Log In Sign up
The largest database of trusted experimental protocols

Goldengate assay

Manufactured by Illumina
Visit Supplier
Sourced in United States, United Kingdom

The GoldenGate assay is a high-throughput genotyping technology developed by Illumina. It allows for the simultaneous interrogation of thousands of genetic markers across multiple samples. The assay utilizes a combination of oligonucleotide ligation, PCR amplification, and fluorescent label detection to generate genotypic data.

Automatically generated - may contain errors

Lab products found in correlation

Genomestudio software, by Illumina (8 mentions) Beadarray reader, by Illumina (5 mentions) Genomestudio, by Illumina (4 mentions) Nanodrop, by Thermo Fisher Scientific (4 mentions) Oragene dna kit, by DNA Genotek (3 mentions) Picogreen assay, by Thermo Fisher Scientific (3 mentions) Qiaamp dna blood kit, by Qiagen (2 mentions) Flexigene dna kit, by Qiagen (2 mentions) Beadstudio genotyping module v 2.3.41, by Illumina (2 mentions) Genomic dna 2 kit, by DNA Genotek (2 mentions) Beadarray platform, by Illumina (2 mentions) Qiamp, by Qiagen (2 mentions) Genomiphi, by GE Healthcare (2 mentions) Qiaamp dna blood midi or maxi kit, by Qiagen (2 mentions) Assay design tool, by Illumina (2 mentions) Beadstudio 3, by Illumina (2 mentions) Genecall software, by Illumina (2 mentions) Taqman snp genotyping assay, by Thermo Fisher Scientific (1 mentions) Qiaamp dna blood maxi kit, by Qiagen (1 mentions) Dna isolation kit for mammalian blood, by Roche (1 mentions)

125 protocols using goldengate assay

1

Genomic DNA Extraction and Genotyping for Psychiatric Disorders

Check if the same lab product or an alternative is used in the 5 most similar protocols
We collected peripheral blood from all of the subjects, and extracted genomic DNA using a QIAamp DNA Blood Maxi Kit (Qiagen, Valencia, CA, USA). Genotyping was performed using golden gate assay (Illumina®) in Riken Genesis (Yokohama, Japan) for the controls and patients with schizophrenia. For patients with bipolar disorder, nine SNPs (8 SNPs shown in Table 4 and rs10160) were selected and genotyped using Taqman SNP Genotyping Assay (Applied Biosystems, Foster City, CA, USA).
Akiyama K., Saito A., Saito S., Ozeki Y., Watanabe T., Fujii K, & Shimoda K. (2019). Association of genetic variants at 22q11.2 chromosomal region with cognitive performance in Japanese patients with schizophrenia. Schizophrenia Research: Cognition, 17, 100134.
+ Open protocol
+ Expand
2

Illumina GoldenGate SNP Genotyping Analysis

Cited 1 time
Check if the same lab product or an alternative is used in the 5 most similar protocols
The Illumina GoldenGate assay was used for SNP genotyping (Jazwinski et al. 2010 (link)). SNPs were selected according to the Illumina Assay Design Tool, and quality control measures were applied using Illumina GenomeStudio. For SNP and sample clustering, the 10th percentile GenCall score was set at 0.4. Samples with call rates below 90% were excluded. UCP2/3 are located in tandem on chromosome 11, and the three SNPs analyzed in this study are polymorphic and in Hardy-Weinberg equilibrium (Table 1). To examine the additive genetic effect of each SNP in the multiple linear regression analysis, its genotype was numerically recoded according to the copy number of the minor allele (0, 1, 2).
Kim S., Myers L., Ravussin E., Cherry K.E, & Jazwinski S.M. (2016). Single nucleotide polymorphisms linked to mitochondrial uncoupling protein genes UCP2 and UCP3 affect mitochondrial metabolism and healthy aging in female nonagenarians. Biogerontology, 17(4), 725-736.
+ Open protocol
+ Expand
3

Genotyping SNPs in T-cell Pathways

Check if the same lab product or an alternative is used in the 5 most similar protocols
DNA was isolated from peripheral blood leukocytes using the DNA Isolation Kit for Mammalian Blood (Roche, Indianapolis, USA), and stored at 4°C prior to further investigation within 2 days. GoldenGate assay (IlluminaInc, San Diego, USA); capable of multiplexing from 96 to 1,536 SNPs in a single reaction over a 3-day period; was used for SNP genotypingaccording to the manufacturer’s instructions. A 96-SNP GoldenGate assay was designed using SNPs selected from the 26 genes of the T-cell pathways. To ensure the accuracy of the genotyping, quality control was performed using exclusion criteria for SNPs as follows: 1) maximum per-person missing rate>5%; 2) Hardy-Weinberg disequilibrium p-value< 0.001; 3) maximum per-SNP missing rate > 5%; 4) minor allele frequency< 0.01.
Zhang Y., Li J., Wang C, & Zhang L. (2015). Association between the Interaction of Key Genes Involved in Effector T-Cell Pathways and Susceptibility to Developallergic Rhinitis: A Population-Based Case-Control Association Study. PLoS ONE, 10(7), e0131248.
+ Open protocol
+ Expand
4

Pigmentation Gene SNP Selection and Genotyping

Cited 1 time
Check if the same lab product or an alternative is used in the 5 most similar protocols
Gene and single nucleotide polymorphism (SNP) selection was performed as previously described [30 (link)]. Sixty-five gene regions were included in this study. They covered a broad range of biological processes, mostly related to pigmentation. We genotyped a total number of 384 tag-SNPs from the selected genes ranging from the hypothetical promoter area (approximately 10 kb upstream) until approximately 5 kb downstream of the gene. SNP codes, locations, and frequencies were obtained from NCBI (www.ncbi.nlm.nih.gov/SNP), HapMap (HapMap.org">www.HapMap.org), and Illumina databases. A minor allele frequency (MAF) threshold of 0.05 in the HapMap CEU population and an ‘Illumina score’ not lower than 0.6 (as recommended by manufacturer) were established to ensure high genotyping success rate of the SNPs selected.
SNP genotyping was done using the Golden Gate Assay according to manufacturer’s protocol (Illumina, San Diego, CA, USA), as previously described [30 (link)].
Hernando B., Ibarrola-Villava M., Fernandez L.P., Peña-Chilet M., Llorca-Cardeñosa M., Oltra S.S., Alonso S., Boyano M.D., Martinez-Cadenas C, & Ribas G. (2016). Sex-specific genetic effects associated with pigmentation, sensitivity to sunlight, and melanoma in a population of Spanish origin. Biology of Sex Differences, 7, 17.
+ Open protocol
+ Expand
5

Teosinte Nested Introgression Lines

Check if the same lab product or an alternative is used in the 5 most similar protocols
The development and genotyping of the 10 teosinte NIL families (58–185 lines per family) was described previously (Liu et al. 2016 ). Briefly, the NILs were developed by backcrossing 10 accessions of geographically diverse Z. mays ssp. parviglumis into the inbred B73 for four generations prior to inbreeding, creating a total of 961 NILs. These NILs were genotyped via a GoldenGate assay (Illumina, San Diego, CA), and a subset of 728 out of the 1106 nested association mapping (NAM) markers were selected based on polymorphism between B73 and the 10 teosinte parents (McMullen et al. 2009 (link); Liu et al. 2016 ). Genotypic data for the teosinte NILs can be accessed from the supplemental data in Liu et al. (2016) . Genotypic ratios revealed by examining marker data shows that the BC4S2 teosinte NIL population averaged ∼95.9% homozygous B73, ∼2.6% heterozygous B73/teosinte, and ∼1.5% homozygous teosinte. An individual teosinte NIL had an average of 2.4 chromosomal segments from teosinte which, when combined, encompass ∼4% of the teosinte genome introgressed into a B73 background (Liu et al. 2016 ).
Karn A., Gillman J.D, & Flint-Garcia S.A. (2017). Genetic Analysis of Teosinte Alleles for Kernel Composition Traits in Maize. G3: Genes|Genomes|Genetics, 7(4), 1157-1164.
+ Open protocol
+ Expand
6

Candidate SNPs Associated with Cognitive Traits

Check if the same lab product or an alternative is used in the 5 most similar protocols
A literature search identified SNPs with evidence of associations with cognitive impairment, depression, fatigue, or circadian rhythms. Preference was given to SNPs in coding regions or known transcription factor binding sites, identified as non-synonymous mutations, and with minor allele frequency ≥.20 in the HapMap CEU population.17 Of 494 SNPs initially identified, 384 were retained after an iterative custom panel design process. Although not included due to a priori hypotheses of associations with HFI, many SNPs were on genes with plausible implications for hot flashes.
DNA was extracted from blood obtained using Gentra Puregene tissue kits (Valencia, CA) according to the manufacturer’s instructions and genotyped using the Illumina GoldenGate™ assay (Illumina, San Diego, CA). Genotypes were determined using the BeadStudio algorithm by the Moffitt Molecular Genomics Core.
Gonzalez B.D., Jim H.S., Donovan K.A., Small B.J., Sutton S.K., Park J., Lin H.Y., Spiess P.E., Fishman M.N, & Jacobsen P.B. (2015). Course and Moderators of Hot Flash Interference During Androgen Deprivation Therapy for Prostate Cancer: A Matched Comparison. The Journal of urology, 194(3), 690-695.
+ Open protocol
+ Expand
7

Expanding Antarctic Fur Seal Genotyping Panel

Cited 2 times
Check if the same lab product or an alternative is used in the 5 most similar protocols
We also added to our list of probe sequences a further set of SNPs that were previously demonstrated to be polymorphic in animals from the study colony. These included 40 SNPs derived from RAD sequencing data that were validated using Sanger sequencing (Humble et al. 2018 (link)), 102 transcriptomic SNPs that were validated using Illumina’s GoldenGate assay (Hoffman et al. 2012 (link)) and 173 cross-amplified SNPs from the Illumina Canine HD BeadChip that were previously found to be polymorphic in 24 Antarctic fur seals (Hoffman et al. 2013a (link)). In addition to these, we included a further six SNPs that were recently discovered from the second exon of the Antarctic fur seal MHC class II DQB locus based on Illumina MiSeq data from 82 Antarctic fur seals (Ottensmann 2018 ).
Humble E., Paijmans A.J., Forcada J, & Hoffman J.I. (2020). An 85K SNP Array Uncovers Inbreeding and Cryptic Relatedness in an Antarctic Fur Seal Breeding Colony. G3: Genes|Genomes|Genetics, 10(8), 2787-2799.
+ Open protocol
+ Expand
8

Integrating Genetic Linkage Maps for Perennial Ryegrass Genome Assembly

Cited 2 times
Check if the same lab product or an alternative is used in the 5 most similar protocols
Marker sequences from the Illumina GoldenGate assay (as presented in the S3 File) and from the previously developed transcriptome map [31 (link)] were aligned to the perennial ryegrass genome [25 (link)] using BLAT [38 (link)]. Only sequences that aligned to a single genomic location were retained. These were then used as markers to integrate the information from both genetic linkage maps for ordering scaffolds from the genome assembly. This was done using ALLMAPS [39 (link)] and equal weighting was applied to both maps. The chain file with the anchored scaffolds is presented in S4 File.
Paina C., Byrne S.L., Studer B., Rognli O.A, & Asp T. (2016). Using a Candidate Gene-Based Genetic Linkage Map to Identify QTL for Winter Survival in Perennial Ryegrass. PLoS ONE, 11(3), e0152004.
+ Open protocol
+ Expand
9

Genotyping of Soybean Accessions

Cited 1 time
Check if the same lab product or an alternative is used in the 5 most similar protocols
A total of 55,159 single nucleotide polymorphisms (SNPs) were genotyped in the 298 accessions, with 52,041 analyzed using an Illumina Infinium assay [45 (link)] and 3,072 SNPs analyzed using the Illumina GoldenGate assay following the protocol described by Fan et al. [49 (link)] and Hyten et al. [50 (link)]. In addition, there were 46 SNP markers genotyped by direct sequencing and a single-base-extension method using the Luminex flow cytometer as described by Choi et al. [51 (link)]. A total of 1,363 SNPs were analyzed using both Illumina GoldenGate and Illumina Infinium assays. After eliminating redundant SNPs, non-polymorphic SNPs and SNPs with >25% missing data, a total of 42,368 SNPs (Additional file 5) remained. The physical positions of these markers in the soybean genome were determined using the whole genome assembly of Williams 82 soybean (Glyma 1.01) at the U.S. Department of Energy, Joint Genome Institute, Walnut Creek, CA (http://www.phytozome.net/soybean).
Hwang E.Y., Song Q., Jia G., Specht J.E., Hyten D.L., Costa J, & Cregan P.B. (2014). A genome-wide association study of seed protein and oil content in soybean. BMC Genomics, 15, 1.
+ Open protocol
+ Expand
10

Genomic DNA Extraction and SNP Genotyping

Cited 1 time
Check if the same lab product or an alternative is used in the 5 most similar protocols
We isolated genomic DNA from fin tissue samples taken from the eight F0 founders, 40 F1 adults, and 633 F2 juveniles, using Proteinase K digestion, phenol-chloroform extraction, and ethanol precipitation74 . We re-suspended DNA in 30μL of TE buffer (10 mM Tris, 1 mM EDTA, pH 8.0) and diluted an aliquot of each sample to a concentration of approx. 25 ng/μL based on PicoGreen® assay (Life Technologies). All F0, F1, and F2 individuals were genotyped at 408 single nucleotide polymorphism (SNP) markers30 (link), which are distributed across the G. aculeatus genome and were polymorphic in our mapping population (Supplementary Table 4). Genotyping was performed with Illumina's GoldenGate assay at the Fred Hutchinson Cancer Research Center (Seattle, WA, USA), using GenomeStudio software (Illumina Inc.) to score genotypes.
We used a Bayesian parentage assignment algorithm75 (link) (R package ‘MasterBayes’76 ) and all SNP genotypes to estimate F1 parentage of every F2 individual. Posterior probabilities of correct assignments of F2 hybrids to their estimated pair of F1 parents were high (mean ± s.d. = 0.999 ± 0.020). Assignments of F1 hybrids to known F0 parents were verified (posterior probability = 1 in every case).
Arnegard M.E., McGee M.D., Matthews B., Marchinko K.B., Conte G.L., Kabir S., Bedford N., Bergek S., Chan Y.F., Jones F.C., Kingsley D.M., Peichel C.L, & Schluter D. (2014). Genetics of ecological divergence during speciation. Nature, 511(7509), 307-311.
+ Open protocol
+ Expand

About PubCompare

Our mission is to provide scientists with the largest repository of trustworthy protocols and intelligent analytical tools, thereby offering them extensive information to design robust protocols aimed at minimizing the risk of failures.

We believe that the most crucial aspect is to grant scientists access to a wide range of reliable sources and new useful tools that surpass human capabilities.

However, we trust in allowing scientists to determine how to construct their own protocols based on this information, as they are the experts in their field.

Ready to get started?

Sign up for free.
Registration takes 20 seconds.
Available from any computer
No download required

Sign up now

Revolutionizing how scientists
search and build protocols!