7 in the excretory phase). The radiomics score (Rad-Score) derived from the CM phase achieved the best predictive ability, with a sensitivity, specificity, and an area under the curve (AUC) of 90.91%, 95.00%, and 0.97 in the training set. In the validation set, the Rad-Score derived from the NP phase achieved the best predictive ability, with a sensitivity, specificity, and an AUC of 72.73%, 85.30%, and 0.84. We constructed a complex model, adding the radiomics score for each of the phases to the clinicoradiological characteristics, and found significantly better performance in the discrimination of the nuclear grades of ccRCCs in all MDCT phases. The highest AUC of 0.99 (95% CI, 0.92–1.00, p < 0.0001) was demonstrated for the CM phase. Our results showed that the MDCT radiomics features may play a role as potential imaging biomarkers to preoperatively predict the WHO/ISUP grade of ccRCCs.

scholarly journals High-resolution limited-angle phase tomography of dense layered objects using deep neural networks

2019 ◽  
Vol 116 (40) ◽  
pp. 19848-19856 ◽  
Author(s):  
Alexandre Goy ◽  
Girish Rughoobur ◽  
Shuai Li ◽  
Kwabena Arthur ◽  
Akintunde I. Akinwande ◽  
...  
Keyword(s):  
Integrated Circuit ◽  
Deep Neural Networks ◽  
Tomographic Reconstruction ◽  
Synthetic Data ◽  
Single Step ◽  
Training Set ◽  
Step Method ◽  
3 Dimensional ◽  
Dimensional Reconstruction ◽  
Optical Domain

We present a machine learning-based method for tomographic reconstruction of dense layered objects, with range of projection angles limited to ±10○. Whereas previous approaches to phase tomography generally require 2 steps, first to retrieve phase projections from intensity projections and then to perform tomographic reconstruction on the retrieved phase projections, in our work a physics-informed preprocessor followed by a deep neural network (DNN) conduct the 3-dimensional reconstruction directly from the intensity projections. We demonstrate this single-step method experimentally in the visible optical domain on a scaled-up integrated circuit phantom. We show that even under conditions of highly attenuated photon fluxes a DNN trained only on synthetic data can be used to successfully reconstruct physical samples disjoint from the synthetic training set. Thus, the need for producing a large number of physical examples for training is ameliorated. The method is generally applicable to tomography with electromagnetic or other types of radiation at all bands.

36 Effect of Blending and Tuning Relationship Matrices in Single-step Genomic BLUP

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 19-20
Author(s):  
Taylor M McWhorter ◽  
Andre Garcia ◽  
Matias Bermann ◽  
Andres Legarra ◽  
Ignacio Aguilar ◽  
...  
Keyword(s):  
Single Step ◽  
Fixation Index ◽  
Genomic Information ◽  
Software Suite ◽  
Genetic Base ◽  
Actual Difference ◽  
The Impact ◽  
Genomic Predictions

Abstract Single-step GBLUP (ssGBLUP) relies on the combination of genomic (G) and pedigree relationships for all (A) and genotyped animals (A22). The procedure implemented in the BLUPF90 software suite first involves combining a small percentage of A22 into G (blending) to avoid singularity problems, then an adjustment to account for the fact the genetic base in G and A22 is different (tuning). However, blending before tuning may not reflect the actual difference between pedigree and genomic base because the blended matrix already contains a portion of A22. The objective of this study was to evaluate the impact of tuning before blending on predictivity, bias, and inflation of GEBV, indirect predictions (IP), and SNP effects from ssGBLUP using American Angus and US Holstein data. We used four different scenarios to obtain genomic predictions: BlendFirst_TunedG2, TuneFirst_TunedG2, BlendFirst_TunedG4, and TuneFirst_TunedG4. TunedG2 adjusts mean diagonals and off-diagonals of G to be similar to the ones in A22, whereas TunedG4 adjusts based on the fixation index. Over 6 million growth records were available for Angus and 5.9 million udder depth records for Holsteins. Genomic information was available on 51,478 Angus and 105,116 Holstein animals. Predictivity and reliability were obtained for 19,056 and 1,711 validation Angus and Holsteins, respectively. We observed the same predictivity and reliability for GEBV or IP in all four scenarios, ranging from 0.47 to 0.60 for Angus and was 0.67 for Holsteins. Slightly less bias was observed when tuning was done before blending. Correlation of SNP effects between scenarios was &gt; 0.99. Refined tuning before blending had no impact on GEBV and marginally reduced the bias. This option will be implemented in the BLUPF90 software suite.

scholarly journals 132 Effects of high-energy diet on viscera and progeny carcass yield in sheep

2019 ◽  
Vol 97 (Supplement_1) ◽  
pp. 71-71
Author(s):  
Sarita Bonagurio Gallo ◽  
Sophia Honigmann ◽  
Luciano Brochine ◽  
Giuliana Micai de Oliveira ◽  
Luis Orlindo Tedeschi
Keyword(s):  
Maternal Diet ◽  
High Energy ◽  
Control Group ◽  
Animal Science ◽  
Carcass Yield ◽  
Orthogonal Contrasts ◽  
Food Engineering ◽  
Omental Fat ◽  
Different Sources ◽  
Different Levels

Abstract The energy required during gestation in sheep is high, and when restricted, fetal development may be affected; thus, knowledge of its deficit needs to be better understood. The objective was to evaluate different levels and sources of energy in the diet of sheep and their influence on the viscera and carcass yield of the offspring. The experiment was carried out at the Faculty of Animal Science and Food Engineering of USP, Brazil, using 56 pregnant ewes, Dorper and Santa Ines breeds, randomly distributed in four treatments: control group (fed according to the 2007 NRC recommendation); and groups with 10% more energy with different sources (starch, ST; protected fat, PF; and chromium, Cr). Ewes were fed the experimental diets at the beginning (50 days) and end of the gestation (50 days). The middle of gestation the ewes received the diet as recommended by the NRC (2007). After weaning at 60 days, 32 uncastrated male lambs were confined for a period of 60 days. In the confinement the lambs received a growth diet, being the same for all the animals. After this period, lambs were slaughtered, and the non-carcass components were weighed, the warm and cold carcass yields were calculated. The experimental design was completely randomized, with eight replicates and four treatments, the means were analyzed by orthogonal contrasts, assuming a significant threshold of 5%. Lambs in the PF treatment had lower cold carcass yield (50.8%, PPP > 0.05). The sheep fed according to the energy recommendation had lambs with greater weight of omental fat, testicular, kidney and rumen. These lambs also had a smaller spleen, heart, liver, and intestine. We concluded that the maternal diet affected the growth and development of progeny by altering the viscera and the carcass yields. Acknowledgment to FAPESP (process 2017/20555–8).

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