Discovery of genomic variations by whole-genome resequencing of the North American Araucana chicken

Autoři: Rooksana E. Noorai aff001;  Vijay Shankar aff002;  Nowlan H. Freese aff003;  Christopher M. Gregorski aff004;  Susan C. Chapman aff004
Působiště autorů: Clemson University Genomics and Bioinformatics Facility, Clemson University, Clemson, South Carolina, United States of America aff001;  Center for Human Genetics, Clemson University, Greenwood, South Carolina, United States of America aff002;  Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, North Carolina, United States of America aff003;  Department of Biological Sciences, College of Science, Clemson University, Clemson, South Carolina, United States of America aff004
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article


Gallus gallus (chicken) is phenotypically diverse, with over 60 recognized breeds, among the myriad species within the Aves lineage. Domestic chickens have been under artificial selection by humans for thousands of years for agricultural purposes. The North American Araucana (NAA) breed arose as a cross between the Chilean “Collonocas” that laid blue eggs and was rumpless and the “Quetros” that had unusual tufts but with tail. NAAs were introduced from South America in the 1940s and have been kept as show birds by enthusiasts since then due to several distinctive traits: laying eggs with blue eggshells, characteristic ear-tufts, a pea comb, and rumplessness. The population has maintained variants for clean-faced and tufted, as well as tailed and rumplessness traits making it advantageous for genetic studies. Genome resequencing of six NAA chickens with a mixture of these traits was done to 71-fold coverage using Illumina HiSeq 2000 paired-end reads. Trimmed and concordant reads were mapped to the Gallus_gallus-5.0 reference genome (galGal5), generated from a female Red Junglefowl (UCD001). To identify candidate genes that are associated with traits of the NAA, their genome was compared with the Korean Araucana, Korean Domestic and White Leghorn breeds. Genomic regions with significantly reduced levels of heterogeneity were detected on five different chromosomes in NAA. The sequence data generated confirm the identity of variants responsible for the blue eggshells, pea comb, and rumplessness traits of NAA and propose one for ear-tufts.

Klíčová slova:

Animal sexual behavior – Bird genetics – Bird genomics – Birds – Chickens – Introns – Peas – Sequence alignment


1. Storey AA, Athens JS, Bryant D, Carson M, Emery K, deFrance S, et al. (2012) Investigating the global dispersal of chickens in prehistory using ancient mitochondrial DNA signatures. PLoS One 7: e39171. doi: 10.1371/journal.pone.0039171 22848352

2. (1873) American Poultry Association.

3. Lenffer J, Nicholas FW, Castle K, Rao A, Gregory S, Poidinger M, et al. (2006) OMIA (Online Mendelian Inheritance in Animals): an enhanced platform and integration into the Entrez search interface at NCBI. Nucleic Acids Res 34: D599–601. doi: 10.1093/nar/gkj152 16381939

4. (2019) Gateway to poultry production and products. Food and Agriculture Organization of the United Nations.

5. Burt DW (2007) Emergence of the Chicken as a Model Organism: Implications for Agriculture and Biology1. Poultry Science 86: 1460–1471. doi: 10.1093/ps/86.7.1460 17575197

6. Dodgson JB, Romanov MN (2004) Use of chicken models for the analysis of human disease. Curr Protoc Hum Genet Chapter 15: Unit 15.15.

7. Schock EN, Chang CF, Youngworth IA, Davey MG, Delany ME, Brugmann SA (2016) Utilizing the chicken as an animal model for human craniofacial ciliopathies. Dev Biol 415: 326–337. doi: 10.1016/j.ydbio.2015.10.024 26597494

8. Li D, Che T, Chen B, Tian S, Zhou X, Zhang G, et al. (2017) Genomic data for 78 chickens from 14 populations. Gigascience 6: 1–5.

9. Cheng Y, Burt DW (2018) Chicken genomics. Int J Dev Biol 62: 265–271. doi: 10.1387/ijdb.170276yc 29616735

10. Brunson C (1991) Araucanas: Rings on Their Ears!: Cathy Brunson.

11. Ekarius C (2007) Storey’s Illustrated Guide to Poultry Breeds: Storey Pub.

12. Hedrick PW (2015) Heterozygote advantage: the effect of artificial selection in livestock and pets. J Hered 106: 141–154. doi: 10.1093/jhered/esu070 25524994

13. Noorai RE, Freese NH, Wright LM, Chapman SC, Clark LA (2012) Genome-wide association mapping and identification of candidate genes for the rumpless and ear-tufted traits of the Araucana chicken. PLoS One 7: e40974. doi: 10.1371/journal.pone.0040974 22844420

14. Freese NH, Lam BA, Staton M, Scott A, Chapman SC (2014) A novel gain-of-function mutation of the proneural IRX1 and IRX2 genes disrupts axis elongation in the Araucana rumpless chicken. PLoS One 9: e112364. doi: 10.1371/journal.pone.0112364 25372603

15. Jeong H, Kim K, Caetano-Anolles K, Kim H, Kim BK, Yi JK, et al. (2016) Whole genome sequencing of Gyeongbuk Araucana, a newly developed blue-egg laying chicken breed, reveals its origin and genetic characteristics. Sci Rep 6: 26484. doi: 10.1038/srep26484 27215397

16. Oh D, Son B, Mun S, Oh MH, Oh S, Ha J, et al. (2016) Whole Genome Re-Sequencing of Three Domesticated Chicken Breeds. Zoolog Sci 33: 73–77. doi: 10.2108/zs150071 26853871

17. Warren WC, Hillier LW, Tomlinson C, Minx P, Kremitzki M, Graves T, et al. (2017) A New Chicken Genome Assembly Provides Insight into Avian Genome Structure. G3 (Bethesda) 7: 109–117.

18. Liu R, Xing S, Wang J, Zheng M, Cui H, Crooijmans R, et al. (2019) A new chicken 55K SNP genotyping array. BMC Genomics 20: 410. doi: 10.1186/s12864-019-5736-8 31117951

19. Qanbari S, Strom TM, Haberer G, Weigend S, Gheyas AA, Turner F, et al. (2012) A high resolution genome-wide scan for significant selective sweeps: an application to pooled sequence data in laying chickens. PLoS One 7: e49525. doi: 10.1371/journal.pone.0049525 23209582

20. Akagi T, Hanada T, Yaegaki H, Gradziel TM, Tao R (2016) Genome-wide view of genetic diversity reveals paths of selection and cultivar differentiation in peach domestication. DNA Res 23: 271–282. doi: 10.1093/dnares/dsw014 27085183

21. Wang Z, Qu L, Yao J, Yang X, Li G, Zhang Y, et al. (2013) An EAV-HP insertion in 5' Flanking region of SLCO1B3 causes blue eggshell in the chicken. PLoS Genet 9: e1003183. doi: 10.1371/journal.pgen.1003183 23359636

22. Morrow BE, McDonald-McGinn DM, Emanuel BS, Vermeesch JR, Scambler PJ (2018) Molecular genetics of 22q11.2 deletion syndrome. Am J Med Genet A 176: 2070–2081. doi: 10.1002/ajmg.a.40504 30380194

23. Napoli E, Tassone F, Wong S, Angkustsiri K, Simon TJ, Song G, et al. (2015) Mitochondrial Citrate Transporter-dependent Metabolic Signature in the 22q11.2 Deletion Syndrome. J Biol Chem 290: 23240–23253. doi: 10.1074/jbc.M115.672360 26221035

24. Wright D, Boije H, Meadows JR, Bed’hom B, Gourichon D, Vieaud A, et al. (2009) Copy number variation in intron 1 of SOX5 causes the Pea-comb phenotype in chickens. PLoS Genet 5: e1000512. doi: 10.1371/journal.pgen.1000512 19521496

25. Gongora J, Rawlence NJ, Mobegi VA, Jianlin H, Alcalde JA, Matus JT, et al. (2008) Indo-European and Asian origins for Chilean and Pacific chickens revealed by mtDNA. Proceedings of the National Academy of Sciences 105: 10308.

26. Storey AA, Ramírez JM, Quiroz D, Burley DV, Addison DJ, Walter R, et al. (2007) Radiocarbon and DNA evidence for a pre-Columbian introduction of Polynesian chickens to Chile. Proceedings of the National Academy of Sciences 104: 10335.

27. Thomson VA, Lebrasseur O, Austin JJ, Hunt TL, Burney DA, Denham T, et al. (2014) Using ancient DNA to study the origins and dispersal of ancestral Polynesian chickens across the Pacific. Proceedings of the National Academy of Sciences 111: 4826.

28. Li D, Li Y, Li M, Che T, Tian S, Chen B, et al. (2019) Population genomics identifies patterns of genetic diversity and selection in chicken. BMC Genomics 20: 263. doi: 10.1186/s12864-019-5622-4 30940068

29. Pampouille E, Berri C, Boitard S, Hennequet-Antier C, Beauclercq SA, Godet E, et al. (2018) Mapping QTL for white striping in relation to breast muscle yield and meat quality traits in broiler chickens. BMC Genomics 19: 202. doi: 10.1186/s12864-018-4598-9 29554873

30. Xue Q, Zhang G, Li T, Ling J, Zhang X, Wang J (2017) Transcriptomic profile of leg muscle during early growth in chicken. PLoS One 12: e0173824. doi: 10.1371/journal.pone.0173824 28291821

31. Yang S, Wang Y, Wang L, Shi Z, Ou X, Wu D, et al. (2018) RNA-Seq reveals differentially expressed genes affecting polyunsaturated fatty acids percentage in the Huangshan Black chicken population. PLoS One 13: e0195132. doi: 10.1371/journal.pone.0195132 29672513

32. Altshuler D, Daly MJ, Lander ES (2008) Genetic mapping in human disease. Science 322: 881–888. doi: 10.1126/science.1156409 18988837

33. Andrews S (2010) FastQC A Quality Control tool for High Throughput Sequence Data.

34. Bolger AM, Lohse M, Usadel B (2014) Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30: 2114–2120. doi: 10.1093/bioinformatics/btu170 24695404

35. Clark K, Karsch-Mizrachi I, Lipman DJ, Ostell J, Sayers EW (2015) GenBank. Nucleic Acids Res 44: D67–72. doi: 10.1093/nar/gkv1276 26590407

36. Hillier LW, Miller W, Birney E, Warren W, Hardison RC, Ponting CP, et al. (2004) Sequence and comparative analysis of the chicken genome provide unique perspectives on vertebrate evolution. Nature 432: 695–716. doi: 10.1038/nature03154 15592404

37. Langmead B, Salzberg SL (2012) Fast gapped-read alignment with Bowtie 2. Nat Methods 9: 357–359. doi: 10.1038/nmeth.1923 22388286

38. Li H (2011) A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics 27: 2987–2993. doi: 10.1093/bioinformatics/btr509 21903627

39. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. (2009) The Sequence Alignment/Map format and SAMtools. Bioinformatics 25: 2078–2079. doi: 10.1093/bioinformatics/btp352 19505943

40. (2015) Picard: a set of command line tools (in Java) for manipulating high-throughput sequencing (HTS) data and formats such as SAM/BAM/CRAM and VCF.

41. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, et al. (2010) The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res 20: 1297–1303. doi: 10.1101/gr.107524.110 20644199

42. DePristo MA, Banks E, Poplin R, Garimella KV, Maguire JR, Hartl C, et al. (2011) A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat Genet 43: 491–498. doi: 10.1038/ng.806 21478889

43. Van der Auwera GA, Carneiro MO, Hartl C, Poplin R, Del Angel G, Levy-Moonshine A, et al. (2013) From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline. Curr Protoc Bioinformatics 43: 11 10 11–33.

44. Freese NH, Norris DC, Loraine AE (2016) Integrated genome browser: visual analytics platform for genomics. Bioinformatics 32: 2089–2095. doi: 10.1093/bioinformatics/btw069 27153568

45. Cingolani P, Patel VM, Coon M, Nguyen T, Land SJ, Ruden DM, et al. (2012) Using Drosophila melanogaster as a Model for Genotoxic Chemical Mutational Studies with a New Program, SnpSift. Front Genet 3: 35. doi: 10.3389/fgene.2012.00035 22435069

46. Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA, et al. (2011) The variant call format and VCFtools. Bioinformatics 27: 2156–2158. doi: 10.1093/bioinformatics/btr330 21653522

47. Kronenberg Z (2016) vcflib. Github.

48. McLaren W, Gil L, Hunt SE, Riat HS, Ritchie GR, Thormann A, et al. (2016) The Ensembl Variant Effect Predictor. Genome Biol 17: 122. doi: 10.1186/s13059-016-0974-4 27268795

49. Zheng X, Levine D, Shen J, Gogarten SM, Laurie C, Weir BS (2012) A high-performance computing toolset for relatedness and principal component analysis of SNP data. Bioinformatics 28: 3326–3328. doi: 10.1093/bioinformatics/bts606 23060615

50. Liaw A, Wiener M (2002) Classification and regression by randomForest. 2: 18–22.

51. Quinlan AR, Hall IM (2010) BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26: 841–842. doi: 10.1093/bioinformatics/btq033 20110278

52. Turner SD (2014) qqman: an R package for visualizing GWAS results using Q-Q and manhattan plots. bioRxiv: 005165.

53. Kanehisa M, Furumichi M, Tanabe M, Sato Y, Morishima K (2017) KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res 45: D353–D361. doi: 10.1093/nar/gkw1092 27899662

54. Kanehisa M, Goto S (2000) KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28: 27–30. doi: 10.1093/nar/28.1.27 10592173

55. Kanehisa M, Sato Y, Kawashima M, Furumichi M, Tanabe M (2016) KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res 44: D457–462. doi: 10.1093/nar/gkv1070 26476454

56. Zerbino DR, Achuthan P, Akanni W, Amode MR, Barrell D, Bhai J, et al. Ensembl 2018. Nucleic Acids Res 46: D754–D761. doi: 10.1093/nar/gkx1098 29155950

57. Wala JA, Bandopadhayay P, Greenwald NF, O’Rourke R, Sharpe T, Stewart C, et al. (2018) SvABA: genome-wide detection of structural variants and indels by local assembly. Genome Res 28: 581–591. doi: 10.1101/gr.221028.117 29535149

58. (2018) Seqtk is a fast and lightweight tool for processing sequences in the FASTA or FASTQ format. It seamlessly parses both FASTA and FASTQ files which can also be optionally compressed by gzip.

59. Xie C, Tammi MT (2009) CNV-seq, a new method to detect copy number variation using high-throughput sequencing. BMC Bioinformatics 10: 80. doi: 10.1186/1471-2105-10-80 19267900

60. Kent WJ (2002) BLAT—the BLAST-like alignment tool. Genome Res 12: 656–664. doi: 10.1101/gr.229202 11932250

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