Evidence map›Paper›PMID 40664476›Full record

ArticleGenome research2025

A high-throughput screening method for selecting feature SNPs to evaluate breed diversity and infer ancestry.

Meilin Zhang, Heng Du, Yu Zhang, Yue Zhuo, Zhen Liu, Yahui Xue, Lei Zhou, Sixuan Zhou, Wanying Li, Jian-Feng Liu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Genome research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
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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

10 authors.

Meilin Zhang *National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture; State Key Laboratory of Animal Biotech Breeding; Frontiers Science Center for Molecular Design Breeding (MOE); College of Animal Science and Technology, China Agricultural University, Beijing 100193, China.
Heng Du *National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture; State Key Laboratory of Animal Biotech Breeding; Frontiers Science Center for Molecular Design Breeding (MOE); College of Animal Science and Technology, China Agricultural University, Beijing 100193, China.
Yu Zhang *National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture; State Key Laboratory of Animal Biotech Breeding; Frontiers Science Center for Molecular Design Breeding (MOE); College of Animal Science and Technology, China Agricultural University, Beijing 100193, China.
Yue ZhuoNational Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture; State Key Laboratory of Animal Biotech Breeding; Frontiers Science Center for Molecular Design Breeding (MOE); College of Animal Science and Technology, China Agricultural University, Beijing 100193, China.
Zhen LiuNational Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture; State Key Laboratory of Animal Biotech Breeding; Frontiers Science Center for Molecular Design Breeding (MOE); College of Animal Science and Technology, China Agricultural University, Beijing 100193, China.
Yahui XueNational Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture; State Key Laboratory of Animal Biotech Breeding; Frontiers Science Center for Molecular Design Breeding (MOE); College of Animal Science and Technology, China Agricultural University, Beijing 100193, China.
Lei ZhouNational Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture; State Key Laboratory of Animal Biotech Breeding; Frontiers Science Center for Molecular Design Breeding (MOE); College of Animal Science and Technology, China Agricultural University, Beijing 100193, China.
Sixuan ZhouInstitute of Animal Husbandry and Veterinary Sciences, Guizhou Academy of Agricultural Sciences, Guiyang, Guizhou 550005, China.
Wanying LiNational Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture; State Key Laboratory of Animal Biotech Breeding; Frontiers Science Center for Molecular Design Breeding (MOE); College of Animal Science and Technology, China Agricultural University, Beijing 100193, China.
Jian-Feng LiuNational Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture; State Key Laboratory of Animal Biotech Breeding; Frontiers Science Center for Molecular Design Breeding (MOE); College of Animal Science and Technology, China Agricultural University, Beijing 100193, China; liujf@cau.edu.cn.ORCID 0000-0002-5766-7864

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As the scale of deep whole-genome sequencing (WGS) data has grown exponentially, hundreds of millions of single nucleotide polymorphisms (SNPs) have been identified in livestock. Utilizing these massive SNP data in population stratification analysis, ancestry prediction, and breed diversity assessments leads to overfitting issues in computational models and creates computational bottlenecks. Therefore, selecting genetic variants that express high amounts of information for use in population diversity studies and ancestry inference becomes critically important. Here, we develop a method, HITSNP, that combines feature selection and machine learning algorithms to select high-representative SNPs that can effectively estimate breed diversity and infer ancestry. HITSNP outperforms existing feature selection methods in estimating accuracy and computational stability. Furthermore, HITSNP offers a new algorithm to predict the number and composition of ancestral populations using a small number of SNPs, and avoiding calculating the number of clusters. Taken together, HITSNP facilitates the research of population structure, animal breeding, and animal resource protection.

Indexed as

BreedingLivestockPolymorphism, Single NucleotideAlgorithmsAnimalsHigh-Throughput Nucleotide SequencingMachine LearningWhole Genome Sequencing

Identifiers

PMID40664476
PMCPMC12315865

What Socratic holds

Textmetadata
LicenceCC BY-NC
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 Socratic graph.