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PreprintCited by 1Genetic and phenotypic traits in livestockGenetic Mapping and Diversity in Plants and AnimalsGenomics and Rare Diseases

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A Framework for Identifying and Prioritizing SNPs in Genes of the Hypothalamic- Pituitary-Gonadal Axis in Guzerat Cattle Using Amplicon-Based NGS

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Marcuzzi, Olivia, Calcaterra, Francisco, Olivera, Leónidas H., Arizmendi, Analía, Henry, Marco R.J.M., Cardoso, Danielle Cunha, Echeverri, Ana M. Loaiza, Liron, Juan P., Fernández, María E., de Oliveira, Denise A. Andrade, Giovambattista, Guillermo·2025·BMC Genomic Data
DOI 10.21203/rs.3.rs-6547904/v1PMC PMC12570696

Preprint — published as 10.1186/s12863-025-01365-8 · Paper page

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Abstract

The combined use of NGS technologies with bioinformatics tools has significantly advanced research by enabling comprehensive analyses of entire genomes, specific genomic regions of interest, and transcriptomes. Targeted NGS methods, which focus on smaller genome fractions, are widely used to study genetic diseases, epigenetic modifications, microbiomes, and environmental DNA, among other applications. This study aimed to develop a roadmap for detecting and selecting polymorphisms in candidate genes by integrating amplicon NGS-Target techniques with bioinformatics analyses. Sixty-eight genes associated with the hypothalamic-pituitary-gonadal (HPG) axis were selected to develop the amplicon NGS assay, comprising 730 regions that cover a total of 136,274 bp. This method was used to sequence 75 Guzerat cattle, a dual-purpose breed from Brazil, renowned for their high rusticity and adaptability. This Zebu cattle exhibit certain limitations, such as delayed puberty onset, which can reduce reproductive efficiency. Using the GATK protocol a total of 2,600 SNPs and 1,615 indels were detected. A series of consecutive filtering steps (maf, the detection of non-synonymous substitution, phylogenetic amino acid conservation, and biochemical properties) were used, resulting in a subset of 30 candidate SNPs. Then, these polymorphisms were analysed using bioinformatic tools (SIFT, PANTHER, PolyPhen2, and MutPred), identifying 5 SNPs with high effect on the protein. Their structure and stability were estimated using AlphaFold and DDMut. Finally, 3 candidate polymorphisms ( IGF1R, LHCGR, TAC3R ) with potentially significant effects on the protein remained to be validated through dynamic simulations or in vitro and in vivo experimental assays.

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Fig. 1 Workflow implemented to select candidate SNPs using the NGS-target amplicon method
Fig. 1 Workflow implemented to select candidate SNPs using the NGS-target amplicon methodCC BY-NC-ND · PubMed Central
Fig. 2 Comparison between the structure of proteins with the reference (blue) and alternative (pink) amino acids. Protein structures were predicted using AlphaFold 2 and visualized with ChimeraX software. CGA (a); HELB (b); IGF1R (c); LEPR (d); LHCG (e); TAC3R (f)
Fig. 2 Comparison between the structure of proteins with the reference (blue) and alternative (pink) amino acids. Protein structures were predicted using AlphaFold 2 and visualized with ChimeraX software. CGA (a); HELB (b); IGF1R (c); LEPR (d); LHCG (e); TAC3R (f)CC BY-NC-ND · PubMed Central
Fig. 3 Reference and alternative amino acids for each gene analysed and visualized using DDMut. Hydrogen bonds (dashed red lines) and the interacting amino acids are shown. CGA (a); HELB (b); IGF1R (c); LEPR (d); LHCG (e); TAC3R (f)
Fig. 3 Reference and alternative amino acids for each gene analysed and visualized using DDMut. Hydrogen bonds (dashed red lines) and the interacting amino acids are shown. CGA (a); HELB (b); IGF1R (c); LEPR (d); LHCG (e); TAC3R (f)CC BY-NC-ND · PubMed Central

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Metadata

Authors
Marcuzzi, O., Calcaterra, F., Olivera, L. H., Arizmendi, A., Henry, M. R. J. M., Cardoso, D. C., Echeverri, A. M. L., Liron, J. P., Fernández, M. E., de Oliveira, D. A. A., & Giovambattista, G.
Affiliations
  • Consejo Nacional de Investigaciones Científicas y Técnicas (AR)
  • Universidade Federal de Minas Gerais (BR)
  • Escola de Veterinária da Universidade Federal de Minas Gerais
  • Universidad Nacional de La Plata
Corresponding author
Giovambattista, Guillermo
ORCID
Venue
BMC Genomic Data
Publisher
Springer Science and Business Media LLC
Published
2025-05-19
Volume
26
Issue
1
Pages
79-79
ISSN
2730-6844 (ISSN-L)
DOI
10.21203/rs.3.rs-6547904/v1
PMC
PMC12570696
OpenAlex
W4415618322
Cited by
1
References
1
Citations by year
2025: 1
FWCI
0.58
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Genomic Data (publishedVersion, cc-by-nc-nd)
  • Research Square (acceptedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • El Servicio de Difusión de la Creación Intelectual (National University of La Plata) (submittedVersion, cc-by-nc-sa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Consejo Nacional de Investigaciones Científicas y Técnicas (PUE-2016)
  • Universidad Nacional de La Plata
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
Keywords
hypothalamic-pituitary-gonadal axis, Guzerat cattle, single nucleotide polymorphisms, candidate gene selection, delayed puberty, IGF-1 receptor, LHCGR, protein stability prediction, non-synonymous variants, Bos indicus
MeSH terms
Animals, Cattle, Hypothalamo-Hypophyseal System, Male, Computational Biology, Polymorphism, Single Nucleotide, High-Throughput Nucleotide Sequencing

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Marcuzzi, O., Calcaterra, F., Olivera, L. H., Arizmendi, A., Henry, M. R. J. M., Cardoso, D. C., Echeverri, A. M. L., Liron, J. P., Fernández, M. E., de Oliveira, D. A. A., & Giovambattista, G. (2025). A Framework for Identifying and Prioritizing SNPs in Genes of the Hypothalamic- Pituitary-Gonadal Axis in Guzerat Cattle Using Amplicon-Based NGS. BMC Genomic Data. https://doi.org/10.21203/rs.3.rs-6547904/v1

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