MARC details
| 000 -LEADER |
|---|
| fixed length control field |
02865nab|a22003137a|4500 |
| 001 - CONTROL NUMBER |
|---|
| control field |
62560 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
MX-TxCIM |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20240919020952.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
200911s2021||||xxk|||p|op||||00||0|eng|d |
| 022 ## - INTERNATIONAL STANDARD SERIAL NUMBER |
|---|
| International Standard Serial Number |
1365-2540 (Online) |
| 024 8# - OTHER STANDARD IDENTIFIER |
|---|
| Standard number or code |
https://doi.org/10.1038/s41437-020-00353-1 |
| 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 |
|---|
| 9 (RLIN) |
15939 |
| Personal name |
Costa-Neto, G. |
| Field link and sequence number |
001712813 |
| Miscellaneous information |
Genetic Resources Program |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc. |
Harlow (United Kingdom) : |
| Name of publisher, distributor, etc. |
Springer Nature, |
| Date of publication, distribution, etc. |
2021. |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Peer review |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Open Access |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
Modern whole-genome prediction (WGP) frameworks that focus on multi-environment trials (MET) integrate large-scale genomics, phenomics, and envirotyping data. However, the more complex the statistical model, the longer the computational processing times, which do not always result in accuracy gains. We investigated the use of new kernel methods and modeling structures involving genomics and nongenomic sources of variation in two MET maize data sets. Five WGP models were considered, advancing in complexity from a main-effect additive model (A) to more complex structures, including dominance deviations (D), genotype x environment interaction (AE and DE), and the reaction-norm model using environmental covariables (W) and their interaction with A and D (AW + DW). A combination of those models built with three different kernel methods, Gaussian kernel (GK), Deep kernel (DK), and the benchmark genomic best linear-unbiased predictor (GBLUP/GB), was tested under three prediction scenarios: newly developed hybrids (CV1), sparse MET conditions (CV2), and new environments (CV0). GK and DK outperformed GB in prediction accuracy and reduction of computation time (similar to up to 20%) under all model-kernel scenarios. GK was more efficient in capturing the variation due to A + AE and D + DE effects and translated it into accuracy gains (similar to up to 85% compared with GB). DK provided more consistent predictions, even for more complex structures such as W + AW + DW. Our results suggest that DK and GK are more efficient in translating model complexity into accuracy, and more suitable for including dominance and reaction-norm effects in a biologically accurate and faster way. |
| 546 ## - LANGUAGE NOTE |
|---|
| Language note |
Text in English |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Evolution |
| Source of heading or term |
AGROVOC |
| 9 (RLIN) |
8815 |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Genomics |
| Source of heading or term |
AGROVOC |
| 9 (RLIN) |
1132 |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Models |
| Source of heading or term |
AGROVOC |
| 9 (RLIN) |
4859 |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Fritsche-Neto, R. |
| 9 (RLIN) |
6507 |
| 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 |
|---|
| Place, publisher, and date of publication |
Harlow (United Kingdom) : Springer Nature, 2021. |
| International Standard Serial Number |
0018-067X |
| Related parts |
v. 126, p. 92-106 |
| Title |
Heredity |
| Record control number |
u444336 |
| 856 8# - ELECTRONIC LOCATION AND ACCESS |
|---|
| Link text |
Open Access through DSpace |
| Uniform Resource Identifier |
https://hdl.handle.net/10883/20953 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Koha item type |
Article |
| Suppress in OPAC |
No |
| Source of classification or shelving scheme |
Dewey Decimal Classification |