Evidence map›Paper›PMID 42732031›Full record

ReviewTAG. Theoretical and applied genetics. Theoretische und angewandte Genetik2026

From family trials to genomic mate allocation: statistical and genomic strategies to accelerate sugarcane genetic improvement.

Andrew Rigby, Felicity Atkin, Ben Hayes, Lee Hickey, Seema Yadav

Abstract readReview
In one paragraph

Review in TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Andrew RigbySugar Research Australia, Meringa, Gordonvale, QLD, Australia.ORCID http://orcid.org/0009-0006-3626-3875
Felicity AtkinPT. Global Papua Abadi, Sermayam, Indonesia.
Ben HayesQueensland Alliance for Agriculture and Food Innovation, Queensland Bioscience Precinct, St. Lucia, Brisbane, QLD, Australia.ORCID http://orcid.org/0000-0002-5606-3970
Lee HickeyQueensland Alliance for Agriculture and Food Innovation, Queensland Bioscience Precinct, St. Lucia, Brisbane, QLD, Australia.ORCID http://orcid.org/0000-0001-6909-7101
Seema YadavQueensland Alliance for Agriculture and Food Innovation, Queensland Bioscience Precinct, St. Lucia, Brisbane, QLD, Australia. seema.yadav@uq.edu.au.ORCID http://orcid.org/0000-0001-7191-7770

Funding

Australian Research Council Training Centre in Predictive Breeding for Agricultural Futures IC230100016
6 · The paper itself

Abstract

Sugarcane (Saccharum spp.) underpins global sugar and bioenergy supply and is increasingly valued as a renewable biomass feedstock. Sustained improvement in commercial traits and resilience is constrained by long breeding cycles, clonal propagation, multi-stage testing, and a highly polyploid, heterozygous, and frequently aneuploid genome with substantial non-additive genetic variation. Genomic selection has demonstrated value for predicting elite-clone performance, yet its operational use remains limited at earlier decision points, including family selection, parent evaluation, and cross design. This review examines the biological, statistical, and genomic factors that shape these decisions, with emphasis on the Australian breeding context based on progeny assessment trials (PATs), clonal assessment trials (CATs), and final assessment trials (FATs). We evaluate challenges arising from family plot means, the use of different full-sib samples as nominal family replicates, spatial heterogeneity, competition, genotype-by-environment interaction, and the partitioning of additive and non-additive effects. We also assess the integration of pedigree and genomic relationship, genotype representation, allele-dosage estimation, aneuploidy, genomic prediction models, and training-population design. We then consider genomic prediction of cross performance and constrained mate allocation as approaches for improving expected family performance, accounting for cross-specific non-additive effects and managing relatedness. We propose a decision-centred framework that links family and clonal data across breeding stages, tracks the propagation of information and uncertainty, and supports parent recycling and cross allocation. We conclude with a practical research agenda for stage-integrated mixed-model and single-step analyses that connect early family evaluation with genomic prediction and cross-level decision support in sugarcane breeding.

Indexed as

Genome, PlantGenomicsPlant BreedingSaccharumGenotypeModels, GeneticPhenotypeSelection, Genetic

Identifiers

PMID42732031
PMCPMC13570833

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.