Coat color and birth weight, as easily selected traits in cattle, play important roles in cattle breeding. Therefore, we carried out a genome-wide association study on birth weight and coat color to identify loci or potential linkage regions in 233 Simmental × Holstein crossbred beef cattle. The results revealed that nine SNPs were significantly associated with coat color (rs137169378, rs110022687, rs136002689, Hypotrichosis_PMel17, PMEL_1, rs134930689, rs383170073, rs109924971, and rs109146332), and these were in RNF41, ZC3H10, ERBB3, PMEL, and OR10A7 on BTA5. Interestingly, rs137169378, rs110022687, rs136002689, Hypotrichosis_PMel17, and PMEL_1 showed strong linkage disequilibrium (r² > 0.8) and were significantly associated with coat color. Notably, Hypotrichosis_PMel17 and PMEL_1 were located in the gene PMEL (p = 2.22 × 10⁻¹⁸). Among the five significant SNPs associated with coat color, the birth weight of heterozygous individuals (AB) was greater than that of homozygous individuals (AA). Notably, the birth weight of heterozygous individuals with Hypotrichosis_PMel17 and PMEL_1 genotypes was significantly greater than that of homozygous individuals (0.01 < p < 0.05). Interestingly, the two loci were homozygous in black/white individuals and heterozygous in gray/white individuals, and the birth weight of heterozygous brown/white individuals (43.82 ± 5.25 kg) was greater than that of homozygous individuals (42.58 ± 3.09 kg). The birth weight of calves with the parental color (41.95 ± 3.53 kg) was significantly lower than that of calves with a non-parental color (43.54 ± 4.78 kg) (p < 0.05), and the birth weight of gray/white individuals (49.40 ± 7.11 kg) was the highest. Overall, PMEL appears to be a candidate gene affecting coat color in cattle, and coat color may have a selective effect on birth weight. This study provides a foundation for the breeding of beef cattle through GWAS for coat color and birth weight.

Background: The genetic dissection of complex traits in livestock continues to pose a significant challenge in the field of animal genetics and breeding. Although traditional genome-wide association studies (GWAS) are capable of localizing genetic variants associated with specific traits, they are insufficient to elucidate the underlying physiological mechanisms. An integrated analysis of multi-trait GWAS and multi-transcriptomic data systematically identifies key tissues and cell types influencing complex traits in beef cattle and elucidates their genetic regulatory basis. Results: We systematically mapped tissue- and cell-type-specific regulatory architectures underlying 20 economically important traits in beef cattle. Tissue-level analyses revealed distinct trait-tissue associations: fatty acid traits, including C16:0 and C20:4, were enriched in liver; carcass traits, including marbling score and carcass weight, in renal cortex/medulla and longissimus dorsi muscle; meat-quality traits such as pH in cartilaginous tissues; and total fat content in bone marrow. At cellular resolution, analysis of eight trait-associated tissues identified 38 discrete cell types. Myofibers were significantly associated with most carcass traits, including rib-eye area and backfat thickness, whereas hepatocytes emerged as key regulators of fatty acid and meat-quality traits, such as C16:0 and crude protein content. Transcription factor analysis identified cell-type-specific regulators: TBX15, SOX6, and TCF12 in myofibers; FOXA2 and NR1H4 in hepatocytes; and IRF8 and IKZF1 in microglia. Notably, hepatocytes and microglia showed complementary, trait-specific association patterns: hepatocytes were enriched for C16:0 associated saturated fatty-acid metabolic pathways, while microglia were enriched for C16:1 and unsaturated fatty-acid–related pathways, suggesting potential cross-tissue coordination in lipid regulation. Conclusions: Our study links specific tissues and cell types to phenotypic variation in beef cattle and identifies core transcriptional regulators and pathways driving trait variation. These cell-resolved maps provide mechanistic insight into how genetic variation shapes economically important traits, offering a valuable resource for functional studies, cell-informed precision breeding strategies, and the design of large-scale molecular phenotyping.