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PLOS One logoLink to PLOS One
. 2020 Apr 2;15(4):e0225084. doi: 10.1371/journal.pone.0225084

Genetic diversity and population structure of indigenous chicken in Rwanda using microsatellite markers

Richard Habimana 1,2,*, Tobias Otieno Okeno 2, Kiplangat Ngeno 2, Sylvere Mboumba 3, Pauline Assami 4, Anique Ahou Gbotto 5, Christian Tiambo Keambou 4,6, Kizito Nishimwe 1, Janvier Mahoro 1, Nasser Yao 4
Editor: Tzen-Yuh Chiang7
PMCID: PMC7117670  PMID: 32240167

Abstract

Rwanda has about 4.5 million of indigenous chicken (IC) that are very low in productivity. To initiate any genetic improvement programme, IC needs to be accurately characterized. The key purpose of this study was to ascertain the genetic diversity of IC in Rwanda using microsatellite markers. Blood samples of IC sampled from 5 agro-ecological zones were collected from which DNA was extracted, amplified by PCR and genotyped using 28 microsatellite markers. A total of 325 (313 indigenous and 12 exotic) chickens were genotyped and revealed a total number of 305 alleles varying between 2 and 22 with a mean of 10.89 per locus. One hundred eighty-six (186) distinct alleles and 60 private alleles were also observed. The frequency of private alleles was highest in samples from the Eastern region, whereas those from the North West had the lowest. The influx of genes was lower in the Eastern agro-ecological zone than the North West. The mean observed heterozygosity was 0.6155, whereas the average expected heterozygosity was 0.688. The overall inbreeding coefficient among the population was 0.040. Divergence from the Hardy-Weinberg equilibrium was significant (p<0.05) in 90% of loci in all the populations. The analysis of molecular variance revealed that about 92% of the total variation originated from variation within populations. Additionally, the study demonstrated that IC in Rwanda could be clustered into four gene groups. In conclusion, there was considerable genetic diversity in IC in Rwanda, which represents a crucial genetic resource that can be conserved or optimized through genetic improvement.

Introduction

Poultry keeping is an agricultural enterprise with a high potential in Rwanda. More than 40% of households keep poultry with indigenous chickens (IC) being the most preferred, accounting for approximately 80% of the reared chicken species [1]. Raising IC is preferred to exotic breeds because of their small cost of production, scavenging capacity and adaptability to harsh environmental conditions. IC production serves a critical role as a source of revenue for resource-limited countryside families [2]. However, the productivity of IC in Rwanda is low. Each mature hen weighs between 0.8 to 1.8 kg and produces an average of 40 to 100 eggs per year. This output is insufficient to meet the needs of the population [3] and mitigate poverty among the smallholder farmers in rural areas. To improve the genetic potential of IC in Rwanda, different crossbreeding programmes between IC and exotic chicken have been initiated. However, these programmes have not been sustainable due to decreased broodiness in the hybridized birds, unpredictable stock, and the high cost of buying and sustaining exotic cocks for breeding purposes. Additionally, recent global efforts to preserve native genetic resources pose a threat to such programmes [4]. There is, therefore, a need for the development of an alternative strategy to genetic improvement and conservation of IC.

Genetic improvement through within-breed selection of IC in Rwanda could be a promising alternative strategy. Nonetheless, genetic enhancements need a resolute breeding objective, sustainable breeding plans, and an in-depth comprehension of the genetic diversity of prevailing genotypes and ecotypes [5]. Therefore, elucidating the genetic characteristics of the prevailing IC stock will not only favor genetic enhancement but will also expedite their preservation [4].

In various parts of the world, the genetic diversity of IC has been assessed using molecular markers including microsatellites [619]. Microsatellites are short, tandemly repeated simple sequences with one to six base pairs in length [20]. Thirty (30) microsatellite markers have been suggested by the Food and Agriculture Organization to be used in the evaluation of genetic diversity in chicken [2021]. These microsatellite markers are appropriate for a wide range of applications and have remained the most commonly used markers in studies of genetic diversity and population structure since the early 1990s [20,22,23] due to their high degree of polymorphism, random distribution across the genome, codominance, and neutrality with respect to selection [24]. Additionally, they are relatively cheaper to genotype and offer more population genetic information per marker than single nucleotide polymorphisms (SNPs) known as biallelic markers [25]. Finally, microsatellites can successfully amplify low DNA concentration or low-quality DNA samples [26].

There is, however, a scarcity of data on the genetic diversity and population structure of IC in Rwanda. The availability of such knowledge could drive the understanding of the origin and genetic variability in the population to guide selection decisions. As a result, it would be possible to develop apposite mating plans to uphold genetic variation and minimize inbreeding in the population, which would promote response to selection. This study evaluated the degree of genetic diversity and phylogenetic relationships between populations of IC in Rwanda using simple sequence repeats (SSR) markers.

Materials and methods

Ethical statement

After a thorough review and approval of sampling procedures and experimental manipulations, ethical permission (Ref: 031/19/DRI September 2, 2019) for the collection of chicken blood samples was obtained from the Research Screening and Ethical Clearance Committee of the College of Agriculture, Animal Sciences and Veterinary Medicine, University of Rwanda. Private grounds were never entered without the consent of chicken owners. The owners of the chicken signed an informed consent form to allow collection of blood sample from their chicken to be used for the experiment. A memorandum of understanding between University of Rwanda, Rwanda Agriculture and Animal Resources Development Board and Ministry of Agriculture had been made to oversee research and consent research activities including procedures to be undertaken in the whole country. Therefore, no specific permissions were needed for each location visited. Every zone was visited in a company of Rwanda Agriculture and Animal Resources Development Board employee who ensured that national and international guidelines were followed. In addition, the chickens were treated humanely, and none of them was sacrificed for this study.

Collection of samples and DNA extraction

In total, 313 distinct IC, previously characterized morphologically (S1 Table) [27], were sampled from five agro-ecological zones [51, 52, 53, 55, and 102 were sampled from Central South (CS), North West (NW), Central North (CN), South West (SW), and East (E), respectively] (S1 Fig and Fig 1). Indigenous Chicken populations were reckoned according to agro-ecological zones [28]. Households having IC were randomly selected considering a minimum distance of 500 meters between them to ensure sampling of unrelated birds [29]. Twelve (12) exotic commercial chicken breeds (2 kuroilers, 5 Isa brown layers and 5 cobb broilers) were included as references. These exotic breeds have been developed from several parent breeds which are not usually divulged by breeder companies and, therefore, are marketed as commercial hybrids under trade names. They were genetically selected for performance traits associated with egg (layers), meat (broilers) or both egg and meat (kuroilers) production (S1 Table).

Fig 1. Map of sampling sites of chicken blood used in this study.

Fig 1

A single blood drop was drawn from veins in the wing of each bird and placed on Whatman FTA filter cards, left to dry in a cool place for approximately one hour, and held in reserve in discrete envelopes at room temperature awaiting further processing. The isolation of genomic DNA was done using Smith and Burgoyne’s boiling method [30]. The quality of genomic DNA was ascertained through gel electrophoresis using 1% agarose. A NanoDrop Spectrophotometer (Thermo Scientific TM Nanodrop 2000) was used to quantify the total DNA, which was adjusted to 10ng/μl before use in the subsequent steps of polymerase chain reaction (PCR) and genotyping.

PCR amplification and DNA polymorphism

Twenty-eight fluorescently-labelled polymorphic SSR markers were chosen based on the extent of polymorphism shown by a high polymorphism information content and the genome coverage consistent across previous studies [31]. The PCR reactions had a total volume of 10μl consisting of 30ng target DNA, 5μl of One Taq 2MM and 0.2μl of each forward and reverse primer. The amplifications were done in a thermocycler (Applied Biosystems 9700 Thermal Cycler Gene Amp®) and entailed the first denaturation at 94°C for 3 minutes, 30 cycles of denaturation at 94ºC for 30 seconds, the primer annealing at temperatures ranging between 58°C and 64°C based on the primer components (Table 1) for 1 minute, and extension at 72°C for 2 minutes. The last extension step was done at 72°C for 10 minutes. The PCR products of different fluorescent tags were combined according to the exhibited colour and intensity of bands to create uniform signal strength. Hi-Di formimide was used to denature the combined amplicons at 95°C for 3 minutes, this step was followed by capillary electrophoresis separation in an ABI3730 DNA genetic analyzer by using GeneScan- 500 Internal LIZ and 1200 Internal LIZ Size Standards. The resultant fragment analysis data and sizes of alleles were counted using GENEMAPPER software v. 4.1 (Applied Biosystems).

Table 1. Sequences and physical information of 28 SSR markers used for PCR amplification.

Nam Allele size (base-pairs) Forward Primer 5'- 3' Reverse primer 3'-5' Annealing temperature
(Tm: oC)
ADL0268 102–116 CTCCACCCCTCTCAGAACTA CAACTTCCCATCTACCTACT 60
MCW0206 221–249 ACATCTAGAATTGACTGTTCAC CTTGACAGTGATGCATTAAATG 60
LEI0166 354–370 CTCCTGCCCTTAGCTACGCA TATCCCCTGGCTGGGAGTTT 60
MCW0295 88–106 ATCACTACAGAACACCCTCTC TATGTATGCACGCAGATATCC 60
MCW0081 112–135 GTTGCTGAGAGCCTGGTGCAG CCTGTATGTGGAATTACTTCTC 60
MCW0014 164–182 TATTGGCTCTAGGAACTGTC GAAATGAAGGTAAGACTAGC 58
MCW0183 296–326 ATCCCAGTGTCGAGTATCCGA TGAGATTTACTGGAGCCTGCC 58
ADL0278 114–126 CCAGCAGTCTACCTTCCTAT TGTCATCCAAGAACAGTGTG 60
MCW0067 176–186 GCACTACTGTGTGCTGCAGTTT GAGATGTAGTTGCCACATTCCGAC 60
MCW0104 190–234 TAGCACAACTCAAGCTGTGAG AGACTTGCACAGCTGTGTACC 60
MCW0123 76–100 CCACTAGAAAAGAACATCCTC GGCTGATGTAAGAAGGGATGA 60
MCW0330 256–300 TGGACCTCATCAGTCTGACAG AATGTTCTCATAGAGTTCCTGC 60
MCW0165 114–118 CAGACATGCATGCCCAGATGA GATCCAGTCCTGCAGGCTGC 60
MCW0069 158–176 GCACTCGAGAAAACTTCCTGCG ATTGCTTCAGCAAGCATGGGAGGA 60
MCW0248 205–225 GTTGTTCAAAAGAAGATGCATG TTGCATTAACTGGGCACTTTC 60
MCW0111 96–120 GCTCCATGTGAAGTGGTTTA ATGTCCACTTGTCAATGATG 60
MCW0020 179–185 TCTTCTTTGACATGAATTGGCA GCAAGGAAGATTTTGTACAAAATC 60
MCW0034 212–246 TGCACGCACTTACATACTTAGAGA TGTCCTTCCAATTACATTCATGGG 60
LEI0234 216–364 ATGCATCAGATTGGTATTCAA CGTGGCTGTGAACAAATATG 60
MCW0103 266–270 AACTGCGTTGAGAGTGAATGC TTTCCTAACTGGATGCTTCTG 64
MCW0222 220–226 GCAGTTACATTGAAATGATTCC TTCTCAAAACACCTAGAAGAC 60
MCW0016 162–206 ATGGCGCAGAAGGCAAAGCGATAT TGGCTTCTGAAGCAGTTGCTATGG 60
MCW0037 154–160 ACCGGTGCCATCAATTACCTATTA GAAAGCTCACATGACACTGCGAAA 64
MCW0098 261–265 GGCTGCTTTGTGCTCTTCTCG CGATGGTCGTAATTCTCACGT 60
LEI0094 247–287 GATCTCACCAGTATGAGCTGC TCTCACACTGTAACACAGTGC 60
MCW0284 235–243 GCCTTAGGAAAAACTCCTAAGG

CAGAGCTGGATTGGTGTCAAG

60
MCW0078 135–147 CCACACGGAGAGGAGAAGGTCT TAGCATATGAGTGTACTGAGCTTC 60
LEI0192 244–370 TGCCAGAGCTTCAGTCTGT GTCATTACTGTTATGTTTATTGC 60
ADL0112 120–134 GGCTTAAGCTGACCCATTAT ATCTCAAATGTAATGCGTGC 58
MCW0216 139–149 GGGTTTTACAGGATGGGACG AGTTTCACTCCCAGGGCTCG 60

Source: FAO [32]

Statistical analysis

Genetic diversity and relationship

The polymorphism information content (PIC) was estimated using Powermarker v.3.25 [6]. GenAlEx v.6.5 was used to estimate the allele frequencies, total alleles, expected heterozygosity (He), observed heterozygosity (Ho), and Wright’s F-statistics as well as other parameters such as inbreeding coefficient over all populations (Fis), among populations (Fit) and within populations (Fst) for 28 microsatellite markers [7]. Jackknifing across populations using FSTAT v.2.9.4 produced standard deviation values that were used to obtain tests of significance per microsatellite locus by creating confidence intervals at 95% and 99% [8].

GENETIX v.4.05.2 was used to estimate genetic variation per breed (He, Ho) and the average number of alleles [9]. Gene flow [10] was calculated using Powermarker v.3.25 [6]. Pairwise Fst values, which are indications of the fraction of genetic variation attributed to population sub-structuring, were calculated for various population pairs using GenAlEx v.6.5 [7]. Analysis of molecular variance (AMOVA) was computed using GenAlEx v.6.5 for within and among pre-grouped populations [7]. Powermarker v 3.25 was used to assess genotype frequencies for nonconformity with Hardy-Weinberg equilibrium (HWE) in addition to linkage disequilibrium by performing Pearson's chi-squared test (χ)2 [6]. GenAlEx v.6.5 [7] was used to approximate Nei’s standard genetic distances [11] among population pairs. The Neighbour-Joining (NJ) programme was used to develop an unrooted NJ cladogram using the Darwin software v.6.0 according to pairwise kinship distance matrix between populations [12]. A consensus tree assessed by 1,000 bootstraps all through the group of loci was created.

Population structure

The possible sum of clusters was approximated using the Evanno method [13] as reported by Dent Earl and Bridgett [14]. A set of rules applied in STRUCTURE v.2.3.4 was used to group entities based on multi-locus genotypes [15]. The evaluation entailed an admixture model alongside interrelated allele frequencies. During the STRUCTURE analysis, 5 replications of K (presumed sum of subpopulations), extending from 1 to 20 were used together with 100,000 reiterations of Markov Chain Monte Carlo (MCMC) and 50,000 burn-in period in the admixture model. Each estimation of K was redone 5 times to ensure the reproducibility of the outcomes. CLUMPAK (CLUMPAK server), which is a tool used to single out clustering types and bundle population structure deductions across K was used. The Factorial Correspondence Analysis (FCA), which is a multivariate model of analysis, was conducted to observe the associations between entities from unlike zones and to evaluate probable admixtures between the populations. The main variables were the frequencies of alleles at all loci in the populations. The FCA was computed using GENETIX v.4.05.2 [9].

Results

Genetic diversity

Marker polymorphism across the studied IC populations

The parameters of the variability of the investigated loci are shown in Table 2. Overall, 305 alleles were observed at the 28 microsatellite loci with an average of 10.89 alleles per microsatellite marker. The total sum of alleles ranged from 2 (MCW0037) to 22 (LEI0192). The effective number of alleles (NE) ranged between 1.6504 (MCW0078) and 8.901 (LEI0234), with an overall mean of 3.8194. The PIC ranged from 0.3488 (MCW0103) to 0.8775 (LEI0234). Out of the total number of alleles, 20% were private alleles (60), whereas ADL0112 revealed the maximum sum of private alleles (6). The within-population insufficiency in heterozygosity (as determined by FIS factor), extended between −1.00 (MCW0037) and 0.338 (LEI0234) with a mean of 0.041 for all loci. The inbreeding coefficient among populations (FIT) values ranged from -1.00 (MCW0037) to 0.354 (LEI0234), with a mean of 0.089. Global population differentiation (evaluated by FST) was estimated at 0.054. The contribution of 28 microsatellites for population segregation (determined by FST statistics) varied from 0.000 (MCW0037) to 0.158 (ADL0268). The overall F-statistics differed significantly (p<0.05) from zero. This differentiation had a significant contribution from all loci. The values for Ho ranged from 0.3015 (MCW0165) to 1 (MCW0037), with an overall mean of 0.6155, while the values of He ranged from 0.394 (MCW0078) to 0.8877 (LEI0234), with a general mean of 0.688. The average number of migrants per generation (Nm) in the whole population and across all the loci was found to be 6.06. Only 10% of the loci in all IC populations, did not differ considerably (p >0.05) from the HWE.

Table 2. Marker polymorphism and diversity parameters across studied IC populations in Rwanda.
Loci MAF NG NA NE NPA He Ho PIC I Fis Fit Fst Nm HWE pV
ADL0112 0.499 27 16 2.720  6 0.632 0.594 0.572 1.318 0.097 0.128 0.034 7.006 0.000
ADL0268 0.245 39 14 6.241  3 0.840 0.582 0.820 2.022 0.176 0.306 0.158 1.332 0.000
ADL0278 0.300 39 12 5.349  4 0.813 0.548 0.789 1.885 0.252 0.283 0.041 5.869 0.000
LEI0094 0.392 45 17 4.360  3 0.771 0.714 0.744 1.867 0.017 0.034 0.017 14.344 0.000
LEI0192 0.317 66 22 5.699  4 0.825 0.775 0.806 2.149 -0.005 0.036 0.041 5.829 0.000
LEI0234 0.177 77 17 8.902  2 0.888 0.569 0.878 2.393 0.338 0.354 0.024 10.202 0.000
MCW0014 0.512 29 10 3.107  1 0.678 0.486 0.645 1.493 0.142 0.263 0.142 1.517 0.000
MCW0016 0.317 39 15 4.699  4 0.787 0.772 0.759 1.841 0.002 0.023 0.021 11.392 0.000
MCW0020 0.305 29 8 4.661  0 0.785 0.720 0.753 1.676 0.050 0.095 0.047 5.027 0.000
MCW0034 0.351 46 14 5.211  5 0.808 0.775 0.788 1.927 -0.003 0.032 0.035 6.965 0.191
MCW0037 0.500 1 2 2.000  0 0.500 1.000 0.375 0.693 -1.000 -1.000 0.000 0.000
MCW0067 0.395 31 11 3.573  1 0.720 0.680 0.679 1.622 0.038 0.137 0.103 2.181 0.000
MCW0069 0.339 26 10 3.671  0 0.728 0.739 0.680 1.503 -0.011 0.028 0.038 6.309 0.104
MCW0078 0.766 11 5 1.650  0 0.394 0.369 0.372 0.820 -0.006 0.006 0.011 21.491 0.015
MCW0081 0.494 42 11 3.001  1 0.667 0.560 0.622 1.483 0.126 0.156 0.034 7.140 0.000
MCW0098 0.465 27 9 2.571  1 0.611 0.523 0.535 1.176 0.105 0.170 0.072 3.212 0.000
MCW0103 0.708 9 6 1.736  2 0.424 0.375 0.349 0.693 0.131 0.160 0.033 7.343 0.000
MCW0104 0.489 43 18 3.271  4 0.694 0.649 0.662 1.701 0.066 0.096 0.033 7.385 0.000
MCW0111 0.595 21 8 2.440  0 0.590 0.483 0.550 1.226 0.110 0.141 0.035 6.800 0.000
MCW0123 0.523 38 14 3.103  3 0.678 0.640 0.650 1.568 0.015 0.031 0.016 15.002 0.000
MCW0165 0.635 7 4 1.924  0 0.480 0.302 0.386 0.755 0.325 0.341 0.024 10.050 0.000
MCW0183 0.292 34 11 5.516  3 0.819 0.659 0.796 1.873 0.119 0.189 0.080 2.885 0.000
MCW0206 0.394 24 9 3.992  2 0.750 0.699 0.714 1.583 -0.004 0.044 0.048 5.000 0.000
MCW0222 0.400 11 6 2.972  2 0.664 0.646 0.600 1.210 -0.030 0.023 0.051 4.641 0.000
MCW0248 0.679 6 4 1.816  1 0.449 0.492 0.366 0.713 -0.236 -0.185 0.041 5.864 0.344
MCW0284 0.368 29 8 3.900  0 0.744 0.689 0.706 1.620 0.050 0.117 0.070 3.321 0.000
MCW0295 0.465 34 13 3.482  3 0.713 0.579 0.680 1.632 0.131 0.214 0.096 2.341 0.000
MCW0330 0.302 26 11 5.376  5 0.814 0.615 0.790 1.827 0.147 0.281 0.157 1.339 0.000
Mean 0.437 30.571 10.893 3.819  2.140 0.688 0.616 0.645 1.510 0.041 0.089 0.054 6.060
Total 305  60

MAF, major allele frequency; NG, number of genotypes; NA, number of alleles; NPA, number of private allele; Ne, number of effective alleles; I, Shannon's information index; He, expected heterozygosity; Ho, observed heterozygosity; PIC, polymorphic information content, Nm: number of migrants, F, inbreeding coefficient over all populations (FIS), among populations (FIT) and within populations (FST), HWE pV, Hardy-Weinberg equilibrium p-value based on chi square test (There is a deviation from HWE at p<0.05)

Genetic diversity indices for IC populations from each agro-ecological zone

Genetic diversity indices for IC from each zone is summarized in Table 3. All the loci were polymorphic. The observed and expected frequencies of heterozygote were not statistically different (p>0.05), hence, the inbreeding coefficient (F) estimates observed were not substantially different from zero. The mean sum of alleles varied from 5.143 to 8.25. The highest count of alleles (8.2) was found in the Eastern IC population. The highest count of private alleles (21) was observed in the Eastern population, while the NW population did not harbor any private allele. The effective sum of alleles ranged from 3.311 to 3.62. The Shannon Index (I), which is an expression of population diversity in a particular habitat, was high in the SW (1.458) and low in exotic chicken (1.305). Furthermore, the lowest observed heterozygosity was in the CS (0.598) while the highest was recorded in exotic chicken population (0.667). The expected heterozygosity in the populations ranged from0.644 (CN) to 0.680 (SW).

Table 3. Common genetic diversity indices as revealed among IC populations in Rwanda.
Populations N %PL NA PA Ne Ho He uHe F I
Central North 51 100 6.929 6 3.354 0.623 0.644 0.650 0.021 1.322
Central South 55 100 7.286 15 3.359 0.598 0.661 0.668 0.077 1.372
Exotic chicken 12 100 5.143 4 3.386 0.667 0.665 0.669 -0.019 1.305
East 102 100 8.250 21 3.367 0.611 0.654 0.657 0.056 1.358
North West 52 100 6.500 0 3.311 0.613 0.645 0.651 0.042 1.306
South West 53 100 7.964 14 3.620 0.626 0.680 0.686 0.063 1.458
Total 325 100 7.011 60 3.400 0.623 0.658 0.668 0.040 1.353

N, Number of chickens, % PL, Proportion of polymorphic loci, NA, number of alleles; PA, number of private allele; Ne, number of effective alleles He, expected heterozygosity Ho, observed heterozygosity uHe: unbiased expected heterozygosity F, inbreeding coefficient I, Shannon's information index.

The p-values of HWE are summarized in Table 4 and confirm that Ho and He did not differ significantly (P>0.05). Thus, taking all the loci into account none of the IC populations diverged from the HWE law.

Table 4. Tests for the Hardy-Weinberg equilibrium probability of loci in the IC population in Rwanda.
Locus North West Central North Central South East North-south Exotic chicken
ADL0112 0.551 0.000 0.000 0.003 0.000 0.028
ADL0268 0.000 0.000 0.163 0.000 0.000 0.330
ADL0278 0.000 0.000 0.000 0.000 0.003 0.349
LEI0094 0.001 0.976 0.000 0.051 0.001 0.812
LEI0192 0.000 0.000 0.000 0.002 0.024 0.913
LEI0234 0.000 0.000 0.000 0.099 0.000 0.720
MCW0014 0.000 0.000 0.000 0.000 0.000 0.634
MCW0016 0.012 0.000 0.000 0.239 0.108 0.200
MCW0020 0.048 0.586 0.190 0.620 0.000 0.980
MCW0034 0.050 0.735 0.316 0.000 0.816 0.412
MCW0037 0.000 0.000 0.000 0.000 0.000 0.001
MCW0067 0.000 0.000 0.870 0.000 0.000 0.095
MCW0069 0.965 0.529 0.971 0.967 0.295 0.279
MCW0078 0.911 0.251 0.985 0.232 0.003 0.916
MCW0081 0.739 0.000 0.000 0.000 0.000 0.004
MCW0098 0.681 0.000 0.000 0.000 0.000 0.005
MCW0103 0.012 0.752 0.000 0.913 0.000 0.574
MCW0104 0.001 1.000 0.355 0.000 0.755 0.213
MCW0111 0.046 0.189 0.127 0.003 0.687 0.545
MCW0123 0.503 0.909 0.000 0.002 0.000 0.003
MCW0165 0.540 0.000 0.004 0.000 0.018 0.327
MCW0183 0.000 0.010 0.000 0.000 0.012 0.001
MCW0206 0.590 0.020 0.009 0.908 0.000 0.658
MCW0222 0.000 0.096 0.000 0.783 0.968 0.283
MCW0248 0.429 0.922 0.057 0.991 0.247 0.035
MCW0284 0.121 0.021 0.846 0.000 0.000 0.437
MCW0295 0.279 0.000 0.017 0.000 0.046 0.015
MCW0330 0.633 0.992 0.000 0.000 0.150 0.001

P-values <0.05 show the genotype frequencies for nonconformity with Hardy-Weinberg Equilibrium (HWE) based on Chi square test

Analysis of molecular variance revealed that ninety-two percent (92%) of the total variation originated from variation within populations (Table 5).

Table 5. Analysis of molecular variance of all loci for the IC population in Rwanda.
Source Degree of freedom Sum square Mean square Estimated variances % of estimated variances
Among Populations 5 574.201 114.840 1.838 8%
Within Populations 319 6346.643 19.895 19.895 92%
Total 324 6920.843 21.733 100%

Genetic relationship

The matrix of pairwise genetic distances between populations (Table 6 and Fig 2) showed low genetic distance (0.029) between NW and CN populations. A similar trend was observed in SW and CS (0.048). On the other hand, by considering only the IC populations, the highest genetic distance was observed between E and SW populations (0.125). The genetic distance between the IC population in Rwanda and exotic chicken was relatively high (0.231).

Table 6. Genetic distance among the IC population in Rwanda.
Populations North West Central North Central South Exotic chicken East
Central North 0.029
Central South 0.094 0.077
Exotic chicken 0.199 0.213 0.231
East 0.112 0.097 0.117 0.196
South West 0.104 0.092 0.048 0.118 0.125

The extent of genetic distinction among the population with regard to allele frequencies (FST) and gene flow (Nm) are presented in Table 7. The results revealed a low genetic differentiation and a high gene flow between CN and NW, and likewise between SW and CS. A relatively high gene differentiation, however, was found between the E population and other populations.

Fig 2. Neighbour-Joining pair-wise of the IC population in Rwanda.

Fig 2

Table 7. Gene flow (upper diagonal) and Gene differentiation (lower diagonal).
Populations Central North Central South Exotic chicken East North West South West
Central North 2.304 1.412 2.051 6.274 2.040
Central South 0.022 0.925 1.471 1.533 3.847
Exotic chicken 0.052 0.058 3.432 1.188 2.791
East 0.025 0.027 0.050 1.783 1.560
North West 0.012 0.026 0.053 0.028 1.471
South West 0.026 0.014 0.036 0.028 0.027

The phylogenetic relationship by the Neighbour-Joining tree showed four (4) IC genetic clusters, namely I, II, III and IV (Fig 3). The eastern population stands alone unlike the other populations: IC populations from the NW clustered together with those from the CN. Few individuals from the SW population clustered together with the exotic chicken in group III, and finally the rest of SW individuals clustered with those from the CS in group II (Fig 3).

Fig 3. Neighbour-Joining tree of the clustering pattern among IC populations in Rwanda.

Fig 3

Population structure

Data from the Bayesian cluster analysis showed the existence of four (4) main gene pools in the whole IC population in Rwanda. The highest value for ΔK was obtained for K = 4 (Table 8 and Fig 4). The first gene pool (I) was composed of CN and NW populations. The second gene pool (II) was made of the Eastern population only. The third (III) included individual from SW and CS and the fourth gene pool (IV) was composed of the remaining individuals of SW and exotic chicken. A high proportion of the admixture was observed in the gene pool III.

Table 8. Number of clusters (K) based on the progression of the average estimate of Ln likelihood of data in IC populations in Rwanda.

K Replication Mean LnP(K) Stdev LnP(K) Ln’(K) ILn”(K)I Delta K
1 5 -27680.120000 0.192354 - - -
2 5 -26645.700000 81.765916 1034.420000 301.520000 3.687600
3 5 -25912.800000 30.968694 732.900000 82.920000 2.677543
4 5 -25262.820000 3.056469 649.980000 558.300000 182.661785
5 5 -25171.140000 37.017671 91.680000 21.920000 0.592150
6 5 -25057.540000 46.761341 113.600000 19.200000 0.410596
7 5 -24963.140000 9.161496 94.400000 81.200000 8.863182
8 5 -24949.940000 63.605566 13.200000 55.340000 0.870050
9 5 -24881.400000 42.680968 68.540000 29.880000 0.700078
10 5 -24842.740000 77.738491 38.660000 87.640000 1.127369
11 5 -24891.720000 114.353824 -48.980000 14.060000 0.122952
12 5 -24954.760000 210.975195 -63.040000 330.240000 1.565302
13 5 -24687.560000 104.370245 267.200000 510.500000 4.891241
14 5 -24930.860000 402.389690 -243.300000 41.440000 0.102985
15 5 -25132.720000 914.525050 -201.860000 542.960000 0.593707
16 5 -24791.620000 296.572178 341.100000 183.320000 0.618129
17 5 -24633.840000 54.568333 157.780000 129.560000 2.374271
18 5 -24605.620000 64.775126 28.220000 204.760000 3.161090
19 5 -24782.160000 498.369745 -176.540000 100.700000 0.202059
20 5 -24858.000000 559.214181 -75.840000 - -

Fig 4. Delta K (ΔK) approximating the more possible number of clusters in IC populations in Rwanda.

Fig 4

The results of the Factorial Correspondence Analysis (FCA) are depicted in Fig 5. It showed tree clusters whereby the Eastern region was still standing alone. NW and CN populations clustered together. Finally, the majority of individuals from the CS, SW and exotic chicken were in the same group.

Fig 5. Factorial correspondence analysis.

Fig 5

Discussion

Genetic diversity

The average PIC was the best index to estimate the polymorphism of alleles [16]. It showed that more information could be obtained from the loci when PIC>0.5. On the other hand, 0.25<PIC<0.5 was an indication of a moderately instructive locus, whereas PIC<0.25 indicated a vaguely informative locus [33]. In this study, 82.3% of all loci were highly informative, which confirmed that they were suitable for estimating the genetic diversity of IC populations in Rwanda. The highest value of PIC (0.87) was that of LEI0234 and the mean PIC was 0.6451. The PIC values found in this study exceeded those (0.29–080) of Cameroon’s IC [17], and (0.31–0.49) of Chinese IC [7,8], but lower than those obtained by Tang for black-bone IC breeds (0.67) [34]. The mean frequency of alleles per marker found in this study (10.89) exceeded those recorded in previous reports in Cameroon (9.04) [17], Ghana (7.8) [35], Iran (5.4) [36], China (3.8) [37], Egypt (7.3) [38], Pakistan (9.1) [39] and Vietnam (6.41) [40]. The values obtained in this study were, however, lower than those from Brazilian (13.3) [41] and were in the same range as those from Ethiopian chicken ecotypes (10.6) [42].

The mean number of effective alleles (3.81) obtained was higher than 3.13 observed in Cameroon [17] and Indian chicken [21]. Heterozygosity can also be considered in genetic diversity. The degree of mean population heterozygosity is an indication of the level of population constancy. Low population heterozygosity informs high population genetic constancy [43]. The present study indicated that Ho of the different IC population varied from 0.3015 to 1 with an overall mean value of 0.6155, while He ranged from 0.394 to 0.887 with an overall average of 0.688.

This study also discovered that the values of Ho and He were similar. As a result, there was no significant difference between zero and the resultant F estimates (0.040), which suggested that the IC populations were in HWE. An implication of this supposition is that the population is under artificial selection, which is indicative of population stability. However, the little variation observed between Ho and He could be attributed to discrepancies in sample size, location, population composition, and the origin of microsatellite markers [44].

The IC populations in Rwanda had a similar level of diversity as their Ethiopian [45], Egyptian [38] and Cameroonian [8] counterparts, but had lower and higher diversity than those observed in southern China [19], European and Asian IC breeds [35], respectively. Among Rwanda IC, all populations showed a significantly high degree of inbreeding, which could have an impact on trait fixation in the populations. This degree of inbreeding exceeded that observed for Yunnan IC breeds (0.25) [8] and Turkish IC (0.301) depicted with 10 SSR loci [44]. The FST value (0.054) revealing the diversity between IC populations in Rwanda was higher than 0.048 for Ethiopian IC ecotypes [46] and (0.003–0.040) for Kenyan IC [47] and lower than 0.080 found in Cameroonian IC [17].

Genetic relationships

Wright’s F-statistics showing the inbreeding coefficient in this study was 0.041, which was higher than 0.03 found in Cameroon [17], but was similar to values obtained in many Chinese IC [18]. The FST permits the approximation of migratory entities in a population per generation (Nm) based on loci. In IC populations in Rwanda, Nm varied from 1.332 to 21.491, with an average of 6.060. This value was higher than that obtained in Cameroun [17].

The number of private alleles (PA) distributed all through the ecotypes showed that there was high genetic diversity between populations. In this study, the number of PA was higher in the East (21) followed by CS (15) and SW (14). The NW population, however, did not exhibit any private allele (0). Despite, the number of private alleles being a good indicator of population relationship and structure, further studies need to be carried out to identify possible traits that may be controlled by these private alleles. The total number of private alleles in this study (60) was higher than that (24) found in Cameroun [17].

Findings from AMOVA showed that the largest portion of the genetic variation in IC populations in Rwanda existed in individuals within the population (92%). A comparable trend was noted in the Tanzanian [48], Ethiopian [17] and Cameroonian [17] IC ecotypes. The quality of the product, cultural uses of chicken, and the ease with which chicken adapts to the environment are the factors that motivate small-scale farmers to rear IC. These factors highlight the importance of within-population diversity as a key incentive in rearing IC [49].

Genetic distance within a population is a useful indicator of separation between various sub-populations. The key assumption of Nei's standard genetic distance is that hereditary dissimilarities are caused by mutations and genetic drift, whereas Reynolds distance assumes that the increase of genetic differences is due to genetic drift only [11]. The genetic distance between IC populations in SW and CS as well as between NW and CN were not significantly different (P>0.05). It was noted that these regions border each other, thereby implying that there is a high likelihood of sharing genetic materials. Another possible explanation is that these regions could be highly favorable to the IC population or IC populations in these regions could be big enough to prevent mutation and genetic drift. The genetic distances reported in this study fluctuated from 0.029 to 0.213. These values are in the range of those found in Egyptian IC [38] and in Chinese IC populations [50]. They are, however, higher than those observed in Chinese Bian chicken [19].

When estimating genetic differentiation using allele frequency in such scenarios, the genetic variance between populations can be explained by four major forces, namely, selection, mutation, migration, and genetic drift [44]. Even though mutation plays a critical role in the long term, short-term evolution is mainly influenced by genetic drift in cases where populations segregated by reproduction [51]. Genetic distance analysis is used to show how close two populations are in relation to each other. The smaller the distance, the closer the two populations are to one another and vice versa [11]. IC populations showed segregation by distance and appeared to be at equipoise under the influence of dispersal and genetic drift. There is a high likelihood that these chickens were present at their current locations earlier than it had been assumed because there was not enough time for segregation due to distance to have come into play. Furthermore, long-distance gene dispersion is not satisfactorily evident to deter genetic deviation. For this, further investigations need to be conducted using more markers, for example, high-density SNP arrays and mitochondrial DNA which was also conducted concurrently with the current study.

Phylogenetic relationship and population structure

The genetic similarity in a collection of breeds with high diversity can be resolved efficiently by cluster analysis, which facilitates the identification of individuals with similar or diverse multi-locus genotypes [52]. A number of IC populations clustered together indicates genetic affinities between them [53]. In our study, the cluster based on the neighbour-joining approach revealed grouping arrays of association and genetic relationships among individuals. These individuals were grouped into four clusters formed by ecotypes from distinct collection sites (NW and CN; SW1 and CS; SW2 with exotic chicken and East alone). This close genetic relationship may indicate a common genetic background [54]. A cluster shows the degree of inbreeding and populations that could be sharing the identical ancestral lineage [55]. There is also similarities in morphological characteristics between the IC populations clustered together [56]. This was confirmed by the structure analysis which revealed four gene pools across IC in Rwanda. These gene pools are distributed exactly according to the different clusters as shown by the neighbour-joining method. The observed gene pools could be accounted for by the sum of private alleles recorded in the population besides the genetic distance between populations. For example, the Eastern region recorded the highest frequency of private alleles, whereas the NW had the lowest number. This observation could be attributed to the large population size of IC in the Eastern region out of all the study sites, which minimized gene inflow in this area. Conversely, the lowest number of IC was noted in the NW region, which could be interpreted to mean that the majority of chicken keepers in this area either buy chicken or exchange cocks from the neighbouring areas such as CN. Consequently, there is a high influx of genes in these regions. This is not surprising since these areas border each other geographically. These findings corroborated the observations of a study conducted in Kenya where the Mantel test had uncovered a positive association between hereditary and geographic distances [57]. Our study also confirmed that geographic distances affected the population’s genetic structure [57]. The portion of SW chicken populations that clustered with the exotic chicken could be attributed to the fact that different crossing programmes between IC and improved chicken breeds have been introduced in that region to improve the genetic potential of IC in Rwanda [58].

Conclusion

The results from this study are the first to recount the genetic diversity and constitution of IC from Rwanda. Overall, the IC populations in Rwanda had high levels of significant genetic variability as per different genetic diversity parameters applied in this study. Therefore, data on genetic diversity estimated by assimilating within and between population variances may inform preservation strategies and the better establishment of priorities. In addition, this study found that IC in Rwanda belongs to four major gene pools that could be preserved independently to uphold their genetic diversity. Generally, these findings provide the fundamental step in the direction of judicious decision-making before the development of genetic enhancement and preservation programmes without interfering with the uniqueness of IC in Rwanda.

Supporting information

S1 Table. Characteristics of indigenous and exotic chickens used in the study.

(DOCX)

S1 Fig. Agro ecological zones in Rwanda.

(PDF)

Acknowledgments

The authors thank University of Rwanda, Rwanda Agriculture and Animal Resources Development Board and Egerton University for their technical support. We acknowledge also chicken owners for allowing collection of blood samples from their chicken.

Data Availability

All relevant data are within the manuscript and its Supporting Information files.

Funding Statement

This study was part of PhD research of the first author and he is thankful to the financial and technical support from BecA-ILRI Hub through Africa Biosciences Challenge Fund (ABCF) programmes. The ABCF Programmes is funded by the Australian Department for Foreign Affairs and Trade (DFAT) through the BecA-CSIRO partnership; the Syngenta Foundation for Sustainable Agriculture (SFSA); the Bill & Melinda Gates Foundation (BMGF); the UK Department for International Development (DFID) and the Swedish International Development Cooperation Agency (SIDA). This material is also based upon work supported by the United States Agency for International Development, as part of the Feed the Future initiative, under the CGIAR Fund, award number BFS-G-11-00002, and the predecessor fund the Food Security and Crisis Mitigation II grant, award number EEM-G-00-04-00013. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Genetic diversity and population structure of indigenous chicken in Rwanda using microsatellite markers

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For additional information about PLOS ONE submissions requirements for ethics oversight of animal work, please refer to http://journals.plos.org/plosone/s/submission-guidelines#loc-animal-research  

Once you have amended this/these statement(s) in the Methods section of the manuscript, please add the same text to the “Ethics Statement” field of the submission form (via “Edit Submission”).

3.  In your Methods section, please provide additional details regarding the chicken used in your study and ensure you have described the source. For more information regarding PLOS' policy on materials sharing and reporting, see https://journals.plos.org/plosone/s/materials-and-software-sharing#loc-sharing-materials.

4. In your Methods section, please provide additional location information of the sampling locations, including geographic coordinates for the data set if available.

5. In your Methods section, please provide additional information regarding the permits you obtained for the work. Please ensure you have included the full name of the authority that approved the sampling locations access and, if no permits were required, a brief statement explaining why.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Partly

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: No

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: No

Reviewer #2: No

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The manuscript reports on a study of genetic diversity and population structure of indigenous chicken in Rwanda using microsatellite markers. The results revealed that the considerable genetic diversity in indigenous chicken in Rwanda, which represents a crucial genetic resource that can be conserved or optimized through genetic improvement. However, the manuscript requires a minor revision before it can be considered further. Overall, I think that the manuscript will need attention in the following areas:

1. The writing of the introduction is poor. The introduction section only introduces the importance of indigenous chicken, and I suggest adding some information about the recent research of microsatellite markers in chickens.

2. In the materials and methods, the twelve exotic chickens (layers and broilers) as a reference data should have a detailed information.

3. Statistical analysis methods are not clearly presented, for example, p-value is mentioned without description of what kind of test. In addition, the p-value should be written consistently in the all manuscript.

4. Line 31: “chicken” should be “chickens”.

5. Line 243: “3,31” should be “3.13”.

6. Line 243: “drift-” should be “drift”.

7. Please carefully check the format of the manuscript, and make sure it fits the journal style.

Reviewer #2: The manuscript entitled “Genetic diversity and population structure of indigenous chicken in Rwanda using microsatellite markers” aimed to evaluate the genetic diversity of IC in Rwanda using microsatellite markers. This is a relevant study since the knowledge of animal genetic resources has become an important issue in order to avoid the genetic erosion. Also, the local chickens are an alternative to sustainable development of livestock. The article is interesting to the journal subject area. However, some points need to be clarified to improve the manuscript understanding are listed below:

1) Introduction: The authors should reformulate the introduction in order to improve the understanding.

For example, I could not understand the sentence: “More than 40% of households keep poultry out of which approximately 80% consists of indigenous chicken (IC).” (lines 50-51).

2) M&M:

Please, specify with more details sample the collection method and the ethical permission for collection. The sentences describing the genetic groups should contain only the breed´s name, number of samples used and also, the number of regions/flocks that samples were collected. I suggest that description of phenotypic traits in a separate table, for better understanding.

3) Discussion:

In general, the discussion is basically descriptive, improving the information of the results previously showed and comparing the results of this and other papers already published. There is no discussion in terms of phylogenetic relationships or evolution, neither about the genetic distance within the breeds studied, which weakens the impact of the paper. The manuscript subject is interesting, but needs to be improved.

The English written should be improved.

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Reviewer #1: No

Reviewer #2: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files to be viewed.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org. Please note that Supporting Information files do not need this step.

PLoS One. 2020 Apr 2;15(4):e0225084. doi: 10.1371/journal.pone.0225084.r002

Author response to Decision Letter 0


25 Feb 2020

Academic editor:

Ethic statement has been amended> Amended ethical clearance has been put in the methods section and also added to the Ethics Statement field of the submission form. Additional information of the sampling locations, including geographic coordinates for the data set were provided in S2 Fig and Fig 1. Additional information regarding the permits I obtained for the work has been provided and the full name of the authority that approved the sampling locations access was given

Reviewer #1:

Introduction has been improved and some information on microsatellite markers has been included in the introduction section (line 61-72). Detailed information on exotic chicken has been discussed and more details were put in supporting information (S1 Table). Statistical analysis has been improved. Concerning tests for the Hardy-Weinberg Equilibrium (HWE), P-value was obtained based on chi square (χ2) test and the nonconformity with HWE based on Chi square test at p<0.05. In addition, p-value has been written consistently throughout the manuscript. The introduction has been reformulated as per your request and it is now understandable. That confusing sentence has been paraphrased and it is now “ More than 40% of households keep poultry with indigenous chickens being the most preferred, accounting for approximately 80% of the reared chicken species”.The collection method has been detailed and ethical statement included.

Chickens, Corrected now line 26

3.13, Corrected now line 307

drift, Corrected now line 350

Reviewer #2:

The introduction has been reformulated as per your request and it is now understandable. That confusing sentence has been paraphrased and it is now “ More than 40% of households keep poultry with indigenous chickens being the most preferred, accounting for approximately 80% of the reared chicken species” . The collection method has been detailed and ethical statement included.

The genetic groups were named based on the agro-ecological zone of origin. In total, 313 IC were sampled from five agro-ecological zones (S2 Fig) and in each region, a specific number of IC was collected according to the size of the region. Phenotypic data was provided in supporting information (S1 Table). You are right. The discussion on phylogenetic relationship or evolution was under the subtitle called population structure; that is why it did not come out clearly. So as to make it clear, apart from its improvement, the subtitle has also been changed into “Phylogenetic relationship and Population structure”.

The discussion on genetic distance is under “genetic relationship subtitle” and it has been improved as per your request.

The English written has been improved as you can see that thru the track changes.

Attachment

Submitted filename: Response to Reviewers.docx

Decision Letter 1

Tzen-Yuh Chiang

10 Mar 2020

Genetic diversity and population structure of indigenous chicken in Rwanda using microsatellite markers

PONE-D-19-29235R1

Dear Dr. HABIMANA,

We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.

Within one week, you will receive an e-mail containing information on the amendments required prior to publication. When all required modifications have been addressed, you will receive a formal acceptance letter and your manuscript will proceed to our production department and be scheduled for publication.

Shortly after the formal acceptance letter is sent, an invoice for payment will follow. To ensure an efficient production and billing process, please log into Editorial Manager at https://www.editorialmanager.com/pone/, click the "Update My Information" link at the top of the page, and update your user information. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, you must inform our press team as soon as possible and no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

With kind regards,

Tzen-Yuh Chiang

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #2: Most of the suggestions were addressed and now the paper is acceptable for publication. There are some spelling errors, which could be corrected in the proofs.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #2: No

Acceptance letter

Tzen-Yuh Chiang

12 Mar 2020

PONE-D-19-29235R1

Genetic diversity and population structure of indigenous chicken in Rwanda using microsatellite markers

Dear Dr. HABIMANA:

I am pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

For any other questions or concerns, please email plosone@plos.org.

Thank you for submitting your work to PLOS ONE.

With kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Tzen-Yuh Chiang

Academic Editor

PLOS ONE

Associated Data

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

    Supplementary Materials

    S1 Table. Characteristics of indigenous and exotic chickens used in the study.

    (DOCX)

    S1 Fig. Agro ecological zones in Rwanda.

    (PDF)

    Attachment

    Submitted filename: Response to Reviewers.docx

    Data Availability Statement

    All relevant data are within the manuscript and its Supporting Information files.


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