Abstract :
[en] The negative energy balance (NEB) state in dairy cows is a critical factor affecting health, reproduction, and production, particularly during early lactation. Multiple blood and milk biomarkers change when dairy cows are in the NEB state. Direct measurement of NEB is impractical for large-scale use due to costs, necessitating reliance on indirect predictors such as milk mid-infrared (MIR) spectrometry-based predicted biomarkers. However, the genetic relationships between NEB and its potential biomarkers remain unclear. This study aimed to (1) compare measured reference NEB with MIR-predicted NEB (PNEB), a novel energy deficit score (EDS), 15 biomarkers, and 3 production traits; (2) estimate genetic parameters among these traits using a 20-trait repeatability model, quantifying the ability of the 19 other studied traits (logit-transformed EDS (LEDS), 15 biomarkers, and 3 production traits) to genetically predict logit-transformed PNEB (LPNEB); and (3) evaluate the causal effects of LPNEB on the 19 traits through a recursive model. Two datasets were used: dataset I (127 cows, 965 records) provided reference data for objective (1), and dataset II (25,287 first-parity cows, 30,634 records) enabled genetic analysis used for objectives (2) and (3). Traits were analyzed using Pearson correlations, multiple-diagonalization expectation maximization REML-based genetic parameter estimation, and recursive modeling. The studied traits had moderate to moderate-high h2 ranging from 0.16 to 0.38. The genetic correlations between LPNEB and the studied traits ranged from -0.60 for LIGF-1 to 0.85 for MIR-predicted blood nonesterified fatty acids (NEFA). Analysis of genetic predictability of LPNEB traits together explained 89% of the genetic variance of LPNEB, with all 15 biomarkers alone contributing the largest fraction with 82%, LEDS alone 65%, NEFA alone 62%, and all traits except LEDS 85%, indicating that LEDS contains useful additional information. Recursive modeling further identified 8 traits, including NEFA and LEDS, as highly dependent on LPNEB, highlighting their potential as robust biomarkers. This study demonstrates the utility of MIR-predicted traits for understanding the genetic mechanisms of NEB and its potential for integration into breeding programs, while emphasizing cautious interpretation of these results due to limitations of MIR-predictions of studied traits to represent directly measured traits.
Funding text :
The China Scholarship Council (Beijing, China) is acknowledged for funding the PhD project of Hongqing Hu. The provision of a dataset collected by the GplusE Project (https://gpluse.eaap.org/) on experimental farms is acknowledged. The computation resources of the University of Li\u00E8ge\u2013Gembloux Agro-Bio Tech (ULi\u00E8ge-GxABT, Gembloux, Belgium) were partly supported by the Fonds de la Recherche Scientifique (FRS-FNRS, Brussels, Belgium), which also provided support through the PDR projects \u201CHTwoTHI\u201D (grant number T.W005.23) and \u201CDEEPSELECT\u201D (grant number T.0095.19). Supplemental materials for this article are available at https://data.mendeley.com/datasets/gh9pwf8fph/2. Author contributions are as follows: Hongqing Hu, funding acquisition, formal analysis, and writing (original draft); S\u00E9bastien Franceschini, data curation and writing (review and editing); Pauline Lemal, validation and writing (review and editing); Cl\u00E9ment Grelet, data curation and writing (review and editing); Yansen Chen, data curation and writing (review and editing); Hadi Atashi, validation and writing (review and editing); Katrien Wijnrocx, validation and writing (review and editing); H\u00E9l\u00E8ne Soyeurt, validation and writing (review and editing); Nicolas Gengler, conceptualization, funding acquisition, methodology, project administration, software, supervision, validation, and writing (review and editing). No human or animal subjects were used, so this analysis did not require approval by an Institutional Animal Care and Use Committee or Institutional Review Board. The authors have not stated any conflicts of interest. Nonstandard abbreviations used: ACE = milk acetone predicted by mid-infrared spectra; B_BHBA = blood \u03B2-hydroxybutyric acid predicted by mid-infrared spectra; BHBA = \u03B2-hydroxybutyrate acid; C10:0 = milk decanoic fatty acid predicted by mid-infrared spectra; C14:0 = milk myristic fatty acid predicted by mid-infrared spectra; C16:0 = milk palmitic fatty acid predicted by mid-infrared spectra; C18:0 = milk stearic fatty acid predicted by mid-infrared spectra; C18:1 cis-9 = milk oleic fatty acid predicted by mid-infrared spectra; CIT = milk citrate predicted by mid-infrared spectra; EB = energy balance; EDS = energy deficit score; EM-REML = expectation maximization REML; FA = fatty acid; FP = milk fat percentage predicted by mid-infrared spectra; GLU = blood glucose predicted by mid-infrared spectra; LACE = log10 milk acetone predicted by mid-infrared spectra; LB_BHBA = log10 blood \u03B2-hydroxybutyric acid predicted by mid-infrared spectra; LCFA = milk long-chain fatty acids predicted by mid-infrared spectra; LEDS = logarithm probability energy deficit score; LIGF-1 = log10 blood insulin-like growth factor 1 predicted by mid-infrared spectra; LM_BHBA = log10 milk \u03B2-hydroxybutyric acid predicted by mid-infrared spectra; LPNEB = logarithm probability negative energy balance predicted by mid-infrared spectra; LRNEB = logarithm measured probability negative energy balance; M_BHBA = milk \u03B2-hydroxybutyric acid predicted by mid-infrared spectra; MCFA = milk medium-chain fatty acids predicted by mid-infrared spectra; MIR = mid-infrared; MY = milk yield; NA = not applicable because data were not available; na = not applicable because trait has no units; NEB = negative energy balance; NEFA = blood nonesterified fatty acids predicted by mid-infrared spectra; PEB = energy balance predicted by mid-infrared spectra; PNEB = negative energy balance predicted by mid-infrared spectra; PP = milk protein percentage predicted by mid-infrared spectra; R2cv = coefficient of determination of cross-validation; REB = measured energy balance; RNEB = measured negative energy balance; RMSEcv = root mean square error of cross-validation; SCFA = milk short-chain fatty acids predicted by mid-infrared spectra.The China Scholarship Council (Beijing, China) is acknowledged for funding the PhD project of Hongqing Hu. The provision of a dataset collected by the GplusE Project ( https://gpluse.eaap.org/ ) on experimental farms is acknowledged. The computation resources of the University of Li\u00E8ge\u2013Gembloux Agro-Bio Tech (ULi\u00E8ge-GxABT, Gembloux, Belgium) were partly supported by the Fonds de la Recherche Scientifique (FRS-FNRS, Brussels, Belgium), which also provided support through the PDR projects \u201CHTwoTHI\u201D (grant number T.W005.23) and \u201CDEEPSELECT\u201D (grant number T.0095.19). Supplemental materials for this article are available at https://data.mendeley.com/datasets/gh9pwf8fph/2 . Author contributions are as follows: Hongqing Hu, funding acquisition, formal analysis, and writing (original draft); S\u00E9bastien Franceschini, data curation and writing (review and editing); Pauline Lemal, validation and writing (review and editing); Cl\u00E9ment Grelet, data curation and writing (review and editing); Yansen Chen, data curation and writing (review and editing); Hadi Atashi, validation and writing (review and editing); Katrien Wijnrocx, validation and writing (review and editing); H\u00E9l\u00E8ne Soyeurt, validation and writing (review and editing); Nicolas Gengler, conceptualization, funding acquisition, methodology, project administration, software, supervision, validation, and writing (review and editing). No human or animal subjects were used, so this analysis did not require approval by an Institutional Animal Care and Use Committee or Institutional Review Board. The authors have not stated any conflicts of interest.
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