This study did not involve live animals, animal experimentation, animal handling, biological sample collection, or any invasive procedures. Therefore, approval from an Institutional Animal Care and Use Committee (IACUC) or other animal ethics committee was not required. The research was conducted exclusively using existing pedigree databases provided by the Asociación de Criadores de Ganado Carora (ASOCRICA), Venezuela, for the Carora cattle population and the National Institute for Agricultural Research (INIA), Venezuela, for the Criollo Limonero cattle population. Permission to access and use these pedigree records for scientific research was obtained from the respective database custodians. All pedigree information was anonymized before analysis, and no personally identifiable information relating to animal owners or breeders was accessed or reported. The study complied with the ethical principles for research using existing archival data and was conducted in accordance with institutional policies and internationally accepted standards for responsible research, data management, and scientific integrity.

The data were extracted and analyzed from January to December 2024. The pedigree datasets included animals born over a 40-year period. Records for the Carora cattle population covered births from January 1984 to December 2023, whereas records for the Criollo Limonero cattle population extended from 1969 to 2023. The pedigree databases were obtained from ASOCRICA and INIA, both located in Venezuela. The geographical distribution of the native Venezuelan Carora and Criollo Limonero dairy cattle populations is presented in Figure 1.

This study was designed as a retrospective pedigree-based population genetics analysis to evaluate classical, ancestral, and new inbreeding coefficients and effective population size in two native Venezuelan dairy cattle breeds. Pedigree completeness, multiple pedigree-derived inbreeding estimators, pedigree purging parameters, effective population size, regression analyses, and correlation analyses were evaluated to characterize long-term genetic diversity and inbreeding dynamics.

The pedigree database of the Carora cattle population (Figure 2) was provided by the ASOCRICA, Venezuela. A total of 80,467 registered animals, comprising 8,067 bulls and 72,411 cows, born between January 1984 and December 2023, were included in the study. These records also incorporated genetic information from sires used through artificial insemination (AI). For the analyses, five populations were defined: one historical population consisting of all animals born between 1984 and 2023 and four decade-based subpopulations (1984–1993, 1994–2003, 2004–2013, and 2014–2023), comprising 8,444, 15,694, 34,987, and 21,350 individuals, respectively.

The pedigree database of the Criollo Limonero cattle population (Figure 2) was provided by the National Institute for Agricultural Research (INIA, Venezuela). The dataset consisted of 3,559 registered animals, including 1,178 bulls and 2,381 cows, born between 1969 and 2023. For comparative analyses, five populations were established, including the historical population (1969–2023) and four decade-based subpopulations (1984–1993, 1994–2003, 2004–2013, and 2014–2023), comprising 699, 460, 1,088, and 462 individuals, respectively.

The PCI was calculated according to the methodology described by Navas et al. [22] using pedigree information from the first through the fifth ancestral generations. In addition, the number of maximum generations (GMax), complete generations (GCom), and equivalent complete generations (GEqu) were calculated as described previously [23].

Before analysis, pedigree records underwent comprehensive quality control to ensure pedigree integrity. Parentage conflicts, duplicate animal identification numbers, and incomplete ancestry records were carefully evaluated. Conflicting records were resolved by cross-checking birth dates, sex, and pedigree consistency, whereas duplicate records were merged or removed after verification. Animals with irreconcilable parentage information or missing critical ancestral records were excluded to minimize bias in the estimation of inbreeding coefficients and effective population size. Individuals with unknown parents were retained as founder animals to preserve pedigree structure without introducing artificial relationships.

Classical and recursive inbreeding coefficients were calculated according to the methodologies described by Wright [1], Sargolzaei et al. [5], and Aguilar et al. [3]. To investigate the contribution of historical ancestry to current inbreeding, pedigree purging methodologies were subsequently applied.

Ballou's ancestral inbreeding coefficient (fa_Ballou) is one of the most widely used pedigree-based measures for evaluating ancestral inbreeding and potential genetic purging [24, 25]. This coefficient represents the cumulative proportion of an individual's genome that has previously been exposed to inbreeding through its ancestors [6], thereby quantifying the historical accumulation of homozygosity over successive generations.

Kalinowski et al. [7] partitioned total inbreeding into two biologically distinct components: ancestral inbreeding (Fa_Kal), representing homozygous alleles that had already been IBD in previous generations, and new inbreeding (F_new), representing alleles becoming IBD for the first time. Because these components are estimated independently from the individual's total inbreeding coefficient, their values may differ substantially. Fa_Kal is equal to zero whenever the traditional inbreeding coefficient is zero because ancestral inbreeding depends on the presence of common ancestors shared through both paternal and maternal pedigree lines.

The AHC was also estimated. The AHC represents the expected number of times that a randomly selected allele has been IBD during pedigree segregation [8]. Kalinowski's inbreeding coefficients and the AHC were estimated using a gene-dropping simulation with 100,000 replicates.

The GRain package version 2.2 [8, 9] was used to calculate Ballou's ancestral inbreeding coefficient [6], the Kalinowski inbreeding coefficients [7], and the AHC defined by Baumung et al. [8]. Initially, the complete pedigree dataset was analyzed; subsequently, a pruned pedigree was generated to satisfy the assumptions required for the gene-dropping procedure implemented in GRain, which requires accurate and conflict-free pedigree information. This dual analytical strategy increased the robustness of comparisons between the substantially different Carora and Criollo Limonero populations by minimizing potential bias associated with pedigree depth and pedigree quality.

The effective population size (Ne) is defined as the number of breeding individuals in an idealized population that would produce the same rate of inbreeding observed in the actual population [26]. Initially, effective population size (Ne) was estimated from the regression coefficient (b) obtained by regressing the classical inbreeding coefficient (F) on the equivalent complete generation [27]. Subsequently, Ne was calculated using the following equation:

The same approach was subsequently applied to estimate the ancestral effective population size, which reflects the rate of ancestral rather than total inbreeding. Accordingly, three additional estimates were obtained: Ne_Bal, Ne_Kal, and Ne_AHC, following the methodology described by Addo et al. [12].

Pedigree analyses were performed using ENDOG version 4.8 [27], POPREP [28], RelaX2 [29], and the GRain package version 2.2 [8, 9], which were used to estimate demographic parameters, genetic diversity indices, gene origin probabilities, and pedigree-based inbreeding coefficients.

Relationships among pedigree inbreeding coefficients were evaluated using simple linear regression analyses with the classical, recursive, and pedigree purging estimators (f_classical, f_recursive, f_Bal, AHC, fa_Kal, and fnew_Kal) as dependent variables and year of birth as a continuous predictor. Prior to regression analyses, the assumptions of linearity, homoscedasticity, and normality of residuals were verified.

For each regression model, the regression coefficient, coefficient of determination (R²), coefficient of variation (CV), standard error of the mean (SEM), and corresponding p-value were calculated. Statistical significance was considered at p < 0.001. Because the analyses were exploratory, no adjustment for multiple comparisons was applied.

Effective population size was estimated using six pedigree-based approaches, including the classical (Ne_classical), recursive (Ne_recursive), Ballou (Ne_Bal), Kalinowski new (Ne_Kal-new), Kalinowski ancestral (Ne_Kal-a), and ancestral history coefficient (Ne_AHC) methods. Finally, Pearson correlation analyses were performed to evaluate relationships among the different pedigree-derived inbreeding coefficients. Unless otherwise specified, statistical significance was established at p < 0.05.

The pedigree-derived inbreeding coefficients estimated using the different methodologies are summarized in Table 2. Overall, all inbreeding coefficients showed an increasing trend across the study period in both breeds. The Carora population consistently exhibited higher inbreeding values than the Criollo Limonero population despite its considerably larger pedigree size.

Among the evaluated estimators, AHC produced the highest inbreeding values in both breeds. In the historical populations, AHC values were 5.16% in the Carora population and 1.72% in the Criollo Limonero population, increasing to 8.59% and 4.17%, respectively, during 2014–2023. Similarly, fa_Ballou increased progressively throughout the study period, reaching 7.84% in the Carora population and 3.98% in the Criollo Limonero population during the final evaluation period. In contrast, fa_Kalinowski consistently produced the lowest inbreeding estimates in both breeds, whereas fnew_Kalinowski showed intermediate values between the classical and ancestral estimators.

The evolution of pedigree-derived inbreeding coefficients is illustrated in Figure 3 using violin plots. These plots summarize the distribution of the inbreeding coefficients for each method by displaying the density distribution together with the median and interquartile range. Overall, the Carora population exhibited higher central values and wider distributions than the Criollo Limonero population, indicating greater variability in pedigree-derived inbreeding estimates.

Distinct distributional patterns were observed among the different pedigree purging methods. The estimators separating ancestral and new inbreeding components showed different distribution patterns, reflecting differences between historical and recently accumulated inbreeding. Greater variability, characterized by wider distributions and more extreme values, was observed for all evaluated methods in the Criollo Limonero population than in the Carora population.

The AHC and fa_Ballou estimators showed the greatest concentration of inbreeding values from approximately 1995 onward in both breeds. In contrast, the classical and Kalinowski estimators exhibited more gradual increases during the same period. In the Carora population, AHC, f_recursive, and fa_Ballou consistently produced higher inbreeding estimates than the classical and Kalinowski methods. Conversely, in the Criollo Limonero population, the distributions of the different estimators showed minimal overlap.

The annual evolution of the pedigree-derived inbreeding coefficients is presented in Figure 4, and the corresponding regression equations and statistical parameters are summarized in Table 3.

Most regression models showed strong temporal relationships, with R² values exceeding 80%. The only exception in the Carora population was f_classical (R² = 80.71%), whereas in the Criollo Limonero population, both fa_Kalinowski and fnew_Kalinowski exhibited R² values below 80%.

From 2011 onward, AHC and fa_Ballou exceeded f_recursive in the Carora population. In contrast, the Criollo Limonero population exhibited greater annual variability in all pedigree-derived inbreeding coefficients after 2014, with marked declines observed in 2020 and 2023.

Different temporal patterns were also observed among the estimators. In the Criollo Limonero population, f_recursive showed a greater increasing trend than AHC and fa_Ballou, whereas the opposite pattern was observed in the Carora population. Moreover, AHC, fa_Ballou, f_classical, and fnew_Kalinowski showed more pronounced declines during specific periods than f_recursive and fa_Kalinowski.

Three distinct groups of pedigree-derived estimators were identified in the Carora population: (1) AHC and fa_Ballou; (2) f_recursive; and (3) f_classical, fa_Kalinowski, and fnew_Kalinowski, which showed the lowest temporal trends. In the Criollo Limonero population, three different groups were identified: (1) f_recursive, AHC, and fa_Ballou; (2) f_classical and fnew_Kalinowski; and (3) fa_Kalinowski.

Pearson correlation coefficients among the different pedigree-derived inbreeding estimators are presented in Table 4.

All pairwise correlations were statistically significant (p < 0.001). In the Carora population, the strongest correlation was observed between AHC and fa_Ballou (r = 0.99), followed by the correlation between f_classical and fnew_Kalinowski (r = 0.98). Moderate correlations were observed between f_classical and fa_Kalinowski and between f_recursive and fnew_Kalinowski, whereas weaker correlations involved the recursive estimator and the ancestral pedigree purging estimators.

In the Criollo Limonero population, the highest correlations were also observed between AHC and fa_Ballou (r = 0.99) and between f_classical and fnew_Kalinowski (r = 0.99). The lowest correlations involved f_recursive and the remaining pedigree-derived estimators, differing from the Carora population, in which f_recursive showed a relatively strong correlation with f_classical (r = 0.77). Overall, the correlations among the pedigree-derived inbreeding estimators were stronger in the Carora population than in the Criollo Limonero population.

The effective population size estimates derived from the different pedigree-based methods are presented in Table 5.

The present study evaluated the temporal evolution of classical, new, and ancestral inbreeding over approximately four decades in two native Venezuelan dairy cattle populations characterized by relatively small effective population sizes. A major strength of this study is its comprehensive analytical framework, which simultaneously evaluated classical and recursive inbreeding coefficients, three pedigree purging estimators, and six effective population size (Ne) estimators using the same pedigree datasets. To our knowledge, this is the first comprehensive pedigree-based comparison of classical, ancestral, and new inbreeding in the Carora and Criollo Limonero cattle populations and one of the most comprehensive pedigree purging analyses conducted in tropical dairy cattle.

The pedigree completeness of the Carora population was lower in the first and third ancestral generations (70.93%–65.62%) than that of the Criollo Limonero population (90.62%–78.17%). Nevertheless, the present findings indicate that population size alone did not determine pedigree completeness. Instead, pedigree recording practices and breeding management appear to have exerted a greater influence on pedigree integrity than the number of registered animals.

Limited pedigree completeness (low PCI) can substantially influence the partitioning of total inbreeding into ancestral and new components. Incomplete pedigrees truncate ancestral lineages, resulting in underestimation of ancestral inbreeding while simultaneously inflating estimates of new inbreeding. Such misclassification is particularly important in populations with incomplete or uneven pedigree records because it may exaggerate recent inbreeding while masking historical purging events. Consequently, pedigree completeness should always be considered when interpreting pedigree-derived estimates of ancestral and new inbreeding.

Comparable observations have been reported in other native cattle breeds. For example, the Berrenda en Negro (1,626 cows and 246 bulls) and Berrenda Colorada (2,651 cows and 523 bulls) breeds exhibited pedigree completeness exceeding 80% for the first and second ancestral generations [30]. These differences may reflect herd book regulations established by breed associations as well as the degree of breed openness resulting from open or closed herd book policies. Similarly, Guzerat cattle in Brazil [31] and Lithuanian dairy cattle [32] have reported pedigree completeness values exceeding 90%, largely attributed to the maintenance of closed herd books.

The Carora breed is currently managed using an open herd book designed to achieve breed purity through a grading-up (absorption) breeding program. This herd book comprises four registration categories: T5 (first-level crossbred), T4 (second-level crossbred), T3 (third-level crossbred), and T1 (pure Carora breed) (ASOCRICA, 2025). In contrast, the Criollo Limonero breed has been managed using a closed herd book within the principal conservation nuclei [15]. These contrasting breeding strategies likely contributed to the observed differences in pedigree completeness and pedigree depth between the two populations.

GEqu provide an estimate of the number of complete ancestral generations available for each individual. In the present study, GEqu reached 5.28 in the Carora population and 4.58 in the Criollo Limonero population. These values remain relatively low compared with those reported for other South American cattle breeds, including the Colombian Blanco Orejinegro breed (9.21) [33], although they are comparable to those reported for the Costeño con Cuernos (3.7), San Martinero (3.8), and Romosinuano (4.8) breeds [34].

Although pedigree recording for both Venezuelan breeds began during the 1960s, greater pedigree depth would be expected, particularly considering that the Colombian Blanco Orejinegro breed, whose pedigree recording began in 1980, has achieved substantially greater equivalent generations [33]. One possible explanation relates to differences in reproductive management. In the Carora population, breeding has relied on both natural mating and AI through a centralized semen distribution program. Although this system facilitates the widespread use of genetically superior sires, many breeding females may remain unregistered, or offspring may be entered into the herd book using only paternal information [18]. Such recording practices inevitably reduce pedigree completeness despite relatively large breeding populations.

In general, estimates of inbreeding and related pedigree parameters are influenced not only by breeding practices, including selection intensity, imported breeding animals, and AI, but also by pedigree completeness, the choice of founder population, and the methodology used to estimate inbreeding [35]. Incomplete pedigree information tends to underestimate true inbreeding; therefore, pedigree completeness values exceeding 0.60 have been recommended to obtain reliable pedigree-based inbreeding estimates [36, 37].

The classical inbreeding coefficients estimated for the Carora and Criollo Limonero populations were 2.35% and 2.05%, respectively. These values were lower than those reported for the Negra Andaluza breed (7.23%) [38], the Berrenda Negra and Berrenda Colorada breeds in Spain (7.0% and 5.7%, respectively) [30], and the Reyna breed of Nicaragua (4.5%–11.2%) [39, 40].

Within Latin America, the estimated inbreeding coefficients were comparable with or slightly higher than those reported for several native cattle breeds, including the Criollo Limonero breed of Venezuela (2.05%) [15], Costeño con Cuernos (0.79%), San Martinero (0.87%), Blanco Orejinegro (0.18%–2.88%), and Romosinuano (1.22%–2.53%) cattle in Colombia [33, 41]. However, they remained lower than those reported for the Mexican Romosinuano (3.7%) and Lechero Tropical (3.48%) breeds [34, 42, 43].

The marked differences between Iberian and Latin American native breeds may largely reflect differences in pedigree depth. European breeds generally possess deeper and more complete genealogical records, whereas pedigree incompleteness in many Latin American breeds may lead to underestimation of classical inbreeding coefficients. Differences in breeding structure, including the number of breeding sires used and reproductive management practices, may also contribute to these contrasting estimates, as previously demonstrated for the Negra Andaluza breed in Spain [38] and the Reyna breed in Nicaragua [40].

Despite the substantial difference in census population size between the Carora and Criollo Limonero populations, their classical inbreeding coefficients were remarkably similar. In the Criollo Limonero population, the relatively small breeding population and limited number of breeding animals likely contribute to the accumulation of inbreeding. Conversely, the larger Carora population benefits from widespread use of AI, allowing a greater number of females to participate in breeding programs and thereby slowing the rate of inbreeding accumulation despite intensive selection [44].

Interestingly, ancestral inbreeding was consistently higher in the Carora population than in the Criollo Limonero population. This finding may reflect differences in the efficiency of genetic purging between the two breeding systems. Intensive directional selection combined with extensive use of AI in the synthetic Carora breed may have increased the exposure of deleterious recessive alleles to selection, thereby facilitating their gradual removal from the population. In contrast, the closed herd book maintained in the Criollo Limonero population may have resulted in different purging dynamics because of its smaller breeding population and more restricted gene flow. These contrasting patterns may have important implications for maintaining adaptation to tropical environments, improving resilience to heat stress, and enhancing the long-term sustainability of locally adapted cattle populations under future climate change scenarios.

Studies evaluating ancestral inbreeding in both indigenous and commercial cattle breeds remain limited. In the present study, ancestral inbreeding estimated using pedigree purging methods increased progressively over time in both the Carora and Criollo Limonero populations, with the Ballou and AHC estimators producing the highest values. Similar trends have been reported for the German Brown Swiss breed by Wirth et al. [45], supporting the usefulness of pedigree purging approaches for evaluating the historical accumulation of inbreeding.

Ballou [6] defined ancestral inbreeding as the cumulative proportion of an individual's genome that has previously been exposed to inbreeding in its ancestors. Consistent with this concept, the Ballou estimator showed marked increases during the last two evaluation periods in both breeds. In the Carora population, fa_Ballou increased from 5.05% to 8.22%, whereas in the Criollo Limonero population it increased from 2.14% to 3.98%. These values were higher than those reported for the Negra Andaluza breed (1.98%) [11], German Brown Swiss (2.2%) [45], German Angler (3.69%), Red-and-White Dual-Purpose cattle (1.39%) [12], and Irish Holstein cattle (6.50%–6.89%) [47], but were comparable with those reported for German Holstein cattle (8.15%) [46].

The progressive increase in ancestral inbreeding observed in both Venezuelan breeds indicates that a considerable proportion of homozygous genomic regions has persisted over multiple generations. Such accumulation reflects the long-term demographic history of these populations and highlights the importance of complementing conventional inbreeding coefficients with pedigree purging estimators when evaluating genetic diversity. Unlike classical inbreeding coefficients, ancestral inbreeding provides additional information regarding the historical exposure of deleterious alleles to selection and the potential occurrence of genetic purging.

The biological consequences of ancestral and new inbreeding have been investigated in several livestock species, although reported effects remain variable [6, 48]. In cattle, recent (new) inbreeding is generally considered more detrimental to economically important traits, including production, fertility, and reproductive performance [47, 49–51]. In contrast, ancestral inbreeding has been associated with both unfavorable effects [52] and beneficial consequences resulting from the gradual purging of deleterious recessive alleles [46].

Evidence supporting genetic purging has also been reported in sheep, dogs, rabbits, and goats [53–58]. Nevertheless, despite the potential reduction in genetic load through purging, the continuous accumulation of inbreeding remains a major concern because of its adverse effects on long-term genetic diversity, population fitness, and economic performance [59, 60]. Therefore, both ancestral and new inbreeding should be considered jointly when designing breeding and conservation strategies, particularly for small or genetically vulnerable livestock populations.

The effective population size (Ne) estimates obtained in the present study were generally lower than those previously reported for the Criollo Limonero [15] and Carora [21] populations. Considerable variation among estimation methods was also observed, highlighting the sensitivity of Ne estimation to the pedigree-derived inbreeding coefficient used.

The relatively low Ne_recursive estimate observed for the Carora population likely reflects the sensitivity of recursive algorithms to incomplete pedigree information rather than the actual demographic structure of the population. Recursive algorithms propagate the effects of unknown parents and missing ancestral information, thereby increasing estimated coancestry and reducing Ne estimates. Consequently, in populations characterized by widespread use of AI and a relatively broad breeding base, such estimates may underestimate the true effective population size because of methodological limitations rather than genuine demographic decline.

Although the Ballou and AHC estimators also produced relatively low Ne estimates, values obtained using the Kalinowski estimator were comparable with those reported previously. Similar methodological differences have been described for the German Angler and Red-and-White Dual-Purpose cattle breeds [12]. These discrepancies probably arise because Ne is traditionally estimated using Wright's classical inbreeding coefficient (F), which assumes an ideal population affected only by finite population size and neutral evolutionary processes. Applying alternative pedigree purging coefficients directly to the classical Ne equation may violate these assumptions and generate biologically unrealistic estimates.

Furthermore, estimation of purged inbreeding is inherently more complex because it depends on the effective purge coefficient (d = 0.5s[1−2h]), which is generally unknown and is frequently estimated simultaneously with the purged inbreeding coefficient itself. Consequently, interpretation of Ne estimates derived from pedigree purging methods should be performed with caution.

An additional consideration is that genetic purging is a gradual evolutionary process requiring multiple generations before measurable effects become apparent. Reliable inference regarding purging therefore depends on sufficiently deep and complete genealogical records extending from the establishment of the population or, more precisely, from the historical bottleneck. The minimum number of generations required depends largely on effective population size. Under completely recessive inheritance, the lower limit can be approximated by tc = √(2Ne) [61]. For example, a population with Ne = 50 would require approximately 10 generations before the effects of purging become detectable. Consequently, pedigree-based measures of purging should not be interpreted as direct evidence of purging unless sufficient time has elapsed for these evolutionary processes to occur.