Biology subjects

Khansefid, M.

Publications and source records attributed to Khansefid, M..

3 recordsLinked to original sources

bioRxiv2026

Toward routine health phenotyping: High-throughput prediction of metabolic, immune, and inflammatory biomarkers from milk mid-infrared spectroscopy in early-lactation dairy cows

This study evaluated the potential of milk mid-infrared (MIR) spectroscopy, combined with routinely available on-farm variables, for predicting serum metabolic, immune, and inflammatory biomarkers in early-lactation cows. Data included 5,936 blood samples from 4,442 cows across 23 Australian dairy herds, with paired milk MIR spectra and serum measurements for up to 14 biomarkers. Prediction models were developed using partial least squares regression and evaluated using nested 10-fold random cross-validation and leave-one-herd-out validation. The results show that while basic herd-test data, including milk fat, protein, and lactose concentration, as well as on-farm variables, including DIM, calving age, breed, and herd could predict serum biomarkers, combining MIR spectra with these on-farm variables produced the best overall performance. In random cross-validation, blood urea nitrogen (BUN) was predicted most accurately (R2 = 0.78), while {beta}-hydroxybutyrate (BHB) and nonesterified fatty acids (NEFA) showed moderate accuracy (R2 = 0.56 and 0.44, respectively). BUN also showed the strongest external validation performance, with leave-one-herd-out R2 = 0.58 and comparable accuracy for predicting records collected after 70 days in milk (R2 = 0.65). BHB and NEFA had moderate leave-one-herd-out accuracy but did not transfer beyond early lactation. Most other biomarkers showed low or inconsistent external validation performance. Overall, MIR spectroscopy combined with on-farm variables shows promise for routine prediction of BUN, BHB and NEFA, which can be used for monitoring and genetic evaluation of, for example, ketosis and energy deficit. Initial random cross-validation results for glucose, bilirubin and cholesterol were promising, but more data is needed to improve the prediction accuracy and robustness of the predictions. HighlightsO_LIMilk MIR can predict several serum biomarkers in early-lactation dairy cows. C_LIO_LIMIR-predicted blood urea nitrogen shows the greatest accuracy and robustness. C_LIO_LIMIR-predicted {beta}-hydroxybutyrate and nonesterified fatty acids show moderate accuracy. C_LIO_LIMost mineral, hepatic, and inflammatory biomarkers had limited accuracy. C_LIO_LIRoutine MIR phenotyping is most promising for BUN, BHB, and NEFA. C_LI SummaryMilk mid-infrared (MIR) spectroscopy and on-farm variables are evaluated as a high-throughput tool to predict health-related serum biomarkers in early-lactation dairy cows. The data include 5,936 paired blood and milk samples from 4,442 cows across 23 Australian dairy herds and up to 14 serum biomarkers. Prediction models are developed using partial least squares regression with nested random cross-validation and leave-one-herd-out validation. Blood urea nitrogen (BUN) shows the greatest and most transferable prediction accuracy across herds and lactation stages. {beta}-hydroxybutyrate (BHB) and nonesterified fatty acids (NEFA) are predicted with moderate accuracy, but only during early lactation. Random cross-validation results for glucose and bilirubin are promising, but larger datasets are needed for robust external validation. Most other mineral, hepatic, and inflammatory biomarkers show limited external prediction accuracy. These results indicate that MIR-based routine health phenotyping is most promising for BUN, BHB, and NEFA.

genetics↗

bioRxiv2026

BayesR3AD: Joint analysis of additive and dominance in Bayesian mixture models

BackgroundGenomic prediction in livestock is predominantly based on additive models, even though dominance and other non-additive effects can contribute appreciably to phenotypic variance for fitness and fertility traits. Bayesian mixture models, such as Bayes R, have proven effective for modelling sparse, heterogeneous additive SNP effects, but most implementations do not explicitly accommodate dominance. In this study, we extended BayesR3 to jointly model additive and dominance marker effects within a unified Bayesian mixture framework, denoted BayesR3AD, and used this method to estimate additive and dominance effects for fertility and cow survival (longevity) in Holstein cattle. ResultsUsing real Holstein genotypes (227,942 animals, 74,626 SNPs), we simulated phenotypes with additive and dominance effects. When dominance was present in the simulated data, BayesR3AD improved prediction accuracy of genetic values by +0.1011 (0.6144 vs 0.5133; {approx}19.7% relative) compared with the additive-only BayesR3 model and recovered additive and dominance variance components without bias. Under purely additive simulations, dominance mixture components were effectively empty, confirming that the extended model shrinks unnecessary dominance effects toward zero. In real fertility data, including calving interval (63,378 records) and survival (68,514 records), BayesR3AD estimated small dominance variance ({approx}1-3% of total genetic variance). The model highlighted a very large additive loci at 57.82 Mb on BTA18 for both calving interval and survival. concordant with previous GWAS studies of Holstein fertility. Additionally, a large dominance effect was found at 44.37 Mb on BTA18 for calving interval implicating a heterozygote advantage that increases fertility. ConclusionsBayesR3AD provides a practical extension of BayesR3 that captures both additive and dominance contributions to genomic prediction. The method is robust, reverting effectively to the additive model when dominance is absent, while delivering accurate variance decomposition, and potential gains in prediction accuracy when dominance is present. Application to Holstein fertility traits demonstrates that dominance can be detected and quantified without compromising additive inference, supporting improved prediction of total genetic merit. While validated in cattle, BayesR3AD can be directly applied to other species to better model and predict traits related to fitness.

genetics↗

bioRxiv2017

Genome variants altering gene splicing in bovine are extensively shared between tissues

BackgroundMammalian phenotypes are shaped by numerous genome variants, many of which may regulate gene transcription or RNA splicing. To identify variants with regulatory functions in cattle, an important economic and model species, we used sequence variants to map a type of expression quantitative trait loci (expression QTLs) that are associated with variations in the RNA splicing, i.e., sQTLs. To further the understanding of regulatory variants, sQTLs were compare with other two types of expression QTLs, 1) variants associated with variations in gene expression, i.e., geQTLs and 2) variants associated with variations in exon expression, i.e., eeQTLs, in different tissues.\n\nResultsUsing whole genome and RNA sequence data from four tissues of over 200 cattle, sQTLs identified using exon inclusion ratios were verified by matching their effects on adjacent intron excision ratios. sQTLs contained the highest percentage of variants that are within the intronic region of genes and contained the lowest percentage of variants that are within intergenic regions, compared to eeQTLs and geQTLs. Many geQTLs and sQTLs are also detected as eeQTLs. Many expression QTLs, including sQTLs, were significant in all four tissues and had a similar effect in each tissue. To verify such expression QTL sharing between tissues, variants surrounding ({+/-}1Mb) the exon or gene were used to build local genomic relationship matrices (LGRM) and estimated genetic correlations between tissues. For many exons, the splicing and expression level was determined by the same cis additive genetic variance in different tissues. Thus, an effective but simple-to-implement meta-analysis combining information from three tissues is introduced to increase power to detect and validate sQTLs. sQTLs and eeQTLs together were more enriched for variants associated with cattle complex traits, compared to geQTLs. Several putative causal mutations were identified, including an sQTL at Chr6:87392580 within the 5th exon of kappa casein (CSN3) associated with milk production traits.\n\nConclusionsUsing novel analytical approaches, we report the first identification of numerous bovine sQTLs which are extensively shared between multiple tissue types. The significant overlaps between bovine sQTLs and complex traits QTL highlight the contribution of regulatory mutations to phenotypic variations.

genomics↗