- Research article
- Open Access
Development and evaluation of the first high-throughput SNP array for common carp (Cyprinus carpio)
© Xu et al.; licensee BioMed Central Ltd. 2014
- Received: 9 September 2013
- Accepted: 17 April 2014
- Published: 24 April 2014
A large number of single nucleotide polymorphisms (SNPs) have been identified in common carp (Cyprinus carpio) but, as yet, no high-throughput genotyping platform is available for this species. C. carpio is an important aquaculture species that accounts for nearly 14% of freshwater aquaculture production worldwide. We have developed an array for C. carpio with 250,000 SNPs and evaluated its performance using samples from various strains of C. carpio.
The SNPs used on the array were selected from two resources: the transcribed sequences from RNA-seq data of four strains of C. carpio, and the genome re-sequencing data of five strains of C. carpio. The 250,000 SNPs on the resulting array are distributed evenly across the reference C.carpio genome with an average spacing of 6.6 kb. To evaluate the SNP array, 1,072 C. carpio samples were collected and tested. Of the 250,000 SNPs on the array, 185,150 (74.06%) were found to be polymorphic sites. Genotyping accuracy was checked using genotyping data from a group of full-siblings and their parents, and over 99.8% of the qualified SNPs were found to be reliable. Analysis of the linkage disequilibrium on all samples and on three domestic C.carpio strains revealed that the latter had the longer haplotype blocks. We also evaluated our SNP array on 80 samples from eight species related to C. carpio, with from 53,526 to 71,984 polymorphic SNPs. An identity by state analysis divided all the samples into three clusters; most of the C. carpio strains formed the largest cluster.
The Carp SNP array described here is the first high-throughput genotyping platform for C. carpio. Our evaluation of this array indicates that it will be valuable for farmed carp and for genetic and population biology studies in C. carpio and related species.
- SNP array
- Linkage disequilibrium
- Identity by state
- Cyprinus carpio
- Common carp
Common carp (Cyprinus carpio) is naturally distributed across Europe and Asia. It was domesticated about 2,000 years ago, and is cultured in over 100 countries worldwide with over 3 million metric tons of global annual production [1, 2]. As a result of selection and breeding efforts over the past centuries, many domesticated strains have been established with distinct economic traits or phenotypes adapted to local environments and to meet consumer demands. China is the largest C. carpio producer, and there are abundant domesticated strains and populations in China, including Sonpu mirror carp, Hebao red carp, Xingguo red carp, Yellow River carp, and Oujiang color carp, as well as many hybrid strains, all of which are the basis and genetic resources for selective breeding using modern genetic tools.
Because of the economic importance of C. carpio for the global aquaculture industry, as well as its importance as a model species for ecology, physiology, and evolutionary studies, over the past decade, researchers have developed a variety of genetic and genomics tool and resources. A large number of genetic markers have been developed, including microsatellites [3, 4], and single nucleotide polymorphisms (SNPs) [5, 6]. A number of genetic linkage maps have been constructed based on these markers [7–10]. The markers have also been used to identify quantitative trait loci (QTLs) associated with economically important traits including growth rate, body shape, and meat quality [4, 11, 12]. A large set of expressed sequence tags (ESTs) have been generated using traditional cloning and Sanger sequencing methods, or next-generation transcriptome sequencing, and a cDNA microarray has been designed and constructed [13–17]. A bacterial artificial chromosome (BAC) library has been built , a BAC-based physical map has been constructed, and a large set of BAC-end sequences (BES) have been generated [19, 20]. The complete mitochondrial genomes of several strains and populations have been sequenced [21–23]. Whole genome exome data were generated for a comparative study with the Danio rerio genome  and, recently, the C. carpio genome consortium has completely sequenced and assembled a draft genome sequence of C. carpio.
A major gap in the C. carpio toolkit is the lack of a high-throughput SNP genotyping platform for genetic research. Such a platform is essential for whole genome association studies (GWAS) of important traits, as well as for genome-assisted selection in breeding programs. Genome-scale SNP genotyping is most efficiently performed using SNP arrays or chips. Arrays of this type have been used widely in genetic studies in humans, as well as in important model organisms and agriculture species.
The reductions in the cost of acquiring sequence data using next-generation sequencing technologies has led to the development of genotyping by sequencing (GBS) approaches, which use whole genome sequencing, reduced representative genome sequencing, or target-enriched DNA sequencing data to determine genotypes. The most popular GBS protocol is restriction-site-associated DNA (RAD) tag sequencing in which DNA fragments flanking particular restriction sites are targeted for sequencing, thereby allowing the discovery and genotyping of SNPs at these targeted locations . Although GBS methods have some advantages for genome-wide SNP discovery and genotyping, especially for species for which a reference genome has not been established, they also have limitations, which include the requirements for complicated DNA library preparation procedures and intensive bioinformatics pipelines. GBS is not suitable for genotyping the very large numbers of individuals or SNP loci that are used commonly in GWAS and genomic selection. In addition, GBS genotyping results are not shared easily among different research groups because the same SNP loci are not assayed in all individuals.
Therefore high-density SNP genotyping arrays remain the tools of choice for high-resolution genetics analysis. Many SNP arrays or chips have been developed for either Illumina or Affymetrix platforms, including the human 500 K array, the Genome-Wide Human SNP Array 5.0 and 6.0, the porcine 60 K SNP array , the bovine 50KSNP array , the chicken 60 K  and 600 K SNP arrays , the canine 22 k SNP array , and the equine 50 K SNP array . These arrays have been used widely for research on selective sweeps, phylogeny, population structure, copy number variations, GWAS, and other aspects [32–36], boosting genome and genetic studies as well as breeding programs of these species.
Although the importance of high-density SNP genotyping arrays has been recognized widely, as yet there are only a few such SNP genotyping arrays for aquaculture species. After the submission of this manuscript, an Affymetrix Axiom® myDesign Custom Array containing 132,033 Atlantic salmon SNPs was developed . Meanwhile, an Affymetrix Axiom Array containing 204,437 putative catfish SNPs was also developed . Although a large research community is working on C. carpio and other closely related Cyprinid species, and genotyping is performed intensively for diverse purposes, no SNP genotyping array is available for C. carpio.
Here, we report the design and validation of the first high-density C. carpio SNP array, the Carp SNP array, based on the Affymetrix Axiom platform. The Carp SNP array was validated with 1,072 samples from various C. carpio populations and strains. To assess its potential use in closely related Cyprinids, we also validated the array in 80 individuals from eight related species. A pilot study was conducted to demonstrate the accuracy and efficiency of the genome-scale genotyping and linkage disequilibrium (LD) decay was analyzed in all samples and in several domesticated strains. Identity by state (IBS) clustering of all samples was conducted, which demonstrated the reliability of the Carp SNP array.
Sequencing and alignment of sequence reads
Genome re-sequencing data
Raw bases (G)
Mapped bases (G)
Mapping rate (%)
Coverage rate (%)
Songpu carp 1
Songpu carp 2
Songpu carp 3
Songpu carp 4
Yellow River carp 1
Yellow River carp 2
Yellow River carp 3
Yellow River carp 4
Heilongjiang River carp 1
Heilongjiang River carp 2
Heilongjiang River carp 3
Hebao carp 1
Hebao carp 2
Hebao carp 3
Hebao carp 4
Oujiang color carp 1
Oujiang color carp 2
Oujiang color carp 3
SNP identification from genome re-sequencing
Songpu mirror carp
Yellow River carp
Heilongjiang River carp
Oujiang color carp
SNP reduction based on flanking sequence quality and close proximity
For quality control, 71-bp fragments spanning each SNP were extracted, including 35-bp upstream and 35-bp downstream of the SNP base. SNPs with flanking sequences that containing over four consecutive ‘G’ or ‘C’ or over six consecutive ‘A’ or ‘T’, and those containing ‘N’ were removed, resulting in 13,431,573 SNPs. Next, GC content was calculated and SNPs with flanking sequences with GC content below 30% or above 70% were removed. The flanking sequences of the remaining 11,307,040 SNPs were mapped to the reference genome, and the 8,450,637 SNPs that mapped uniquely were kept for further selection. SNPs located very close to each other are less likely to be assayed successfully during genotyping because of interference from neighboring variants. Clustering of SNPs can be a result of the mis-alignment of reads because of the presence of the indels (insertions or deletions) at the beginning or end of reads . Based on advice from Affymetrix scientists, we removed SNPs that were within 10 bp of each other or there were more than two variants within 35 bp. After these steps, 3,719,260 SNPs remained in the final pool for selection. Priority was given to SNPs in coding sequences, and then the genome re-sequencing SNPs were selected on the basis of their quality scores and spacing on the genome. Finally, a total of 378,815 SNPs were submitted for probe design.
SNP reduction based on in-silicoanalysis of conversion values
SNP selection for the final Carp array
Number of SNPs during SNP array designation
Evaluation of the SNP array in C. carpiostrains
Evaluation of SNP array in all samples
Related species of C. carpio
Poly high resolution
No minor homology
Mono high resolution
Call rate below threshold
Off Target Variation (OTV)
Accuracy of genotyping for the SNP array
High accuracy is a vital parameter for a genotyping platform. In this study, we assessed the genotyping accuracy of our Carp array using data from a family comprising two parents and 80 offspring. PLINK software was applied with the ‘Mendel’ parameter. Any genotypes not concordant between parents and offspring were regarded as genotyping errors. We estimated the accuracy to be 99.6% on average, and after excluding one sample because of multiple inconsistencies with the inheritance pattern expected on the basis of the declared pedigree, the genotyping accuracy increased to 99.8% on average, showing the high genotyping quality of the Carp array. Thus, in subsequent research, this array will be of great importance in trait association analysis, QTL mapping, and marker assisted selection.
Extensive assessment of the SNP array in Cyprinids
Evaluation of SNP array in eight Cyprinus carpio related species
C. carassius(n = 10)
M. piceus(n = 10)
C. idella(n = 10)
H. molitrix(n = 10)
H. nobilis(n = 10)
M. amblycephala(n = 10)
D. rerio(n = 15)
L. waleckii(n = 5)
Poly high resolution
No minor homology
Mono high resolution
Call rate below threshold
Off Target Variation (OTV)
Linkage disequilibrium (LD) analysis
Population structure analysis through identity by state (IBS) clustering
We developed the Carp SNP array which is the first high-throughput genotyping platform for C. carpio. After evaluation with large samples, nearly three fourths of the designed 250,000 SNPs proved to be polymorphic in C. carpio. Besides, the Carp SNP array was also evaluated in related species. LD was calculated and longer haplotype blocks were observed in domesticated strains. IBS was conducted and most of the samples were assigned to different clusters. This study indicates that the Carp SNP array will be valuable for farmed carp and for genetic and population biology studies in C. carpio and related species.
This study was approved by the Animal Care and Use Committee (ACUC) of the Centre for Applied Aquatic Genomics at the Chinese Academy of Fishery Sciences. All sampling procedures complied with the guidelines of ACUC on the care and use of animals for scientific purposes.
Sample collection and genome re-sequencing
Five strains (here a “strain” is defined as a domestic population with unique characteristics; different strains belong to the same species) of C. carpio comprising 18 accessions (here “accession” means individual) were collected. The five strains were Songpu carp from Heilongjiang Fishery Research Institute, Yellow River carp from Henan Academy of Fishery Sciences, Heilongjiang River carp from Fuyuan County in Heilongjiang Province, Hebao carp from Wuyuan County in Jiangxi Province, and Oujiang color carp from Longquan County in Zhejiang Province. Fin chips or blood samples were collected and DNA was extracted using a DNeasy Blood & Tissue Kit (Qiagen, Shanghai, China). The samples are listed in Table 1. DNA library preparation and sequencing were carried out at the HudsonAlpha Genomic Services Laboratory (Huntsville, AL, USA) following the manufacturer’s instructions. After KAPA quantitation and dilution, the library was sequenced on Illumina HiSeq 2000 to generate 101 bp paired-end reads.
The paired-end reads from each accession were aligned to the reference genome using BWA  to generate sequence alignment/map SAM files. After mapping, SNPs were identified on the basis of the mpileup files generated by SAMtools . The variant call format (VCF) files were manipulated further using custom-made scripts for primary filtration based on depth and quality.
SNP selection was carried out in multiple steps using different criteria. All the filtration parameters were set to minimize the risk of false positive sites and to select SNPs that were relatively evenly distributed across the genome. All the original SNPs were classified to six different databases and selected in a certain order. First, non-synonymous SNPs and SNPs in UTR regions were selected; then other transcriptome SNPs were added; and finally, strain-shared and strain-specific SNPs were added to the pool of candidate SNPs. During the SNP selection steps, several custom-made scripts were used to qualify flanking sequences. To ensure an even distribution of SNPs over the genome, a custom-made algorithm (described below) was used. When a new SNP was introduced into the final pool, a threshold of t bases was set and SNPs within the t bases were excluded. For SNPs that originated from the transcriptome data, t was set lower than 2 kb so that all the cSNPs were included in the final pool. For SNPs from the genome re-sequencing data, t was set over 10 kb because most of these SNPs were from non-coding regions.
Evaluation of the SNP array
To evaluate the Carp SNP array, 1,072 samples from C. carpio and 80 samples from carp-related species were collected. Genomic DNA was extracted from blood using a DNeasy 96 Blood & Tissue Kit (Qiagen). All the DNA samples were quantified by NanodropND-1000 spectrophotometer (NanoDrop Technologies Inc., Wilmington, DE, USA) and sent to GeneSeek (Lansing, MI) for genotyping. The genotype data were extracted and converted to Ped/Map format. PLINK software  was used to classify the SNPs and extract the data for the different species. Mendelian analysis and LD decay were also conducted with PLINK using the “--mendel” and “--r2” parameters. Mendelian analysis was conducted on family data for two parents and 80 offspring, following the procedure reported previously . X-Y plots were drawn using the average r2 values (Y axis) and the physical distances (X axis) for each pair of SNPs each kilo base-pair. IBS clustering was conducted with PLINK using the “--mds-plot 2”, “--cluster”, and “--genome” parameters, with a P-value threshold of 1E-3. The PLINK MDS file was extracted and a scatter plot was drawn using d$C1 (X axis) and d$C2 (Y axis) in the R software package (version 3.0.2, Vienna, Austria).
We acknowledge grant support from the National High-Technology Research and Development Program of China (863 program; 2011AA100401 and 2011AA100402), National Department Public Benefit Research Foundation of China (200903045), and China Ministry of Agriculture “948” Program (No. 2013- Z12). PX would like to thank the Visiting Professorship Program, Deanship of Scientific Research, College of Sciences at King Saud University, Riyadh.
- Fisheries F: Aquaculture Department: The State of World Fisheries and Aquaculture 2006. 2007, Rome: Food and Agriculture Organization of the United NationsGoogle Scholar
- Bostock J, McAndrew B, Richards R, Jauncey K, Telfer T, Lorenzen K, Little D, Ross L, Handisyde N, Gatward I, Corner R: Aquaculture: global status and trends. Philos T R Soc B. 2010, 365 (1554): 2897-2912. 10.1098/rstb.2010.0170.View ArticleGoogle Scholar
- Ji P, Zhang Y, Li C, Zhao Z, Wang J, Li J, Xu P, Sun X: High throughput mining and characterization of microsatellites from common carp genome. Int J Mol Sci. 2012, 13 (8): 9798-9807.PubMed CentralPubMedView ArticleGoogle Scholar
- Zheng X, Kuang Y, Lv W, Cao D, Zhang X, Li C, Lu C, Sun X: A consensus linkage map of common carp (Cyprinus carpio L.) to compare the distribution and variation of QTLs associated with growth traits. Sci China Life Sci. 2013, 56 (4): 351-359. 10.1007/s11427-012-4427-3.PubMedView ArticleGoogle Scholar
- Xu J, Ji P, Zhao Z, Zhang Y, Feng J, Wang J, Li J, Zhang X, Zhao L, Liu G, Xu P, Sun X: Genome-wide SNP discovery from transcriptome of four common carp strains. PLoS One. 2012, 7 (10): e48140-10.1371/journal.pone.0048140.PubMed CentralPubMedView ArticleGoogle Scholar
- Kongchum P, Palti Y, Hallerman EM, Hulata G, David L: SNP discovery and development of genetic markers for mapping innate immune response genes in common carp (Cyprinus carpio). Fish Shellfish Immunol. 2010, 29 (2): 356-361. 10.1016/j.fsi.2010.04.013.PubMedView ArticleGoogle Scholar
- Zhang X, Zhang Y, Zheng X, Kuang Y, Zhao Z, Zhao L, Li C, Jiang L, Cao D, Lu C, Xu P, Sun X: A consensus linkage map provides insights on genome character and evolution in common carp (Cyprinus carpio L.). Mar Biotechnol. 2013, 15 (3): 275-312. 10.1007/s10126-012-9485-9.PubMedView ArticleGoogle Scholar
- Zhao L, Zhang Y, Ji P, Zhang X, Zhao Z, Hou G, Huo L, Liu G, Li C, Xu P, Sun X: A dense genetic linkage map for common carp and its integration with a BAC-based physical map. PLoS One. 2013, 8 (5): e63928-10.1371/journal.pone.0063928.PubMed CentralPubMedView ArticleGoogle Scholar
- Wong GK, Liu B, Wang J, Zhang Y, Yang X, Zhang Z, Meng Q, Zhou J, Li D, Zhang J, Ni P, Li S, Ran L, Li H, Li R, Zheng H, Lin W, Li G, Wang X, Zhao W, Li J, Ye C, Dai M, Ruan J, Zhou Y, Li Y, He X, Huang X, Tong W, Chen J, et al: A genetic variation map for chicken with 2.8 million single-nucleotide polymorphisms. Nature. 2004, 432 (7018): 717-722. 10.1038/nature03156.PubMedView ArticleGoogle Scholar
- Cheng L, Liu L, Yu X, Wang D, Tong J: A linkage map of common carp (Cyprinus carpio) based on AFLP and microsatellite markers. Anim Genet. 2010, 41 (2): 191-198. 10.1111/j.1365-2052.2009.01985.x.PubMedView ArticleGoogle Scholar
- Liu J, Zhang L, Xu L, Ren H, Lu J, Zhang X, Zhang S, Zhou X, Wei C, Zhao F, Du L: Analysis of copy number variations in the sheep genome using 50 K SNP BeadChip array. BMC Genomics. 2013, 14: 229-10.1186/1471-2164-14-229.PubMed CentralPubMedView ArticleGoogle Scholar
- Zhang Y, Xu P, Lu C, Kuang Y, Zhang X, Cao D, Li C, Chang Y, Hou N, Li H: Genetic linkage mapping and analysis of muscle fiber-related QTLs in common carp (Cyprinus carpio L.). Mar Biotechnol. 2011, 13 (3): 376-392. 10.1007/s10126-010-9307-x.PubMedView ArticleGoogle Scholar
- Xu J, Huang W, Zhong C, Luo D, Li S, Zhu Z, Hu W: Defining global gene expression changes of the hypothalamic-pituitary-gonadal axis in female sGnRH-antisense transgenic common carp (Cyprinus carpio). PLoS One. 2011, 6 (6): e21057-10.1371/journal.pone.0021057.PubMed CentralPubMedView ArticleGoogle Scholar
- Williams DR, Li W, Hughes MA, Gonzalez SF, Vernon C, Vidal MC, Jeney Z, Jeney G, Dixon P, McAndrew B, Bartfai R, Orban L, Trudeau V, Rogers J, Matthews L, Fraser EJ, Gracey AY, Cossins AR: Genomic resources and microarrays for the common carp Cyprinus carpio L. J Fish Biol. 2008, 72 (9): 2095-2117. 10.1111/j.1095-8649.2008.01875.x.View ArticleGoogle Scholar
- Christoffels A, Bartfai R, Srinivasan H, Komen H, Orban L: Comparative genomics in cyprinids: common carp ESTs help the annotation of the zebrafish genome. BMC Bioinforma. 2006, 7 (Suppl 5): S2-10.1186/1471-2105-7-S5-S2.View ArticleGoogle Scholar
- Ji P, Liu G, Xu J, Wang X, Li J, Zhao Z, Zhang X, Zhang Y, Xu P, Sun X: Characterization of common carp transcriptome: sequencing, de novo assembly, annotation and comparative genomics. PLoS One. 2012, 7 (4): e35152-10.1371/journal.pone.0035152.PubMed CentralPubMedView ArticleGoogle Scholar
- Moens LN, van der Ven K, Van Remortel P, Del‒Favero J, De Coen W: Gene expression analysis of estrogenic compounds in the liver of common carp (Cyprinus carpio) using a custom cDNA microarray. J Biochem Mol Toxicol. 2007, 21 (5): 299-311. 10.1002/jbt.20190.PubMedView ArticleGoogle Scholar
- Li Y, Xu P, Zhao Z, Wang J, Zhang Y, Sun XW: Construction and characterization of the BAC library for common carp Cyprinus carpio L. and establishment of microsynteny with zebrafish Danio rerio. Mar Biotechnol. 2011, 13 (4): 706-712. 10.1007/s10126-010-9332-9.PubMedView ArticleGoogle Scholar
- Xu P, Li J, Li Y, Cui R, Wang J, Zhang Y, Zhao Z, Sun X: Genomic insight into the common carp (Cyprinus carpio) genome by sequencing analysis of BAC-end sequences. BMC Genomics. 2011, 12: 188-10.1186/1471-2164-12-188.PubMed CentralPubMedView ArticleGoogle Scholar
- Xu P, Wang J, Wang J, Cui R, Li Y, Zhao Z, Ji P, Zhang Y, Li J, Sun X: Generation of the first BAC-based physical map of the common carp genome. BMC Genomics. 2011, 12 (1): 537-10.1186/1471-2164-12-537.PubMed CentralPubMedView ArticleGoogle Scholar
- Mabuchi K, Miya M, Senou H, Suzuki T, Nishida M: Complete mitochondrial DNA sequence of the Lake Biwa wild strain of common carp (<i > Cyprinus carpio</i > L.): further evidence for an ancient origin. Aquaculture. 2006, 257 (1): 68-77.View ArticleGoogle Scholar
- Mabuchi K, Song H: The complete mitochondrial genome of the Japanese ornamental koi carp (Cyprinus carpio) and its implication for the history of koi. Mitochondrial DNA. 2013, 0: 1-2.Google Scholar
- Wang B, Ji P, Wang J, Sun J, Wang C, Xu P, Sun X: The complete mitochondrial genome of the Oujiang color carp, Cyprinus carpio var. color (Cypriniformes, Cyprinidae). Mitochondrial DNA. 2013, 24 (1): 19-21. 10.3109/19401736.2012.710230.PubMedView ArticleGoogle Scholar
- Henkel CV, Dirks RP, Jansen HJ, Forlenza M, Wiegertjes GF, Howe K, van den Thillart GE, Spaink HP: Comparison of the exomes of common carp (Cyprinus carpio) and zebrafish (Danio rerio). Zebrafish. 2012, 9 (2): 59-67. 10.1089/zeb.2012.0773.PubMed CentralPubMedView ArticleGoogle Scholar
- Sun X, Yu J, Xu P, Wang X, Liu G, Li J, Zhang X, Kuang Y: Towards the Complete Genome: Progress of Common Carp Genome Project. 2012, San Diego: Plant and Animal Genome XXGoogle Scholar
- Baird NA, Etter PD, Atwood TS, Currey MC, Shiver AL, Lewis ZA, Selker EU, Cresko WA, Johnson EA: Rapid SNP discovery and genetic mapping using sequenced RAD markers. PLoS One. 2008, 3 (10): e3376-10.1371/journal.pone.0003376.PubMed CentralPubMedView ArticleGoogle Scholar
- Ramos AM, Crooijmans RP, Affara NA, Amaral AJ, Archibald AL, Beever JE, Bendixen C, Churcher C, Clark R, Dehais P, Hansen MS, Hedegaard J, Hu ZL, Kerstens HH, Law AS, Megens HJ, Milan D, Nonneman DJ, Rohrer GA, Rothschild MF, Smith TP, Schnabel RD, Van Tassell CP, Taylor JF, Wiedmann RT, Schook LB, Groenen MA: Design of a high density SNP genotyping assay in the pig using SNPs identified and characterized by next generation sequencing technology. PLoS One. 2009, 4 (8): e6524-10.1371/journal.pone.0006524.PubMed CentralPubMedView ArticleGoogle Scholar
- Matukumalli LK, Lawley CT, Schnabel RD, Taylor JF, Allan MF, Heaton MP, O’Connell J, Moore SS, Smith TP, Sonstegard TS, Van Tassell CP: Development and characterization of a high density SNP genotyping assay for cattle. PLoS One. 2009, 4 (4): e5350-10.1371/journal.pone.0005350.PubMed CentralPubMedView ArticleGoogle Scholar
- Groenen M, Megens H-J, Zare Y, Warren W, Hillier L, Crooijmans R, Vereijken A, Okimoto R, Muir W, Cheng H: The development and characterization of a 60 K SNP chip for chicken. BMC Genomics. 2011, 12 (1): 274-10.1186/1471-2164-12-274.PubMed CentralPubMedView ArticleGoogle Scholar
- Kranis A, Gheyas AA, Boschiero C, Turner F, Yu L, Smith S, Talbot R, Pirani A, Brew F, Kaiser P, Hocking PM, Fife M, Salmon N, Fulton J, Strom TM, Haberer G, Weigend S, Preisinger R, Gholami M, Qanbari S, Simianer H, Watson KA, Woolliams JA, Burt DW: Development of a high density 600 K SNP genotyping array for chicken. BMC Genomics. 2013, 14: 59-10.1186/1471-2164-14-59.PubMed CentralPubMedView ArticleGoogle Scholar
- Meurs KM, Mauceli E, Lahmers S, Acland GM, White SN, Lindblad-Toh K: Genome-wide association identifies a deletion in the 3′ untranslated region of striatin in a canine model of arrhythmogenic right ventricular cardiomyopathy. Hum Genet. 2010, 128 (3): 315-324. 10.1007/s00439-010-0855-y.PubMed CentralPubMedView ArticleGoogle Scholar
- McCue ME, Bannasch DL, Petersen JL, Gurr J, Bailey E, Binns MM, Distl O, Guerin G, Hasegawa T, Hill EW, Leeb T, Lindgren G, Penedo MC, Roed KH, Ryder OA, Swinburne JE, Tozaki T, Valberg SJ, Vaudin M, Lindblad-Toh K, Wade CM, Mickelson JR: A high density SNP array for the domestic horse and extant Perissodactyla: utility for association mapping, genetic diversity, and phylogeny studies. PLoS Genet. 2012, 8 (1): e1002451-10.1371/journal.pgen.1002451.PubMed CentralPubMedView ArticleGoogle Scholar
- Utsunomiya YT, Perez O’Brien AM, Sonstegard TS, Van Tassell CP, Do Carmo AS, Meszaros G, Solkner J, Garcia JF: Detecting loci under recent positive selection in dairy and beef cattle by combining different genome-wide scan methods. PLoS One. 2013, 8 (5): e64280-10.1371/journal.pone.0064280.PubMed CentralPubMedView ArticleGoogle Scholar
- Boitard S, Rocha D: Detection of signatures of selective sweeps in the Blonde d’Aquitaine cattle breed. Anim Genet. 2013, 44 (5): 579-583. 10.1111/age.12042.PubMedView ArticleGoogle Scholar
- Bourret V, Kent MP, Primmer CR, Vasemägi A, Karlsson S, Hindar K, McGinnity P, Verspoor E, Bernatchez L, Lien S: SNP‒array reveals genome‒wide patterns of geographical and potential adaptive divergence across the natural range of Atlantic salmon (Salmo salar). Mol Ecol. 2013, 22 (3): 532-551. 10.1111/mec.12003.PubMedView ArticleGoogle Scholar
- Wade CM, Giulotto E, Sigurdsson S, Zoli M, Gnerre S, Imsland F, Lear TL, Adelson DL, Bailey E, Bellone RR, Blocker 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, Guerin G: Genome sequence, comparative analysis, and population genetics of the domestic horse. Science. 2009, 326 (5954): 865-867. 10.1126/science.1178158.PubMed CentralPubMedView ArticleGoogle Scholar
- Houston RD, Taggart JB, Cezard T, Bekaert M, Lowe NR, Downing A, Talbot R, Bishop SC, Archibald AL, Bron JE, Penman DJ, Davassi A, Brew F, Tinch AE, Gharbi K, Hamilton A: Development and validation of a high density SNP genotyping array for Atlantic salmon (Salmo salar). BMC Genomics. 2014, 15: 90-10.1186/1471-2164-15-90.PubMed CentralPubMedView ArticleGoogle Scholar
- Liu S, Sun L, Li Y, Sun F, Jiang Y, Zhang Y, Zhang J, Feng J, Kaltenboeck L, Kucuktas H: Development of the catfish 250 K SNP array for genome-wide association studies. BMC Res Notes. 2014, 7: 135-10.1186/1756-0500-7-135.PubMed CentralPubMedView ArticleGoogle Scholar
- Abecasis GR, Altshuler D, Auton A, Brooks LD, Durbin RM, Gibbs RA, Hurles ME, McVean GA: A map of human genome variation from population-scale sequencing. Nature. 2010, 467 (7319): 1061-1073. 10.1038/nature09534.PubMedView ArticleGoogle Scholar
- Pemberton TJ, Absher D, Feldman MW, Myers RM, Rosenberg NA, Li JZ: Genomic patterns of homozygosity in worldwide human populations. Am J Hum Genet. 2012, 91 (2): 275-292. 10.1016/j.ajhg.2012.06.014.PubMed CentralPubMedView ArticleGoogle Scholar
- Tarazona-Santos E, Tishkoff SA: Divergent patterns of linkage disequilibrium and haplotype structure across global populations at the interleukin-13 (IL13) locus. Genes Immunity. 2005, 6 (1): 53-65.PubMedGoogle Scholar
- Shifman S, Kuypers J, Kokoris M, Yakir B, Darvasi A: Linkage disequilibrium patterns of the human genome across populations. Hum Mol Genet. 2003, 12 (7): 771-776. 10.1093/hmg/ddg088.PubMedView ArticleGoogle Scholar
- Van Inghelandt D, Reif JC, Dhillon BS, Flament P, Melchinger AE: Extent and genome-wide distribution of linkage disequilibrium in commercial maize germplasm. Theor Appl Genet. 2011, 123 (1): 11-20. 10.1007/s00122-011-1562-3.PubMedView ArticleGoogle Scholar
- Boyko AR: The domestic dog: man’s best friend in the genomic era. Genome Biol. 2011, 12 (2): 216-10.1186/gb-2011-12-2-216.PubMed CentralPubMedView ArticleGoogle Scholar
- Laurie CC, Nickerson DA, Anderson AD, Weir BS, Livingston RJ, Dean MD, Smith KL, Schadt EE, Nachman MW: Linkage disequilibrium in wild mice. PLoS Genet. 2007, 3 (8): e144-10.1371/journal.pgen.0030144.PubMed CentralPubMedView ArticleGoogle Scholar
- R W, S P, E T, J H, K W, J B: Genomic selection using low density marker panels with application to a sire line in pigs. Genet Sel Evol. 2013, 45: 28-10.1186/1297-9686-45-28.View ArticleGoogle Scholar
- Khatkar MS, Moser G, Hayes BJ, Raadsma HW: Strategies and utility of imputed SNP genotypes for genomic analysis in dairy cattle. BMC Genomics. 2012, 13: 538-10.1186/1471-2164-13-538.PubMed CentralPubMedView ArticleGoogle Scholar
- Finlay EK, Berry DP, Wickham B, Gormley EP, Bradley DG: A genome wide association scan of bovine tuberculosis susceptibility in Holstein-Friesian dairy cattle. PLoS One. 2012, 7 (2): e30545-10.1371/journal.pone.0030545.PubMed CentralPubMedView ArticleGoogle Scholar
- Kim BY, Jin HJ, Kim JY: Genome-wide association analysis of Sasang constitution in the Korean population. J Altern Complement Med. 2012, 18 (3): 262-269. 10.1089/acm.2010.0764.PubMed CentralPubMedView ArticleGoogle Scholar
- He S, Liu H, Chen Y, Kuwahara M, Nakajima T, Zhong Y: Molecular phylogenetic relationships of Eastern Asian Cyprinidae (pisces: cypriniformes) inferred from cytochrome b sequences. Sci China C Life Sci. 2004, 47 (2): 130-138. 10.1360/03yc0034.PubMedView ArticleGoogle Scholar
- He S, Mayden RL, Wang X, Wang W, Tang KL, Chen WJ, Chen Y: Molecular phylogenetics of the family Cyprinidae (Actinopterygii: Cypriniformes) as evidenced by sequence variation in the first intron of S7 ribosomal protein-coding gene: further evidence from a nuclear gene of the systematic chaos in the family. Mol Phylogenet Evol. 2008, 46 (3): 818-829. 10.1016/j.ympev.2007.06.001.PubMedView ArticleGoogle Scholar
- Wang X, Li J, He S: Molecular evidence for the monophyly of East Asian groups of Cyprinidae (Teleostei: Cypriniformes) derived from the nuclear recombination activating gene 2 sequences. Mol Phylogenet Evol. 2007, 42 (1): 157-170. 10.1016/j.ympev.2006.06.014.PubMedView ArticleGoogle Scholar
- Li H, Durbin R: Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009, 25 (14): 1754-1760. 10.1093/bioinformatics/btp324.PubMed CentralPubMedView ArticleGoogle Scholar
- Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R: The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009, 25 (16): 2078-2079. 10.1093/bioinformatics/btp352.PubMed CentralPubMedView ArticleGoogle Scholar
- Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, de Bakker PI, Daly MJ, Sham PC: PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007, 81 (3): 559-575. 10.1086/519795.PubMed CentralPubMedView ArticleGoogle Scholar
- Boichard D, Chung H, Dassonneville R, David X, Eggen A, Fritz S, Gietzen KJ, Hayes BJ, Lawley CT, Sonstegard TS, Van Tassell CP, VanRaden PM, Viaud-Martinez KA, Wiggans GR: Design of a bovine low-density SNP array optimized for imputation. PLoS One. 2012, 7 (3): e34130-10.1371/journal.pone.0034130.PubMed CentralPubMedView ArticleGoogle Scholar
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