Genome-wide genotype data were used to generate principal components (PCs) to control for ancestral heterogeneity in each analysis. Genotypes for the Discovery and Brain Bank Cohorts were based on data from Illumina (San Diego CA) HumanOmni2.5-8 BeadChips as described previously [1 (link), 36 (link)] and in the Supplementary Methods (Additional file 1 ). DNAm was assessed in the Discovery and the Brain Bank Cohorts using the Illumina EPIC 850K BeadChips. An EWAS consortium-derived pipeline was used to clean the DNAm data from both cohorts [37 (link)]. Additional information about the quality control (QC) pipeline is available in Additional file 1 .
Humanomni2.5 8 beadchip
Manufactured by Illumina
Sourced in Canada, Denmark, United States
The HumanOmni2.5-8 BeadChip is a high-density genotyping microarray produced by Illumina. It is designed to interrogate over 2.5 million genetic markers across the human genome.
Automatically generated - may contain errors
Lab products found in correlation
Humanexome v1.0 beadchip, by Labcorp (2 mentions)
Iplex adme pgx panel, by Labcorp (2 mentions)
Oragene dna kit, by DNA Genotek (2 mentions)
Humanomniexpress 12 beadchip, by Illumina (2 mentions)
Humanomniexpressexome 8 v1.2 beadchip, by Illumina (2 mentions)
Genomestudio software, by Illumina (2 mentions)
Genomestudio v2010, by Illumina (1 mentions)
Repli g kit, by Qiagen (1 mentions)
Extract n amp kit, by Merck Group (1 mentions)
Infinium lcg assay, by Illumina (1 mentions)
Genomestudio software version 2011, by Illumina (1 mentions)
Hiscan system, by Illumina (1 mentions)
Qubit 2, by Thermo Fisher Scientific (1 mentions)
Genome wide human snp array 5, by Thermo Fisher Scientific (1 mentions)
Genome wide human snp array 6, by Thermo Fisher Scientific (1 mentions)
Infinium 2 assay, by Illumina (1 mentions)
Flexi gene dna kits, by Qiagen (1 mentions)
Humanexome v1.0 beadchip, by Illumina (1 mentions)
Humanomniexpress 24 beadchip, by Illumina (1 mentions)
Genomestudio, by Illumina (1 mentions)
24 protocols using humanomni2.5 8 beadchip
1
Genome-wide Genotyping and Epigenomic Analysis
2
DNA Extraction and Genotyping from Dried Blood Spots
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
DNA was extracted from dried blood spot samples (DBSS) collected from each subject close to birth, and stored at the Swedish phenylketonuria screening registry and the Danish Newborn Screening Biobank. Two disks were punched from each participant’s DBSS. DNA was extracted using the Extract-N-amp kit (Sigma-Aldrich) as described previously [11 (link)–13 (link)]. The extracted DNA was whole-genome-amplified using the REPLIg kit (QIAGEN; Danish samples) or the GenomePlex single cell whole genome amplification (WGA) kit (Sigma-Aldrich; Swedish samples) according to the manufactures’ instructions. Both kits have been shown to perform well in the downstream analyses used in this study [11 (link)]. Genotyping was performed using HumanOmni2.5-8 BeadChips (Illumina) at Aros, Denmark. WGA specific cluster files were generated and genotypes called using GenomeStudio V2010.3 (Illumina) and GenomeStudio Genotyping Analysis Module 1.8.4. Four subjects (one Swedish case, one Danish case, and two Danish controls) had a call-rate <97 % and were excluded from further analyses, whereas all other samples had a call-rate >97 %, indicating good DNA quality. SNPs were excluded based on the following criteria in either data set: call-rate <95 %, minor allele frequency <1 %, Hardy–Weinberg test P < 1 × 10−6, and missing genotypes non-randomly distributed between cases and controls.
3
Genome-Wide SNP Genotyping Using Illumina BeadChips
4
Pharmacogenomic Analysis of Furosemide Response
5
Whole-Genome Genotyping of High-Quality DNA
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
High quality DNA (200ng), quantified by Qubit 2.0 (Invitrogen), was used as the starting material for whole-genome genotyping experiments following the manufacturer’s specifications. Briefly, the genomic DNA was denatured at room temperature (RT) for 10 mins using 0.1N NaOH, neutralized and used for whole genome amplification (WGA) under isothermal conditions, at 37°C for 20 hrs. Post-WGA, the DNA was enzymatically fragmented at 37°C for 1hr. The fragmented DNA was precipitated with isopropanol at 4°C and resuspended in hybridization buffer. The samples were then denatured at 95°C for 20 mins, cooled at RT for 30 mins and 35µl of each sample was loaded onto the Illumina HumanOmni 2.5-8 beadchip for hybridization (20hrs at 48°C) in a hybridization chamber. The unhybridized probes were washed away and the Chips (HumanOmni 2.5-8 v1.0 and v1.1,
Additional file 2 ) were prepared for staining, single base extension and scanning using Illumina’s HiScan system.
We filtered the SNP locations to retain only those, called without any error, contained within the exome boundaries as per the sequencing baits, and which were callable (covered by at least five sequencing reads). At these locations, we estimated the overlap for individual SNP calls, i.e., chr/pos/ref/alt and for no calls; i.e., chr/pos/ref/ref; between sequencing and array platforms (
Additional file 16 ).
We filtered the SNP locations to retain only those, called without any error, contained within the exome boundaries as per the sequencing baits, and which were callable (covered by at least five sequencing reads). At these locations, we estimated the overlap for individual SNP calls, i.e., chr/pos/ref/alt and for no calls; i.e., chr/pos/ref/ref; between sequencing and array platforms (
6
Genome-Wide Genotyping and Imputation
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
All of the 10,004 KARE study samples were genotyped using the Affymetrix Genome-Wide Human SNP array 5.0. After quality control, 8,842 subjects and 352,228 SNPs remained for analyses. For in silico replication, 4,302 individuals from the HEXA cohort were genotyped using the Affymetrix Genome-Wide Human SNP array 6.0. After quality control, 3,703 samples and 646,062 SNPs remained. Genotype calling methods and quality control criteria for samples and SNPs of both cohorts have been previously described [11 (link),19 (link)].
SNP imputation was conducted using the IMPUTE program [20 (link)] based on International HapMap (phase 2, release 22, NCBI build 36 and dbSNP build 126;http://hapmap.ncbi.nlm.nih.gov/ ) data from JPT and CHB populations. We used 1,573,409 SNPs for the KARE study and 1,984,393 SNPs for the HEXA cohort after excluding imputed SNPs of unsatisfactory quality for genetic analyses [19 (link)].
The results for replication stage 2 were generated from 356 individuals from the Ehime study using the Illumina Human Omni 2.5-8 BeadChip and 485 individuals from the Amagasaki study using the Illumina HumanHap 550 k Quad BeadChip.
SNP imputation was conducted using the IMPUTE program [20 (link)] based on International HapMap (phase 2, release 22, NCBI build 36 and dbSNP build 126;
The results for replication stage 2 were generated from 356 individuals from the Ehime study using the Illumina Human Omni 2.5-8 BeadChip and 485 individuals from the Amagasaki study using the Illumina HumanHap 550 k Quad BeadChip.
7
Genetic Variants in Urate Transporter URAT1 Gene
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
The URAT1 protein is coded by SLC22A12 and has an amino acid length of 553. We used the whole-genome sequences (WGS) of participants of the TMM cohort study to identify the SLC22A12 variants. The TMM project was conducted by the Tohoku University Tohoku Medical Megabank Organization (ToMMo) and the Iwate Medical University Iwate Tohoku Medical Megabank Organization (IMM) (Kuriyama et al. 2016 (link)). A total of 3392 candidates were selected considering the traceability of participant information, and the quality and abundance of DNA samples for SNP array genotyping and WGS analysis. All candidates were genotyped with Illumina HumanOmni2.5-8 BeadChip (Omni2.5). From the candidates, 2306 samples were selected by filtering out close relatives of individual subjects, based on mean identity-by-descent (IBD; PIHAT in PLINK version 1.07) values indicating relatedness closer than third-degree relatives.
8
Evaluating Chromatin Interactions in Prostate Cancer
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
To evaluate functional relevance of the chromatin interactions, we aligned our interaction peaks with the genomic locations of GENCODE genes (v19; http://www.gencodegenes.org ), and histone modifications including active enhancers (H3K27ac, H3K4me1), active promoters (H3K4me3) and (H3K4me2), which usually overlapped with transcription factor binding regions51 (link)52 (link)53 (link)54 (link). For the most significant interactions, we examined the binding of genome organizer proteins CTCF, RAD21 and prostate specific transcription factors: AR, FOXA1, HOXB13 in prostate specific cell lines LNCaP, VCaP and NCIH660. eQTL analysis was performed using RNA-seq data and SNP genotyping data (Illumina HumanOmni2.5–8 Beadchip) derived from 476 normal prostate tissues in our laboratory. Linear regression was used to correlate genotypes of selected SNPs and expression level (RPKM) of candidate genes.
9
Genotyping of SNPs and Microdeletion in DNA
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Saliva was collected and DNA extracted using Oragene-DNA kits (DNA Genotek) following manufacturer protocols. SNP genotyping for rs2143340 was conducted as part of a larger Illumina HumanOmni2.5-8 bead chip, with genotyping calls screened for quality control measures. The call rate for rs2143340 in the GRaD sample was 0.983.
READ1 genotyping was conducted using PCR amplification and Sanger sequencing at the Yale W.M. Keck DNA Sequencing Facility using standard protocols as previously described30 . Primer sequences and amplification protocol were as previously described11 (link). READ1 alleles were called from chromatograms using a custom program written in C++ (Dr. Yong Kong, available upon request). If the calling program identified errors, chromatograms were manually examined and deconvoluted for allele calling. The call rate for READ1 allele genotyping was 0.987.
The 2445 bp microdeletion on 6p22, which encompasses the READ1 allele within breakpoints in intron 2 of DCDC2, was genotyped by allele specific PCR and agarose-gel electrophoresis. Primer sequences, amplification protocol, and gel electrophoresis for genotyping were as previously described11 (link). The genotyping call rate for the microdeletion was 0.972.
READ1 genotyping was conducted using PCR amplification and Sanger sequencing at the Yale W.M. Keck DNA Sequencing Facility using standard protocols as previously described30 . Primer sequences and amplification protocol were as previously described11 (link). READ1 alleles were called from chromatograms using a custom program written in C++ (Dr. Yong Kong, available upon request). If the calling program identified errors, chromatograms were manually examined and deconvoluted for allele calling. The call rate for READ1 allele genotyping was 0.987.
The 2445 bp microdeletion on 6p22, which encompasses the READ1 allele within breakpoints in intron 2 of DCDC2, was genotyped by allele specific PCR and agarose-gel electrophoresis. Primer sequences, amplification protocol, and gel electrophoresis for genotyping were as previously described11 (link). The genotyping call rate for the microdeletion was 0.972.
10
Genomic DNA Analysis of Foreskin Fibroblasts
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Genomic DNA samples from 62 foreskin fibroblasts, prepared in parallel with the HKC strains, were analyzed by SNP array hybridization utilizing a HumanOmni2.5‐8 Beadchip (www.illumina.com ). PLINK 1.07 and GCTA analysis tools were used for data quality control and processing. Two samples with low genotyping rate (< 95%) were removed. After data quality control (call rate ≥ 0.95, minor allele frequency (MAF)> 0.05 and LD threshold = 0.2), 53837 SNPs were used for PCA. The package “snpStats” was used as statistical tool for SNP association studies, and the package ‘SNPRelate” (Zheng et al, 2012 ) was used to calculate the eigenvectors and eigenvalues for principal component analysis (PCA) based on filtered SNPs. The first two principal components were used to create the PCA plot. Raw and processed genotyping data were deposited on the GEO Omnibus database (GSE156977).
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
Revolutionizing how scientists
search and build protocols!