Genomic inbreeding and selection signatures in the local dairy breed Icelandic Cattle

Unraveling inbreeding patterns and selection signals in Alpine Grey cattle

Inbreeding plays a crucial role in livestock breeding, influencing genetic diversity and phenotypic traits. Genomic data have helped address limitations posed by incomplete pedigrees, providing deeper insights into breed genetic diversity. This study assesses inbreeding levels via pedigree and genomic approaches and analyzes old and recent inbreeding using runs of homozygosity (ROH), and selection signals in Alpine Grey cattle. Pedigree data from 165 575 individuals, analyzed with INBUPGF90 software, computed inbreeding coefficients. Genomic-based coefficients derived from PLINK v1.9. or DetectRUNS R package analyses of 1 180 individuals' genotypes. Common single nucleotide polymorphisms within ROH pinpointed genomic regions, aggregating into "ROH islands" indicative of selection pressure. Overlaps with USCS Genome Browser unveiled gene presence. Moderate correlations (0.20-0.54) existed between pedigree and genomic coefficients, with most genomic estimators having higher (>0.8) correlation values. Inbreeding averaged 0.04 in < 8 Mb ROH segments, and 0.03 in > 16 Mb segments; > 90% of ROHs were < 8 Mb, indicating ancient inbreeding prevalence. Recent inbreeding proved less detrimental than in cosmopolitan breeds. Two major ROH islands on chromosomes 6 and 7 harbored genes linked to immune response, disease resistance (PYURF, HERC3), and fertility (EIF4EBP3, SRA1). This study underscores the need for detailed inbreeding analyses to understand genetic characteristics and historical changes in local breeds like Alpine Grey cattle. Genomic insights, especially from ROH, facilitated overcoming pedigree limitations, illuminating breed genetic diversity. Our findings reveal ancient inbreeding's enduring genetic impact and ROH islands potential for selective sweeps, elucidating traits in Alpine Grey cattle.

Ancestral alleles defined for 70 million cattle variants using a population-based likelihood ratio test

BACKGROUND

The study of ancestral alleles provides insights into the evolutionary history, selection, and genetic structures of a population. In cattle, ancestral alleles are widely used in genetic analyses, including the detection of signatures of selection, determination of breed ancestry, and identification of admixture. Having a comprehensive list of ancestral alleles is expected to improve the accuracy of these genetic analyses. However, the list of ancestral alleles in cattle, especially at the whole genome sequence level, is far from complete. In fact, the current largest list of ancestral alleles (~ 42 million) represents less than 28% of the total number of detected variants in cattle. To address this issue and develop a genomic resource for evolutionary studies, we determined ancestral alleles in cattle by comparing prior derived whole-genome sequence variants to an out-species group using a population-based likelihood ratio test.

RESULTS

Our study determined and makes available the largest list of ancestral alleles in cattle to date (70.1 million) and includes 2.3 million on the X chromosome. There was high concordance (97.6%) of the determined ancestral alleles with those from previous studies when only high-probability ancestral alleles were considered (29.8 million positions) and another 23.5 million high-confidence ancestral alleles were novel, expanding the available reference list to improve the accuracies of genetic analyses involving ancestral alleles. The high concordance of the results with previous studies implies that our approach using genomic sequence variants and a likelihood ratio test to determine ancestral alleles is appropriate.

CONCLUSIONS

Considering the high concordance of ancestral alleles across studies, the ancestral alleles determined in this study including those not previously listed, particularly those with high-probability estimates, may be used for further genetic analyses with reasonable accuracy. Our approach that used predetermined variants in species and the likelihood ratio test to determine ancestral alleles is applicable to other species for which sequence level genotypes are available.

A genome-wide comparison between selected and unselected Valle del Belice sheep reveals differences in population structure and footprints of recent selection

About three decades of breeding and selection in the Valle del Belìce sheep are expected to have left several genomic footprints related to milk production traits. In this study, we have assembled a dataset with 451 individuals of the Valle del Belìce sheep breed: 184 animals that underwent directional selection for milk production and 267 unselected animals, genotyped for 40,660 single-nucleotide polymorphisms (SNPs). Three different statistical approaches, both within (iHS and ROH) and between (Rsb) groups, were used to identify genomic regions potentially under selection. Population structure analyses separated all individuals according to their belonging to the two groups. A total of four genomic regions on two chromosomes were jointly identified by at least two statistical approaches. Several candidate genes for milk production were identified, corroborating the polygenic nature of this trait and which may provide clues to potential new selection targets. We also found candidate genes for growth and reproductive traits. Overall, the identified genes may explain the effect of selection to improve the performances related to milk production traits in the breed. Further studies using high-density array data, would be particularly relevant to refine and validate these results.

Impact of kinship matrices on genetic gain and inbreeding with optimum contribution selection in a genomic dairy cattle breeding program

BACKGROUND

Genomic selection has increased genetic gain in dairy cattle, but in some cases it has resulted in higher inbreeding rates. Therefore, there is need for research on efficient management of inbreeding in genomically-selected dairy cattle populations, especially for local breeds with a small population size. Optimum contribution selection (OCS) minimizes the increase in average kinship while it maximizes genetic gain. However, there is no consensus on how to construct the kinship matrix used for OCS and whether it should be based on pedigree or genomic information. VanRaden's method 1 (VR1) is a genomic relationship matrix in which centered genotype scores are scaled with the sum of 2p(1-p) where p is the reference allele frequency at each locus, and VanRaden's method 2 (VR2) scales each locus with 2p(1-p), thereby giving greater weight to loci with a low minor allele frequency. We compared the effects of nine kinship matrices on genetic gain, kinship, inbreeding, genetic diversity, and minor allele frequency when applying OCS in a simulated small dairy cattle population. We used VR1 and VR2, each using base animals, all genotyped animals, and the current generation of animals to compute reference allele frequencies. We also set the reference allele frequencies to 0.5 for VR1 and the pedigree-based relationship matrix. We constrained OCS to select a fixed number of sires per generation for all scenarios. Efficiency of the different matrices were compared by calculating the rate of genetic gain for a given rate of increase in average kinship.

RESULTS

We found that: (i) genomic relationships were more efficient than pedigree-based relationships at managing inbreeding, (ii) reference allele frequencies computed from base animals were more efficient compared to reference allele frequencies computed from recent animals, and (iii) VR1 was slightly more efficient than VR2, but the difference was not statistically significant.

CONCLUSIONS

Using genomic relationships for OCS realizes more genetic gain for a given amount of kinship and inbreeding than using pedigree relationships when the number of sires is fixed. For a small genomic dairy cattle breeding program, we recommend that the implementation of OCS uses VR1 with reference allele frequencies estimated either from base animals or old genotyped animals.

Invited review: Good practices in genome-wide association studies to identify candidate sequence variants in dairy cattle

Genotype data from dairy cattle selection programs have greatly facilitated GWAS to identify variants related to economic traits. Results can enhance the accuracy of genomic prediction, analyze more complex models that go beyond additive effects, elucidate the genetic architecture of a trait, and finally, decipher the underlying biology of traits. The entire process, comprising data generation, quality control, statistical analyses, interpretation of association results, and linking results to biology should be designed and executed to minimize the generation of false-positive and false-negative associations and misleading links to biological processes. This review aims to provide general guidelines for data analysis that address data quality control, association tests, adjustment for population stratification, and significance evaluation to improve the reliability of conclusions. We also provide guidance on post-GWAS strategy and the interpretation of results. These guidelines are tailored to dairy cattle, which are characterized by long-range linkage disequilibrium, large half-sib families, and routinely collected phenotypes, requiring different approaches than those applied in human GWAS. We discuss common limitations and challenges that have been overlooked in the analysis and interpretation of GWAS to identify candidate sequence variants in dairy cattle.

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 &lt;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.

Molecular evolution and signatures of selective pressures on Bos, focusing on the Nelore breed (Bos indicus)

Evolutionary history leads to genome changes over time, especially for species that have experienced intense selective pressures over a short period. Here, we investigated the genomic evolution of Bos species by searching for potential selection signatures, focusing on Nelore, an economically relevant cattle breed in Brazil. We assessed the genomic processes determining the molecular evolution across Nelore and thirteen other related taxa by evaluating (i) amino acid sequence conservation, (ii) the dN/dS ratio, and (iii) gene families' turnover rate (λ). Low conserved regions potentially associated with fatty acid metabolism seem to reflect differences in meat fat content in taxa with different evolutionary histories. All Bos species presented genes under positive selection, especially B. indicus and Nelore, which include transport protein cobalamin, glycolipid metabolism, and hormone signaling. These findings could be explained by constant selective pressures to obtain higher immune resistance and efficient metabolism. The gene contraction rate across the Nelore + B. indicus branch was almost nine times higher than that in other lineages (λ = 0.01043 vs. 0.00121), indicating gene losses during the domestication process. Amino acid biosynthesis, reproductive and innate immune system-related pathways were associated with genes recognized within the most frequent rapidly evolving gene families and in genes under positive selection, supporting the substantial relevance of such traits from a domestication perspective. Our data provide new insights into how the genome may respond to intense artificial selection in distinct taxa, and reinforces the presence of selective pressures on traits potentially relevant for future animal breeding investments.

The impact of using different ancestral reference populations in assessing crossbred population admixture and influence on performance

Crossbreeding is a process in which animals from different breeds are mated together. The animals produced will exhibit a combination of both additive and non-additive genetic improvement from parental breeds that increase heterozygosity and negate inbreeding depression. However, crossbreeding may also break up the unique and often beneficial gene combinations in parental breeds, possibly reducing performance potential as the benefits of heterosis depends on the type of crossbreeding systems used and heritability of the traits. This effect of crossbreeding, especially on the genome architecture, is still poorly understood with respect to 3-breed crossbreeding systems. Thus, this study examined variation in genomic ancestry estimations relative to pedigree-based estimations and correlated breed composition to key production and health traits. Two rotational crossbred populations, referenced as ProCROSS and Grazecross were assessed and totaled 607 crossbred cattle. ProCROSS is a product of rotational crossbreeding of Viking Red (VKR), Holstein (HOL), and Montbeliarde (MON). In contrast, Grazecross consists of Viking Red (VKR), Normande (NOR), and Jersey (JER). Both breeding programs were aimed at capitalizing on the positive effect of heterosis. The VKR is a marketing term for Swedish Red, Danish Red, and Finnish Ayrshire breed which complicated breed determination. Therefore, genomic breed composition estimates were compared using two different representations of VKR, one of which was based on parents used in the crossing system and a second based on genotypes from the ancestral breeds that comprise VKR. Variation of breed composition estimates were assessed between pedigree and genome-based predictions. Lastly, Genomic estimations were correlated with production and health traits by comparing extreme performance groups to identify the relationship between breed ancestry and performance. With the exception of the JER breed composition in Grazecross, all other estimates of the purebred contribution to the ProCROSS and Grazecross showed a significant difference in their genomic breed estimation when using the VKR ancestral versus the VKR parental reference populations for admixture analysis. These observations were expected given the different relationship of each VKR representation to the crossbred cattle. Further analysis showed that regardless of which VKR reference population was used, the degree of MON and HOL breed composition plays a significant role in milk and fat production in ProCROSS, while the degree of VKR and NOR ancestry were related to improved health performance in Grazecross. In all, identifying the most appropriate and informative animals to use as reference animals in admixture analysis is an important factor when interpreting results of relationship and population structure, but some degree of uncertainty exists when assessing the relationship of breed composition to phenotypic performance.

Opportunities of Genomics for the Use of Semen Cryo-Conserved in Gene Banks

Shortly after the introduction of cryo-conserved semen in the main farm animal species, gene banks were founded. Safeguarding farm animal genetic diversity for future use was and is the main objective. A sampling of sires was based on their pedigree and phenotypic information. Nowadays, DNA information from cryo-conserved sires and from animals in the living populations has become available. The combination of their DNA information can be used to realize three opportunities: 1) to make the gene bank a more complete archive of genetic diversity, 2) to determine the history of the genetic diversity from the living populations, and 3) to improve the performance and genetic diversity of living populations. These three opportunities for the use of gene bank sires in the genomic era are outlined in this study, and relevant recent literature is summarized to illustrate the great value of a gene bank as an archive of genetic diversity.

Five SNPs Within the FGF5 Gene Significantly Affect Both Wool Traits and Growth Performance in Fine-Wool Sheep (Ovis aries)

Fibroblast growth factor 5 (FGF5) gene, a member of fibroblast growth factor superfamily, plays significant roles in the regulation of the hair growth cycle during the development of mammalian hair follicles as well as the skeletal muscle development. In this study, DNA sequencing was used to scan the putative SNPs within the full-length of FGF5 gene, and SNPscan high-throughput technique was applied in the individual genotyping of 604 crossbred sheep. 10 SNPs were identified within FGF5 gene while five of them located in intron 1 could be genotyped, namely SNP1 (g. 105914953 G > A), SNP2 (g. 105922232 T > C), SNP3 (g. 105922244 A > G), SNP4 (g. 105922334 A > T) and SNP5 (g. 105922340 G > T). All these SNPs were in accord with the Hardy-Weinberg equilibrium (P > 0.05), and displayed the moderate polymorphism with PIC values ranging from 0.302 to 0.374. Thereafter, the correlation analysis between each SNP locus and economic traits including wool length, greasy wool weight and growth performance of sheep was systematically implemented. In our results, SNP1, SNP3, SNP4 and SNP5 were significantly associated with wool length, greasy wool weight and growth traits of SG sheep (P < 0.05); SNP1, SNP2, SNP3, and SNP4 were significantly correlated with wool length and growth traits of SSG sheep (P < 0.05). Meanwhile, our study revealed a strong linkage disequilibrium (LD) relationship among these SNPs (r² > 0.33), except for SNP3 and SNP4 sites (r² = 0.30). Combination genotype analysis showed that combination genotypes were significantly associated with mean fiber diameter of SG (P < 0.05), and body weight trait of SSG (P < 0.01). The above findings suggested that these SNP loci might affect economic traits synergistically and could be regarded as potential molecular markers for improving both wool production and growth performance of fine-wool sheep, which lay a molecular foundation for the breeding of fine dual-purpose sheep thereby accelerating the pace of sheep breeding.