Using residual regressions to quantify and map signal leakage in genomic prediction
CONCLUSIONS: Residual single-marker regression analysis is a simple approach that can be used to detect regional genomic signals that are poorly captured by a model and to indicate ways to fix such problems.PMID:37550618 | PMC:PMC10405418 | DOI:10.1186/s12711-023-00830-1 (Source: Genet Sel Evol)
Source: Genet Sel Evol - August 7, 2023 Category: Genetics & Stem Cells Authors: Bruno D Valente Gustavo de Los Campos Alexander Grueneberg Ching-Yi Chen Roger Ros-Freixedes William O Herring Source Type: research
Estimation and consequences of direct-maternal genetic and environmental covariances in models for genetic evaluation in broilers
CONCLUSIONS: In this paper, we propose a simple approach to estimate the environmental direct-maternal covariance using standard software for REML analysis. The genetic covariance between dam and offspring was negative whereas the corresponding environmental covariance was positive. Considering both covariances in models for genetic evaluation increased the accuracy of predicted breeding values.PMID:37550635 | PMC:PMC10405509 | DOI:10.1186/s12711-023-00829-8 (Source: Genet Sel Evol)
Source: Genet Sel Evol - August 7, 2023 Category: Genetics & Stem Cells Authors: H élène Romé Thinh T Chu Danye Marois Chyong-Huoy Huang Per Madsen Just Jensen Source Type: research
Using residual regressions to quantify and map signal leakage in genomic prediction
CONCLUSIONS: Residual single-marker regression analysis is a simple approach that can be used to detect regional genomic signals that are poorly captured by a model and to indicate ways to fix such problems.PMID:37550618 | PMC:PMC10405418 | DOI:10.1186/s12711-023-00830-1 (Source: Genet Sel Evol)
Source: Genet Sel Evol - August 7, 2023 Category: Genetics & Stem Cells Authors: Bruno D Valente Gustavo de Los Campos Alexander Grueneberg Ching-Yi Chen Roger Ros-Freixedes William O Herring Source Type: research
Estimation and consequences of direct-maternal genetic and environmental covariances in models for genetic evaluation in broilers
CONCLUSIONS: In this paper, we propose a simple approach to estimate the environmental direct-maternal covariance using standard software for REML analysis. The genetic covariance between dam and offspring was negative whereas the corresponding environmental covariance was positive. Considering both covariances in models for genetic evaluation increased the accuracy of predicted breeding values.PMID:37550635 | PMC:PMC10405509 | DOI:10.1186/s12711-023-00829-8 (Source: Genet Sel Evol)
Source: Genet Sel Evol - August 7, 2023 Category: Genetics & Stem Cells Authors: H élène Romé Thinh T Chu Danye Marois Chyong-Huoy Huang Per Madsen Just Jensen Source Type: research
Using residual regressions to quantify and map signal leakage in genomic prediction
CONCLUSIONS: Residual single-marker regression analysis is a simple approach that can be used to detect regional genomic signals that are poorly captured by a model and to indicate ways to fix such problems.PMID:37550618 | PMC:PMC10405418 | DOI:10.1186/s12711-023-00830-1 (Source: Genet Sel Evol)
Source: Genet Sel Evol - August 7, 2023 Category: Genetics & Stem Cells Authors: Bruno D Valente Gustavo de Los Campos Alexander Grueneberg Ching-Yi Chen Roger Ros-Freixedes William O Herring Source Type: research
Estimation and consequences of direct-maternal genetic and environmental covariances in models for genetic evaluation in broilers
CONCLUSIONS: In this paper, we propose a simple approach to estimate the environmental direct-maternal covariance using standard software for REML analysis. The genetic covariance between dam and offspring was negative whereas the corresponding environmental covariance was positive. Considering both covariances in models for genetic evaluation increased the accuracy of predicted breeding values.PMID:37550635 | PMC:PMC10405509 | DOI:10.1186/s12711-023-00829-8 (Source: Genet Sel Evol)
Source: Genet Sel Evol - August 7, 2023 Category: Genetics & Stem Cells Authors: H élène Romé Thinh T Chu Danye Marois Chyong-Huoy Huang Per Madsen Just Jensen Source Type: research
Using residual regressions to quantify and map signal leakage in genomic prediction
CONCLUSIONS: Residual single-marker regression analysis is a simple approach that can be used to detect regional genomic signals that are poorly captured by a model and to indicate ways to fix such problems.PMID:37550618 | DOI:10.1186/s12711-023-00830-1 (Source: Genet Sel Evol)
Source: Genet Sel Evol - August 7, 2023 Category: Genetics & Stem Cells Authors: Bruno D Valente Gustavo de Los Campos Alexander Grueneberg Ching-Yi Chen Roger Ros-Freixedes William O Herring Source Type: research
Estimation and consequences of direct-maternal genetic and environmental covariances in models for genetic evaluation in broilers
CONCLUSIONS: In this paper, we propose a simple approach to estimate the environmental direct-maternal covariance using standard software for REML analysis. The genetic covariance between dam and offspring was negative whereas the corresponding environmental covariance was positive. Considering both covariances in models for genetic evaluation increased the accuracy of predicted breeding values.PMID:37550635 | DOI:10.1186/s12711-023-00829-8 (Source: Genet Sel Evol)
Source: Genet Sel Evol - August 7, 2023 Category: Genetics & Stem Cells Authors: H élène Romé Thinh T Chu Danye Marois Chyong-Huoy Huang Per Madsen Just Jensen Source Type: research
deepGBLUP: joint deep learning networks and GBLUP framework for accurate genomic prediction of complex traits in Korean native cattle
CONCLUSIONS: We introduced a novel genomic prediction algorithm, deepGBLUP, which successfully integrates deep learning networks and GBLUP framework. Through comprehensive evaluations on the Korean native cattle data and simulated data, deepGBLUP consistently achieved superior performance across various traits, marker densities, training sizes, heritabilities, and QTL effects. Therefore, deepGBLUP is an efficient method to estimate an accurate genomic value. The source code and manual for deepGBLUP are available at https://github.com/gywns6287/deepGBLUP .PMID:37525091 | PMC:PMC10392020 | DOI:10.1186/s12711-023-00825-y (Source: Genet Sel Evol)
Source: Genet Sel Evol - July 31, 2023 Category: Genetics & Stem Cells Authors: Hyo-Jun Lee Jun Heon Lee Cedric Gondro Yeong Jun Koh Seung Hwan Lee Source Type: research
deepGBLUP: joint deep learning networks and GBLUP framework for accurate genomic prediction of complex traits in Korean native cattle
CONCLUSIONS: We introduced a novel genomic prediction algorithm, deepGBLUP, which successfully integrates deep learning networks and GBLUP framework. Through comprehensive evaluations on the Korean native cattle data and simulated data, deepGBLUP consistently achieved superior performance across various traits, marker densities, training sizes, heritabilities, and QTL effects. Therefore, deepGBLUP is an efficient method to estimate an accurate genomic value. The source code and manual for deepGBLUP are available at https://github.com/gywns6287/deepGBLUP .PMID:37525091 | PMC:PMC10392020 | DOI:10.1186/s12711-023-00825-y (Source: Genet Sel Evol)
Source: Genet Sel Evol - July 31, 2023 Category: Genetics & Stem Cells Authors: Hyo-Jun Lee Jun Heon Lee Cedric Gondro Yeong Jun Koh Seung Hwan Lee Source Type: research
deepGBLUP: joint deep learning networks and GBLUP framework for accurate genomic prediction of complex traits in Korean native cattle
CONCLUSIONS: We introduced a novel genomic prediction algorithm, deepGBLUP, which successfully integrates deep learning networks and GBLUP framework. Through comprehensive evaluations on the Korean native cattle data and simulated data, deepGBLUP consistently achieved superior performance across various traits, marker densities, training sizes, heritabilities, and QTL effects. Therefore, deepGBLUP is an efficient method to estimate an accurate genomic value. The source code and manual for deepGBLUP are available at https://github.com/gywns6287/deepGBLUP .PMID:37525091 | PMC:PMC10392020 | DOI:10.1186/s12711-023-00825-y (Source: Genet Sel Evol)
Source: Genet Sel Evol - July 31, 2023 Category: Genetics & Stem Cells Authors: Hyo-Jun Lee Jun Heon Lee Cedric Gondro Yeong Jun Koh Seung Hwan Lee Source Type: research
deepGBLUP: joint deep learning networks and GBLUP framework for accurate genomic prediction of complex traits in Korean native cattle
CONCLUSIONS: We introduced a novel genomic prediction algorithm, deepGBLUP, which successfully integrates deep learning networks and GBLUP framework. Through comprehensive evaluations on the Korean native cattle data and simulated data, deepGBLUP consistently achieved superior performance across various traits, marker densities, training sizes, heritabilities, and QTL effects. Therefore, deepGBLUP is an efficient method to estimate an accurate genomic value. The source code and manual for deepGBLUP are available at https://github.com/gywns6287/deepGBLUP .PMID:37525091 | PMC:PMC10392020 | DOI:10.1186/s12711-023-00825-y (Source: Genet Sel Evol)
Source: Genet Sel Evol - July 31, 2023 Category: Genetics & Stem Cells Authors: Hyo-Jun Lee Jun Heon Lee Cedric Gondro Yeong Jun Koh Seung Hwan Lee Source Type: research
deepGBLUP: joint deep learning networks and GBLUP framework for accurate genomic prediction of complex traits in Korean native cattle
CONCLUSIONS: We introduced a novel genomic prediction algorithm, deepGBLUP, which successfully integrates deep learning networks and GBLUP framework. Through comprehensive evaluations on the Korean native cattle data and simulated data, deepGBLUP consistently achieved superior performance across various traits, marker densities, training sizes, heritabilities, and QTL effects. Therefore, deepGBLUP is an efficient method to estimate an accurate genomic value. The source code and manual for deepGBLUP are available at https://github.com/gywns6287/deepGBLUP .PMID:37525091 | PMC:PMC10392020 | DOI:10.1186/s12711-023-00825-y (Source: Genet Sel Evol)
Source: Genet Sel Evol - July 31, 2023 Category: Genetics & Stem Cells Authors: Hyo-Jun Lee Jun Heon Lee Cedric Gondro Yeong Jun Koh Seung Hwan Lee Source Type: research
deepGBLUP: joint deep learning networks and GBLUP framework for accurate genomic prediction of complex traits in Korean native cattle
CONCLUSIONS: We introduced a novel genomic prediction algorithm, deepGBLUP, which successfully integrates deep learning networks and GBLUP framework. Through comprehensive evaluations on the Korean native cattle data and simulated data, deepGBLUP consistently achieved superior performance across various traits, marker densities, training sizes, heritabilities, and QTL effects. Therefore, deepGBLUP is an efficient method to estimate an accurate genomic value. The source code and manual for deepGBLUP are available at https://github.com/gywns6287/deepGBLUP .PMID:37525091 | DOI:10.1186/s12711-023-00825-y (Source: Genet Sel Evol)
Source: Genet Sel Evol - July 31, 2023 Category: Genetics & Stem Cells Authors: Hyo-Jun Lee Jun Heon Lee Cedric Gondro Yeong Jun Koh Seung Hwan Lee Source Type: research
Using pre-selected variants from large-scale whole-genome sequence data for single-step genomic predictions in pigs
CONCLUSIONS: The benefit of using sequence data depends on the line, the size of the genotyped population, and how the WGS variants are preselected. When WGS data are available on hundreds of thousands of animals, using sequence data presents an advantage but this remains limited in pigs.PMID:37495982 | PMC:PMC10373252 | DOI:10.1186/s12711-023-00831-0 (Source: Genet Sel Evol)
Source: Genet Sel Evol - July 26, 2023 Category: Genetics & Stem Cells Authors: Sungbong Jang Roger Ros-Freixedes John M Hickey Ching-Yi Chen Justin Holl William O Herring Ignacy Misztal Daniela Lourenco Source Type: research
