Estimation of effective population size and inbreeding coefficient parameters based on genomic chip data in Iranian Holstein cattle

Abstract
Introduction: Holstein cattle, originating from Northern Europe, have spread to countries like Iran, primarily due to their high milk production. Breeding programs for this breed have made significant progress based on economic traits such as milk, fat, and protein yield. In Iran, Holsteins play a crucial role in dairy production, and breeding programs aim to enhance genetic quality. Understanding the population structure of this breed is essential for achieving high productivity, involving genetic diversity analysis and parameters like linkage disequilibrium (LD), effective population size (Ne), and inbreeding coefficient. Genomic technologies, such as genomic chips, assist breeders in devising precise strategies to improve desirable traits. LD provides insights into allele relationships, population history, and Ne, with reduced Ne leading to inbreeding, genetic diversity loss, and potential production issues. High inbreeding negatively affects productivity and health. Recent advancements in sequencing technologies allow for more accurate estimations of Ne and inbreeding. This study aimed to investigate the structure of linkage disequilibrium (LD), effective population size (Ne), and inbreeding in Iranian Holstein cattle populations.
Materials and Methods: In the present study, genomic data from 25 and 43 individuals of the Iranian Holstein populations were utilized. Quality control and data screening were performed using the Plink 1.9 software. Specifically, loci and individuals with more than 5% missing genotypes, SNP markers with a minor allele frequency (MAF) less than 5%, and SNP markers that were out of Hardy-Weinberg equilibrium based on Bonferroni correction were excluded. The corrected pairwise r² values for SNP markers within a 40 Mbp range and the effective population size (Ne) for both past and current generations were calculated using the SNeP ver1.1 software. The r² values and Ne estimates for past and current generations were determined using the pairwise r² values of SNP markers, with distances up to 20 Mbp, using the SNeP ver1.1 software. The calculated r² values were adjusted for sample size within each population (Barbato et al., 2015). Subsequently, the corrected LD information for each population was used to calculate the effective population size. Inbreeding coefficients were calculated using four methods: the genetic relationship matrix (FGRM), homozygosity rate (FHOM), gamete correlation (FUNI), and runs of homozygosity (FROH). The FGRM, FHOM, and FUNI values were obtained using the GCTA software. To estimate FROH, ROH values were first calculated using the PLINK ver1.9 software, and then FROH values were derived using the related formula.
Results and Discussion: This study utilized two genomic datasets. The first dataset included 43 animals and 47,843 SNP markers, which, after excluding incomplete data and markers with a minor allele frequency (MAF) under 5%, resulted in 40,105 markers. These markers, with an average distance of 62.99 kb, were used for further analysis in 41 animals. The second dataset, consisting of 25 animals and 54,609 SNP markers, after removing markers with missing genotypes and those with MAF under 5%, included 41,535 markers. These markers, with an average distance of 60.29 kb, were used for final analysis in 25 animals. After quality control, 13,551 common SNP markers were found between the two datasets, though the analyses were conducted independently for each dataset. The r² values for the IRI-S1 and IRI-S2 populations were 0.311 and 0.268, respectively, decreasing to 0.03 and 0.47 at a 38 Mb distance. This reduction indicates a diminishing phase of LD as the distance between markers increases. Effective population size (Ne) is crucial for maintaining genetic diversity and ensuring the survival of Holstein populations. Accurate Ne estimation is essential to prevent genetic decline and make informed decisions for genetic conservation. One common method for estimating Ne involves using linkage disequilibrium (LD) information, which depends on the availability of extensive genetic marker data. In this study, the SNeP software was used to estimate Ne, providing a valuable tool for understanding the demographic history of Holstein cattle. The effective population size (Ne) in the current generation for IRI-S1 and IRI-S2 was 121 and 130, respectively, while the lowest Ne values, recorded in the five preceding generations, were 103 and 99. Inbreeding coefficients using the FGRM, FHOM, and FUNI methods were 0.035 for IRI-S1 and ranged from 0.036 to 0.047 for IRI-S2. Additionally, the highest inbreeding coefficients based on FROH were 0.095 for IRI-S1 and 0.091 for IRI-S2, observed at MAF values below 0.01. These results indicate that as MAF decreases, inbreeding increases, while FROH values decrease as MAF increases from 0.01 to 0.02.
Conclusion:
The results indicate a significant decrease in linkage disequilibrium (LD) with increasing marker distance in the studied populations. This declining trend aligns with previous studies in various cattle and poultry breeds, reflecting the impact of population history and selective breeding programs. The estimates related to the effective population size (Ne) indicate a significant decline in Ne in recent generations. The estimated Ne values for the current generation were 121 and 130 for IRI-S1 and IRI-S2, respectively, while for the past 2,000 generations, they were 2,463 and 3,382. This declining trend raises concerns about reduced genetic diversity and increased risks associated with inbreeding, such as decreased disease resistance.The effective population size (Ne) has sharply decreased in recent generations, remaining lower than indigenus Iranian breeds (Ne = 150-200) but higher than some international purebred populations. While the inbreeding level in Iran is lower than Canadian and American Holsteins, it exhibits a faster upward trend. To preserve genetic diversity and control inbreeding, implementing genomic strategies—such as ROH-based mating management, enhancing sire diversity, and continuous Ne monitoring—is critical. These findings provide a foundation for optimizing breeding programs in Iran.