Literature DB >> 30941726

Whole genome sequencing analysis of horse populations inhabiting the Korean Peninsula and Przewalski's horse.

Ha-Seung Seong1, Nam-Young Kim2, Dae Cheol Kim3, Nam-Hyun Hwang1, Da-Hye Son1, Jong Suh Shin1, Joon-Hee Lee4, Won-Hyong Chung5, Jung-Woo Choi6.   

Abstract

BACKGROUND: The Jeju horse is an indigenous horse breed in Korea. However, there is a severe lack of genomic studies on Korean horse breeds.
OBJECTIVE: The objective of this study was to report genomic characteristics of domestic horse populations that inhabit South Korea (Jeju, Jeju crossbred, and Thoroughbred) and a wild horse breed (Przewalski's horse).
RESULTS: Using the equine reference genome assembly (EquCab 2.0), more than ~ 6.5 billion sequence reads were successfully mapped, which generated an average of 40.87-fold coverage throughout the genome. Using these data, we detected a total of 12.88 million SNPs, of which 73.7% were found to be novel. All the detected SNPs were deeply annotated to retrieve SNPs in gene regions using the RefSeq and Ensemble gene sets. Approximately 27% of the total SNPs were located within genes, whereas the remaining 73% were found in intergenic regions. Using 129,776 coding SNPs, we retrieved a total of 49,171 nonsynonymous SNPs in 12,351 genes. Furthermore, we identified a total of 10,770 deleterious nonsynonymous SNPs which are predicted to affect protein structure or function.
CONCLUSION: We showed numerous genomic variants from domestic and wild horse breeds. These results provide a valuable resource for further studies on functions of SNP-containing genes, and can aid in determining the molecular basis underlying variation in economically important traits of horses.

Entities:  

Keywords:  Jeju horse; Przewalski's horse; Re-sequencing; Single-nucleotide polymorphism

Mesh:

Year:  2019        PMID: 30941726     DOI: 10.1007/s13258-019-00795-w

Source DB:  PubMed          Journal:  Genes Genomics        ISSN: 1976-9571            Impact factor:   1.839


  29 in total

1.  Genome-wide analyses of the Jeju, Thoroughbred, and Jeju crossbred horse populations using the high density SNP array.

Authors:  Nam Young Kim; Ha-Seung Seong; Dae Cheol Kim; Nam Geon Park; Byoung Chul Yang; Jun Kyu Son; Sang Min Shin; Jae Hoon Woo; Moon Cheol Shin; Ji Hyun Yoo; Jung-Woo Choi
Journal:  Genes Genomics       Date:  2018-08-11       Impact factor: 1.839

2.  Association of single nucleotide polymorphisms in CAPN1, CAST and MB genes with meat color of Brahman and crossbreed cattle.

Authors:  Susan Castro; Marcela Ríos; Yurany Ortiz; Carlos Manrique; Ariel Jiménez; Fernando Ariza
Journal:  Meat Sci       Date:  2016-02-11       Impact factor: 5.209

3.  Comprehensive characterization of horse genome variation by whole-genome sequencing of 88 horses.

Authors:  V Jagannathan; V Gerber; S Rieder; J Tetens; G Thaller; C Drögemüller; T Leeb
Journal:  Anim Genet       Date:  2018-12-07       Impact factor: 3.169

4.  The Sequence Alignment/Map format and SAMtools.

Authors:  Heng Li; Bob Handsaker; Alec Wysoker; Tim Fennell; Jue Ruan; Nils Homer; Gabor Marth; Goncalo Abecasis; Richard Durbin
Journal:  Bioinformatics       Date:  2009-06-08       Impact factor: 6.937

5.  Genome sequence, comparative analysis, and population genetics of the domestic horse.

Authors:  C M Wade; E Giulotto; S Sigurdsson; M Zoli; S Gnerre; F Imsland; T L Lear; D L Adelson; E Bailey; R R Bellone; H Blöcker; O Distl; R C Edgar; M Garber; T Leeb; E Mauceli; J N MacLeod; M C T Penedo; J M Raison; T Sharpe; J Vogel; L Andersson; D F Antczak; T Biagi; M M Binns; B P Chowdhary; S J Coleman; G Della Valle; S Fryc; G Guérin; T Hasegawa; E W Hill; J Jurka; A Kiialainen; G Lindgren; J Liu; E Magnani; J R Mickelson; J Murray; S G Nergadze; R Onofrio; S Pedroni; M F Piras; T Raudsepp; M Rocchi; K H Røed; O A Ryder; S Searle; L Skow; J E Swinburne; A C Syvänen; T Tozaki; S J Valberg; M Vaudin; J R White; M C Zody; E S Lander; K Lindblad-Toh
Journal:  Science       Date:  2009-11-06       Impact factor: 47.728

6.  Horse domestication and conservation genetics of Przewalski's horse inferred from sex chromosomal and autosomal sequences.

Authors:  Allison N Lau; Lei Peng; Hiroki Goto; Leona Chemnick; Oliver A Ryder; Kateryna D Makova
Journal:  Mol Biol Evol       Date:  2008-10-17       Impact factor: 16.240

7.  A framework for variation discovery and genotyping using next-generation DNA sequencing data.

Authors:  Mark A DePristo; Eric Banks; Ryan Poplin; Kiran V Garimella; Jared R Maguire; Christopher Hartl; Anthony A Philippakis; Guillermo del Angel; Manuel A Rivas; Matt Hanna; Aaron McKenna; Tim J Fennell; Andrew M Kernytsky; Andrey Y Sivachenko; Kristian Cibulskis; Stacey B Gabriel; David Altshuler; Mark J Daly
Journal:  Nat Genet       Date:  2011-04-10       Impact factor: 38.330

8.  Fast and accurate long-read alignment with Burrows-Wheeler transform.

Authors:  Heng Li; Richard Durbin
Journal:  Bioinformatics       Date:  2010-01-15       Impact factor: 6.937

9.  Analysis of horse genomes provides insight into the diversification and adaptive evolution of karyotype.

Authors:  Jinlong Huang; Yiping Zhao; Wunierfu Shiraigol; Bei Li; Dongyi Bai; Weixing Ye; Dorjsuren Daidiikhuu; Lihua Yang; Burenqiqige Jin; Qinan Zhao; Yahan Gao; Jing Wu; Wuyundalai Bao; Anaer Li; Yuhong Zhang; Haige Han; Haitang Bai; Yanqing Bao; Lele Zhao; Zhengxiao Zhai; Wenjing Zhao; Zikui Sun; Yan Zhang; He Meng; Manglai Dugarjaviin
Journal:  Sci Rep       Date:  2014-05-14       Impact factor: 4.379

10.  Trimmomatic: a flexible trimmer for Illumina sequence data.

Authors:  Anthony M Bolger; Marc Lohse; Bjoern Usadel
Journal:  Bioinformatics       Date:  2014-04-01       Impact factor: 6.937

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  1 in total

Review 1.  Ten years of the horse reference genome: insights into equine biology, domestication and population dynamics in the post-genome era.

Authors:  T Raudsepp; C J Finno; R R Bellone; J L Petersen
Journal:  Anim Genet       Date:  2019-09-30       Impact factor: 3.169

  1 in total

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