Design of a High Density SNP Genotyping Assay in the Pig Using SNPs Identified and Characterized by Next Generation Sequencing Technology

Ramos, A. M.; Crooijmans, R.P.M.A.; Affara, Nabeel A.; Amaral, Andreia J.; Archibald, Alan; Beever, Jonathan E.; Berthou, Christian; Churcher, Carol; Clark, Richard; Dehais, Patrick; Hansen, Mark; Hedegaard, Jakob; Hu, Zhi-Liang; Kerstens, Hindrik H. D.; Law, Andy; Megens, Hendrik‐Jan; Milan, Denis; Nonneman, D.; Rohrer, G. A.; Rothschild, Max F.; Smith, Ted; Schnabel, Robert D.; Tassell, Curtis P. Van; Taylor, Jeremy F.; Wiedmann, Ralph T.; Schook, Lawrence B.; Groenen, Martien A. M.

doi:10.1371/journal.pone.0006524

Cited by 579 publications

(581 citation statements)

References 29 publications

Supporting

Mentioning

563

Contrasting

Unclassified

Order By: Relevance

“…In our previous study, 1,899 F 2 pigs were genotyped for 183 microsatellite markers [6]. Here, 928 F 2 pigs and their F 1 and F 0 ancestors, 320 Erhualian and 383 Sutai pigs were genotyped for 62193 single nucleotide polymorphisms (SNPs) on the Illumina Porcine 60K Beadchip [7]. The 60K SNP data of the 928 F 2 pigs and their F 0 and F 1 ancestors (reference animals) were further employed to impute the 60K SNP genotypes of 815 F 2 pigs (target animals) that were not genotyped by 60K SNP chip by using the following approach [8].…”

Section: Methodsmentioning

confidence: 99%

Identification of loci affecting teat number by genome-wide association studies on three pig populations

Tang

Zhang

Yang

et al. 2016

Asian-Australas J Anim Sci

View full text Add to dashboard Cite

ObjectiveThree genome-wide association studies (GWAS) and a meta-analysis of GWAS were conducted to explore the genetic mechanisms underlying variation in pig teat number.MethodsWe performed three GWAS and a meta-analysis for teat number on three pig populations, including a White Duroc×Erhualian F2 resource population (n = 1,743), a Chinese Erhualian pig population (n = 320) and a Chinese Sutai pig population (n = 383).ResultsWe detected 24 single nucleotide polymorphisms (SNPs) that surpassed the genome-wide significant level on Sus Scrofa chromosomes (SSC) 1, 7, and 12 in the F2 resource population, corresponding to four loci for pig teat number. We highlighted vertnin (VRTN) and lysine demethylase 6B (KDM6B) as two interesting candidate genes at the loci on SSC7 and SSC12. No significant associated SNPs were identified in the meta-analysis of GWAS.ConclusionThe results verified the complex genetic architecture of pig teat number. The causative variants for teat number may be different in the three populations

show abstract

Section: Methodsmentioning

confidence: 99%

Identification of loci affecting teat number by genome-wide association studies on three pig populations

Tang

Zhang

Yang

et al. 2016

Asian-Australas J Anim Sci

View full text Add to dashboard Cite

show abstract

“…2013) with the PorcineSNP60 BeadChip (Illumina, Inc.), designed by Ramos et al . (2009). Then, 612 F 2 animals were genotyped with the 9K tagSNP set and the GeneSeek Genomic Profiler for Porcine LD (version 1) (GGP‐Porcine LD; Badke et al .…”

Section: Methodsmentioning

confidence: 99%

“…Animals, as well as their sires, were genotyped using the Illumina PorcineSNP60 BeadChip (Ramos et al . 2009). Records of carcass composition and pork quality traits were collected, as described by Nonneman et al .…”

Section: Methodsmentioning

confidence: 99%

Meta‐analysis of genome‐wide association from genomic prediction models

Rubio

Duarte²,

Bates³

et al. 2015

Animal Genetics

View full text Add to dashboard Cite

SummaryGenome‐wide association (GWA) studies based on GBLUP models are a common practice in animal breeding. However, effect sizes of GWA tests are small, requiring larger sample sizes to enhance power of detection of rare variants. Because of difficulties in increasing sample size in animal populations, one alternative is to implement a meta‐analysis (MA), combining information and results from independent GWA studies. Although this methodology has been used widely in human genetics, implementation in animal breeding has been limited. Thus, we present methods to implement a MA of GWA, describing the proper approach to compute weights derived from multiple genomic evaluations based on animal‐centric GBLUP models. Application to real datasets shows that MA increases power of detection of associations in comparison with population‐level GWA, allowing for population structure and heterogeneity of variance components across populations to be accounted for. Another advantage of MA is that it does not require access to genotype data that is required for a joint analysis. Scripts related to the implementation of this approach, which consider the strength of association as well as the sign, are distributed and thus account for heterogeneity in association phase between QTL and SNPs. Thus, MA of GWA is an attractive alternative to summarizing results from multiple genomic studies, avoiding restrictions with genotype data sharing, definition of fixed effects and different scales of measurement of evaluated traits.

show abstract

“…2009). SNP loci with minor allele frequencies (MAF) ≤ 0.01, call rate ≤ 0.95 and extreme departure from Hardy–Weinberg equilibrium when assessed using a false discovery rate of 1% were removed.…”

Section: Methodsmentioning

confidence: 99%

Efficiency of genomic prediction for boar taint reduction in Danish Landrace pigs

et al. 2015

View full text Add to dashboard Cite

SummaryGenetic selection against boar taint, which is caused by high skatole and androstenone concentrations in fat, is a more acceptable alternative than is the current practice of castration. Genomic predictors offer an opportunity to overcome the limitations of such selection caused by the phenotype being expressed only in males at slaughter, and this study evaluated different approaches to obtain such predictors. Samples from 1000 pigs were included in a design which was dominated by 421 sib pairs, each pair having one animal with high and one with low skatole concentration (≥0.3 μg/g). All samples were measured for both skatole and androstenone and genotyped using the Illumina SNP60 porcine BeadChip for 62 153 single nucleotide polymorphisms. The accuracy of predicting phenotypes was assessed by cross‐validation using six different genomic evaluation methods: genomic best linear unbiased prediction (GBLUP) and five Bayesian regression methods. In addition, this was compared to the accuracy of predictions using only QTL that showed genome‐wide significance. The range of accuracies obtained by different prediction methods was narrow for androstenone, between 0.29 (Bayes Lasso) and 0.31 (Bayes B), and wider for skatole, between 0.21 (GBLUP) and 0.26 (Bayes SSVS). Relative accuracies, corrected for h 2, were 0.54–0.56 and 0.75–0.94 for androstenone and skatole respectively. The whole‐genome evaluation methods gave greater accuracy than using only the QTL detected in the data. The results demonstrate that GBLUP for androstenone is the simplest genomic technology to implement and was also close to the most accurate method. More specialised models may be preferable for skatole.

show abstract

Design of a High Density SNP Genotyping Assay in the Pig Using SNPs Identified and Characterized by Next Generation Sequencing Technology

Cited by 579 publications

References 29 publications

Identification of loci affecting teat number by genome-wide association studies on three pig populations

Identification of loci affecting teat number by genome-wide association studies on three pig populations

Meta‐analysis of genome‐wide association from genomic prediction models

Efficiency of genomic prediction for boar taint reduction in Danish Landrace pigs

Contact Info

Product

Resources

About