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. 2024 Aug 7;103(11):104136. doi: 10.1016/j.psj.2024.104136

Inter and intra population genetic variability in ducks under conservation programs

Anna Wolc *,†, Mirosław Lisowski ‡,1, Bartosz Grajewski ‡, Lidia Lewko §, Tomasz Szwaczkowski ║,2
PMCID: PMC11399794  PMID: 39208486

Abstract

This study focuses on estimation of the inter and intra population genetic variability of 6 duck populations. Microsatellite loci were used to assess the genetic variation and population structure of 6 duck populations under a conservation program in Poland. DNA polymorphism was assessed using 24 microsatellite markers and 50 individuals from each population. Polymorphism information content (PIC), heterozygosity with 2 estimators of genetic differentiation (FST and GST), and Nei's standard genetic distance were calculated. The results showed that these 6 endangered duck populations showed high genetic polymorphism. Observed heterozygosity within populations ranged from 0.14 to 0.83, with the average value of 0.58. PIC within populations ranged from 0.038 (P-8 and P-9 lines) to 0.89 (LsA line). Average number of alleles in the studied populations ranged from 4.5 (KhO-1 line) to 7.3 (LsA line). Based on the results, the pairs of lines LsA: P-33 and P-8: P-9 were found to be the most related; and the most genetically distant group was KhO-1 line, which originated as a cross between Khaki Campbell with Orpington duck.

Key words: genetic resource, genetic distance, genetic diversity, microsatellite marker

INTRODUCTION

There are currently about 200 breeds of ducks in the world. However, despite their important socio-economic role and the relatively large number of breeds that exist, information on genetic diversity of ducks is limited. Only a few highly productive populations are used to produce commercial hybrids. These are mainly Pekin ducks, produced by global breeding companies. Such an approach, not only in case of ducks, leads inevitably to extinction of breeds that don't have any currently perceived important economic impact (Oldenbroek, 1999) but can contain valuable genetic variation for future use.

Animal genetic resources include all species, breeds and varieties of livestock and poultry which, according to recommendations of the Commission on Genetic Resources for Food and Agriculture – FAO, and should be protected for economic, scientific, and cultural reasons (Alderson, 1990; NRC, 1993; Polak et al., 2021). Long term selection towards improved production, however, leads to a reduction of genetic diversity in livestock as specific highly prolific and productive breeds are exclusively utilized. To eliminate unfavourable loss of genetic diversity due to intensive selection, genetic reserves are needed. In Poland, genetic reserve flocks of ducks have been maintained in situ since the 1970s (Polak et al., 2021). Molecular markers can be used to evaluate these populations to guide prioritization of conservation efforts (Zhang et al., 2019).

So far, research (Kokoszynski et al. 2019) on Polish duck populations covered by genetic resources flocks has focused on carcass composition and some meat quality traits. There have been limited studies about genetic diversity in these populations, except for 1 comparative study with Italian duck populations by Carco et al. (2018). Today, most genetic diversity studies utilize single nucleotide polymorphism (SNP) markers. These are biallelic and are useful when large numbers of markers are available. However, genetic characterization of diversity within and between populations can be done using microsatellite markers. Microsatellites are characterized by a core sequence that consists of several tandemly repeated units with a length of 1-6 base pairs. The repetitive nature of these markers results in multiple alleles per locus (Wu et al., 2008). Compared to biallelic SNPs, microsatellites are 2 to 3 times more informative (Fernández et al. 2013). Hence, this type of genetic markers is still successfully and routinely applied to analyse the molecular variability of many species (Choi et al., 2018), including duck (Lai et al., 2020; Debnath et al., 2023).

Animal genetic resources, regardless of their geographical location, are perceived as elements of the world's cultural heritage. To our knowledge, local duck populations included in in situ genetic resources conservation programs have not been the subject of comprehensive research on genetic diversity to date.

The objective of this study was to estimate the inter and intra population genetic variability of 6 duck populations to evaluate the effectiveness of the existing conservation program in protecting duck genetic resources.

MATERIAL

Genetic Resources

Duck breeds utilized in this study are all maintained at the Genetic Resources Station for Waterfowl in Dworzyska near Kórnik, owned by National Institute of Animal Science Experimental Station in Kołuda Wielka. The material originated from local and foreign origin birds and synthetic lines which have been kept in the genetic resources program in situ since the 1970s. There are 6 unique duck lines within the genetic conservation program: Mini duck (K-2), crossbreed duck (KhO-1), English Pekin (LsA), Danish Pekin (P-8), French Pekin (P-9) and Local Pekin (P-33). The K-2 population was created in the mid-1970s by crossing mallard ducks with Pekin drakes. In turn, the KhO-1 population was created in the late 1970s by crossing Khaki Campbell and Orpington ducks. The origin of the LsA population was 3 English Pekin flocks imported to Poland also in the late 1970s which were subsequently crossed with other lines. The populations P-8 and P-9 came from 600 hatching eggs brought to Poland from Denmark in 1983 and from France in 1978, respectively. The P-33 population is the result of crossbreeding local Polish ducks with birds imported from the Netherlands. After including these lines in the genetic resources conservation program, the sizes of all populations are approximately equal: from 204 to 227 total individuals (170–190 females and 34–40 males). Additional phenotype descriptions of these populations are given in the Table 1.

Table 1.

Description of the duck populations utilized in this study, including their physical characteristics production type and economic significance.

Population Characteristics Productive Purpose Economic importance
Mini duck Small body weight. Stocky and compact build. Broad chest, short neck and legs. Amateur and backyard breeding. High quality meat.
Crossbreed duck Light-brown plumage. Darker, black-brown neck in males. Small and quite vertical torso. Due to the original shape and colour of the plumage, they are suitable for keeping in water reservoirs in parks, gardens, and zoos. High egg production, high slaughter yield, birds' resistance to adverse environmental and nutritional conditions.
English Pekin White plumage. Harmonious body shape.
The breast is well developed, wide and convex.
Strong legs spread wide.		 They are a rich source of genetic variation that can be used to create new breeds. High performance value.
Very good health.
Danish Pekin White plumage.
Harmonious body shape with a slightly raised torso. The breast is full and wide. Strong legs spread wide.		 This population may be useful in work to obtain commercial hybrids. Excellent performance values, well-muscled and a high level of reproductive characteristics.
French Pekin White plumage. Relatively large head and long neck. The back is rounded and long. The torso is cylindrical and quite vertical. Multipurpose population. They have better reproductive characteristics than meat ones. A valuable population for research and breeding work.
Polish Pekin White plumage. A delicate figure with a more vertical posture. Multipurpose population. Backyard breeding. These ducks are characterized by high dietary value of meat, low fat content in the carcass and good quality feathers.
Birds' resistance to adverse environmental and nutritional conditions.
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METHODS

Samples Collection and DNA Isolation

DNA was isolated from whole blood collected from the clavicle vein. All experiments were approved by the 2nd Local Ethical Commission for Animal Experiments in Krakow (resolution no. 503/2007). DNeasy Tissue from Qiagen was used for DNA extraction. DNA concentration was assessed by spectrometry and electrophoresis on 0.8% agarose gel.

Microsatellite Analysis

The primers for amplification of microsatellite sequences were chosen based on the literature, mostly from waterfowl (Buchholz et al., 1998; Huang et al., 2005, 2006; Maak et al., 2003).

Using PCR with fluorescent tagged primers, 24 loci were amplified. Reactions were carried out in 4 loci multiplex with Type-it Microsatellite PCR Kit (Qiagen). Reactions were in a 10μl volume which contained 5μl of 2x concentrated reaction mix Type-it, 1μl of DNA matrix (approx. 50ng), and with each of the primers at 0.25μM concentration. Amplification was done in a 2720 Thermal Cycler (Applied Biosystems) with an initial denaturation step at 95°C for 5 min followed by 30 cycles of 95°C for 30s, primer-specific hybridization temperature (see Table 2) at 50 to 60°C for 90 s, 72°C for 30 s, with a final extension step at 60°C for 30 min. Each amplification product was diluted with water (10–100 times), 1μl was added to 9μl of formamid containing 0.5μl size standard DNA GeneScan-600 LIZ Size Standard (Applied Biosystems) and denatured for 5 min. at 95°C. Capillary electrophoresis was performed in ABI Prism 3130XL (Applied Biosystems), with 36 cm long capillaries, polymer POP7 (https://www.thermofisher.com/order/catalog/product/4393708) and G5 filter. The allele sizes were read in Peak Scanner v 1.0 (2006, Applied Biosystems, www.appliedbiosystems.com).

Table 2.

Description of the microsatellite markers: including their chromosomal locations, GenBank accession numbers, Tm, Dye and primer sequence.

Locus Chromosomal location* GenBank accession Tm (°C) Dye Primer sequences (5`–3`)
CAUD038 9:14555012-14555071 AY493283 55 NED GATAATGGCTGGCTCCTTGA GACCACAACATCGTGCAGAG
CAUD024 1:184977427-184977486 AY493269 55 VIC TCGCATTAAGCTCTGATCT ATCAACAGAATCCAAAATATG
CAUD050 4:3786088-3786147 AY493295 55 6-Fam GGACAAGTGGCATGTGTCAT GGCTTCTGTGCTCCTCAGAT
CAUD117 1:134970773-134970832 AY587036 55 PET GCCTTCATTCCTCTGCTAC GCTCATCCCTGCTGCTCA
CAUD069 CAU1 AY493314 50 NED CAGCATTATTATTTCAGAAGG CTCATTCCAATTCCTCTGTA
CAUD070 CAU2 AY493315 50 6-Fam GTAACAACTCAGTGCTTTCAA GTAAGTATTGACAGAGACATC
CAUD120 20:10548501-10548560 AY587039 50 PET AATATCCTGTCGCCGTGGT AATTCTTGCTGAGATTATAGAG
CAUD126 CAU1 AY587045 50 VIC TTGCCACATAAACCCACTAC CAGAGAATTTTAGTAAGAGT
CAUD111 CAU5 AY587030 50 NED TGACATTACACACCCAAAC CAAGGGCAGGGGTAAGGAT
CAUD013 14:14024200-14024259 AY493258 50 6-Fam ACAATAGATTCCAGATGCTGAA ATGTCTGAGTCCTCGGAGC
CAUD022 CAU7 AY493267 50 PET CATGCTGAGTGTCCTATCCT CCAGGTCAGGCGTGTGCT
CAUD124 12:13261098-13261157 AY587043 50 VIC CCAGCCAAGAACCTCCAGT CTTTGAATGTCCATGTAGCAG
CAUD093 1:53563719-53563778 AY493338 50 PET AGAGCGGTGTGAGAGCAGAG GATATCGCTCGCAATTTTGG
CAUD112 1:47985196-47985255 AY587031 50 6-Fam CAACTGACAGAGAGGCACG GACTGTGTTTCCAATGCTCC
CAUD060 CAU2 AY493305 50 NED AGAAAGCTCCTGTATGTGAT ATGCTGGTGTGAGATTTGAA
CAUD040 28:4819663-4819722 AY493285 50 VIC TGTGTAACCCTGATAGACTGA TCCCACCCCAAACCCTGC
CAUD019 20:2571875-2571934 AY493264 55 VIC CTTAGCCCAGTGAAGCATG GCAGACTTTTACTTATGACTC
CAUD086 1:53563719-53563778 AY493331 55 6-Fam AACACAGCTTCACCCCACAG GCAGAGCGGTGTGAGAGCA
CAUD136 2:144222943-144223002 AY587055 55 PET GTTGCATGAAAAAGGAAAGG GGAAGATAGAAGATGGAATG
CAUD036 UNK AY493281 55 6-Fam AAGTTGGGAGAGGAGTCAG CTAAGGCTTTTCCAGAATGC
CAUD091 CAU3 AY493336 60 NED GAAAAAGGCAGCACAGCAC GCAAAGTTGAGGCATGTAATC
CAUD039 1:54663830-54663889 AY493284 55 VIC GGGACATCTCTTGGAGCAAA AGTGAAAGCTGCTGCTGGAT
CAUD026 6:35089244-35089303 AY493271 55 VIC ACGTCACATCACCCCACAG CTTTGCCTCTGGTGAGGTTC
CAUD082 UNK AY493327 55 VIC ATGTAAAGCAAGGAAGAGCC AAGAGTCTGAGCCAAGCAC
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⁎

Locations based on: Ensembl search, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1461431/, https://hal.science/hal-01702555/document, UNK – no chromosomal assignment found.

Genetic Diversity Analyses

Genetic diversity was assessed using 24 microsatellite markers for 50 individuals (40 females and 10 males) from each population. Each population was characterized by the following characteristics: number of alleles per locus, observed and expected heterozygosity, polymorphic information content (PIC) and deviation from Hardy-Weinberg equilibrium. These parameters were estimated using the CERVUS program (Marshall et al., 1998). FIS was estimated for each locus following Weir and Cockerham (1984) and coefficient of gene differentiation Gst between populations (Nei, 1973). Significance of Fis was tested in FSTAT 2.9.3 (updated in 2001 from Goudet (1995). P-value for Fis within samples, based on 2880 randomizations.

Phylogenic tree with 100 bootstrap samples was constructed using Nei's standard genetic distance (Nei, 1972), in Populations 1.2.31 (Langella, 1999) and edited in TREEVIEW (Page, 1996). Admixture between populations was evaluated using STRUCTURE (Evanno et al. 2005) assuming independent loci and 4 to 10 populations, admixture level inferred from the data and prior population assignment according to population assignment at sampling.

The STRUCTURE analysis was implemented using an admixture model and a Markov Chain Monte Carlo (MCMC) chain of 20,000 rounds where the initial 1,000 were discarded as burn-in period and K (number of clusters) ranging from 2 to 10. For each value of K, 20 independent runs were performed. DeltaK method (Evanno et al., 2005) was used to evaluate optimal number of clusters.

Principal Components Analysis was performed in Genetix (Belkhir et al., 1996).

Data analysis was performed using CERVUS (Marshall et al. 1998), FSTAT 2.9.3 (updated in 2001 from Goudet (1995), Microsat (Minch et al. 1997) and GENEPOP (Rousset, 2008).

RESULTS AND DISCUSSION

Genetic Variation

The 24 microsatellite loci were dispersed across 13 chromosomes, including both macro and microchromosomes, with 2 loci (CAUD36 and CAUD82) for which no chromosomal assignment has been made. The parameters describing heterozygosity, polymorphism, as well as genetic equilibrium are listed in Table 3, Table 4.

Table 3.

Summary statistics of the microsatellite markers in the populations LsA, P33 and P8.

LsA
 P33
 P8
Locus k HO He PIC HW k H0 He PIC HW K H0 He PIC HW
CAUD050 7 0.653 0.804 0.769 NS 7 0.860 0.828 0.795 NS 5 0.319 0.513 0.438 NS
CAUD024 12 0.776 0.886 0.865 ND 14 0.840 0.891 0.871 ND 8 0.429 0.769 0.734 NS
CAUD038 13 0.776 0.773 0.736 NS 10 0.680 0.716 0.686 NS 4 0.265 0.277 0.262 ND
CAUD117 10 0.878 0.839 0.812 NS 9 0.800 0.820 0.786 NS 9 0.796 0.835 0.804 NS
CAUD070 14 0.857 0.873 0.849 NS 14 0.826 0.874 0.851 NS 7 0.740 0.731 0.682 NS
CAUD120 3 0.480 0.485 0.397 NS 4 0.638 0.585 0.537 NS 4 0.780 0.741 0.684 NS
CAUD126 9 0.796 0.866 0.842 NS 11 0.809 0.816 0.785 NS 9 0.820 0.738 0.700 NS
CAUD069 11 0.820 0.798 0.769 NS 11 0.872 0.869 0.845 NS 10 0.900 0.853 0.825 NS
CAUD111 6 0.760 0.771 0.727 NS 4 0.580 0.538 0.484 NS 5 0.571 0.604 0.537 NS
CAUD013 4 0.460 0.567 0.491 NS 5 0.820 0.746 0.694 NS 6 0.560 0.645 0.572 NS
CAUD022 3 0.480 0.597 0.508 NS 3 0.460 0.594 0.517 NS 3 0.440 0.424 0.368 NS
CAUD124 4 0.600 0.661 0.606 NS 4 0.720 0.719 0.655 NS 5 0.520 0.556 0.468 NS
CAUD060 16 0.449 0.908 0.890 ND 17 0.633 0.893 0.874 ND 9 0.755 0.839 0.807 NS
CAUD112 3 0.460 0.472 0.413 NS 3 0.694 0.663 0.582 NS 3 0.490 0.409 0.331 NS
CAUD093 5 0.673 0.686 0.628 NS 4 0.878 0.687 0.627 * 4 0.620 0.707 0.647 NS
CAUD040 14 0.720 0.867 0.843 NS 9 0.776 0.823 0.790 NS 9 0.694 0.786 0.757 NS
CAUD019 10 0.600 0.800 0.764 NS 9 0.673 0.813 0.779 NS 5 0.260 0.775 0.730 ***
CAUD086 4 0.280 0.556 0.499 * 5 0.367 0.457 0.414 NS 3 0.440 0.653 0.573 NS
CAUD136 2 0.100 0.132 0.122 ND 3 0.292 0.384 0.348 NS 2 0.040 0.040 0.038 ND
CAUD091 4 0.878 0.745 0.689 NS 4 0.500 0.634 0.552 NS 4 0.660 0.623 0.549 NS
CAUD039 5 0.653 0.658 0.602 NS 4 0.688 0.669 0.603 NS 4 0.511 0.460 0.390 NS
CAUD036 5 0.286 0.684 0.613 *** 5 0.167 0.639 0.569 *** 3 0.000 0.230 0.209 ND
CAUD026 5 0.320 0.568 0.520 NS 4 0.286 0.617 0.535 *** 3 0.500 0.594 0.517 NS
CAUD082 7 0.680 0.717 0.674 NS 6 0.776 0.819 0.784 NS 6 0.720 0.708 0.651 NS
Average 7.3 0.601 0.696 0.651 - 7.4 0.561 0.712 0.665 - 5.4 0.534 0.605 0.553 -
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K: number of alleles per locus; HO: observed heterozygosity; He: expected heterozygosity; PIC: polymorphic information content; HW: Hardy-Weinberg equilibrium; ND: not defined.

NS, *, *** - statistically significant deviation from HW for P > 0.05, P ≤ 0.05 and P ≤ 0.001, respectively

50 birds per population.

Table 4.

Summary statistics of the microsatellite markers in the populations P9, KhO-1 and K2 (50 birds per population).

P9
 KhO-1
 K2
Locus k HO He PIC HW k H0 He PIC HW K H0 He PIC HW
CAUD050 7 0.653 0.710 0.657 NS 4 0.638 0.605 0.556 NS 6 0.250 0.737 0.687 *
CAUD024 10 0.735 0.841 0.811 NS 8 0.787 0.845 0.817 NS 10 0.490 0.868 0.843 NS
CAUD038 9 0.633 0.752 0.717 NS 8 0.745 0.785 0.742 NS 13 0.755 0.831 0.802 NS
CAUD117 9 0.837 0.789 0.754 NS 6 0.383 0.460 0.403 NS 5 0.583 0.758 0.710 NS
CAUD070 11 0.714 0.692 0.666 NS 7 0.604 0.665 0.609 NS 8 0.727 0.807 0.770 NS
CAUD120 4 0.813 0.716 0.658 NS 2 0.604 0.505 0.375 NS 4 0.545 0.563 0.463 NS
CAUD126 10 0.796 0.855 0.828 NS 6 0.604 0.615 0.580 NS 9 0.659 0.824 0.790 NS
CAUD069 11 0.878 0.811 0.775 NS 7 0.625 0.650 0.603 NS 8 0.886 0.854 0.824 ND
CAUD111 4 0.780 0.708 0.644 NS 5 0.520 0.591 0.544 NS 7 0.720 0.741 0.690 NS
CAUD013 4 0.520 0.492 0.395 NS 3 0.200 0.187 0.177 ND 6 0.800 0.780 0.735 NS
CAUD022 3 0.540 0.533 0.416 NS 3 0.400 0.371 0.337 NS 3 0.400 0.620 0.533 NS
CAUD124 4 0.800 0.724 0.665 NS 5 0.520 0.549 0.443 NS 5 0.760 0.777 0.731 NS
CAUD060 14 0.780 0.883 0.863 NS 5 0.440 0.751 0.697 ** 9 0.574 0.787 0.745 NS
CAUD112 3 0.560 0.600 0.526 NS 3 0.540 0.622 0.534 NS 3 0.511 0.553 0.464 NS
CAUD093 4 0.640 0.624 0.554 NS 4 0.540 0.538 0.454 NS 5 0.574 0.668 0.613 NS
CAUD040 11 0.540 0.857 0.832 NS 4 0.860 0.542 0.438 *** 12 0.796 0.771 0.741 NS
CAUD019 8 0.440 0.710 0.667 ** 6 0.420 0.698 0.651 ** 8 0.809 0.781 0.740 NS
CAUD086 3 0.540 0.552 0.466 NS 4 0.280 0.647 0.593 *** 4 0.174 0.432 0.367 ***
CAUD136 2 0.040 0.040 0.038 ND 2 0.122 0.276 0.236 ND 3 0.261 0.543 0.473 **
CAUD091 4 0.630 0.658 0.596 NS 3 0.620 0.588 0.518 NS 5 0.512 0.727 0.677 NS
CAUD039 5 0.413 0.482 0.439 NS 3 0.420 0.512 0.455 NS 7 0.651 0.780 0.741 NS
CAUD036 4 0.065 0.324 0.301 ND 4 0.600 0.680 0.612 NS 5 0.302 0.613 0.531 **
CAUD026 3 0.220 0.296 0.267 ND 3 0.220 0.392 0.333 NS 4 0.260 0.427 0.389 NS
CAUD082 7 0.820 0.719 0.663 NS 3 0.660 0.652 0.572 NS 5 0.460 0.422 0.395 NS
Average 6.4 0.599 0.640 0.591 4.5 0.515 0.572 0.512 6.4 0.561 0.694 0.644
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K: number of alleles per locus; HO: observed heterozygosity; He: expected heterozygosity; PIC: polymorphic information content; HW: Hardy-Weinberg equilibrium; ND: not defined.

NS, **, *** - statistically significant deviation of HW for P > 0.05, P ≤ 0.01 and P ≤ 0.001, respectively.

The total number of observed alleles across the 24 microsatellites was 244, with an average of 6.2 alleles per locus. Average number of alleles per locus ranged from 2.3 for CAUD136 to 11.7 for CAUD060 with the total number of alleles being between 3 (CAUD120 and CAUD022) and 24 (CAUD060) (Table 3, Table 4). The number of detected alleles is comparable to that reported by Carco et al. (2018) with a similar panel who identified 261 alleles with an average of 11.36 alleles across 2 Italian and 2 Polish duck breeds, while Hariyono et al. (2019) identified 153 alleles across 22 loci in Indonesian ducks with average of 6.96 alleles. Chepiha et al. (2021) identified 99 alleles for 20 loci and 89 alleles for 17 loci in Ukrainian Black White Breasted and Ukrainian Clay, respectively. As expected, the number of alleles in local duck breeds is larger compared to experimental and commercial populations under long term selection. For example, Zhang et al. (2021) in experimental duck population found the average number of alleles per locus was 4.133 with a range from 2 to 7.

Observed average heterozygosity within populations ranged from 0.14 to 0.83, with the average value being 0.58. PIC measures the quantity of information of each microsatellite and depends on the number of alleles identified and the allele frequencies (Purwantini and Purwantini, 2010). The scale of PIC index reflects the proportion of heterozygotes in populations. The average number of alleles in the studied populations ranged from 4.5 (KhO-1) to 7.3 (LsA). The highest average heterozygosity was estimated in the P-33 line (0.65), and the lowest in the KhO-1 line (0.51). Khan Ahmadi et al. (2007) estimated a similar level of heterozygosity for Pekin (0.53) and Muscovy (0.44) ducks. Even higher variability in heterozygosity was reported for Asian ducks: Seo et al. (2016) reported 0.49 heterozygosity of south-east Asian ducks, while Li et al. (2010) obtained a very high estimate of 0.86.

Heterozygosity and PIC are perceived as the most important indicators utilized to determine the genetic variation of population (Huang et al., 2005). The diversity of a locus is generally considered low when PIC < 0.25 and high when PIC > 0.5 (Botstein et al., 1980). In this study, the average PIC of all sites and all populations was 0.73, with 24 microsatellites showing high diversity (Table 3, Table 4). It is important to note that significant variation between loci and populations did exist, ranging from 0.038 (CAUD136 in the P-8 and P-9 lines) to 0.89 (CAUD060 in the LsA line). Joint PICs across the 6 populations studied were high, and only for 2 loci (CAUD022 and CAUD136) were these parameters smaller than 0.5. This is similar to reports in literature (Su and Chen, 2009; Pham et al., 2022). In contrast, relatively low PICs were found for 2 Italian local duck populations (Carco et al., 2018). The average frequency of private alleles was 0.085, which provides an estimate of on average 0.6 migrants per generation. The estimates of PIC between 0.51 in KhO-1 and 0.67 in P-33 indicates smaller genetic diversity in the analysed populations compared to results of Wu et al. (2008) who found that the PIC for all populations was around 0.75.

Inbreeding coefficients (FIS) for 24 loci of 6 duck populations are listed in Table 5. The average inbreeding coefficients for the analysed populations were close to zero. Considering averages of all analysed loci jointly, the lowest inbreeding coefficient was estimated in the P-9 line (0.078) and the highest in the K-2 line (0.205). However, it is worth noting the large differences between individual markers with FIS values ranging from -0.597 for CAUD040 in the KhO-1 line to 1 for CAUD0036 marker in P-8 line. Negative FIS estimates were obtained in each population, however, their distribution among loci was quite diverse. The numbers of negative FIS values varied from 4 (LsA) to 11 (P-9). Generally, the Pekin duck lines were characterized by larger numbers of negative inbreeding coefficients compared to other populations. At the same time, high FIS values were obtained for some loci, indicating a high level of undesirable homozygosity. The applied permutation test indicates the significance of Fis for 5 populations, except P-9 (adjusted nominal level of 5% is 0.00035).

Table 5.

Inbreeding coefficient (Fis) analysis for 24 loci in 6 duck populations.

LsA P-33 P-8 P-9 KhO-1 K-2
CAUD050 0.190 −0.039 0.381 0.081 −0.055 0.664
CAUD024 0.126 0.058 0.446 0.127 0.069 0.438
CAUD038 −0.003 0.050 0.042 0.160 0.052 0.092
CAUD117 −0.046 0.025 0.048 −0.061 0.169 0.232
CAUD070 0.018 0.055 −0.012 −0.032 0.093 0.100
CAUD120 0.010 −0.093 −0.054 −0.136 −0.199 0.032
CAUD126 0.082 0.010 −0.113 0.070 0.018 0.202
CAUD069 −0.028 −0.004 −0.056 −0.083 0.038 −0.039
CAUD111 0.014 −0.079 0.055 −0.103 0.121 0.029
CAUD013 0.190 −0.101 0.133 −0.058 −0.071 −0.026
CAUD022 0.198 0.227 −0.039 −0.014 −0.079 0.357
CAUD124 0.094 −0.002 0.065 −0.106 0.054 0.022
CAUD060 0.508 0.294 0.101 0.118 0.417 0.272
CAUD112 0.026 −0.047 −0.201 0.067 0.133 0.077
CAUD093 0.019 −0.282 0.124 −0.026 −0.004 0.142
CAUD040 0.171 0.059 0.118 0.373 −0.597 −0.033
CAUD019 0.252 0.173 0.667 0.383 0.401 −0.035
CAUD086 0.499 0.198 0.328 0.022 0.570 0.600
CAUD136 0.241 0.241 −0.010 −0.010 0.559 0.522
CAUD091 −0.179 0.213 −0.059 0.043 −0.054 0.298
CAUD039 0.007 −0.028 −0.112 0.145 0.181 0.167
CAUD036 0.585 0.741 1.000 0.801 0.118 0.509
CAUD026 0.439 0.540 0.159 0.258 0.441 0.393
CAUD082 0.053 0.054 −0.016 −0.141 −0.013 −0.092
Mean 0.144 0.096 0.125 0.078 0.098 0.205
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Estimated levels of heterozygosity of the studied populations was not as expected based on their method of origination. Three of the populations (KhO-1, K-2 and P-33) resulted from crossbreeding and this were expected to have high rates of heterozygosity. From this perspective, the highest inbreeding level in the K-2 population is surprising. Generally, obtained averages of variability parameters within populations are relatively similar. Khan Ahmadi et al. (2007) reported that low average heterozygosity may be attributed to the small number of alleles in given population. Some deficits of heterozygotes in the studied lines were observed. From the perspective of conservation programs, this is an undesirable result.

Hardy-Weinberg equilibrium was demonstrated for nearly all the loci analysed. This is undoubtedly good from the perspective of maintaining genetic variability within each of the 6 populations. Significant or highly significant deviations from genetic balance have been demonstrated only for a few loci: CAUD086 (for LsA, KhO-1, K-2), CAUD036 (for LsA, P-33, K-2), CAUD093 (for P-33), CAUD026 (for P-33), CAUD019 (for P8, P-9, KhO-1), CAUD060 (for KhO-1), CAUD040 (for KhO-1) and CAUD136 (for K-2). Similar trends (some loci with deviations from the Hardy-Weinberg equilibrium) can also be seen in research conducted by other authors (Lai et al., 2020; Pham et al., 2022) on indigenous duck populations under the conservation programs. Other research, such as by Hsiao et al. (2008), showed no loci with significant deviation from Hardy-Weinberg equilibrium in Tsaya duck.

Genetic Diversity Among the Studied Populations

Estimated genetic distances are given in (Table 6). The 2 pairs of lines, LsA with P-33 and P-8 with P-9, were found to be the most related, while the most genetically distant group was the KhO-1 line. The estimates of FST suggest large (>0.15) or moderate (0.05–0.15) genetic differentiation between the analysed populations (Wright, 1978). The lowest FST (0.069) was found between lines P-33 and LsA which can be explained by participation of P-33 in creation of synthetic line LsA. Similar range of FST values were reported by Su et al. (2007) between local Chinese duck populations. Similar conclusions can be drawn based on GST, indicating the highest genetic differentiation was between KhO-1 and other populations, with the majority of genetic variation originating between populations while maintaining some level of within population heterozygosity.

Table 6.

Genetic distances between the population with pairwise Fst (above diagonal) and coefficients of gene differentation (Gst; below diagonal).

Lines K-2 KhO-1 LsA P-33 P-8 P-9
K-2 0.221 0.156 0.159 0.233 0.227
KhO-1 0.527 - 0.227 0.219 0.327 0.292
LsA 0.469 0.559 - 0.069 0.112 0.112
P-33 0.507 0.531 0.173 - 0.124 0.134
P-8 0.644 0.795 0.232 0.27 - 0.109
P-9 0.693 0.728 0.259 0.334 0.203 -
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The tree of genealogical distances is shown in Figure 1. The results from microsatellite data agree with the known history of the analysed lines: small distances were found between the P-8 line (Danish Pekin) and the P-9 line (French Pekin). Larger distance was observed between those 2 groups and birds from the P-33 line (Polish Pekin) and LsA (English Pekin). The most genetically distant breed from the Pekin type ducks were animals from the KhO-1 line, which were created as a cross between Khaki Campbell and Orpington breeds, showing clear distinction between these populations with different breed origins.

Figure 1.

Figure 1

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The tree of genealogical distances.

The first 4 principal components explained 5.93%, 4.53%, 2.99%, and 2.44% of genetic variations, respectively, which indicates that there was not a very strongly dominating component (see Figure 2, Figure 3). PCA1 vs PCA2 clearly separated K-2 population (pink) and KhO-1 (green) from the remaining populations (Figure 2, Figure 3). PCA1 vs PCA3 separated populations LsA (yellow) and P-33 (blue) from P-8 (white) and P-9 (grey) which confirms the results of the phylogenetic tree.

Figure 2.

Figure 2

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Principal components analysis PCA1 vs PCA2. Populations: K-2 (pink), KhO-1 (green), LsA (yellow), P-33 (blue), P-8 (white), P-9 (grey).

Figure 3.

Figure 3

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Principal components analysis PCA1 vs PCA3. Populations: K-2 (pink), KhO-1 (green), LsA (yellow), P-33 (blue), P-8 (white), P-9 (grey).

STRUCTURE confirmed 6 distinctive populations with low level (less than 10%) of misclassified or admixed individuals (Figure 4). Additional genotyping of current breeding stock may be recommended to ensure no genetic introgression between the populations. K values in the range of 2 to 10 were tested. Large increase in likelihood was observed going from 2 to 6 populations suggesting that there are 6 populations involved; smaller additional improvement was observed between 6 and 8 populations which likely represents known history of KhO-1 and K2 being crossbreds back in 1970s (Figure 5A). However, deltaK method confirmed K=6 as the optimal number of clusters (Figure 5B)

Figure 4.

Figure 4

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Population admixture identified by STRUCTURE with samples ordered according to original population assignment and coloured according to clustering algorithm. (1 - K-2; 2 - KhO-1; 3 – LsA; 4 – P-33; 5 – P-8; 6 – P-9). The clustering diagrams obtained for K values ranging from 2 to 10. (K=10 was very similar to K=9).

Figure 5.

Figure 5

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Log-likelihood (A) and delta K (B) for the K-values ranging from 2 to 10 estimated by the STRUCTURE.

CONCLUSIONS

The analysis of microsatellite variation showed the presence of acceptable levels of genetic variation in the duck populations maintained as genetic reserves, indicating that the conservation program was successful. Some markers had a large number of segregating alleles and indicated differentiation of the populations. Molecular tools can be used to better maintain genetic diversity in the conservation program and preserve the unique alleles of different populations. Generally, the parameters of genetic variability for these populations are similar to the genetic variability information obtained by other authors with other duck populations. This confirms the validity of the implemented concept of continued protection of these genetic groups.

DISCLOSURES

The authors declare that they have no conflict of interest.

ACKNOWLEDGMENTS

The research was financed by the National Science Centre Poland - grant no. NN311295635. The authors would like to thank the anonymous reviewers for their valuable comments improving our manuscript. We are grateful to Dr. Janet Fulton and Dr. Luke Kramer for language correction of the manuscript and, above all, useful comments.

Footnotes

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.psj.2024.104136.

Appendix. Supplementary materials

Photo1. Mini duck (K-2)

mmc1.jpg (3.3MB, jpg)

Photo2-Crossbreed duck (KhO-1)

mmc2.jpg (3.7MB, jpg)

Photo3-English Pekin (LsA)

mmc3.jpg (3.3MB, jpg)

Photo4-Polish Pekin (P-33)

mmc4.jpg (3.5MB, jpg)

Photo5-Danish Pekin (P-8)

mmc5.jpg (3.6MB, jpg)

Photo6-French Pekin (P-9)

mmc6.jpg (3.3MB, jpg)

REFERENCES

  1. Alderson L. C.A.B. Int.; Wallingford, UK: 1990. Genetic conservation of domestic livestock. [Google Scholar]
  2. Belkhir K., Borsa P., Chikhi L., Raufaste N., Bonhomme F. Université de Montpellier II; Montpellier (France): 1996. GENETIX 4.05, logiciel sous Windows TM pour la génétique des populations. Laboratoire Génome, Populations, Interactions, CNRS UMR 5171. [Google Scholar]
  3. Botstein D., White R.L., Skolnick M., Davis R.W. Construction of a genetic linkage map in man using rkkestriction fragment length polymorphisms. Am. J. Hum. Genet. 1980;32:314–331. [PMC free article] [PubMed] [Google Scholar]
  4. Buchholz W.G., Pearce J.M., Pierson B.J., Scribner K.T. Dinucleotide repeat polymorphisms in waterfowl (family Anatidae): characterization of a sex-linked (Z-specific) and 14 autosomal loci. Anim. Genet. 1998;29:323–325. [PubMed] [Google Scholar]
  5. Carcò G., Grajewski B., Cassandro M., Lisowski M., Szwaczkowski T. Genetic variability of some Italian and Polish duck breeds. Ital. J. Anim. Sci. 2018;17:899–906. [Google Scholar]
  6. Chepiha A.M., Kostenko S.O., Doroshenko M.S., Konoval O.M., Lu L., Sydorenko O.V., Svyrydenko N.P., Dzhus P.P., Drahulian M.V. Assessment Ukrainian breeds ducks genetic diversity by microsatellite loci. Ukr. J. Ecol. 2021;11:105–112. [Google Scholar]
  7. Choi N., Seo D., Manjula P., Lee J.H. Genetic diversity studies using molecular genetic markers. J. Anim. Breed. Genom. 2018;2:21–27. [Google Scholar]
  8. Debnath J., Debnath S., Sarkar D., Debroy B., Kumar M., Kumar-Dass A A. Genetic characterisation of indigenous duck of Tripura state in India using microsatellite markers. Trop. Anim. Health Prod. 2023;55:198. doi: 10.1007/s11250-023-03629-w. [DOI] [PubMed] [Google Scholar]
  9. Evanno G., Regnaut S., Gouder J. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Molec. Ecol. 2005;14:2611–2620. doi: 10.1111/j.1365-294X.2005.02553.x. [DOI] [PubMed] [Google Scholar]
  10. Fernández M.E., Goszczynski D.E., Lirón J.P., Villegas-Castagnasso E.E., Carino M.H., Ripoli M.V., Rogberg-Muñoz A., Posik D.M., Peral-García P., Giovambattista G. Comparison of the effectiveness of microsatellites and SNP panels for genetic identification, traceability and assessment of parentage in an inbred Angus herd. Genet. Mol. Biol. 2013;36:185–191. doi: 10.1590/S1415-47572013000200008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Goudet J. FSTAT: A computer program to calculate F-statistics. J. Hered. 1995;86:485–486. [Google Scholar]
  12. Hariyono D.N.H., Maharani D., Cho S., Manjula P., Seo D., Choi N., Sidadolog J.H.P., Lee J.-H. Genetic diversity and phylogenetic relationship analyzed by microsatellite markers in eight Indonesian local duck populations. Asian Austral. J. Anim. 2019;32:31–37. doi: 10.5713/ajas.18.0055. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Hsiao M.C., Lin H.C., Hsu Y.C., Hu Y.H., Li S.H., Lee S.R. Isolation and characterization of microsatellite markers in Tsaiya duck. Asian Austral. J. Anim. 2008;21:624–627. [Google Scholar]
  14. Huang Y., Tu J., Cheng X., Tang B., Hu X., Liu Z., Feng J., Lou Y., Lin L., Xu K., Zhao Y., Li N. Characterization of 35 novel microsatellite DNA markers from the duck (Anas platyrhynchos) genome and cross-amplification in other birds. Genet. Sel. Evol. 2005;37:455–472. doi: 10.1186/1297-9686-37-5-455. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Huang Y., Zhao Y., Haley C., Hu S., Hao J., Wu C. A genetic and cytogenetic map for the duck (Anas platyrhynchos) Genet. 2006;173:287–296. doi: 10.1534/genetics.105.053256. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Khan Ahmadi A., Rahimi G., Vafaei A., Sayyazadeh H. Microsatellite analysis of genetic diversity in pekin (Anas platyrhynchos) and muscovy (Cairina Moschata) duck populations. Int. J. Poult. Sci. 2007;6:378–382. [Google Scholar]
  17. Kokoszyński D., Wasilewski R., Stęczny K., Kotowicz M., Hrnčar C., Arpášová H. Carcass composition and selected meat quality traits of Pekin ducks from genetic resources flocks. Poult. Sci. 2019;98:3029–3039. doi: 10.3382/ps/pez073. [DOI] [PubMed] [Google Scholar]
  18. Lai F.Y., Chang Y.Y., Chein Y.C., Lin E.C., Liu H.C., Huang J.F., Ding S.T., Wang P.H. Monitoring of genetically close Tsaiya duck populations using novel microsatellite markers with high polymorphism. Asian Austral. J. Anim. 2020;33:888–901. doi: 10.5713/ajas.19.0175. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Langella O. 1999. Populations, 1.2.30. CNRS UPR9034. www.bioinformatics.org/populations Accessed on Oct 05, 2024
  20. Li H.F., Zhu W.Q., Song W.T., Shu J.T., Han W., Chen K.W. Origin and genetic diversity of Chinese domestic ducks. Mol. Phylogenet. Evol. 2010;57:634–640. doi: 10.1016/j.ympev.2010.07.011. [DOI] [PubMed] [Google Scholar]
  21. Maak S., Wimmers K., Weigend S., Neumann K. Isolation and characterization of 18 microsatellites in the Peking duck (Anas platyrhynchos) and their application in other waterfowl species. Mol. Ecol. Notes. 2003;3:224–227. [Google Scholar]
  22. Marshall T.C., Slate J., Kruuk L.E.B., Pemberton J.M. Statistical confidence for likelihood-based paternity inference in natural populations. Mol. Ecol. 1998;7:639–655. doi: 10.1046/j.1365-294x.1998.00374.x. [DOI] [PubMed] [Google Scholar]
  23. Minch E., Ruiz-Linares A., Goldstein D., Feldman M., Cavalli-Sforza L.L. Stanford University; Stanford, CA: 1997. MICROSAT: A computer program for calculating various statistics on microsatellite allele data, ver. 1.5d. [Google Scholar]
  24. Nei M. Genetic distance between populations. Am. Nat. 1972;106:283–292. [Google Scholar]
  25. Nei M. Analysis of gene diversity in subdivided populations. P. Natl. Acad. Sci. USA. 1973;70:3321–3323. doi: 10.1073/pnas.70.12.3321. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. NRC . Natl. Acad. Press; Washington. DC: 1993. Managing global genetic resources: Livestock, National research council committee on managing global genetic resources. [Google Scholar]
  27. Oldenbroek J.K. In: Genebanks and the conservation of animal genetic resources. Oldenbroek J.K., editor. DLO Institute for Animal Science and Health; Lelystad, Netherlands: 1999. Introduction; pp. 1–10. [Google Scholar]
  28. Page R.D.M. TREEVIEW: An application to display phylogenetic trees on personal computers. Comput. App. Biosci. 1996;12:357–358. doi: 10.1093/bioinformatics/12.4.357. [DOI] [PubMed] [Google Scholar]
  29. Pham L.D., Do D.N., Nam L.Q., Van Ba N., Ninh P.H., Thuy P.D., Van Son P., Thieu P.C. Evaluation of genetic diversity and population structure in four indigenous duck breeds in Vietnam. Anim. Biotechnol. 2022;33:1065–1072. doi: 10.1080/10495398.2020.1868485. [DOI] [PubMed] [Google Scholar]
  30. Polak G., Krupiński J., Calik J., Kawęcka A., Krawczyk J., Majewska A., Sikora J., Sosin-Bzducha E., Szyndler-Nedza M., Tomczyk-Wrona I. The risk status of Polish local breeds under conservation programmes – new approach. Ann. Anim. Sci. 2021;21:125–140. [Google Scholar]
  31. Purwantini I., Purwantini D. An estimation of genetic variation in Indonesian local duck using Microsatellite Markers. Asian J. Poult. Sci. 2010;4:198–204. [Google Scholar]
  32. Rousset F. Genepop'007: a complete re-implementation of the genepop software for Windows and Linux. Mol. Ecol. Resour. 2008;8:103–106. doi: 10.1111/j.1471-8286.2007.01931.x. [DOI] [PubMed] [Google Scholar]
  33. Seo D., Bhuiyan S.A., Sultana H., Heo J.M., Lee J.-H. Genetic diversity analysis of south and east Asian duck populations using highly polymorphic microsatellite markers. Asian Austral. J. Anim. 2016;29:471–478. doi: 10.5713/ajas.15.0915. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Su Y., Long R., Chen G., Wu X., Xie K., Wan J. Genetic Analysis of six endangered local duck populations in china based on microsatellite markers. J. Genet. Genomics. 2007;34:1010–1018. doi: 10.1016/S1673-8527(07)60114-3. [DOI] [PubMed] [Google Scholar]
  35. Su Y., Chen G.H. DNA microsatellite analysis of genetic diversity among Chinese indigenous laying-type ducks. Czech J. Anim. Sci. 2009;54:128–135. [Google Scholar]
  36. Weir B.S., Cockerham C.C. Estimating F-statistics for the analysis of population structure. Evolution. 1984;38:1358–1370. doi: 10.1111/j.1558-5646.1984.tb05657.x. [DOI] [PubMed] [Google Scholar]
  37. Wright S. The University of Chicago Press; Chicago: 1978. Evolution and the genetics of population, variability within and among natural populations. [Google Scholar]
  38. Wu Y., Liu X.L., Hou S.S., Huang W. Study on genetic diversity of six duck populations with microsatellite DNA. Asian Austral. J. Anim. 2008;21:776–783. [Google Scholar]
  39. Zhang Y., Wang L., Bian Y., Wang Z., Xu Q., Chang G., Chen G. Marginal diversity analysis of conservation of Chinese domestic duck breeds. Sci. Rep. 2019;9:13141. doi: 10.1038/s41598-019-49652-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Zhang X., He Y., Zhang W., Wang Y., Liu X., Cui A., Gong Y., Lu J., Liu X., Huo X., Lv J., Guo M., Du X., Han L., Chen H., Chen J., Li C., Chen Z. Development of microsatellite marker system to determine the genetic diversity of experimental chicken, duck, goose and pigeon populations. Biomed Res Int. 2021;2021 doi: 10.1155/2021/8851888. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Photo1. Mini duck (K-2)

mmc1.jpg (3.3MB, jpg)

Photo2-Crossbreed duck (KhO-1)

mmc2.jpg (3.7MB, jpg)

Photo3-English Pekin (LsA)

mmc3.jpg (3.3MB, jpg)

Photo4-Polish Pekin (P-33)

mmc4.jpg (3.5MB, jpg)

Photo5-Danish Pekin (P-8)

mmc5.jpg (3.6MB, jpg)

Photo6-French Pekin (P-9)

mmc6.jpg (3.3MB, jpg)

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