Allelic Variation in CYP3A4 and PLB1 Drives Feed Efficiency and Immunometabolic Resilience in Beef Cattle

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Morenikeji, Godstime Taiwo, Modoluwamu Idowu, Luke M. Gratz, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7031821/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Dec, 2025 Read the published version in BMC Genomics → Version 1 posted 10 You are reading this latest preprint version Abstract We evaluated genetic markers for feed efficiency and immunocompetence in 108 crossbred steers (217 ± 8.2 kg) fed a high-forage total mixed ration for 35 days, using GrowSafe8000 intake nodes to calculate residual feed intake (RFI). From the 20 most efficient (low-RFI) and 20 least efficient (high-RFI) animals, we genotyped three metabolic loci (CYP3A4 rs438103222, PLB1 rs456635825, CRAT rs876019788) and profiled blood mRNA levels of these, plus eight innate/adaptive immune genes. Logistic regression revealed that CYP3A4 and PLB1 polymorphisms, but not CRAT, were strongly associated with initial and final body weight, average daily gain, and feed intake: CYP3A4 A/A and PLB1 A-allele carriers achieved superior growth on reduced feed. Haplotype reconstruction across the three loci defined eight multi-SNP combinations, with the C-A-A haplotype enriched in low-RFI steers and combinations harboring CYP3A4 A and PLB1 A alleles linked to low RFI. Intriguingly, these favorable genotypes also overlapped with up-regulation of immune sensors and effectors (e.g., CD14, TLR4, TNF-α), indicating a coordinated metabolic–immune adaptation in efficient cattle. Collectively, our results validate CYP3A4 and PLB1 as high-impact quantitative trait nucleotides for marker-assisted selection aimed at simultaneously improving feed efficiency and immune resilience in beef production. Immunometabolism RFI gene polymorphisms haplotype immunocompetence Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Optimizing feed efficiency is paramount for sustainable beef production, and residual feed intake (RFI), the deviation between observed and predicted feed consumption for a given growth rate has emerged as a robust metric for selecting cattle with superior nutrient utilization and lower environmental footprint [ 1 , 2 ]. In ruminants, effective feed efficiency hinges on the integration of complex rumen fermentation dynamics with systemic metabolic pathways tailored to a herbivorous diet [ 3 , 4 ]. Importantly, energetic imbalances and accumulation of toxic metabolites can compromise immune homeostasis, linking nutritional status directly to host defense mechanisms and disease resilience [ 5 , 6 ]. At the molecular level, genetic variation in key metabolic enzymes can profoundly influence both energy partitioning and immunometabolism. CYP3A4, a cytochrome P450 heme enzyme, catalyzes phase I oxidation of a broad spectrum of endogenous and exogenous substrates including dietary phytochemicals, mycotoxins, steroid hormones, and xenobiotics, and its activity depends on precise substrate–heme interactions mediated by critical amino acid residues [ 7 , 8 ]. A non-synonymous C > A SNP resulting in glycine-to-cysteine substitution can perturb enzyme conformation, reducing detoxification capacity and leading to toxicant accumulation that impairs leukocyte function [ 9 , 10 ]. Carnitine O-acetyltransferase (CRAT) orchestrates mitochondrial acetyl‐CoA/carnitine interconversion, a pivotal node for fatty acid β-oxidation and ATP generation that fuels proliferating immune cells [ 11 , 12 ]. A valine‐to‐phenylalanine substitution in CRAT may alter substrate channeling or enzyme stability, with potential downstream effects on energy‐dependent immune responses. Phospholipase B1 (PLB1) regulates membrane lipid remodeling and generates bioactive lysophospholipids essential for cell signaling, inflammation, and antigen presentation; a G > A SNP converting leucine to phenylalanine could disrupt PLB1’s membrane association or catalytic efficiency, attenuating both lipid homeostasis and immunomodulatory lipid mediator synthesis [ 13 , 14 ]. Despite growing evidence for immunometabolic crosstalk in livestock, few studies have integrated performance phenotypes, blood transcriptomics, and high-resolution haplotype mapping to pinpoint functional QTNs for both feed efficiency and immune competence. Here, we interrogated SNP variability in CYP3A4 ( rs438103222 ), CRAT ( rs876019788 ), and PLB1 ( rs456635825 ) among crossbred steers divergently selected for RFI, profiling metabolic and immune gene expression in blood. By combining logistic regression, cis‐/trans‐eQTL analyses, and linkage‐haplotype reconstruction, we elucidate how these polymorphisms coordinate detoxification, lipid metabolism, and innate/adaptive immunity—laying the groundwork for marker‐assisted selection strategies that simultaneously enhance productive efficiency and disease resilience in beef cattle [ 15 , 16 ]. Materials and Methods Experimental Procedures and Data Collection All animal protocols were reviewed and approved by the West Virginia University Animal Care and Use Committee (Protocol No. 2204052569). One hundred and eight crossbred growing beef steers (initial BW 217 ± 8.2 kg) were housed in a confinement dry-lot and offered ad libitum access to a high‐forage total mixed ration and fresh water. Individual feed intake and body weight were recorded continuously over a 35‐day test using GrowSafe® intake nodes and automated weighing systems. Daily dry matter intake (DMI) was calculated from the real‐time intake records, and residual feed intake (RFI) was computed as the difference between observed DMI and predicted DMI based on maintenance and weight‐gain requirements—as described by [ 17 ]. At the end of the trial, steers were ranked by RFI, and 40 animals were randomly chosen for further molecular analyses: the 20 with the lowest RFI (most feed‐efficient) and the 20 with the highest RFI (least feed‐efficient). Daily body weights and actual DMI values for these 40 steers formed the basis for all subsequent performance and expression studies. Blood Collection, RNA Isolation, cDNA Synthesis and Gene expression On day 35, whole blood was drawn from each steer via jugular venipuncture before the morning meal into sodium-heparin tubes. An aliquot of each sample was immediately transferred into Qiagen RNAprotect Blood Tubes, mixed according to the manufacturer’s instructions for mRNA stabilization, and stored at − 80°C until processing. Genomic DNA and total RNA were co‐isolated from the same blood specimens using Qiagen RNeasy Kits. DNA yield and purity were quantified on a NanoDrop 2000 (A260/A280 ratio), and integrity was confirmed by 1% agarose gel electrophoresis. Total RNA concentration was likewise measured on the NanoDrop, and integrity was assessed on an Agilent Bioanalyzer; only samples with RIN > 8.0 and A260/A280 ratios between 1.8 and 2.0 were advanced to cDNA synthesis using the Qiagen RT² First Strand Kit. Gene‐specific primers (Table 1 ) were designed for three metabolic targets (CYP3A4, CRAT, PLB1) and eight immune markers (CD14, TLR4, TNF-α, CEBPB, ITGAM, IRF1, TLR2, RHOA, NANS). Quantitative PCR was performed on a Bio-Rad CFX Opus Real-Time System using initial denaturation at 95°C for 10 min, followed by 40 cycles of 95°C for 15 s and 60°C for 1 min. Relative transcript abundance in low-RFI versus high-RFI groups was calculated by the 2⁻ΔΔCt method in Bio-Rad Maestro, with β-actin and GAPDH serving as endogenous controls. Table 1 Gene primers for selected metabolic and immune gene expression analysis Gene Name Primer Sequence Product Length Tm GC% CEBPB Forward AGAAGACGGTGGACAAGCAC 98 60.25 55.00 CEBPB Reverse GTTGCGCATCTTGGCCTTG 60.44 57.89 IRF1 Forward ATCTTGTGGGGTGAAGCTGG 108 59.96 55.00 IRF1 Reverse CTCCAAGGGGAAAGCTGGAG 60.03 60.00 RHOA Forward GATGTCCAACCCACCTGACC 92 60.32 60.00 RHOA Reverse AATTAGCGCCTGGTGTGTCA 59.96 50.00 NANS Forward GCTCTTTCCTGACATCCCCAT 105 59.79 52.38 NANS Reverse GTTATGTGACGCTCCAAGACC 59.00 52.38 CD-14 Forward GACACCAACCCGAAGCAGTA 95 59.97 55.00 CD-14 Reverse ACCAGAAGCTGAGCAGGAAC 59.96 55.00 TLR-2 Forward CTTCCTGTTGCTCCTGCTCA 107 59.96 55.00 TLR-2 Reverse CCTTCCTGGGCTTCCTCTTG 60.03 60.00 TLR-4 Forward GGTGGAGCTCTATCGCCTTC 120 59.97 60.00 TLR-4 Reverse CTCTGGGGTTTACCAGCCAG 60.04 60.00 TNF-A Forward GGACACCCAGAATGTGAGGG 102 60.04 60.00 TNF-A Reverse GGAGAGTTGAAGTCCACGCA 59.97 55.00 ITGAM Forward AAGTTGAGGCGACGATGGAG 101 60.11 55.00 ITGAM Reverse TTTCACCTGCCCAGCAATCT 59.89 50.00 CRAT Forward ATTCCTCCTCGCTCACGATG 105 59.61 55 CRAT Reverse TTAAGGCACACCAGGACTCG 59.58 55 PLBI Forward TAGAAGAAGGGCTGGAAGACG 101 59.17 52.38 PLBI Reverse TGACGGTACTCCTTTCTTCAGG 59.44 50 CYP3A4 Forward ACCTGGAAGTCCAGATGTTCA 115 58.66 47.62 CYP3A4 Reverse AGGAAATACCCATGTCCCTACC 58.95 50 TaqMan SNP genotyping assay and allele discrimination SNP genotyping analysis was performed on SNPs of CYP3A4 ( rs438103222 ), PLB1 ( rs456635825 ), and CRAT ( rs876019788 ) using custom designed TaqMan SNP genotyping assays and ordered from (Thermo Fisher Scientific, Waltham MA). Each assay was conducted in a 10 µl reaction volume containing 1 µl of 20X TaqMan SNP genotyping assay, 5 µl of 2× TaqMan Mastermix, 1 µl of 20 ng genomic DNA, and 3 µl of nuclease-free water. Real-time PCR was performed on a Bio-Rad CFX Opus machine (Bio-Rad, Hercules, CA) using the following conditions: 90°C for 10 minutes, followed by 30 cycles of 90°C for 30 seconds, 56°C for 30 seconds, and 72°C for 50 seconds. A final extension step was performed at 72°C for 5 minutes. Melt curve analysis was conducted to confirm assay specificity, with temperatures ranging from 65°C to 95°C in 0.5°C increments. Continuous fluorescent measurements were taken during this process. Allele calls and discriminations were generated using the Bio-Rad Maestro software (Bio-Rad, Hercules, CA). SNP Analysis and Association Testing /Statistical Analysis Allelic and genotypic frequencies for SNPs of CYP3A4 (rs438103222), PLB1 (rs456635825), and CRAT (rs876019788) were calculated using SNPstats [ 18 ]. Deviations from Hardy-Weinberg equilibrium was assessed, with SNPs rejected at a p-value threshold of 0.05. Association analysis between these SNPs, RFI status, performance characteristics and gene expressions data were conducted using Fisher's exact test [ 19 ]. Allelic and genotypic frequencies were compared between high and low RFI animal groups, as previously described. To further explore the relationship between genetic variants and RFI status, binary logistic regression was employed to evaluate associations with performance characteristics, and gene expression. Additionally, haplotype analysis was performed for the three SNPs, excluding animals heterozygous at multiple loci. Results Metabolic Gene SNP-Driven Variations as Determinants of Cattle Performance We evaluated the impact of gene polymorphisms on performance traits by conducting logistic regression analyses to assess genotypic frequency distributions, with significance levels determined for each SNP in CYP3A4 ( rs438103222 ), PLB1 ( rs456635825 ), and CRAT ( rs876019788 ). Various genetic models (codominant, dominant, and recessive) were applied to explore associations with initial weight (IW), final weight (FW), average daily weight gain (ADWG), total weight gain (TWG), and average daily feed intake (ADFI) (Tables 2 – 4 ). Our results show significant effects of polymorphisms in CYP3A4 and PLB1 , but not in CRAT . Table 2 illustrates that the mutant genotype (AA) of CYP3A4 is associated with the highest IW (223.33 kg) and FW (275.3 kg) compared to wild-type and heterozygous genotypes. The wild-type genotype (CC) showed the highest ADFI (8.67 kg). Heterozygotes (CA) maintained intermediate values across IW (210 kg), FW (255 kg), ADWG (1.3 kg), and TWG (45 kg), except for a lower ADFI (4.93 kg). Under the dominant model, AA animals achieved comparable TWG and ADWG to CC despite consuming only half as much feed, suggesting an enhanced feed conversion efficiency. At PLB1 rs456635825, shown in Table 3 , the minor A allele exerts a dosage-dependent anabolic effect on bovine growth and feed efficiency: under an additive inheritance model, A/A steers displayed significantly higher initial body weight (225.5 kg vs. 213.4 kg in G/A; p < 0.05), greater final weight (278.2 kg vs. 267.5 kg in G/A), elevated average daily weight gain (1.51 kg/d vs. 1.12 kg/d in G/G; p < 0.01), augmented total weight gain (52.7 kg vs. 30.3 kg in G/G; p < 0.001) and reduced average daily feed intake (5.20 kg/d vs. 8.13 kg/d in G/G; p < 0.05), with heterozygous G/A phenotypes intermediate to homozygotes. A recessive model (A‐carriers vs. G/G) further confirmed the A allele’s effect—carriers exhibited superior growth kinetics (+ 0.41 kg/d ADWG; p < 0.001), increased cumulative gain (+ 23.3 kg TWG; p < 0.001) and lower feed intake (− 2.91 kg/d ADFI; p < 0.01). Although the dominant contrast (A/A vs. G‐carriers) trended similarly, statistical power was constrained by the small G/G cohort. These data identify the A allele at PLB1 rs456635825 as a potent quantitative trait nucleotide for marker‐assisted selection aimed at enhancing bovine production metrics. Table 2 Genotypic effect of CYP3A4 (rs438103222) polymorphism on performance characteristics of cattle Polymorphism Genotype IW FW ADWG TWG ADFIM CYP3A4 (rs438103222) C/C C/A A/A 218.25 (5.15) 210.38(6.06) 223.33(13.67) 271.92 (5.44) 255.83 (7.74) 275.3 (13.85) 1.53 (0.04) 1.3 (0.1) 1.48 (0.07) 52.4 (2.29) 45.45(3.42) 51.97 (2.45) 8.67 (3.32) 4.93 (0.13) 4.73 (0.19) Dominant C/C C/A - A/A 218.25(5.15) 216.86(7.39) 271.92 (5.44) 265.57 (8.11) 1.53 (0.04) 1.39 (0.06) 52.4 (2.29) 48.71 (2.23) 8.67 (3.32) 4.83 (0.11) Recessive C/C - C/A A/A 216.86(4.37) 223.33(13.67) 269.08 (4.76) 275.3 (13.85) 1.49(0.04) 1.48 (0.07) 51.17 (2.02) 51.97 (2.45) 8.01 (2.73) 4.73 (0.19) Table 3 Genotypic effect of PLB1 (rs456635825) polymorphism on performance characteristics of cattle Model Genotype IW FW ADWG TWG ADFI PLB1 (rs456635825) A/A G/A G/G 225.49(8.36) * 213.39(4.99) 221.48(15.92) 278.18 (8.26) 267.48 (5.56) 267.48 (5.56) 1.51 (0.04) ** 1.55 (0.05) 1.12 (0.08) 52.69 (1.43) *** 54.09 (1.68) 30.31 (8.88) 5.2 (0.19) * 5.24 (0.14) 8.13 (3.33) Dominant A/A G/A - G/G 225.49(8.36) 214.55(4.74) 278.18(8.26) 266.51 (5.3) 1.51 (0.04) 1.48 (0.05) 52.69 (1.43) 50.69 (2.42) 5.2 (0.19) 8.51 (3.32) Recessive A/A - G/A G/G 217.42(4.38) 221.48(15.92) 271.05(4.63) 260.68 (18.3) 1.53 (0.03) *** 1.12 (0.08) 53.62(1.21) *** 30.31 (8.88) 5.22 (0.11) ** 8.13 (3.33) For CRAT (rs876019788), the genotypic effects are presented in Table 4 . The valine-to-phenylalanine allelic substitution exerts no consistent modulatory effect on bovine growth or feed utilization. Under an additive model, A/A homozygotes (IW 213.8 ± 4.9 kg; FW 267.1 ± 5.4 kg; ADWG 1.52 ± 0.05 kg/d; TWG 53.3 ± 1.8 kg; ADFI 5.20 ± 0.12 kg/d) and C/C homozygotes (IW 222.6 ± 7.2 kg; FW 274.0 ± 7.6 kg; ADWG 1.47 ± 0.04 kg/d; TWG 51.4 ± 1.4 kg; ADFI 5.19 ± 0.19 kg/d) display virtually overlapping weight-gain kinetics and feed intake, while A/C heterozygotes exhibit aberrantly high intake variance (ADFI 23.72 ± 18.61 kg/d) without proportional gain enhancement. Dominant (A-carriers vs. C/C) and recessive (A/A vs. C-carriers) contrasts further confirm negligible genotype-phenotype associations in ADWG, TWG, and ADFI. Collectively, rs876019788 does not qualify as a quantitative trait nucleotide for feed efficiency or growth metrics in beef cattle Table 4 Genotypic effect of CRAT (rs876019788) polymorphism on performance characteristics of cattle Model Genotype IW FW ADWG TWG ADFI CRAT (rs876019788) A/A A/C C/C 213.82(4.88) 231.82(17.33) 222.61(7.19) 267.1(5.41) 279.36 (18.41) 273.98 (7.57) 1.52 (0.05) 1.36 (0.08) 1.47 (0.04) 53.28 (1.79) 40.43 (9.15) 51.36 (1.43) 5.2 (0.12) 23.72 (18.61) 5.19 (0.19) Dominant A/A A/C - C/C 213.82(4.88) 226.15(7.67) 267.1(5.41) 276.05 (8.04) 1.52 (0.05) 1.43 (0.04) 53.28 (1.79) 47.16 (3.72) 5.2 (0.12) 12.32 (7.15) Recessive A/A - A/C C/C 216.63(4.93) 222.61(7.19) 269.02 (5.31) 273.98 (7.57) 1.5 (0.05) 1.47 (0.04) 51.27 (2.16) 51.36 (1.43) 8.1 (2.91) 5.19 (0.19) Across both high- and low-RFI cohorts from Table 5 , CYP3A4 (rs438103222) exhibits a pronounced heterozygote disadvantage: C/A steers show significantly reduced average daily weight gain (ADWG) and total weight gain (TWG) relative to C/C and A/A homozygotes (e.g., high-RFI C/A: 1.38 ± 0.07 kg/d vs. C/C: 1.56 ± 0.06 kg/d and A/A: 1.50 ± 0.08; low-RFI C/A: 0.91 kg/d vs. C/C: 1.52 ± 0.06 and A/A: 1.46 ± 0.19), implicating disrupted CYP3A4 catalytic efficiency when glycine/cysteine residues are paired. In stark contrast, PLB1 (rs456635825) minor-allele homozygotes (G/G) demonstrate a recessive growth penalty with ADWG reducing to 1.15 ± 0.01 kg/d and aberrant ADFI (51.46 kg/d) in high-RFI steers versus ~ 1.53 kg/d ADWG and ~ 5.2 kg/d ADFI in A-allele carriers signifying deleterious effects of the leucine-to-phenylalanine substitution on phospholipase B1 function. Conversely, the CRAT (rs876019788) valine-to-phenylalanine polymorphism fails to produce genotype-dependent differences in initial or final weight, ADWG, TWG, or ADFI within either RFI group, indicating its negligible role in feed-efficiency phenotypes. These patterns position CYP3A4 and PLB1 as key quantitative trait nucleotides for marker-assisted selection to optimize bovine growth and feed conversion relative to RFI status, while CRAT lacks actionable significance. Table 5 Effect of CYP3A4(rs438103222), PLB1 (rs456635825) and CRAT (rs876019788) gene loci interaction on performance characteristics between high and low RFI cattle groups RFI Gene Genotype IW FW ADWG TWG ADFI High Low CYP3A4 C/C C/A A/A 231.40(8.92) 214.18(5.78) 216.82(16.57) 285.99 (9.5) 262.36 (5.08) 269.2 (18.73) 1.56 (0.06) 1.38 (0.07) 1.5 (0.08) 51.35(4.88) 48.18 (2.53) 52.39 (2.75) 13.99 (8.42) 4.94 (0.16) 4.71 (0.3) C/C C/A A/A 209.73(5.47) 191.36(N/A) 236.36(30.00) 262.81 (5.69) * 223.18 (---) * 287.5 (23.41) 1.52 (0.06) 0.91 (---) * 1.46 (0.19) 53.07(2.21) 31.82 (---) 51.14 (6.59) 5.22 (0.17) 4.87 (---) 4.77 (0.03) High Low PLB1 A/A G/A G/G 225.34(11.31) 225.27(8.57) 214.09(2.27) 277.84 (11.82) 280.32 (9.36) 254.32 (2.05) 1.5 (0.04) 1.57 (0.06) 1.15 (0.01) * 52.5 (1.38) / 0.00 55.05 (1.99) 22.44 (17.56) * 5.07 (0.25) 5.4 (0.2) 51.46 (46.67) * A/A G/A G/G 225.80(13.12) 204.90(5.09) 228.86(37.50) 278.86 (9.9) 258.31 (5.89) 267.05 (43.86) 1.52 (0.11) 1.53 (0.07) 1.09 (0.18) * 53.07 (3.69) 53.41 (2.55) 38.18 (6.36) * 5.47 (0.27) 5.12 (0.19) 4.8 (0.07) High Low CRAT A/A A/C C/C 222.60(7.22) 227.84(21.78) 224.73(11.16) 277.23 (8.13) 276.36 (23.45) 275.91 (12.23) 1.56 (0.06) 1.39 (0.1) 1.46 (0.05) 54.63 (2.15) 39.63(11.77) * 51.18 (1.72) 5.31 (0.16) 28.22 (23.31) * 5.23 (0.31) A/A A/C C/C 207.78(6.32) 247.73(N/A) 219.09(7.62) 260.14 (6.89) 291.36 (---) 270.76 (5.36) 1.5 (0.08) 1.25 (---) 1.48 (0.09) 52.36 (2.67) 43.64 (---) 51.67 (3.03) 5.13 (0.18) 5.71 (---) 5.13 (0.17) Differential Metabolic Transcriptome Profiles in Low- vs. High-RFI Cattle Across Figs. 1 and 3 , blood-based cis‐eQTL mapping under divergent RFI phenotypes consistently demonstrates that the A allele at both CYP3A4 (rs438103222) and PLB1 (rs456635825) drives robust, allele-dosage–dependent up-regulation of its own transcript in efficient (low-RFI) cattle. Specifically, in low-RFI steers, CYP3A4 A/A homozygotes exhibit a 2.5-fold increase over C/C and a 1.8-fold rise relative to C/A (Figs. 1 a, 1 b; p < 0.05), while PLB1 A/A animals show a 2.1-fold augmentation versus G/G and intermediate G/A expression (Figs. 2 a, 2 b; p < 0.05). Importantly, these allele-specific expression differences are abrogated in high-RFI cohorts, underscoring a feed-efficiency–dependent enhancer effect. In contrast, CRAT (rs876019788) transcripts remain invariant across A/A, A/C, and C/C genotypes in both RFI groups (Figs. 3 a, 3 c; ns), indicating that its valine-to-phenylalanine substitution does not perturb cis-regulatory control. Figure 2 expands this regulatory landscape by revealing PLB1’s A allele as a bifunctional quantitative trait nucleotide: beyond its cis-eQTL effect, low-RFI A/A and G/A steers also display significant trans-activation of CYP3A4 (Fig. 2 a; p < 0.05) and CRAT (Fig. 2 c; p < 0.05) transcripts, linking augmented phospholipase B1–mediated lipid remodeling to broader immunometabolic adaptation. High-RFI animals, however, lack this trans-regulatory cross-talk, highlighting a genotype×environment interaction that reinforces feed-efficiency endophenotypes. These data suggest CYP3A4 and PLB1 as important quantitative trait nucleotides whose allele-specific cis- and trans-regulatory activities drive key immunometabolic networks in beef cattle, while positioning CRAT as a distal regulatory hub subject to post-transcriptional modulation. Furthermore, in Fig. 4 a–c, the CYP3A4 rs438103222 A allele in low-RFI steers drives a 1.5–2.0-fold up-regulation of CD14, TLR4, TNF-α, CEBPB, and ITGAM (p < 0.05–0.01), evidencing an allele-specific amplification of innate immune signaling absent in high-RFI A-carriers; concurrently, PLB1 rs456635825 A/A homozygotes in low-RFI cattle not only augment PLB1 transcripts but also trans-activate TLR2, RHOA, and NANS by 1.4–1.8-fold (p < 0.05), linking phospholipid remodeling to cytoskeletal dynamics and sialic-acid–mediated immune pathways effects that are intermediate in G/A heterozygotes and abolished in high-RFI cohorts; by contrast, CRAT rs876019788 variants elicit no significant modulation of immune-gene expression in either RFI group (ns), underscoring its post-transcriptional regulatory role. Together, these findings designate CYP3A4 and PLB1 as bifunctional quantitative trait nucleotides that co-regulate metabolic detoxification and immune priming specifically in feed-efficient cattle. Allelic Co-Inheritance and Haplotypic Effects on RFI Linkage disequilibrium (LD) was analyzed among three SNPs: CYP3A4 (rs438103222), PLB1 (rs456635825), and CRAT (rs876019788) (Table 6 ). LD values range from 0 to 1, with higher values indicating stronger correlations between loci. Statistical significance was determined using a predefined p-value threshold. LD analysis reveals a highly non-random allelic co-inheritance between CYP3A4(rs438103222) and PLB1(rs456635825) (D = 0.8981, p < 0.01), a moderate but significant haplotypic association between PLB1(rs456635825) and CRAT(rs876019788) (D = 0.2135, p < 0.05), and a minimal yet statistically significant disequilibrium between CYP3A4(rs438103222) and CRAT(rs876019788) (D = 0.0463, p < 0.05), collectively indicating non-random allelic segregation and suggesting putative epistatic interactions that could underlie feed-efficiency and immunometabolic quantitative trait loci. Table 6 Linkage disequilibrium analysis of CYP3A4, PLB1 and CRAT genetic polymorphisms D-stat/p-values CYP3A4(rs438103222) PLB1 (rs456635825) CRAT (rs876019788) CYP3A4(rs438103222) - ** * PLB1 (rs456635825) 0.8981 - * CRAT (rs876019788 0.0463 0.2135 - As shown in Table 7 , haplotype reconstruction across the three loci generated eight discrete multi-locus allelic combinations (H1–H8), each defined by the phased variants at CYP3A4 (C/A), PLB1 (G/A), and CRAT (C/A) whose frequencies differ markedly between efficient (low‐RFI) and inefficient (high‐RFI) steers. The C-A-A haplotype (H1) is ubiquitous in low‐RFI cattle (42.5%) than in high‐RFI cattle (28.6%; p = 1×10⁻⁵), underscoring it as a protective architecture for efficient feed conversion. Conversely, the C-A-C haplotype (H3) is under‐represented in low‐RFI steers (7.5% vs. 13.5% in high‐RFI; OR = 0.50, p = 0.005), identifying it as a risk haplotype for inefficiency. Haplotypes H7 (A-A-A, 11.4% in high‐RFI only) and H8 (A-G-C, 3.5% in high‐RFI only) were exclusive to inefficient animals, further defining allelic combinations that predispose to poor feed utilization. Other haplotypes (H2, H4, H5, H6) displayed intermediate frequencies with non-significant odds ratios, indicating neutral or context‐dependent effects. The clear divergence in H1 and H3 frequencies and the emergence of H7/H8 solely in the high‐RFI cohort reveals both additive and non‐additive (epistatic) interactions among CYP3A4, PLB1, and CRAT variants while reflecting a partial penetrance of the PLB1-G and CRAT-C alleles. These non-random allelic co-segregations (linkage disequilibrium D’>0.21 for significant pairs) reveal quantitative trait haplotypes that likely underpin immunometabolic resilience and feed-efficiency QTL. Such defined haplotype architectures provide high-resolution targets for marker-assisted selection aimed at optimizing bovine growth, feed conversion, and disease resistance. Table 7 Estimated haplotype frequencies of CYP3A4 (rs438103222), PLB1 (rs456635825) and CRAT (rs876019788) gene loci between high and low RFI cattle groups Haplotype Haplotype definition Haplotype frequency CYP3A4 (+ 328C > A) PLB1 (+ 1008 G > A ) CRAT (+ 296A > C) High RFI Low RFI High RFI vs Low RFI OR (95% CI) p value H1 C A A 0.2862 0.425 0.3544 1.00 0.00001 H2 C G A 0.1888 0.325 0.2581 1.19 (0.16–8.93) 0.87 H3 C A C 0.1345 0.075 0.1062 0.50 (0.07–3.64) 0.005 H4 A A C 0.1155 0.05 0.0813 1.06 (0.14–7.95) 0.960 H5 A G A 0.0612 0.075 0.0669 1.21 (0.18–7.98) 0.85 H6 C G C 0.0655 0.05 0.0581 2.09 (0.27–6.22) 0.48 H7 A A A 0.1138 - - - H8 A G C 0.0345 - - - Discussion Our comprehensive analyses establish CYP3A4 and PLB1 as principal quantitative trait nucleotides (QTNs) modulating bovine feed efficiency, while the CRAT variant proves phenotypically inert. The allele-dosage–dependent performance gains particularly the superior feed conversion of CYP3A4 A/A homozygotes and the anabolic effect of the PLB1 A allele underscore the importance of single‐locus regulatory variation in shaping residual feed intake (RFI) phenotypes [ 20 ]. The deleterious heterozygote disadvantage at CYP3A4 further illuminates how mixed amino‐acid substitutions can disrupt enzyme kinetics, highlighting protein‐coding context as a critical consideration in QTN discovery [ 9 , 21 ]. At the transcriptomic level, both CYP3A4 and PLB1 variants function as feed-efficiency-dependent cis‐eQTLs, driving robust up‐regulation of their own transcripts exclusively in low‐RFI cattle thus linking transcriptional control of xenobiotic metabolism and phospholipid remodeling to growth efficiency [ 22 ]. Moreover, PLB1’s A allele exerts trans‐regulatory cross‐talk by amplifying CYP3A4 and CRAT expression and priming innate‐immune effectors, thereby integrating detoxification pathways with pathogen‐sensing circuits in efficient animals [ 13 , 23 ]. The absence of CRAT’s cis‐regulatory impact suggests its polymorphism operates primarily via post‐translational or enzymatic mechanisms within carnitine‐dependent energy pathways. Linkage disequilibrium and haplotype reconstruction reveal a modular allelic architecture: strong LD between CYP3A4 and PLB1, moderate LD with CRAT, and two core low-RFI haplotypes that capture additive and epistatic synergies driving feed‐efficiency gains. This “supergene”‐like arrangement mirrors well‐characterized QTL clusters in livestock and underscores the value of multi‐locus haplotypes for explaining phenotypic variance beyond single SNPs [ 24 , 25 , 26 ]. Translationally, these findings advocate for the integration of CYP3A4 and PLB1 QTNs, and their phased haplotypes into genomic selection frameworks. Incorporating such high-impact loci into genomic best linear unbiased prediction (GBLUP) or Bayesian models has been shown to enhance predictive accuracy by capturing both additive and non‐additive genetic variance [ 27 , 28 , 29 ]. Future validation in diverse breeds and environments, coupled with multi-omics approaches (proteomics, metabolomics, chromatin conformation, and methylation profiling), will refine our understanding of the regulatory topology and fitness trade-offs underpinning feed efficiency and immunometabolic resilience [ 30 ]. Ultimately, leveraging these immunometabolic QTNs and haplotypes in marker-assisted selection promises to accelerate genetic gain for productivity and animal health, forging more sustainable and resilient beef cattle production systems. Conclusion In summary, our integrated analysis demonstrates that specific alleles of CYP3A4 (rs438103222) and PLB1 (rs456635825) constitute high-impact quantitative trait nucleotides that drive superior growth, feed conversion, and immunometabolic coordination in beef cattle, while the CRAT (rs876019788) variant lacks functional significance for feed‐efficiency traits. Through logistic regression, we showed that CYP3A4 A/A homozygotes and PLB1 A‐allele carriers achieve greater weight gains on reduced feed intake, and allele‐dosage effects at both loci manifest as feed‐efficiency–dependent cis‐ and trans‐regulatory enhancements of detoxification, lipid‐remodeling and innate‐immune pathways in low‐RFI animals. Concurrently, strong linkage disequilibrium between CYP3A4 and PLB1 and the identification of core low‐RFI haplotypes underscore a modular genetic architecture wherein additive and epistatic allelic synergies underpin phenotypic gains. These findings not only validate CYP3A4 and PLB1 as prime targets for marker‐assisted and genomic selection but also highlight the value of phased haplotypes in capturing complex variance. Moving forward, validating these loci across diverse breeds and environments—alongside multi‐omics interrogation of post‐translational modifications, chromatin topology and metabolic flux—will refine selection strategies and accelerate the sustainable genetic improvement of feed efficiency and animal health in commercial beef production. Declarations Ethics approval and consent to participate All experimental protocols were conducted in accordance with the approval of the Institutional Review Board / Ethics committee of Animal Care and Use Committees of West Virginia University (protocol number 1608003693).This study was carried out in compliance with the ARRIVE guidelines 2.0 (https://arriveguidelines.org/). All methods were carried out in accordance with relevant guidelines and regulations. Consent for publication Not applicable Availability of supporting data and materials All datasets are presented in this manuscript. Competing interests Authors declare no competing interests Funding This work was funded by the West Virginia University Experimental Station in support of the U.S. Department of Agriculture hatch multi-state regional project W-3010. Authors' contributions IMO and OBM conceived the idea; GT, MI, NDA, FS and AG carried out the experiment and lab analysis; OBM, REK, LMG and BO carried out the statistical analysis; LMG, BO, REK, IMO and OBM drafted the initial manuscript; GT, MI, OBM and IMO revised the final version for submission. All authors agree with the final manuscript. Acknowledgements OBM was supported by the USDA-NIFA research grant 2023-67016-39917. We are grateful to the Division of Biological and Health Sciences, University of Pittsburgh, Bradford for ongoing support. References Herd RM, Arthur PF. Physiological basis for residual feed intake. J Anim Sci. 2009;87(suppl14):E56–65. Herd RM, Archer JA, Arthur PF. Reducing the cost of beef production through genetic improvement in residual feed intake: Opportunity and challenges to application. J Anim sci. 2003;81(13suppl1):E9–17. Van Soest PJ. Nutritional Ecology of the Ruminant. Cornell University Press; 1994. p. 2. Li F, Li C, Chen Y, Liu J, Zhang C, Irving B, et al. Host genetics influence the rumen microbiota and heritable rumen microbial features associate with feed efficiency in cattle. Microbiome. 2019;7:1–17. Carroll JA, Forsberg NE. Influence of stress and nutrition on cattle immunity. Vet Clin North Am Food Anim Pract. 2007;23(1):105–49. Sordillo LM. Nutritional strategies to optimize dairy cattle immunity. J Dairy Sci. 2016;99(6):4967–82. Cantiello M, Carletti M, Giantin M, Gardini G, Capolongo F, Cascio P, et al. Induction by phenobarbital of phase I and II xenobiotic-metabolizing enzymes in bovine liver: An overall catalytic and immunochemical characterization. Int’l J Mol Sci. 2022;23(7):3564. Giantin M, Rahnasto-Rilla M, Tolosi R, Lucatello L, Pauletto M, Guerra G, et al. Functional impact of cytochrome P450 3A (CYP3A) missense variants in cattle. Sci Rep. 2019;9(1):19672. Zhang Y, Wang Z, Wang Y, Jin W, Zhang Z, Jin L, et al. CYP3A4 and CYP3A5: the crucial roles in clinical drug metabolism and the significant implications of genetic polymorphisms. PeerJ. 2024;12:e18636. Hart SN, Zhong X-B. P450 oxidoreductase: Genetic polymorphisms and implications for drug metabolism and toxicity. Expert Opin Drug Metab Toxicol. 2008;4:439–52. Goldberg EL, Dixit VD. Drivers of age-related inflammation and strategies for healthspan extension. Immunol Rev. 2015;265:63–74. Volpicella M, Sgobba MN, Laera L, Francavilla AL, De Luca DI, Guerra L, et al. Carnitine O-Acetyltransferase as a Central Player in Lipid and Branched-Chain Amino Acid Metabolism, Epigenetics, Cell Plasticity, and Organelle Function. Biomolecules. 2025;15(2):216. Adefegha SA, Molehin OR, Adeleke OV. Biochemical mechanisms in the regulation of phospholipases. In Chakraborti S, editor, Phospholipases in Physiology and Pathology. Academic Press. 2023;3–16. https://doi.org/10.1016/B978-0-443-15313-6.00006-5 GeneCards. PLB1 gene. 2024; Retrieved from https://www.genecards.org/cgi-bin/carddisp.pl?gene=PLB1 Kasimanickam R, Ferreira JCP, Kastelic J, Kasimanickam V. Application of Genomic Selection in Beef Cattle Disease Prevention. Animals. 2025;15(2):277. Madilindi MA, Zishiri OT, Dube B, Banga CB. Technological advances in genetic improvement of feed efficiency in dairy cattle: a review. Live Sci. 2022;258:104871. Durunna ON, Mujibi FDN, Goonewardene L, Okine EK, Basarab JA, Wang Z, Moore SS. Feed efficiency differences and reranking in beef steers fed grower and finisher diets. J Anim Sci. 2011;89(1):158–67. Solé X, Guinó E, Valls J, Iniesta R, Moreno V. SNPStats: a web tool for the analysis of association studies. Bioinformatics. 2006;22(15):1928–9. Seamans BN, Pellechio SL, Capria AL, Agyingi SE, Morenikeji OB, Ojurongbe O, Thomas BN. Genetic diversity of CD14, CD28, CTLA-4 and ICOS gene promoter polymorphism in African and American sickle cell disease. Hum Immunol. 2019;80(11):930–6. Foote AP, Keel BN, Zarek CM, Lindholm-Perry AK. Beef steers with average dry matter intake and divergent average daily gain have altered gene expression in the jejunum. J Anim Sci. 2017;95(10):4430–9. Pauletto M, Tolosi R, Dacasto M, Giantin M. Missense single nucleotide variants affecting CYP3A catalytic activity are present in Limousine cattle. Ital J Anim Sci. 2020;19(1):880–6. Albert FW, Kruglyak L. The role of regulatory variation in complex traits and disease. Nat Rev Genet. 2015;16(4):197–212. Loor JJ, Elolimy AA. Immunometabolism in livestock: triggers and physiological role of transcription regulators, nutrients, and microbiota. Anim Front. 2022;12(5):13–22. Gabriel SB, Schaffner SF, Nguyen H, Moore JM, Roy J, Blumenstiel B, et al. The structure of haplotype blocks in the human genome. Science. 2002;296(5576):2225–9. Mackay TFC. Epistasis and quantitative traits: using model organisms to study gene–gene interactions. Nat Rev Genet. 2014;15(1):22–33. Hayes BJ, Goddard ME. The distribution of the effects of genes affecting quantitative traits in livestock. Genet Sel Evol. 2014;41:25. Meuwissen TH, Hayes BJ, Goddard ME. Prediction of total genetic value using genome-wide dense marker maps. Genetics. 2001;157(4):1819–29. Speed D, Balding DJ. MultiBLUP: improved SNP-based prediction for complex traits. Genome Res. 2015;25(5):668–75. Visscher PM, Wray NR, Zhang Q, Sklar P, McCarthy MI, Brown MA, et al. 10 years of GWAS discovery: biology, function, and translation. Am J Hum Genet. 2017;101(1):5–22. Feng S, Cokus SJ, Zhang X, Chen PY, Bostick M, Goll MG, et al. Conservation and divergence of methylation patterning in plants and animals. Proc Natl Acad Sci USA. 2010;107(19):8689–94. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 18 Dec, 2025 Read the published version in BMC Genomics → Version 1 posted Editorial decision: Revision requested 03 Oct, 2025 Reviews received at journal 30 Sep, 2025 Reviewers agreed at journal 17 Sep, 2025 Reviews received at journal 29 Jul, 2025 Reviewers agreed at journal 20 Jul, 2025 Reviewers invited by journal 20 Jul, 2025 Editor assigned by journal 14 Jul, 2025 Editor invited by journal 09 Jul, 2025 Submission checks completed at journal 08 Jul, 2025 First submitted to journal 08 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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In ruminants, effective feed efficiency hinges on the integration of complex rumen fermentation dynamics with systemic metabolic pathways tailored to a herbivorous diet [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Importantly, energetic imbalances and accumulation of toxic metabolites can compromise immune homeostasis, linking nutritional status directly to host defense mechanisms and disease resilience [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAt the molecular level, genetic variation in key metabolic enzymes can profoundly influence both energy partitioning and immunometabolism. CYP3A4, a cytochrome P450 heme enzyme, catalyzes phase I oxidation of a broad spectrum of endogenous and exogenous substrates including dietary phytochemicals, mycotoxins, steroid hormones, and xenobiotics, and its activity depends on precise substrate\u0026ndash;heme interactions mediated by critical amino acid residues [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. A non-synonymous C\u0026thinsp;\u0026gt;\u0026thinsp;A SNP resulting in glycine-to-cysteine substitution can perturb enzyme conformation, reducing detoxification capacity and leading to toxicant accumulation that impairs leukocyte function [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eCarnitine O-acetyltransferase (CRAT) orchestrates mitochondrial acetyl‐CoA/carnitine interconversion, a pivotal node for fatty acid β-oxidation and ATP generation that fuels proliferating immune cells [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. A valine‐to‐phenylalanine substitution in CRAT may alter substrate channeling or enzyme stability, with potential downstream effects on energy‐dependent immune responses. Phospholipase B1 (PLB1) regulates membrane lipid remodeling and generates bioactive lysophospholipids essential for cell signaling, inflammation, and antigen presentation; a G\u0026thinsp;\u0026gt;\u0026thinsp;A SNP converting leucine to phenylalanine could disrupt PLB1\u0026rsquo;s membrane association or catalytic efficiency, attenuating both lipid homeostasis and immunomodulatory lipid mediator synthesis [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite growing evidence for immunometabolic crosstalk in livestock, few studies have integrated performance phenotypes, blood transcriptomics, and high-resolution haplotype mapping to pinpoint functional QTNs for both feed efficiency and immune competence. Here, we interrogated SNP variability in \u003cem\u003eCYP3A4\u003c/em\u003e (\u003cem\u003ers438103222\u003c/em\u003e), \u003cem\u003eCRAT\u003c/em\u003e (\u003cem\u003ers876019788\u003c/em\u003e), and \u003cem\u003ePLB1\u003c/em\u003e (\u003cem\u003ers456635825\u003c/em\u003e) among crossbred steers divergently selected for RFI, profiling metabolic and immune gene expression in blood. By combining logistic regression, cis‐/trans‐eQTL analyses, and linkage‐haplotype reconstruction, we elucidate how these polymorphisms coordinate detoxification, lipid metabolism, and innate/adaptive immunity\u0026mdash;laying the groundwork for marker‐assisted selection strategies that simultaneously enhance productive efficiency and disease resilience in beef cattle [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cb\u003eExperimental Procedures and Data Collection\u003c/b\u003e\u003c/p\u003e\u003cp\u003e All animal protocols were reviewed and approved by the West Virginia University Animal Care and Use Committee (Protocol No. 2204052569). One hundred and eight crossbred growing beef steers (initial BW 217\u0026thinsp;\u0026plusmn;\u0026thinsp;8.2 kg) were housed in a confinement dry-lot and offered ad libitum access to a high‐forage total mixed ration and fresh water. Individual feed intake and body weight were recorded continuously over a 35‐day test using GrowSafe\u0026reg; intake nodes and automated weighing systems. Daily dry matter intake (DMI) was calculated from the real‐time intake records, and residual feed intake (RFI) was computed as the difference between observed DMI and predicted DMI based on maintenance and weight‐gain requirements\u0026mdash;as described by [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. At the end of the trial, steers were ranked by RFI, and 40 animals were randomly chosen for further molecular analyses: the 20 with the lowest RFI (most feed‐efficient) and the 20 with the highest RFI (least feed‐efficient). Daily body weights and actual DMI values for these 40 steers formed the basis for all subsequent performance and expression studies.\u003c/p\u003e\u003cp\u003e\u003cb\u003eBlood Collection, RNA Isolation, cDNA Synthesis and Gene expression\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOn day 35, whole blood was drawn from each steer via jugular venipuncture before the morning meal into sodium-heparin tubes. An aliquot of each sample was immediately transferred into Qiagen RNAprotect Blood Tubes, mixed according to the manufacturer\u0026rsquo;s instructions for mRNA stabilization, and stored at \u0026minus;\u0026thinsp;80\u0026deg;C until processing. Genomic DNA and total RNA were co‐isolated from the same blood specimens using Qiagen RNeasy Kits. DNA yield and purity were quantified on a NanoDrop 2000 (A260/A280 ratio), and integrity was confirmed by 1% agarose gel electrophoresis. Total RNA concentration was likewise measured on the NanoDrop, and integrity was assessed on an Agilent Bioanalyzer; only samples with RIN\u0026thinsp;\u0026gt;\u0026thinsp;8.0 and A260/A280 ratios between 1.8 and 2.0 were advanced to cDNA synthesis using the Qiagen RT\u0026sup2; First Strand Kit. Gene‐specific primers (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) were designed for three metabolic targets (CYP3A4, CRAT, PLB1) and eight immune markers (CD14, TLR4, TNF-α, CEBPB, ITGAM, IRF1, TLR2, RHOA, NANS). Quantitative PCR was performed on a Bio-Rad CFX Opus Real-Time System using initial denaturation at 95\u0026deg;C for 10 min, followed by 40 cycles of 95\u0026deg;C for 15 s and 60\u0026deg;C for 1 min. Relative transcript abundance in low-RFI versus high-RFI groups was calculated by the 2⁻ΔΔCt method in Bio-Rad Maestro, with β-actin and GAPDH serving as endogenous controls.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGene primers for selected metabolic and immune gene expression analysis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGene Name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrimer Sequence\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eProduct Length\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTm\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGC%\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCEBPB Forward\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAGAAGACGGTGGACAAGCAC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e60.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e55.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCEBPB Reverse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGTTGCGCATCTTGGCCTTG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e60.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e57.89\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIRF1 Forward\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eATCTTGTGGGGTGAAGCTGG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e59.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e55.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIRF1 Reverse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCTCCAAGGGGAAAGCTGGAG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e60.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e60.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRHOA Forward\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGATGTCCAACCCACCTGACC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e60.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e60.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRHOA Reverse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAATTAGCGCCTGGTGTGTCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e59.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e50.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNANS Forward\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGCTCTTTCCTGACATCCCCAT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e59.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e52.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNANS Reverse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGTTATGTGACGCTCCAAGACC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e59.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e52.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD-14 Forward\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGACACCAACCCGAAGCAGTA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e59.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e55.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD-14 Reverse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eACCAGAAGCTGAGCAGGAAC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e59.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e55.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTLR-2 Forward\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCTTCCTGTTGCTCCTGCTCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e59.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e55.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTLR-2 Reverse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCCTTCCTGGGCTTCCTCTTG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e60.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e60.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTLR-4 Forward\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGGTGGAGCTCTATCGCCTTC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e59.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e60.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTLR-4 Reverse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCTCTGGGGTTTACCAGCCAG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e60.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e60.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTNF-A Forward\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGGACACCCAGAATGTGAGGG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e60.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e60.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTNF-A Reverse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGGAGAGTTGAAGTCCACGCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e59.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e55.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eITGAM Forward\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAAGTTGAGGCGACGATGGAG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e60.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e55.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eITGAM Reverse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTTTCACCTGCCCAGCAATCT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e59.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e50.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCRAT Forward\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eATTCCTCCTCGCTCACGATG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e59.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCRAT Reverse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTTAAGGCACACCAGGACTCG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e59.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLBI Forward\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTAGAAGAAGGGCTGGAAGACG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e59.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e52.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLBI Reverse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTGACGGTACTCCTTTCTTCAGG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e59.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCYP3A4 Forward\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eACCTGGAAGTCCAGATGTTCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e58.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e47.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCYP3A4 Reverse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAGGAAATACCCATGTCCCTACC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e58.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eTaqMan SNP genotyping assay and allele discrimination\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSNP genotyping analysis was performed on SNPs of \u003cem\u003eCYP3A4\u003c/em\u003e (\u003cem\u003ers438103222\u003c/em\u003e), \u003cem\u003ePLB1\u003c/em\u003e (\u003cem\u003ers456635825\u003c/em\u003e), and \u003cem\u003eCRAT\u003c/em\u003e (\u003cem\u003ers876019788\u003c/em\u003e) using custom designed TaqMan SNP genotyping assays and ordered from (Thermo Fisher Scientific, Waltham MA). Each assay was conducted in a 10 \u0026micro;l reaction volume containing 1 \u0026micro;l of 20X TaqMan SNP genotyping assay, 5 \u0026micro;l of 2\u0026times; TaqMan Mastermix, 1 \u0026micro;l of 20 ng genomic DNA, and 3 \u0026micro;l of nuclease-free water. Real-time PCR was performed on a Bio-Rad CFX Opus machine (Bio-Rad, Hercules, CA) using the following conditions: 90\u0026deg;C for 10 minutes, followed by 30 cycles of 90\u0026deg;C for 30 seconds, 56\u0026deg;C for 30 seconds, and 72\u0026deg;C for 50 seconds. A final extension step was performed at 72\u0026deg;C for 5 minutes. Melt curve analysis was conducted to confirm assay specificity, with temperatures ranging from 65\u0026deg;C to 95\u0026deg;C in 0.5\u0026deg;C increments. Continuous fluorescent measurements were taken during this process. Allele calls and discriminations were generated using the Bio-Rad Maestro software (Bio-Rad, Hercules, CA).\u003c/p\u003e\u003cp\u003e\u003cb\u003eSNP Analysis and Association Testing /Statistical Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAllelic and genotypic frequencies for SNPs of CYP3A4 (rs438103222), PLB1 (rs456635825), and CRAT (rs876019788) were calculated using SNPstats [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Deviations from Hardy-Weinberg equilibrium was assessed, with SNPs rejected at a p-value threshold of 0.05. Association analysis between these SNPs, RFI status, performance characteristics and gene expressions data were conducted using Fisher's exact test [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Allelic and genotypic frequencies were compared between high and low RFI animal groups, as previously described. To further explore the relationship between genetic variants and RFI status, binary logistic regression was employed to evaluate associations with performance characteristics, and gene expression. Additionally, haplotype analysis was performed for the three SNPs, excluding animals heterozygous at multiple loci.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eMetabolic Gene SNP-Driven Variations as Determinants of Cattle Performance\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe evaluated the impact of gene polymorphisms on performance traits by conducting logistic regression analyses to assess genotypic frequency distributions, with significance levels determined for each SNP in \u003cem\u003eCYP3A4\u003c/em\u003e (\u003cem\u003ers438103222\u003c/em\u003e), \u003cem\u003ePLB1\u003c/em\u003e (\u003cem\u003ers456635825\u003c/em\u003e), and \u003cem\u003eCRAT\u003c/em\u003e (\u003cem\u003ers876019788\u003c/em\u003e). Various genetic models (codominant, dominant, and recessive) were applied to explore associations with initial weight (IW), final weight (FW), average daily weight gain (ADWG), total weight gain (TWG), and average daily feed intake (ADFI) (Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOur results show significant effects of polymorphisms in \u003cem\u003eCYP3A4\u003c/em\u003e and \u003cem\u003ePLB1\u003c/em\u003e, but not in \u003cem\u003eCRAT\u003c/em\u003e. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates that the mutant genotype (AA) of \u003cem\u003eCYP3A4\u003c/em\u003e is associated with the highest IW (223.33 kg) and FW (275.3 kg) compared to wild-type and heterozygous genotypes. The wild-type genotype (CC) showed the highest ADFI (8.67 kg). Heterozygotes (CA) maintained intermediate values across IW (210 kg), FW (255 kg), ADWG (1.3 kg), and TWG (45 kg), except for a lower ADFI (4.93 kg). Under the dominant model, AA animals achieved comparable TWG and ADWG to CC despite consuming only half as much feed, suggesting an enhanced feed conversion efficiency.\u003c/p\u003e\u003cp\u003eAt PLB1 rs456635825, shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the minor A allele exerts a dosage-dependent anabolic effect on bovine growth and feed efficiency: under an additive inheritance model, A/A steers displayed significantly higher initial body weight (225.5 kg vs. 213.4 kg in G/A; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), greater final weight (278.2 kg vs. 267.5 kg in G/A), elevated average daily weight gain (1.51 kg/d vs. 1.12 kg/d in G/G; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), augmented total weight gain (52.7 kg vs. 30.3 kg in G/G; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and reduced average daily feed intake (5.20 kg/d vs. 8.13 kg/d in G/G; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with heterozygous G/A phenotypes intermediate to homozygotes. A recessive model (A‐carriers vs. G/G) further confirmed the A allele\u0026rsquo;s effect\u0026mdash;carriers exhibited superior growth kinetics (+\u0026thinsp;0.41 kg/d ADWG; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), increased cumulative gain (+\u0026thinsp;23.3 kg TWG; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and lower feed intake (\u0026minus;\u0026thinsp;2.91 kg/d ADFI; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Although the dominant contrast (A/A vs. G‐carriers) trended similarly, statistical power was constrained by the small G/G cohort. These data identify the A allele at PLB1 rs456635825 as a potent quantitative trait nucleotide for marker‐assisted selection aimed at enhancing bovine production metrics.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGenotypic effect of \u003cem\u003eCYP3A4 (rs438103222)\u003c/em\u003e polymorphism on performance characteristics of cattle\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePolymorphism\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGenotype\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIW\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFW\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eADWG\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTWG\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eADFIM\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCYP3A4 (rs438103222)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC/C\u003c/p\u003e\u003cp\u003eC/A\u003c/p\u003e\u003cp\u003eA/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e218.25 (5.15)\u003c/p\u003e\u003cp\u003e210.38(6.06)\u003c/p\u003e\u003cp\u003e223.33(13.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e271.92 (5.44)\u003c/p\u003e\u003cp\u003e255.83 (7.74)\u003c/p\u003e\u003cp\u003e275.3 (13.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.53 (0.04)\u003c/p\u003e\u003cp\u003e1.3 (0.1)\u003c/p\u003e\u003cp\u003e1.48 (0.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e52.4 (2.29)\u003c/p\u003e\u003cp\u003e45.45(3.42)\u003c/p\u003e\u003cp\u003e51.97 (2.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.67 (3.32)\u003c/p\u003e\u003cp\u003e4.93 (0.13)\u003c/p\u003e\u003cp\u003e4.73 (0.19)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDominant\u003c/p\u003e\u003cp\u003eC/C\u003c/p\u003e\u003cp\u003eC/A - A/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e218.25(5.15)\u003c/p\u003e\u003cp\u003e216.86(7.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e271.92 (5.44)\u003c/p\u003e\u003cp\u003e265.57 (8.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.53 (0.04)\u003c/p\u003e\u003cp\u003e1.39 (0.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e52.4 (2.29)\u003c/p\u003e\u003cp\u003e48.71 (2.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.67 (3.32)\u003c/p\u003e\u003cp\u003e4.83 (0.11)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRecessive\u003c/p\u003e\u003cp\u003eC/C - C/A\u003c/p\u003e\u003cp\u003eA/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e216.86(4.37)\u003c/p\u003e\u003cp\u003e223.33(13.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e269.08 (4.76)\u003c/p\u003e\u003cp\u003e275.3 (13.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.49(0.04)\u003c/p\u003e\u003cp\u003e1.48 (0.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e51.17 (2.02)\u003c/p\u003e\u003cp\u003e51.97 (2.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.01 (2.73)\u003c/p\u003e\u003cp\u003e4.73 (0.19)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGenotypic effect of \u003cem\u003ePLB1 (rs456635825)\u003c/em\u003e polymorphism on performance characteristics of cattle\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGenotype\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIW\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFW\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eADWG\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTWG\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eADFI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLB1 (rs456635825)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eA/A\u003c/p\u003e\u003cp\u003eG/A\u003c/p\u003e\u003cp\u003eG/G\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e225.49(8.36) *\u003c/p\u003e\u003cp\u003e213.39(4.99)\u003c/p\u003e\u003cp\u003e221.48(15.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e278.18 (8.26)\u003c/p\u003e\u003cp\u003e267.48 (5.56)\u003c/p\u003e\u003cp\u003e267.48 (5.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.51 (0.04) **\u003c/p\u003e\u003cp\u003e1.55 (0.05)\u003c/p\u003e\u003cp\u003e1.12 (0.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e52.69 (1.43) ***\u003c/p\u003e\u003cp\u003e54.09 (1.68)\u003c/p\u003e\u003cp\u003e30.31 (8.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.2 (0.19) *\u003c/p\u003e\u003cp\u003e5.24 (0.14)\u003c/p\u003e\u003cp\u003e8.13 (3.33)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDominant\u003c/p\u003e\u003cp\u003eA/A\u003c/p\u003e\u003cp\u003eG/A - G/G\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e225.49(8.36)\u003c/p\u003e\u003cp\u003e214.55(4.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e278.18(8.26)\u003c/p\u003e\u003cp\u003e266.51 (5.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.51 (0.04)\u003c/p\u003e\u003cp\u003e1.48 (0.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e52.69 (1.43)\u003c/p\u003e\u003cp\u003e50.69 (2.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.2 (0.19)\u003c/p\u003e\u003cp\u003e8.51 (3.32)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRecessive\u003c/p\u003e\u003cp\u003eA/A - G/A\u003c/p\u003e\u003cp\u003eG/G\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e217.42(4.38)\u003c/p\u003e\u003cp\u003e221.48(15.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e271.05(4.63)\u003c/p\u003e\u003cp\u003e260.68 (18.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.53 (0.03) ***\u003c/p\u003e\u003cp\u003e1.12 (0.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e53.62(1.21) ***\u003c/p\u003e\u003cp\u003e30.31 (8.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.22 (0.11) **\u003c/p\u003e\u003cp\u003e8.13 (3.33)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFor CRAT (rs876019788), the genotypic effects are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The valine-to-phenylalanine allelic substitution exerts no consistent modulatory effect on bovine growth or feed utilization. Under an additive model, A/A homozygotes (IW 213.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9 kg; FW 267.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4 kg; ADWG 1.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05 kg/d; TWG 53.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8 kg; ADFI 5.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12 kg/d) and C/C homozygotes (IW 222.6\u0026thinsp;\u0026plusmn;\u0026thinsp;7.2 kg; FW 274.0\u0026thinsp;\u0026plusmn;\u0026thinsp;7.6 kg; ADWG 1.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04 kg/d; TWG 51.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4 kg; ADFI 5.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19 kg/d) display virtually overlapping weight-gain kinetics and feed intake, while A/C heterozygotes exhibit aberrantly high intake variance (ADFI 23.72\u0026thinsp;\u0026plusmn;\u0026thinsp;18.61 kg/d) without proportional gain enhancement. Dominant (A-carriers vs. C/C) and recessive (A/A vs. C-carriers) contrasts further confirm negligible genotype-phenotype associations in ADWG, TWG, and ADFI. Collectively, rs876019788 does not qualify as a quantitative trait nucleotide for feed efficiency or growth metrics in beef cattle\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGenotypic effect of \u003cem\u003eCRAT (rs876019788)\u003c/em\u003e polymorphism on performance characteristics of cattle\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGenotype\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIW\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFW\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eADWG\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTWG\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eADFI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCRAT (rs876019788)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eA/A\u003c/p\u003e\u003cp\u003eA/C\u003c/p\u003e\u003cp\u003eC/C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e213.82(4.88)\u003c/p\u003e\u003cp\u003e231.82(17.33)\u003c/p\u003e\u003cp\u003e222.61(7.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e267.1(5.41)\u003c/p\u003e\u003cp\u003e279.36 (18.41)\u003c/p\u003e\u003cp\u003e273.98 (7.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.52 (0.05)\u003c/p\u003e\u003cp\u003e1.36 (0.08)\u003c/p\u003e\u003cp\u003e1.47 (0.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e53.28 (1.79)\u003c/p\u003e\u003cp\u003e40.43 (9.15)\u003c/p\u003e\u003cp\u003e51.36 (1.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.2 (0.12)\u003c/p\u003e\u003cp\u003e23.72 (18.61)\u003c/p\u003e\u003cp\u003e5.19 (0.19)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDominant\u003c/p\u003e\u003cp\u003eA/A\u003c/p\u003e\u003cp\u003eA/C - C/C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e213.82(4.88)\u003c/p\u003e\u003cp\u003e226.15(7.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e267.1(5.41)\u003c/p\u003e\u003cp\u003e276.05 (8.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.52 (0.05)\u003c/p\u003e\u003cp\u003e1.43 (0.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e53.28 (1.79)\u003c/p\u003e\u003cp\u003e47.16 (3.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.2 (0.12)\u003c/p\u003e\u003cp\u003e12.32 (7.15)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRecessive\u003c/p\u003e\u003cp\u003eA/A - A/C\u003c/p\u003e\u003cp\u003eC/C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e216.63(4.93)\u003c/p\u003e\u003cp\u003e222.61(7.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e269.02 (5.31)\u003c/p\u003e\u003cp\u003e273.98 (7.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.5 (0.05)\u003c/p\u003e\u003cp\u003e1.47 (0.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e51.27 (2.16)\u003c/p\u003e\u003cp\u003e51.36 (1.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.1 (2.91)\u003c/p\u003e\u003cp\u003e5.19 (0.19)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAcross both high- and low-RFI cohorts from Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, CYP3A4 (rs438103222) exhibits a pronounced heterozygote disadvantage: C/A steers show significantly reduced average daily weight gain (ADWG) and total weight gain (TWG) relative to C/C and A/A homozygotes (e.g., high-RFI C/A: 1.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07 kg/d vs. C/C: 1.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06 kg/d and A/A: 1.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08; low-RFI C/A: 0.91 kg/d vs. C/C: 1.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06 and A/A: 1.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19), implicating disrupted CYP3A4 catalytic efficiency when glycine/cysteine residues are paired. In stark contrast, PLB1 (rs456635825) minor-allele homozygotes (G/G) demonstrate a recessive growth penalty with ADWG reducing to 1.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01 kg/d and aberrant ADFI (51.46 kg/d) in high-RFI steers versus ~\u0026thinsp;1.53 kg/d ADWG and ~\u0026thinsp;5.2 kg/d ADFI in A-allele carriers signifying deleterious effects of the leucine-to-phenylalanine substitution on phospholipase B1 function. Conversely, the CRAT (rs876019788) valine-to-phenylalanine polymorphism fails to produce genotype-dependent differences in initial or final weight, ADWG, TWG, or ADFI within either RFI group, indicating its negligible role in feed-efficiency phenotypes. These patterns position CYP3A4 and PLB1 as key quantitative trait nucleotides for marker-assisted selection to optimize bovine growth and feed conversion relative to RFI status, while CRAT lacks actionable significance.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEffect of CYP3A4(rs438103222), PLB1 (rs456635825) and CRAT (rs876019788) gene loci interaction on performance characteristics between high and low RFI cattle groups\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRFI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGene\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGenotype\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIW\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFW\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eADWG\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTWG\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eADFI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCYP3A4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eC/C\u003c/p\u003e\u003cp\u003eC/A\u003c/p\u003e\u003cp\u003eA/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e231.40(8.92)\u003c/p\u003e\u003cp\u003e214.18(5.78)\u003c/p\u003e\u003cp\u003e216.82(16.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e285.99 (9.5)\u003c/p\u003e\u003cp\u003e262.36 (5.08)\u003c/p\u003e\u003cp\u003e269.2 (18.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.56 (0.06)\u003c/p\u003e\u003cp\u003e1.38 (0.07)\u003c/p\u003e\u003cp\u003e1.5 (0.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e51.35(4.88)\u003c/p\u003e\u003cp\u003e48.18 (2.53)\u003c/p\u003e\u003cp\u003e52.39 (2.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e13.99 (8.42)\u003c/p\u003e\u003cp\u003e4.94 (0.16)\u003c/p\u003e\u003cp\u003e4.71 (0.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eC/C\u003c/p\u003e\u003cp\u003eC/A\u003c/p\u003e\u003cp\u003eA/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e209.73(5.47)\u003c/p\u003e\u003cp\u003e191.36(N/A)\u003c/p\u003e\u003cp\u003e236.36(30.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e262.81 (5.69) *\u003c/p\u003e\u003cp\u003e223.18 (---) *\u003c/p\u003e\u003cp\u003e287.5 (23.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.52 (0.06)\u003c/p\u003e\u003cp\u003e0.91 (---) *\u003c/p\u003e\u003cp\u003e1.46 (0.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e53.07(2.21)\u003c/p\u003e\u003cp\u003e31.82 (---)\u003c/p\u003e\u003cp\u003e51.14 (6.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.22 (0.17)\u003c/p\u003e\u003cp\u003e4.87 (---)\u003c/p\u003e\u003cp\u003e4.77 (0.03)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePLB1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eA/A\u003c/p\u003e\u003cp\u003eG/A\u003c/p\u003e\u003cp\u003eG/G\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e225.34(11.31)\u003c/p\u003e\u003cp\u003e225.27(8.57)\u003c/p\u003e\u003cp\u003e214.09(2.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e277.84 (11.82)\u003c/p\u003e\u003cp\u003e280.32 (9.36)\u003c/p\u003e\u003cp\u003e254.32 (2.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.5 (0.04)\u003c/p\u003e\u003cp\u003e1.57 (0.06)\u003c/p\u003e\u003cp\u003e1.15 (0.01) *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e52.5 (1.38) / 0.00\u003c/p\u003e\u003cp\u003e55.05 (1.99)\u003c/p\u003e\u003cp\u003e22.44 (17.56) *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.07 (0.25)\u003c/p\u003e\u003cp\u003e5.4 (0.2)\u003c/p\u003e\u003cp\u003e51.46 (46.67) *\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eA/A\u003c/p\u003e\u003cp\u003eG/A\u003c/p\u003e\u003cp\u003eG/G\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e225.80(13.12)\u003c/p\u003e\u003cp\u003e204.90(5.09)\u003c/p\u003e\u003cp\u003e228.86(37.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e278.86 (9.9)\u003c/p\u003e\u003cp\u003e258.31 (5.89)\u003c/p\u003e\u003cp\u003e267.05 (43.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.52 (0.11)\u003c/p\u003e\u003cp\u003e1.53 (0.07)\u003c/p\u003e\u003cp\u003e1.09 (0.18) *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e53.07 (3.69)\u003c/p\u003e\u003cp\u003e53.41 (2.55)\u003c/p\u003e\u003cp\u003e38.18 (6.36) *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.47 (0.27)\u003c/p\u003e\u003cp\u003e5.12 (0.19)\u003c/p\u003e\u003cp\u003e4.8 (0.07)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCRAT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eA/A\u003c/p\u003e\u003cp\u003eA/C\u003c/p\u003e\u003cp\u003eC/C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e222.60(7.22)\u003c/p\u003e\u003cp\u003e227.84(21.78)\u003c/p\u003e\u003cp\u003e224.73(11.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e277.23 (8.13)\u003c/p\u003e\u003cp\u003e276.36 (23.45)\u003c/p\u003e\u003cp\u003e275.91 (12.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.56 (0.06)\u003c/p\u003e\u003cp\u003e1.39 (0.1)\u003c/p\u003e\u003cp\u003e1.46 (0.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e54.63 (2.15)\u003c/p\u003e\u003cp\u003e39.63(11.77) *\u003c/p\u003e\u003cp\u003e51.18 (1.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.31 (0.16)\u003c/p\u003e\u003cp\u003e28.22 (23.31) *\u003c/p\u003e\u003cp\u003e5.23 (0.31)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eA/A\u003c/p\u003e\u003cp\u003eA/C\u003c/p\u003e\u003cp\u003eC/C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e207.78(6.32)\u003c/p\u003e\u003cp\u003e247.73(N/A)\u003c/p\u003e\u003cp\u003e219.09(7.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e260.14 (6.89)\u003c/p\u003e\u003cp\u003e291.36 (---)\u003c/p\u003e\u003cp\u003e270.76 (5.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.5 (0.08)\u003c/p\u003e\u003cp\u003e1.25 (---)\u003c/p\u003e\u003cp\u003e1.48 (0.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e52.36 (2.67)\u003c/p\u003e\u003cp\u003e43.64 (---)\u003c/p\u003e\u003cp\u003e51.67 (3.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.13 (0.18)\u003c/p\u003e\u003cp\u003e5.71 (---)\u003c/p\u003e\u003cp\u003e5.13 (0.17)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eDifferential Metabolic Transcriptome Profiles in Low- vs. High-RFI Cattle\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAcross Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, blood-based cis‐eQTL mapping under divergent RFI phenotypes consistently demonstrates that the A allele at both CYP3A4 (rs438103222) and PLB1 (rs456635825) drives robust, allele-dosage\u0026ndash;dependent up-regulation of its own transcript in efficient (low-RFI) cattle. Specifically, in low-RFI steers, CYP3A4 A/A homozygotes exhibit a 2.5-fold increase over C/C and a 1.8-fold rise relative to C/A (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while PLB1 A/A animals show a 2.1-fold augmentation versus G/G and intermediate G/A expression (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Importantly, these allele-specific expression differences are abrogated in high-RFI cohorts, underscoring a feed-efficiency\u0026ndash;dependent enhancer effect. In contrast, CRAT (rs876019788) transcripts remain invariant across A/A, A/C, and C/C genotypes in both RFI groups (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec; ns), indicating that its valine-to-phenylalanine substitution does not perturb cis-regulatory control.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e expands this regulatory landscape by revealing PLB1\u0026rsquo;s A allele as a bifunctional quantitative trait nucleotide: beyond its cis-eQTL effect, low-RFI A/A and G/A steers also display significant trans-activation of CYP3A4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and CRAT (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) transcripts, linking augmented phospholipase B1\u0026ndash;mediated lipid remodeling to broader immunometabolic adaptation. High-RFI animals, however, lack this trans-regulatory cross-talk, highlighting a genotype\u0026times;environment interaction that reinforces feed-efficiency endophenotypes. These data suggest CYP3A4 and PLB1 as important quantitative trait nucleotides whose allele-specific cis- and trans-regulatory activities drive key immunometabolic networks in beef cattle, while positioning CRAT as a distal regulatory hub subject to post-transcriptional modulation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFurthermore, in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea\u0026ndash;c, the CYP3A4 rs438103222 A allele in low-RFI steers drives a 1.5\u0026ndash;2.0-fold up-regulation of CD14, TLR4, TNF-α, CEBPB, and ITGAM (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u0026ndash;0.01), evidencing an allele-specific amplification of innate immune signaling absent in high-RFI A-carriers; concurrently, PLB1 rs456635825 A/A homozygotes in low-RFI cattle not only augment PLB1 transcripts but also trans-activate TLR2, RHOA, and NANS by 1.4\u0026ndash;1.8-fold (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), linking phospholipid remodeling to cytoskeletal dynamics and sialic-acid\u0026ndash;mediated immune pathways effects that are intermediate in G/A heterozygotes and abolished in high-RFI cohorts; by contrast, CRAT rs876019788 variants elicit no significant modulation of immune-gene expression in either RFI group (ns), underscoring its post-transcriptional regulatory role. Together, these findings designate CYP3A4 and PLB1 as bifunctional quantitative trait nucleotides that co-regulate metabolic detoxification and immune priming specifically in feed-efficient cattle.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eAllelic Co-Inheritance and Haplotypic Effects on RFI\u003c/b\u003e\u003c/p\u003e\u003cp\u003eLinkage disequilibrium (LD) was analyzed among three SNPs: CYP3A4 (rs438103222), PLB1 (rs456635825), and CRAT (rs876019788) (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). LD values range from 0 to 1, with higher values indicating stronger correlations between loci. Statistical significance was determined using a predefined p-value threshold. LD analysis reveals a highly non-random allelic co-inheritance between CYP3A4(rs438103222) and PLB1(rs456635825) (D\u0026thinsp;=\u0026thinsp;0.8981, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), a moderate but significant haplotypic association between PLB1(rs456635825) and CRAT(rs876019788) (D\u0026thinsp;=\u0026thinsp;0.2135, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and a minimal yet statistically significant disequilibrium between CYP3A4(rs438103222) and CRAT(rs876019788) (D\u0026thinsp;=\u0026thinsp;0.0463, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), collectively indicating non-random allelic segregation and suggesting putative epistatic interactions that could underlie feed-efficiency and immunometabolic quantitative trait loci.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLinkage disequilibrium analysis of CYP3A4, PLB1 and CRAT genetic polymorphisms\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD-stat/p-values\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCYP3A4(rs438103222)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003ePLB1 (rs456635825)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eCRAT (rs876019788)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCYP3A4(rs438103222)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePLB1 (rs456635825)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.8981\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCRAT (rs876019788\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0463\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, haplotype reconstruction across the three loci generated eight discrete multi-locus allelic combinations (H1\u0026ndash;H8), each defined by the phased variants at CYP3A4 (C/A), PLB1 (G/A), and CRAT (C/A) whose frequencies differ markedly between efficient (low‐RFI) and inefficient (high‐RFI) steers. The C-A-A haplotype (H1) is ubiquitous in low‐RFI cattle (42.5%) than in high‐RFI cattle (28.6%; p\u0026thinsp;=\u0026thinsp;1\u0026times;10⁻⁵), underscoring it as a protective architecture for efficient feed conversion. Conversely, the C-A-C haplotype (H3) is under‐represented in low‐RFI steers (7.5% vs. 13.5% in high‐RFI; OR\u0026thinsp;=\u0026thinsp;0.50, p\u0026thinsp;=\u0026thinsp;0.005), identifying it as a risk haplotype for inefficiency. Haplotypes H7 (A-A-A, 11.4% in high‐RFI only) and H8 (A-G-C, 3.5% in high‐RFI only) were exclusive to inefficient animals, further defining allelic combinations that predispose to poor feed utilization.\u003c/p\u003e\u003cp\u003eOther haplotypes (H2, H4, H5, H6) displayed intermediate frequencies with non-significant odds ratios, indicating neutral or context‐dependent effects. The clear divergence in H1 and H3 frequencies and the emergence of H7/H8 solely in the high‐RFI cohort reveals both additive and non‐additive (epistatic) interactions among CYP3A4, PLB1, and CRAT variants while reflecting a partial penetrance of the PLB1-G and CRAT-C alleles. These non-random allelic co-segregations (linkage disequilibrium D\u0026rsquo;\u0026gt;0.21 for significant pairs) reveal quantitative trait haplotypes that likely underpin immunometabolic resilience and feed-efficiency QTL. Such defined haplotype architectures provide high-resolution targets for marker-assisted selection aimed at optimizing bovine growth, feed conversion, and disease resistance.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEstimated haplotype frequencies of CYP3A4 (rs438103222), PLB1 (rs456635825) and CRAT (rs876019788) gene loci between high and low RFI cattle groups\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHaplotype\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eHaplotype definition\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eHaplotype frequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eCYP3A4 (+\u0026thinsp;328C\u0026thinsp;\u0026gt;\u0026thinsp;A)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003ePLB1\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(+\u0026thinsp;1008\u003c/b\u003e\u003cb\u003eG\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/b\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eCRAT\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(+\u0026thinsp;296A\u0026thinsp;\u0026gt;\u0026thinsp;C)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eHigh RFI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eLow RFI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e\u003cb\u003eHigh RFI vs Low RFI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003eOR (95% CI)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003ep\u003c/b\u003e \u003cb\u003evalue\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.2862\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.425\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.3544\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.00001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.1888\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.325\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.2581\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e1.19 (0.16\u0026ndash;8.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.1345\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.1062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e0.50 (0.07\u0026ndash;3.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.1155\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0813\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e1.06 (0.14\u0026ndash;7.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.960\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0612\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0669\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e1.21 (0.18\u0026ndash;7.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0655\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0581\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e2.09 (0.27\u0026ndash;6.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.1138\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0345\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur comprehensive analyses establish CYP3A4 and PLB1 as principal quantitative trait nucleotides (QTNs) modulating bovine feed efficiency, while the CRAT variant proves phenotypically inert. The allele-dosage\u0026ndash;dependent performance gains particularly the superior feed conversion of CYP3A4 A/A homozygotes and the anabolic effect of the PLB1 A allele underscore the importance of single‐locus regulatory variation in shaping residual feed intake (RFI) phenotypes [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The deleterious heterozygote disadvantage at CYP3A4 further illuminates how mixed amino‐acid substitutions can disrupt enzyme kinetics, highlighting protein‐coding context as a critical consideration in QTN discovery [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAt the transcriptomic level, both CYP3A4 and PLB1 variants function as feed-efficiency-dependent cis‐eQTLs, driving robust up‐regulation of their own transcripts exclusively in low‐RFI cattle thus linking transcriptional control of xenobiotic metabolism and phospholipid remodeling to growth efficiency [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Moreover, PLB1\u0026rsquo;s A allele exerts trans‐regulatory cross‐talk by amplifying CYP3A4 and CRAT expression and priming innate‐immune effectors, thereby integrating detoxification pathways with pathogen‐sensing circuits in efficient animals [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The absence of CRAT\u0026rsquo;s cis‐regulatory impact suggests its polymorphism operates primarily via post‐translational or enzymatic mechanisms within carnitine‐dependent energy pathways.\u003c/p\u003e\u003cp\u003eLinkage disequilibrium and haplotype reconstruction reveal a modular allelic architecture: strong LD between CYP3A4 and PLB1, moderate LD with CRAT, and two core low-RFI haplotypes that capture additive and epistatic synergies driving feed‐efficiency gains. This \u0026ldquo;supergene\u0026rdquo;‐like arrangement mirrors well‐characterized QTL clusters in livestock and underscores the value of multi‐locus haplotypes for explaining phenotypic variance beyond single SNPs [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTranslationally, these findings advocate for the integration of CYP3A4 and PLB1 QTNs, and their phased haplotypes into genomic selection frameworks. Incorporating such high-impact loci into genomic best linear unbiased prediction (GBLUP) or Bayesian models has been shown to enhance predictive accuracy by capturing both additive and non‐additive genetic variance [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Future validation in diverse breeds and environments, coupled with multi-omics approaches (proteomics, metabolomics, chromatin conformation, and methylation profiling), will refine our understanding of the regulatory topology and fitness trade-offs underpinning feed efficiency and immunometabolic resilience [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Ultimately, leveraging these immunometabolic QTNs and haplotypes in marker-assisted selection promises to accelerate genetic gain for productivity and animal health, forging more sustainable and resilient beef cattle production systems.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, our integrated analysis demonstrates that specific alleles of CYP3A4 (rs438103222) and PLB1 (rs456635825) constitute high-impact quantitative trait nucleotides that drive superior growth, feed conversion, and immunometabolic coordination in beef cattle, while the CRAT (rs876019788) variant lacks functional significance for feed‐efficiency traits. Through logistic regression, we showed that CYP3A4 A/A homozygotes and PLB1 A‐allele carriers achieve greater weight gains on reduced feed intake, and allele‐dosage effects at both loci manifest as feed‐efficiency\u0026ndash;dependent cis‐ and trans‐regulatory enhancements of detoxification, lipid‐remodeling and innate‐immune pathways in low‐RFI animals. Concurrently, strong linkage disequilibrium between CYP3A4 and PLB1 and the identification of core low‐RFI haplotypes underscore a modular genetic architecture wherein additive and epistatic allelic synergies underpin phenotypic gains. These findings not only validate CYP3A4 and PLB1 as prime targets for marker‐assisted and genomic selection but also highlight the value of phased haplotypes in capturing complex variance. Moving forward, validating these loci across diverse breeds and environments\u0026mdash;alongside multi‐omics interrogation of post‐translational modifications, chromatin topology and metabolic flux\u0026mdash;will refine selection strategies and accelerate the sustainable genetic improvement of feed efficiency and animal health in commercial beef production.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll experimental protocols were conducted in accordance with the approval of the Institutional Review Board / Ethics committee of Animal Care and Use Committees of West Virginia University (protocol number 1608003693).This study was carried out in compliance with the ARRIVE guidelines 2.0 (https://arriveguidelines.org/). All methods were carried out in accordance with relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of supporting data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll datasets are presented in this manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors declare no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by the West Virginia University Experimental Station in support of the U.S. Department of Agriculture hatch multi-state regional project W-3010.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIMO and OBM conceived the idea; GT, MI, NDA, FS and AG carried out the experiment and lab analysis; OBM, REK, LMG and BO carried out the statistical analysis; LMG, BO, REK, IMO and OBM drafted the initial manuscript; GT, MI, OBM and IMO revised the final version for submission. All authors agree with the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOBM was supported by the USDA-NIFA research grant 2023-67016-39917. We are grateful to the Division of Biological and Health Sciences, University of Pittsburgh, Bradford for ongoing support.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHerd RM, Arthur PF. Physiological basis for residual feed intake. J Anim Sci. 2009;87(suppl14):E56\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHerd RM, Archer JA, Arthur PF. Reducing the cost of beef production through genetic improvement in residual feed intake: Opportunity and challenges to application. J Anim sci. 2003;81(13suppl1):E9\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVan Soest PJ. Nutritional Ecology of the Ruminant. 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Proc Natl Acad Sci USA. 2010;107(19):8689\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Immunometabolism, RFI, gene polymorphisms, haplotype, immunocompetence","lastPublishedDoi":"10.21203/rs.3.rs-7031821/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7031821/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWe evaluated genetic markers for feed efficiency and immunocompetence in 108 crossbred steers (217\u0026thinsp;\u0026plusmn;\u0026thinsp;8.2 kg) fed a high-forage total mixed ration for 35 days, using GrowSafe8000 intake nodes to calculate residual feed intake (RFI). From the 20 most efficient (low-RFI) and 20 least efficient (high-RFI) animals, we genotyped three metabolic loci (CYP3A4 rs438103222, PLB1 rs456635825, CRAT rs876019788) and profiled blood mRNA levels of these, plus eight innate/adaptive immune genes. Logistic regression revealed that CYP3A4 and PLB1 polymorphisms, but not CRAT, were strongly associated with initial and final body weight, average daily gain, and feed intake: CYP3A4 A/A and PLB1 A-allele carriers achieved superior growth on reduced feed. Haplotype reconstruction across the three loci defined eight multi-SNP combinations, with the C-A-A haplotype enriched in low-RFI steers and combinations harboring CYP3A4 A and PLB1 A alleles linked to low RFI. Intriguingly, these favorable genotypes also overlapped with up-regulation of immune sensors and effectors (e.g., CD14, TLR4, TNF-α), indicating a coordinated metabolic\u0026ndash;immune adaptation in efficient cattle. Collectively, our results validate CYP3A4 and PLB1 as high-impact quantitative trait nucleotides for marker-assisted selection aimed at simultaneously improving feed efficiency and immune resilience in beef production.\u003c/p\u003e","manuscriptTitle":"Allelic Variation in CYP3A4 and PLB1 Drives Feed Efficiency and Immunometabolic Resilience in Beef Cattle","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-23 07:33:05","doi":"10.21203/rs.3.rs-7031821/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-03T11:42:52+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-01T01:15:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"178420038241610038633466231013536868962","date":"2025-09-17T07:00:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-29T06:39:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"106298911076612768063249418154834181346","date":"2025-07-21T01:18:14+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-20T22:30:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-14T18:53:09+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-09T15:06:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-09T02:20:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Genomics","date":"2025-07-09T02:17:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"af7ce4da-aa8b-412a-9f9a-5b709d32a689","owner":[],"postedDate":"July 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-22T16:06:57+00:00","versionOfRecord":{"articleIdentity":"rs-7031821","link":"https://doi.org/10.1186/s12864-025-12412-0","journal":{"identity":"bmc-genomics","isVorOnly":false,"title":"BMC Genomics"},"publishedOn":"2025-12-18 15:58:28","publishedOnDateReadable":"December 18th, 2025"},"versionCreatedAt":"2025-07-23 07:33:05","video":"","vorDoi":"10.1186/s12864-025-12412-0","vorDoiUrl":"https://doi.org/10.1186/s12864-025-12412-0","workflowStages":[]},"version":"v1","identity":"rs-7031821","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7031821","identity":"rs-7031821","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}