MARC details
| 000 -LEADER |
|---|
| fixed length control field |
03122nab|a22003737a|4500 |
| 001 - CONTROL NUMBER |
|---|
| control field |
64322 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
MX-TxCIM |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20240919020953.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
202101s2021||||xxk|||p|op||||00||0|eng|d |
| 022 ## - INTERNATIONAL STANDARD SERIAL NUMBER |
|---|
| International Standard Serial Number |
0018-067X |
| 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/s41437-021-00474-1 |
| 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 |
|---|
| Personal name |
Lopez-Cruz, M. |
| 9 (RLIN) |
2348 |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Multi-generation genomic prediction of maize yield using parametric and non-parametric sparse selection indices |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc. |
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. |
Genomic prediction models are often calibrated using multi-generation data. Over time, as data accumulates, training data sets become increasingly heterogeneous. Differences in allele frequency and linkage disequilibrium patterns between the training and prediction genotypes may limit prediction accuracy. This leads to the question of whether all available data or a subset of it should be used to calibrate genomic prediction models. Previous research on training set optimization has focused on identifying a subset of the available data that is optimal for a given prediction set. However, this approach does not contemplate the possibility that different training sets may be optimal for different prediction genotypes. To address this problem, we recently introduced a sparse selection index (SSI) that identifies an optimal training set for each individual in a prediction set. Using additive genomic relationships, the SSI can provide increased accuracy relative to genomic-BLUP (GBLUP). Non-parametric genomic models using Gaussian kernels (KBLUP) have, in some cases, yielded higher prediction accuracies than standard additive models. Therefore, here we studied whether combining SSIs and kernel methods could further improve prediction accuracy when training genomic models using multi-generation data. Using four years of doubled haploid maize data from the International Maize and Wheat Improvement Center (CIMMYT), we found that when predicting grain yield the KBLUP outperformed the GBLUP, and that using SSI with additive relationships (GSSI) lead to 5–17% increases in accuracy, relative to the GBLUP. However, differences in prediction accuracy between the KBLUP and the kernel-based SSI were smaller and not always significant. |
| 546 ## - LANGUAGE NOTE |
|---|
| Language note |
Text in English |
| 591 ## - CATALOGING NOTES |
|---|
| Affiliation |
De los Campos, G. : No CIMMYT Affiliation |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Source of heading or term |
AGROVOC |
| 9 (RLIN) |
1132 |
| Topical term or geographic name as entry element |
Genomics |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Source of heading or term |
AGROVOC |
| 9 (RLIN) |
4859 |
| Topical term or geographic name as entry element |
Models |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Source of heading or term |
AGROVOC |
| 9 (RLIN) |
1130 |
| Topical term or geographic name as entry element |
Genetics |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Beyene, Y. |
| 9 (RLIN) |
870 |
| Field link and sequence number |
INT2891 |
| Miscellaneous information |
Global Maize Program |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Gowda, M. |
| 9 (RLIN) |
795 |
| Field link and sequence number |
I1705963 |
| Miscellaneous information |
Global Maize Program |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Crossa, J. |
| Miscellaneous information |
Genetic Resources Program |
| Field link and sequence number |
CCJL01 |
| 9 (RLIN) |
59 |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Perez-Rodriguez, P. |
| 9 (RLIN) |
2703 |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
De los Campos, G. |
| 9 (RLIN) |
2349 |
| Field link and sequence number |
CCAG01 |
| Miscellaneous information |
Genetic Resources Program |
| 773 0# - HOST ITEM ENTRY |
|---|
| Title |
Heredity |
| Place, publisher, and date of publication |
United Kingdom : Springer Nature, 2021. |
| International Standard Serial Number |
0018-067X |
| Related parts |
v. 127, no. 5, p. 423-432 |
| 856 4# - ELECTRONIC LOCATION AND ACCESS |
|---|
| Link text |
Open Access through DSpace |
| Uniform Resource Identifier |
https://hdl.handle.net/10883/21694 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Koha item type |
Article |
| Suppress in OPAC |
No |
| Source of classification or shelving scheme |
Dewey Decimal Classification |