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BMC genomics2025; 26(1); 1086; doi: 10.1186/s12864-025-12256-8

Comparison between SNP array and imputed data to estimate population structure and ROH hotspots in horse breeds.

Abstract: Single nucleotide polymorphism (SNP) arrays are commonly used for studying the genomic structure and diversity of livestock breeds, but whole-genome sequencing (WGS) provides higher-resolution genomic data. Genotype imputation has become a standard practice for increasing the genomic resolution of association studies. This work aimed to extend imputation to biodiversity analyses, comparing SNP array data before and after imputation. A 40 k SNP dataset of 281 horses from 12 breeds (DS) was imputed to sequence-level using a reference panel of 327 sequenced individuals, generating approximately 9 million markers after filtering (DS). Both datasets were used to study genetic variability, population structure and runs of homozygosity (ROH). Results: Genetic indices and relationships showed similar trends for both datasets, with high Pearson correlations and Mantel test values (> 0.8) indicating that the imputed data are a reliable alternative to SNP array data for genetic studies. Multidimensional scaling and admixture analyses highlighted how the genetic proximity between breeds observed for the DS was amplified by the imputation process in cases of those breeds with a few sequences included in the WGS reference panel. ROH investigation showed overlapping homozygosity regions between the two datasets, highlighting the benefits of having more markers for gene and QTL annotation. Of the 141 ROH islands identified in the DS, 79 overlapped perfectly with those found in the imputed data. Validation with the reference panel of 327 sequenced horses revealed a single ROH island on ECA11 shared across all three datasets, containing genes associated with morphology and behavioral traits. Conclusions: High correlations between SNP array and imputed data indicate that imputed genotypes provide a reliable alternative for assessing population structure and genetic diversity in horse breeds. Specifically, imputation can enhance the detection of ROH and the annotation of genes within ROH islands, with the reliability of these results depending on the quality of the reference panel and its representation of the studied breeds, among others.
© 2025. The Author(s).
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Publication Date: 2025-11-29 PubMed ID: 41318401PubMed Central: PMC12670763DOI: 10.1186/s12864-025-12256-8Google Scholar: Lookup
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  • Journal Article
  • Comparative Study

Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

Overview

  • This study compares the use of SNP arrays and imputed whole-genome sequencing (WGS) data to analyze population structure and runs of homozygosity (ROH) hotspots in horse breeds.
  • It evaluates whether imputed data, generated by enhancing lower-resolution SNP array data using a WGS reference panel, can reliably replace SNP array data for genetic diversity and structure studies.

Introduction to Genetic Tools Used

  • SNP arrays: Tools that genotype a fixed number of known single nucleotide polymorphisms (SNPs), typically tens of thousands, useful for genomic diversity and structure analysis but with limited resolution.
  • Whole-genome sequencing (WGS): Provides comprehensive genomic data at single-nucleotide resolution, offering greater detail but at higher cost and complexity.
  • Genotype imputation: A statistical method that predicts unobserved genotypes by leveraging a reference panel of sequenced genomes to increase marker density from SNP arrays to sequence-level, aimed at improving genomic resolution without the full cost of WGS.

Research Objectives

  • To apply genotype imputation for biodiversity analysis in horse populations by increasing SNP array data to sequence-level marker density.
  • To compare key genetic analyses – including measures of genetic variability, population structure, and ROH detection – between the original SNP array data and the imputed high-density data.

Methodology

  • Samples: 281 horses from 12 distinct breeds genotyped by a 40,000 SNP (40k) SNP array dataset.
  • Imputation: Used a reference panel of 327 fully sequenced horses to impute the 40k SNP data, resulting in approximately 9 million high-quality variant markers after filtering.
  • Analyses performed on both datasets:
    • Genetic variability indices
    • Population structure assessments via multidimensional scaling and admixture analyses
    • Runs of homozygosity (ROH) identification and characterization

Key Findings

  • Genetic variability and relationships:
    • Both datasets showed similar trends in genetic indices and pairwise genetic relationships among breeds.
    • High statistical concordance was observed, with Pearson correlation and Mantel test values above 0.8, confirming the reliability of imputed data for genetic studies.
  • Population structure:
    • Multidimensional scaling and admixture analyses showed genetic proximities between breeds.
    • Imputation amplified the observed genetic proximity, especially in breeds that had fewer sequences in the reference panel, indicating an effect of reference panel representation on results.
  • Runs of homozygosity (ROH):
    • There was significant overlap in homozygosity regions identified in both datasets.
    • The imputed dataset, with more markers, allowed improved annotation of genes and quantitative trait loci (QTL) within ROH islands.
    • Out of 141 ROH islands found using the SNP array data, 79 perfectly overlapped ROH islands identified with imputed data.
    • Validation using the reference panel’s sequenced horses discovered a shared ROH island on equine chromosome 11 (ECA11) associated with genes controlling morphology and behavior, present in all three datasets.

Conclusions and Implications

  • Imputed genotypes strongly correlate with SNP array data and thus represent a reliable alternative for assessing population structure and genetic diversity in horses.
  • Imputation enhances detection and characterization of ROH, leading to better insight into genomic regions linked to important traits.
  • The success of imputation-based analyses depends on factors such as the quality and breed representation within the WGS reference panel.
  • This approach can optimize cost-effectiveness by leveraging existing SNP array data for high-resolution genetic analyses without requiring full WGS for all samples.

Cite This Article

APA
Chessari G, Reich P, Criscione A, Falker-Gieske C, Mastrangelo S, Tumino S, Bordonaro S, Marletta D, Tetens J. (2025). Comparison between SNP array and imputed data to estimate population structure and ROH hotspots in horse breeds. BMC Genomics, 26(1), 1086. https://doi.org/10.1186/s12864-025-12256-8

Publication

BMC genomics
ISSN: 1471-2164
NlmUniqueID: 100965258
Country: England
Language: English
Volume: 26
Issue: 1
Pages: 1086
PII: 1086

Researcher Affiliations

Chessari, Giorgio
  • Department of Agriculture, Food and Environment, University of Catania, Catania, 95131, Italy. giorgio.chessari@unict.it.
  • Department of Animal Sciences, Georg-August-University Göttingen, Göttingen, 37077, Germany. giorgio.chessari@unict.it.
Reich, Paula
  • Department of Animal Sciences, Georg-August-University Göttingen, Göttingen, 37077, Germany.
  • Center for Integrated Breeding Research (CiBreed), Georg-August- University Göttingen, Göttingen, 37075, Germany.
Criscione, Andrea
  • Department of Agriculture, Food and Environment, University of Catania, Catania, 95131, Italy.
Falker-Gieske, Clemens
  • Department of Animal Sciences, Georg-August-University Göttingen, Göttingen, 37077, Germany.
  • Center for Integrated Breeding Research (CiBreed), Georg-August- University Göttingen, Göttingen, 37075, Germany.
Mastrangelo, Salvatore
  • Department of Agricultural, Food and Forestry Sciences, University of Palermo, Palermo, 90128, Italy.
Tumino, Serena
  • Department of Agriculture, Food and Environment, University of Catania, Catania, 95131, Italy.
Bordonaro, Salvatore
  • Department of Agriculture, Food and Environment, University of Catania, Catania, 95131, Italy.
Marletta, Donata
  • Department of Agriculture, Food and Environment, University of Catania, Catania, 95131, Italy.
Tetens, Jens
  • Department of Animal Sciences, Georg-August-University Göttingen, Göttingen, 37077, Germany.
  • Center for Integrated Breeding Research (CiBreed), Georg-August- University Göttingen, Göttingen, 37075, Germany.

MeSH Terms

  • Animals
  • Polymorphism, Single Nucleotide
  • Horses / genetics
  • Homozygote
  • Breeding
  • Genotype
  • Genetics, Population
  • Whole Genome Sequencing
  • Genomics / methods

Conflict of Interest Statement

Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

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