PMC Oxford University Press is a department of the University of Oxford. A variety of methods exist to control for population stratification, of which the most common is to perform principal component analysis on the genome-wide data, and then use the resulting components as covariates in association analysis. Computation time and memory resources required by two different software packages (BEAGLE and IMPUTE2) were also evaluated. A number of challenges were encountered due to the complexity of using two different imputation software packages, multiple ancestral populations, and many different genotyping platforms. The following procedures/parameters were used in the post-imputation quality control by PLINK1.90: sample . (post-imputation) . GM PLoS One. Methods 7, 331331 10.1038/nmth0510-331 Unable to load your collection due to an error, Unable to load your delegates due to an error. Aulchenko This initial calling is performed by automated softwarehowever, the algorithms to perform this calling sometimes fail to identify valid clusters, especially when patterns of clustering are unusual. Frequency polygon showing the number of variants at each info value post-imputation, including poor-quality variants to be excluded (info <0.15) and higher-quality variants that should be kept (info >0.85). Example scripts are provided at https://github.com/JoniColeman/gwas_scripts . Last updated: 2021-03-04 Checks: 6 1 Knit directory: PSYMETAB/ This reproducible R Markdown analysis was created with workflowr (version 1.6.0). DE His research interests are the genetics of complex psychiatric and co-morbid disorders. eCollection 2022. However, this relies on large sample sizes to allow for reliable calling of the genotypes. J Post Imputation Quality Control (QC) Post imputation QC was previously completed for a cross-disorder genome-wide study on the OCD dataset. Search for other works by this author on: The genome revolution and its role in understanding complex diseases, Molecular genetic testing and the future of clinical genomics, Data quality control in genetic case-control association studies, Quality control for genome-wide association studies, Genome-wide association studies: a primer, The psychiatric GWAS consortium: big science comes to psychiatry, Practical aspects of imputation-driven meta-analysis of genome-wide association studies, GCTA: a tool for genome-wide complex trait analysis, Variance component model to account for sample structure in genome-wide association studies, Advantages and pitfalls in the application of mixed-model association methods, Whole-genome genotyping with the single-base extension assay, Collection of blood, saliva, and buccal cell samples in a pilot study on the danish nurse cohort: comparison of the response rate and quality of genomic DNA, Prospects for whole-genome linkage disequilibrium mapping of common disease genes, Detecting association in a case-control study while correcting for population stratification, zCall: a rare variant caller for array-based genotyping: genetics and population analysis, GenABEL: an R library for genome-wide association analysis, PLINK: a tool set for whole-genome association and population-based linkage analyses, Genome-wide association studies of quantitative traits with related individuals: little (power) lost but much to be gained, Second-generation PLINK: rising to the challenge of larger and richer datasets, A critical evaluation of genomic control methods for genetic association studies. In smaller cohorts, a more stringent MAF cut-off is recommended, as the minor allele count will be lower, which limits the value of conclusions from the analysis of these variants. Therefore, the ADHD sample did not need to undergo additional QC measures. PLoS ONE 4:e8398, The 1000 Genomes Project Consortium (2012) An integrated map of genetic variation from 1,092 human genomes. Bray D, Hook H, Zhao R, Keenan JL, Penvose A, Osayame Y, Mohaghegh N, Chen X, Parameswaran S, Kottyan LC, Weirauch MT, Siggers T. Cell Genom. First, the exonic content allows rare coding variation to be assayed in large numbers of samples without the high costs of sequencing these variants [ 26 ]. Any reference papers or site describing post imputation quality control would be highly appreciated. Anderson 2017 Feb 28;12(2):e0172082. SH BMC Bioinformatics 11:134. FH As such, some clusters must be identified by manual recalling by a bioinformatician. Post-imputation quality control consisted of checking chunk integrity (along the chromosome) and minor allele frequency for imputed variants (compared to the reference panel). This increases downstream flexibility at the expense of losing the more informative probabilistic calls. 2009 Dec 22;4(12):e8398 . The introduction of mixed linear model association analysis is an example of this, allowing for an approach to control for population structure that is as yet not available in PLINK2, although the implementation of GCTA code into PLINK2 is expected in the near future [ 9 , 11 , 23 ]. 1000 Genomes Imputation Cookbook 2.3.1. Chen G, Shriner D, Zhang J, Zhou J, Adikaram P, Doumatey AP, Bentley AR, Adeyemo A, Rotimi CN. PR Genet. Out of the 365 SNPs previously reported in facial GWASs , 301 were included Setting the threshold for the P -value of the HardyWeinberg test to be low ( P <110 5 ) decreases the probability of excluding deviations that result from processes of interest. et al. Quality control, imputation and analysis of genome-wide genotyping data from the Illumina HumanCoreExome microarray Jonathan R. I. Coleman, Jonathan Coleman is a PhD student at the MRC Social, Genetic and Developmental Psychiatry Centre (SGDP), using genomic methods to explore differential response to psychological treatments for anxiety disorders. The value of the array in smaller cohorts is in providing an inexpensive means to assay thousands of variants that are in high LD with a considerably greater number. The main benefits of the HumanCoreExome as a low-cost microarray are twofold. 2011 Nov;35(7):632-7 It is worth noting that the exonic content of the HumanCoreExome chip was specifically designed to target coding variants, with much of this content having a population MAF<1% [ 17 ]. MeSH government site. 2018 Feb 13;19(1):23. doi: 10.1186/s12881-018-0534-8. volume132,pages 10731075 (2013)Cite this article. MK Neither choice in this context is wrong, but the choice made has consequences, and as such needs to be considered and reported [ 11 ]. It is worth noting that some downstream analysis programs impose much more severe IBD cut-offs (GREML estimation in GCTA, which produces an estimate of heritability from all assayed variants, uses 0.025), while other analyses account for between-sample relatedness as part of the analysis [ 9 , 21 ]. After quality control applied to the 50 K SNP chip, 5905, 4114 and 3665 SNPs were removed by HWE, MAF and genotyping call-rate filters, respectively, 29,587 SNPs remained for subsequent analyses. et al. However, the phenomenon of LD can exaggerate or obscure similarities, as a shared region of high LD results in more shared variants than one of low LD, even if the two regions are the same size. This site needs JavaScript to work properly. Pac Symp Biocomput. Imputation increased the. See this image and copyright information in PMC. Daniel Shriner. For full access to this pdf, sign in to an existing account, or purchase an annual subscription. Imputation and quality control steps for combining multiple genome-wide datasets. 2010 Dec;34(8):816-34 PF The precise pairwise relationships will differ subtly depending on whether the GRM is made using the genotype data before or after imputation (as well as on the programme used), and so the results of the association study will also differ slightly. Quality control, imputation and analysis of genome-wide genotyping data from the Illumina HumanCoreExome microarray Jonathan R. I. Coleman,Jonathan Colemanis a PhD student at the MRC Social, Genetic and Developmental Psychiatry Centre (SGDP), using genomic methods to explore differential response to psychological treatments for anxiety disorders. https://doi.org/10.1007/s00439-013-1336-x. The second database is required only for local imputation, and downloading the latest release of the 1,000 Genomes Project data. et al. 2009;67(2):104-15. doi: 10.1159/000179558. Well-executed recalling and quality control of genotype data reduces biases within GWAS studies and increases the probability of successful replication. Visscher Peter Google Scholar, Li Y, Willer CJ, Ding J, Scheet P, Abecasis GR (2010) MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. For example, the graphs below show most of the worst-performing variants have info<0.15, and there is an enrichment of high-quality variants with info>0.85. Genet. In current standard practice, 5 , 6 , 7 variant-level imputation quality metrics such as IMPUTE's INFO, 8 minimac's Rsq, 9 or Beagle's DR2 10 2012 Nov 1;491(7422):56-65 Removal of such missing variants and samples is best conducted in an iterative manner, removing variants genotyped in<90% of the samples, then samples with<90% of variants and continuing with increasing stringency to a user-defined final threshold (typically in the range of 9599% completeness, depending on the required stringency of quality control). We hope that the provision of this simple protocol will ensure the general standard of GWAS remains high, and will simplify the combination of independent studies into the collaborative meta-analyses that have become a hallmark of success in genomics. et al. doi: 10.1371/journal.pone.0172082. At worst, poor quality control can lead to systematic biases in outcome and increased false-positive (and false-negative) associations [ 4 ]. b Rare variants (minor allele frequency <0.05) Full size image References HM However, such guidance is not easily available to groups outside these consortia. Before Imputation. SK Genotype imputation is used to predict genotypes that are not experimentally determined in a study sample (Marchini and Howie 2010). For this reason, the rarest variants should be discarded from the analysis. Genotype_Imputation_Pipeline. Coors A, Imtiaz MA, Boenniger MM, Aziz NA, Ettinger U, Breteler MMB. Ramnarine S, Zhang J, Chen LS, Culverhouse R, Duan W, Hancock DB, Hartz SM, Johnson EO, Olfson E, Schwantes-An TH, Saccone NL. Nat Rev Genet 11:499511, Article The electronic MEdical Records and GEnomics (eMERGE) network brings together DNA biobanks linked to electronic health records (EHRs) from multiple institutions. eCollection 2021 Jan 14. we compared two alternative methods for post-imputation qc filtering, first, the impute-info score, which is associated with the imputed allele frequency estimate which ranges from 1,. Step 1.3. In this example, a MAF cut-off of 0.01 appears to remove most of the SNPs with low info scores. Pitfalls of merging GWAS data: lessons learned in the eMERGE network and quality control procedures to maintain high data quality. Rare-variant genome-wide association studies: a new frontier in genetic analysis of complex traits, An integrated map of genetic variation from 1,092 human genomes, Fast and accurate genotype imputation in genome-wide association studies through pre-phasing, A flexible and accurate genotype imputation method for the next generation of genome-wide association studies, ProbABEL package for genome-wide association analysis of imputed data, Efficient Bayesian mixed-model analysis increases association power in large cohorts, FaST linear mixed models for genome-wide association studies, The Author 2015. Once an LD-pruned data set is obtained, individuals can be compared pairwise to establish the proportion of variants they share identical-by-state (IBS). . All clinical investigation was conducted according to the principles expressed in the Declaration of Helsinki. The ADHD sample was cleaned prior to upload to the site 7. Kang 10.1186/1471-2105-11-134 Pan However, caution is advised when studying cohorts in which consanguineous relationships are common, as high inbreeding coefficients are expected in these samples. . Genotyping support was provided by the Coriell Institute for Medical Research. The site is secure. ic, a post-Imputation data checking program Background ic is a set of programs designed to produce a single html page visual summary of one or more imputed data sets from the most common imputation programs. Post-imputation quality control. The steps are likely to be applicable to data from other arrays, with the caveat that differences in array content may require alteration of the various thresholds discussed. Finally, it is necessary to exclude variants missing in multiple samples when using hard-called data, as variants imputed with a certainty below threshold are marked as missing rather than being excluded. Y I thank Adebowale Adeyemo and two anonymous reviewers for their helpful comments. H -, PLoS Genet. To make effective use of the array in this manner requires imputation of the data to a reference population, most commonly the 1000 Genomes Reference [ 27 ]. Here, I illustrate the con- Gerome Breen is a senior lecturer at the SGDP, and Theme Lead for the Genomics and Biomarkers and BioResource for Mental and Neurological Health themes at the NIHR BRC MH. Cumulative frequency curve showing the same data as Figure 1 . Goddard G Additive genetic effect of GCKR, G6PC2, and SLC30A8 variants on fasting glucose levels and risk of type 2 diabetes. Amos Folarin is a senior software developer and bioinformatician at the NIHR BRC MH Bioinformatics Core, using bioinformatics for drug screening, target identification and disease analysis. Verma, S.S. et al. His interests include developing new methods to understand the genetic architecture of, and epidemiological relationship between, psychiatric and other medical disorders. G The .gov means its official. 2022 Aug 19;12(1):337. doi: 10.1038/s41398-022-02093-8. Calculating Polygenic Risk Scores (PRS) in UK Biobank: A Practical Guide for Epidemiologists. If your data passed this steps, your job is added to our imputation queue and will be processed as soon as possible. Before Bethesda, MD 20894, Web Policies The complexities of genotyping and recalling are beyond the scope of this protocol, but guidance is available from array manufacturers and as referenced in the Supplementary Data [ 13 ]. Monomorphic variants should be removed (MAF=0), as well as variants that are extremely rare in the cohort (see the earlier discussion of MAF removals). High heritability of ascending aortic diameter and trans-ancestry prediction of thoracic aortic disease. 2.1 Quality Control of Genotype Data 2.2 Convert Genotype Data to Build 37 2.3 Convert Genotype Files Into IMPUTE format 3 Pre-Phasing (autosomal chromosomes only) 3.1 Sliding Window Analyses 3.2 Pre-Phasing using IMPUTE2 4 Pre-Phasing using SHAPEIT (recommended) 5 Imputation 6 X-Chromosome Imputation 7 Association Analysis Introduction Authors This protocol describes the basic analytical steps required to conduct a genome-wide association study; it is expected that DNA genotyping and genotype recalling have already been performed. Jonathan R. I. Coleman, Jack Euesden, Hamel Patel, Amos A. Folarin, Stephen Newhouse, Gerome Breen, Quality control, imputation and analysis of genome-wide genotyping data from the Illumina HumanCoreExome microarray, Briefings in Functional Genomics, Volume 15, Issue 4, July 2016, Pages 298304, https://doi.org/10.1093/bfgp/elv037. Hamel Patel is a PhD student at the SGDP and the National Institute for Health Research Biomedical Research Centre for Mental Health (NIHR BRC MH) Bioinformatics Core, South London and Maudsley NHS Trust. The final step presented in this protocol is to perform the association analysis itself. HHS Vulnerability Disclosure, Help Using PLINK, the imputed genotype posterior probabilities in the VCF files were converted to Oxford-format (.gen) best-guess genotypes for the GenEpi interaction analyses. A unified approach to genotype imputation and haplotype-phase inference for large data sets of trios and unrelated individuals. Imputation accuracy statistics can be classified into two types: (1) statistics that compare imputed to genotyped data and (2) statistics produced without reference to true genotypes. The exact analysis performed depends on the research question being investigated and the covariates included. One method to detect this is to evaluate the deviation from HardyWeinberg equilibrium at each variant. Gunderson Careers. doi: 10.1371/journal.pone.0160733. Zuvich RL, Armstrong LL, Bielinski SJ, Bradford Y, Carlson CS, Crawford DC, Crenshaw AT, de Andrade M, Doheny KF, Haines JL, Hayes MG, Jarvik GP, Jiang L, Kullo IJ, Li R, Ling H, Manolio TA, Matsumoto ME, McCarty CA, McDavid AN, Mirel DB, Olson LM, Paschall JE, Pugh EW, Rasmussen LV, Rasmussen-Torvik LJ, Turner SD, Wilke RA, Ritchie MD. We suggest an alternative, regressing principal components on outcome directly, and keeping only those that explain variance in the outcome at a rate above chance for use as covariates in the GWAS. All rights reserved. JI The Supplementary Data uses IMPUTE2 [ 28 , 29 ] to impute to the full 1000 Genomes Reference population. CM Replication, including combining individual studies in meta-analyses is central to genomics. Craddock Center for Research on Genomics and Global Health, National Human Genome Research Institute, Building 12A, Room 4047, 12 South Dr., MSC 5635, Bethesda, MD, 20892-5635, USA, You can also search for this author in C PF 2.4. imputation and analysis pipeline, which prepares raw genetic data, performs pre-imputation quality control, phasing, imputation, post-imputation quality control, population stratification analysis, and genome-wide association with statistical data analysis, including result visualization. eCollection 2022. Policy. autoencoders are neural networks tasked with the problem of simply reconstructing the original input data, with constraints applied to the network architecture or transformations applied to the input data in order to achieve a desired goal like dimensionality reduction or compression, and de-noising or de-masking ( abouzid et al., 2019; liu et Carey Lert-Itthiporn W, Suktitipat B, Grove H, Sakuntabhai A, Malasit P, Tangthawornchaikul N, Matsuda F, Suriyaphol P. BMC Med Genet. Front. Wagner Sul imputation quality control comprised the input datasets for imputation, which used IMPUTE224 with 1000 Genomes25 Phase 1 v3 as the reference panel. Clipboard, Search History, and several other advanced features are temporarily unavailable. 10.1016/j.ajhg.2009.01.005 The eMERGE imputed dataset will serve as a valuable resource for discovery, leveraging the clinical data that can be mined from the EHR. 2017;22:368-379. doi: 10.1142/9789813207813_0035. The analysis of thousands of variants allows novel findings to be made, and targets for replication to be established. The F statistic is a function of the deviation of the observed number of heterozygote variants from that expected under HardyWeinberg equilibrium. Isaacs BMC Bioinformatics. Lewis Deviations from HardyWeinberg equilibrium as a result of genotyping artefacts are not expected to differ between cases and controls, but biologically relevant deviations are more likely to occur in cases [ 5 ]. Lorraine Southam1, Kalliope Panoutsopoulou2,NWilliamRayner3,4,KayChapman1, Caroline Durrant3, Teresa Ferreira3, Nigel Arden5,6,AndrewCarr1,PanosDeloukas2, Michael Doherty7, John Loughlin8, Andrew McCaskie8,9,WilliamEROllier10 . DB This list is part of IMPUTE2 output or could be additional list of SNPs that we wish to exclude for other reasons. eMERGE [email protected]. Taken together, HWE testing is recommended for post-imputation quality control for all markers, regardless of whether genotypes were experimentally determined or imputed. Pre-analytical steps partly inform these thresholds. PLoS Genet 5:e1000529, Article KL BK The window size of 1500 variants corresponds to the large, high LD chromosome 8 inversion, while the shift of 10% represents a trade-off between efficiency and thoroughness [ 5 ]. Yang A generic coalescent-based framework for the selection of a reference panel for imputation. Paaniuc B, Avinery R, Gur T, Skibola CF, Bracci PM, Halperin E. Genet Epidemiol. Evaluating Imputation Algorithms for Low-Depth Genotyping-By-Sequencing (GBS) Data. ME 2022 Springer Nature Switzerland AG. J. Hum. The best method is to plot a frequency curve ( Figure 1 ) or cumulative distribution ( Figure 2 ) of the info score and assign the threshold at the inflexion point. In males, F 1, because all X chromosome variants are hemizygous, and so no heterozygotes are observed. For the smallest studies, where fewer than 1000 individuals are investigated, a cut-off of 5% should be consideredthis is in line with the analysis program GenAbel, for example, which uses a minor allele count of 5 as its cut-off [ 18 ]. NA Controlling for population structure and genotyping platform bias in the eMERGE multi-institutional biobank linked to Electronic Health Records. ProbABEL package for genome-wide association analysis of imputed data. FOIA Service 124, 439450. However, this format remains computationally burdensome at presentfor example, it is not yet possible to store dosage data as a file input type in PLINK, akin to the PLINK binary format. Imputation results were evaluated using the following metrics: accuracy of imputation, allelic R (2) (estimated correlation between the imputed and true genotypes), and the relationship between allelic R (2) and minor allele frequency. and transmitted securely. HHS Vulnerability Disclosure, Help The 1000 Genomes cosmopolitan reference panel was used for imputation. -, Genet Epidemiol. Can anyone kindly explain me the possible ways of quality control of imputed data? Federal government websites often end in .gov or .mil. This method compares the deviation of each individual from the population mean at each variant in the data set, and then compares individuals pairwise to establish a value for overall genetic similarity. Corvin Pre-phasing done with Eagle 2.3. Ethics approval for the Howard University Family Study was obtained from the Howard University Institutional Review Board and written informed consent was obtained from each participant. Lee However, this is an imprecise measurefemale subjects with high F have been reported in the 1000 Genomes reference population ( https://www.cog-genomics.org/plink2/basic_stats ). Disclaimer, National Library of Medicine Wijmenga Quality control after genotype imputation, Traffic: 1734 users visited in the last hour, https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0137601, A: Beagle imputation results quality control, User Agreement and Privacy S Provided by the Springer Nature SharedIt content-sharing initiative, Over 10 million scientific documents at your fingertips, Not logged in As a result, including closely related individuals can skew analysis; genetic variants shared because of close relatedness can become falsely associated with phenotypic similarity that also results from close relatedness. A Population Stratification and Phenotype Prep Module are provided, which assists in the removal of ancestral backgrounds deemed unwanted though a PCA-based approach and normalizing . Learn more about Institutional subscriptions, Howie BN, Donnelly P, Marchini J (2009) A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. Visscher Would you like email updates of new search results? All data sets are not perfect. Neale PMID: 23842951 . This has benefits over removing all variants and samples beneath the final threshold, as fewer samples are lost using the iterative procedure (at the expense of a slight increase in variant exclusions). Impact of Hardy-Weinberg disequilibrium on post-imputation quality control Hum Genet. Jack Euesden is a PhD student at the SGDP. PLoS One. Different sources recommend different thresholds to exclude poorly imputed data. Zaitlen Aulchenko Shriner, D. Impact of HardyWeinberg disequilibrium on post-imputation quality control. Please enable it to take advantage of the complete set of features! The protocol provided with this article provides a straightforward introduction to the basics of GWAS that will increase standardization of GWAS studies between different groups. The CRGGH is supported by the National Human Genome Research Institute, the National Institute of Diabetes and Digestive and Kidney Diseases, the Center for Information Technology, and the Office of the Director at the National Institutes of Health (Z01HG200362).

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