Genomic signatures of near-extinction and rebirth of the crested ibis and other endangered bird species
- Shengbin Li†1,
- Bo Li†2,
- Cheng Cheng†1, 2, 4,
- Zijun Xiong2,
- Qingbo Liu1,
- Jianghua Lai1,
- Hannah V Carey5,
- Qiong Zhang2, 6,
- Haibo Zheng1,
- Shuguang Wei1,
- Hongbo Zhang1,
- Liao Chang1, 2,
- Shiping Liu2,
- Shanxin Zhang1,
- Bing Yu1,
- Xiaofan Zeng2,
- Yong Hou2,
- Wenhui Nie7,
- Youmin Guo1,
- Teng Chen1,
- Jiuqiang Han1,
- Jian Wang2, 8,
- Jun Wang2, 9, 10,
- Chen Chen11,
- Jiankang Liu1, 4,
- Peter J Stambrook12,
- Ming Xu13,
- Guojie Zhang2, 9,
- M Thomas P Gilbert14, 15,
- Huanming Yang2, 6, 8, 16Email author,
- Erich D Jarvis17Email author,
- Jun Yu1, 3Email author and
- Jianqun Yan1Email author
© Li et al.; licensee BioMed Central. 2014
Received: 14 October 2014
Accepted: 2 December 2014
Published: 11 December 2014
Nearly one-quarter of all avian species is either threatened or nearly threatened. Of these, 73 species are currently being rescued from going extinct in wildlife sanctuaries. One of the previously most critically-endangered is the crested ibis, Nipponia nippon. Once widespread across North-East Asia, by 1981 only seven individuals from two breeding pairs remained in the wild. The recovering crested ibis populations thus provide an excellent example for conservation genomics since every individual bird has been recruited for genomic and demographic studies.
Using high-quality genome sequences of multiple crested ibis individuals, its thriving co-habitant, the little egret, Egretta garzetta, and the recently sequenced genomes of 41 other avian species that are under various degrees of survival threats, including the bald eagle, we carry out comparative analyses for genomic signatures of near extinction events in association with environmental and behavioral attributes of species. We confirm that both loss of genetic diversity and enrichment of deleterious mutations of protein-coding genes contribute to the major genetic defects of the endangered species. We further identify that genetic inbreeding and loss-of-function genes in the crested ibis may all constitute genetic susceptibility to other factors including long-term climate change, over-hunting, and agrochemical overuse. We also establish a genome-wide DNA identification platform for molecular breeding and conservation practices, to facilitate sustainable recovery of endangered species.
These findings demonstrate common genomic signatures of population decline across avian species and pave a way for further effort in saving endangered species and enhancing conservation genomic efforts.
The International Union for Conservation of Nature (IUCN) and Bird Life Species has recognized over 20% of approximately 10,000 extant bird species as being threatened. As of 2014, the IUCN RedList has declaredfive, 1,373, and 959 species as extinct in the wild, threatened, and near threatened, respectively. Between 1988 and 2008, the conservation status of 235 species was upgraded to higher categories of endangerment, as compared to just 32 species that were downgraded . Furthermore, historical records document the extinction of at least 150 avian species since the 16th century. The principal threats leading to avian population decline have been linked to man-made environmental disasters, including over-hunting, habitat loss, pesticide abuse, and invasive species introduction . To combat the ongoing decline, conservation efforts have been made, such as protective legislation, habitat restoration, captive breeding, and reintroduction, and all are responsible for the successful recovery of 49 species that were near-extinct between 1994 and 2004 .
Recent conservation genetic studies - have demonstrated that small populations are susceptible to allelic drift, leading to allele loss/fixation, and the process can be accelerated by inbreeding. Likewise, in small captive populations, rapid genetic deterioration, such as inbreeding depression and genetic adaptation to artificial environment, can also occur . Deleterious mutation tends to accumulate due to reduced selective strength . Furthermore, extinction rate in small wild populations increases significantly as heterozygosity decreases . Several genetic studies have attempted to characterize this effect from conservation-related bottlenecks among avian species, albeit based on limited markers of allozymes or microsatellites ,. It has been proposed that studies using up-to-date and more informative markers at a genome-scale will be necessary .
To provide genome-scale insights into the near-extinction and rescue, we sequenced the genomes of multiple individuals from both the crested ibis (n = 9; from the rescued population) and its co-habitant, not-endangered close-relative, the little egret (Egretta garzetta; n = 6; from the same order Pelecaniformes; diverged approximately 57 million years ago) . We compared their genome sequences with those of 41 other avian species described in companion publications in this issue and elsewhere , which include seven Endangered + Vulnerable (EV) species listed by the IUCN within the recent past (crowned crane, Balearica regulorum; macQueen’s bustard, Chlamydotis macqueenii; brown mesite, Mesitornis unicolor; kea, Nestor notabilis; dalmatian pelican, Pelecanus crispus; white-tailed eagle, Haliaeetus albicilla; and bald eagle, Haliaeetus leucocephalus) and 31 Least Concern (LC) species (Additional file 1: Table S3). We found common genomic signatures among the endangered or recently endangered species and that in the ibis was associated with feeding behavior, climate change, environmental hazard, and man-made disasters. We also found that the ibis populations are rapidly evolving and possess greater genetic diversity than expected in the recovery process. To better assist protection and recovery efforts for the crested ibis, we developed technical platforms and molecular tools, which may also be useful for the rescue and protection of other endangered wildlife.
The reference genome assemblies and annotations of the crested ibis and its cohabitant the little egret
Data statistics of the crested ibis and the little egret
De novo sequence
Insert size (bp)
Total data (Gb)
Sequence coverage ( x genome)
Total data (Gb)
Sequence coverage (x genome)
2-20 × 103
Total size (Gb)
Total size (Gb)
Total length (Mb)
Total length (Mb)
Total data (Gb)
Sequence coverage (x genome)
Total data (Gb)
Sequence coverage (x genome)
Mutation rateb (θS) (×10-3)
Mutation rateb (θS) (×10-3)
Low heterozygosity among the EV species, and its ongoing loss but with signs of increased diversity in the recovered crested ibis population
Heterozygous SNPs in nine representative avian species
Heterozygosity of whole genome (10 -3) a
Heterozygosity of whole exons (10 -3) a
Great black cormorant
EV (Once EN)
EV (Once VU)
To confirm this genomic signature at the population level, we analyzed SNP and STR calls, using the moderate-coverage genome sequences (approximately 20×) of eight crested ibis and five little egret individuals, which were sampled from the same populations as used for the reference genomes. We found a dramatic one-eighth (8 times less) SNP density reduction of the autosomes in the crested ibis population as compared to the little egret population (Additional file 1: Tables S9 and S10). The average frequency of short (1 to 2 bp) STR loci for the crested ibis genomes (0.7%; 2-bp, ≥4 alleles; n = 6) was an order of magnitude lower than that of the little egret genomes (8.0%, n = 6; Figure 2c). The longer STR loci (3- to 6-bp) also showed magnitudes lower frequencies, similar to what were seen in the crested ibis (Figure 2c).
Taking advantage of the extensive identity-tracking data from the living crested ibis populations, we asked if there is still ongoing heterozygosity reduction over time during species recovery. By analyzing 31 well-defined STR loci (4-bp unit), we estimated Ht/H0 , (heterozygosity at generation t/initial heterozygosity) from 105 individuals (Additional file 1: Table S11) and found a negative Ht/H0 correlation (r = -0.61) with population recovery time (with a slope of 0.017 units lost per year; Figure 2d). The SNP-based Ht/H0 of the eight re-sequenced ibis individuals also displays a strong negative correlation with population recovery time (r = -0.66), although there was no significant change with time, most likely due to the limited data points (Figure 2d). To further investigate the genetic basis of this heterozygosity reduction, we calculated fixation index among four sub-populations derived from the two original breeding pairs, including their offspring kept in the original Yangxian Nature Reserve population. We found remarkably large fixation indices among the sub-populations (Additional file 2: Figure S3), despite that the first population split occurred only about 20 years ago. This points to the presence of signs for rapidly increased genomic diversity between separated populations, even though its smaller population size and physical isolation all lead to ongoing heterozygosity reduction.
Accumulation of deleterious mutations in the threatened species
We tested whether the higher NS/S ratios can be attributed to stronger linkage disequilibrium due to inbreeding in a small effective population , using the multiple sequenced individuals. The crested ibis population has a slow linkage disequilibrium (LD) decay with reduced r 2 correlation coefficient at half of its maximum and at a distance of approximately 60 kb as compared to the little egret population with a distance of approximately 1 kb (Figure 3b). A similar slow LD decay has been observed in highly-inbred domestic species, such as horse  and dog . Furthermore, the synonymous SNP fraction of the derived alleles at a low frequency of 0.1 in the crested ibis population is just half that of the non-synonymous SNPs, whereas the two values are either higher or comparable in the little egret (Figure 3c). It appears that the decreased proportion of low-frequency synonymous SNPs relative to non-synonymous SNPs is a result of inbreeding fixation in the small crested ibis population.
Genes involved in brain function and cytochrome P450 metabolism have allelic fixation in the recovered crested ibis population
Population bottlenecks of the crested ibis and immune genes
To search for possible genetic causes for the recorded animal deaths among the recovered population, we scrutinized the records and found that the crested ibis population in the wild, while in the human-assisted recovery, has still been suffering from parasitic infection and other diseases, which account for 46.3% of total deaths from 1981 to 2003 (Figure 5c) . Since the major histocompatibility complexes (MHC) and its genetic variants are critical for immunity , we analyzed the nucleotide sequences of the classical MHC class II β gene (BLB) antigen binding region (encoding a protein for presenting antigenic peptides to helper T cells). This gene shows a much lower genetic diversity (1 locus with ≥3 amino acid alleles) in the antigen binding domain than its homolog of the little egret (6 loci with ≥3 amino acid alleles; Additional file 2: Figure S5). Such a low level of genetic diversity in the BLB and other MHC genes may result in defective immunity of the crested ibis population.
Historic agrochemical overuse and relevant mutated genes in threatened avian species
Overuse of various nondegradable agrochemicals has been suggested as one of the principal reasons for the population decline of seven of the eight EV species examined: bald eagle , white-tailed eagle , kea , Adele penguin , emperor penguin , chimney swift , and the Dalmatian pelican . To investigate whether agrochemical use also contributed to the decline of the crested ibis survival, we first examined the relevant historical evidence. In Japan, during the Meiji Restoration (in the late 19th century), traditional protection measures were disregarded and rampant hunting rapidly reduced the crested ibis population to the extent that by the time when the species was enlisted for protection on the hunting ordinances in 1908, it was almost extinct there . Although relevant evidence was poorly documented in Korea, Northern China, and Russia in the first half of the 20th century, in central China, the crested ibis was common in Gansu and Shaanxi Provinces before 1950 but nearly extinct by the end of 1950s. We found a negative correlation between the estimated crested ibis population size in the Gansu and Shaanxi Provinces and the amount of fertilizers and pesticides used in the region (Figure 5d). These findings suggest that overuse of agrochemicals may be associated with very dramatic and obvious decline in the crested ibis population of the region from which our genomes were sequenced.
We compared 6,332 orthologs genes among EV (all are carnivorous species; n = 8) and also to the LC carnivorous species (n = 15), since carnivorous species are also often apex predators and more sensitive to agrochemicals ,,,. We identified 44 genes that have a significantly higher rate of being inactivated (null mutations that alter protein structure) in the EV carnivorous species, and only nine genes with a significantly higher rate in the LC carnivorous species (Fisher’s exact test, P <0.05; Additional file 1: Table S16). Among them, 17 genes are metabolism related enzymes; for instance, one of them, SLCO1A2, a sodium-independent transporter mediating cellular uptake of organic anions in the liver , has lost its function in three threatened species (37.5%) and in none (0%) of the LC species. Another, HACL1, catalyzing a carbon-carbon cleavage reaction, is necessary for the catabolism of phytanoic acid in carnivores , which has lost its function in three threatened species. CHIA, which degrades chitin-chitotriose and participates in the defense against pathogens , has lost its function in five threatened species. These findings suggest that carnivorous EV species have a greater genetic susceptibility to agrochemicals.
Genome-wide STR profiling of the crested ibis population for marker-assisted breeding
Our genome-wide analysis on the endangered crested ibis and seven other recently endangered and rescued avian species across the Neoaves phylogenetic tree provides direct evidence at a genomic-scale for support of previous hypothesis and novel insights into consequences of heterozygosity loss, deleterious mutation accumulations, population bottlenecks, and genetic drifts. The convergent inactivation (or pseudogenization) of xenobiotic metabolism related genes in the ibis and other endangered top predators suggests a reduction of adaptive genetic plasticity in these species to agrochemical overuse. However, the increasing genomic diversity among the isolated ibis populations derived from the offspring of the last wild pair identified in 1981 indicate that rapidly diverging sequences in the recovering ibis population are being fixed in less than 10 generations.
Our genome-wide data are important for exploring causative factors of the near-extinction and exact demographic reconstruction of the endangered species, and both are necessary for distinguishing long-term climate change from recent human-mediated events ,. In our case, we identified distant bottlenecks due to the past glaciations and the most recent bottleneck that is clearly unrelated to global glaciations (although severe periodic temperature drops may happen to accelerate the process) but associated with some man-made factors. The man-made induced bottleneck was much more severe than the glaciations. It appears that the crested ibis is more sensitive to these environmental challenges than the little egret. Similar events may have affected endangered non-avian species, such as the giant panda . However, our observations on the EV avian species provide an avian model for conservation genomics, which is distinctly different from giant panda whose genetic diversity remains high , albeit with a similar population size (approximately 2,000 for the crested ibis vs. approximately 2,500 for the giant panda). We propose that, regardless of the past conservation success, an immediate evaluation of genetic diversity and sequence variation should be imposed for risk assessment on all endangered species.
The genetic drift for fixation of changes in brain and metabolism genes of the rescued crested ibis population in China is intriguing, and could mean either deleterious mutations in these genes or the possibility of enhanced functions for certain brain behaviors and enhanced metabolism of toxins for survival of the species. Consistent with the former possibility, the surviving crested ibis in China may have gone through a change in foraging behavior . As a wading bird, the crested ibis uses a ‘remote touch’ mechanism to detect the movement of their prey in the mud through a series of sensory receptors , and either sacrifice or gain of sensing and digesting abilities are all relevant. We do not know if this change occurred demographically before or through genetic drift after the two breeding pairs were rescued in 1981.
One interpretation of the fixation findings based on the methods we used is that there have been selective sweeps for specific SNPs in specific brain and metabolism genes over the past 30 years. These sweeps could have artificially occurred due to controlled inbreeding, or naturally occur due to selection. But such selective sweeps within such a short time, for animals that reach sexual maturity at around 3 years with limited generations seems remarkable; although we see more rapid increasing diversity in the genome than expected. An alternative, more likely interpretation is that greater fixation of these alleles was already present in the two last breeding pairs before near extinction due to demographic differences . This difference is difficult to test considering all the animals we sequenced are descended from the last seven individuals in the wild from one population in 1981, where all others are extinct. If a demographic explanation were true, it would mean that these alleles became fixed through a bottleneck affect reducing the polymorphisms in the genomes by genetic drift.
One question that can now be better addressed is why the crested ibis nearly suffered extinction, whereas its cohabitant, the little egret, did not? One possible reason is that while both species exploit aquatic environments, such as eating mollusks, crustaceans, fish, and frogs, the little egret also consumes plant seeds in the winter or under drought and thus still thrives strongly in the same habitat. This foraging behavioral difference is consistent with genetic differences in enzymes for food digestion. Another possible reason is that the little egret might have become resident birds and gone through a bottleneck already by changing their foraging behavior at the same time.
Our genome-wide STR markers and its application to conservation genomics also provide a more powerful platform for breeding and tracking of endangered species both in partial captivity and in the wild. With this method, we believe that it is possible that immediate genome sequencing and evaluation of genetic diversity and loss-of-function genes for risk assessment can be done for generating rescuing strategies for other currently endangered avian species.
Our study is the first, of which we are aware, to conduct genome-scale analyses of species that were endangered, including near extinct, across a vertebrate class. With a focus on the crested ibis, we were able to identify genetic associations before, during, and after the near extinction events and population bottlenecks. We confirmed some expected changes, but genome-wide, such as reduced heterozygosity, accumulation of deleterious mutations, and susceptibility to agrochemical overuse by humans. We also identified seemingly positive changes in the recovering crested ibis population, such as more rapidly increasing genetic diversity between new populations than expected, and changes in some gene families that could potentially be related to surviving extinction or recovery. Our genome-scale derived STR platform is now assisting in that recovery. We hope that the knowledge and lessons learned from this study will be applicable to not only the one-quarter of avian species that are threatened or near threatened, but to threatened species broadly.
Materials and methods
For de novo assembly, we extracted DNA samples from peripheral venous blood of a 3-year-old female crested ibis in the Yangxian County Reserve and a male little egret captured from the same county in southern Qinling Mountains, Shaanxi Province, China. For our resequencing effort, blood DNA samples were from eight crested ibis and five little egret individuals from the same locality. For meta-analysis of endangered and least concerned species, we used the genome sequences of 41 additional avian species (Additional file 1: Table S3) . For DNA profiling, we used 105 individual crested ibis from four sub-populations of Yangxian, Huayang, Louguan, and Ningshan Counties (Additional file 1: Table S11).
Skin cells were grown in DMEM medium supplemented with 15% fetal bovine serum. Metaphase preparations for flow sorting were generated . The crested ibis chromosomes were numbered according to convention . Chromosome preparations were stained with Hoechst 33258 (Sigma, St Louis, MO, USA) and Chromomycin A3 (Sigma) and then sorted  (MoFlo, DAKO, Glostrup, Denmark DAKO).
For genome assembly, we constructed sequencing libraries with variable insert sizes (180 bp, 500 bp, 800 bp, 2 kb, 5 kb, 10 kb, and 20 kb) by following the manufacturer’s instruction for Illumina’s HiSeq 2000. Sequences of approximately 266 Gb and 127 Gb (reads length: 100 bp for libraries with insert size <2 kb, 45 bp for other libraries) were generated for the crested ibis and the little egret, respectively, and after quality-filtering, approximately 194 Gb (roughly 156×) and about 90 Gb (71×) sequences survived for the assembly. To achieve better contiguity, we also used a new physical mapping technology developed by the Argus System (OpGen) and its assembly software (Genome-Builder), which produced, based on KpnI digest, a large optical mapping dataset (about 282 Gb) from 34 high-density MapCards and containing 799,678 single-molecule restriction maps (>250 kb) with an average size of 353 kb.
The genome sequences for the crested ibis and little egret were assembled by using the de Bruijn graph-based assembler SOAPdenovo . Prior to assembly, potential sequencing errors were removed or corrected based on k-mer frequency methodology. Reads from libraries with insert sizes ranging from 170 bp to 800 bp were split into 41-mers to construct de Bruijn graphs and contigs. The paired-end reads were aligned to construct scaffolds. Super-scaffolds for the crested ibis were constructed and aided with optical mapping data. The crested ibis chromosomes were built by using super-scaffolds based on conserved synteny between the assembly and genome data of chicken and zebra finch.
To assess the large-scale and local assembly accuracy, we also sequenced (Sanger sequencing technology) and assembled (phred-phrap-consed) eight randomly selected fosmids (average approximately 39 kb long) from a genomic library for the crested ibis (same DNA used for the reference assembly). We also assessed the completeness and accuracy of our assembly using 98,881 transcripts from blood, which were sequenced and assembled independently. A total of 94,709 assembled transcripts (>95%) were mapped to the assembly (BLASTN, E <10-5, coverage ≥90%), yielding a single-base accuracy of approximately 98% for the assembled sequences with >20 reads coverage and excluding sequence gaps.
Gene and repeat annotations
To predict genes, we used both homology-based and de novo methods as follows. First, we obtained protein sequences of chicken, zebra finch, and human from Ensembl (release 60) and mapped them onto the genome assemblies using Tblastn with E-value 1e-5. All high-score segments were grouped into gene-like structures (genBlastA ). The homologous genome sequences with flanking sequences (2,000 bp) were aligned to the protein sequences by using Genewise  to define gene models. We clustered predicted transcripts >100 bp and took cross-species synteny into account (otherwise, a transcript with the best aligning score was used). Single-exon genes containing one or >1 frame shift errors and multi-exon genes containing >3 frame errors were not taken into account. Second, we clustered transcripts using TopHat  and Cufflinks  and aligned them (>150 bp) to SwissProt/TrEMBL database  with a cutoff E-value <1e-5. Third, we predicted protein-coding genes (>150 bp) using Genscan  (gene model parameters trained with Homo sapiens genes) and Augustus  (gene model parameters trained with chicken genes) and defined TE-derived proteins (BlastP with E-value <1e-5 and >50% alignment).
For the reference gene set, we constructed gene models following three criteria: (1) candidate genes clustered with >100 bp overlap; (2) one cluster per gene (homology-based model > RNA-seq model > de novo predicted model); and (3) if not (2), 30% alignment to a known protein in the SwissProt/TrEMBL database  (>2 exons). Functional annotations were based on the best match principle using SwissProt, InterPro , and KEGG  databases. Treefam  was used to define gene family (Blastp, E-value <1e-7; Hscore >10; minimum edge density >1/3) and CAFE  to define gene loss and gain.
We annotated transposable elements (TEs) based on homology to RepBase sequencesusing RepeatProteinMask and RepeatMasker  with default parameters. We also constructed de novo repeat libraries (transposable elements) using RepeatModeler (http://repeatmasker.org/RepeatModeler.html) with default parameters.
Resequencing data analysis
Resequencing reads were generated from a single-size insert library (500 bp) per individual and mapped high-quality reads onto the references with BWA , followed by removal of unmapped reads (average quality <10 or average map quality <20 or multiple-site reads). SNPs were called by using SOAPsnp  with thresholds of quality score ≥20, sequencing depth >8X and <40X, copy number of flanking sequences < = 2, >3 uniquely mapped reads, and distance between SNPs ≥5 bp.
We calculated the correlation coefficient (r 2 ) of alleles at SNP locus after setting -maxdistance 300 -dprime -minGeno 0.6 -minMAF 0.1 -hwcutoff 0.001 using the Haploview software . Since sample size is an important parameter influencing LD patterns, we randomly selected five crested ibises three times to repeat the experiment and the analysis. To reconstruct ancient demographic history, we ran the PSMC program (parameters: -N30, -t15, -r5, and -p ‘4 + 25*2 + 4 + 6’) using autosomal sequences (scaffold length ≥50 kb and a total of 478,758 heterozygous loci). We performed bootstrapping (100 times) to estimate the variance of simulated results and estimated the neutral mutation rate μ (mutations per base per generation) using the estimated genome-wide nucleotide divergence (10.31%) and divergence time (38.98 × 106) between the crested ibis and the little egret. Based on mean generation time (3 years for crested ibis), we calculated μ = (0.1031 × 3)/(2 × 38.98 × 106) = 3.968 × 10-9 mutations per generation for the crested ibis.
To reconstruct recent demographic history, we used the ∂a∂i program  and paired-end reads (500 bp in size) from nine samples (eight re-sequencing individuals and one de novo assembly individual). To minimize the effect of low-coverage sequencing, we extracted the sites that were covered by high-quality reads (at least six of nine individuals covered by >2X reads). To prepare for ∂a∂i program, we called 1,420,399 SNPs using a published method . The little egret reference genome sequence was used to infer ancestral alleles. We considered four models and chose the one with highest maximum log-likelihood value. The ancestral population size (Na) was estimated on the basis of the calculated θ value and the mutation rate. Population size and corresponding time were derived from parameters scaled based on Na.
Purifying selection analysis
For each 500-kb window, we determined the number of reads corresponding to the most and least abundant SNP alleles (nMAJ and nMIN), H p = 2∑nMAJ∑nMIN/(∑nMAJ + ∑nMIN)2, and transformed H p into Z scores: ZH p = (H p-μH p)/σH p . We applied a threshold of ZH p = -2.326 (q <0.01 in normal distribution) for putative selective sweeps.
Genome-wide STR profiling
We defined STRs using Tandem Repeat Finder  (parameters: Match = 2, Mismatch = 7, Delta = 7, PM = 80, PI = 10, Minscore = 30, and MaxPeriod = 6), which were validated in the following steps. DNA was extracted with the E.Z.N.A.™ Blood DNA Kit (Omega Bio-Tek Inc., USA) according to its instruction (E.Z.N.A.™ Blood DNA Isolation Protocols, Revised June 2009). All DNA samples were quantified with the TIANamp Genomic DNA Kit. PCR amplification was performed in a reaction volume of 25 μL with MicroAmp® reaction tubes (Applied Biosystems, CA, USA; the GeneAmp® PCR Systems 9700 with gold-plated silver or silver 96-well blocks). Amplified products were separated in ABI3730 DNA Genetic Analyzer 48-capillary array system (Applied Biosystems) according to the manufacturer’s protocol. The genotypes were analyzed by using Genemapper 3.5 (Applied Biosystems).
Genome data of crested ibis and little egret are uploaded to NCBI (PRJNA232572 and PRJNA232959). The raw reads in the SRA (SRP035852 and SRP035853). The NCBI accession numbers of the assembled genomes of all species are described in Additional file 1: Table S3.
- BLB :
MHC class II β gene
- CHIA :
DNA identification profiling (DIP) platform
Combined EN and VU
- HACL1 :
2-hydroxyacyl-CoA lyase 1
- Hp :
Heterozygosity in 500-kb sliding windows
Heterozygosity at generation t/initial heterozygosity
International Union for Conservation of Nature
Kyoto Encyclopedia of Genes and Genomes
Major histocompatibility complexes
Million years ago
Pairwise sequential Markovian coalescent
Reads per kilobase per million
- SLCO1A2 :
Solute carrier organic anion transporter family member 1 A2
Single nucleotide polymorphisms
- ZHp :
Z transformations of Hp
We thank Laurie Goodman for help in editing the manuscript and Zhaozheng Guo for overseeing experimental bioethics. We thank Jing Liu, Hao Li, Liang Yang, Xiaojing Wei, Fang Cao, and Rong Su in Xi’an Jiaotong University for help in experiment. The Ibis and Egret sequencing and analyses part of this project was supported financially with funds from the state forestry administration of the People’s Republic of China (No. 2012717), Higher Education Innovation Fund of Xi’an Jiaotong University, and Dr. Xi Guang Assistant to the President, Kwang-Hua Education Foundation (No. 2010822), and Dr. Yan Hong Assistant to the President, Dr. Chu Longfei and Dr. Ding Haihua in Shaanxi branch of State Forestry Administration, the Breeding and Releasing programs of crested ibis population (No. 2010, No. 2008), the Shenzhen Municipal Government of China (Nos. CXB201108250096A, JC201005260191A), ShenZhen Engineering Laboratory for Genomics-Assisted Animal Breeding, and China National GeneBank-Shenzhen. EDJ’s contribution was supported by HHMI.
- Attenborough D: Help us build a brighter future: the need to save the world’s most threatened birds. 2008, BirdLife International, CambridgeGoogle Scholar
- Collar NJ, Andreev AV, Chan S: Threatened Birds of Asia: The BirdLife International Red Data Book. 2001, BirdLife International, Cambridge, 5Google Scholar
- Brooke Mde L, Butchart SH, Garnett ST, Crowley GM, Mantilla-Beniers NB, Stattersfield AJ: Rates of movement of threatened bird species between IUCN red list categories and toward extinction. Conserv Biol. 2008, 22: 417-427. 10.1111/j.1523-1739.2008.00905.x.PubMedView ArticleGoogle Scholar
- Frankham R: Genetics and extinction. Biol Conserv. 2005, 126: 131-140. 10.1016/j.biocon.2005.05.002.View ArticleGoogle Scholar
- Saccheri I, Kuussaari M, Kankare M, Vikman P, Fortelius W, Hanski I: Inbreeding and extinction in a butterfly metapopulation. Nature. 1998, 392: 491-494. 10.1038/33136.View ArticleGoogle Scholar
- Keller LF, Waller DM: Inbreeding effects in wild populations. Trends Ecol Evol. 2002, 17: 230-241. 10.1016/S0169-5347(02)02489-8.View ArticleGoogle Scholar
- Lande R: Mutation and conservation. Conserv Biol. 1995, 9: 782-791. 10.1046/j.1523-1739.1995.09040782.x.View ArticleGoogle Scholar
- Woodworth L, Montgomery M, Briscoe D, Frankham R: Rapid genetic deterioration in captive populations: causes and conservation implications. Conserv Genet. 2002, 3: 277-288. 10.1023/A:1019954801089.View ArticleGoogle Scholar
- Evans SR, Sheldon BC: Interspecific patterns of genetic diversity in birds: correlations with extinction risk. Conserv Biol. 2008, 22: 1016-1025. 10.1111/j.1523-1739.2008.00972.x.PubMedView ArticleGoogle Scholar
- Spielman D, Brook BW, Frankham R: Most species are not driven to extinction before genetic factors impact them. Proc Natl Acad Sci U S A. 2004, 101: 15261-15264. 10.1073/pnas.0403809101.PubMedPubMed CentralView ArticleGoogle Scholar
- Kohn MH, Murphy WJ, Ostrander EA, Wayne RK: Genomics and conservation genetics. Trends Ecol Evol. 2006, 21: 629-637. 10.1016/j.tree.2006.08.001.PubMedView ArticleGoogle Scholar
- Liu YZ: Rediscovery of crested ibis Nipponia nippon in Qinling Mountain. Chin J Zool. 1981, 27: 237-Google Scholar
- Shi D, Cao Y: Chinese Crested Ibis. 2001, China Forestry Press, BeijingGoogle Scholar
- Ding C: Research on the Crested Ibis. 2004, Shanghai Scientific and Technological Educational Publishing House Press, ShanghaiGoogle Scholar
- Jarvis ED, Mirarab S, Aberer AJ, Li B, Houde P, Li C, Ho SYW, Faircloth BC, Nabholz B, Howard JT, Suh A, Weber CC, Fonseca RR, Li J, Zhang F, Li H, Zhou L, Narula N, Liu L, Ganapathy G, Boussau B, Bayzid MS, Zavidovych V, Subramanian S, Gabaldón T, Capella- Gutiérrez S, Huerta-Cepas J, Rekepalli B, Munch K, Schierup M, et al: Using whole genomes to resolve the tree of life of modern birds. Science. 2014, 346: 1320-1331. 10.1126/science.1253451.PubMedPubMed CentralView ArticleGoogle Scholar
- Zhang G, Li C, Li Q, Li B, Larkin DM, Lee C, Storz JF, Antunes A, Greenwold MJ, Meredith RW, Ödeen A, Cui J, Zhou Q, Xu L, Pan H, Wang Z, Jin L, Zhang P, Hu H, Yang W, Hu J, Xiao J, Yang Z, Liu Y, Xie Q, Yu H, Lian J, Wen P, Zhang F, Li H, et al: Comparative genomics across modern bird species reveal insights into avian genome evolution and adaptation. Science. 2014, 346: 1311-1320. 10.1126/science.1251385.PubMedPubMed CentralView ArticleGoogle Scholar
- Li R, Zhu H, Ruan J, Qian W, Fang X, Shi Z, Li Y, Li S, Shan G, Kristiansen K, Li S, Yang H, Wang J, Wang J: De novo assembly of human genomes with massively parallel short read sequencing. Genome Res. 2010, 20: 265-272. 10.1101/gr.097261.109.PubMedPubMed CentralView ArticleGoogle Scholar
- Yarmolinsky DA, Zuker CS, Ryba NJ: Common sense about taste: from mammals to insects. Cell. 2009, 139: 234-244. 10.1016/j.cell.2009.10.001.PubMedPubMed CentralView ArticleGoogle Scholar
- Warren WC, Clayton DF, Ellegren H, Arnold AP, Hillier LW, Kunstner A, Searle S, White S, Vilella AJ, Fairley S, Heger A, Kong L, Ponting CP, Jarvis ED, Mello CV, Minx P, Lovell P, Velho TA, Ferris M, Balakrishnan CN, Sinha S, Blatti C, London SE, Li Y, Lin YC, George J, Sweedler J, Southey B, Gunaratne P, Watson M, et al: The genome of a songbird. Nature. 2010, 464: 757-762. 10.1038/nature08819.PubMedPubMed CentralView ArticleGoogle Scholar
- International Chicken Genome Sequencing Consortium: Sequence and comparative analysis of the chicken genome provide unique perspectives on vertebrate evolution. Nature. 2004, 432: 695-716. 10.1038/nature03154.View ArticleGoogle Scholar
- Reed DH, Frankham R: Correlation between fitness and genetic diversity. Conserv Biol. 2003, 17: 230-237. 10.1046/j.1523-1739.2003.01236.x.View ArticleGoogle Scholar
- Messaoudi I, Patino JAG, Dyall R, LeMaoult J, Nikolich-Zugich J: Direct link between mhc Polymorphism, T cell avidity, and diversity in immune defense. Science. 2002, 298: 1797-1800. 10.1126/science.1076064.PubMedView ArticleGoogle Scholar
- Fang X, Zhang Y, Zhang R, Yang L, Li M, Ye K, Guo X, Wang J, Su B: Genome sequence and global sequence variation map with 5.5 million SNPs in Chinese rhesus macaque. Genome Biol. 2011, 12: R63-10.1186/gb-2011-12-7-r63.PubMedPubMed CentralView ArticleGoogle Scholar
- Kim EB, Fang X, Fushan AA, Huang Z, Lobanov AV, Han L, Marino SM, Sun X, Turanov AA, Yang P, Yim SH, Zhao X, Kasaikina MV, Stoletzki N, Peng C, Polak P, Xiong Z, Kiezun A, Zhu Y, Chen Y, Kryukov GV, Zhang Q, Peshkin L, Yang L, Bronson RT, Buffenstein R, Wang B, Han C, Li Q, Chen L, et al: Genome sequencing reveals insights into physiology and longevity of the naked mole rat. Nature. 2011, 479: 223-227. 10.1038/nature10533.PubMedPubMed CentralView ArticleGoogle Scholar
- Li R, Fan W, Tian G, Zhu H, He L, Cai J, Huang Q, Cai Q, Li B, Bai Y, Zhang Z, Zhang Y, Wang W, Li J, Wei F, Li H, Jian M, Li J, Zhang Z, Nielsen R, Li D, Gu W, Yang Z, Xuan Z, Ryder OA, Leung FC, Zhou Y, Cao J, Sun X, Fu Y, et al: The sequence and de novo assembly of the giant panda genome. Nature. 2009, 463: 311-317. 10.1038/nature08696.PubMedPubMed CentralView ArticleGoogle Scholar
- Gymrek M, Golan D, Rosset S, Erlich Y: lobSTR: A short tandem repeat profiler for personal genomes. Genome Res. 2012, 22: 1154-1162. 10.1101/gr.135780.111.PubMedPubMed CentralView ArticleGoogle Scholar
- Crow JF, Kimura M: An Introduction to Population Genetics Theory. 1970, Harper & Row, New YorkGoogle Scholar
- Reumers J, Schymkowitz J, Ferkinghoff-Borg J, Stricher F, Serrano L, Rousseau F: SNPeffect: a database mapping molecular phenotypic effects of human non-synonymous coding SNPs. Nucleic Acids Res. 2005, 33: D527-D532. 10.1093/nar/gki086.PubMedPubMed CentralView ArticleGoogle Scholar
- Mettler LE, Gregg TG: Population genetics and evolution. 1969, Prentice-Hall, Inc., Englewood Cliffs (NJ)Google Scholar
- Biswas S, Akey JM: Genomic insights into positive selection. Trends Genet. 2006, 22: 437-446. 10.1016/j.tig.2006.06.005.PubMedView ArticleGoogle Scholar
- Wade CM, Giulotto E, Sigurdsson S, Zoli M, Gnerre S, Imsland F, Lear TL, Adelson DL, Bailey E, Bellone RR, Blöcker H, Distl O, Edgar RC, Garber M, Leeb T, Mauceli E, MacLeod JN, Penedo MC, Raison JM, Sharpe T, Vogel J, Andersson L, Antczak DF, Biagi T, Binns MM, Chowdhary BP, Coleman SJ, Della Valle G, Fryc S, Guérin G, et al: Genome sequence, comparative analysis, and population genetics of the domestic horse. Science. 2009, 326: 865-867. 10.1126/science.1178158.PubMedPubMed CentralView ArticleGoogle Scholar
- Lindblad-Toh K, Wade CM, Mikkelsen TS, Karlsson EK, Jaffe DB, Kamal M, Clamp M, Chang JL, Kulbokas EJ, Zody MC, Mauceli E, Xie X, Breen M, Wayne RK, Ostrander EA, Ponting CP, Galibert F, Smith DR, DeJong PJ, Kirkness E, Alvarez P, Biagi T, Brockman W, Butler J, Chin CW, Cook A, Cuff J, Daly MJ, DeCaprio D, Gnerre S, et al: Genome sequence, comparative analysis and haplotype structure of the domestic dog. Nature. 2005, 438: 803-819. 10.1038/nature04338.PubMedView ArticleGoogle Scholar
- Rubin CJ, Zody MC, Eriksson J, Meadows JR, Sherwood E, Webster MT, Jiang L, Ingman M, Sharpe T, Ka S, Hallböök F, Besnier F, Carlborg O, Bed’hom B, Tixier-Boichard M, Jensen P, Siegel P, Lindblad-Toh K, Andersson L: Whole-genome resequencing reveals loci under selection during chicken domestication. Nature. 2010, 464: 587-591. 10.1038/nature08832.PubMedView ArticleGoogle Scholar
- Lands B: Consequences of essential fatty acids. Nutrients. 2012, 4: 1338-1357. 10.3390/nu4091338.PubMedPubMed CentralView ArticleGoogle Scholar
- Li H, Durbin R: Inference of human population history from individual whole-genome sequences. Nature. 2011, 475: 493-496. 10.1038/nature10231.PubMedPubMed CentralView ArticleGoogle Scholar
- Gutenkunst RN, Hernandez RD, Williamson SH, Bustamante CD: Inferring the joint demographic history of multiple populations from multidimensional SNP frequency data. PLoS Genet. 2009, 5: e1000695-10.1371/journal.pgen.1000695.PubMedPubMed CentralView ArticleGoogle Scholar
- Zheng B, Xu Q, Shen Y: The relationship between climate change and Quaternary glacial cycles on the Qinghai–Tibetan Plateau: review and speculation. Quaternary Int. 2002, 97–98: 93-101. 10.1016/S1040-6182(02)00054-X.View ArticleGoogle Scholar
- Donaldson GM, Shutt JL, Hunter P: Organochlorine contamination in bald eagle eggs and nestlings from the canadian great lakes. Arch Environ Contam Toxicol. 1999, 36: 70-80. 10.1007/s002449900444.PubMedView ArticleGoogle Scholar
- Helander B, Olsson A, Bignert A, Asplund L, Litzen K: The role of DDE, PCB, coplanar PCB and eggshell parameters for reproduction in the white-tailed sea eagle (Haliaeetus albicilla) in Sweden. Ambio. 2002, 31: 386-403.PubMedView ArticleGoogle Scholar
- Youl JM: Lead exposure in free-ranging kea (Nestor notabilis), takahe (Porphyrio hochstetteri) and Australasian harriers (Circus approximans) in New Zealand. 2009, Massey University, Palmerston NorthGoogle Scholar
- Shinsuke Tanabe AS, Hidaka H, Tatsukawa R: Transfer rates and pattern of PCB isomers and congeners and p, p’-DDE from mother to egg in Adelie Penguin (Pygoscelis adeliae). Chemosphere. 1986, 15: 9-Google Scholar
- Corsolini S, Borghesi N, Ademollo N, Focardi S: Chlorinated biphenyls and pesticides in migrating and resident seabirds from East and West Antarctica. Environ Int. 2011, 37: 1329-1335. 10.1016/j.envint.2011.05.017.PubMedView ArticleGoogle Scholar
- Nocera JJ, Blais JM, Beresford DV, Finity LK, Grooms C, Kimpe LE, Kyser K, Michelutti N, Reudink MW, Smol JP: Historical pesticide applications coincided with an altered diet of aerially foraging insectivorous chimney swifts. 2012, 279:3114–3120
- Albanis TA, Hela GD, Hatzilakos D: Organochlorine residues in eggs of Pelecanus crispus and its prey in wetlands of Amvrakikos Gulf, North-western Greece. Chemosphere. 1995, 31: 9-10.1016/0045-6535(95)00302-O.View ArticleGoogle Scholar
- Badagnani I, Castro RA, Taylor TR, Brett CM, Huang CC, Stryke D, Kawamoto M, Johns SJ, Ferrin TE, Carlson EJ, Burchard EG, Giacomini KM: Interaction of Methotrexate with organic-anion transporting polypeptide 1A2 and its genetic variants. J Pharmacol Exp Ther. 2006, 318: 521-529. 10.1124/jpet.106.104364.PubMedView ArticleGoogle Scholar
- Foulon V, Antonenkov VD, Croes K, Waelkens E, Mannaerts GP, Van Veldhoven PP, Casteels M: Purification, molecular cloning, and expression of 2-hydroxyphytanoyl-CoA lyase, a peroxisomal thiamine pyrophosphate-dependent enzyme that catalyzes the carbon-carbon bond cleavage during alpha-oxidation of 3-methyl-branched fatty acids. Proc Natl Acad Sci U S A. 1999, 96: 10039-10044. 10.1073/pnas.96.18.10039.PubMedPubMed CentralView ArticleGoogle Scholar
- Maizels RM: Infections and allergy - helminths, hygiene and host immune regulation. Curr Opin Immunol. 2005, 17: 656-661. 10.1016/j.coi.2005.09.001.PubMedView ArticleGoogle Scholar
- Steiner CC, Putnam AS, Hoeck PEA, Ryder OA: Conservation genomics of threatened animal species. Annu Rev Anim Biosci. 2013, 1: 261-281. 10.1146/annurev-animal-031412-103636.PubMedView ArticleGoogle Scholar
- Zhao S, Zheng P, Dong S, Zhan X, Wu Q, Guo X, Hu Y, He W, Zhang S, Fan W, Zhu L, Li D, Zhang X, Chen Q, Zhang H, Zhang Z, Jin X, Zhang J, Yang H, Wang J, Wang J, Wei F: Whole-genome sequencing of giant pandas provides insights into demographic history and local adaptation. Nat Genet. 2012, 45: 67-71. 10.1038/ng.2494.PubMedView ArticleGoogle Scholar
- Cunningham SJ, Alley MR, Castro I, Potter MA, Cunningham M, Pyne MJ: Bill morphology of ibises suggests a remote-tactile sensory system for prey detection. Auk. 2010, 127: 308-316. 10.1525/auk.2009.09117.View ArticleGoogle Scholar
- Nielsen R, Williamson S, Kim Y, Hubisz MJ, Clark AG, Bustamante C: Genomic scans for selective sweeps using SNP data. Genome Res. 2005, 15: 1566-1575. 10.1101/gr.4252305.PubMedPubMed CentralView ArticleGoogle Scholar
- Yang F, Carter NP, Shi L, Ferguson-Smith MA: A comparative study of karyotypes of muntjacs by chromosome painting. Chromosoma. 1995, 103: 642-652. 10.1007/BF00357691.PubMedView ArticleGoogle Scholar
- Ladjali-Mohammedi K, Bitgood JJ, Tixier-Boichard M, Ponce De Leon FA: International system for standardized avian karyotypes (ISSAK): standardized banded karyotypes of the domestic fowl (Gallus domesticus). Cytogenet Cell Genet. 1999, 86: 271-276. 10.1159/000015318.PubMedView ArticleGoogle Scholar
- Ng BL, Carter NP: Factors affecting flow karyotype resolution. Cytometry A. 2006, 69: 1028-1036. 10.1002/cyto.a.20330.PubMedView ArticleGoogle Scholar
- She R, Chu JS, Wang K, Pei J, Chen N: GenBlastA: enabling BLAST to identify homologous gene sequences. Genome Res. 2009, 19: 143-149. 10.1101/gr.082081.108.PubMedPubMed CentralView ArticleGoogle Scholar
- Birney E, Clamp M, Durbin R: GeneWise and Genomewise. Genome Res. 2004, 14: 988-995. 10.1101/gr.1865504.PubMedPubMed CentralView ArticleGoogle Scholar
- Trapnell C, Pachter L, Salzberg SL: TopHat: discovering splice junctions with RNA-Seq. Bioinformatics. 2009, 25: 1105-1111. 10.1093/bioinformatics/btp120.PubMedPubMed CentralView ArticleGoogle Scholar
- Roberts A, Pimentel H, Trapnell C, Pachter L: Identification of novel transcripts in annotated genomes using RNA-Seq. Bioinformatics. 2011, 27: 2325-2329. 10.1093/bioinformatics/btr355.PubMedView ArticleGoogle Scholar
- Bairoch A, Apweiler R: The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000. Nucleic Acids Res. 2000, 28: 45-48. 10.1093/nar/28.1.45.PubMedPubMed CentralView ArticleGoogle Scholar
- Salamov AA, Solovyev VV: Ab initio gene finding in Drosophila genomic DNA. Genome Res. 2000, 10: 516-522. 10.1101/gr.10.4.516.PubMedPubMed CentralView ArticleGoogle Scholar
- Stanke M, Waack S: Gene prediction with a hidden Markov model and a new intron submodel. Bioinformatics. 2003, 19: 215-225. 10.1093/bioinformatics/btg1080.View ArticleGoogle Scholar
- Mulder N, Apweiler R: InterPro and InterProScan: tools for protein sequence classification and comparison. Methods Mol Biol. 2007, 396: 59-70. 10.1007/978-1-59745-515-2_5.PubMedView ArticleGoogle Scholar
- Kanehisa M, Goto S: KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000, 28: 27-30. 10.1093/nar/28.1.27.PubMedPubMed CentralView ArticleGoogle Scholar
- Li H, Coghlan A, Ruan J, Coin LJ, Heriche JK, Osmotherly L, Li R, Liu T, Zhang Z, Bolund L, Wong GK, Zheng W, Dehal P, Wang J, Durbin R: TreeFam: a curated database of phylogenetic trees of animal gene families. Nucleic Acids Res. 2006, 34: D572-D580. 10.1093/nar/gkj118.PubMedPubMed CentralView ArticleGoogle Scholar
- De Bie T, Cristianini N, Demuth JP, Hahn MW: CAFE: a computational tool for the study of gene family evolution. Bioinformatics. 2006, 22: 1269-1271. 10.1093/bioinformatics/btl097.PubMedView ArticleGoogle Scholar
- Steinberg S, Chen L, Wei L, Moser A, Moser H, Cutting G, Braverman N: The PEX Gene Screen: molecular diagnosis of peroxisome biogenesis disorders in the Zellweger syndrome spectrum. Mol Genet Metab. 2004, 83: 252-263. 10.1016/j.ymgme.2004.08.008.PubMedView ArticleGoogle Scholar
- Li R, Li Y, Fang X, Yang H, Wang J, Kristiansen K: SNP detection for massively parallel whole-genome resequencing. Genome Res. 2009, 19: 1124-1132. 10.1101/gr.088013.108.PubMedPubMed CentralView ArticleGoogle Scholar
- Barrett JC, Fry B, Maller J, Daly MJ: Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics. 2005, 21: 263-265. 10.1093/bioinformatics/bth457.PubMedView ArticleGoogle Scholar
- Yi X, Liang Y, Huerta-Sanchez E, Jin X, Cuo ZX, Pool JE, Xu X, Jiang H, Vinckenbosch N, Korneliussen TS, Zheng H, Liu T, He W, Li K, Luo R, Nie X, Wu H, Zhao M, Cao H, Zou J, Shan Y, Li S, Yang Q, Ni P, Tian G, Xu J, Liu X, Jiang T, Wu R, Asan, et al: Sequencing of 50 human exomes reveals adaptation to high altitude. Science. 2010, 329: 75-78. 10.1126/science.1190371.PubMedPubMed CentralView ArticleGoogle Scholar
- Benson G: Tandem repeats finder: a program to analyze DNA sequences. Nucleic Acids Res. 1999, 27: 573-580. 10.1093/nar/27.2.573.PubMedPubMed CentralView ArticleGoogle Scholar
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