Evidence map›Paper›PMID 41152765›Full record

ArticleBMC genomic data2025

A framework for identifying and prioritizing SNPs in genes of the hypothalamic- pituitary-gonadal axis in Guzerat cattle using amplicon-based NGS.

Olivia Marcuzzi, Francisco Calcaterra, Leónidas H Olivera, Analía Arizmendi, Marco R J M Henry, Danielle Cunha Cardoso, Ana M Loaiza Echeverri, Juan P Liron, María E Fernández, Denise A Andrade de Oliveira and 1 more

Abstract read
In one paragraph

Article in BMC genomic data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

11 authors.

Olivia MarcuzziInstituto de Genética Veterinaria (IGEVET, CONICET), Facultad de Ciencias Veterinarias, Universidad Nacional de La Plata, 60 Y 118 S/N, La Plata, 1900, Argentina.
Francisco CalcaterraInstituto de Genética Veterinaria (IGEVET, CONICET), Facultad de Ciencias Veterinarias, Universidad Nacional de La Plata, 60 Y 118 S/N, La Plata, 1900, Argentina.
Leónidas H OliveraInstituto de Genética Veterinaria (IGEVET, CONICET), Facultad de Ciencias Veterinarias, Universidad Nacional de La Plata, 60 Y 118 S/N, La Plata, 1900, Argentina.
Analía ArizmendiInstituto de Genética Veterinaria (IGEVET, CONICET), Facultad de Ciencias Veterinarias, Universidad Nacional de La Plata, 60 Y 118 S/N, La Plata, 1900, Argentina.
Marco R J M HenryLaboratório de Genética, Escola de Veterinária da Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil.
Danielle Cunha CardosoLaboratório de Genética, Escola de Veterinária da Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil.
Ana M Loaiza EcheverriLaboratório de Genética, Escola de Veterinária da Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil.
Juan P LironInstituto de Genética Veterinaria (IGEVET, CONICET), Facultad de Ciencias Veterinarias, Universidad Nacional de La Plata, 60 Y 118 S/N, La Plata, 1900, Argentina.
María E FernándezInstituto de Genética Veterinaria (IGEVET, CONICET), Facultad de Ciencias Veterinarias, Universidad Nacional de La Plata, 60 Y 118 S/N, La Plata, 1900, Argentina.
Denise A Andrade de OliveiraLaboratório de Genética, Escola de Veterinária da Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil.
Guillermo GiovambattistaInstituto de Genética Veterinaria (IGEVET, CONICET), Facultad de Ciencias Veterinarias, Universidad Nacional de La Plata, 60 Y 118 S/N, La Plata, 1900, Argentina. guillermogiovambattista@gmail.com.

Funding

Consejo Nacional de Investigaciones Científicas y Técnicas,Argentina PUE-2016 N◦22920160100004COUnivesidad nacional de La Plata Grant V247
6 · The paper itself

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.

Indexed as

High-Throughput Nucleotide SequencingHypothalamo-Hypophyseal SystemPolymorphism, Single NucleotideAnimalsCattleComputational BiologyMaleBovineGenotyping by sequencingPolymorphismProtein modelingReproductive traits

Identifiers

PMID41152765
PMCPMC12570696

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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.