Steffimol Rose Chacko Kaitholil

PubMed2026

Genome-wide cis-expression Quantitative Trait Loci (eQTL) and transcriptomic signals reveal distinct molecular regulation across correlated feed efficiency traits.

INTRODUCTION: Feed efficiency (FE) is a complex trait which determines livestock production profitability, yet the molecular mechanisms behind it remain unclear. This study investigated the blood transcriptomic profile of lambs, alongside genotype data with the aim to uncover the genetic basis of FE traits such as absolute dry matter intake (DMIabsolute), DMI adjusted for body size (DMIadjusted), average daily live weight gain (ADG), and residual feed intake (RFI). MATERIALS AND METHODS: Bulk RNA-Seq and genotype data were analysed using three complementary approaches: differential gene expression (DGE) analysis, weighted gene co-expression network analysis (WGCNA), and cis-expression Quantitative Trait Loci (cis-eQTL) mapping. These methods were used independently to identify genes and regulatory networks associated with FE traits and to investigate evidence supporting multi-trait candidate gene selection. RESULTS: DGE analysis revealed 2, 24, 85 and 4 differentially expressed genes for DMIabsolute, DMIadjusted, ADG, and RFI (Padjusted < 0.05), functionally enriched in sensory perception, ATP-dependent chromatin remodeling, Notch signaling and immune response pathways. 9 gene modules significantly associated with the FE traits (P ≤ 0.05) with correlations ranging from r = -0.56 to 0.49, were identified using WGCNA. Single nucleotide polymorphism (SNP)-level cis-eQTL analysis identified 93 eSNPs associated with 74 genes (false discovery rate (FDR) < 0.05), while permutation-derived gene level analysis identified 280 eGenes (FDR < 0.2, empirical P < 0.03). Across the three analyses, applying thresholds of DGE (Padjusted < 0.05), WGCNA (correlation, P ≤ 0.05), and cis-eQTL gene-level significance (empirical P < 0.05), multiple overlapping genes were identified including DNMT3A, KANSL1, NCOR1 for DMIadjusted, ACOX2, FANCF, CIMIP2B, LOC101115106, ARMH2, LOC132657496 for ADG, and LOC114114576 for RFI representing regulators of variations in FE. DISCUSSION: The integration of DGE, WGCNA, and cis-eQTL analyses identified key genes and regulatory mechanisms associated with variation in FE traits. These results highlight that integrated multi-trait candidate gene identification approaches can reveal key genes that lower feed intake while maintaining animal growth, supporting breeding strategies aimed at improving efficiency and long-term economic sustainability in sheep.

average daily gain (ADG)↗

PubMed2026

Blood-derived gene expression profiles associated with dietary microalgae oil intake and methane emission variation in lambs.

BACKGROUND: Minimising methane (CH4) emissions from livestock production is a global priority, and feed modifications, such as supplementing diets with microalgae, have previously been shown to help reducing enteric CH4 production. This study explored blood-derived host gene expression profiles from twenty lambs supplemented with increasing levels of microalgae oil to investigate their transcriptional responses associated with varying microalgae oil levels while also exploring the host systemic responses towards varied CH4 productions. RESULTS: Findings revealed no significant changes in CH4 production with increasing levels of microalgae oil intake through phenotypic analysis (P = 0.18). However inter-individual variations in CH4 production ranged from 27.02 to 47.86 g/day throughout the study period. Blood RNA-Sequencing identified 64 significant genes including DHCR7, DHCR24, HMGCS1, INSIG1, LSS, MSMO1, and SQLE, which were involved in lipid metabolism, and steroid biosynthesis that became enriched alongside increasing microalgae oil intake levels thereby contributing to a positive impact on lambs' metabolic functions. Additionally, seven significant blood-expressed host genes (NME4, MARCHF3, PLXNB3, LOC132657460, LOC121819234, LOC105603087, LOC101116551) functionally enriched in nucleotide metabolic pathways and immune responses were identified to have significant positive associations with increasing CH4 production. Importantly, this study found no overlap between genes associated with microalgae oil intake and those linked to CH4 emissions. CONCLUSIONS: Findings suggest that microalgae oil intake and inter-individual variations in CH₄ production are associated with distinct blood-derived transcriptional responses. Although such signals should be interpreted as proxies for systemic host responses rather than direct measures of rumen-specific processes, these results emphasise the importance of considering host-associated molecular variations alongside dietary CH₄-mitigation strategies.

Animals↗