Sajjad Toghiani

Identifying causal genetic variants underlying economically important traits in dairy cattle is essential for understanding their genetic basis and optimizing breeding programs. The growing availability of sequenced reference genomes and individuals with both phenotypic and genotypic data notably enhances our ability to detect genetic associations and further pinpoint causal effects. This comprehensive GWAS of dairy cattle used deregressed breeding values as phenotypes and analyzed 11,292,243 quality-controlled, imputed sequence variants from 50,309 Holstein bulls. The number of bulls with available phenotypes ranged from 23,121 to 50,309 across 30 complex traits categorized into production and yield, type, and longevity and health. We performed GWAS using our SLEMM-GWA approach, which accounts for the varying reliability of deregressed breeding values across individuals and demonstrates computational efficiency for large sample sizes and sequence data. This analysis identified 381 significant association peaks, of which 126 are novel findings. Subsequent Bayesian fine-mapping provided statistical prioritization by assigning posterior conditional inclusion probabilities to individual variants and genes, yielding a list of credible candidate genes-an advancement over conventional GWAS reporting of all proximal genes. This prioritization offered direct statistical support for previously reported genes and, more importantly, identified credible candidate genes within the 126 newly discovered peaks for specific traits, including AOPEP, GC, E2F6, MGST1, VPS13B, ZNF652, ASPH, SFMBT1, and MAPRE2. These findings enhance the understanding of the genetic architecture of these complex dairy traits and provide valuable insights for the refinement of genomic selection strategies and breeding programs in Holstein cattle.

Inbreeding depression (InD) refers to the mean reduction in trait values due to inbreeding, with detrimental effects on survival, production, and reproduction traits that have been observed in many natural and domesticated populations. Despite efforts to measure how much reduction in the traits of interest was caused by InD, the genetic and molecular basis of these declines remains unclear, particularly in dairy cattle. In this research, we used a linear mixed model to partition the InD of 3 production traits in 245,517 genotyped Jersey cows from the Council on Dairy Cattle Breeding (Bowie, MD) database. We mapped 9,532,696 imputed sequence variants into 5 functional annotation categories (i.e., intron, promoter, genomic evolutionary rate profiling [GERP] constrained elements, coding sequence, untranslated regions [UTR], and remaining). We estimated the effects of InD attributed to each functional annotation category by a mixed model method accounting for additive effects and relatedness through a genomic relationship matrix. The InD for milk yield was significantly enriched for promoter regions (enrichment ratio [R_k] = 20.11, SE = 6.44), UTR regions (R_k = 57.96, SE = 16.62) and GERP regions (R_k = 35.91, SE = 7.00). The enrichment ratio R_k represents the disproportionate effect that annotation-specific homozygosity has on the trait mean compared with the magnitude of InD on the whole-genome level. Similarly, protein yield showed significant enrichment of InD for promoter regions (R_k = 15.25, SE = 5.45), UTR regions (R_k = 46.44, SE = 14.07), and GERP regions (R_k = 32.73, SE = 5.92), whereas fat yield showed significant enrichment of InD for UTR regions (R_k = 40.20, SE = 12.77) and GERP regions (R_k = 28.72, SE = 5.34). Our results indicate that certain functional annotations in dairy cattle genome are disproportionally responsible for the detrimental effects of inbreeding, which could be vulnerable to deleterious mutations. This research can help better elucidate the genetic and molecular basis of InD in dairy cattle genome and potentially guide breeding strategies for genomic selection.

Calf diarrhea (DIAR) and respiratory illnesses (RESP) are leading causes of calf mortality. This study aimed to develop a comprehensive US national genomic evaluation for these important calf health traits using producer-recorded data from the National Cooperator Database. Analyses included 207,602 calf records for DIAR (age 3 to 60 d) and 681,741 records for RESP (age 3 to 365 d) from all breeds (97.5% data from Holstein and Jersey calves) born between 2013 and 2024. Both traits were modeled as binary variables (0 = diseased, 100 = healthy) with breeding values expressed as disease resistance in percentage points. Overall incidence was 14.46% for DIAR and 16.05% for RESP, with Jerseys showing higher incidence than Holsteins. Heritability estimates from the all-breed model were 0.026 (DIAR) and 0.022 (RESP). Genomic PTA reliabilities exceeded pedigree PTA reliabilities, with young sires (born ≥2020) exhibiting high reliability for genetic improvement opportunities in calf health. Holsteins exhibited higher genomic reliabilities for both traits compared with Jerseys, likely due to the larger dataset. A moderate genetic correlation (0.22) was observed between DIAR and RESP, and correlations with heifer livability, another currently evaluated calf health trait, were 0.13 (DIAR) and 0.35 (RESP). Correlations of these health traits with other traits were low. These findings demonstrate the feasibility of including DIAR and RESP in the US national genetic evaluation system, enabling more effective breeding strategies to improve calf health and survivability.

Background/Objectives: Genomic selection (GS) has improved accuracy compared to traditional methods. However, accuracy tends to plateau beyond a certain marker density. Prioritizing influential SNPs could further enhance the accuracy of GS. The fixation index (F_ST) allows for the identification of SNPs under selection pressure. Although the F_ST method was shown to be able to prioritize SNPs across the whole genome and to increase accuracy, its performance could be further improved by focusing on the prioritization process within QTL regions. Methods: A trait with heritability of 0.1 and 0.4 was generated under different simulation scenarios (number of QTL, size of SNP windows around QTL, and number of selected SNPs within a QTL region). In total, six simulation scenarios were analyzed. Each scenario was replicated five times. The population comprised 30K animals from the last 2 generations (G9–G10) of a 10-generation (G1–G10) selection process. All animals in G9–10 were genotyped with a 600K SNP panel. F_ST scores were calculated for all 600K SNPs. Two prioritization scenarios were used: (1) selecting the top 1% SNPs with the highest F_ST scores, and (2) selecting a predetermined number of SNPs within each QTL window. GS accuracy was evaluated using the correlation between true and estimated breeding values for 5000 randomly selected animals from G10. Results: Prioritizing SNPs using F_ST scores within QTL window regions increased accuracy by 5 to 18%, with the 50-SNP windows showing the best performance. Conclusions: The increase in GS accuracy warrants the testing of the algorithm when the number and position of QTL are unknown.

Most genotypes in the National Cooperator Database now originate from cows, but most previous studies validating genomic predictions have primarily focused on bulls. This study paired official within-breed genomic predicted transmitting ability (GPTA) and parent average (PA) for genotyped heifer calves born between 2019 and 2021 using the August 2021 database with their corresponding performance deviations (PDEV) for 17 different traits. The PDEV data became available when the heifers completed their first lactation and were extracted from the August 2023 database in which at least one PDEV value for those 17 traits existed for each genotyped heifer record. The separate breed analyses included records for 219 Ayrshires (AY), 2,715 Brown Swiss (BS), 1,055 Guernseys (GU), 949,904 Holsteins (HO), and 125,275 Jerseys (JE). These validation cows were heifer calves born between 2019 and 2021. However, due to timing or recording patterns, each trait had missing or incomplete PDEV data, leading to unbalanced distributions of records across traits. The squared accuracy of genomic prediction, or genomic reliability (r²), was divided by the corresponding heritability for each trait, as only the heritable portion of cow records could be predicted, and this reliability varied across different traits and breeds. For HO and JE, the predictive ability of GPTA outperformed PA in predicting cow PDEV for yield, productive life, somatic cell score, fertility, and health traits. The improvement ranged from 33% to 142% compared with the predictive ability of the PA. However, the results for AY, BS, and GU breeds were less consistent due to the smaller number of genotyped heifers. The r² gains in those breeds were smaller and aligned with the published reliabilities of GPTA. Weighted and unweighted regressions of PDEV on GPTA and PA traits mostly exceeded the expected value of 2.00 when predicting the future trait PDEV using GPTA or PA. The larger number of observations and lower standard error of the weighted regression coefficient prediction in HO and JE breeds contributed to more stable and consistent regression coefficients for all traits except milk fever and heifer livability. Our study suggests that herd owners may experience greater benefits from genomics than originally forecast.

Large datasets allow estimation of feed required for individual milk components or body maintenance. Phenotypic regressions are useful for nutrition management, but genetic regressions are more useful in breeding programs. Dry matter intake records from 8,513 lactations of 6,621 Holstein cows were predicted from phenotypes or genomic evaluations for milk components and body size traits. The mixed models also included DIM, age-parity subclass, trial date, management group, and BW change during 28- and 42-d feeding trials in mid lactation. Phenotypic regressions of DMI on milk (0.014 ± 0.006), fat (3.06 ± 0.01), and protein (4.79 ± 0.25) were much less than corresponding genomic regressions (0.08 ± 0.03, 11.30 ± 0.47, and 9.35 ± 0.87, respectively) or sire genomic regressions multiplied by 2 (0.048 ± 0.04, 6.73 ± 0.94, and 4.98 ± 1.75). Thus, marginal feed costs as fractions of marginal milk revenue were higher from genetic than phenotypic regressions. According to the ECM formula, fat production requires 69% more DMI than protein production. In the phenotypic regression, it was estimated that protein production requires 56% more DMI than fat. However, the genomic regression for the animal showed a difference of only 21% more DMI for protein compared with fat, whereas the sire genomic regressions indicated approximately 35% more DMI for fat than protein. Estimates of annual maintenance in kilograms DMI/kilograms BW per lactation were similar from phenotypic regression (5.9 ± 0.14), genomic regression (5.8 ± 0.31), and sire genomic regression multiplied by 2 (5.3 ± 0.55) and are larger than those estimated by the National Academies for Science, Engineering, and Medicine based on NE_L equations. Multiple regressions on genomic evaluations for the 5 type traits in body weight composite (BWC) showed that strength was the type trait most associated with BW and DMI, agreeing with the current BWC formula, whereas other traits were less useful predictors, especially for DMI. The Net Merit formula used to weight different genetic traits to achieve an economically optimal overall selection response was revised in 2021 to better account for these estimated regressions. To improve profitability, breeding programs should select smaller cows with negative residual feed intake that produce more milk, fat, and protein.

This study leveraged a growing dataset of producer-recorded phenotypes for mastitis, reproductive diseases (metritis and retained placenta), and metabolic diseases (ketosis, milk fever, and displaced abomasum) to investigate the potential presence of inbreeding depression for these disease traits. Phenotypic, pedigree, and genomic information were obtained for 354,043 and 68,292 US Holstein and Jersey cows, respectively. Total inbreeding coefficients were calculated using both pedigree and genomic information; the latter included inbreeding estimates obtained using a genomic relationship matrix and runs of homozygosity. We also generated inbreeding coefficients based on the generational inbreeding for recent and old pedigree inbreeding, for different run-of-homozygosity length classes, and for recent and old homozygous-by-descent segment-based inbreeding. Estimates on the liability scale revealed significant evidence of inbreeding depression for reproductive-disease traits, with an increase in total pedigree and genomic inbreeding showing a notable effect for recent inbreeding. However, we found inconsistent evidence for inbreeding depression for mastitis or any metabolic diseases. Notably, in Holsteins, the probability of developing displaced abomasum decreased with inbreeding, particularly for older inbreeding. Estimates of disease probability for cows with low, average, and high inbreeding levels did not significantly differ across any inbreeding coefficient and trait combination, indicating that although inbreeding may affect disease incidence, it likely plays a smaller role compared with management and environmental factors.

This study aimed at evaluating the quality of imputation accuracy (IA) by marker (IA_m) and by individual (IA_i) in US crossbred dairy cattle. Holstein × Jersey crossbreds were used to evaluate imputation accuracy from a low (7K) to medium density (50K) SNP chip. Crossbred animals, as well as their sires (53), dams (77), and maternal grandsires (63), were all genotyped with a 78K SNP chip. Seven different scenarios of reference populations were tested in which some scenarios used different family relationships and some other added random unrelated purebred and crossbred individuals to those different family relationship scenarios. The same scenarios were tested on Holstein and Jersey purebred animals to compare these outcomes to those attained in crossbred animals. The genotype imputation was performed with findhap (version 4) software. There were no significant differences in IA results depending on whether the sire of imputed individuals was Holstein and the dam was Jersey, or vice versa. The IA increased significantly with the addition of related individuals in the reference population, from 86.70 ± 0.06% when only sires or dams were included in the reference population to 90.09 ± 0.06% when sires, dams, and maternal grandsires genomic information were combined in the reference population. In all scenarios including related individuals in the reference population, IA_m and IA_i were significantly superior in purebred Jersey and Holstein animals than in crossbreds, ranging from 90.75 ± 0.06 to 94.02 ± 0.06%, and from 90.88 ± 0.11 to 94.04 ± 0.10%, respectively. Additionally, a scenario called S_PB+D_LD that is similar to the genomic evaluations performed on US crossbred dairy was tested. In this scenario, the information from the 5 evaluated breeds (Ayrshire, Brown Swiss, Guernsey, Holstein, and Jersey) genotyped with a 50K SNP chip and genomic information from the dams genotyped with a 7K SNP chip were combined in the reference population and the IA_m and IA_i were 80.87 ± 0.06%, and 80.85 ± 0.08%, respectively. Adding randomly nonrelated genotyped individuals in the reference population reduced IA for both purebred and crossbred cows, except for scenario S_PB+D_LD, where adding crossbreds to the reference population increased IA values. Our findings demonstrate that IA for US Holstein × Jersey crossbred ranged from 85 to 90%, and emphasize the significance of designing and defining the reference population for improved IA.

This study aimed to evaluate the economic impact of improving feed efficiency on breeding objectives for Iranian Holsteins. Production and economic data from seven dairy herds were used to estimate the economic values of different traits, and a meta-analysis was conducted to analyze the genetic relationships between feed efficiency and other traits. Economic weights were calculated for various traits, with mean values per cow and per year across herds estimated at USD 0.34/kg for milk yield, USD 6.93/kg for fat yield, USD 5.53/kg for protein yield, USD −1.68/kg for dry matter intake, USD −1.70/kg for residual feed intake, USD 0.47/month for productive life, and USD −2.71/day for days open. The Iranian selection index was revised to improve feed efficiency, and the feed efficiency sub-index (FE$) introduced by the Holstein Association of the United States of America was adopted to reflect Iran’s economic and production systems. However, there were discrepancies between Iranian and US genetic coefficients in the sub-index, which could be attributed to differences in genetic and phenotypic parameters, as well as the economic value of each trait. More accurate estimates of economic values for each trait in FE$ could be obtained by collecting dry matter intake from Iranian herds and conducting genetic evaluations for residual feed intake.