MARC details
| 000 -LEADER |
|---|
| fixed length control field |
03251nab|a22003857a|4500 |
| 001 - CONTROL NUMBER |
|---|
| control field |
65754 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
MX-TxCIM |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20240919020954.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
20229s2022||||mx |||p|op||||00||0|eng|d |
| 022 ## - INTERNATIONAL STANDARD SERIAL NUMBER |
|---|
| International Standard Serial Number |
1664-8021 (Online) |
| 024 8# - OTHER STANDARD IDENTIFIER |
|---|
| Standard number or code |
https://doi.org/10.3389/fgene.2022.966775 |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
MX-TxCIM |
| 041 ## - LANGUAGE CODE |
|---|
| Language code of text/sound track or separate title |
eng |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Montesinos-Lopez, O.A. |
| Field link and sequence number |
I1706800 |
| 9 (RLIN) |
2700 |
| Miscellaneous information |
Genetic Resources Program |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Multi-trait genome prediction of new environments with partial least squares |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Name of publisher, distributor, etc. |
Frontiers, |
| Date of publication, distribution, etc. |
2022. |
| Place of publication, distribution, etc. |
Switzerland : |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Peer review |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Open Access |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
The genomic selection (GS) methodology proposed over 20 years ago by Meuwissen et al. (Genetics, 2001) has revolutionized plant breeding. A predictive methodology that trains statistical machine learning algorithms with phenotypic and genotypic data of a reference population and makes predictions for genotyped candidate lines, GS saves significant resources in the selection of candidate individuals. However, its practical implementation is still challenging when the plant breeder is interested in the prediction of future seasons or new locations and/or environments, which is called the “leave one environment out” issue. Furthermore, because the distributions of the training and testing set do not match, most statistical machine learning methods struggle to produce moderate or reasonable prediction accuracies. For this reason, the main objective of this study was to explore the use of the multi-trait partial least square (MT-PLS) regression methodology for this specific task, benchmarking its performance with the Bayesian Multi-trait Genomic Best Linear Unbiased Predictor (MT-GBLUP) method. The benchmarking process was performed with five actual data sets. We found that in all data sets the MT-PLS method outperformed the popular MT-GBLUP method by 349.8% (under predictor E + G), 484.4% (under predictor E + G + GE; where E denotes environments, G genotypes and GE the genotype by environment interaction) and 15.9% (under predictor G + GE) across traits. Our results provide empirical evidence of the power of the MT-PLS methodology for the prediction of future seasons or new environments. Furthermore, the comparison between single univariate-trait (UT) versus MT for GBLUP and PLS gave an increase in prediction accuracy of MT-GBLUP versus UT-GBLUP, but not for MT-PLS versus UT-PLS. |
| 546 ## - LANGUAGE NOTE |
|---|
| Language note |
Text in English |
| 591 ## - CATALOGING NOTES |
|---|
| Affiliation |
Montesinos-Lopez, O.A. : No CIMMYT Affiliation |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Genotypes |
| Source of heading or term |
AGROVOC |
| 9 (RLIN) |
1134 |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Genotype environment interaction |
| Source of heading or term |
AGROVOC |
| 9 (RLIN) |
1133 |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Machine learning |
| Source of heading or term |
AGROVOC |
| 9 (RLIN) |
11127 |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Forecasting |
| Source of heading or term |
AGROVOC |
| 9 (RLIN) |
2701 |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Marker-assisted selection |
| Source of heading or term |
AGROVOC |
| 9 (RLIN) |
10737 |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Montesinos-Lopez, A. |
| 9 (RLIN) |
2702 |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Bernal Sandoval, D.A. |
| 9 (RLIN) |
29339 |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Mosqueda-Gonzalez, B.A. |
| 9 (RLIN) |
19441 |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Valenzo-Jiménez, M.A. |
| 9 (RLIN) |
29340 |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Crossa, J. |
| Miscellaneous information |
Genetic Resources Program |
| Field link and sequence number |
CCJL01 |
| 9 (RLIN) |
59 |
| 773 0# - HOST ITEM ENTRY |
|---|
| Title |
Frontiers in Genetics |
| Related parts |
v. 13, art. 966775 |
| Place, publisher, and date of publication |
Switzerland : Frontiers, 2022. |
| Record control number |
58093 |
| International Standard Serial Number |
1664-8021 |
| 856 ## - ELECTRONIC LOCATION AND ACCESS |
|---|
| Link text |
Open Access through DSpace |
| Uniform Resource Identifier |
https://hdl.handle.net/10883/22290 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Koha item type |
Article |
| Suppress in OPAC |
No |
| Source of classification or shelving scheme |
Dewey Decimal Classification |