Human660w quad beadchip

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The Human660W-Quad BeadChip is a high-throughput genotyping array designed by Illumina. It is capable of interrogating approximately 660,000 genetic variants across the human genome.

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BeadChips (68 citations) Human660W-Quad (7 citations) Human660W-Quad v1 DNA Analysis BeadChips (2 citations)

43 protocols using human660w quad beadchip

Genotyping and Quality Control for Raine Study

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TEST and BATS participants were genotyped using the Illumina Human 660W-Quad bead chip. A total of 1903 individuals from the Raine Study (some did not participate in the eye study) were genotyped in two different batches: 1593 individuals were genotyped in 2009 using the Human 660W-Quad bead chip and a further 310 individuals were genotyped in 2012 using the Illumina Human-OmniExpress bead chip.
As part of quality control (QC), the data were filtered by single nucleotide polymorphism (SNP) call rate <0.95, a Hardy-Weinberg equilibrium (HWE) p-value< 10⁻⁶ and a minor allele frequency (MAF) >0.01. To exclude population outliers, a principal component analysis (PCA) was carried out using SNPs with genotyping rate >0.98. Identical SNPs with the 1000 Genome panel were identified for the PCA analysis. All the samples beyond six standard deviations from PC1 and PC2 of 1000 Genomes British population were excluded. Individuals with identity-by-descent (IBD) estimate > 0.24 with another participants were also removed from the analysis.

Yazar S., Cuellar-Partida G., McKnight C.M., Quach-Thanissorn P., Mountain J.A., Coroneo M.T., Pennell C.E., Hewitt A.W., MacGregor S, & Mackey D.A. (2015). Genetic and environmental factors in conjunctival UV autofluorescence. JAMA ophthalmology, 133(4), 406-412.

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Genetic Profiling of Dopaminergic Pathways

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Genetic information was collected via saliva samples and derived through
genome-wide sequencing using the Illumina Human660W-Quad BeadChip, which
contains over 550,000 genetic markers (including SNPs and copy number
variations); the Illumina Human660W-Quad BeadChip was designed on Hapmap
release 21 reference data (see http://hapmap.ncbi.nlm.nih.gov/; additional information
can be found at https://www.ncbi.nlm.nih.gov/variation/news/NCBI_retiring_HapMap/).
Genotyping assignments were made, and reproducibility was examined using
a clustering algorithm in Illumina’s Genome Studio software. SNPs with
<99.9% reproducibility were flagged and investigated further (Jernigan et al.,
2016). For this project, quality control was conducted on the
final genetic dataset, including filtering for minor allele frequency
<1% and Hardy–Weinberg Equilibrium, p < 0.0001.
Additional information on the specific PING genetics protocol can be
found at http://pingstudy.ucsd.edu/resources/genomics-core.html.
We selected 265 ACGT-coded SNPs from nine genes associated with
dopaminergic transmission and metabolism pathways, language development
and psychopathology using PLINK (Version 1.07; Purcell et al., 2007 (link)) for
model building (see Table 3).

Evans C.L., Sawyer K.S., Levy S.A., Conklin J.P., McDonough E, & Gansler D.A. (2022). Factors in the neurodevelopment of negative urgency: Findings from a community-dwelling sample. Brain and Neuroscience Advances, 6, 23982128221079548.

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Genome-wide Linkage Analysis of Family 1

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For the genome-wide linkage analysis, approximately 200 ng of genomic DNA of the 12 individuals from the Family 1 was used for the genotyping analysis by using Illumina Human 660W-Quad BeadChip. Briefly, each sample was whole-genome amplified, fragmented, precipitated and resuspended in appropriate hybridization buffer. Denatured samples were hybridized on prepared Illumina Human 660-Quad BeadChip. After hybridization, the BeadChip oligonucleotides were extended d by a single labeled base, which was detected by fluorescence imaging with an Illumina Bead Array Reader. Normalized bead intensity data obtained for each sample were loaded into the Illumina GenomStudio software, which converted fluorescence in tensities into SNP genotypes.
After quality control filtering, 589,209 SNPs were remained for linkage analysis. Familial relationship check based on IBD sharing was carried out to confirm the collected pedigree information. Multipoint parametric linkage analysis was performed in Merlin by using the pruned autosomal SNPs (with LD <0.1 in population data) and assuming dominant inheritance with a disease allele frequency of 0.001, penetrance rate of 0.99 and phenocopy rate of 0.01. The linkage critical regions were determined by haplotype co-segregation analysis using Haplopainter.

Liu H., Li Y., Hung K.K., Wang N., Wang C., Chen X., Sheng D., Fu X., See K., Foo J.N., Low H., Liany H., Irwan I.D., Liu J., Yang B., Chen M., Yu Y., Yu G., Niu G., You J., Zhou Y., Ma S., Wang T., Yan X., Goh B.K., Common J.E., Lane B.E., Sun Y., Zhou G., Lu X., Wang Z., Tian H., Cao Y., Chen S., Liu Q., Liu J, & Zhang F. (2014). Genome-Wide Linkage, Exome Sequencing and Functional Analyses Identify ABCB6 as the Pathogenic Gene of Dyschromatosis Universalis Hereditaria. PLoS ONE, 9(2), e87250.

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Illumina Genotyping of Stenosis Cohort

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DNA was extracted using the whole blood sample collected and subjected to genotyping using Illumina Human610-Quad BeadChip (Illumina, San Diego, CA, USA) and Illumina Human660W-Quad BeadChip (Illumina, San Diego, CA, USA), as described by Hager et al 2012.27 (link) Among the study population, 1394 patients from Stenosis Group, and 705 patients from Stenosis Group had genotyped data available and were included in the genetic association using PLINK.28 (link) Using PLINK, quality control (QC) was applied, and variants were filtered out. Sex checks were performed using PLINK and variants were excluded for having >5% missing genotyping rates and <1% MAF (minor allele frequency), and for failing HWE (Hardy-Weinberg Equilibrium) test (P > 0.05).

Al Hageh C., Chacar S., Venkatachalam T., Gauguier D., Abchee A., Chammas E., Hamdan H., O’Sullivan S., Zalloua P, & Nader M. (2023). Genetic Variants in PHACTR1 & LPL Mediate Restenosis Risk in Coronary Artery Patients. Vascular Health and Risk Management, 19, 83-92.

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Opioid Dependence Relapse Genetics

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The Comorbidity and Trauma Study (CATS) has been previously described ^{35 (link), 36 (link)}. Briefly, opioid dependent patients were recruited from OST clinics in the greater Sydney area of Australia between 2003 and 2008, a period during which the vast majority of the sample were likely to be either currently receiving, or to have previously received, methadone treatment. Ethics approvals were obtained from the institutional review boards of University of New South Wales, Washington University in Saint Louis, Queensland Institute of Medical Research and the relevant NSW area heath service. The current analyses were restricted to individuals who met DSM-IV criteria for opioid dependence. During the interview, opioid dependent individuals were asked if they ever had a period of abstinence from opioids. Those endorsing such a period were asked if they ever had a subsequent relapse. Genotyping was performed using the Illumina Human660W-Quad BeadChip at the Johns Hopkins Center for Inherited Disease Research (CIDR) and principal components were identified using the smartpca program in the Eigensoft 3.0 package ^{37 (link)}. An additive model was used in logistic regression analysis to examine the effect of rs10485058 genotype on relapse in 1215 patients of European descent, while controlling for age, sex, and two principal components.

Crist R.C., Doyle G.A., Nelson E.C., Degenhardt L., Martin N.G., Montgomery G.W., Saxon A.J., Ling W, & Berrettini W.H. (2016). A polymorphism in the OPRM1 3′ untranslated region is associated with methadone efficacy in treating opioid dependence. The pharmacogenomics journal, 18(1), 173-179.

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Opioid Dependence Relapse Genetics

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Ancestry and Admixture Analysis of PING Participants

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As described in detail previously [4 (link)], saliva samples were sent to Scripps Translational Research Institute (STRI) for analysis. Once extracted, genomic DNA was genotyped with Illumina Human660W-Quad BeadChip. Replication and quality control filters (that is, sample call rate >99, call rates >95%, minor allele frequency >5%) were performed [48 (link)]. To assess genetic ancestry and admixture proportions in the PING participants, a supervised clustering approach implemented in the ADMIXTURE software was used [49 (link)]. Using this approach, a GAF was developed for each participant, representing the proportion of ancestral descent for each of six major continental populations: African, Central Asian, East Asian, European, Native American and Oceanic. Implementation of ancestry and admixture proportions in the PING subjects is described in detail elsewhere [50 (link)]. A more complete description of the genetic ancestry of the PING sample is presented elsewhere [51 (link)].

Piccolo L.R., Merz E.C., He X., Sowell E.R, & Noble K.G. (2016). Age-Related Differences in Cortical Thickness Vary by Socioeconomic Status. PLoS ONE, 11(9), e0162511.

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Genetic Ancestry and Admixture Analysis

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A complete description of the genetic ancestry of the PING sample is presented elsewhere (Akshoomoff et al., 2014 (link); Fjell, et al., 2012 ). Briefly, saliva samples were analyzed in the Scripps Translational Research Institute (STRI). Genomic DNA was genotyped with Illumina Human660W-Quad BeadChip and replication and quality control filters (sample call rate >99, call rates >95%, minor allele frequency >5%) were performed (Bakken, et al., 2012 ). A supervised clustering approach implemented in the ADMIXTURE software was used to assess genetic ancestry and admixture proportions in the PING participants (Alexander & Lange, 2011 (link)). Through this approach, a GAF was developed for each participant, representing the proportion of ancestral descent for each of six major continental populations: African, Central Asian, East Asian, European, Native American and Oceanic.

Piccolo L.R, & Noble K.G. (2017). Perceived stress is associated with smaller hippocampal volume in adolescence: Perceived stress effects in adolescent brain. Psychophysiology, 55(5), e13025.

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Robust Genome-Wide Association Studies

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Samples were genotyped using the Affymetrix 6.0 and Illumina (610 Quad, Human660W-quad Beadchip, Omni5, OmniExpress Beadchip, Oncoarray) platforms. Each of the GWAS was subjected to rigorous standardised quality control independently prior to imputation, which was performed via the Michigan imputation server (https://imputationserver.sph.umich.edu/) based on the Haplotype Reference Consortium (HRC)^{29 (link)}. After imputation, each site was filtered to include only imputed variants with information score>0.6 and further quality controls checks were implemented (genotype rate >95%, minor allele frequencies >0.01, and Hardy-Weinberg equilibrium (HWE) >x10⁻⁵ in controls). Finally, the data were pooled and final quality control was performed on the pooled GWAS set including checks for missingness, duplicates, sex mismatch, abnormal heterozygosity, cryptic relatedness, population outliers (principal components analyses: Eigenstrat), and genomic inflation (λ > 1.00). Additional information on the MM GWAS studies contributing in the InterLymph consortium are showed in supplementary table 1.

Macauda A., Piredda C., Clay-Gilmour A.I., Sainz J., Buda G., Markiewicz M., Barington T., Ziv E., Hildebrandt M.A., Belachew A.A., Varkonyi J., Prejzner W., Druzd-Sitek A., Spinelli J., Andersen N.F., Hofmann J.N., Dudziński M., Martinez-Lopez J., Iskierka-Jazdzewska E., Milne R.L., Mazur G., Giles G.G., Ebbesen L.H., Rymko M., Jamroziak K., Subocz E., Reis R.M., Garcia-Sanz R., Suska A., Haastrup E.K., Zawirska D., Grzasko N., Vangsted A.J., Dumontet C., Kruszewski M., Dutka M., Camp N.J., Waller R.G., Tomczak W., Pelosini M., Raźny M., Marques H., Abildgaard N., Wątek M., Jurczyszyn A., Brown E.E., Berndt S., Butrym A., Vachon C.M., Norman A.D., Slager S.L., Gemignani F., Canzian F, & Campa D. (2021). Expression quantitative trait loci of genes predicting outcome are associated with survival of multiple myeloma patients. International journal of cancer, 149(2), 327-336.

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Genetic Ancestry Analysis of PING Participants

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Saliva samples were sent to Scripps Translational Research Institute (STRI) for analysis. Once extracted, genomic DNA was genotyped with Illumina Human660W-Quad BeadChip. Replication and quality control filters (i.e. sample call rate >99, call rates >95%, minor allele frequency >5%) were performed^{47 (link)}. To assess genetic ancestry and admixture proportions in the PING participants, a supervised clustering approach implemented in the ADMIXTURE software was used^{48 (link)}. Using this approach, a genetic ancestry factor (GAF) was developed for each participant, representing the proportion of ancestral descent for each of six major continental populations: African, Central Asian, East Asian, European, Native American and Oceanic. Implementation of ancestry and admixture proportions in the PING subjects is described in detail elsewhere^{45 (link)}. See also Akshoomoff and colleagues^{44 (link)} for a more complete description of the genetic ancestry of the PING sample.

Noble K.G., Houston S.M., Brito N.H., Bartsch H., Kan E., Kuperman J.M., Akshoomoff N., Amaral D.G., Bloss C.S., Libiger O., Schork N.J., Murray S.S., Casey B.J., Chang L., Ernst T.M., Frazier J.A., Gruen J.R., Kennedy D.N., Zijl P.V., Mostofsky S., Kaufmann W.E., Kenet T., Dale A.M., Jernigan T.L, & Sowell E.R. (2015). Family Income, Parental Education and Brain Structure in Children and Adolescents. Nature neuroscience, 18(5), 773-778.

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