Evidence map›Paper›PMID 41257573›Full record

ArticleBMC genomics2025

Machine learning-based discovery of informative SNPs for population assignment through whole genome sequencing.

Hui Liang, Yugang He, Jingfang Si, Xin Su, Xue Wang, Huaming Mao, Yi Zhang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC genomics, 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

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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

2 citing papers in PubMed.

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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

7 authors.

Hui LiangState Key Laboratory of Animal Biotech Breeding, National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China.
Yugang HeState Key Laboratory of Animal Biotech Breeding, National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China.
Jingfang SiState Key Laboratory of Animal Biotech Breeding, National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China.
Xin SuState Key Laboratory of Animal Biotech Breeding, National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China.
Xue WangState Key Laboratory of Animal Biotech Breeding, National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China.
Huaming MaoCollege of Animal Science and Technology, Yunnan Agricultural University, Kunming, 650201, China.
Yi ZhangState Key Laboratory of Animal Biotech Breeding, National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China. yizhang@cau.edu.cn.

Funding

National Key Research and Development Program of China 2021YFD1200904The earmarked fund CARS36
6 · The paper itself

Abstract

backgroundWhole-genome sequencing (WGS) data contain a large proportion of genetic information for farm animals, while machine learning algorithms offer a powerful approach for discovering population-informative genetic markers for population assignment. These methods can enhance population classification, supporting genetic resource conservation and optimizing breeding strategies.

methodsWGS data from 187 individuals representing eight water buffalo breeds (four river type breeds, i.e., Mediterranean, Murrah, Nili Ravi, and Binglangjiang, and four swamp type breeds, i.e., Diandongnan, Dehong, Guizhoubai, and Shanghai) were analyzed. Multiple single nucleotide polymorphism (SNP) selection methods including the population genetic differentiation index (F

resultsPopulation genetic analysis revealed hierarchical structures among the eight water buffalo breeds, with two divergent groups (swamp type buffaloes and river type buffaloes) and relatively closely related breeds within each group. Among the classifiers, SVM and NB outperformed RF and KNN in terms of assignment accuracy. Accuracy improved with an increasing number of SNPs, reaching over 85% for all classifiers with 10,000 SNPs. The mRMR method provided the highest accuracy, achieving 97.6% with 2000 SNPs when paired with the SVM classifier, followed by Relief-F (94%) and F

conclusionBy integrating machine learning-based feature selection with machine learning classifier, this study provides an effective framework for mining highly informative genetic markers for population assignment from whole-genome sequencing data.

Indexed as

BuffaloesGenetics, PopulationMachine LearningPolymorphism, Single NucleotideWhole Genome SequencingAlgorithmsAnimalsSupport Vector Machine

Identifiers

PMID41257573
PMCPMC12751628

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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.