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Flavio S Schenkel

Publications and source records attributed to Flavio S Schenkel.

2 recordsLinked to original sources
PubMed2026

Genomic background of gestation length and calving-related traits in Holstein cattle.

The reproductive success of cows directly influences the profitability of dairy farms. Reproductive traits, particularly calving-related traits, generally have low heritability but sufficient additive genetic variance to enable genetic progress through genomic selection. Thus, the primary objectives of this study were to estimate genetic parameters and perform single-step genome-wide association studies (ssGWAS) for calf size, calving ease, gestation length, and stillbirth in Holstein cattle. Variance components were estimated based on animal models and Bayesian inference using a data set containing 226,717 animals with phenotypic records, 15,761 animals genotyped with 45,101 SNP markers, and 461,819 animals in the pedigree. SNP effects were estimated using the single-step GBLUP method. For direct and maternal genetic effects, heritability estimates (posterior standard deviation) ranged from 0.001 (0.002) for gestation length in heifers to 0.16 (0.001) for gestation length in cows. Genetic correlations ranged from -0.57 (0.01) between calving ease and stillbirth in heifers to 0.74 (0.01) between gestation length evaluated in heifers and cows. The ssGWAS results supported a highly polygenic architecture for calving-related traits, with most genomic signals not reaching genome-wide significance. A genome-wide significant association was detected for calving ease in cows on BTA23, highlighting FARS2 as a positional candidate gene. The strongest GWAS signals for each trait harbored additional biologically important candidate genes, including NPPA, NPPB, BCHE, EPHA4, DLD, and GTF2I. Given the generally low heritability estimates and the predominantly polygenic architecture observed for these traits, genomic selection may contribute to the genetic improvement of calving-related traits in Holstein cattle, with potential benefits for cow welfare, calf survival, and overall dairy production efficiency.

dairy cattle↗
PubMed2026

Validation of a national genetic evaluation for methane emission in Holstein cattle.

Lactanet Canada launched a genomic evaluation for methane efficiency for Holsteins in April 2023, utilizing milk mid-infrared-predicted methane (CH4) emissions (CH4MIR) as a proxy. This study validated the methane efficiency genomic evaluation using genotyped cows with CH4MIR and CH4 records from GreenFeed systems (CH4GF), along with relative breeding values (RBV) for methane production and methane efficiency from the April 2023 evaluation. In Lactanet's methane efficiency evaluation, a higher RBV indicates more desirable, lower-emitting animals. For the validation, RBV were categorized into quintiles for the CH4MIR dataset and tertiles for the CH4GF dataset to evaluate trends across the RBV distribution. Mean CH4MIR decreased progressively across RBV quintiles for both traits, with all pairwise comparisons among quintiles significantly different. Similarly, CH4GF emissions declined across RBV tertiles, with significant differences observed between the lowest and highest tertiles. Additional analyses using RBV threshold categories confirmed that cows with the highest RBV consistently exhibited lower methane emissions. Linear regression analyses further demonstrated a negative relationship between RBV and methane emissions, supporting the predictive ability of the genomic evaluation. These findings confirm that Canada's genomic evaluation for methane efficiency effectively differentiates cows by methane emission potential, reinforcing its potential as a tool for genetic selection to reduce methane emissions in dairy cattle.

Journal Article↗