Comparative Analysis of Genotype Imputation Strategies for SNPs Calling from RNA-seq

Overview

Journal BMC Genomics

Publisher Biomed Central

Specialty Genetics

Date 2025 Mar 14

PMID 40082746

Authors

Kaixuan Guo

Zhanming Zhong

Haonan Zeng

Changliang Zhang

Teddy Tinashe Chitotombe

Jinyan Teng

Yahui Gao

Zhe Zhang

Affiliations

Soon will be listed here.

Abstract

Background: RNA 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.

Results: This 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).

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

References

Nagalakshmi U, Wang Z, Waern K, Shou C, Raha D, Gerstein M . The transcriptional landscape of the yeast genome defined by RNA sequencing. Science. 2008; 320(5881):1344-9. PMC: 2951732. DOI: 10.1126/science.1158441. View

Lister R, OMalley R, Tonti-Filippini J, Gregory B, Berry C, Millar A . Highly integrated single-base resolution maps of the epigenome in Arabidopsis. Cell. 2008; 133(3):523-36. PMC: 2723732. DOI: 10.1016/j.cell.2008.03.029. View

Deelen P, Zhernakova D, de Haan M, van der Sijde M, Bonder M, Karjalainen J . Calling genotypes from public RNA-sequencing data enables identification of genetic variants that affect gene-expression levels. Genome Med. 2015; 7(1):30. PMC: 4423486. DOI: 10.1186/s13073-015-0152-4. View

Piskol R, Ramaswami G, Li J . Reliable identification of genomic variants from RNA-seq data. Am J Hum Genet. 2013; 93(4):641-51. PMC: 3791257. DOI: 10.1016/j.ajhg.2013.08.008. View

Mortazavi A, Williams B, McCue K, Schaeffer L, Wold B . Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods. 2008; 5(7):621-8. DOI: 10.1038/nmeth.1226. View

Gondret F, Vincent A, Houee-Bigot M, Siegel A, Lagarrigue S, Causeur D . A transcriptome multi-tissue analysis identifies biological pathways and genes associated with variations in feed efficiency of growing pigs. BMC Genomics. 2017; 18(1):244. PMC: 5361837. DOI: 10.1186/s12864-017-3639-0. View

Savary C, Kim A, Lespagnol A, Gandemer V, Pellier I, Andrieu C . Depicting the genetic architecture of pediatric cancers through an integrative gene network approach. Sci Rep. 2020; 10(1):1224. PMC: 6985191. DOI: 10.1038/s41598-020-58179-0. View

Jehl F, Desert C, Klopp C, Brenet M, Rau A, Leroux S . Chicken adaptive response to low energy diet: main role of the hypothalamic lipid metabolism revealed by a phenotypic and multi-tissue transcriptomic approach. BMC Genomics. 2020; 20(1):1033. PMC: 6937963. DOI: 10.1186/s12864-019-6384-8. View

Montgomery S, Sammeth M, Gutierrez-Arcelus M, Lach R, Ingle C, Nisbett J . Transcriptome genetics using second generation sequencing in a Caucasian population. Nature. 2010; 464(7289):773-7. PMC: 3836232. DOI: 10.1038/nature08903. View

10.

Barbeira A, Dickinson S, Bonazzola R, Zheng J, Wheeler H, Torres J . Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics. Nat Commun. 2018; 9(1):1825. PMC: 5940825. DOI: 10.1038/s41467-018-03621-1. View

11.

Mele M, Ferreira P, Reverter F, DeLuca D, Monlong J, Sammeth M . Human genomics. The human transcriptome across tissues and individuals. Science. 2015; 348(6235):660-5. PMC: 4547472. DOI: 10.1126/science.aaa0355. View

12.

Liu S, Gao Y, Canela-Xandri O, Wang S, Yu Y, Cai W . A multi-tissue atlas of regulatory variants in cattle. Nat Genet. 2022; 54(9):1438-1447. PMC: 7613894. DOI: 10.1038/s41588-022-01153-5. View

13.

Teng J, Gao Y, Yin H, Bai Z, Liu S, Zeng H . A compendium of genetic regulatory effects across pig tissues. Nat Genet. 2024; 56(1):112-123. PMC: 10786720. DOI: 10.1038/s41588-023-01585-7. View

14.

Adetunji M, Lamont S, Abasht B, Schmidt C . Variant analysis pipeline for accurate detection of genomic variants from transcriptome sequencing data. PLoS One. 2019; 14(9):e0216838. PMC: 6756534. DOI: 10.1371/journal.pone.0216838. View

15.

Morrison A, Huang Z, Yu B, Metcalf G, Liu X, Ballantyne C . Practical Approaches for Whole-Genome Sequence Analysis of Heart- and Blood-Related Traits. Am J Hum Genet. 2017; 100(2):205-215. PMC: 5294677. DOI: 10.1016/j.ajhg.2016.12.009. View

16.

Canovas A, Rincon G, Islas-Trejo A, Wickramasinghe S, Medrano J . SNP discovery in the bovine milk transcriptome using RNA-Seq technology. Mamm Genome. 2010; 21(11-12):592-8. PMC: 3002166. DOI: 10.1007/s00335-010-9297-z. View

17.

Bakhtiarizadeh M, Alamouti A . RNA-Seq based genetic variant discovery provides new insights into controlling fat deposition in the tail of sheep. Sci Rep. 2020; 10(1):13525. PMC: 7419499. DOI: 10.1038/s41598-020-70527-8. View

18.

Cai W, Hu J, Zhang Y, Guo Z, Zhou Z, Hou S . Cis-eQTLs in seven duck tissues identify novel candidate genes for growth and carcass traits. BMC Genomics. 2024; 25(1):429. PMC: 11061949. DOI: 10.1186/s12864-024-10338-7. View

19.

De Marino A, Mahmoud A, Bose M, Bircan K, Terpolovsky A, Bamunusinghe V . A comparative analysis of current phasing and imputation software. PLoS One. 2022; 17(10):e0260177. PMC: 9581364. DOI: 10.1371/journal.pone.0260177. View

20.

Teng J, Zhao C, Wang D, Chen Z, Tang H, Li J . Assessment of the performance of different imputation methods for low-coverage sequencing in Holstein cattle. J Dairy Sci. 2022; 105(4):3355-3366. DOI: 10.3168/jds.2021-21360. View