Evidence map›Paper›PMID 40856885›Full record

ReviewMolecular biology reports2025

Genome-wide association studies in forestry.

Xiangrun Meng, Yinyin Fu, Yanran Qi, Zhuoying Jin, Peng Li, Yalin Sang

Abstract readReview
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In one paragraph

Review in Molecular biology reports, 2025. 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

6 authors.

Xiangrun Meng *State Forestry and Grassland Administration Key Laboratory of Silviculture in downstream areas of the Yellow River, College of Forestry, ShandongAgricultural University, Taian, Shandong, China.
Yinyin Fu *State Key Laboratory of Tree Genetics and Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, P. R. China.
Yanran QiState Forestry and Grassland Administration Key Laboratory of Silviculture in downstream areas of the Yellow River, College of Forestry, ShandongAgricultural University, Taian, Shandong, China.
Zhuoying JinState Key Laboratory of Tree Genetics and Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, P. R. China.
Peng LiState Forestry and Grassland Administration Key Laboratory of Silviculture in downstream areas of the Yellow River, College of Forestry, ShandongAgricultural University, Taian, Shandong, China. lipeng0706@sdau.edu.cn.
Yalin SangState Forestry and Grassland Administration Key Laboratory of Silviculture in downstream areas of the Yellow River, College of Forestry, ShandongAgricultural University, Taian, Shandong, China. sangyl@sdau.edu.cn.

Funding

Shandong Provincial Department of Science and Technolog 2024LZGC025;ZR2024QC117
6 · The paper itself

Abstract

The extended generation cycles and high genomic heterozygosity of forest trees have long hindered investigations into the genetic basis of quantitative traits, impeding progress in molecular breeding applications. Recent advances in genome-wide association studies (GWAS), empowered by next-generation sequencing technologies, now offer unprecedented opportunities to dissect complex trait architectures and identify causal allelic variants in tree species. This review critically examines the evolving role of GWAS in forest tree genetics, emphasizing its achievements in mapping quantitative trait loci (QTLs) and characterizing functionally relevant alleles for breeding. We further analyze the unresolved challenge of “missing heritability” in tree GWAS and propose integrative approaches to mitigate this gap, including the development of high-throughput phenotyping platforms for capturing trait dynamics across environments, synergistic integration of multi-omics data (genomics, transcriptomics, epigenomics) via advanced computational models, and construction of pan-genome references to resolve structural variations in highly heterozygous genomes. Finally, we discuss the translational potential of GWAS-driven strategies in modern forestry, particularly for enhancing marker-assisted selection of climate-adaptive traits, optimizing wood properties, and shortening domestication timelines. By bridging methodological innovations with practical breeding applications, this synthesis aims to accelerate the translation of genetic discoveries into sustainable forest management practices.

Indexed as

ForestryGenome-Wide Association StudyTreesChromosome MappingForestsGenome, PlantGenomicsPhenotypePlant BreedingQuantitative Trait LociForest treesGenome-wide association study (GWAS)Missing heritabilityMolecular breedingQuantitative trait loci (QTL)

Identifiers

PMID40856885

What OpenQuestion holds

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Registered trials

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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.