Valentin Costes
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View article: Genetic regulation of sperm DNA methylation in cattle through meQTL mapping
Genetic regulation of sperm DNA methylation in cattle through meQTL mapping Open
Background DNA methylation (DNAm) plays an important functional role and is influenced by genetic variants known as methylation QTLs (meQTLs). The majority of meQTL studies have been conducted in human blood. Despite its unique landscape, …
The effect of environmental enrichment on immune cell DNA methylation profiles depends on the parity of sows Open
The aim of this study was to identify epigenetic markers that reflected positive affective states in multiparous pregnant sows. The animals were housed during gestation in either a conventional (C) environment (2.4 m² per sow), featuring a…
Unveiling inter-embryo variability in spindle length over time: Towards quantitative phenotype analysis Open
How can inter-individual variability be quantified? Measuring many features per experiment raises the question of choosing them to recapitulate high-dimensional data. Tackling this challenge on spindle elongation phenotypes, we showed that…
View article: Genetic determinism of sperm DNA methylation in French Holstein cattle
Genetic determinism of sperm DNA methylation in French Holstein cattle Open
Session 69. Epigenetics, adaptation and intergenerational transmission
Multi-omics data integration for the identification of biomarkers for bull fertility Open
Bull fertility is an important economic trait, and the use of subfertile semen for artificial insemination decreases the global efficiency of the breeding sector. Although the analysis of semen functional parameters can help to identify in…
Only three principal components account for inter-embryo variability of the spindle length over time Open
How does inter-individual variability emerge? When measuring a large number of features per experiment/individual, this question becomes non-trivial. One challenge lies in choosing features to recapitulate high-dimension data. In this pape…
Additional file 3 of Predicting male fertility from the sperm methylome: application to 120 bulls with hundreds of artificial insemination records Open
Additional file 3: Table S2. Listing the functional parameters measured on each semen sample.
Additional file 8 of Predicting male fertility from the sperm methylome: application to 120 bulls with hundreds of artificial insemination records Open
Additional file 8: Table S7. Listing the differentially methylated genes found in common with four human studies.
Additional file 5 of Predicting male fertility from the sperm methylome: application to 120 bulls with hundreds of artificial insemination records Open
Additional file 5: Table S4. Listing the fertility-related DMRs, their methylation status in each fertility group and their annotation regarding genome features.
Additional file 10 of Predicting male fertility from the sperm methylome: application to 120 bulls with hundreds of artificial insemination records Open
Additional file 10: Table S9. Listing the primers used for pyrosequencing validation.
Additional file 9 of Predicting male fertility from the sperm methylome: application to 120 bulls with hundreds of artificial insemination records Open
Additional file 9: Table S8. Listing the semen samples used for pyrosequencing validation.
Additional file 7 of Predicting male fertility from the sperm methylome: application to 120 bulls with hundreds of artificial insemination records Open
Additional file 7: Table S6. Listing the differentially methylated genes relevant to fertility together with supporting references.
Additional file 6 of Predicting male fertility from the sperm methylome: application to 120 bulls with hundreds of artificial insemination records Open
Additional file 6: Table S5. Listing the CpGs10 obtained after alignment of the RRBS sequences on a Repbase artificial genome, their coverage and DNA methylation status in each sample.
Additional file 2 of Predicting male fertility from the sperm methylome: application to 120 bulls with hundreds of artificial insemination records Open
Additional file 2: Table S1. Listing the semen samples, information on their origin and processing, and the field fertility of the corresponding bulls.
Additional file 4 of Predicting male fertility from the sperm methylome: application to 120 bulls with hundreds of artificial insemination records Open
Additional file 4: Table S3. Listing the fertility-related DMCs, their DNA methylation status in each sample and their annotation regarding genome features.
The epigenome of male germ cells and the programming of phenotypes in cattle Open
International audience