This study aimed to identify inbreeding coefficient (F) estimators useful for improvement programs in a small Holstein population through the evaluation of different methodologies in the Mexican Holstein population. F was estimated as follows: (a) from pedigree information (Fped); (b) through runs of homozygosity (Froh); (c) from the number of observed and expected homozygotic SNP in the individuals (Fgeno); (d) through the genomic relationship matrix (Fmg). The study included information from 4277 animals with pedigree records and 100,806 SNP. The average and standard deviation values of F were 3.11 ± 2.30 for Fped, −0.02 ± 3.55 for Fgeno, 2.77 ± 0.71 for Froh and 3.03 ± 3.05 for Fmg. The correlations between coefficients varied from 0.30 between Fped and Froh, to 0.96 between Fgeno and Fmg. Differences in the level of inbreeding among the parent’s country of origin were found regardless of the method used. The correlations among genomic inbreeding coefficients were high; however, they were low with Fped, so further research on this topic is required.

The objective of this study was to evaluate the proportion of genetic variance explained by single nucleotide polymorphism markers, individually or clustered in 1, 2, and 5 Mb windows, for milk yield, fat yield, protein yield, fat content, protein content, and somatic cell score in Mexican Holstein cattle. The analysis included data from 640,746 lactation records of 358,857 cows born between 1979 and 2019, distributed in 353 herds in 18 states of Mexico. The analysis included genotypic data on 7,713 cows and 577 sires, with information on 88,911 markers previously imputed and filtered by quality control. Genomic scans via the single-step genomic best linear unbiased prediction method were performed using BLUPF90 software. A total of 162 markers were significantly associated (p<0.01) with the phenotypic traits evaluated, and the SNP markers were distributed across chromosomes 1, 3, 5, 6, 10, 12, 14, 16, 18, 20, 22, and 29. When the size of the genomic windows was increased from 1 to 5 Mb, a greater proportion of genetic variance was explained by the SNPs within the window, and a greater number of windows explained more than 1% of the genetic variance. The most significant regions were associated with two or more phenotypic traits, such as one region on chromosome 14 that harbors the DGAT1, EXOSC4, PPP1R16A, and FOXH1 genes, which affect all the traits under study. In general, the utilization of genomic windows resulted in a greater proportion of genetic variance explained by milk production traits.

Se evaluó la asociación entre los sistemas de conformación de tipo y la producción de leche (PL), grasa (PG) y proteína (PP) a través de la correlación entre las características de conformación (CC) y las predicciones de valores genéticos para leche (HTPL), grasa (HTPGK) y proteína (HTPPK) de ganado Holstein, para determinar en qué medida las CC influyen en la producción de los animales. El estudio incluyó 31,711 registros de CC, 87,871 de PL, 65,593 de PG y 43,717 de PP. Las CC se agruparon en cuatro sistemas generales: estructura y capacidad (SEC), patas y pezuñas (SP), ubre (SU) y anca (SA). También se evaluó el efecto de las clases de puntos finales (PF) en los niveles de PL, PG y PP a través de un análisis de varianza. La HTPL tuvo una correlación de 0.28 con SU, 0.17 con SA, 0.08 con SP y -0.05 con SEC (P<0.001). Las correlaciones de HTPGK y HTPPK con los diferentes sistemas fueron bajas, destacando las encontradas con SU, 0.15 y 0.21 (P<0.001), respectivamente. Las asociaciones fenotípicas y las correlaciones de los valores genéticos reportadas en este estudio sugieren que la selección de vacas por CC puede modificar la producción de leche, grasa y proteína.

Summary: We investigated the use of different sources of genomic information, such as single nucleotide polymorphisms (SNPs), haplotypes (HAP), and runs of homozygosity (ROH), to predict productive, health, and reproductive traits in Mexican Holstein cattle. The use of models including genomic information from DNA markers increased the predictive performance and yielded higher estimates of genetic variance compared with models that included only fixed effects of complex traits in Mexican Holstein cattle.

El presente estudio evaluó el impacto de la inclusión del coeficiente de consanguinidad (F) en la estimación de parámetros genéticos, predicción de valores genéticos y su confiabilidad para producción de leche, sus componentes (grasa y proteína) y conteo de células somáticas (CCS) en ganado Holstein de México. Se analizaron registros productivos ajustados a 305 días de ~368 mil vacas de 353 hatos con información genealógica y genómica disponible. La estimación de componentes de varianza se realizó mediante el método de Máxima Verosimilitud Restringida (REML), y la predicción de valores genéticos mediante las metodologías de Mejor Predicción Lineal Insesgada (BLUP) y de un solo paso (ssGBLUP), comparando escenarios que excluyeron o incluyeron F en la matriz de relaciones aditivas. La inclusión de F tuvo un efecto limitado sobre los componentes de varianza; encontrándose una disminución en la varianza genética de producción de leche y una disminución en la estimación de heredabilidad especialmente para producción de grasa. Además, su incorporación influyó en la confiabilidad y predicción de los valores genéticos, especialmente en características de baja heredabilidad, donde se registraron incrementos moderados en la precisión de la evaluación genética. Las correlaciones entre los valores genéticos y las confiabilidades obtenidas con y sin F fueron altas para la mayoría de las características, aunque CCS mostró mayor sensibilidad a su inclusión. En conjunto, los resultados sugieren que, en poblaciones con niveles moderados de consanguinidad, la inclusión de F no modifica sustancialmente la estimación de parámetros genéticos, pero puede mejorar la precisión de las evaluaciones genéticas en características específicas.

The objective of this study was to compare the proportion of explained genetic variance (EXGV) and the reliability of genomic breeding values (GBVs) predictions for milk yield (MY), fat yield (FY), protein yield (PY) fat percentage (FP), protein percentage (PP), and somatic cell score (SCS) in Holstein cattle. Three types of genomic information were evaluated. (a) SNP-ALL: this analysis included 88,911 single nucleotide polymorphisms (SNP) from 8290 animals. (b) HAP-PSEUDOSNP: haplotypes, defined based on high linkage disequilibrium (LD, r² ≥ 0.80) between SNPs, which were encoded as pseudo-SNPs, with a total of 35,552 pseudo-SNPs and 8331 animals included. (c) SNP-HAP: analysis using only individual SNPs included in the haplotypes (without recoding); for this analysis, 33,010 SNPs and 8192 individuals were retained. All analyses were conducted using the single-step genome-wide association study method implemented in the BLUPF90 software package. The results showed that the inclusion of SNPs with high LD (SNP-HAP) increases the reliability of GBVs’ predictions compared to the SNP-ALL analysis; average reliability increased between 0.05 and 0.11. Moreover, the SNP-HAP analysis resulted in a twofold increase in the EXGV for all traits, likely due to increased estimates of individual marker effects compared to the SNP-ALL analysis.