Genetic Diversity in the Italian Holstein Dairy Cattle Based on Pedigree and SNP Data Prior and After Genomic Selection

Impact of inbreeding on production, fertility, and health traits in German Holstein dairy cattle utilizing various inbreeding estimators

In dairy cattle production, it is important to understand how inbreeding affects production, fertility, and health traits. However, there is still limited use of genomic information to estimate inbreeding, despite advancements in genotyping technologies. To address this gap, we investigated the effect of inbreeding on German Holstein dairy cattle using both pedigree-based and genomic-based inbreeding estimators. We employed one method based on pedigree information (Fped) together with 6 genomic-based methods, including 3 genome-wide complex trait analysis software estimators (Fhat1, Fhat2, Fhat3), VanRaden's first method (FVR1, with observed allele frequencies, and FVR0.5, when allele frequencies are set to 0.5), and one based on runs of homozygosity (Froh). Data from 24,489 cows with both phenotypes and genotypes were used, with a pedigree including 232,780 animals born between 1970 and 2018. We analyzed the effects of inbreeding depression on production, fertility, and health traits separately, using single-trait linear animal models as well as threshold models to account for the binary nature of the health traits. For the health traits, we transformed solutions from the liability scale to a probability scale for easier interpretation. Our results showed that the mean inbreeding coefficients from all estimators ranged from -0.003 to 0.243, with negative values observed for most genomic-based methods. We found out that a 1% increase in inbreeding caused a depression ranging from 25.94 kg (Fhat1) to 40.62 kg (Fhat3), 1.18 kg (Fhat2) to 1.70 kg (Fhat3), 0.90 kg (Fhat2) to 1.45 kg (Froh and Fhat3), 0.19 (Fped) to 0.34 d (Fhat3) for 305-d milk yield, fat, protein, and calving interval, respectively. The health traits showed very slight gradual changes when inbreeding was increased steadily from 0% to 50%, with digital dermatitis showing a rather contrasting trend to that of mastitis, which increased the more an animal was inbred. Overall, our study highlights the importance of considering both pedigree-based and genomic-based inbreeding estimators when assessing the impact on inbreeding, emphasizing that not all inbreeding is harmful.

Genomic prediction and genome-wide association study using combined genotypic data from different genotyping systems: application to apple fruit quality traits

With advances in next-generation sequencing technologies, various marker genotyping systems have been developed for genomics-based approaches such as genomic selection (GS) and genome-wide association study (GWAS). As new genotyping platforms are developed, data from different genotyping platforms must be combined. However, the potential use of combined data for GS and GWAS has not yet been clarified. In this study, the accuracy of genomic prediction (GP) and the detection power of GWAS increased for most fruit quality traits of apples when using combined data from different genotyping systems, Illumina Infinium single-nucleotide polymorphism array and genotyping by random amplicon sequencing-direct (GRAS-Di) systems. In addition, the GP model, which considered the inbreeding effect, further improved the accuracy of the seven fruit traits. Runs of homozygosity (ROH) islands overlapped with the significantly associated regions detected by the GWAS for several fruit traits. Breeders may have exploited these regions to select promising apples by breeders, increasing homozygosity. These results suggest that combining genotypic data from different genotyping platforms benefits the GS and GWAS of fruit quality traits in apples. Information on inbreeding could be beneficial for improving the accuracy of GS for fruit traits of apples; however, further analysis is required to elucidate the relationship between the fruit traits and inbreeding depression (e.g. decreased vigor).

Genetic Variation and Composition of Two Commercial Estonian Dairy Cattle Breeds Assessed by SNP Data

The aims of this study were to assess the genomic relatedness of Estonian and selected European dairy cattle breeds and to examine the within-breed diversity of two Estonian dairy breeds using genome-wide SNP data. This study was based on a genotyped heifer population of the Estonian Red (ER) and Estonian Holstein (EH) breeds, including about 10% of all female cattle born in 2017-2020 (sample sizes n = 215 and n = 2265, respectively). The within-breed variation study focused on the level of inbreeding using the ROH-based inbreeding coefficient. The genomic relatedness analyses were carried out among two Estonian and nine European breeds from the WIDDE database. Admixture analysis revealed the heterogeneity of ER cattle with a mixed pattern showing several ancestral populations containing a relatively low proportion (1.5-37.0%) of each of the reference populations used. There was a higher FROH in EH (FROH = 0.115) than in ER (FROH = 0.044). Compared to ER, the long ROHs of EH indicated more closely related parents. The paternal origin of the genetic material used in breeding had a low effect on the inbreeding level. However, among EH, the highest genomic inbreeding was estimated in daughters of USA-born sires.

Exploring genome-wide differentiation and signatures of selection in Italian and North American Holstein populations

Among Italian dairy cattle, the Holstein is the most reared breed for the production of Parmigiano Reggiano protected designation of origin cheese, which represents one of the most renowned products in the entire Italian dairy industry. In this work, we used a medium-density genome-wide data set consisting of 79,464 imputed SNPs to study the genetic structure of Italian Holstein breed, including the population reared in the area of Parmigiano Reggiano cheese production, and assessing its distinctiveness from the North American population. Multidimensional scaling and ADMIXTURE approaches were used to explore the genetic structure among populations. We also investigated putative genomic regions under selection among these 3 populations by combining 4 different statistical methods based either on allele frequencies (single marker and window-based) or extended haplotype homozygosity (EHH; standardized log-ratio of integrated EHH and cross-population EHH). The genetic structure results allowed us to clearly distinguish the 3 Holstein populations; however, the most remarkable difference was observed between Italian and North American stock. Selection signature analyses identified several significant SNPs falling within or closer to genes with known roles in several traits such as milk quality, resistance to disease, and fertility. In particular, a total of 22 genes related to milk production have been identified using the 2 allele frequency approaches. Among these, a convergent signal has been found in the VPS8 gene which resulted to be involved in milk traits, whereas other genes (CYP7B1, KSR2, C4A, LIPE, DCDC1, GPR20, and ST3GAL1) resulted to be associated with quantitative trait loci related to milk yield and composition in terms of fat and protein percentage. In contrast, a total of 7 genomic regions were identified combining the results of standardized log-ratio of integrated EHH and cross-population EHH. In these regions candidate genes for milk traits were also identified. Moreover, this was also confirmed by the enrichment analyses in which we found that the majority of the significantly enriched quantitative trait loci were linked to milk traits, whereas the gene ontology and pathway enrichment analysis pointed to molecular functions and biological processes involved in AA transmembrane transport and methane metabolism pathway. This study provides information on the genetic structure of the examined populations, showing that they are distinguishable from each other. Furthermore, the selection signature analyses can be considered as a starting point for future studies in the identification of causal mutations and consequent implementation of more practical application.

Single-Step Genomic Prediction of Superovulatory Response Traits in Japanese Black Donor Cows

We assessed the performance of single-step genomic prediction of breeding values for superovulatory response traits in Japanese Black donor cows. A total of 25,332 records of the total number of embryos and oocytes (TNE) and the number of good embryos (NGE) per flush for 1874 Japanese Black donor cows were collected during 2008 and 2022. Genotype information on 36,426 autosomal single-nucleotide polymorphisms (SNPs) for 575 out of the 1,874 cows was used. Breeding values were predicted exploiting a two-trait repeatability animal model. Two genetic relationship matrices were used, one based on pedigree information (A matrix) and the other considering both pedigree and SNP marker genotype information (H matrix). Estimated heritabilities of TNE and NGE were 0.18 and 0.11, respectively, when using the H matrix, which were both slightly lower than when using the A matrix (0.26 for TNE and 0.16 for NGE). Estimated genetic correlations between the traits were 0.61 and 0.66 when using H and A matrices, respectively. When the variance components were the same in breeding value prediction, the mean reliability was greater when using the H matrix than when using the A matrix. This advantage seems more prominent for cows with low reliability when using the A matrix. The results imply that introducing single-step genomic prediction could boost the rate of genetic improvement of superovulatory response traits, but efforts should be made to maintain genetic diversity when performing selection.

High throughput analysis of MHC class I and class II diversity of Zambian indigenous cattle populations

The classical MHC class I and class II molecules play key roles in determining the antigenic-specificity of CD8+ and CD4+ T-cell responses-as such characterisation of the repertoire of MHCI and MHCII allelic diversity is fundamental to our ability to understand, and potentially, exploit how genetic diversity influences the outcome of immune responses. Cattle remain one of the most economically livestock species, with particular importance to many small-holder farmers in low-and-middle income countries (LMICs). However, our knowledge of MHC (BoLA) diversity in the indigenous breeds that form the mainstay of cattle populations in many LMICs remains very limited. In this study we develop a MiSeq-based platform to enable the rapid analysis of BoLA-DQA and BoLA-DQB, and combine this with similar platforms to analyse BoLA-I and BoLA-DRB repertoires, to study a large cohort of cattle (~800 animals) representing the 3 major indigenous breeds (Angoni, Barotse, Tonga) in Zambia. The data presented confirms the capacity of this high-throughput and high-resolution approach to provide a full characterisation of the MHCI-MHCII genotypes of cattle for which little previous MHC sequence data has been obtained. The cattle in Zambia were found to express a diverse range of MHCI, MHCII and extended MHCI-MHCII haplotypes. The combined MHCI-MHCII genotyping now possible opens new opportunities to rapidly expand our knowledge of MHC diversity in cattle that could find applications in a related translational disciplines such as vaccine development.

Pedigree and genome-based patterns of homozygosity in the South African Ayrshire, Holstein, and Jersey breeds

The erosion of genetic diversity limits long-term genetic gain and impedes the sustainability of livestock production. In the South African (SA) dairy industry, the major commercial dairy breeds have been applying estimated breeding values (EBVs) and/or have been participating in Multiple Across Country Evaluations (MACE). The transition to genomic estimated breeding values (GEBVs) in selection strategies requires monitoring of the genetic diversity and inbreeding of current genotyped animals, especially considering the comparatively small population sizes of global dairy breeds in SA. This study aimed to perform a homozygosity-based evaluation of the SA Ayrshire (AYR), Holstein (HST), and Jersey (JER) dairy cattle breeds. Three sources of information, namely 1) single nucleotide polymorphism (SNP) genotypes (3,199 animals genotyped for 35,572 SNPs) 2) pedigree records (7,885 AYR; 28,391 HST; 18,755 JER), and 3) identified runs of homozygosity (ROH) segments were used to quantify inbreeding related parameters. The lowest pedigree completeness was for the HST population reducing from a value of 0.990 to 0.186 for generation depths of one to six. Across all breeds, 46.7% of the detected ROH were between 4 megabase pairs (Mb) and 8 Mb in length. Two conserved homozygous haplotypes were identified in more than 70% of the JER population on Bos taurus autosome (BTA) 7. The JER breed displayed the highest level of inbreeding across all inbreeding coefficients. The mean (± standard deviation) pedigree-based inbreeding coefficient (FPED) ranged from 0.051 (±0.020) for AYR to 0.062 (±0.027) for JER, whereas SNP-based inbreeding coefficients (FSNP) ranged from 0.020 (HST) to 0.190 (JER) and ROH-based inbreeding coefficients, considering all ROH segment coverage (FROH), ranged from 0.053 (AYR) to 0.085 (JER). Within-breed Spearman correlations between pedigree-based and genome-based estimates ranged from weak (AYR: 0.132 between FPED and FROH calculated for ROH <4Mb in size) to moderate (HST: 0.584 between FPED and FSNP). Correlations strengthened between FPED and FROH as the ROH length category was considered lengthened, suggesting a dependency on breed-specific pedigree depth. The genomic homozygosity-based parameters studied proved useful in investigating the current inbreeding status of reference populations genotyped to implement genomic selection in the three most prominent South African dairy cattle breeds.

Herd Level Yield Gap Analysis in a Local Scale Dairy Farming System: A Practical Approach to Discriminate between Nutritional and Other Constraining Factors

This study performed a yield gap analysis to help farmers understand whether their constraints were mainly due to nutritional factors or management and health issues. Twenty-nine farms were periodically evaluated. Milk yield (MY), dry matter intake (DMI), total mixed ration (TMR) composition and homogeneity index (HI), TMR digestibility, income over feed cost (IOFC), and MY summer-winter ratio (SWR) were collected. Farms were divided and compared according to the average annual MY: Low (L), Medium (M) and High (H), characterised by <31.1, 31.1-36.7 and >36.7 kg/head/day. An ANOVA mixed model and a stepwise regression to assess the relationship between nutritional variables and MY were run. H farms showed higher IOFC (p < 0.001), DMI (p = 0.006), DDM (p < 0.001), digestible crude protein (DCP, p = 0.019), HI (p = 0.09), SWR (p = 0.041) and lower HI coefficient of variation (p = 0.04). The conversion of DDM into milk was higher in H and M farms. Stepwise regression for MY selected DDM and CP (R² = 0.716, p < 0.05). M farms were mainly constrained by nutritional factors, whereas L farms were also affected by other factors such as those related to management and health.

The role of inbreeding depression on productive performance in the Italian Holstein breed

Inbreeding depression has become an urgent issue in cosmopolitan breeds where the massive genetic progress achieved in the latest generations is counterbalanced by a dramatic loss of genetic diversity causing increased health issues. Thus, the aim of this study was to estimate inbreeding depression on productive traits in Holstein dairy cattle. More precisely, we aimed to i) determine the level of inbreeding in 27,735 Italian Holstein dairy cows using pedigree and genotype data, ii) quantify the effect of inbreeding on 305-d in milk yield (MY; kg), fat yield (FY; kg), and protein yield (PY; kg) based on different statistical approaches, iii) determine if recent inbreeding has a more harmful impact than ancestral ones, and iv) quantify chromosomal homozygosity effect on productive traits. Quality control was performed on the autosomal chromosomes resulting in a final dataset of 84,443 single nucleotide polymorphisms. Four statistical models were used to evaluate the presence of inbreeding depression, which included linear regression analysis and division of FPED and FROH into percentile classes. Moreover, FROH was partitioned into i) length classes to assess the role of recent and ancestral inbreeding and ii) chromosome-specific contributions (FROH-CHR). Results evidenced that inbreeding negatively impacted the productive performance of Italian Holstein Friesian cows. However, differences between the estimated FPED and FROH coefficients resulted in different estimates of inbreeding depression. For instance, a 1% increase in FPED and FROH was associated with a decrease in MY of about 44 and 61 kg (P < 0.01). Further, when considering the extreme inbreeding percentile classes moving from the 5th lowest to the 95th highest, there was a reduction of -263 kg and -561 kg per lactation for FPED and FROH. Increased inbreeding, estimated by FPED and FROH, had also a negative effect on PY and FY, either fit as a regressor or percentile classes. When evaluating the impact of inbreeding based on runs of homozygosity (ROH) length classes, longer ROH (over 8 Mb) had a negative effect in all traits, indicating that recent inbreeding might be more harmful than the ancestral one. Finally, results within chromosome homozygosity highlighted specific chromosomes with a more deleterious effect on productive traits.

Genomic inbreeding coefficients using imputed genotypes: assessing differences among SNP panels in Holstein-Friesian dairy cows

The objective of this study was to evaluate the effect of imputation of single nucleotide polymorphisms (SNP) on the estimation of genomic inbreeding coefficients. Imputed genotypes of 68,127 Italian Holstein dairy cows were analyzed. Cows were initially genotyped with two high density (HD) SNP panels, namely the Illumina Infinium BovineHD BeadChip (678 cows; 777,962 SNP) and the Genomic Profiler HD-150K (641 cows; 139,914 SNP), and four medium density (MD): GeneSeek Genomic Profiler 3 (10,679 cows; 26,151 SNP), GeneSeek Genomic Profiler 4 (33,394 cows; 30,113 SNP), GeneSeek MD (12,030 cows; 47,850 SNP) and the Labogena MD (10,705 cows; 41,911 SNP). After imputation, all cows had genomic information on 84,445 SNP. Seven genomic inbreeding estimators were tested: (i) four PLINK v1.9 estimators (F, F_hat1,2,3), (ii) two genomic relationship matrix (grm) estimators [VanRaden's 1^st method, but with observed allele frequencies (F_grm) and VanRaden's 3^rd method that is allelic free and pedigree dependent (F_grm2)], and (iii) a runs of homozygosity (roh) - based estimator (F_roh). Genomic inbreeding coefficients of each SNP panel were compared with genomic inbreeding coefficients derived from the 84,445 imputation SNP. Coefficients of the HD SNP panels were consistent between genotyped-imputed SNP (Pearson correlations ~99%), while variability across SNP panels and estimators was observed in the MD SNP panels, with Labogena MD providing, on average, more consistent estimates. The robustness of Labogena MD, can be partly explained by the fact that 97.85% of the SNP of this panel is included in the 84,445 SNP selected by ANAFIBJ for routine genomic imputations, while this percentage for the other MD SNP panels varied between 55 and 60%. Runs of homozygosity was the most robust estimator. Genomic inbreeding estimates using imputation SNP are influenced by the SNP number of the SNP panel that are included in the imputed SNP, and performance of genomic inbreeding estimators depends on the imputation.

Fitting Genomic Prediction Models with Different Marker Effects among Prefectures to Carcass Traits in Japanese Black Cattle

We fitted statistical models, which assumed single-nucleotide polymorphism (SNP) marker effects differing across the fattened steers marketed into different prefectures, to the records for cold carcass weight (CW) and marbling score (MS) of 1036, 733, and 279 Japanese Black fattened steers marketed into Tottori, Hiroshima, and Hyogo prefectures in Japan, respectively. Genotype data on 33,059 SNPs was used. Five models that assume only common SNP effects to all the steers (model 1), common effects plus SNP effects differing between the steers marketed into Hyogo prefecture and others (model 2), only the SNP effects differing between Hyogo steers and others (model 3), common effects plus SNP effects specific to each prefecture (model 4), and only the effects specific to each prefecture (model 5) were exploited. For both traits, slightly lower values of residual variance than that of model 1 were estimated when fitting all other models. Estimated genetic correlation among the prefectures in models 2 and 4 ranged to 0.53 to 0.71, all <0.8. These results might support that the SNP effects differ among the prefectures to some degree, although we discussed the necessity of careful consideration to interpret the current results.

Analysis of Family Structure and Paternity Test of Tan Sheep in Yanchi Area, China

Tan sheep is a special breed of locally protected sheep in China, one of the best quality meat sheep in the world. Due to the unclear pedigree of the rams on the Ningxia Tan sheep breeding farm, we investigated 74 rams in the field and explored a new method for family division. Genomic DNA was extracted from the blood of breeding rams. Using Plink software, GCTA tools and R language, we analyzed the genetic structure, kinship, and inbreeding coefficient of the breeding sheep, which revealed the genetic relationship between the individuals. The results showed that there was no obvious clustering phenomenon in the PCA, and the genetic background of the samples was similar. The G matrix and IBS distance matrix indicated that most individuals were far away from each other. Paternity testing identified 24 pairs of unknown parent-child pairs, and all the Tan sheep could be divided into 12 families, which provided a reference for sheep breeding. The average inbreeding coefficient based on the ROH of this population was 0.049, so there was a low degree of inbreeding and the rams in the field were able to maintain high genetic diversity. Overall, we explored a more accurate method through paternity and kinship analysis; it provides a scientific basis for pedigree construction, which has an important application value for Tan sheep breeding.

Genomic characterization of autozygosity and recent inbreeding trends in all major breeds of US dairy cattle

Maintaining a genetically diverse dairy cattle population is critical to preserving adaptability to future breeding goals and avoiding declines in fitness. This study characterized the genomic landscape of autozygosity and assessed trends in genetic diversity in 5 breeds of US dairy cattle. We analyzed a sizable genomic data set containing 4,173,679 pedigreed and genotyped animals of the Ayrshire, Brown Swiss, Guernsey, Holstein, and Jersey breeds. Runs of homozygosity (ROH) of 2 Mb or longer in length were identified in each animal. The within-breed means for number and the combined length of ROH were highest in Jerseys (62.66 ± 8.29 ROH and 426.24 ± 83.40 Mb, respectively; mean ± SD) and lowest in Ayrshires (37.24 ± 8.27 ROH and 265.05 ± 85.00 Mb, respectively). Short ROH were the most abundant, but moderate to large ROH made up the largest proportion of genome autozygosity in all breeds. In addition, we identified ROH islands in each breed. This revealed selection patterns for milk production, productive life, health, and reproduction in most breeds and evidence for parallel selective pressure for loci on chromosome 6 between Ayrshire and Brown Swiss and for loci on chromosome 20 between Holstein and Jersey. We calculated inbreeding coefficients using 3 different approaches, pedigree-based (F_PED), marker-based using a genomic relationship matrix (F_GRM), and segment-based using ROH (F_ROH). The average inbreeding coefficient ranged from 0.06 in Ayrshires and Brown Swiss to 0.08 in Jerseys and Holsteins using F_PED, from 0.22 in Holsteins to 0.29 in Guernsey and Jerseys using F_GRM, and from 0.11 in Ayrshires to 0.17 in Jerseys using F_ROH. In addition, the effective population size at past generations (5-100 generations ago), the yearly rate of inbreeding, and the effective population size in 3 recent periods (2000-2009, 2010-2014, and 2015-2018) were determined in each breed to ascertain current and historical trends of genetic diversity. We found a historical trend of decreasing effective population size in the last 100 generations in all breeds and breed differences in the effect of the recent implementation of genomic selection on inbreeding accumulation.