Maximum a posteriori Threshold Genomic Prediction Model for Ordinal Traits
Copyright © 2020 Montesinos-Lopez et al..
Due to the ever-increasing data collected in genomic breeding programs, there is a need for genomic prediction models that can deal better with big data. For this reason, here we propose a Maximum a posteriori Threshold Genomic Prediction (MAPT) model for ordinal traits that is more efficient than the conventional Bayesian Threshold Genomic Prediction model for ordinal traits. The MAPT performs the predictions of the Threshold Genomic Prediction model by using the maximum a posteriori estimation of the parameters, that is, the values of the parameters that maximize the joint posterior density. We compared the prediction performance of the proposed MAPT to the conventional Bayesian Threshold Genomic Prediction model, the multinomial Ridge regression and support vector machine on 8 real data sets. We found that the proposed MAPT was competitive with regard to the multinomial and support vector machine models in terms of prediction performance, and slightly better than the conventional Bayesian Threshold Genomic Prediction model. With regard to the implementation time, we found that in general the MAPT and the support vector machine were the best, while the slowest was the multinomial Ridge regression model. However, it is important to point out that the successful implementation of the proposed MAPT model depends on the informative priors used to avoid underestimation of variance components.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2020 |
|---|---|
| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:10 |
|---|---|
| Enthalten in: |
G3 (Bethesda, Md.) - 10(2020), 11 vom: 05. Nov., Seite 4083-4102 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Montesinos-López, Abelardo [VerfasserIn] |
|---|
| Links: |
|---|
| Anmerkungen: |
Date Completed 21.06.2021 Date Revised 21.06.2021 published: Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1534/g3.120.401733 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM315059168 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM315059168 | ||
| 003 | DE-627 | ||
| 005 | 20231226201912.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231225s2020 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1534/g3.120.401733 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1050.xml |
| 035 | |a (DE-627)NLM315059168 | ||
| 035 | |a (NLM)32934017 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Montesinos-López, Abelardo |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Maximum a posteriori Threshold Genomic Prediction Model for Ordinal Traits |
| 264 | 1 | |c 2020 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 21.06.2021 | ||
| 500 | |a Date Revised 21.06.2021 | ||
| 500 | |a published: Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2020 Montesinos-Lopez et al. | ||
| 520 | |a Due to the ever-increasing data collected in genomic breeding programs, there is a need for genomic prediction models that can deal better with big data. For this reason, here we propose a Maximum a posteriori Threshold Genomic Prediction (MAPT) model for ordinal traits that is more efficient than the conventional Bayesian Threshold Genomic Prediction model for ordinal traits. The MAPT performs the predictions of the Threshold Genomic Prediction model by using the maximum a posteriori estimation of the parameters, that is, the values of the parameters that maximize the joint posterior density. We compared the prediction performance of the proposed MAPT to the conventional Bayesian Threshold Genomic Prediction model, the multinomial Ridge regression and support vector machine on 8 real data sets. We found that the proposed MAPT was competitive with regard to the multinomial and support vector machine models in terms of prediction performance, and slightly better than the conventional Bayesian Threshold Genomic Prediction model. With regard to the implementation time, we found that in general the MAPT and the support vector machine were the best, while the slowest was the multinomial Ridge regression model. However, it is important to point out that the successful implementation of the proposed MAPT model depends on the informative priors used to avoid underestimation of variance components | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Research Support, U.S. Gov't, Non-P.H.S. | |
| 650 | 4 | |a Bayesian Threshold Genomic Prediction model | |
| 650 | 4 | |a EM algorithm | |
| 650 | 4 | |a GenPred | |
| 650 | 4 | |a Genomic Prediction | |
| 650 | 4 | |a Shared data resources | |
| 650 | 4 | |a genomic selection | |
| 650 | 4 | |a maximum a posteriori estimation | |
| 650 | 4 | |a multinomial Ridge regression | |
| 650 | 4 | |a support vector machine | |
| 700 | 1 | |a Gutierrez-Pulido, Humberto |e verfasserin |4 aut | |
| 700 | 1 | |a Montesinos-López, Osval Antonio |e verfasserin |4 aut | |
| 700 | 1 | |a Crossa, José |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t G3 (Bethesda, Md.) |d 2011 |g 10(2020), 11 vom: 05. Nov., Seite 4083-4102 |w (DE-627)NLM215888499 |x 2160-1836 |7 nnns |
| 773 | 1 | 8 | |g volume:10 |g year:2020 |g number:11 |g day:05 |g month:11 |g pages:4083-4102 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1534/g3.120.401733 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 10 |j 2020 |e 11 |b 05 |c 11 |h 4083-4102 | ||