Analysis of the current status of genetic diversity in Liaoning Huoyan goose based on microsatellite markers

Abstract

In this study, microsatellite loci in geese were initially screened, after which a multiplex PCR system was constructed and the genetic diversity and genetic structure of 40 samples from each of the three Huoyan goose populations in Liaoning were analyzed using the STR typing test. The results showed that 12 microsatellite loci were screened, and one quadruple PCR system, two triple PCR systems and one double PCR system were constructed with consistent and reliable reproducibility. A total of 90 alleles were detected in 120 samples, the number of alleles(Na), observed heterozygosity (Ho), expected heterozygosity (He) and polymorphic information content(PIC) of the three populations ranged from 5.083 to 6.083, from 0.149 to 0.184, from 0.555 to 0.577 and from 0.503 to 0.512.respectively, the populations present an unbalanced state, and the genetic diversity of three populations was highly polymorphic and highly inbred. The results of Principal Coordinates Analysis (PCoA) and genetic structure analysis showed that the genetic backgrounds of the three populations of Huoyan geese were similar, with close affinities and frequent gene exchanges.

1. Introduction

China boasts a wide variety of goose breeds, which are geographically widespread and economically valuable. The goose industry occupies a significant position within the broader context of animal husbandry.1, 2 Following a lengthy period of artificial selection and breeding, in association with the influence of the natural environment, a range of distinct goose breeds have emerged, exhibiting a variety of characteristics pertaining to their appearance, body size, growth performance and reproductive performance, etc.3, 4 The China's National Livestock and Poultry Resource Breeds List (2020) showed that there are currently 30 local goose breeds in China, in addition to three cultivated breed packages and six introduced breed packages. The Huoyan goose is an excellent local goose breed in China,4, 5, 6 native to the Wulong River basin in the Laiyang area of Shandong Province, so it is known as the Wulong goose in Shandong Province, and due to historical reasons, a large number of people from Shandong migrated to northeastern China and brought it here.7, 8 Huoyan goose is a small goose breed with a compact body shape and a medium-sized head. It is characterized by a scar-like notch on both upper eyelids, which is the unique feature of this breed.9, 10 The Huoyan goose has a wide range of dietary habits, strong resistance and adaptability, tolerance of roughage and high reproductive performance, and is known for its high egg-laying performance.10, 11

Genetic diversity is a crucial factor in enabling species to adapt to their living environment and is a prerequisite for their development and evolution. Therefore, it is of paramount importance to detect and conserve the genetic diversity of species.12, 13, 14, 15 In recent years, many molecular markers have been used to study genetic diversity and genetic structure and background of various species.15, 16, 17 Microsatellite markers are second-generation molecular markers, also known as short tandem repeats (STR), which consist of 1–6 nucleotide tandem repeats and conserved flanking sequences.18, 19, 20 These repeat sequences are widely and uniformly distributed in eukaryotic genomes, exhibit high levels of polymorphism, and are inherited in a co-dominant manner.16, 17, 20 Microsatellite markers are widely used in population genetic diversity analysis, genetic structure analysis, parentage identification, construction of genetic linkage maps and the genetic background between populations.21, 22, 23, 24 Emel Özkan Ünal et al.²³ used microsatellite markers to analysis Genetic diversity and genetic structure in Turkish Water Buffalo population. Marcos et al.²⁵ used microsatellite markers to analyze genetic diversity and kinship of local goat breeds in Brazil. In addition, microsatellite markers have been used to conduct tests on many outstanding goose breeds, such as greylag goose (Anser anser), Canada goose(Branta canadensis L.), Hawaiian goose, white-fronted goose (Anser albifrons), the white Roman goose and a few Chinese breeds.This also demonstrates their applicability in aspects such as genetic diversity analysis and phylogenetic relationship analysis among populations.26, 27 Multiplex PCR (MPCR), also known as compound PCR, is a technique that allows for the simultaneous amplification of multiple target gene fragments by placing two or more pairs of primers into a single PCR system. This is in contrast to the conventional PCR approach which can only amplify a single gene fragment using a single pair of primers at a time. This is an inefficient and time-consuming process. In research, the simultaneous amplification of multiple microsatellite loci is often required. The utilization of multiplex PCR can significantly enhance the efficiency of experimental procedures and minimize the number of samples required.28, 29, 30, 31

In this study, a four-group multiplex PCR system was constructed for the screened microsatellite markers, and then the genetic diversity of three populations of Huoyan goose in Liaoning was analyzed, aiming at exploring the genetic variation of each population, and evaluating the degree of intra-population differentiation and inbreeding, so as to reveal their genetic structure. This, in turn, provides a theoretical basis for the preservation of Huoyan goose.

2. Materials and methods

2.1. Animals

In this study, purebred Huoyan geese were randomly selected from each of the three regional Huoyan goose breeding bases in Liaoning Province (Changtu Goose Factory, Liaoyang A Goose Factory, Liaoyang B Goose Factory); and 25 Sanhua geese were selected from a goose factory in Haicheng. The animal study protocol was approved by the Animal Care and Use Committee of Shenyang Agricultural University (2023030204).

2.2. Sample collection and blood DNA extraction

Blood samples were collected from the subwing vein using disposable vacuum blood collection tubes. Each goose was sampled with 2 mL of blood, which was then transferred immediately into test tubes containing glucose-citrate anticoagulant solution, which was divided and stored at −20℃. The DNA extraction of blood samples was performed according to the instructions of the Blood Genomic DNA Extraction Kit (TianGen Biotech (Beijing) Co.,Ltd., Beijing, China,DP348). DNA purity and concentration were detected by measuring the absorbance at 260 nm and its ratio to the absorbance at 280 nm (NanoDrop, Thermo Scientific, Waltham, MA, USA).

2.3. Selection of microsatellite markers and establishment of multiplex PCR system

The 12 microsatellite loci used for the evaluation of genetic diversity of geese were selected from the pre-tests of the research group and from GenBank and the literature(Table 1). Primier imformation of four multiplex PCR sets of microsatellites in Huoyan Goose(The microsatellite ID,annealing temperature, primer sequence and Fluorescent labelling (Table 1). The primers were synthesized by Suzhou Genewiz Biotech Co.,Ltd. (Suzhou, China), and fluorescent markers such as FAM, HEX and ROX were used at the 5′ end of the upstream primers of each pair of primers, respectively. After agarose gel electrophoresis for the screening of microsatellite loci, the screened microsatellite loci were then grouped according to the size of their fragment and Tm and optimized for the reaction system, the reaction procedure and the ratio of primer concentration, the selection of the appropriate annealing temperature, etc., and a reasonable combination of different microsatellite markers, and the optimal multiplex PCR system was finally determined through continuous optimization.

Table 1.

Primier imformation of four multiplex PCR sets of microsatellites in Huoyan Goose(The microsatellite ID,annealing temperature, primer sequence and Fluorescent labeling.

Multiplex PCR Groups	Loci	Primer sequence	TM/℃	Maker type
1	CKW23	FP:ATGTCACAGTGTTGATCCCAAC RP:TAAGAACTTTTACTATGCTTACATCCA	57	5′AM
	CKW21	FP:CCCAGAACAGTGCTAGAA GAGG RP:AGCGAGTCACTCCAGTAC CTTC		5′HEX
	CKW13	FP:AGGCTGAGGTGGGAGAAT TTAT RP:TTCTTCCACTTCTCCCAAA GAA		5′ROX
	HY84	FP:GTGGGTCATAGGGGCTTGT RP:CTCCACCTACTGCTACCTA ATC		5′FAM
2	ZAAS039	FP:GGACATGGGGAGTGAAACAT RP: CAGTCTAGCTCGTCCCTGCT	58.6	5′HEX
	CKW15	FP:AGGCATGATATCTGTCCCTGAT RP:TTTCAGTGCAATTACCCATTCA		5′FAM
	APL515899	FP:TCAACCAGTGGTCAGAGAAAAA RP:AGGTCAGCCCCCATTTTAGT		5′FAM
3	CKW24	FP:ACAAGAGTGTTGGGAGGGA RP:GAGTAGGAACAGGTAAGCCAT	54.7	5′FAM
	TTUCG5	FP:TGTATTTGGGGCAAATGTGA RP:TCCGGTCTGTAAAGTCAGACAA		5′FAM
4	AuLul	FP:CATGGGTGTTTAAGGGGTAT RP:TAAGACTTGCGTGAGGAATA	55.6	5′FAM
	APL515888	FP:TTAGTAGCATGTCAGGTTTATT RP:GCTTGTAGACTTCAGAGTTC		5′HEX
	CKW14	FP:AACTGATCCGGCAGAAAACTAA RP:ACTTAGCATGCACCTTCACAAA		5′FAM

2.4. STR genotyping test

The amplified PCR products with fluorescent labeling in the previous step were sent to Suzhou Genewiz Biotech Co.,Ltd. (Suzhou, China) for capillary electrophoresis detection by ABI3730XL sequencer, and the results of genotyping were interpreted using GeneMapper Software 5.

2.5. Statistical analysis

The obtained genotyping results were entered into Excel 2019, and genetic diversity parameters such as the number of alleles (Na), the number of effective alleles (Ne), the expected heterozygosity (He), the observed heterozygosity (Ho), and the F-statistics (Fit, Fis, and Fst) were computed using GenAlex 6.503 software, as well as the population genetic distance parameter and the principal coordinates analysis (PCoA) based on this; calculation of polymorphic information content (PIC) and Hardy-Weinberg equilibrium (HWE) using Cervus software; and construction of UPMGA genetic evolution tree based on Nei's genetic distance using MEGAX software. Population genetic structure was analyzed using STRUCTURE 2.3.4 with preset K values from 1 to 9, and each K value was run five times to derive the Ln P(D) value and then the ΔK value was calculated in Excel 2019, and the optimal K value was determined based on the fact that the ΔK was the maximum, and ΔK was plotted versus the K values of the different hypothetical taxa using GraphPad Prism10 software.

3. Results

3.1. Screening of microsatellite primers and establishment of multiplex PCR system

By agarose electrophoresis of the PCR products of microsatellite loci, it was found that 12 of the microsatellite loci were better amplified with better primer specificity and a single clear band of amplification. The 12 pairs of microsatellite primers screened were grouped to find out the optimal annealing temperature and optimize the reaction system, and a 4-group multiplex PCR system was successfully constructed (Table 1). The agarose gel electrophoresis is shown in Fig. 1. In the electrophoretic graphs of one group of quadruple PCR system, two groups of triple PCR system and one group of double PCR system, clear bands without overlapping can be seen. In addition, in order to verify whether this multiplex PCR system can be applied to other breeds of geese, the system was utilized to amplify the genomic DNA of 25 Sanhua geese (Fig. 2), and agarose gel electrophoresis showed clear and single bands, which are distinguishable from each other and do not appear as dimers. The system was reproducible in each individual and could be used for subsequent analysis, and it is clear that the multiplex PCR system constructed in this study has the opportunity to be generalized.

Fig. 1.

Agarose gel electrophoresis of 4 sets of multiplex PCR systems(Huoyan goose).

Fig. 2.

Agarose gel electrophoresis of 4 sets of multiplex PCR systems (Sanhua goose).

3.2. Genetic diversity analysis of microsatellite locus

PCR amplification products were sequenced and analyzed using GenAlex for alleles and genotypes at 12 microsatellite loci for each individual. The results showed a total of 90 alleles were detected at the 12 microsatellite loci in the three Huoyan goose populations, with an average of 7.5 alleles per microsatellite locus. The highest number of alleles (28) was found in the CKW21 locus, and the lowest number of alleles (2) was found in the CKW23 loci. The number of Ne at each locus ranged from 1.368 to 9.530, with an average value of 3.064, with the highest number of effective alleles (9.530) at locus CKW21 (Table 2). As shown in Table 2, the PIC was calculated by Cervus software in this experiment and ranged from 0.245 to 0.895, with an average PIC value of 0.524. Seven of the twelve microsatellite loci were highly polymorphic (PIC > 0.5), four were moderately polymorphic (0.25 < PIC < 0.50), and one was low polymorphic (PIC < 0.25). The Ho ranged from 0 to 0.781, with a mean value of 0.181; the He ranged from 0.265 to 0.893, with a mean value of 0.524. The mean value of the Ho was smaller than the mean value of the He, and the genotypic frequencies deviated, with some deletion of the heterozygotes at each locus. Ten of the 12 loci significantly deviated from HWE (p < 0.01). The polymorphic information content of the three groups is similar, but the Na, Ne, and Ho values in the CT group are relatively higher (Table 3).

Table 2.

Genetic diversity parameter among 12 loci in Huoyan Goose.

Locus	Na	Ne	Ho	He	I	PIC	HWE
CKW21	19	9.530	0.781	0.893	2.251	0.895	NS
CKW13	4	2.294	0.134	0.548	0.919	0.501	***
HY84	2	1.949	0.008	0.487	0.695	0.377	***
CKW23	2	1.957	0	0.489	0.682	0.371	***
ZAAS039	4	2.329	0.125	0.570	1.011	0.535	***
CKW15	3	1.368	0.008	0.265	0.493	0.245	ND
APL515899	2	1.547	0	0.334	0.525	0.302	***
CKW24	6	4.319	0.138	0.767	1.613	0.755	***
TTUCG5	8	4.354	0.392	0.767	1.665	0.745	***
AuLul	4	2.603	0.053	0.601	1.091	0.560	***
APL515888	6	2.527	0.419	0.601	1.189	0.563	**
CKW14	3	1.988	0.108	0.490	0.867	0.440	***
Mean	5.08	3.064	0.181	0.568	1.108	0.524

Notes: **indicates statistical significance (p < 0.01);*** indicates statistical significance (p < 0.001);NS, No significant difference; ND, not detected.

Table 3.

Summary of genetic diversity of the three populations of Huoyan goose.

Population	Na	Ne	I	Ho	He	PIC
CT	6.083	3.166	1.119	0.209	0.555	0.503
LY1	5.417	2.995	1.108	0.149	0.579	0.512
LY2	5.083	3.092	1.098	1.184	0.584	0.509
Mean	5.528	3.064	1.108	0.181	0.573	0.508

Notes:The number of alleles (Na), number of effective alleles(Ne), observed heterozygosity (Ho), expected heterozygosity (He),Shannon's Information Index(I),polymorphism information content(PIC). Thegroups of Huoyan goose from three geographical regions(CT = Liaoning Changtu,LY1=North of Liaoyang, Liaoning Province,LY2=South of Liaoyang, Liaoning Province.

The results of the analysis of the Fit value, the FST value and the Fis value are shown in Table 4. The data of the F-statistics showed that among the 12 microsatellite loci, the mean value of Fis was 0.743, which indicated a higher degree of inbreeding in the population, and the average value of Fst was 0.027 (Fst < 0.05), and the degree of genetic differentiation is relatively low, and the degree of genetic differentiation among subpopulations was not obvious. Fit ranged from −0.059 to 0.977, with a mean value of 0.745.

Table 4.

F-statistics and gene flow of 12 microsatellite loci in Huoyan goose.

Locus	Fis	Fit	Fst	Nm
CKW21	0.126	0.133	0.009	28.23
CKW13	0.755	0.767	0.049	4.485
HY84	0.983	0.983	0.020	12.06
CKW23	1.000	1.000	0.006	44.31
ZAAS039	0.781	0.796	0.069	3.367
CKW15	0.969	0.969	0.013	18.325
APL51589	1.000	1.000	0.077	3.004
CKW24	0.820	0.825	0.026	9.435
TTUCG5	0.489	0.496	0.014	17.47
AuLul	0.911	0.914	0.027	8.855
APL515888	0.303	0.307	0.005	48.25
CKW14	0.779	0.782	0.013	18.78
Mean	0.743	0.748	0.027	18.07

Notes: Fis: the inbreeding coefficient within individuals relative to the subpopulation; Fit: the inbreeding coefficient within individuals relative to the total; Fst: the inbreeding coefficient within subpopulations relative to the total; Nm: Gene flow.

3.3. Population genetic Relationships and genetic structure

The genetic distance and genetic similarity among the three Huoyan goose populations are shown in Table 5, in which Liaoyang group 2 (LY2) and Liaoyang group 1 (LY1) have the closest genetic distance (0.049) and the highest genetic similarity (0.953), and Liaoyang group 2 (LY2) is the furthest away from Changtu group (CT) (0.057); the clustering results of the threeHuoyan goose populations are shown in Fig. 3. The three populations were divided into two groups, LY1 and LY2 were clustered into one group; CT group was a separate group.

Table 5.

Nei’s genetic distance and genetic similarity between the four groups.

Group	CT	LY1	LY2
CT	–	0.946	0.945
LY1	0.055	–	0.953
LY2	0.057	0.049	–

Notes: Genetic distance is found below the diagonal, Genetic similarity is above the diagonal. The groups of Huoyan goose from three geographicaregions (CT: LiaoningChangtu, LY1: North of Liaoyang, Liaoning Province, LY2: South of Liaoyang, Liaoning Province.

Fig. 3.

Nei’s genetic distance UPGMA cluster plot of three Huoyan goose groups.

The test samples were subjected to principal coordinate analysis (PCoA) using GenAlex software. The results of principal coordinate individual clustering can reflect the degree of differentiation of the population, the PCoA analysis of the three populations is shown in Fig. 4, which is consistent with the results of the UPGMA clustering analysis (Fig. 2). The LY1 and LY2 populations being genetically closer to each other, and the CT population being genetically farther away from the other two populations, with a certain genetic distance. In the direction of main coordinate 1, the three Huoyan goose groups were roughly divided into three taxa, and the distribution of individuals in the three taxa was more evenly distributed, mixed and with a large amount of interpenetration, suggesting that there was a great deal of genetic exchange among the three, and that they were more closely related. Using STRUCTURE software to construct the genetic structure map of 75 Huoyan goose individuals (Fig. 5), preset the number of subpopulations of the population as 2 ∼ 9, that is, the K value is 2 ∼ 9, and set up 5 repetitions to get the optimal number of taxa for individuals; with reference to the graph of K value versus ΔK(Fig. 6), when K = 3, a peak appeared, and the ΔK value was the largest; it matched the results of the PCoA of the population; therefore, it would be more appropriate to take K = 3 is the best value for the test samples, and it is more appropriate to divide the test samples into three subpopulations.

Fig. 4.

PCoA analysis of three Huoyan goose groups.

Fig. 5.

Bayesian clustering of Huoyan individuals. Bar plots show the individual membership coefficients at 2–3,where K is the member of inferred genetic clusters in the model. CT: LiaoningChangtu, LY1: North of Liaoyang,Liaoning Province, LY2: South of Liaoyang, Liaoning Province.

Fig. 6.

Curve of change in Delta K with changing K value.

4. Discussion

4.1. Establishment of multiplex PCR system

The establishment of multiplex PCR system can save experimental time and improve experimental efficiency.29, 30, 31, 32 The key to the establishment of multiplex PCR system is the similarity of the annealing temperature of each primer pair in the system, and the amplification product fragments should exist at a certain distance. The multiplex PCR is now commonly used in the diagnosis of genetic diseases, polymorphism analysis, detection of pathogenic microorganisms, paternity analysis, as well as identification of individuals and species.31, 32 Sahu et al.³³ established a multiplex PCR system for rapid detection of pathogenic bacterial contamination in aquatic products by screening, which can be used by quality inspectors for quality testing. Lu et al.³⁴ screened 36 pairs of microsatellite Loci with good polymorphisms and established a 2-group multiplex PCR system through whole genome prediction of eels; by performing simulation analysis of different parental numbers, the results showed that the accuracy of the 2-group multiplex PCR system could reach 95 % when the parental sex was known and unknown. Fu et al.³⁵ screened 18 microsatellite primers with good polymorphisms and constructed an 8-group triple PCR system for the analysis of genetic diversity and genetic structure of largemouth bass populations. Currently, multiplex PCR systems are more frequently used in the field of aquatic animal genetic breeding, and there are fewer reports on the successful construction of multiplex PCR systems for poultry species.

In this study, by screening geese-related microsatellite loci, and continuously optimizing and adjusting the primers and PCR reaction system, we constructed four sets of multiplex PCR systems, which are expected to be used for the analysis of the genetic diversity and genetic structure of Huoyan goose in Liaoning, including two sets of triple PCR systems and one set of quadruple PCR system, as well as one set of double PCR system, which can provide convenience for the subsequent related tests, and reduce the cost. This system will provide a convenient and cost-effective means of analyzing the genetic variation in the population of Huoyan geese in Liaoning. In addition, the results of genomic DNA amplification of Sanhua geese using the constructed multiplex PCR system showed that the system was reproducible and stable, no dimer appeared, and the amplification effect was better, or it could be used for the genetic diversity analysis of other breeds of geese and could be used for subsequent analysis, and it is clear that the multiplex PCR system constructed in this study has the opportunity to be generalized.

In addition, fluorescent markers such as FAM, HEX and ROX were used at the 5′ end of the upstream primers of each microsatellite primer. Genotyping after PCR amplification using fluorescently labeled primers can eliminate the tedious process of polypropylene electrophoresis genotyping, which, together with the established multiplex PCR system, greatly improves the experimental efficiency.

4.2. Analysis of genetic diversity in three populations

China's local goose breeds are rich in resources, but due to the long-term closed breeding, the progress in the selection and breeding of good breeds is slow, and no excellent selection system and breeding methods have been formed, so the genetic diversity of many good breeds has not been completely preserved.36, 37 In recent years, due to the limited scale of the Huoyan goose conservation farms, it has been difficult to maintain a sufficient population size, which may lead to an intensification of genetic drift. Moreover, artificial selection has been biased towards certain economic traits, neglecting the protection of the genetic diversity of the population, resulting in a relatively homogeneous population structure. Genetic diversity is the basis for the long-term survival, evolution and development of a species or population. The higher its genetic diversity, the greater its ability to adapt to changes in the external environment. Molecular markers can effectively quantify the genetic variation among populations, thus providing a deeper understanding of the conservation and development of a variety.25, 36 Therefore, the study of the genetic diversity of Huoyan goose will be of great significance for the utilization of their breed resources and genetic breeding.

Polymorphic information content (PIC) is an important indicator for detecting genetic diversity, with higher values indicating higher genetic diversity in a population.³⁷ According to the criteria proposed by Bostein³⁸ a for measuring the PIC of genetic variation, of the 12 microsatellite loci selected in this study, seven loci were highly polymorphic (PIC > 0.5), four were moderately polymorphic (0.25 < PIC < 0.5), and only loci CKW15 was lowly polymorphic (PIC < 0.25), and the mean PIC value was 0.524. The results are higher than those of the study conducted by Joanna Warzecha et al.²⁶ Joanna Warzecha et al. employed 15 microsatellite markers to detect the White Kołuda® goose and 12 conservative flocks. The PIC values ranged from 0.165 to 0.813, with an average PIC value of 0.463.Hower, This result is similar to the Polymorphic Information Content (PIC) value of the Huoyan goose population measured by Liu et al.³⁹ It indicates that the genetic diversity level of the Huoyan goose is at a high level of polymorphism. These loci possess multiple alleles, exhibit a relatively balanced distribution, and can provide sufficient information for the evaluation of the genetic diversity of Huoyan geese. It also indicates that the genetic diversity of the three Huoyan Goose populations is rich, the selection potential is high, and the level of genetic variation is relatively high.

Genetic heterozygosity, which can be categorized into observed heterozygosity (Ho) and expected heterozygosity (He), is a more appropriate parameter for measuring genetic variation in a population. If a population is more heterozygous, then it is more genetically diverse and has higher population fitness, and vice versa.⁴⁰ In this study, the average Ho of the three groups was 0.181 and the average He was 0.568; the average He was higher than the average Ho. The results were lower than those of the studies Lai et al.⁴¹ The Ho is less than the He results are similar to those of Lai et al. and Warzecha et al.²⁶ When the observed heterozygosity is approximately equal to the expected heterozygosity, the population is said to be in Hardy − Weinberg equilibrium.42, 43 This equilibrium state is indicative of a relatively stable genetic structure within the population. In this study, significant differences were observed between Ho and He. Notably, 9 out of the 12 experimental sites deviated significantly from Hardy − Weinberg equilibrium (HWE) (p < 0.01). These findings suggest that the population may have been influenced by certain factors, including selection, migration, genetic drift, and others.13, 44, 45 This process can lead to an elevated frequency of homozygosity, random fluctuations in allele frequencies, and the loss or fixation of alleles.46, 47 This phenomenon may be attributable to breeders' breeding strategies. For instance, artificial selection can cause rapid alterations in the frequency of specific alleles, leading to deviations from Hardy − Weinberg equilibrium (HWE).48, 49 Additionally, the practice of inbreeding can result in an overabundance of homozygotes and a deficiency of heterozygotes. This imbalance ultimately leads to inbreeding depression, which has significant implications for the genetic health and viability of the population under study.50, 51, 52 At the same time, the coefficient of inbreeding (Fis) within a population reflects the lack of heterozygosity among individuals of each subpopulation, and the value of Fis in the present study was 0.743, which also indicates that the three populations have a high degree of inbreeding, and that there was a gradual decrease in heterozygous genes. However, it also showed that the three Huoyan goose populations have done a better job of pure breeding selection and the degree of uniformity is more consistent.

From the results of PIC value and genetic heterozygosity, The three Huo Yan goose populations exhibited relatively rich genetic diversity, but demonstrated a high level of inbreeding. The reason might be that a high level of inbreeding within the population reduces heterozygosity, yet the number of alleles remains unchanged, merely increasing the number of homozygotes.53, 54 As a result, the observed heterozygosity (Ho) decreases, while the polymorphism information content (PIC) remains at a normal level. Alternatively, it could be due to selective pressure that leads to a reduction in heterozygosity. Given that the loci possess a relatively large number of alleles, the PIC value remains high.⁵⁵ In the field of animal breeding, the effective management of genetic diversity and efforts to mitigate inbreeding depression constitute the most central and critical tasks. Therefore, it is necessary to establish a more scientific and standardized conservation norms and selection system, using traditional conservation measures such as inbreeding control, random mating and appropriate introduction of foreign breeds.27, 51, 56, 57 To safeguard genetic diversity and promote healthy reproduction within the population, it is imperative to maintain an adequate breeding population and effectively prevent the accumulation of inbreeding effects caused by too small population size.58, 59 When faced with the situation of limited population size, the clustering strategy can be adopted, that is, the population is dividing the population into multiple subgroups, and the gene flow process in the large population can be simulated by periodic rotation of individuals between these subgroups, thereby optimizing and preserving the genetic structure of the population.59, 60 Establish a complete and systematic genealogical file to provide solid data support for subsequent breeding work through continuous tracking and accurate recording of individual kinship. In the planning of breeding, priority should be given to the selection of distantly related individuals for combination, so as to effectively increase genetic diversity and minimize the risk of inbreeding decline.61, 62, 63 At the same time, the occurrence of full-sib or parent–child mating must be strictly avoided to ensure the healthy reproduction and genetic stability of the population. The effective population size and genetic diversity index are regularly assessed, and new provenances are introduced or breeding schemes are adjusted if necessary.20, 64

In addition, Warzecha et al.²⁶ analyzed the genetic diversity of Polish geese from 13 populations and detected 19 and 15 alleles in the CKW21 and TTUCG5 loci, respectively, which were consistent with their results, in this study, CKW21 and TTUCG5 were also the best loci for polymorphism, which were observed in all the samples with respectively 28 and 11 alleles, respectively, and the two loci also had the highest PIC values; similar results were obtained by Parada et al.⁵⁶ and Mindek et al.⁵⁷ It would appear that the CKW21 and TTUCG5 loci exhibit considerable polymorphism in a number of breeds of geese, which makes them suitable for use in genetic diversity analysis.

4.3. Genetic structure and differentiation in three populations of exotic geese

The three indices of F-statistics, Fis, Fit and Fst can be used to reflect the degree of differentiation and inbreeding among populations. Wright's F − statistic, the F − statistic, is used to evaluate the level of inbreeding in population structures.⁶⁵ Fis, Fit, and Fst are inbreeding coefficients at different levels. Fst is mainly used to measure the degree of genetic differentiation among subpopulations, it can be regarded as the correlation of alleles within a group relative to the entire population, representing the ratio of variance between subpopulations and within the total population. Conversely, Fis and Fit are associated with the heterozygosity of individuals in relation to subpopulations or the total population.66, 67. Fis represents the correlation between alleles within an individual and the population to which the individual belongs. It is the difference between the observed and expected values of heterozygotes within sub − populations.⁶⁸ Fit is the proportion of genetic diversity resulting from differences in allele frequencies among populations, reflecting the heterozygosity of an individual relative to the entire total population. When the value of Fst is less than 0.05, it is a low differentiation level, between 0.05 and 0.15 it belongs to the medium differentiation level; between 0.15 and 0.25 it is a to the high differentiation level; and when the value of Fst is more than 0.25, it belongs to the very high differentiation level.57, 69, 70, 71 Warzecha et al.²⁶ used microsatellite markers to analyze the genetic structure of 12 populations of Polish geese and obtained a mean value of the coefficient of genetic differentiation between populations of 0.075. The results obtained are similar to those of this experiment, In this study, the indices of genetic differentiation between populations ranged from 0.009 to 0.069, with a mean value of 0.027. Only about 2.7 % of the genetic variation can be attributed to differences among subgroups, indicating low substructure in the studied populations. Similar results were also obtained in the research on guinea fowl by Aïcha Edith Soara et al.⁷² and Kayang et al. reported that the Wright’s fixation indices (FIS = 0.169, FST = 0.012 and FIT = 0.171) found in indigenous guinea fowl population of Togo were low. The three populations belonged to a moderately low level of differentiation, and the population differences among the three populations were small, with little genetic differentiation and a high degree of inbreeding. Gene flow, also known as gene migration, is commonly estimated using Wright's F − statistics in population genetics. The calculation formula is $\mathit{Nm}=\frac{1-Fst}{4Fst}$, when the value of Nm is greater than 1, it indicates a high level of gene exchange between populations.73, 74 This leads to an increase in the similarity among populations and a higher degree of population homogeneity. In this experiment, the average value of Nm is 18.07, which suggests that the three populations exhibit a high degree of genetic consistency.

UPGMA cluster analysis based on Nei's genetic distance showed that LY1 and LY2 populations were the least genetically distant, and the LY1 population was the furthest genetically distant from the CT population, which was consistent with the genetic differentiation results, and indicated that the LY1 and LY2 populations were more closely related to each other. In addition, the results of PCoA also showed that the LY1 and LY2 populations were more closely related to each other and roughly divided into three populations in the direction of principal coordinate 1 and the gene distributions of the three populations were more mixed. From the results of clustering and PCoA, there was a certain correlation with geographic location, which may be due to the fact that both LY1 and LY2 populations are located in the same city, which is relatively close to each other geographically, and therefore they are genetically closer. In addition, the three populations are more closely related to each other and have frequent gene exchange.

The genetic structure of a population can provide information about the origin and composition of individual pedigrees. The results of the genetic structure analysis were more consistent with the UPGMA cluster analysis tree. The genetic structure analysis of the three populations showed that it is optimal to divide the three populations into three taxa, that is, the K value = 3. When K = 3, all three populations showed mixed genotypes, with the CT population having a larger blue portion and differing slightly in genetic structure from the LY1 and LY2 populations. When K = 4, the CT population also had some differences compared to the other two populations. There was some genetic exchange among the three populations, and the genetic backgrounds were more similar, with some genetic homology. This may be due to the frequent movement of exclusionary goose seedlings in the region, which gave the three populations a great opportunity to exchange genes. In addition, it may also be due to the elimination of genetic differentiation among the populations due to continuous inbreeding, resulting in a reduction of population polymorphism.

5. Conclusion

In conclusion, The genetic diversity within the three Huoyan goose populations was found to be abundant,and were more closely related to each other, and the CT population had higher genetic diversity and heterozygosity than the LY1 and LY2 populations, which had more potential for selection.

6. Institutional Review Board

The animal study protocol was approved by the Animal Care and Use Committee of Shenyang Agricultural University (2023030204).

CRediT authorship contribution statement

Qianhui Wang: Writing – review & editing, Writing – original draft, Validation, Software, Resources, Methodology, Formal analysis, Data curation. Jiaming Wang: Supervision, Software, Resources, Project administration, Methodology, Funding acquisition, Conceptualization. Yan Zheng: Software, Project administration, Methodology, Data curation, Conceptualization. Changjiang Li: Supervision, Software, Resources, Funding acquisition, Formal analysis. Xingtang Dou: Software, Project administration, Methodology, Investigation, Funding acquisition. Zhongzan Cao: Visualization, Resources, Methodology, Investigation, Conceptualization. Yue Gao: Software, Resources, Methodology, Funding acquisition. Bing Xue: Resources, Project administration, Methodology, Funding acquisition. Di Han: Supervision, Project administration, Methodology, Funding acquisition, Conceptualization. Xinhong Luan: Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization.

Funding

Natural Science Foundation Project of Liaoning Provincial Department of Science and Technology (Grant No. 2023-MS-066).

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability

Data will be made available on request.