scholarly journals Estimation of Variance Components and Genomic Prediction for Individual Birth Weight Using Three Different Genome-Wide SNP Platforms in Yorkshire Pigs

Animals ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 2219
Author(s):  
Jungjae Lee ◽  
Sang-Min Lee ◽  
Byeonghwi Lim ◽  
Jun Park ◽  
Kwang-Lim Song ◽  
...  
Keyword(s):  
Birth Weight ◽  
Bayesian Methods ◽  
Genomic Prediction ◽  
Snp Genotyping ◽  
Suitable Model ◽  
Prediction Ability ◽  
Breeding Values ◽  
Yorkshire Pigs ◽  
Estimated Breeding Values ◽  
Estimation Of Variance

This study estimates the individual birth weight (IBW) trait heritability and investigates the genomic prediction efficiency using three types of high-density single nucleotide polymorphism (SNP) genotyping panels in Korean Yorkshire pigs. We use 38,864 IBW phenotypic records to identify a suitable model for statistical genetics, where 698 genotypes match our phenotypic records. During our genomic analysis, the deregressed estimated breeding values (DEBVs) and their reliabilities are used as derived response variables from the estimated breeding values (EBVs). Bayesian methods identify the informative regions and perform the genomic prediction using the IBW trait, in which two common significant window regions (SSC8 27 Mb and SSC15 29 Mb) are identified using the three genotyping platforms. Higher prediction ability is observed using the DEBV-including parent average as a response variable, regardless of the SNP genotyping panels and the Bayesian methods, relative to the DEBV-excluding parent average. Hence, we suggest that fine-mapping studies targeting the identified informative regions in this study are necessary to find the causal mutations to improve the IBW trait’s prediction ability. Furthermore, studying the IBW trait using a genomic prediction model with a larger genomic dataset may improve the genomic prediction accuracy in Korean Yorkshire pigs.

scholarly journals Genomic Analysis Using Bayesian Methods under Different Genotyping Platforms in Korean Duroc Pigs

Animals ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 752 ◽  
Author(s):  
Jungjae Lee ◽  
Yongmin Kim ◽  
Eunseok Cho ◽  
Kyuho Cho ◽  
Soojin Sa ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Genome Wide Association Study ◽  
Genomic Analysis ◽  
Snp Markers ◽  
Snp Genotyping ◽  
Muscle Area ◽  
Bayesian Approaches ◽  
Breeding Values ◽  
Estimated Breeding Values ◽  
Response Variables

Genomic evaluation has been widely applied to several species using commercial single nucleotide polymorphism (SNP) genotyping platforms. This study investigated the informative genomic regions and the efficiency of genomic prediction by using two Bayesian approaches (BayesB and BayesC) under two moderate-density SNP genotyping panels in Korean Duroc pigs. Growth and production records of 1026 individuals were genotyped using two medium-density, SNP genotyping platforms: Illumina60K and GeneSeek80K. These platforms consisted of 61,565 and 68,528 SNP markers, respectively. The deregressed estimated breeding values (DEBVs) derived from estimated breeding values (EBVs) and their reliabilities were taken as response variables. Two Bayesian approaches were implemented to perform the genome-wide association study (GWAS) and genomic prediction. Multiple significant regions for days to 90 kg (DAYS), lean muscle area (LMA), and lean percent (PCL) were detected. The most significant SNP marker, located near the MC4R gene, was detected using GeneSeek80K. Accuracy of genomic predictions was higher using the GeneSeek80K SNP panel for DAYS (Δ2%) and LMA (Δ2–3%) with two response variables, with no gains in accuracy by the Bayesian approaches in four growth and production-related traits. Genomic prediction is best derived from DEBVs including parental information as a response variable between two DEBVs regardless of the genotyping platform and the Bayesian method for genomic prediction accuracy in Korean Duroc pig breeding.

scholarly journals Genomic selection strategies for clonally propagated crops

2020 ◽  
Author(s):  
Christian R. Werner ◽  
R. Chris Gaynor ◽  
Daniel J. Sargent ◽  
Alessandra Lillo ◽  
Gregor Gorjanc ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Genetic Gain ◽  
Genomic Prediction ◽  
Breeding Program ◽  
Selection Methods ◽  
Breeding Programs ◽  
Breeding Values ◽  
Parent Selection ◽  
Estimated Breeding Values ◽  
Selection Of

AbstractFor genomic selection in clonal breeding programs to be effective, crossing parents should be selected based on genomic predicted cross performance unless dominance is negligible. Genomic prediction of cross performance enables a balanced exploitation of the additive and dominance value simultaneously. Here, we compared different strategies for the implementation of genomic selection in clonal plant breeding programs. We used stochastic simulations to evaluate six combinations of three breeding programs and two parent selection methods. The three breeding programs included i) a breeding program that introduced genomic selection in the first clonal testing stage, and ii) two variations of a two-part breeding program with one and three crossing cycles per year, respectively. The two parent selection methods were i) selection of parents based on genomic estimated breeding values, and ii) selection of parents based on genomic predicted cross performance. Selection of parents based on genomic predicted cross performance produced faster genetic gain than selection of parents based on genomic estimated breeding values because it substantially reduced inbreeding when the dominance degree increased. The two-part breeding programs with one and three crossing cycles per year using genomic prediction of cross performance always produced the most genetic gain unless dominance was negligible. We conclude that i) in clonal breeding programs with genomic selection, parents should be selected based on genomic predicted cross performance, and ii) a two-part breeding program with parent selection based on genomic predicted cross performance to rapidly drive population improvement has great potential to improve breeding clonally propagated crops.

scholarly journals Comparison of Marker-Based Genomic Estimated Breeding Values and Phenotypic Evaluation for Selection of Bacterial Spot Resistance in Tomato

2018 ◽  
Vol 108 (3) ◽  
pp. 392-401 ◽  
Author(s):  
Debora Liabeuf ◽  
Sung-Chur Sim ◽  
David M. Francis
Keyword(s):  
Prediction Models ◽  
Phenotypic Selection ◽  
Hybrid Population ◽  
Bacterial Spot ◽  
Training Population ◽  
Prediction Ability ◽  
Breeding Values ◽  
Genomic Estimated Breeding Values ◽  
Estimated Breeding Values ◽  
Efficiency Of Selection

Bacterial spot affects tomato crops (Solanum lycopersicum) grown under humid conditions. Major genes and quantitative trait loci (QTL) for resistance have been described, and multiple loci from diverse sources need to be combined to improve disease control. We investigated genomic selection (GS) prediction models for resistance to Xanthomonas euvesicatoria and experimentally evaluated the accuracy of these models. The training population consisted of 109 families combining resistance from four sources and directionally selected from a population of 1,100 individuals. The families were evaluated on a plot basis in replicated inoculated trials and genotyped with single nucleotide polymorphisms (SNP). We compared the prediction ability of models developed with 14 to 387 SNP. Genomic estimated breeding values (GEBV) were derived using Bayesian least absolute shrinkage and selection operator regression (BL) and ridge regression (RR). Evaluations were based on leave-one-out cross validation and on empirical observations in replicated field trials using the next generation of inbred progeny and a hybrid population resulting from selections in the training population. Prediction ability was evaluated based on correlations between GEBV and phenotypes (rg), percentage of coselection between genomic and phenotypic selection, and relative efficiency of selection (rg/rp). Results were similar with BL and RR models. Models using only markers previously identified as significantly associated with resistance but weighted based on GEBV and mixed models with markers associated with resistance treated as fixed effects and markers distributed in the genome treated as random effects offered greater accuracy and a high percentage of coselection. The accuracy of these models to predict the performance of progeny and hybrids exceeded the accuracy of phenotypic selection.

scholarly journals Using Machine Learning To Improve the Accuracy of Genomic Prediction on Reproduction Traits in Pigs

2021 ◽  
Author(s):  
Xue Wang ◽  
Shaolei Shi ◽  
Guijiang Wang ◽  
Wenxue Luo ◽  
Xia Wei ◽  
...  
Keyword(s):  
Machine Learning ◽  
Genomic Prediction ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Single Step ◽  
Support Vector ◽  
Prediction Ability ◽  
Reproduction Traits ◽  
Yorkshire Pigs ◽  
Robust Prediction

Abstract Background Recently, machine learning (ML) is becoming attractive in genomic prediction, while its superiority in genomic prediction and the choosing of optimal ML methods are needed investigation. Results In this study, 2566 Chinese Yorkshire pigs with reproduction traits records were used, they were genotyped with GenoBaits Porcine SNP 50K and PorcineSNP50 panel. Four ML methods, including support vector regression (SVR), kernel ridge regression (KRR), random forest (RF) and Adaboost.R2 were implemented. Through 20 replicates of five-fold cross-validation, the genomic prediction abilities of ML methods were explored. Compared with genomic BLUP(GBLUP), single-step GBLUP (ssGBLUP) and Bayesian method BayesHE, our results indicated that ML methods significantly outperformed. The prediction accuracy of ML methods was improved by 19.3%, 15.0% and 20.8% on average over GBLUP, ssGBLUP and BayesHE, ranging from 8.9–24.0%, 7.6–17.5% and 11.1–24.6%, respectively. In addition, ML methods yielded smaller mean squared error (MSE) and mean absolute error (MAE) in all scenarios. ssGBLUP yielded improvement of 3.7% on average compared to GBLUP, and the performance of BayesHE was close to GBLUP. Among four ML methods, SVR and KRR had the most robust prediction abilities, which yielded higher accuracies, lower bias, lower MSE and MAE, and comparable computing efficiency as GBLUP. RF demonstrated the lowest prediction ability and computational efficiency among ML methods. Conclusion Our findings demonstrated that ML methods are more efficient than traditional genomic selection methods, and it could be new options for genomic prediction.

scholarly journals Direct and correlated responses to selection for autumn lambing in sheep

2020 ◽  
Vol 52 (1) ◽  
Author(s):  
Masood Asadi-Fozi ◽  
Heather L. Bradford ◽  
David R. Notter
Keyword(s):  
Birth Weight ◽  
Selection Line ◽  
Sheep Population ◽  
Estrous Cycles ◽  
Breeding Values ◽  
Scrotal Circumference ◽  
Ram Effect ◽  
Selection For ◽  
Estimated Breeding Values ◽  
Sheep Production

Abstract Background Seasonal reproduction limits productivity, flexibility, and profitability in commercial sheep production. Hormonal and (or) photoperiodic manipulation can be used to control estrous cycles in sheep and reduce limitations that are imposed by the seasonal anestrous but are often impractical or incompatible with the extensive management systems preferred for ruminant livestock. Thus, the current study investigated the use of selection to improve realized fertility (i.e., the proportion of ewes that lambed) following an out-of-season spring joining period (May and June) in a crossbred sheep population. Results Over 17 years, estimated breeding values (EBV) for fertility in selected (S) ewes increased by 0.175 (0.01 per year). The mean EBV for fertility of S ewes was greater than that of control ewes by year 10 (P = 0.02), and the fertility of adult (≥ 3 years old) ewes reached 0.88 ± 0.05 by year 17. Lambing began approximately 140 days after the introduction of rams, and 64% of the S ewes that lambed did so in the first 17 days of the potential lambing season, which indicated that most of the S ewes were cycling at the time of ram introduction and were not induced to cycle by the introduction of breeding males (i.e., the so-called “ram effect”). Animals in the S line had modest increases in body weight and scrotal circumference. A modest negative trend in the additive maternal effect on birth weight was observed but was reversed by additional selection on EBV for maternal birth weight. The heritability of litter size in autumn lambing was low (0.04) and could potentially limit the response to selection for this trait. Conclusions Selection improved realized ewe fertility in out-of-season mating, with absolute increases of approximately 1% per year in the percentage of joined ewes that lambed in the autumn. Genetic antagonisms with other performance traits were generally small. A modest antagonism with maternal breeding values for birth weight was observed but it could be accommodated by selection on EBV for maternal birth weight. Our results support results of previous studies that indicate that these selected ewes had one of the shortest seasonal anestrous periods reported for temperate sheep breeds and that spring-lambing lactating ewes from the selection line were capable of relatively rapid rebreeding in the spring.

Genomic prediction ability for carcass composition indicator traits in Nellore cattle

2021 ◽  
Vol 245 ◽  
pp. 104421
Author(s):  
Rosiane P. Silva ◽  
Rafael Espigolan ◽  
Mariana P. Berton ◽  
Raysildo B. Lôbo ◽  
Cláudio U. Magnabosco ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Carcass Composition ◽  
Prediction Ability ◽  
Nellore Cattle ◽  
Indicator Traits

scholarly journals GPS Coordinates for Modelling Correlated Herd Effects in Genomic Prediction Models Applied to Hanwoo Beef Cattle

Animals ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 2050
Author(s):  
Beatriz Castro Dias Cuyabano ◽  
Gabriel Rovere ◽  
Dajeong Lim ◽  
Tae Hun Kim ◽  
Hak Kyo Lee ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Genomic Prediction ◽  
Prediction Models ◽  
Phenotypic Expression ◽  
Genetic Evaluation ◽  
Genomic Breeding ◽  
Breeding Values ◽  
Korean Cattle ◽  
Evaluation Programs ◽  
The Impact

It is widely known that the environment influences phenotypic expression and that its effects must be accounted for in genetic evaluation programs. The most used method to account for environmental effects is to add herd and contemporary group to the model. Although generally informative, the herd effect treats different farms as independent units. However, if two farms are located physically close to each other, they potentially share correlated environmental factors. We introduce a method to model herd effects that uses the physical distances between farms based on the Global Positioning System (GPS) coordinates as a proxy for the correlation matrix of these effects that aims to account for similarities and differences between farms due to environmental factors. A population of Hanwoo Korean cattle was used to evaluate the impact of modelling herd effects as correlated, in comparison to assuming the farms as completely independent units, on the variance components and genomic prediction. The main result was an increase in the reliabilities of the predicted genomic breeding values compared to reliabilities obtained with traditional models (across four traits evaluated, reliabilities of prediction presented increases that ranged from 0.05 ± 0.01 to 0.33 ± 0.03), suggesting that these models may overestimate heritabilities. Although little to no significant gain was obtained in phenotypic prediction, the increased reliability of the predicted genomic breeding values is of practical relevance for genetic evaluation programs.

Sign in / Sign up

Export Citation Format

Share Document