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Identification of genomic variants causing sperm abnormalities and reduced male fertility
Whole genome sequencing has identified millions of bovine genetic variants; however, there is currently little understanding about which variants affect male fertility. It is imperative that we begin to link detrimental genetic variants to sperm phenotypes via the analysis of semen samples and measu...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2018
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6503949/ https://www.ncbi.nlm.nih.gov/pubmed/29454799 http://dx.doi.org/10.1016/j.anireprosci.2018.02.007 |
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author | Taylor, Jeremy F. Schnabel, Robert D. Sutovsky, Peter |
author_facet | Taylor, Jeremy F. Schnabel, Robert D. Sutovsky, Peter |
author_sort | Taylor, Jeremy F. |
collection | PubMed |
description | Whole genome sequencing has identified millions of bovine genetic variants; however, there is currently little understanding about which variants affect male fertility. It is imperative that we begin to link detrimental genetic variants to sperm phenotypes via the analysis of semen samples and measurement of fertility for bulls with alternate genotypes. Artificial insemination (AI) bulls provide a useful model system because of extensive fertility records, measured as sire conception rates (SCR). Genetic variants with moderate to large effects on fertility can be identified by sequencing the genomes of fertile and subfertile or infertile sires identified with high or low SCR as adult AI bulls or yearling bulls that failed Breeding Soundness Evaluation. Variants enriched in frequency in the sequences of subfertile/infertile bulls, particularly those likely to result in the loss of protein function or predicted to be severely deleterious to genes involved in sperm protein structure and function, semen quality or sperm morphology can be designed onto genotyping assays for validation of their effects on fertility. High throughput conventional and image-based flow cytometry, proteomics and cell imaging can be used to establish the functional effects of variants on sperm phenotypes. Integrating the genetic, fertility and sperm phenotype data will accelerate biomarker discovery and validation, improve routine semen testing in bull studs and identify new targets for cost-efficient AI dose optimization approaches such as semen nanopurification. This will maximize semen output from genetically superior sires and will increase the fertility of cattle. Better understanding of the relationships between male genotype and sperm phenotype may also yield new diagnostic tools and treatments for human male and idiopathic infertility. |
format | Online Article Text |
id | pubmed-6503949 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
record_format | MEDLINE/PubMed |
spelling | pubmed-65039492019-07-01 Identification of genomic variants causing sperm abnormalities and reduced male fertility Taylor, Jeremy F. Schnabel, Robert D. Sutovsky, Peter Anim Reprod Sci Article Whole genome sequencing has identified millions of bovine genetic variants; however, there is currently little understanding about which variants affect male fertility. It is imperative that we begin to link detrimental genetic variants to sperm phenotypes via the analysis of semen samples and measurement of fertility for bulls with alternate genotypes. Artificial insemination (AI) bulls provide a useful model system because of extensive fertility records, measured as sire conception rates (SCR). Genetic variants with moderate to large effects on fertility can be identified by sequencing the genomes of fertile and subfertile or infertile sires identified with high or low SCR as adult AI bulls or yearling bulls that failed Breeding Soundness Evaluation. Variants enriched in frequency in the sequences of subfertile/infertile bulls, particularly those likely to result in the loss of protein function or predicted to be severely deleterious to genes involved in sperm protein structure and function, semen quality or sperm morphology can be designed onto genotyping assays for validation of their effects on fertility. High throughput conventional and image-based flow cytometry, proteomics and cell imaging can be used to establish the functional effects of variants on sperm phenotypes. Integrating the genetic, fertility and sperm phenotype data will accelerate biomarker discovery and validation, improve routine semen testing in bull studs and identify new targets for cost-efficient AI dose optimization approaches such as semen nanopurification. This will maximize semen output from genetically superior sires and will increase the fertility of cattle. Better understanding of the relationships between male genotype and sperm phenotype may also yield new diagnostic tools and treatments for human male and idiopathic infertility. 2018-02-10 2018-07 /pmc/articles/PMC6503949/ /pubmed/29454799 http://dx.doi.org/10.1016/j.anireprosci.2018.02.007 Text en https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/ (https://creativecommons.org/licenses/by/4.0/) ). |
spellingShingle | Article Taylor, Jeremy F. Schnabel, Robert D. Sutovsky, Peter Identification of genomic variants causing sperm abnormalities and reduced male fertility |
title | Identification of genomic variants causing sperm abnormalities and reduced male fertility |
title_full | Identification of genomic variants causing sperm abnormalities and reduced male fertility |
title_fullStr | Identification of genomic variants causing sperm abnormalities and reduced male fertility |
title_full_unstemmed | Identification of genomic variants causing sperm abnormalities and reduced male fertility |
title_short | Identification of genomic variants causing sperm abnormalities and reduced male fertility |
title_sort | identification of genomic variants causing sperm abnormalities and reduced male fertility |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6503949/ https://www.ncbi.nlm.nih.gov/pubmed/29454799 http://dx.doi.org/10.1016/j.anireprosci.2018.02.007 |
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