Contribution of both positive selection and relaxation of selective constraints to degeneration of flyability during geese domestication

Ye Wang; Yaodong Hu; Daqian He; Shiyi Chen; Siming Li; Dan Lan; Peng Ren; Zhenping Lin; Yiping Liu

doi:10.1371/journal.pone.0185328

Abstract

Flyability is the most discrepant trait between modern-day geese and their wild ancestors, and the degeneration of flyability is a key marker of the successful domestication of wild geese. In light of the relatively short history of domestic geese, intense artificial selection is thought to play an important role in the degeneration of flyability. However, the underlying mechanism behind this phenomenon has seldom been investigated. In this study, we applied a molecular evolutionary approach to the evaluation of partial breeds of domestic geese in order to look for genes involved in the selection pressure toward degeneration of flyability. The haplotype networks, pairwise fixation index (F_ST) values, and analysis of molecular variance results all clearly illustrated a population variance between Landes geese and partial Chinese domestic geese. We also detected signatures of positive artificial selection in the COX2 and COX3 genes, and related selection in the HBB gene. Our results support the independent origins of partial European domestic geese and Chinese domestic geese. In addition, both positive artificial selection and the relaxation of functional constraints appeared to play important roles in the degeneration of flyability in domestic geese.

Citation: Wang Y, Hu Y, He D, Chen S, Li S, Lan D, et al. (2017) Contribution of both positive selection and relaxation of selective constraints to degeneration of flyability during geese domestication. PLoS ONE 12(9): e0185328. https://doi.org/10.1371/journal.pone.0185328

Editor: Tzen-Yuh Chiang, National Cheng Kung University, TAIWAN

Received: April 27, 2017; Accepted: September 11, 2017; Published: September 25, 2017

Copyright: © 2017 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The sequences obtained in this study were deposited in GenBank (Accession numbers: COX2, KX435055-KX435226; COX3, KX435227-KX435398; HBA1, KX435399-KX435570; HBA2, KX435571-KX435742; HBB, KX435915-KX436086, EVC2P, KX435743-KX435914). All other data sets supporting the conclusions of this article are included within this article and its additional files.

Funding: This work was funded by the National Natural Science Foundation of China (Nos. 31172181 and 31660663) and the National Waterfowl Industrial Technology System (No. CARS-43-4 and 2016YFD0500510).

Competing interests: The authors have declared that no competing interests exist.

Abbreviations: AMOVA, analysis of molecular variance; B, Anser cygnoides; bce, Before the Common Era; COX, Cytochrome c oxidase; EVC2P, class II-related endogenous retrovirus; G, gray plumage Landes goose; H, spotted feather Landes goose; Hb, hemoglobin; HBA1, gene coding for α^D; HBA2, gene coding for α^A; HBB, gene coding for β^A; HKA, Hudson-Kreitman-Aguade test; Hui, Anser anser; S, Lion-head goose; SC, Sichuan white goose; Z, Eastern Zhejiang white goose; Zi, Zi goose

Introduction

Flyability is the most discrepant trait between modern-day geese and their wild ancestors. Wild geese were one of the most celebrated high-altitude performers for their stamina and the heights they achieved. Bar-headed geese (Anser indicus) were known to pass over the Himalayas in only one day and to climb between 4,000 and 6,000 m in 7–8 h [1]. In contrast, modern-day domestic geese perform poorly at flying, and most of them have lost the ability to gain altitude. The degeneration of flyability is a key marker of the successful domestication of geese, since their ability to maintain flight would be detrimental to their artificial breeding. Recent archaeological discoveries have indicated that geese domestication began at around 1,700 bce, which means that domestic geese have been in existence for less than 4,000 years [2–4]. It is remarkable that these birds had lost their ability to fly in such a relatively short time span without significant accompanying morphological changes. So far, few studies have concentrated on degeneration of flyability, and little is known about the physiological and molecular basis of the phenomenon. Historically, artificial selection has been considered to be the main driving force behind trait changes occurring during domestication; for instance, the coat color of swine and the semi-dwarf phenotype of rice [5]. Therefore, we speculated that artificial selection might be responsible for the loss of flyability in geese during their domestication.

Flight is one of the most energy-consuming movements in animals [6]. Studies performed on barnacle geese (Branta leucopsis) and bar-headed geese (A. indicus) revealed that the flying state required 10–20-fold more oxygen (O₂) than the walking state [7, 8]. As a specialized flying mammal, the metabolic rate of bats is 3–5 times higher than that of similarly sized terrestrial mammals during strenuous exercise [9, 10]. Typically, the mitochondrial respiratory chain produces almost 95% of the adenosine triphosphate required for movement [11]. Cytochrome c oxidase (COX, also named Complex IV) is the terminal oxidase of the mitochondrial respiratory chain and yields twice the amount of energy of the other two steps in electron transport. Furthermore, COX has been found to represent the rate-limiting step in intact cells during respiration. This reinforces the assumption that COX plays a large role in control of the respiratory chain and thus of global energy production [12–14]. COX is composed of nuclear genes and mitochondrial genes, and three mitochondrial genes are encoded within its catalytic core (viz., COX1, COX2, and COX3) [15]. A mitochondrial study performed on avian subjects demonstrated that functional constraints caused by limited locomotion play an important role in the evolution of mitochondrial DNA (mtDNA) [16]. The authors of that study found that with the degeneration of flyability, the mtDNA accumulated more nonsynonymous nucleotide substitutions, where the relaxation of selective constraints was deemed to be the dominant driving force [16]. In view of this, the COX genes could reveal some signatures of selection following the obvious degeneration of flight in domestic geese. The efficiency of O₂ transport is another critical factor affecting the locomotion of birds. Hemoglobin (Hb), the carrier of O₂ in blood, is a tetrameric molecule composed of two α (or α-like) chains and two β (or β-like) chains that combine to produce major isoHb (α^Aβ^A) and minor isoHb (α^Dβ^A) molecules. The α^D, α^A, and β^A chains are encoded by the HBA1, HBA2, and HBB genes, respectively [17]. Studies performed on waterfowls, frogs, and deer mice suggest that Hb can adapt to the high-altitude conditions found in nature [18–20]. In a comparative study [20], differences were found between the Hb molecules of highland and lowland waterfowls. Obviously, the Hb genes are sensitive to O₂ pressure and the flyability of birds. Hence, we surmised that the Hb genes may reveal trace evidence of the selection that affects the flyability of domestic geese.

Poor flying performance in domestic geese is correlated with less energy consumption and more energy storage, and is an important prerequisite for the successful domestication of geese. Although it is a reasonable assumption that artificial selection had affected the flyability of domestic geese, the target and type of selection involved remains unclear. Therefore, to provide direct evidence of selection pressure on the loss of flyability during domestication and to identify the type of selection utilized, we focused on the comparison of two mitochondrial genes (COX2 and COX3) and three nuclear genes (HBA1, HBA2, and HBB) in domestic geese and their wild ancestors. Chinese and European domestic geese reportedly had originated independently from the swan goose (Anser cygnoides) and the Greylag Landaise goose (Anser anser), respectively [21]. It is interesting that both lineages had lost their flyability during domestication, which reveals a parallel evolutionary tendency. Thus, another goal of this study was to look for any differences in the above-mentioned genes between these two lineages at both the population and genetic levels.

Materials and methods

Ethics statement

The protocols used in this study were approved by the Institutional Animal Care and Use Committee of Sichuan Agricultural University (Permit No. DKY-S20131105).

Sample collection

Blood samples were collected from 172 individuals representing the following: one breed of European domestic geese (viz., the Landaise goose, divided into the gray plumage Landes group (G) and the spotted feather Landes group (H)); four breeds of Chinese domestic geese (viz., the Lion-head goose (S), Sichuan white goose (SC), Eastern Zhejiang white goose (Z), and Zi goose (Zi)); and two wild ancestors (viz., A. cygnoides (B) and A. anser (Hui)). To avoid accidental collection from related individuals, we obtained samples from geese with clear pedigrees only. The information obtained from the samples is presented in S1 Table.

Primer design, PCR amplification, and sequencing

Genomic DNA was isolated from the blood samples using a standard phenol-chloroform extraction protocol. Primers for COX2 and COX3 were designed using the known mitochondrial genome of A. anser (GenBank Accession No. NC_011196.1). The primers used to amplify the partial sequence of EVC2P were designed using the class II-related endogenous retrovirus of Anser albifrons (GenBank Accession No. AY820076.1) [22]. Amplification of HBA1, HBA2, and HBB was performed according to the method described by McCracken et al. [23]. All primers used in this study are listed in S2 Table.

Polymerase chain reaction (PCR) amplification was performed using a 50 μL reaction mixture containing 100 ng of DNA, 10 mmol/L of Tris-HCl (pH 8.3), 2.5 mmol/L of MgCl₂, 50 mmol/L of KCl, 10 mM of each dNTP, 10 pmol/L of each primer, and 1 unit of Taq polymerase (Takara, Dalian, China). The PCR protocol included a 7 min preheat at 95°C; followed by 35 cycles of 30 s at 94°C, 30 s at 56–68°C, 1–2 min at 72°C; and a final extension of 10 min at 72°C. The PCR products were sequenced at Sangon Biotech (Shanghai, China) using an ABI 3730 sequencer (Applied Biosystems, Foster City, CA, USA) and the primers listed in S2 Table. Samples with rare variants were verified by resequencing.

Data analyses

All sequences acquired in this study were edited and assembled using BioEdit version 7.0 [24]. Multiple sequence alignment was performed using ClustalX 2.0 [25], and variable sites at each gene were analyzed using MEGA6 [26]. The number of haplotypes (H), haplotype diversity (Hd), nucleotide diversity (π), average number of nucleotide differences (K), Tajima’s D test, Fu and Li’s D test, and Fu’s Fs H test were determined for each population, using DnaSP 5.0 [27]. The haplotype networks were constructed using the pegas, plyr, and reshape packages of R program, and were modified manually for improved illustration.

Adaptation to various selection pressures always increases the degree of variance among subpopulations. Thus, we applied the F_ST value to measure the degree of variance for each population, and detected outlier loci based on the F_ST value at the single nucleotide polymorphism (SNP) level. The computation was performed from 10,000 simulations with 100 demes per group, under the assumption of a hierarchical island model, by ARLEQUIN version 3.5 [28]. To determine the amount of genetic variance at each hierarchical level, we also performed an analysis of molecular variance (AMOVA) based on the haplotype frequencies and pairwise differences in ARLEQUIN. To further study the patterns of polymorphism and divergence, Hudson-Kreitman-Aguade (HKA) tests, allowing comparison with the neutral model, were implemented for each gene by using DnaSP 5.0. The ratio of nonsynonymous (d_N) to synonymous (d_S) substitutions and standard errors were calculated using 1,000 bootstrap replicates via MEGA6, and statistical significance was evaluated using the Z test.

Results

Sequence variation and molecular diversity

Whole DNA sequences of COX2, COX3, HBA1, and HBA2 as well as partial sequences of HBB (intron 1, exon 2, and intron 2) and EVC2P were analyzed in this study. We found that there were relatively fewer polymorphic sites in the COX2 and COX3 genes of the Zi, A. cygnoides, and G populations than in those of the other populations, as well as fewer variable sites in COX3 than in COX2. A relatively large number of mutations were found in EVC2P, with a low divergence among all populations except A. anser (S3 Table). In addition, we screened several population-specific mutations using the amino acid sequences. Mutation E88K of COX2 occurred with a high frequency in Landaise geese and moderate frequency in A. cygnoides and A. anser, but was hardly found in Chinese domestic geese (S1 File). No other population-specific amino acid mutation was found among the other genes (S2–S5 Files).

The molecular diversity indices (θ_π and θ_s) in COX2 of B, G, S, and Zi were obviously lower than those in the other populations (Fig 1a). A similar trend of a smaller average θ value was observed for COX3 (Fig 1b). For the HBA1, HBA2, HBB, and EVC2P fragments, an even distribution of θ_π and θ_s was observed among the populations, where the θ values for HBB and EVC2P were much higher than those for HBA1 and HBA2 (Fig 1c–1f). The nucleotide diversity of EVC2P was much richer and more regular than that of the other genes, with its mean π value being about 10-fold larger. Extremely low π values were identified for COX2, COX3, and HBA1, whereas the π values of HBA2 and HBB were relatively higher and more stable.

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Fig 1. Molecular diversity indices of six gene fragments. (A) COX2, (B) COX3, (C) EVC2P, (D) HBA1, (E) HBA2, and (F) HBB.

θ_k, average number of nucleotide differences; θ_H, haplotype diversity; θ_S, number of polymorphic sites; θ_π, nucleotide diversity.

https://doi.org/10.1371/journal.pone.0185328.g001

Population differentiation due to genetic structures

A total of 49, 16, 44, 44, and 102 haplotypes were detected in the COX2, COX3, HBA1, HBA2, and HBB genes, respectively. The networks of the two mitochondrial genes (COX2 and COX3) showed COX3 as being more conservative than COX2. A distinct variance in main haplotypes between the wild ancestors and domestic geese was observed for COX2 (Fig 2a and 2b). Divergent clusters of main haplotypes in HBA1, HBA2, and HBB were found simultaneously in the European geese (Hui and G) and Chinese geese (Fig 2c–2e).

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Fig 2. Haplotype networks of five genes across each population. (A) COX2, (B) COX3, (C) HBA1, (D) HBA2, and (E) HBB.

https://doi.org/10.1371/journal.pone.0185328.g002

To further estimate the degree of population variance, we calculated the pairwise F_ST value. Interestingly, compared with COX2 and COX3, significant differences in the three karyogenes HBA1, HBA2, and HBB were found between the Chinese geese populations and the Landaise geese (G and H) (Fig 3c–3e). Furthermore, the F_ST values of COX3 (0–0.15) for each population were much lower than those of COX2 (0–0.7), which again proved that COX3 was more evolutionarily conserved than COX2 (Fig 3a and 3b).

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Fig 3. Matrix of pairwise F_ST for each gene across populations. (A) COX2, (B) COX3, (C) HBA1, (D) HBA2, and (E) HBB.

https://doi.org/10.1371/journal.pone.0185328.g003

The hierarchical AMOVA was conducted, using the percentages of total genetic variation partitioned among groups (Chinese geese and A. cygnoides, Landaise geese and A. anser), among populations within groups (among each goose breed within groups), and within populations (individuals) (Table 1). Significant evidence from the six genes indicated that 50.2–90.86% of variances came from individuals, and only 9.14–49.8% of variances came from groups and populations. Compared with the other genes, no significant variance from the groups was observed for EVC2P, whereas the variance percentages from groups and populations were nearly equal for COX2 and COX3. On the contrary, the variance from groups was larger than that from populations for HBA1, HBB, and especially HBA2, which also suggested apparent variance between the Chinese geese group and the Landaise geese.

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Table 1. Results of the hierarchical AMOVA for grouping hypothesis^{^a}.

https://doi.org/10.1371/journal.pone.0185328.t001

We then screened the outliers at the SNP level to further evaluate the variance between the Chinese geese group and the Landaise geese. An extremely significant outlier locus (268) was found in the COX2 gene, and two extremely significant outlier loci (3 and 696) were found in the COX3 gene (Fig 4a and 4b). We also identified an extremely significant outlier locus (179) and two significant outlier loci (2 and 7) in the HBA1 gene (Fig 4c). A significant outlier locus (107) in the HBB gene was also detected (Fig 4e). These results also illustrated the variance between the two geographical groups.

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Fig 4. Differentiated loci between partial Chinese domestic geese and Landes geese. (A) COX2, (B) COX3, (C) HBA1, (D) HBA2, and (E) HBB.

F_ST: locus-specific genetic divergence among populations. Heterozygosity: measure of heterozygosity per locus. Single-locus F_ST values are plotted against heterozygosity, with the red-filled circles representing loci significant at the 1% level, and blue-filled circles representing loci significant at the 5% level.

https://doi.org/10.1371/journal.pone.0185328.g004

Test for selection

The possible influence of artificial selection on the above-mentioned genes was first examined by Tajima’s D test and Fu and Li’s D test (S4 Table). Nearly all the significant values were found to be negative in our analysis. Test values examined by the two methods were all significantly negative for the COX2, COX3, and HBA1 genes of descendants of A. cygnoides (DAC; including the S, SC, Z, and Zi populations). For the HBA1 gene, both Tajima’s D and Fu and Li’s D tests were found to be significant for S, Zi, H, DAC, and descendants of A. anser (DAA: including the G and H populations). For the HBA2 gene, only the G population was found to be significantly negative by both methods. Only Fu and Li’s D test detected significant negativity for the HBB gene in DAC. Except for the B and G populations, we detected significantly negative values for Fu’s Fs H test in all the other populations (S4 Table). To distinguish the effects of population dynamics and direct selection, we next employed the HKA neutrality test to compare the divergence of the candidate genes from the neutral EVC2P pseudogene. The HKA test results showed that only the COX2 gene in the SC population (3.906, p < 0.05) was significantly different (Table 2).

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Table 2. Values of the Hudson-Kreitman-Aguade test for each gene across populations^{^a}.

https://doi.org/10.1371/journal.pone.0185328.t002

Historically, relaxation of functional constraints has been considered to be the main player during animal domestication. To evaluate whether relaxed selection had acted on these genes, we calculated the d_N/d_S value of the protein coding region for each gene (S5 Table). Significant purifying selection was discovered in the DAC population at COX2, and in the DAC, Z, DAA, and H populations at COX3. With regard to the three nuclear genes, a significant accumulation of nonsynonymous mutations was found in HBA1 in the Hui population and in HBB in the Zi population, which indicated relaxation of selective pressure. Interestingly, no synonymous mutation was found in the HBB gene in any population. In summary, the two mitochondrial genes seemed to undergo purifying selection, whereas the three nuclear genes showed signs of relaxed selection.

Discussion

Population variance between European domestic geese and Chinese domestic geese

It is widely believed that Chinese domestic geese originated from A. cygnoides and European domestic geese originated from A. anser. However, most of the evidence supporting this belief originated from maternal phylogenetic studies involving sequencing of a control region of mtDNA. This means that those results lack evidence at the nuclear and population genetics levels. In this study, we analyzed two mitochondrial genes and three nuclear genes from two wild ancestors (A. cygnoides and A. anser), Landaise geese (G and H populations), and four breeds of Chinese domestic geese (SC, S, Z, and Zi populations). Both the haplotype networks and the pairwise F_ST values of HBA1, HBA2, and HBA3 showed a clear divergence between the European geese and the Chinese geese, indicating an independent genetic structure for the two goose populations (Figs 2 and 3). In addition, for the HBA1, HBA2, and HBA3 genes, AMOVA supported our finding of larger variations between groups than between populations within groups. This means that apart from the variation between individuals, the divergence between the European geese and the Chinese geese was larger than that found within the varieties of geese. That is, our results suggest a distinct genetic variance between European geese and Chinese geese, which is consistent with the results of Shi et al. [21]. Compared with the three nuclear genes, the two mitochondrial genes appeared to be more conserved, which could be attributed to the more elastic spatial structure of the Hb protein as compared with the structure of COX. The haplotype network of COX3 was significantly different between the two main geese groups, which may be due to its functional constraint or to gene introgression between the two ancestral groups as we previously reported [29]. The present study found molecular evidence of population variance between the Landaise geese and partial Chinese domestic geese, which supported the theory of an independent origin for European and Chinese domestic geese. However, the limited number of samples and genes evaluated in our study prevented a more comprehensive understanding of the origin of these groups of geese, and thus more targeted studies are needed.

Signatures of positive selection and relaxed selection

The flyability of wild geese is the primary barrier preventing their early domestication owing to their limited feeding options, which makes the domestication of geese difficult. Unexpectedly, the domestication of wild geese was found to have occurred later than that of most other domestic animals, which suggested to us that intense artificial selection may have resulted in the degeneration of flyability [3]. As most studies have reported, the availability of energy directly affects the ability of birds to fly and always generates some molecular evidence [16, 30]. In this study, we worked under the assumption that energy metabolism and O₂ transportation played an important role in the loss of flyability among domestic geese. Hence, we focused our study on Hb and the terminal oxidase of the mitochondrial respiratory chain. In our analysis, the molecular diversity of the five functional genes studied was much less than that of EVC2P; in fact, EVC2P had an appearance characteristic of a pseudogene that nearly evolved neutrally (Fig 1, Table 1, and S3 Table). These results are consistent with those of a previous report [31] evaluating pigs, which claimed that a population bottleneck could not explain the limited diversity of five candidate genes. It was suggested that those five genes suffered from selection pressure. Thus, we performed the neutral test for further verification, and evidence of both purifying selection and population expansion was discovered (S4 Table). With the increase in the human population, the number of domestic animals, including geese, has multiplied rapidly during recent years [5]. The results of the HKA test were obviously different from those of the neutral test, in that a statistically significant difference was found only in the COX2 gene of the SC population. Under the stress of artificial selection, some neutral polymorphisms in the ancestral population may suddenly become advantageous; therefore, the beneficial alleles had an initial frequency (designated p) in the progenitor of the domesticated species. According to the report by Innan and Kim [32], p has a strong effect on the likelihood of detecting the signature of selection during domestication. When p was relative large, the HKA test became robust at detecting selection, which might explain the bias between the HKA and neutral tests.

Finally, we calculated the d_N/d_S values to better understand the selection involved and were able to detect the signature of purifying selection in the COX2 and COX3 genes. In the study of Shen et al. [16], COX3 was found to vary significantly between strongly and weakly locomotive birds [16]. However, with the degeneration of flyability, COX3 showed relaxed selection in that study, which is in contrast to our results. It should be noted that the samples used in the two studies were quite different from one another. Shen et al. [16] evaluated various species of birds, most of which were under natural selection, whereas we concentrated on domestic geese that were under the influence of artificial selection. In a recent report, Wang et al. [33] found that positive selection, rather than relaxation of functional constraints, drove the degeneration of vision during chicken domestication. Historically, both vision and flyability were crucial for a bird’s survival. Therefore, the results of that study suggested to us that positive artificial selection played an important role in the degeneration of flyability in domestic geese. Interestingly, although most of the P-values in this study were not significant, a clear trend of relaxed selection was found in the HBB gene, which is consistent with the results of Shen et al. [16]. That is, with the degeneration of flyability, some functional genes accumulated more nonsynonymous than synonymous nucleotide substitutions, which indicates a relaxation of selective constraints.

Flight is one of the most crucial abilities affecting a bird’s viability, including its ability to mate, forage, and avoid predators. Unlike their wild ancestors, domestic geese living under a predictable farm environment have more accessibility to food and safety. These interventions by humans have reduced the pressure for flight in domestic geese, which may have eventually led to the degeneration of flyability. Unlike other traits, flight is a characteristic trait affecting survival and is hard to lose under natural selection. From the results of this study, we speculate that both positive artificial selection and relaxed selection have affected the ability of domestic geese to fly. Presumably, the progenitors of domestic geese that possessed weaker flyability were more easily caught, reared, and domesticated. As suggested by Shen et al. [34], genes encoding oxidative phosphorylation may have been sensitive to flight and suffered from positive artificial selection, whereas other genes related to energy metabolism, such as Hb-coding genes, may have been affected by the relaxation of functional constraints at a later time.

Loss of flight is considered a classic case of Darwin’s natural selection theory. So far, 26 families of birds in 17 different orders have been found to have lost the ability to fly, with obvious limb modifications. Recently, Burga et al. [35] made a breakthrough in analyzing the flightlessness mechanism of birds. They detected significant positive selection and found that the combined effects of variants in genes related to cilia or Hedgehog (Hh) signaling may be responsible for the reduction in growth of both the keel and wings in Phalacrocorax harrisi, leading to the bird’s flightlessness. They concluded that positive selection had played an important role in the fixation of the variants associated with loss of flight in birds with apparent limb modifications. However, these studies about flightless birds have seriously ignored another situation; namely, flightlessness without limb modifications, of which domesticated geese are a distinct example. Therefore, the findings in our study provide good supplemental information to present knowledge about the molecular mechanisms of flightlessness in birds. Unfortunately, even though we showed evidence of artificial selection, the limited number of genes analyzed and samples obtained did not provide comprehensive information about the mechanism behind the degeneration of flyability in domestic geese. Targeted studies will be helpful to supplement our hypothesis.

Conclusions

Our study provides preliminary insight into the selection pressure acting on the degeneration of flyability in domestic geese. Combined with the results of previous studies, our findings support the independent origin of partial European domestic geese and Chinese domestic geese. It appears that both positive artificial selection and the relaxation of functional constraints played an important role in the degeneration of flyability in domestic geese.

Supporting information

S1 Table. Summary of sample collection for this study.

https://doi.org/10.1371/journal.pone.0185328.s001

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S2 Table. Primers used to amplify the COX2 and COX3 sequences, and to provide partial sequences of HBA1, HBA2, and HBB.

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S3 Table. Nucleotide diversity of each gene across populations.

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S4 Table. Results of the neutral test based on Tajima’s D, Fu and Li’s D, and Fu’s Fs H tests.

Statistically significant values are marked with *, **, or ***, and N indicates data that could not be calculated.

https://doi.org/10.1371/journal.pone.0185328.s004

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S5 Table. Estimates of the evolutionary divergence and positive selection for each gene.

Analyses of the numbers of synonymous (d_S) and nonsynonymous (d_N) substitutions per site were conducted using the Nei-Gojobori method in MEGA 6. Standard error estimates were obtained using a bootstrap procedure (1,000 replicates). All positions containing alignment gaps and missing data were eliminated. P-values were calculated using the codon-based Z test with the alternative hypothesis of d_N > d_S. Significant P-values are in bold font; N indicates data that could not be calculated.

https://doi.org/10.1371/journal.pone.0185328.s005

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S1 File. Amino acid mutations of COX2.

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S2 File. Amino acid mutations of COX3.

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S3 File. Amino acid mutations of HBA1.

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S4 File. Amino acid mutations of HBA2.

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S5 File. Amino acid mutations of HBB.

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Acknowledgments

We would like to thank Prof. Jiwen Wang at the Institute of Animal Genetics and Breeding, Sichuan Agricultural University for the helpful comments and suggestions.

References

1. Hawkes LA, Balachandran S, Batbayar N, Butler PJ, Frappell PB, Milsom WK, et al. The trans-Himalayan flights of bar-headed geese (Anser indicus). Proceedings of the National Academy of Sciences of the United States of America. 2011;108(23):9516–9. pmid:21628594
- View Article
- PubMed/NCBI
- Google Scholar
2. Mannermaa K. Goose: Domestication: Springer New York; 2014. 3096–8 p.
3. Ericson PG, Tyrberg T. The early history of the Swedish avifauna: a review of the subfossil record and early written sources: Kungl. vitterhets historie och antikvitets akademien; 2004.
4. Albarella U. Alternate fortunes? The role of domestic ducks and geese from Roman to Medieval times in Britain. Documenta Archaeobiologiae. 2005:249–58.
- View Article
- Google Scholar
5. Diamond J. Evolution, consequences and future of plant and animal domestication. Nature. 2002;418(6898):700–7. pmid:12167878
- View Article
- PubMed/NCBI
- Google Scholar
6. Maina J. What it takes to fly: the structural and functional respiratory refinements in birds and bats. Journal of Experimental Biology. 2000;203(20):3045–64.
- View Article
- Google Scholar
7. Ward S, Bishop C, Woakes A, Butler P. Heart rate and the rate of oxygen consumption of flying and walking barnacle geese (Branta leucopsis) and bar-headed geese (Anser indicus). Journal of Experimental Biology. 2002;205(21):3347–56.
- View Article
- Google Scholar
8. Bishop C, Butler P. Physiological modelling of oxygen consumption in birds during flight. The Journal of experimental biology. 1995;198(10):2153–63.
- View Article
- Google Scholar
9. Bishop KL. The evolution of flight in bats: narrowing the field of plausible hypotheses. The Quarterly review of biology. 2008;83(2):153–69. pmid:18605533
- View Article
- PubMed/NCBI
- Google Scholar
10. Thomas SP, SUTHERS RA. The physiology and energetics of bat flight. Journal of Experimental Biology. 1972;57(2):317–35.
- View Article
- Google Scholar
11. Morán M, Moreno-Lastres D, Marín-Buera L, Arenas J, Martín MA, Ugalde C. Mitochondrial respiratory chain dysfunction: implications in neurodegeneration. Free Radical Biology and Medicine. 2012;53(3):595–609. pmid:22595027
- View Article
- PubMed/NCBI
- Google Scholar
12. Pacelli C, Latorre D, Cocco T, Capuano F, Kukat C, Seibel P, et al. Tight control of mitochondrial membrane potential by cytochrome c oxidase. Mitochondrion. 2011;11(2):334–41. pmid:21147274
- View Article
- PubMed/NCBI
- Google Scholar
13. Follmann K, Arnold S, Ferguson-Miller S, Kadenbach B. Cytochrome c oxidase from eucaryotes but not from procaryotes is allosterically inhibited by ATP. IUBMB Life. 1998;45(5):1047–55.
- View Article
- Google Scholar
14. Fontanesi F, Soto IC, Horn D, Barrientos A. Assembly of mitochondrial cytochrome c-oxidase, a complicated and highly regulated cellular process. American Journal of Physiology-Cell Physiology. 2006;291(6):C1129–C47. pmid:16760263
- View Article
- PubMed/NCBI
- Google Scholar
15. Shinzawa-Itoh K, Aoyama H, Muramoto K, Terada H, Kurauchi T, Tadehara Y, et al. Structures and physiological roles of 13 integral lipids of bovine heart cytochrome c oxidase. The EMBO journal. 2007;26(6):1713–25. pmid:17332748
- View Article
- PubMed/NCBI
- Google Scholar
16. Shen Y-Y, Shi P, Sun Y-B, Zhang Y-P. Relaxation of selective constraints on avian mitochondrial DNA following the degeneration of flight ability. Genome research. 2009;19(10):1760–5. pmid:19617397
- View Article
- PubMed/NCBI
- Google Scholar
17. Storz JF, Moriyama H. Mechanisms of hemoglobin adaptation to high altitude hypoxia. High altitude medicine & biology. 2008;9(2):148–57.
- View Article
- Google Scholar
18. Weber RE, Ostojic H, Fago A, Dewilde S, Van Hauwaert M-L, Moens L, et al. Novel mechanism for high-altitude adaptation in hemoglobin of the Andean frog Telmatobius peruvianus. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology. 2002;283(5):R1052–R60. pmid:12376398
- View Article
- PubMed/NCBI
- Google Scholar
19. Storz JF, Sabatino SJ, Hoffmann FG, Gering EJ, Moriyama H, Ferrand N, et al. The molecular basis of high-altitude adaptation in deer mice. PLoS Genet. 2007;3(3):e45. pmid:17397259
- View Article
- PubMed/NCBI
- Google Scholar
20. Mccracken KG, Barger CP, Sorenson MD. Phylogenetic and structural analysis of the HbA (α/β) and HbD (α/β) hemoglobin genes in two high-altitude waterfowl from the Himalayas and the Andes: Bar-headed goose (Anser indicus) and Andean goose (Chloephaga melanoptera). Molecular Phylogenetics & Evolution. 2010;56(2):649–58.
- View Article
- Google Scholar
21. Shi XW, Wang JW, Zeng FT, Qiu XP. Mitochondrial DNA Cleavage Patterns Distinguish Independent Origin of Chinese Domestic Geese and Western Domestic Geese. Biochemical Genetics. 2006;44(5–6):237–45. pmid:16957989
- View Article
- PubMed/NCBI
- Google Scholar
22. Gifford R, Kabat P, Martin J, Lynch C, Tristem M. Evolution and distribution of class II-related endogenous retroviruses. Journal of virology. 2005;79(10):6478–86. pmid:15858031
- View Article
- PubMed/NCBI
- Google Scholar
23. McCracken KG, Barger CP, Sorenson MD. Phylogenetic and structural analysis of the HbA (α A/β A) and HbD (α D/β A) hemoglobin genes in two high-altitude waterfowl from the Himalayas and the Andes: Bar-headed goose (Anser indicus) and Andean goose (Chloephaga melanoptera). Molecular Phylogenetics and Evolution. 2010;56(2):649–58. pmid:20434566
- View Article
- PubMed/NCBI
- Google Scholar
24. Hall TA, editor BioEdit: a user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucleic acids symposium series; 1999.
25. Larkin MA, Blackshields G, Brown N, Chenna R, McGettigan PA, McWilliam H, et al. Clustal W and Clustal X version 2.0. Bioinformatics. 2007;23(21):2947–8. pmid:17846036
- View Article
- PubMed/NCBI
- Google Scholar
26. Tamura K, Stecher G, Peterson D, Filipski A, Kumar S. MEGA6: molecular evolutionary genetics analysis version 6.0. Molecular biology and evolution. 2013:mst197.
- View Article
- Google Scholar
27. Librado P, Rozas J. DnaSP v5: a software for comprehensive analysis of DNA polymorphism data. Bioinformatics. 2009;25(11):1451–2. pmid:19346325
- View Article
- PubMed/NCBI
- Google Scholar
28. Excoffier L, Lischer HE. Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Molecular ecology resources. 2010;10(3):564–7. pmid:21565059
- View Article
- PubMed/NCBI
- Google Scholar
29. Sun J, Zhang S, He D-Q, Chen S-Y, Duan Z-Y, Yao Y-G, et al. Matrilineal genetic structure of domestic geese. The Journal of Poultry Science. 2014;51(2):130–7.
- View Article
- Google Scholar
30. Scott GR, Milsom WK. Flying high: a theoretical analysis of the factors limiting exercise performance in birds at altitude. Respiratory physiology & neurobiology. 2006;154(1):284–301.
- View Article
- Google Scholar
31. Li J, Yang H, Li J, Li H, Ning T, Pan X, et al. Artificial selection of the melanocortin receptor 1 gene in Chinese domestic pigs during domestication. Heredity. 2010;105(3):274–81. pmid:20179735
- View Article
- PubMed/NCBI
- Google Scholar
32. Innan H, Kim Y. Pattern of polymorphism after strong artificial selection in a domestication event. Proceedings of the National Academy of Sciences of the United States of America. 2004;101(29):10667–72. pmid:15249682
- View Article
- PubMed/NCBI
- Google Scholar
33. Wang MS, Zhang R, Su LY, Li Y, Peng MS, Liu HQ, et al. Positive selection rather than relaxation of functional constraint drives the evolution of vision during chicken domestication. Cell Research. 2016;26(5):556. pmid:27033669
- View Article
- PubMed/NCBI
- Google Scholar
34. Shen Y-Y, Liang L, Zhu Z-H, Zhou W-P, Irwin DM, Zhang Y-P. Adaptive evolution of energy metabolism genes and the origin of flight in bats. Proceedings of the National Academy of Sciences. 2010;107(19):8666–71.
- View Article
- Google Scholar
35. Burga A, Wang W, Ben-David E, Wolf PC, Ramey AM, Verdugo C, et al. A genetic signature of the evolution of loss of flight in the Galapagos cormorant. Science. 2017;356(6341):eaal3345. pmid:28572335
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. Hawkes LA, Balachandran S, Batbayar N, Butler PJ, Frappell PB, Milsom WK, et al. The trans-Himalayan flights of bar-headed geese (Anser indicus). Proceedings of the National Academy of Sciences of the United States of America. 2011;108(23):9516–9. pmid:21628594
View Article
PubMed/NCBI
Google Scholar

[2] View Article

[3] PubMed/NCBI

[4] Google Scholar

[ref2] 2. Mannermaa K. Goose: Domestication: Springer New York; 2014. 3096–8 p.

[ref3] 3. Ericson PG, Tyrberg T. The early history of the Swedish avifauna: a review of the subfossil record and early written sources: Kungl. vitterhets historie och antikvitets akademien; 2004.

[ref4] 4. Albarella U. Alternate fortunes? The role of domestic ducks and geese from Roman to Medieval times in Britain. Documenta Archaeobiologiae. 2005:249–58.
View Article
Google Scholar

[8] View Article

[9] Google Scholar

[ref5] 5. Diamond J. Evolution, consequences and future of plant and animal domestication. Nature. 2002;418(6898):700–7. pmid:12167878
View Article
PubMed/NCBI
Google Scholar

[11] View Article

[12] PubMed/NCBI

[13] Google Scholar

[ref6] 6. Maina J. What it takes to fly: the structural and functional respiratory refinements in birds and bats. Journal of Experimental Biology. 2000;203(20):3045–64.
View Article
Google Scholar

[15] View Article

[16] Google Scholar

[ref7] 7. Ward S, Bishop C, Woakes A, Butler P. Heart rate and the rate of oxygen consumption of flying and walking barnacle geese (Branta leucopsis) and bar-headed geese (Anser indicus). Journal of Experimental Biology. 2002;205(21):3347–56.
View Article
Google Scholar

[18] View Article

[19] Google Scholar

[ref8] 8. Bishop C, Butler P. Physiological modelling of oxygen consumption in birds during flight. The Journal of experimental biology. 1995;198(10):2153–63.
View Article
Google Scholar

[21] View Article

[22] Google Scholar

[ref9] 9. Bishop KL. The evolution of flight in bats: narrowing the field of plausible hypotheses. The Quarterly review of biology. 2008;83(2):153–69. pmid:18605533
View Article
PubMed/NCBI
Google Scholar

[24] View Article

[25] PubMed/NCBI

[26] Google Scholar

[ref10] 10. Thomas SP, SUTHERS RA. The physiology and energetics of bat flight. Journal of Experimental Biology. 1972;57(2):317–35.
View Article
Google Scholar

[28] View Article

[29] Google Scholar

[ref11] 11. Morán M, Moreno-Lastres D, Marín-Buera L, Arenas J, Martín MA, Ugalde C. Mitochondrial respiratory chain dysfunction: implications in neurodegeneration. Free Radical Biology and Medicine. 2012;53(3):595–609. pmid:22595027
View Article
PubMed/NCBI
Google Scholar

[31] View Article

[32] PubMed/NCBI

[33] Google Scholar

[ref12] 12. Pacelli C, Latorre D, Cocco T, Capuano F, Kukat C, Seibel P, et al. Tight control of mitochondrial membrane potential by cytochrome c oxidase. Mitochondrion. 2011;11(2):334–41. pmid:21147274
View Article
PubMed/NCBI
Google Scholar

[35] View Article

[36] PubMed/NCBI

[37] Google Scholar

[ref13] 13. Follmann K, Arnold S, Ferguson-Miller S, Kadenbach B. Cytochrome c oxidase from eucaryotes but not from procaryotes is allosterically inhibited by ATP. IUBMB Life. 1998;45(5):1047–55.
View Article
Google Scholar

[39] View Article

[40] Google Scholar

[ref14] 14. Fontanesi F, Soto IC, Horn D, Barrientos A. Assembly of mitochondrial cytochrome c-oxidase, a complicated and highly regulated cellular process. American Journal of Physiology-Cell Physiology. 2006;291(6):C1129–C47. pmid:16760263
View Article
PubMed/NCBI
Google Scholar

[42] View Article

[43] PubMed/NCBI

[44] Google Scholar

[ref15] 15. Shinzawa-Itoh K, Aoyama H, Muramoto K, Terada H, Kurauchi T, Tadehara Y, et al. Structures and physiological roles of 13 integral lipids of bovine heart cytochrome c oxidase. The EMBO journal. 2007;26(6):1713–25. pmid:17332748
View Article
PubMed/NCBI
Google Scholar

[46] View Article

[47] PubMed/NCBI

[48] Google Scholar

[ref16] 16. Shen Y-Y, Shi P, Sun Y-B, Zhang Y-P. Relaxation of selective constraints on avian mitochondrial DNA following the degeneration of flight ability. Genome research. 2009;19(10):1760–5. pmid:19617397
View Article
PubMed/NCBI
Google Scholar

[50] View Article

[51] PubMed/NCBI

[52] Google Scholar

[ref17] 17. Storz JF, Moriyama H. Mechanisms of hemoglobin adaptation to high altitude hypoxia. High altitude medicine & biology. 2008;9(2):148–57.
View Article
Google Scholar

[54] View Article

[55] Google Scholar

[ref18] 18. Weber RE, Ostojic H, Fago A, Dewilde S, Van Hauwaert M-L, Moens L, et al. Novel mechanism for high-altitude adaptation in hemoglobin of the Andean frog Telmatobius peruvianus. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology. 2002;283(5):R1052–R60. pmid:12376398
View Article
PubMed/NCBI
Google Scholar

[57] View Article

[58] PubMed/NCBI

[59] Google Scholar

[ref19] 19. Storz JF, Sabatino SJ, Hoffmann FG, Gering EJ, Moriyama H, Ferrand N, et al. The molecular basis of high-altitude adaptation in deer mice. PLoS Genet. 2007;3(3):e45. pmid:17397259
View Article
PubMed/NCBI
Google Scholar

[61] View Article

[62] PubMed/NCBI

[63] Google Scholar

[ref20] 20. Mccracken KG, Barger CP, Sorenson MD. Phylogenetic and structural analysis of the HbA (α/β) and HbD (α/β) hemoglobin genes in two high-altitude waterfowl from the Himalayas and the Andes: Bar-headed goose (Anser indicus) and Andean goose (Chloephaga melanoptera). Molecular Phylogenetics & Evolution. 2010;56(2):649–58.
View Article
Google Scholar

[65] View Article

[66] Google Scholar

[ref21] 21. Shi XW, Wang JW, Zeng FT, Qiu XP. Mitochondrial DNA Cleavage Patterns Distinguish Independent Origin of Chinese Domestic Geese and Western Domestic Geese. Biochemical Genetics. 2006;44(5–6):237–45. pmid:16957989
View Article
PubMed/NCBI
Google Scholar

[68] View Article

[69] PubMed/NCBI

[70] Google Scholar

[ref22] 22. Gifford R, Kabat P, Martin J, Lynch C, Tristem M. Evolution and distribution of class II-related endogenous retroviruses. Journal of virology. 2005;79(10):6478–86. pmid:15858031
View Article
PubMed/NCBI
Google Scholar

[72] View Article

[73] PubMed/NCBI

[74] Google Scholar

[ref23] 23. McCracken KG, Barger CP, Sorenson MD. Phylogenetic and structural analysis of the HbA (α A/β A) and HbD (α D/β A) hemoglobin genes in two high-altitude waterfowl from the Himalayas and the Andes: Bar-headed goose (Anser indicus) and Andean goose (Chloephaga melanoptera). Molecular Phylogenetics and Evolution. 2010;56(2):649–58. pmid:20434566
View Article
PubMed/NCBI
Google Scholar

[76] View Article

[77] PubMed/NCBI

[78] Google Scholar

[ref24] 24. Hall TA, editor BioEdit: a user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucleic acids symposium series; 1999.

[ref25] 25. Larkin MA, Blackshields G, Brown N, Chenna R, McGettigan PA, McWilliam H, et al. Clustal W and Clustal X version 2.0. Bioinformatics. 2007;23(21):2947–8. pmid:17846036
View Article
PubMed/NCBI
Google Scholar

[81] View Article

[82] PubMed/NCBI

[83] Google Scholar

[ref26] 26. Tamura K, Stecher G, Peterson D, Filipski A, Kumar S. MEGA6: molecular evolutionary genetics analysis version 6.0. Molecular biology and evolution. 2013:mst197.
View Article
Google Scholar

[85] View Article

[86] Google Scholar

[ref27] 27. Librado P, Rozas J. DnaSP v5: a software for comprehensive analysis of DNA polymorphism data. Bioinformatics. 2009;25(11):1451–2. pmid:19346325
View Article
PubMed/NCBI
Google Scholar

[88] View Article

[89] PubMed/NCBI

[90] Google Scholar

[ref28] 28. Excoffier L, Lischer HE. Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Molecular ecology resources. 2010;10(3):564–7. pmid:21565059
View Article
PubMed/NCBI
Google Scholar

[92] View Article

[93] PubMed/NCBI

[94] Google Scholar

[ref29] 29. Sun J, Zhang S, He D-Q, Chen S-Y, Duan Z-Y, Yao Y-G, et al. Matrilineal genetic structure of domestic geese. The Journal of Poultry Science. 2014;51(2):130–7.
View Article
Google Scholar

[96] View Article

[97] Google Scholar

[ref30] 30. Scott GR, Milsom WK. Flying high: a theoretical analysis of the factors limiting exercise performance in birds at altitude. Respiratory physiology & neurobiology. 2006;154(1):284–301.
View Article
Google Scholar

[99] View Article

[100] Google Scholar

[ref31] 31. Li J, Yang H, Li J, Li H, Ning T, Pan X, et al. Artificial selection of the melanocortin receptor 1 gene in Chinese domestic pigs during domestication. Heredity. 2010;105(3):274–81. pmid:20179735
View Article
PubMed/NCBI
Google Scholar

[102] View Article

[103] PubMed/NCBI

[104] Google Scholar

[ref32] 32. Innan H, Kim Y. Pattern of polymorphism after strong artificial selection in a domestication event. Proceedings of the National Academy of Sciences of the United States of America. 2004;101(29):10667–72. pmid:15249682
View Article
PubMed/NCBI
Google Scholar

[106] View Article

[107] PubMed/NCBI

[108] Google Scholar

[ref33] 33. Wang MS, Zhang R, Su LY, Li Y, Peng MS, Liu HQ, et al. Positive selection rather than relaxation of functional constraint drives the evolution of vision during chicken domestication. Cell Research. 2016;26(5):556. pmid:27033669
View Article
PubMed/NCBI
Google Scholar

[110] View Article

[111] PubMed/NCBI

[112] Google Scholar

[ref34] 34. Shen Y-Y, Liang L, Zhu Z-H, Zhou W-P, Irwin DM, Zhang Y-P. Adaptive evolution of energy metabolism genes and the origin of flight in bats. Proceedings of the National Academy of Sciences. 2010;107(19):8666–71.
View Article
Google Scholar

[114] View Article

[115] Google Scholar

[ref35] 35. Burga A, Wang W, Ben-David E, Wolf PC, Ramey AM, Verdugo C, et al. A genetic signature of the evolution of loss of flight in the Galapagos cormorant. Science. 2017;356(6341):eaal3345. pmid:28572335
View Article
PubMed/NCBI
Google Scholar

[117] View Article

[118] PubMed/NCBI

[119] Google Scholar

Figures

Abstract

Introduction

Materials and methods

Ethics statement

Sample collection

Primer design, PCR amplification, and sequencing

Data analyses

Results

Sequence variation and molecular diversity

Population differentiation due to genetic structures

Test for selection

Discussion

Population variance between European domestic geese and Chinese domestic geese

Signatures of positive selection and relaxed selection

Conclusions

Supporting information

S1 Table. Summary of sample collection for this study.

S2 Table. Primers used to amplify the COX2 and COX3 sequences, and to provide partial sequences of HBA1, HBA2, and HBB.

S3 Table. Nucleotide diversity of each gene across populations.

S4 Table. Results of the neutral test based on Tajima’s D, Fu and Li’s D, and Fu’s Fs H tests.

S5 Table. Estimates of the evolutionary divergence and positive selection for each gene.

S1 File. Amino acid mutations of COX2.

S2 File. Amino acid mutations of COX3.

S3 File. Amino acid mutations of HBA1.

S4 File. Amino acid mutations of HBA2.

S5 File. Amino acid mutations of HBB.

Acknowledgments

References