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This study aimed to develop a haplotype-segment matching-based maternal grandsire discovery approach applicable to different SNP array densities and segment window sizes in the absence of dam genotypes， thereby providing a reliable technical approach for pedigree reconstruction. The study utilized genomic data from 6 117 dairy cows to construct datasets of varying SNP densities， including low-density （5K， 10K） and medium-density （20K， 50K） SNP panels. For each dataset， the genome was partitioned into fixed-length haplotype segments of 5， 10， 35， 75， and 120 SNPs， followed by haplotype inference. Based on the known genotypes of the offspring's sire， paternal haplotypes were excluded to infer maternal haplotypes， which were then compared with those of candidate bulls on a segment-by-segment basis to calculate the haplotype matching rate （HMR）. The bull with the highest HMR was designated as the putative maternal grandsire （MGS）. A total of 345 individuals with complete and validated pedigree information were used to verify method accuracy. Furthermore， genomic relatedness coefficients and homozygosity levels were calculated to investigate the potential effects of kinship structure and inbreeding on identification outcomes. The haplotype-segment matching method achieved more than 90% assignment accuracy across most combinations of SNP-array density and segment window length. The 20K array with a 35-SNPs window yielded the highest accuracy at 98%， demonstrating robust performance. Nonetheless， the optimal decision thresholds for the haplotype match rate and the resulting accuracies varied substantially with parameter settings， indicating that SNP density and window length were key determinants of performance. Additional analyses showed that kinship structure and inbreeding levels significantly affected matching performance and assignment accuracy， suggesting that the population genetic background should be considered in practical applicatons. The haplotype-segment matching （HSM）-based method demonstrated high accuracy in maternal grandsire identification under conditions of missing maternal genotype data， making it a valuable tool for pedigree correction in large-scale commercial herds. Optimizing SNP density and haplotype window length based on the genetic background of the population is essential to enhance identification efficiency and robustness in practical applications.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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