Evidence map›Paper›PMID 42688996›Full record

ArticleHorticulture research2026

Advances in genomics-driven genetic decoding and genomic design breeding in tomato.

Zihui Ding, Zhangjun Fei, Sanwen Huang, Yaoyao Wu

Abstract read
In one paragraph

Article in Horticulture research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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

4 authors.

Zihui DingState Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, College of Horticulture, Nanjing Agricultural University, Nanjing 210095, China.
Zhangjun FeiBoyce Thompson Institute, Cornell University, Ithaca, NY 14853, USA.ORCID https://orcid.org/0000-0001-9684-1450
Sanwen HuangNational Key Laboratory of Tropical Crop Breeding, Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong 518120, China.ORCID https://orcid.org/0000-0002-8547-5309
Yaoyao WuState Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, College of Horticulture, Nanjing Agricultural University, Nanjing 210095, China.ORCID https://orcid.org/0000-0003-0766-1541

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tomatoes are highly nutritious and represent one of the important vegetable fruits worldwide. Both historically and moving forward, genetic decoding and precision breeding remain fundamental to tomato improvement. Here, we summarize pivotal advances in decoding tomato genomes across domestication, improvement and evolution processes and provide a perspective on future breeding through precision design. In-depth population genetic studies have revealed how artificial selection systematically prioritized yield-related alleles at the cost of narrowing genetic diversity, especially at flavor-related loci-highlighting the urgent need to reconcile these trade-offs. Comparative genomics across species, viewed through an evolutionary lens, has uncovered critical insights into functional genes, deepening our understanding of the genetic architecture and regulatory mechanisms underlying key traits. Collectively, these advances have enabled precise identification and functional characterization of key genetic elements, paving the way for systematic redomestication of tomato through precision genomic design. Looking ahead, more efficient and precise breeding strategies will be required to accelerate genetic gains in tomato in the coming decades. The integration of recent genomic advances, coupled with genomic selection and artificial intelligence, into genomic design breeding offers a transformative framework, unlocking unprecedented opportunities for developing highly flavorful and consumer-customized tomato varieties.

Identifiers

PMID42688996
PMCPMC13537829

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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.