Evidence map›Paper›PMID 40082746›Full record

ArticleBMC genomics2025

Comparative analysis of genotype imputation strategies for SNPs calling from RNA-seq.

Kaixuan Guo, Zhanming Zhong, Haonan Zeng, Changliang Zhang, Teddy Tinashe Chitotombe, Jinyan Teng, Yahui Gao, Zhe Zhang

Abstract readComparative Study
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
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1 · What the graph read from it

What it found

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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

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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

8 authors.

Kaixuan GuoState Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China.
Zhanming ZhongState Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China.
Haonan ZengState Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China.
Changliang ZhangState Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China.
Teddy Tinashe ChitotombeState Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China.
Jinyan TengState Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China.
Yahui GaoState Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China.
Zhe ZhangState Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China. zhezhang@scau.edu.cn.

Funding

China Agriculture Research System CARS-35Guangxi Science and Technology Program Project GuikeJB23023003Specific university discipline construction project 2023B10564001
6 · The paper itself

Abstract

backgroundRNA sequencing (RNA-seq) is a powerful tool for transcriptome profiling, enabling integrative studies of expression quantitative trait loci (eQTL). As it identifies fewer genetic variants than DNA sequencing (DNA-seq), reference panel-based genotype imputation is often required to enhance its utility.

resultsThis study evaluated the accuracy of genotype imputation using SNPs called from RNA-seq data (RNA-SNPs). SNP features from 6,567 RNA-seq samples across 28 pig tissues were used to mask whole genome sequencing (WGS) data, with the Pig Genomic Reference Panel (PGRP) serving as the reference panel. Three imputation software tools (i.e., Beagle, Minimac4, and Impute5) were employed to perform the imputation. The result showed that RNA-SNPs achieved higher imputation accuracy (CR: 0.895 ~ 0.933; r²: 0.745 ~ 0.817) than SNPs from GeneSeek Genomic Profiler Porcine SNP50 BeadChip (Chip-SNPs) (CR: 0.873 ~ 0.909; r²: 0.629 ~ 0.698), and lower accuracy in "intergenic" regions. After imputation, quality control (QC) by minor allele frequency (MAF) and imputation quality (DR²) could improve r² but reduce SNP retention. Among software, Minimac4 takes the least runtime in single-thread setting, while Beagle performed best in multi-thread setting and phasing. Impute5 takes up minimal memory usage but requires the maximum runtime. All tools demonstrated comparable global accuracy (CR: 0.906 ~ 0.917; r²: 0.780 ~ 0.787).

conclusionsThis study offers practical guidance for conducting RNA-SNP imputation strategies in genome and transcriptome research.

Indexed as

Polymorphism, Single NucleotideRNA-SeqSequence Analysis, RNAAnimalsGenotypeQuantitative Trait LociSoftwareSwineGenotype imputationPigRNA-seq

Identifiers

PMID40082746
PMCPMC11907794

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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.