Genome-wide association and genomic prediction for a reproductive index summarizing fertility outcomes in U.S. Holsteins

Genetic Diversity, Admixture, and Selection Signatures in a Rarámuri Criollo Cattle Population Introduced to the Southwestern United States

Rarámuri Criollo (RC) cattle have been raised by the isolated Tarahumara communities of Chihuahua, Mexico, for nearly 500 years, mostly under natural selection and minimal management. RC cattle were introduced to the United States Department of Agriculture-Agricultural Research Service Jornada Experimental Range (RCJER) in 2005 to begin evaluations of beef production performance and their adaptation to the harsh ecological and climatic conditions of the Northern Chihuahuan Desert. While this research unveiled crucial information on their phenotypic plasticity and adaptation, the genetic diversity and structure of the RCJER population remains poorly understood. This study analyzed the genetic diversity, population structure, ancestral composition, and selection signatures of the RCJER herd using a ~64 K SNP array. The RCJER herd exhibits moderate genetic diversity and low population stratification with no evident clustering, suggesting a shared genetic background among different subfamilies. Admixture analysis revealed the RCJER herd represents a distinctive genetic pool within the Criollo cattle breeds, with significant Iberian ancestry. Selection signatures identified candidate genes and quantitative trait loci (QTL) for traits associated with milk composition, growth, meat and carcass, reproduction, metabolic homeostasis, health, and coat color. The RCJER population represents a distinctive genetic resource adapted to harsh environmental conditions while maintaining productive and reproductive attributes. These findings are crucial to ensuring the long-term genetic conservation of the RCJER and their strategic expansion into locally adapted beef production systems in the USA.

Detecting and characterizing copy number variation in a large commercial U.S. Holstein cattle population

BACKGROUND

Copy number variations (CNVs) are an important source of genomic variation that play an active role in modulating biological processes by altering gene expression and dosage. These structural variants involve duplications and deletions of segments usually exceeding 1 kilobase in size, dispersed throughout the genome of humans and livestock individuals. We mapped CNVs from high-density single-nucleotide polymorphism (SNP) genotyping array data on 3,601 Holsteins. Following, we explored their association with reported quantitative trait loci (QTLs), genes, and biological pathways, unveiling the potential biological contributions of CNVs to economically important traits in the dairy industry and breeding programs.

RESULTS

We identified 4,113 non-redundant high-confidence CNVs, of which 78% were deletions and 22% duplications, distributed across all bovine autosomal chromosomes (BTA). Out of the 1,184 compiled CNV regions (CNVRs) covering 3.02% of the autosomal genome, 199 novel CNVRs were mapped. QTLs overlapping with CNVRs detected in this study were enriched for 140 economically important traits, such as milk yield, conception and pregnancy rates, susceptibility to diseases and length of productive life, indicating that CNVs likely underlie productive, reproductive and health performance in Holstein dairy cattle. Moreover, detected CNVRs overlapped with 2,788 annotated genes, including those linked to milk production, fertility, and immune response in cattle, such as DGAT1, AFF1, and ADAMTS13 genes. Furthermore, the gene set analysis revealed GO terms related to metabolic processes, immune system, response to stimulus, and cellular binding activities. Notably, enriched GO terms highlighted relevant genes to cattle health and reproduction overlapping CNVRs, such as DEFB4A, GATA3, GNB1, and PPP1R1B.

CONCLUSIONS

We mapped and demonstrated the characteristics of genome-wide distributed CNVs in a large commercial Holstein population genotyped with a high-density SNP array. Collectively, the results emphasize the role of CNVs as a valuable resource of genetic variation and contribute to better understand the genetic architecture of economic complex traits in dairy cattle. Furthermore, these findings may provide opportunities for the development of novel and enhanced genomic selection strategies in Holstein cattle.

The economic impact of purulent vaginal discharge in dairy herds within a single lactation

The goal of this study was to calculate the cost of purulent vaginal discharge (PVD) in dairy cows. The dataset included 11,051 cows from 16 dairy herds located in 4 regions of the United States. Purulent vaginal discharge was characterized as a mucopurulent, purulent, or reddish-brownish vaginal discharge collected at 28 ± 7 DIM. Gross profit was calculated as the difference between incomes and expenses, and the cost of PVD was calculated by subtracting the gross profit of cows with PVD from the gross profit of cows without PVD. Continuous outcomes such as milk production (kg/cow), milk sales ($/cow), cow sales ($/cow), feed costs ($/cow), reproductive management costs ($/cow), replacement costs ($/cow), and gross profit ($/cow) were analyzed using linear mixed effects models. Pregnancy and culling by 305 DIM were analyzed by generalized linear mixed effects models using logistic regression. Models included the fixed effects of PVD, metritis, parity, region, season of calving, and morbidity in the first 60 DIM, as well as the interactions between PVD and metritis, PVD and parity group, and PVD and morbidity. Farm and the interaction between PVD and farm were considered random effects in all the statistical models. A stochastic analysis was conducted using 10,000 iterations with varying relevant inputs. Cows with PVD produced less milk (9,753.2 ± 333.6 vs. 9,994.6 ± 330.9 kg/cow), were less likely to be pregnant (70.7 ± 1.7% vs. 78.9 ± 1.2%), and were more likely to be culled by 305 DIM (34.6 ± 1.7% vs. 27.2 ± 1.3%) compared with cows without PVD. Consequently, milk sales (4,744.7 ± 162.3 vs. 4,862.1 ± 161.0 $/cow) and residual cow value (1,079.6 ± 23.0 vs. 1,179.3 ± 20.3 $/cow) were lesser for cows with PVD. Replacement (639.4 ± 26.4 vs. 526.0 ± 23.4 $/cow) and reproductive management costs (76.3 ± 2.5 vs. 69.0 ± 2.4 $/cow) were greater for cows with PVD. The mean cost of PVD was $202. The stochastic analysis also showed a mean cost of $202, ranging from $152 to $265. The robust dataset and the stochastic analysis strengthen both the external and internal validity of our findings, offering a deeper understanding of the economic consequences of PVD. In conclusion, PVD resulted in large economic losses to dairy herds by being associated with decreased milk yield, impaired reproduction, and greater culling.

Validation of loci and genes associated with fertility in Holstein cows using gene-set enrichment analysis-SNP and genotype-by-sequencing

BACKGROUND

The financial strain fertility issues cause the dairy cattle industry is substantial, with over $7 billion in lost revenue accrued annually due to a relatively low cow conception rate (CCR; 30-43%) for US dairy cows. While CCR has been improving through genomic selection, identification of causal mutations would help improve the rate of genetic progress with genomic selection and provide a better understanding of infertility. The objectives of this study were to: (1) identify genes and gene-sets associated with CCR to the first breeding (CCR1) and the number of breedings required to conceive (TBRD) in Holstein cows and (2) identify putative functional variants associated with CCR1 and TBRD through a custom genotype-by-sequencing array. The study consisted of 1,032 cows (494 pregnant to first breeding, 472 pregnant to subsequent [2-20] services, and 66 that never conceived). Cows were artificially inseminated, and pregnancy was determined 35d later by rectal palpation of uterine contents. Gene-set enrichment analyses with SNP data (GSEA-SNP) were conducted for CCR1 and TBRD with a normalized enrichment score (NES) ≥ 3.0 required for significance. Leading edge genes (LEG) and positional candidate genes from this and 26 additional studies were used to validate 100 loci associated (P < 1 × 10- 5) with cow fertility using a custom sequencing genotyping array of putative functional variants (exons, promoters, splice sites, and conserved regions).

RESULTS

GSEA-SNP identified 95 gene-sets (1,473 LEG) enriched for CCR1 and 67 gene sets enriched (1,438 LEG) for TBRD (NES ≥ 3). Thirty-four gene-sets were shared between CCR1 and TBRD along with 788 LEG. The association analysis for TBRD identified three loci: BTA1 at 83 Mb, BTA1 at 145 Mb, and BTA 20 at 46 Mb (P < 1 × 10- 5). The loci associated with TBRD contained candidate genes with functions relating to implantation and uterine receptivity. No loci were associated with CCR1, however a single locus on BTA1 at 146 Mb trended toward significance with an FDR of 0.04.

CONCLUSIONS

The validation of three loci associated with CCR and TBRD in Holsteins can be used to improve fertility through genomic selection and provide insight into understanding infertility.

Integrating Genomic Selection and a Genome-Wide Association Study to Improve Days Open in Thai Dairy Holstein Cattle: A Comprehensive Genetic Analysis

Days open (DO) is a critical economic and reproductive trait that is commonly employed in genetic selection. Making improvements using conventional genetic techniques is exceedingly challenging. Therefore, new techniques are required to improve the accuracy of genetic selection using genomic data. This study examined the genetic approaches of traditional AIREML and single-step genomic AIREML (ssGAIREML) to assess genetic parameters and the accuracy of estimated breeding values while also investigating SNP regions associated with DO and identifying candidate genes through a genome-wide association study (GWAS). The dataset included 59415 DO records from 36368 Thai-Holstein crossbred cows and 882 genotyped animals. The cows were classified according to their Holstein genetic proportion (breed group, BG) as follows: BG1 (>93.7% Holstein genetics), BG2 (87.5% to 93.6% Holstein genetics), and BG3 (<87.5% Holstein genetics). AIREML was utilized to estimate genetic parameters and variance components. The results of this study reveal that the average DO values for BG1, BG2, and BG3 were 97.64, 97.25, and 96.23 days, respectively. The heritability values were estimated to be 0.02 and 0.03 for the traditional AIREML and ssGAIREML approaches, respectively. Depending on the dataset, the ssGAIREML method produced more accurate estimated breeding values than the traditional AIREML method, ranging from 40.5 to 45.6%. The highest values were found in the top 20% of the dam dataset. For the GWAS, we found 12 potential candidate genes (DYRK1A, CALCR, MIR489, MIR653, SLC36A1, GNA14, GNAQ, TRNAC-GCA, XYLB, ACVR2B, SLC22A14, and EXOC2) that are believed to have a significant influence on days open. In summary, the ssGAIREML method has the potential to enhance the accuracy and heritability of reproductive values compared to those obtained using conventional AIREML. Consequently, it is a viable alternative for transitioning from conventional methodologies to the ssGAIREML method in the breeding program for dairy cattle in Thailand. Moreover, the 12 identified potential candidate genes can be utilized in future studies to select markers for days open in regard to dairy cattle.