Development of the catfish 250K SNP array for genome-wide association studies
© Liu et al.; licensee BioMed Central Ltd. 2014
Received: 11 December 2013
Accepted: 28 February 2014
Published: 10 March 2014
Quantitative traits, such as disease resistance, are most often controlled by a set of genes involving a complex array of regulation. The dissection of genetic basis of quantitative traits requires large numbers of genetic markers with good genome coverage. The application of next-generation sequencing technologies has allowed discovery of over eight million SNPs in catfish, but the challenge remains as to how to efficiently and economically use such SNP resources for genetic analysis.
In this work, we developed a catfish 250K SNP array using Affymetrix Axiom genotyping technology. The SNPs were obtained from multiple sources including gene-associated SNPs, anonymous genomic SNPs, and inter-specific SNPs. A set of 640K high-quality SNPs obtained following specific requirements of array design were submitted. A panel of 250,113 SNPs was finalized for inclusion on the array. The performance evaluated by genotyping individuals from wild populations and backcross families suggested the good utility of the catfish 250K SNP array.
This is the first high-density SNP array for catfish. The array should be a valuable resource for genome-wide association studies (GWAS), fine QTL mapping, high-density linkage map construction, haplotype analysis, and whole genome-based selection.
KeywordsCatfish Fish Genome SNP array GWAS Genotyping
Catfish is the most important aquaculture species in the United States. In recent years, catfish industry encounters great challenges including devastating diseases which cause the largest economic loss. Improved brood stocks with a high level of disease resistance are desperately needed. Quantitative traits, such as disease resistance, are most often controlled by a set of genes involving a complex array of regulation [1–3]. The dissection of genetic basis of these traits requires large numbers of genetic markers.
Single nucleotide polymorphisms (SNPs) are now the markers of choice because they are the most abundant genetic variations widely distributed in the genome, and are generally bi-allelic polymorphisms that are amenable to automated genotyping . SNPs are efficient for genome-wide association studies (GWAS) because linkage disequilibrium can be detected with high-density SNPs when dealing with complex traits. Simultaneous analysis of thousands of SNPs have enabled genome-wide association studies for performance and production traits in chicken [5, 6], pig [7, 8], cattle [9–11], horse  and sheep [13, 14]. However, such studies have not been possible with most aquaculture species including catfish due to the lack of genome-wide SNP markers and high-throughput SNP genotyping platforms.
Until recently genome-scale SNP identification was a great challenge for most non-model species. The next-generation sequencing technologies enabled efficient identification of SNPs from genomes of various organisms . With the availability of a large number of SNPs, the challenge then is how to genotype these SNPs efficiently and economically.
A variety of SNP array platforms are available, of which Illumina iSelect HD Custom BeadChip (Illumina, San Diego, CA), the Sequenom MassArray (Sequenom, San Diego, CA) and Affymetrix GeneChip Custom Array (Affymetrix, Santa Clara, CA) are widely used. More recently, Affymetrix adopted the Axiom genotyping technology that allows development of customized arrays containing 1,500 to 2.6 million SNPs . These platforms differ in their requirements for SNP marker numbers, sample size, cost and automation.
SNP arrays have been developed in several livestock species, including cattle , horse , pig , sheep , dog  and chicken . For instance, the Illumina BovineSNP50 Beadchip featuring approximately 54,000 informative SNP probes was first developed for detecting variations in cattle breeds . Two cattle SNP arrays with higher density were recently developed . In dog, Illumina developed the CanineSNP20 BeadChip with 20K SNPs, and the recent CanineHD BeadChip containing over 170,000 SNPs . A 60K chicken SNP array powered by Illumina iSelect BeadChip was designed to contain 57,636 SNPs . Recently, a high density 600K chicken SNP array was developed with Affymetrix Axiom genotyping technology . Apparently, there are no technology barriers for the development of high density SNP arrays, but the economic challenge is still tremendous. Even though the unit cost per genotype is currently extremely low, the total costs for the high density SNP arrays with high numbers of SNPs can still be beyond the economic powers of researchers working with species within small research communities.
High-density SNP arrays have not been developed for aquaculture species. Only low to medium density arrays were used in several aquaculture species. The Illumina GoldenGate Assay was used to evaluate 384 rainbow trout SNPs, resulting in a validation rate of 48% for the tested SNPs . The GoldenGate Assay was also used to genotype 384 catfish EST-derived SNPs to assess the factors affecting SNP validation rates . A custom Illumina iSelect SNP array containing approximately 6K SNP markers from Atlantic salmon was developed and used for linkage mapping and QTL analysis [28, 29].
SNP resources required for the development of a high-density SNP array have been developed in catfish. Over two million gene-associated SNPs were identified in channel catfish and blue catfish, respectively, using next-generation sequencing. Of these putative gene-associated SNPs, approximately 400,000-500,000 were of high quality . In a recent study, over eight million SNPs were identified in channel catfish by whole genome sequencing of one wild and four aquaculture populations . With the availability of these SNP resources, we report here the development and performance evaluation of the 250K catfish SNP array using the Affymetrix Axiom genotyping technology.
Results and discussion
Selection of SNPs for the SNP array
One of the most important goals of the SNP array development is to have a good coverage of the genome. The first task was to select a subset of SNPs from all identified SNPs, gene-associated as well as anonymous SNPs. From a large pool of the previously identified SNPs [30, 31], the following selection criteria were used for the initial selection of SNPs: 1) For gene-associated SNPs, at least one but no more than two SNPs per transcript contig were selected; 2) For anonymous SNPs, one SNP was selected for small contigs of less than 4 kb, but two SNPs were selected for contigs larger than 4 kb, with their spacing being the largest within the contig. In addition, a set of sequence features were also considered for the selection of the initial SNPs (see Methods).
The initial in house selection resulted in 641,489 SNPs that were submitted to Affymetrix for in-silico analysis to assess the predicted performance on Axiom arrays. Both forward and reverse probes of each SNP were assigned with p-convert values, which were derived from a random forest model to predict the probability of the SNP conversion on the array. The model considers factors including probe sequence, binding energy and the expected degree of non-specific hybridization to multiple genomic regions (personal communication with Affymetrix). SNP probes with high p-convert values are expected to convert on the SNP array with high probability.
SNPs included on the 250K array
Summary of SNPs used for the catfish 250K SNP array design
SNPs selected for array
SNPs passed Affymetrix evaluation
SNPs included on the array
Summary of the catfish 250K SNP array
Total number of SNPs
Number of SNPs tiled with single probe
Number of SNPs tiled with two probes
Total number of probes
Number of data quality control probes
Inclusion of gene-associated SNPs should enhance the conversion rates because genes and their associated sequences are more unique in the genome than the non-coding genomic sequences. In addition, as genes are broadly distributed across all 29 pairs of chromosomes of the catfish genome , SNPs derived from genes should reflect the distribution of genes within the genome. However, as genes are not entirely evenly distributed, inter-marker spacing is not equal. Genomic SNPs from anonymous regions would fill the gaps. A subset of inter-specific SNPs are included, which are useful for genetic analysis of the inter-specific hybrid catfish system. The hybrid catfish produced by crossing channel catfish female with blue catfish male are widely used in the catfish industry because the hybrids possess several superior performance and production traits to both of their parents.
Distribution of SNP spacing
Due to the lack of a fully assembled genome sequence, the inter-marker spacing is probably underestimated. The current draft genome assembly had a total genome size of 830.5 Mb, but the catfish genome is approximately 950 Mb. In addition, the spacing from the most external SNPs of each scaffold to the next marker is not included in the assessment. Therefore, the overall marker spacing could have been slightly larger.
The SNP distribution was also evaluated with regard to association with bacterial artificial chromosome (BAC) end sequences (BES). A total of 22,051 SNPs on the array are associated with 16,832 BES derived from 14,849 BAC clones. Accordingly, such SNPs are associated with 2,853 (86.3% of 3,307) contigs from the catfish physical map developed by Xu et al. , including 1,141 contigs that were not able to integrate with linkage map constructed mainly using microsatellite markers . Such BAC associated SNPs are useful because they are separated by a known distance in the genome (BAC insert size of 161 Kb on average ), and their use can facilitate full integration of genetic linkage and physical maps.
The catfish have a genome size of ~ 1 billion base pairs. With the 250,000 SNPs, theoretically, the average SNP marker intervals are about 4 kb in the catfish genome. We should acknowledge that it is too costly to develop a SNP array with millions of markers for catfish, like it has been done in human and other model species, since far less funding support is available for aquaculture species. For the same budget related reason, the number of samples genotyped for GWAS is limited for aquaculture species as well. Therefore, different strategies should be utilized when conducting genome-wide genetic analysis using the catfish 250K SNP array. The genome regions underlying production and performance traits can be first located through the whole genome scanning with the 250K SNPs genotyped from hundreds of samples. To further investigate the SNP effects, it’s cost-saving to use other SNP genotyping platforms such as Sequenom MassArray (Sequenom, San Diego, CA) to genotype thousands of individuals with denser SNPs from the targeted regions.
Performance of the catfish 250K SNP array
Genotyping performance of the SNP array
Performance assessment of the catfish 250K SNP array
Avg. SNP call rate***
Although polymorphism ultimately dictates how useful the array, the results present herein is related to the testing samples used in this study. In the case of the catfish 250K SNP array, the situation is further complicated by inclusion of intra-specific as well as inter-specific SNPs. Inter-specific SNPs are not expected to be polymorphic within channel catfish or blue catfish, but are polymorphic in inter-specific hybrids. One obvious question we are interested to ask is how many of the 250K SNPs represented real SNPs (the validation rate). Here, a total of 137,459 SNPs were polymorphic in wild fish; 130,685 SNPs were polymorphic in BC1 fish, and 156,357 SNPs were polymorphic in BC3 fish. Taken together, of the 241,812 converted SNPs, a total of 200,860 SNPs (83.1%) were polymorphic in at least one testing population, demonstrating a high SNP validation rate.
The catfish hybrid system is not only important to the industry but also interesting for the genetic studies because the inter-specific hybrids exhibit significant heterosis. The high conversion and polymorphic rates achieved by genotyping catfish from backcross hybrid families as well as wild populations suggested good performance and utility of this SNP array.
Assessment of informativeness of SNPs on the array
Relationships between design score and SNP performance
Performance of gene-associated SNPs versus anonymous SNPs
Performance of intra-specific and inter-specific SNPs
Performance of transition and transversion SNPs
SNP transferability to other related catfish species
Transferability of SNPs to other catfish species
Blue catfish (Rio Grande)
Blue catfish (D&B)
Brown bullhead catfish
In spite of the low polymorphic rates, the number of SNPs that were polymorphic was still notable with various species of catfishes. Over 12,000 SNPs were polymorphic for bullhead catfish and white catfish, suggesting its applicability for genetic analysis in related catfish taxa. The polymorphic rates evaluated here are probably underestimated because only 10 individuals were genotyped. Polymorphic rates would increase if more fish had been genotyped. Although these estimates are quite preliminary, the very low polymorphic rates observed in D&B strain of blue catfish suggest that this strain may have experienced inbreeding and might have had a small number of founders, at least twice less diverse than the Rio Grande strain (Table 4).
In this study, we report the development of the catfish 250K SNP array using Affymetrix Axiom genotyping technology. The SNPs were well-spaced in the genome. Distribution of minor allele frequency indicated that SNPs with uniform MAFs were included on the array. The performance evaluation of the SNP array by genotyping samples from pedigree families and unrelated wild populations suggested high SNP conversion rates (~80%) and high polymorphic rates (over 50%) can be obtained in all the examined samples. Technically, we showed that the Affymetrix design score (p-convert value) adequately predicted SNP probe performance and the inclusion of alternative probes greatly increased the SNP conversion rates, especially for SNPs with probes that had low design scores. The catfish 250K SNP array should be valuable for genome-wide association studies, fine QTL mapping, high-density linkage mapping, haplotype analysis, and whole genome-based selection.
All procedures involving the handling and treatment of fish used during this study were approved by the Auburn University Institutional Animal Care and Use Committee (AU-IACUC) prior to initiation.
SNP selection and array design
Gene-associated SNPs were generated from Liu et al. . Anonymous SNPs from non-coding genomic regions were from Sun et al. . SNPs were filtered following the specific requirements for the Affymetrix SNP array design. Flanking sequences of 35 bp for each SNP were extracted where no other variations (SNPs and/or Indels) were allowed within 30 bp of SNPs. The balanced A/T/G/C of flanking sequences was required with GC content of 30%-70%. No repetitive elements were allowed in flanking sequences. In addition, single base repeats of G or C greater than 4 and A or T greater than 6 were not allowed.
All SNPs passing the in house selection using the above criteria were submitted to Affymetrix for design score assessment, where a p-convert value was assigned to each of the two probes flanking a SNP, respectively. SNPs with probes of high p-convert values were more likely to be convertible. A p-convert value threshold was determined by excluding the tail of lowest performing probes to facilitate selection of final SNP list. For the SNPs with both probes passing the p-convert value threshold, only the probe with the greater value was selected. For the SNPs with both probes having low p-convert values, both probes were selected to ensure a high probability of conversion.
To select well-spaced SNPs, at least one but no more than two SNPs per transcript contig were selected for gene-associated SNPs. At the time of the SNP selection, only the initial preliminary catfish genome assembly was available (255,858 contigs with mean length of 2,996 bp and N50 of 6,027 bp, unpublished data). The preliminary assembly was used to facilitate SNP selection according to contig length for the anonymous SNPs. One SNP per contig was selected from the contigs with lengths less than 4 kb. Two SNPs per contig were selected from the contigs with lengths greater than 4 kb. For the two SNPs selected from one contig, the SNPs with the largest distances were selected to ensure good spacing in the genome.
In addition, A/T and C/G SNPs were not selected because the two alleles of these SNPs match the same dye, and additional distinct probes in different physical locations on the array are required to distinguish them. Non-polymorphic 31-mers were randomly picked from non-repetitive regions of the genome for data quality control (QC) probes. The QC probes along with the final list of SNPs were submitted to Affymetrix for production of Axiom genotyping arrays.
Assessment of SNP spacing
To assess the genome distribution of SNPs on the array, all the 250,113 SNPs with 35-bp up and downstream flanking sequences (71 bp in total) were aligned with the latest draft genome assembly now available (62,461 scaffolds with N50 of 3 Mb, covering a total size of 850 Mb, unpublished) using BLAST to determine SNP positions. The inter-SNP spacing was determined based on SNP positions in the scaffolds. The distance between SNPs at the end of sequences and the next SNP was not included because it’s not possible to assess their inter-marker interval. Similarly, SNP flanking sequences were aligned with the catfish BAC end sequences (BES) [36, 37] to identify BES associated SNPs.
SNP array performance evaluation
Three different sample sources were used for genotyping to assess the SNP array performance: 1) 192 unrelated channel catfish from wild populations; 2) 192 catfish hybrids from the 1st generation of backcrossing and 3) 192 catfish hybrids from the 3rd generation of backcrossing. Samples from wild populations were channel catfish collected for a previous study . The hybrids from the 1st generation of backcrossing were produced by backcrossing the inter-specific F1 hybrids (channel female x blue male) with a male channel catfish, and the 3rd generation of backcross hybrids were produced by backcrossing the 2nd generation of backcross hybrids with a male channel catfish.
Blood samples (300-500μl) were collected in a 1-ml syringe and immediately expelled into a 15-ml tube containing 5 ml of cell lysis buffer (100 mM NaCl, 10 mM Tris, pH 8, 25 mM EDTA, 0.5% SDS, and 0.1 mg/ml freshly made proteinase K) for DNA isolation. DNA was isolated as previously described [39, 40]. Picogreen dye (Quant-iT Pico Green, Invitrogen) was used in order to quantify double-stranded DNA according to the manufacturer’s protocol. The integrity of DNA samples was checked by 1% agarose gel electrophoresis stained with ethidium bromide.
Genomic DNA samples were arranged in a 96-well microtiter plate, and normalized to a final concentration of 50 ng/μl with a final volume of 10 μl. Genotyping with the catfish 250K SNP array was outsourced to GeneSeek (Lincoln, NE, USA).
SNP transferability to other related catfish species
A set of DNA samples were tested from other related catfish species, including blue catfish (Ictalurus furcatus) of Rio Grande strain (10) and D&B strain (10), 10 brown bullhead catfish (Ameiurus nebulosus) and 10 white catfish (A. catus). The DNA preparation, SNP genotyping and analysis were the same as mentioned above.
This project was supported by Agriculture and Food Research Initiative Competitive Grant no. 2009-35205-05101 and 2010-65205-20356 from the USDA National Institute of Food and Agriculture (NIFA). The authors are grateful for the support provided by colleagues from Affymetrix Bioinformatics Services and the custom genotyping program, especially Lucy Reynolds and Ali Pirani. Thanks go to the scientists and leaders of the Center for Applied Aquatic Genomics, Chinese Academy of Fishery science including Dr. Xiaowen Sun, Dr. Peng Xu, Director Yingjie Liu, Vice President Jieren Li, Vice President Qing Liu, and President Xianliang Zhang for their collaboration with the SNP project. It is only because of their collaboration that made the cost of the catfish array affordable with our limited resources. We thank Dr. Geoff Waldbieser for his collaboration in generating the preliminary catfish whole genome assembly. We thank all the lab colleagues who provided assistance with sample collection. The first author was supported by a scholarship from the China Scholarship Council.
- Gui JF, Zhu ZY: Molecular basis and genetic improvement of economically important traits in aquaculture animals. Chin Sci Bull. 2012, 57 (15): 1751-1760. 10.1007/s11434-012-5213-0.View ArticleGoogle Scholar
- Zheng XH, Kuang YY, Lv WH, Cao DC, Zhang XF, Li C, Lu CY, Sun XW: 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
- Odegard J, Baranski M, Gjerde B, Gjedrem T: Methodology for genetic evaluation of disease resistance in aquaculture species: challenges and future prospects. Aquac Res. 2011, 42: 103-114.View ArticleGoogle Scholar
- Kruglyak L: The use of a genetic map of biallelic markers in linkage studies. Nat Genet. 1997, 17 (1): 21-24. 10.1038/ng0997-21.PubMedView ArticleGoogle Scholar
- Abasht B, Lamont SJ: Genome-wide association analysis reveals cryptic alleles as an important factor in heterosis for fatness in chicken F-2 population. Anim Genet. 2007, 38 (5): 491-498. 10.1111/j.1365-2052.2007.01642.x.PubMedView ArticleGoogle Scholar
- Wolc A, Arango J, Settar P, Fulton JE, O’Sullivan NP, Preisinger R, Habier D, Fernando R, Garrick DJ, Hill WG, Dekkers JM: Genome-wide association analysis and genetic architecture of egg weight and egg uniformity in layer chickens. Anim Genet. 2012, 43: 87-96.PubMedView ArticleGoogle Scholar
- Becker D, Wimmers K, Luther H, Hofer A, Leeb T: A genome-wide association study to detect QTL for commercially important traits in swiss large white boars. Plos One. 2013, 8 (2): e55951-10.1371/journal.pone.0055951.PubMedPubMed CentralView ArticleGoogle Scholar
- Sahana G, Kadlecova V, Hornshoj H, Nielsen B, Christensen OF: A genome-wide association scan in pig identifies novel regions associated with feed efficiency trait. J Anim Sci. 2013, 91 (3): 1041-1050. 10.2527/jas.2012-5643.PubMedView ArticleGoogle Scholar
- Meuwissen THE, Hayes BJ, Goddard ME: Prediction of total genetic value using genome-wide dense marker maps. Genetics. 2001, 157 (4): 1819-1829.PubMedPubMed CentralGoogle Scholar
- Khatkar MS, Nicholas FW, Collins AR, Zenger KR, Cavanagh JA, Barris W, Schnabel RD, Taylor JF, Raadsma HW: Extent of genome-wide linkage disequilibrium in Australian Holstein-Friesian cattle based on a high-density SNP panel. BMC Genomics. 2008, 9: 187-10.1186/1471-2164-9-187.PubMedPubMed CentralView ArticleGoogle Scholar
- Kim ES, Berger PJ, Kirkpatrick BW: Genome-wide scan for bovine twinning rate QTL using linkage disequilibrium. Anim Genet. 2009, 40 (3): 300-307. 10.1111/j.1365-2052.2008.01832.x.PubMedView ArticleGoogle Scholar
- Brooks SA, Gabreski N, Miller D, Brisbin A, Brown HE, Streeter C, Mezey J, Cook D, Antczak DF: Whole-genome SNP association in the horse: identification of a deletion in myosin Va responsible for lavender foal syndrome. Plos Genet. 2010, 6 (4): e1000909-10.1371/journal.pgen.1000909.PubMedPubMed CentralView ArticleGoogle Scholar
- Kijas JW, Townley D, Dalrymple BP, Heaton MP, Maddox JF, McGrath A, Wilson P, Ingersoll RG, McCulloch R, McWilliam S, Tang D, McEwan J, Cockett N, Oddy VH, Nicholas FW, Raadsma H, the International Sheep Genomics Consortium: A genome wide survey of SNP variation reveals the genetic structure of sheep breeds. Plos One. 2009, 4 (3): e4668-10.1371/journal.pone.0004668.PubMedPubMed CentralView ArticleGoogle Scholar
- Becker D, Tetens J, Brunner A, Burstel D, Ganter M, Kijas J, Drogemuller C, Consortium ISG: Microphthalmia in texel sheep is associated with a missense mutation in the paired-like homeodomain 3 (PITX3) gene. Plos One. 2010, 5 (1): e8689-10.1371/journal.pone.0008689.PubMedPubMed CentralView ArticleGoogle Scholar
- Davey JW, Hohenlohe PA, Etter PD, Boone JQ, Catchen JM, Blaxter ML: Genome-wide genetic marker discovery and genotyping using next-generation sequencing. Nat Rev Genet. 2011, 12 (7): 499-510. 10.1038/nrg3012.PubMedView ArticleGoogle Scholar
- Hoffmann TJ, Kvale MN, Hesselson SE, Zhan YP, Aquino C, Cao Y, Cawley S, Chung E, Connell S, Eshragh J, Ewing M, Gollub J, Henderson M, Hubbell E, Iribarren C, Kaufman J, Lao RZ, Lu Y, Ludwig D, Mathauda GK, McGuire W, Mei G, Miles S, Purdy MM, Quesenberry C, Ranatunga D, Rowell S, Sadler M, Shapero MH, Shen L, et al: Next generation genome-wide association tool: design and coverage of a high-throughput european-optimized SNP array. Genomics. 2011, 98 (2): 79-89. 10.1016/j.ygeno.2011.04.005.PubMedPubMed CentralView ArticleGoogle Scholar
- Matukumalli LK, Lawley CT, Schnabel RD, Taylor JF, Allan MF, Heaton MP, O’Connell J, Moore SS, Smith TPL, 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.PubMedPubMed CentralView 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, Røed 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.PubMedPubMed CentralView ArticleGoogle Scholar
- Ramos AM, Crooijmans RPMA, 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.PubMedPubMed CentralView ArticleGoogle Scholar
- Miller JM, Poissant J, Kijas JW, Coltman DW, Consortium ISG: A genome-wide set of SNPs detects population substructure and long range linkage disequilibrium in wild sheep. Mol Ecol Resour. 2011, 11 (2): 314-322. 10.1111/j.1755-0998.2010.02918.x.PubMedView 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 striat in in a canine model of arrhythmogenic right ventricular cardiomyopathy. Hum Genet. 2010, 128 (3): 351-324. 10.1007/s00439-010-0851-2.View ArticleGoogle Scholar
- Groenen MAM, Megens HJ, Zare Y, Warren WC, Hillier LW, Crooijmans RPMA, Vereijken A, Okimoto R, Muir WM, Cheng HH: The development and characterization of a 60K SNP chip for chicken. BMC Genomics. 2011, 12 (1): 274-10.1186/1471-2164-12-274.PubMedPubMed CentralView ArticleGoogle Scholar
- Rincon G, Weber KL, Van Eenennaam AL, Golden BL, Medrano JF: Hot topic: performance of bovine high-density genotyping platforms in holsteins and jerseys. J Dairy Sci. 2011, 94 (12): 6116-6121. 10.3168/jds.2011-4764.PubMedView ArticleGoogle Scholar
- Mogensen MS, Karlskov-Mortensen P, Proschowsky HF, Lingaas F, Lappalainen A, Lohi H, Jensen VF, Fredholm M: Genome-wide association study in Dachshund: identification of a major locus affecting intervertebral disc calcification. J Hered. 2011, 102 (Suppl 1): S81-86. 10.1093/jhered/esr021.PubMedView 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 600K SNP genotyping array for chicken. BMC Genomics. 2013, 14: 59-10.1186/1471-2164-14-59.PubMedPubMed CentralView ArticleGoogle Scholar
- Sanchez CC, Smith TP, Wiedmann RT, Vallejo RL, Salem M, Yao J, Rexroad CE: Single nucleotide polymorphism discovery in rainbow trout by deep sequencing of a reduced representation library. BMC Genomics. 2009, 10: 559-10.1186/1471-2164-10-559.PubMedPubMed CentralView ArticleGoogle Scholar
- Wang SL, Sha ZX, Sonstegard TS, Liu H, Xu P, Somridhivej B, Peatman E, Kucuktas H, Liu ZJ: Quality assessment parameters for EST-derived SNPs from catfish. BMC Genomics. 2008, 9: 450-10.1186/1471-2164-9-450.PubMedPubMed CentralView ArticleGoogle Scholar
- Lien S, Gidskehaug L, Moen T, Hayes BJ, Berg PR, Davidson WS, Omholt SW, Kent MP: A dense SNP-based linkage map for Atlantic salmon (Salmo salar) reveals extended chromosome homeologies and striking differences in sex-specific recombination patterns. BMC Genomics. 2011, 12: 615-10.1186/1471-2164-12-615.PubMedPubMed CentralView ArticleGoogle Scholar
- Gutierrez AP, Lubieniecki KP, Davidson EA, Lien S, Kent MP, Fukui S, Withler RE, Swift B, Davidson WS: Genetic mapping of quantitative trait loci (QTL) for body-weight in Atlantic salmon (Salmo salar) using a 6.5 K SNP array. Aquaculture. 2012, 358: 61-70.View ArticleGoogle Scholar
- Liu SK, Zhou ZC, Lu JG, Sun FY, Wang SL, Liu H, Jiang YL, Kucuktas H, Kaltenboeck L, Peatman E, Liu ZJ: Generation of genome-scale gene-associated SNPs in catfish for the construction of a high-density SNP array. BMC Genomics. 2011, 12: 53-10.1186/1471-2164-12-53.PubMedPubMed CentralView ArticleGoogle Scholar
- Sun L, Liu S, Wang R, Zhang Y, Zhang J, Jiang Y, Bao L, Kaltenboeck L, Waldbieser G, Kucuktas H: Snp Variations In The Catfish Genome: Factors To Consider For Snp Identification Using Next Generation Sequencing. (in review).
- Xu P, Wang SL, Liu L, Thorsen J, Kucuktas H, Liu ZJ: A BAC-based physical map of the channel catfish genome. Genomics. 2007, 90 (3): 380-388. 10.1016/j.ygeno.2007.05.008.PubMedView ArticleGoogle Scholar
- Ninwichian P, Peatman E, Liu H, Kucuktas H, Somridhivej B, Liu SK, Li P, Jiang YL, Sha ZX, Kaltenboeck L, Abernathy JW, Wang WQ, Chen F, Lee Y, Wong LL, Wang SL, Lu JG, Liu ZJ: Second-generation genetic linkage Map of catfish and its integration with the BAC-based physical Map. G3-Genes Genom Genet. 2012, 2 (10): 1233-1241.Google Scholar
- Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, McCarthy MI, Ramos EM, Cardon LR, Chakravarti A, Cho JH, Guttmacher AE, Kong A, Kruglyak L, Mardis E, Rotimi CN, Slatkin M, Valle D, Whittemore AS, Boehnke M, Clark AG, Eichler EE, Gibson G, Haines JL, Mackay TF, McCarroll SA, Visscher PM: Finding the missing heritability of complex diseases. Nature. 2009, 461 (7265): 747-753. 10.1038/nature08494.PubMedPubMed CentralView ArticleGoogle Scholar
- Wang SL, Peatman E, Abernathy J, Waldbieser G, Lindquist E, Richardson P, Lucas S, Wang M, Li P, Thimmapuram J, Liu L, Vullaganti D, Kucuktas H, Murdock C, Small BC, Wilson M, Liu H, Jiang YL, Lee Y, Chen F, Lu JG, Wang WQ, Xu P, Somridhivej B, Baoprasertkul P, Quilang J, Sha Z, Bao B, Wang Y, Wang Q: Assembly of 500,000 inter-specific catfish expressed sequence tags and large scale gene-associated marker development for whole genome association studies. Genome Biol. 2010, 11 (1): R8-10.1186/gb-2010-11-1-r8.PubMedPubMed CentralView ArticleGoogle Scholar
- Xu P, Wang SL, Liu L, Peatman E, Somridhivej B, Thimmapuram J, Gong G, Liu ZJ: Channel catfish BAC-end sequences for marker development and assessment of syntenic conservation with other fish species. Anim Genet. 2006, 37 (4): 321-326. 10.1111/j.1365-2052.2006.01453.x.PubMedView ArticleGoogle Scholar
- Liu H, Jiang YL, Wang SL, Ninwichian P, Somridhivej B, Xu P, Abernathy J, Kucuktas H, Liu ZJ: Comparative analysis of catfish BAC end sequences with the zebrafish genome. BMC Genomics. 2009, 10: 592-10.1186/1471-2164-10-592.PubMedPubMed CentralView ArticleGoogle Scholar
- Simmons M, Mickett K, Kucuktas H, Li P, Dunham R, Liu ZJ: Comparison of domestic and wild channel catfish (Ictalurus punctatus) populations provides no evidence for genetic impact. Aquaculture. 2006, 252 (2–4): 133-146.View ArticleGoogle Scholar
- Kucuktas H, Wang SL, Li P, He CB, Xu P, Sha ZX, Liu H, Jiang YL, Baoprasertkul P, Somridhivej B, Wang Y, Abernathy J, Guo XM, Liu L, Muir W, Liu ZJ: Construction of genetic linkage maps and comparative genome analysis of catfish using gene-associated markers. Genetics. 2009, 181 (4): 1649-1660. 10.1534/genetics.108.098855.PubMedPubMed CentralView ArticleGoogle Scholar
- Liu ZJ, Li P, Argue B, Dunham R: Random amplified polymorphic DNA markers: usefulness for gene mapping and analysis of genetic variation of catfish. Aquaculture. 1999, 174 (1-2): 59-68. 10.1016/S0044-8486(99)00007-1.View ArticleGoogle Scholar
- Didion JP, Yang HN, Sheppard K, Fu CP, McMillan L, de Villena FPM, Churchill GA: Discovery of novel variants in genotyping arrays improves genotype retention and reduces ascertainment bias. BMC Genomics. 2012, 13: 34-10.1186/1471-2164-13-34.PubMedPubMed CentralView ArticleGoogle Scholar
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