All data based on genome sequences from previous generations provides comprehensive information on the polymorphic loci that characterize a given breed and differentiate it from others. Association analysis with imputed sequences, mainly when applied to multiple traits simultaneously, is a powerful approach to detecting candidate causal variants underlying complex phenotypes. Twelve quantitative trait loci (QTL) affecting different morphological characteristics of the mammary gland were detected in the German Fleckvieh cattle population. Most of the QTLs were located in non-coding regions of the genome but in close proximity to candidate genes that could be involved in mammary gland morphology (
SP5, GC, NPFFR2, CRIM1, RXFP2, TBX5, RBM19, and
ADAM12)
[39][27]. In Holstein cattle, genetic analyses of udder conformation traits have been developed
[19][28]. A genome-wide association has been described with five udder conformation traits, including anterior udder attachment, central suspensory ligament, posterior udder attachment height, posterior udder attachment width, and udder depth
[19][28]. Heritability and standard errors for these five udder traits ranged from 0.04 ± 0.00 to 0.49 ± 0.03. In th
ise study, phenotypic data were measured on 1000 Holstein cows, and the GeneSeek Genomic Profiler (GGP) Bovine 100 K SNP chip was used to analyze genotypic data
[19][28]. Numerous candidate genes were identified within 200 kb of significant SNPs. Among all significant SNPs associated with udder conformation traits, most of them were located within the following genes: Microsomal Glutathione S-Transferase 1 (
MGST1), Microsomal Glutathione S-Transferase 2 (
MGST2), Microtubule-associated scaffold protein 1 (
MTUS1), LOC101903734,
LOC112447118, Structural maintenance of chromosomes protein 5 and 6 (
SMC5-SMC6), Parkin RBR E3 ubiquitin-protein ligase (
PRKN), Syntaxin-Binding Protein 6 (
STXBP6), glutamate ionotropic receptor delta-type subunit 2 (
GRID2), E2F transcription factor 8 (
E2F8), Cadherin 11 (
CDH11), Forkhead Box P1 (
FOXP1), Localisation factor complex 1 (
SLF1), transmembrane protein 117 (
TMEM117), SET binding factor 2 (
SBF2), CGPVitamin D binding protein (
GC), galectin 2 (
LGALS2), adhesion G protein-coupled receptor B3 (
ADGRB3), and Glutamate-Cysteine Ligase Catalytic Subunit
(GCLC) as well as
KEGG pathway genes. While one of the SNPs on
Chr6 was close (50 kb) to the ubiquitin-conjugating enzyme E2 K gene (
UBE2K), three SNPs on
Chr21,
Chr29, and
Chr18 were located close (100 kb) to
STXBP6,
E2F8, and
CDH11, respectively
[19][28].
5. Evaluation Criteria Based on Genomic and Phenotypic Traits: Udder Health, Production, and Longevity
5.1. Genomic, Genotypic, and Phenotypic Udder Traits: Impact on Mammary Gland Health
The development of national health registration programs began several decades ago. Accordingly, the first reports of genetic results based on health traits in the United States started in 2004
[41][29]. A few years later, in 2007, Canada reported large amounts of data on mastitis incidence in cows
[42,43][30][31]. Available data based on the mastitis incidence can be analyzed by multi-trait models using SCS data and other derived traits indicative of SCS in early lactation, partial SCS, test day SCS, anterior udder attachment, udder depth, and BCS to perform traditional and genomic evaluations for mastitis resistance
[35,43,44][23][31][32]. Udder health traits associated with bacteriological testing of quarters maximize the information on infection; however, these tests are not practical on a population scale
[45][33].
Nevertheless, several milk enzymes are possible indicators of tissue damage at the mammary gland level
[45][33]. The heritabilities are around 0.2 for SCCs and 0.1 for other traits, reflecting genetic variations in the immunological defenses and their effect on the teat, phagocytosis, and immune response
[45][33]. There is a positive relationship between somatic cell count (SCC) and milk yield at the genetic level but a strong negative relationship between them at the phenotypic level
[45][33]. Genetic correlation refers to the degree to which the same genes affect two different traits, in this case, SCC and milk yield.
Furthermore, it was observed that the
CHL1 gene is up-regulated with stress levels and influences the mechanisms of the immune system
[49][34]. Therefore, the activity interactions between the genes may fluctuate depending on environmental stressors. On the other hand, other authors report that
CFAP69,
STEAP2, and
ITGB3BP genes located on chromosome 4 are related to the mechanisms of the mammary gland immune system
[50,51][35][36]. The objective measures of mammary gland conformation and linear type scores are used to predict mastitis in experimental linear classification programs
[1]. However, the relationships between conformation and mastitis are often inconsistent due to moderate or low correlations between mastitis indicators
[46][37]. Selection to reduce the occurrence of cows with deep udders, especially low rear udders, open teats, teats that are set back, and also short and wide teats, can improve efforts to reduce the incidence of mastitis, along with better health control, therapeutic managing, and proper milking procedures
[46][37].
There are factors associated with the distribution of clinical bovine mastitis between the hind and forequarters, in addition to risk factors related to certain aspects of lactation, udder conformation, and management practices
[54,55,56][38][39][40]. Thus, programs to select sires whose progeny have the lowest SCCs should be carefully planned, taking into account the interpretation of SCCs as an immunological defense mechanism
[11,27,57,58][11][22][41][42]. High SCCs decrease the likelihood of subsequent infections; however, there is a heritable variation in SCCs in dairy cattle. Therefore, using SCCs as a defense marker should be studied in greater depth
[45][33]. Clinical mastitis in the hindquarters was found to be more common among primiparous cows, with a prevalence rate of 61.9% in affected cases
[54][38].
5.2. Genomic, Genotypic, and Phenotypic Udder Traits: Effects on Milk Production and Quality
Traditionally, dairy cattle have been selected for their ability to produce milk in quantity and quality
[20][25]. The traditional approach to dairy cattle selection has changed, and secondary traits are now included in the selection indices, with less emphasis on milk production and more interest in milk quality and other non-productive traits
[20,53,63][25][43][44]. Greater emphasis on non-productive traits is reflected in the industry’s desire to breed less productive but more functional dairy cattle
[20][25].
The phenotypic and genomic traits related to milk yield, protein, and fat are included in the performance tests in young bulls
[8,11][8][11]. Several linear-type traits have been added to the factor analysis. Heritability (h2) estimates for milk yield and milk quality-derived traits ranged from 0.125 to 0.219
[11,45][11][33]. Moreover, genetic estimates for milk yield and milk quality traits were negatively correlated with traits explaining udder conformation (−0.40) and rear muscularity
[11]. The latter genetic traits also showed a negative correlation with udder volume (−0.28). Additionally, head typicality and rear leg traits were not correlated with milk yield and milk quality but were negatively correlated with meat-related traits such as rear muscularity (−0.32)
[11]. The consequence of these results is that the use of the current selection index, which is mainly focused on milk production traits, may lead to a deterioration of all other traits. Therefore, more appropriate selection indices should consider an association between genetic and functional traits
[11,23,37,57][11][26][41][45].
The selection and correlation of different traits for milk production based on the selection of AI bulls preferred for high transmission capacity is now possible
[3,27,57][3][22][41]. The estimates regarding sire selection criteria may vary; however, the selection for milk production traits effectively increases milk production
[8]. Nonetheless, in a research project evaluating the direct and correlated effects of single-trait selection on milk yield all selective breeding groups increased productivity, but also undesirable responses correlated with selection for milk production were detected
[27][22]. Sometimes, bull sires from different genetic lines were selected by progeny testing based on the first lactation productivity and milk quality by associating traits for fat-corrected milk production, percentage of daughters discarded at the first lactation, and daughters’ udder conformation. However, one study showed no differences regarding milk yield among lines
[57][41].
It has been observed by RNA sequencing that
DDIT3,
RPL23A,
SESN2, and
NR4A1 genes are significantly and differentially expressed between mammary glands of lactating Holstein cows with extremely high or low protein and fat percentages. Therefore, these four genes could affect milk production and composition traits
[76][46]. In addition, the genes identified in another study, such as
PGM1 and
ARL4A, were related to milk production traits, lactose synthesis, glucose metabolism
[77][47], and milk production or composition
[78][48].
Linear evaluation plays a vital role in estimating the milk production of dairy cows; however, these evaluations, sometimes taken through subjective methods, can show variations
[5,31][5][49]. On the other hand, objective methods for estimating milk productivity provide more accurate and reliable data but require more sophisticated technologies
[5]. In one study, sire-derived type traits were analyzed in cows with low, medium, or high production performance during the first lactation
[80][50]. The factors examined in the model included herd-year-season, age at calving, the month of calving, recording status interaction, change in herd size, and season.
5.3. Genomic, Genotypic, and Phenotypic Udder Traits: Influence on True and Functional Longevity
Longevity-related traits have garnered growing interest among various milk-producing species, including sheep, cattle, goats, and other animals, as efforts to enhance the longevity of these animals continue to gain importance. Therefore, understanding the impact of genomic, genotypic, and phenotypic udder traits is crucial in determining dairy cows’ true and functional longevity. Functional longevity is defined as the number of days between first calving and culling, that is to say, the length of cows´ productive life
[81][51]. Functional longevity is an economically important trait to increase the profitability of dairy management
[82][52]. In different bovine dairy herds, the reasons for culling cows can be voluntary (mainly because of low productivity) or involuntary (mainly because of health and low fertility)
[81,83][51][53]. The longevity-derived traits include (i) true longevity (all reasons for culling, including productivity) and (ii) functional longevity (all reasons for culling except productivity)
[82][52]. Conformation traits and functional longevity have been related by survival analysis (Cox proportional hazards models) in first-lactation Holstein cows
[81][51]. The dairy character had the strongest correlation between a composite trait and functional longevity, followed by the udder final score
[81,84][51][54].
A more accurate methodology for improving animal longevity is necessary to decrease the involuntary culling rates rather than extending traits that influence the herd life
[82][52]. Therefore, the proportional hazard model is helpful for assessing genetic fitness for the traits influencing herd life. However, the differences between estimates made with the proportional hazards models and those made with linear animal models for one or more traits are unclear. Productive traits, udder traits, and leg and hoof traits are genetically correlated with longevity; consequently, these traits are used to assess longevity indirectly
[12,16][12][16]. The reliability of genetic fitness-related estimates for longevity is increased by combining direct and indirect estimates
[82][52]. Therefore, these genetic correlations should be reviewed periodically in different dairy cattle production systems as they vary according to the year of birth
[82][52]. In light of this, QTLs affecting economically relevant traits were studied for eight US Holstein cattle genetic lines
[85][55]. A marker on chromosome 14 associated with differences in fat yield, fat percentage, and milk yield was observed in two genetic lines. Other markers located on chromosomes 16 and 20 were related to differences in udder depth and anterior udder attachment, respectively. A marker on chromosome 27 was associated with a difference in milkability index. These additional markers complete the quantitative trait locus mapping to identify QTLs affecting economically important traits in a selected commercial Holstein population
[85][55].
The relationship between different conformation traits and functional longevity is essential in dairy cows and has been evaluated for years using survival analyses
[80,81][50][51]. As mentioned, the highest correlations among descriptive traits were observed for longevity-udder attachment and longevity-udder depth
[26,35,43,46,59][21][23][31][37][56]. However, functional longevity decreased with decreasing body condition in dairy cows
[81][51]. Other relevant factors affecting longevity could be related to chronic stress in dairy cattle.
5.4. Relationship between Genomic, Genotypic, and Phenotypic Udder Traits: Health, Production, and Longevity
The intricate interplay between genomic, genotypic, and phenotypic udder traits is fundamental to understanding the complex relationship between health, production, and longevity in dairy cattle. The dairy cattle sector is significantly impacted by the economic costs associated with the high incidence and prevalence of clinical and subclinical mastitis, including expenses related to treatment, production losses, and reduced animal welfare
[59,93][56][57]. The large databases generated have allowed for assessing the incidence of this health problem and investigating the genetic background of clinical mastitis and its relationships with other udder-derived traits of interest for the dairy industry
[59][56]. There is persistent controversy about low milk SCCs and susceptibility to mastitis
[94][58]. However, high SCCs in milk may indicate inflammation or infection of the mammary gland
[94][58].
Other studies involved molecular genome mapping results which provided information on quantitative trait loci (QTL) related to mastitis resistance and provided a better understanding of the genetic relevance of the traits
[37][45]. Many countries have implemented selection programs based on a linear decrease in SCCs for increasing mastitis resistance
[37][45]. Improving the selection accuracy for mastitis resistance includes advances in modeling, an optimal combination of mastitis-related traits, and associated udder predictors
[37][45]. In addition, the definition of the overall breeding objective that includes udder-related conformational and functional traits and the inclusion of molecular-based information is now available from QTL
[37,96][45][59]. These cutting-edge studies will lead to a better understanding of the genetic background of mastitis resistance and allow for more accurate selection, improving udder health, animal welfare, and profitability in the future modern dairy industry
[59,97][56][60].
Within the domain of conformation traits, a study was conducted to assess the impact of udder morphological characteristics on milk production in
Bos indicus cows
[104,105][61][62]. First, the udder diameter and height, teat length and diameter, and milk production were measured, and finally, the study determined the values of udder morphological characteristics in local zebu cows. The results showed that the udder size was highly and positively correlated with milk production. These findings will be instrumental in genetic improvement programs for zebu cows
[106][63].
Dairy cattle offer an attractive model for investigating the genes responsible for the substantial diversity observed both within and between mammalian species concerning their milk volume, protein, and fat composition, highlighting the potential significance of genomic traits. Many phenotypes for these traits and the complete genome sequence of the key founders of modern dairy cattle populations are available. Association tests were conducted on Holstein and Jersey cattle with exceptional phenotypes to identify variants within the target regions, while gene expression data were analyzed to pinpoint potential candidate genes such as
BTRC, MGST1, SLC37A1, STAT5A, STAT5B, PAEP, VDR, CSF2RB, MUC1, NCF4, and
GHDC associated with milk production
[108][64]. In
Bos taurus, 141 and five novel genes related to milk production and SCS have been identified, respectively. These novel genes were also found to be functionally related to heat tolerance (e.g.,
SLC45A2, IRAG1, and
LOC101902172), longevity (e.g.,
SYT10 and
LOC101903327), and fertility
[109][65].