Genomic prediction in hybrid breeding: I. Optimizing the training set design Article Swipe
Albrecht E. Melchinger
,
Rohan L. Fernando
,
Christian Stricker
,
Chris‐Carolin Schön
,
Hans-Jürgen Auinger
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1007/s00122-023-04413-y
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1007/s00122-023-04413-y
Key message Training sets produced by maximizing the number of parent lines, each involved in one cross, had the highest prediction accuracy for H0 hybrids, but lowest for H1 and H2 hybrids. Abstract Genomic prediction holds great promise for hybrid breeding but optimum composition of the training set (TS) as determined by the number of parents ( n TS ) and crosses per parent ( c ) has received little attention. Our objective was to examine prediction accuracy ( $$r_{a}$$ ) of GCA for lines used as parents of the TS (I1 lines) or not (I0 lines), and H0, H1 and H2 hybrids, comprising crosses of type I0 × I0, I1 × I0 and I1 × I1, respectively, as function of n TS and c . In the theory, we developed estimates for $$r_{a}$$ of GBLUPs for hybrids: (i) $$\hat{r}_{a}$$ based on the expected prediction accuracy, and (ii) $$\tilde{r}_{a}$$ based on $$r_{a}$$ of GBLUPs of GCA and SCA effects. In the simulation part, hybrid populations were generated using molecular data from two experimental maize data sets. Additive and dominance effects of QTL borrowed from literature were used to simulate six scenarios of traits differing in the proportion ( τ SCA = 1%, 6%, 22%) of SCA variance in σ G 2 and heritability ( h 2 = 0.4, 0.8). Values of $$\tilde{r}_{a}$$ and $$\hat{r}_{a}$$ closely agreed with $$r_{a}$$ for hybrids. For given size N TS = n TS × c of TS, $$r_{a}$$ of H0 hybrids and GCA of I0 lines was highest for c = 1. Conversely, for GCA of I1 lines and H1 and H2 hybrids, c = 1 yielded lowest $$r_{a}$$ with concordant results across all scenarios for both data sets. In view of these opposite trends, the optimum choice of c for maximizing selection response across all types of hybrids depends on the size and resources of the breeding program.
Related Topics
Concepts
Biology
Plant biochemistry
Genomic selection
Training (meteorology)
Set (abstract data type)
Training set
Evolutionary biology
Computational biology
Genetics
Biotechnology
Artificial intelligence
Gene
Computer science
Genotype
Single-nucleotide polymorphism
Meteorology
Programming language
Physics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s00122-023-04413-y
- https://link.springer.com/content/pdf/10.1007/s00122-023-04413-y.pdf
- OA Status
- hybrid
- Cited By
- 13
- References
- 68
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385490429
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4385490429Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1007/s00122-023-04413-yDigital Object Identifier
- Title
-
Genomic prediction in hybrid breeding: I. Optimizing the training set designWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-08-01Full publication date if available
- Authors
-
Albrecht E. Melchinger, Rohan L. Fernando, Christian Stricker, Chris‐Carolin Schön, Hans-Jürgen AuingerList of authors in order
- Landing page
-
https://doi.org/10.1007/s00122-023-04413-yPublisher landing page
- PDF URL
-
https://link.springer.com/content/pdf/10.1007/s00122-023-04413-y.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://link.springer.com/content/pdf/10.1007/s00122-023-04413-y.pdfDirect OA link when available
- Concepts
-
Biology, Plant biochemistry, Genomic selection, Training (meteorology), Set (abstract data type), Training set, Evolutionary biology, Computational biology, Genetics, Biotechnology, Artificial intelligence, Gene, Computer science, Genotype, Single-nucleotide polymorphism, Meteorology, Programming language, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
13Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 5, 2024: 7, 2023: 1Per-year citation counts (last 5 years)
- References (count)
-
68Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4385490429 |
|---|---|
| doi | https://doi.org/10.1007/s00122-023-04413-y |
| ids.doi | https://doi.org/10.1007/s00122-023-04413-y |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/37532821 |
| ids.openalex | https://openalex.org/W4385490429 |
| fwci | 4.67054193 |
| mesh[0].qualifier_ui | |
| mesh[0].descriptor_ui | D000069600 |
| mesh[0].is_major_topic | True |
| mesh[0].qualifier_name | |
| mesh[0].descriptor_name | Plant Breeding |
| mesh[1].qualifier_ui | |
| mesh[1].descriptor_ui | D023281 |
| mesh[1].is_major_topic | True |
| mesh[1].qualifier_name | |
| mesh[1].descriptor_name | Genomics |
| mesh[2].qualifier_ui | |
| mesh[2].descriptor_ui | D010641 |
| mesh[2].is_major_topic | False |
| mesh[2].qualifier_name | |
| mesh[2].descriptor_name | Phenotype |
| mesh[3].qualifier_ui | |
| mesh[3].descriptor_ui | D018745 |
| mesh[3].is_major_topic | False |
| mesh[3].qualifier_name | |
| mesh[3].descriptor_name | Genome, Plant |
| mesh[4].qualifier_ui | |
| mesh[4].descriptor_ui | D003198 |
| mesh[4].is_major_topic | False |
| mesh[4].qualifier_name | |
| mesh[4].descriptor_name | Computer Simulation |
| mesh[5].qualifier_ui | |
| mesh[5].descriptor_ui | D008957 |
| mesh[5].is_major_topic | False |
| mesh[5].qualifier_name | |
| mesh[5].descriptor_name | Models, Genetic |
| mesh[6].qualifier_ui | |
| mesh[6].descriptor_ui | D000069600 |
| mesh[6].is_major_topic | True |
| mesh[6].qualifier_name | |
| mesh[6].descriptor_name | Plant Breeding |
| mesh[7].qualifier_ui | |
| mesh[7].descriptor_ui | D023281 |
| mesh[7].is_major_topic | True |
| mesh[7].qualifier_name | |
| mesh[7].descriptor_name | Genomics |
| mesh[8].qualifier_ui | |
| mesh[8].descriptor_ui | D010641 |
| mesh[8].is_major_topic | False |
| mesh[8].qualifier_name | |
| mesh[8].descriptor_name | Phenotype |
| mesh[9].qualifier_ui | |
| mesh[9].descriptor_ui | D018745 |
| mesh[9].is_major_topic | False |
| mesh[9].qualifier_name | |
| mesh[9].descriptor_name | Genome, Plant |
| mesh[10].qualifier_ui | |
| mesh[10].descriptor_ui | D003198 |
| mesh[10].is_major_topic | False |
| mesh[10].qualifier_name | |
| mesh[10].descriptor_name | Computer Simulation |
| mesh[11].qualifier_ui | |
| mesh[11].descriptor_ui | D008957 |
| mesh[11].is_major_topic | False |
| mesh[11].qualifier_name | |
| mesh[11].descriptor_name | Models, Genetic |
| mesh[12].qualifier_ui | |
| mesh[12].descriptor_ui | D000069600 |
| mesh[12].is_major_topic | True |
| mesh[12].qualifier_name | |
| mesh[12].descriptor_name | Plant Breeding |
| mesh[13].qualifier_ui | |
| mesh[13].descriptor_ui | D023281 |
| mesh[13].is_major_topic | True |
| mesh[13].qualifier_name | |
| mesh[13].descriptor_name | Genomics |
| mesh[14].qualifier_ui | |
| mesh[14].descriptor_ui | D010641 |
| mesh[14].is_major_topic | False |
| mesh[14].qualifier_name | |
| mesh[14].descriptor_name | Phenotype |
| mesh[15].qualifier_ui | |
| mesh[15].descriptor_ui | D018745 |
| mesh[15].is_major_topic | False |
| mesh[15].qualifier_name | |
| mesh[15].descriptor_name | Genome, Plant |
| mesh[16].qualifier_ui | |
| mesh[16].descriptor_ui | D003198 |
| mesh[16].is_major_topic | False |
| mesh[16].qualifier_name | |
| mesh[16].descriptor_name | Computer Simulation |
| mesh[17].qualifier_ui | |
| mesh[17].descriptor_ui | D008957 |
| mesh[17].is_major_topic | False |
| mesh[17].qualifier_name | |
| mesh[17].descriptor_name | Models, Genetic |
| type | article |
| title | Genomic prediction in hybrid breeding: I. Optimizing the training set design |
| biblio.issue | 8 |
| biblio.volume | 136 |
| biblio.last_page | 176 |
| biblio.first_page | 176 |
| topics[0].id | https://openalex.org/T10594 |
| topics[0].field.id | https://openalex.org/fields/13 |
| topics[0].field.display_name | Biochemistry, Genetics and Molecular Biology |
| topics[0].score | 0.9998999834060669 |
| topics[0].domain.id | https://openalex.org/domains/1 |
| topics[0].domain.display_name | Life Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1311 |
| topics[0].subfield.display_name | Genetics |
| topics[0].display_name | Genetic and phenotypic traits in livestock |
| topics[1].id | https://openalex.org/T11468 |
| topics[1].field.id | https://openalex.org/fields/13 |
| topics[1].field.display_name | Biochemistry, Genetics and Molecular Biology |
| topics[1].score | 0.9965000152587891 |
| topics[1].domain.id | https://openalex.org/domains/1 |
| topics[1].domain.display_name | Life Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1311 |
| topics[1].subfield.display_name | Genetics |
| topics[1].display_name | Genetic Mapping and Diversity in Plants and Animals |
| topics[2].id | https://openalex.org/T11229 |
| topics[2].field.id | https://openalex.org/fields/11 |
| topics[2].field.display_name | Agricultural and Biological Sciences |
| topics[2].score | 0.9879999756813049 |
| topics[2].domain.id | https://openalex.org/domains/1 |
| topics[2].domain.display_name | Life Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1110 |
| topics[2].subfield.display_name | Plant Science |
| topics[2].display_name | Genetics and Plant Breeding |
| funders[0].id | https://openalex.org/F4320323383 |
| funders[0].ror | https://ror.org/02kkvpp62 |
| funders[0].display_name | Technische Universität München |
| is_xpac | False |
| apc_list.value | 3490 |
| apc_list.currency | EUR |
| apc_list.value_usd | 4490 |
| apc_paid.value | 3490 |
| apc_paid.currency | EUR |
| apc_paid.value_usd | 4490 |
| concepts[0].id | https://openalex.org/C86803240 |
| concepts[0].level | 0 |
| concepts[0].score | 0.8653287291526794 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[0].display_name | Biology |
| concepts[1].id | https://openalex.org/C2993199473 |
| concepts[1].level | 3 |
| concepts[1].score | 0.7398602366447449 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q185973 |
| concepts[1].display_name | Plant biochemistry |
| concepts[2].id | https://openalex.org/C2992444039 |
| concepts[2].level | 5 |
| concepts[2].score | 0.5473369359970093 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q17145598 |
| concepts[2].display_name | Genomic selection |
| concepts[3].id | https://openalex.org/C2777211547 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5446065068244934 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q17141490 |
| concepts[3].display_name | Training (meteorology) |
| concepts[4].id | https://openalex.org/C177264268 |
| concepts[4].level | 2 |
| concepts[4].score | 0.521625816822052 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q1514741 |
| concepts[4].display_name | Set (abstract data type) |
| concepts[5].id | https://openalex.org/C51632099 |
| concepts[5].level | 2 |
| concepts[5].score | 0.43497616052627563 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q3985153 |
| concepts[5].display_name | Training set |
| concepts[6].id | https://openalex.org/C78458016 |
| concepts[6].level | 1 |
| concepts[6].score | 0.43130743503570557 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q840400 |
| concepts[6].display_name | Evolutionary biology |
| concepts[7].id | https://openalex.org/C70721500 |
| concepts[7].level | 1 |
| concepts[7].score | 0.42004215717315674 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q177005 |
| concepts[7].display_name | Computational biology |
| concepts[8].id | https://openalex.org/C54355233 |
| concepts[8].level | 1 |
| concepts[8].score | 0.4077135920524597 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q7162 |
| concepts[8].display_name | Genetics |
| concepts[9].id | https://openalex.org/C150903083 |
| concepts[9].level | 1 |
| concepts[9].score | 0.40148016810417175 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q7108 |
| concepts[9].display_name | Biotechnology |
| concepts[10].id | https://openalex.org/C154945302 |
| concepts[10].level | 1 |
| concepts[10].score | 0.20965754985809326 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[10].display_name | Artificial intelligence |
| concepts[11].id | https://openalex.org/C104317684 |
| concepts[11].level | 2 |
| concepts[11].score | 0.18116557598114014 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q7187 |
| concepts[11].display_name | Gene |
| concepts[12].id | https://openalex.org/C41008148 |
| concepts[12].level | 0 |
| concepts[12].score | 0.1803562045097351 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[12].display_name | Computer science |
| concepts[13].id | https://openalex.org/C135763542 |
| concepts[13].level | 3 |
| concepts[13].score | 0.09993335604667664 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q106016 |
| concepts[13].display_name | Genotype |
| concepts[14].id | https://openalex.org/C153209595 |
| concepts[14].level | 4 |
| concepts[14].score | 0.07556033134460449 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q501128 |
| concepts[14].display_name | Single-nucleotide polymorphism |
| concepts[15].id | https://openalex.org/C153294291 |
| concepts[15].level | 1 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q25261 |
| concepts[15].display_name | Meteorology |
| concepts[16].id | https://openalex.org/C199360897 |
| concepts[16].level | 1 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q9143 |
| concepts[16].display_name | Programming language |
| concepts[17].id | https://openalex.org/C121332964 |
| concepts[17].level | 0 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[17].display_name | Physics |
| keywords[0].id | https://openalex.org/keywords/biology |
| keywords[0].score | 0.8653287291526794 |
| keywords[0].display_name | Biology |
| keywords[1].id | https://openalex.org/keywords/plant-biochemistry |
| keywords[1].score | 0.7398602366447449 |
| keywords[1].display_name | Plant biochemistry |
| keywords[2].id | https://openalex.org/keywords/genomic-selection |
| keywords[2].score | 0.5473369359970093 |
| keywords[2].display_name | Genomic selection |
| keywords[3].id | https://openalex.org/keywords/training |
| keywords[3].score | 0.5446065068244934 |
| keywords[3].display_name | Training (meteorology) |
| keywords[4].id | https://openalex.org/keywords/set |
| keywords[4].score | 0.521625816822052 |
| keywords[4].display_name | Set (abstract data type) |
| keywords[5].id | https://openalex.org/keywords/training-set |
| keywords[5].score | 0.43497616052627563 |
| keywords[5].display_name | Training set |
| keywords[6].id | https://openalex.org/keywords/evolutionary-biology |
| keywords[6].score | 0.43130743503570557 |
| keywords[6].display_name | Evolutionary biology |
| keywords[7].id | https://openalex.org/keywords/computational-biology |
| keywords[7].score | 0.42004215717315674 |
| keywords[7].display_name | Computational biology |
| keywords[8].id | https://openalex.org/keywords/genetics |
| keywords[8].score | 0.4077135920524597 |
| keywords[8].display_name | Genetics |
| keywords[9].id | https://openalex.org/keywords/biotechnology |
| keywords[9].score | 0.40148016810417175 |
| keywords[9].display_name | Biotechnology |
| keywords[10].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[10].score | 0.20965754985809326 |
| keywords[10].display_name | Artificial intelligence |
| keywords[11].id | https://openalex.org/keywords/gene |
| keywords[11].score | 0.18116557598114014 |
| keywords[11].display_name | Gene |
| keywords[12].id | https://openalex.org/keywords/computer-science |
| keywords[12].score | 0.1803562045097351 |
| keywords[12].display_name | Computer science |
| keywords[13].id | https://openalex.org/keywords/genotype |
| keywords[13].score | 0.09993335604667664 |
| keywords[13].display_name | Genotype |
| keywords[14].id | https://openalex.org/keywords/single-nucleotide-polymorphism |
| keywords[14].score | 0.07556033134460449 |
| keywords[14].display_name | Single-nucleotide polymorphism |
| language | en |
| locations[0].id | doi:10.1007/s00122-023-04413-y |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S96685181 |
| locations[0].source.issn | 0040-5752, 1432-2242 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 0040-5752 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Theoretical and Applied Genetics |
| locations[0].source.host_organization | https://openalex.org/P4310319900 |
| locations[0].source.host_organization_name | Springer Science+Business Media |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310319900, https://openalex.org/P4310319965 |
| locations[0].source.host_organization_lineage_names | Springer Science+Business Media, Springer Nature |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://link.springer.com/content/pdf/10.1007/s00122-023-04413-y.pdf |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Theoretical and Applied Genetics |
| locations[0].landing_page_url | https://doi.org/10.1007/s00122-023-04413-y |
| locations[1].id | pmid:37532821 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306525036 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | PubMed |
| locations[1].source.host_organization | https://openalex.org/I1299303238 |
| locations[1].source.host_organization_name | National Institutes of Health |
| locations[1].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | publishedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | True |
| locations[1].raw_source_name | TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/37532821 |
| locations[2].id | pmh:oai:pubmedcentral.nih.gov:10397156 |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S2764455111 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | False |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | PubMed Central |
| locations[2].source.host_organization | https://openalex.org/I1299303238 |
| locations[2].source.host_organization_name | National Institutes of Health |
| locations[2].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[2].license | cc-by |
| locations[2].pdf_url | https://pmc.ncbi.nlm.nih.gov/articles/PMC10397156/pdf/122_2023_Article_4413.pdf |
| locations[2].version | submittedVersion |
| locations[2].raw_type | Text |
| locations[2].license_id | https://openalex.org/licenses/cc-by |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | Theor Appl Genet |
| locations[2].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/10397156 |
| locations[3].id | pmh:oai:mediatum.ub.tum.de:node/1763558 |
| locations[3].is_oa | True |
| locations[3].source.id | https://openalex.org/S4306400453 |
| locations[3].source.issn | |
| locations[3].source.type | repository |
| locations[3].source.is_oa | False |
| locations[3].source.issn_l | |
| locations[3].source.is_core | False |
| locations[3].source.is_in_doaj | False |
| locations[3].source.display_name | mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) |
| locations[3].source.host_organization | https://openalex.org/I62916508 |
| locations[3].source.host_organization_name | Technical University of Munich |
| locations[3].source.host_organization_lineage | https://openalex.org/I62916508 |
| locations[3].license | other-oa |
| locations[3].pdf_url | |
| locations[3].version | submittedVersion |
| locations[3].raw_type | article |
| locations[3].license_id | https://openalex.org/licenses/other-oa |
| locations[3].is_accepted | False |
| locations[3].is_published | False |
| locations[3].raw_source_name | |
| locations[3].landing_page_url | https://mediatum.ub.tum.de/1763558 |
| indexed_in | crossref, pubmed |
| authorships[0].author.id | https://openalex.org/A5047834524 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-8346-7786 |
| authorships[0].author.display_name | Albrecht E. Melchinger |
| authorships[0].countries | DE |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I62916508 |
| authorships[0].affiliations[0].raw_affiliation_string | Plant Breeding, TUM School of Life Sciences, Technical University of Munich, 85354, Freising, Germany |
| authorships[0].institutions[0].id | https://openalex.org/I62916508 |
| authorships[0].institutions[0].ror | https://ror.org/02kkvpp62 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I62916508 |
| authorships[0].institutions[0].country_code | DE |
| authorships[0].institutions[0].display_name | Technical University of Munich |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Albrecht E. Melchinger |
| authorships[0].is_corresponding | True |
| authorships[0].raw_affiliation_strings | Plant Breeding, TUM School of Life Sciences, Technical University of Munich, 85354, Freising, Germany |
| authorships[1].author.id | https://openalex.org/A5045484613 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-5821-099X |
| authorships[1].author.display_name | Rohan L. Fernando |
| authorships[1].countries | US |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I173911158 |
| authorships[1].affiliations[0].raw_affiliation_string | Department of Animal Science, Iowa State University, Ames, IA, 50011, USA |
| authorships[1].institutions[0].id | https://openalex.org/I173911158 |
| authorships[1].institutions[0].ror | https://ror.org/04rswrd78 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I173911158 |
| authorships[1].institutions[0].country_code | US |
| authorships[1].institutions[0].display_name | Iowa State University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Rohan Fernando |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Department of Animal Science, Iowa State University, Ames, IA, 50011, USA |
| authorships[2].author.id | https://openalex.org/A5107300242 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-4784-3147 |
| authorships[2].author.display_name | Christian Stricker |
| authorships[2].countries | DE |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I62916508 |
| authorships[2].affiliations[0].raw_affiliation_string | Plant Breeding, TUM School of Life Sciences, Technical University of Munich, 85354, Freising, Germany |
| authorships[2].institutions[0].id | https://openalex.org/I62916508 |
| authorships[2].institutions[0].ror | https://ror.org/02kkvpp62 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I62916508 |
| authorships[2].institutions[0].country_code | DE |
| authorships[2].institutions[0].display_name | Technical University of Munich |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Christian Stricker |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Plant Breeding, TUM School of Life Sciences, Technical University of Munich, 85354, Freising, Germany |
| authorships[3].author.id | https://openalex.org/A5011477310 |
| authorships[3].author.orcid | |
| authorships[3].author.display_name | Chris‐Carolin Schön |
| authorships[3].countries | DE |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I62916508 |
| authorships[3].affiliations[0].raw_affiliation_string | Plant Breeding, TUM School of Life Sciences, Technical University of Munich, 85354, Freising, Germany |
| authorships[3].institutions[0].id | https://openalex.org/I62916508 |
| authorships[3].institutions[0].ror | https://ror.org/02kkvpp62 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I62916508 |
| authorships[3].institutions[0].country_code | DE |
| authorships[3].institutions[0].display_name | Technical University of Munich |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Chris-Carolin Schön |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Plant Breeding, TUM School of Life Sciences, Technical University of Munich, 85354, Freising, Germany |
| authorships[4].author.id | https://openalex.org/A5033987599 |
| authorships[4].author.orcid | https://orcid.org/0000-0001-5055-5328 |
| authorships[4].author.display_name | Hans-Jürgen Auinger |
| authorships[4].countries | DE |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I62916508 |
| authorships[4].affiliations[0].raw_affiliation_string | Plant Breeding, TUM School of Life Sciences, Technical University of Munich, 85354, Freising, Germany |
| authorships[4].institutions[0].id | https://openalex.org/I62916508 |
| authorships[4].institutions[0].ror | https://ror.org/02kkvpp62 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I62916508 |
| authorships[4].institutions[0].country_code | DE |
| authorships[4].institutions[0].display_name | Technical University of Munich |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Hans-Jürgen Auinger |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Plant Breeding, TUM School of Life Sciences, Technical University of Munich, 85354, Freising, Germany |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://link.springer.com/content/pdf/10.1007/s00122-023-04413-y.pdf |
| open_access.oa_status | hybrid |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Genomic prediction in hybrid breeding: I. Optimizing the training set design |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10594 |
| primary_topic.field.id | https://openalex.org/fields/13 |
| primary_topic.field.display_name | Biochemistry, Genetics and Molecular Biology |
| primary_topic.score | 0.9998999834060669 |
| primary_topic.domain.id | https://openalex.org/domains/1 |
| primary_topic.domain.display_name | Life Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1311 |
| primary_topic.subfield.display_name | Genetics |
| primary_topic.display_name | Genetic and phenotypic traits in livestock |
| related_works | https://openalex.org/W230091440, https://openalex.org/W2233261550, https://openalex.org/W2810751659, https://openalex.org/W258997015, https://openalex.org/W2997094352, https://openalex.org/W2021663171, https://openalex.org/W3216976533, https://openalex.org/W2803260331, https://openalex.org/W2782972808, https://openalex.org/W2551249631 |
| cited_by_count | 13 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 5 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 7 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 1 |
| locations_count | 4 |
| best_oa_location.id | doi:10.1007/s00122-023-04413-y |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S96685181 |
| best_oa_location.source.issn | 0040-5752, 1432-2242 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | 0040-5752 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Theoretical and Applied Genetics |
| best_oa_location.source.host_organization | https://openalex.org/P4310319900 |
| best_oa_location.source.host_organization_name | Springer Science+Business Media |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310319900, https://openalex.org/P4310319965 |
| best_oa_location.source.host_organization_lineage_names | Springer Science+Business Media, Springer Nature |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://link.springer.com/content/pdf/10.1007/s00122-023-04413-y.pdf |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Theoretical and Applied Genetics |
| best_oa_location.landing_page_url | https://doi.org/10.1007/s00122-023-04413-y |
| primary_location.id | doi:10.1007/s00122-023-04413-y |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S96685181 |
| primary_location.source.issn | 0040-5752, 1432-2242 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 0040-5752 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Theoretical and Applied Genetics |
| primary_location.source.host_organization | https://openalex.org/P4310319900 |
| primary_location.source.host_organization_name | Springer Science+Business Media |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310319900, https://openalex.org/P4310319965 |
| primary_location.source.host_organization_lineage_names | Springer Science+Business Media, Springer Nature |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://link.springer.com/content/pdf/10.1007/s00122-023-04413-y.pdf |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Theoretical and Applied Genetics |
| primary_location.landing_page_url | https://doi.org/10.1007/s00122-023-04413-y |
| publication_date | 2023-08-01 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W2030718898, https://openalex.org/W2025978835, https://openalex.org/W3171679384, https://openalex.org/W2037303870, https://openalex.org/W2120575449, https://openalex.org/W2921844463, https://openalex.org/W2230276206, https://openalex.org/W2977202545, https://openalex.org/W2157467511, https://openalex.org/W2007720118, https://openalex.org/W2145350980, https://openalex.org/W2013866489, https://openalex.org/W2130060388, https://openalex.org/W1525668218, https://openalex.org/W2132595545, https://openalex.org/W1977688774, https://openalex.org/W3011172731, https://openalex.org/W2303043072, https://openalex.org/W2116938825, https://openalex.org/W2788143107, https://openalex.org/W2001293313, https://openalex.org/W2139189039, https://openalex.org/W2149786357, https://openalex.org/W2135734970, https://openalex.org/W2026611985, https://openalex.org/W4293258550, https://openalex.org/W1966648718, https://openalex.org/W2148306906, https://openalex.org/W2072214908, https://openalex.org/W2253398379, https://openalex.org/W2948907476, https://openalex.org/W3118332892, https://openalex.org/W2341634274, https://openalex.org/W2618271825, https://openalex.org/W2086299449, https://openalex.org/W2078823676, https://openalex.org/W2028942657, https://openalex.org/W4289518846, https://openalex.org/W1993662265, https://openalex.org/W2024616053, https://openalex.org/W2141099825, https://openalex.org/W2030590757, https://openalex.org/W2033670977, https://openalex.org/W2117709702, https://openalex.org/W2130244527, https://openalex.org/W4225574660, https://openalex.org/W2462242997, https://openalex.org/W2078870394, https://openalex.org/W2551419474, https://openalex.org/W2785291720, https://openalex.org/W2805275973, https://openalex.org/W3011342500, https://openalex.org/W2271902495, https://openalex.org/W1982553420, https://openalex.org/W2189194177, https://openalex.org/W1993188061, https://openalex.org/W2006026084, https://openalex.org/W2102273814, https://openalex.org/W1970968485, https://openalex.org/W2067715889, https://openalex.org/W2611530529, https://openalex.org/W3036465674, https://openalex.org/W2626365918, https://openalex.org/W2193480576, https://openalex.org/W4230921330, https://openalex.org/W2009857485, https://openalex.org/W4281810392, https://openalex.org/W2030156721 |
| referenced_works_count | 68 |
| abstract_inverted_index.( | 57, 65, 79, 239, 255 |
| abstract_inverted_index.) | 60, 67, 88 |
| abstract_inverted_index.. | 133 |
| abstract_inverted_index.1 | 346 |
| abstract_inverted_index.2 | 252, 257 |
| abstract_inverted_index.= | 242, 258, 304, 331, 345 |
| abstract_inverted_index.G | 251 |
| abstract_inverted_index.N | 302 |
| abstract_inverted_index.c | 66, 132, 308, 330, 344, 377 |
| abstract_inverted_index.h | 256 |
| abstract_inverted_index.n | 58, 129, 305 |
| abstract_inverted_index.1. | 332 |
| abstract_inverted_index.H0 | 24, 320 |
| abstract_inverted_index.H1 | 29, 107, 340 |
| abstract_inverted_index.H2 | 31, 109, 342 |
| abstract_inverted_index.I0 | 115, 120, 325 |
| abstract_inverted_index.I1 | 118, 122, 337 |
| abstract_inverted_index.In | 134, 201, 367 |
| abstract_inverted_index.TS | 59, 98, 130, 303, 306 |
| abstract_inverted_index.as | 50, 94, 126 |
| abstract_inverted_index.by | 6, 52 |
| abstract_inverted_index.in | 15, 236, 249 |
| abstract_inverted_index.of | 10, 45, 55, 89, 96, 113, 128, 149, 194, 196, 222, 233, 246, 262, 309, 319, 324, 336, 369, 376, 385, 393 |
| abstract_inverted_index.on | 166, 185, 388 |
| abstract_inverted_index.or | 101 |
| abstract_inverted_index.to | 75, 229 |
| abstract_inverted_index.we | 137 |
| abstract_inverted_index.× | 116, 119, 123, 307 |
| abstract_inverted_index.σ | 250 |
| abstract_inverted_index.τ | 240 |
| abstract_inverted_index.(I0 | 103 |
| abstract_inverted_index.(I1 | 99 |
| abstract_inverted_index.(i) | 153 |
| abstract_inverted_index.1%, | 243 |
| abstract_inverted_index.6%, | 244 |
| abstract_inverted_index.For | 299 |
| abstract_inverted_index.GCA | 90, 197, 323, 335 |
| abstract_inverted_index.H0, | 106 |
| abstract_inverted_index.I0, | 117 |
| abstract_inverted_index.I1, | 124 |
| abstract_inverted_index.Key | 1 |
| abstract_inverted_index.Our | 72 |
| abstract_inverted_index.QTL | 223 |
| abstract_inverted_index.SCA | 199, 241, 247 |
| abstract_inverted_index.TS, | 310 |
| abstract_inverted_index.all | 361, 383 |
| abstract_inverted_index.and | 30, 61, 105, 108, 121, 131, 171, 198, 219, 253, 274, 322, 339, 341, 391 |
| abstract_inverted_index.but | 26, 42 |
| abstract_inverted_index.for | 23, 28, 39, 91, 140, 151, 297, 329, 334, 363, 378 |
| abstract_inverted_index.had | 18 |
| abstract_inverted_index.has | 68 |
| abstract_inverted_index.not | 102 |
| abstract_inverted_index.one | 16 |
| abstract_inverted_index.per | 63 |
| abstract_inverted_index.set | 48 |
| abstract_inverted_index.six | 231 |
| abstract_inverted_index.the | 8, 19, 46, 53, 97, 135, 167, 202, 237, 373, 389, 394 |
| abstract_inverted_index.two | 213 |
| abstract_inverted_index.was | 74, 327 |
| abstract_inverted_index.(TS) | 49 |
| abstract_inverted_index.(ii) | 172 |
| abstract_inverted_index.0.4, | 259 |
| abstract_inverted_index.22%) | 245 |
| abstract_inverted_index.both | 364 |
| abstract_inverted_index.data | 211, 216, 365 |
| abstract_inverted_index.each | 13 |
| abstract_inverted_index.from | 212, 225 |
| abstract_inverted_index.sets | 4 |
| abstract_inverted_index.size | 301, 390 |
| abstract_inverted_index.type | 114 |
| abstract_inverted_index.used | 93, 228 |
| abstract_inverted_index.view | 368 |
| abstract_inverted_index.were | 207, 227 |
| abstract_inverted_index.with | 288, 357 |
| abstract_inverted_index.0.8). | 260 |
| abstract_inverted_index.based | 165, 184 |
| abstract_inverted_index.given | 300 |
| abstract_inverted_index.great | 37 |
| abstract_inverted_index.holds | 36 |
| abstract_inverted_index.lines | 92, 326, 338 |
| abstract_inverted_index.maize | 215 |
| abstract_inverted_index.part, | 204 |
| abstract_inverted_index.sets. | 217, 366 |
| abstract_inverted_index.these | 370 |
| abstract_inverted_index.types | 384 |
| abstract_inverted_index.using | 209 |
| abstract_inverted_index.GBLUPs | 150, 195 |
| abstract_inverted_index.Values | 261 |
| abstract_inverted_index.across | 360, 382 |
| abstract_inverted_index.agreed | 287 |
| abstract_inverted_index.choice | 375 |
| abstract_inverted_index.cross, | 17 |
| abstract_inverted_index.hybrid | 40, 205 |
| abstract_inverted_index.lines) | 100 |
| abstract_inverted_index.lines, | 12 |
| abstract_inverted_index.little | 70 |
| abstract_inverted_index.lowest | 27, 348 |
| abstract_inverted_index.number | 9, 54 |
| abstract_inverted_index.parent | 11, 64 |
| abstract_inverted_index.traits | 234 |
| abstract_inverted_index.Genomic | 34 |
| abstract_inverted_index.closely | 286 |
| abstract_inverted_index.crosses | 62, 112 |
| abstract_inverted_index.depends | 387 |
| abstract_inverted_index.effects | 221 |
| abstract_inverted_index.examine | 76 |
| abstract_inverted_index.highest | 20, 328 |
| abstract_inverted_index.hybrids | 321, 386 |
| abstract_inverted_index.lines), | 104 |
| abstract_inverted_index.message | 2 |
| abstract_inverted_index.optimum | 43, 374 |
| abstract_inverted_index.parents | 56, 95 |
| abstract_inverted_index.promise | 38 |
| abstract_inverted_index.results | 359 |
| abstract_inverted_index.theory, | 136 |
| abstract_inverted_index.trends, | 372 |
| abstract_inverted_index.yielded | 347 |
| abstract_inverted_index.Abstract | 0, 33 |
| abstract_inverted_index.Additive | 218 |
| abstract_inverted_index.Training | 3 |
| abstract_inverted_index.accuracy | 22, 78 |
| abstract_inverted_index.borrowed | 224 |
| abstract_inverted_index.breeding | 41, 395 |
| abstract_inverted_index.effects. | 200 |
| abstract_inverted_index.expected | 168 |
| abstract_inverted_index.function | 127 |
| abstract_inverted_index.hybrids, | 25, 110, 343 |
| abstract_inverted_index.hybrids. | 32, 298 |
| abstract_inverted_index.hybrids: | 152 |
| abstract_inverted_index.involved | 14 |
| abstract_inverted_index.opposite | 371 |
| abstract_inverted_index.produced | 5 |
| abstract_inverted_index.program. | 396 |
| abstract_inverted_index.received | 69 |
| abstract_inverted_index.response | 381 |
| abstract_inverted_index.simulate | 230 |
| abstract_inverted_index.training | 47 |
| abstract_inverted_index.variance | 248 |
| abstract_inverted_index.$$r_{a}$$ | 80, 141, 186, 289, 311, 349 |
| abstract_inverted_index.<mml:math | 81, 142, 155, 174, 187, 264, 276, 290, 312, 350 |
| abstract_inverted_index.accuracy, | 170 |
| abstract_inverted_index.developed | 138 |
| abstract_inverted_index.differing | 235 |
| abstract_inverted_index.dominance | 220 |
| abstract_inverted_index.estimates | 139 |
| abstract_inverted_index.generated | 208 |
| abstract_inverted_index.molecular | 210 |
| abstract_inverted_index.objective | 73 |
| abstract_inverted_index.resources | 392 |
| abstract_inverted_index.scenarios | 232, 362 |
| abstract_inverted_index.selection | 380 |
| abstract_inverted_index.<mml:msub> | 83, 144, 157, 176, 189, 266, 278, 292, 314, 352 |
| abstract_inverted_index.attention. | 71 |
| abstract_inverted_index.comprising | 111 |
| abstract_inverted_index.concordant | 358 |
| abstract_inverted_index.determined | 51 |
| abstract_inverted_index.literature | 226 |
| abstract_inverted_index.maximizing | 7, 379 |
| abstract_inverted_index.prediction | 21, 35, 77, 169 |
| abstract_inverted_index.proportion | 238 |
| abstract_inverted_index.simulation | 203 |
| abstract_inverted_index.</mml:math> | 87, 148, 164, 183, 193, 273, 285, 296, 318, 356 |
| abstract_inverted_index.</mml:msub> | 86, 147, 163, 182, 192, 272, 284, 295, 317, 355 |
| abstract_inverted_index.<mml:mover> | 158, 177, 267, 279 |
| abstract_inverted_index.Conversely, | 333 |
| abstract_inverted_index.composition | 44 |
| abstract_inverted_index.populations | 206 |
| abstract_inverted_index.</mml:mover> | 161, 180, 270, 282 |
| abstract_inverted_index.experimental | 214 |
| abstract_inverted_index.heritability | 254 |
| abstract_inverted_index.respectively, | 125 |
| abstract_inverted_index.$$\hat{r}_{a}$$ | 154, 275 |
| abstract_inverted_index.$$\tilde{r}_{a}$$ | 173, 263 |
| abstract_inverted_index.<mml:mi>a</mml:mi> | 85, 146, 162, 181, 191, 271, 283, 294, 316, 354 |
| abstract_inverted_index.<mml:mi>r</mml:mi> | 84, 145, 159, 178, 190, 268, 280, 293, 315, 353 |
| abstract_inverted_index.<mml:mo>^</mml:mo> | 160, 281 |
| abstract_inverted_index.<mml:mo>~</mml:mo> | 179, 269 |
| abstract_inverted_index.xmlns:mml="http://www.w3.org/1998/Math/MathML"> | 82, 143, 156, 175, 188, 265, 277, 291, 313, 351 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5047834524 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I62916508 |
| citation_normalized_percentile.value | 0.95183622 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |