UK

您所在的位置：网站首页 › genotype calls ukb怎么下载 › UK

UK

2024-07-13 02:59| 来源: 网络整理| 查看: 265

UK Biobank GWAS Table of Contents Goals Files Phenotypes and applications Phenotype output Phenotype to Genotype linking Sample and Variant QC Sample QC Genotype QC Association in Hail Association Model Single phenotype association workflow Summary stat output Quick Manhattan and QQ plot UK Biobank Updates Goals: Collect UK Biobank phenotypes from collaborating applications When necessary, convert phenotypes into clean case/control, true/false, or quantitative values using the PHESANT algorithm Use the same sample and variant QC across all phenotypes Run SNP association on UKBB imputed dosage BGEN files using hail on the google cloud platform Provide per-SNP summary stats for all approved phenotypes GWAS Results available for viewing and download via聽https://biobankengine.stanford.edu Files 1_merge_mfi.sh - merge UKBB mfi files 2_fam_sqc_merge.R - merge ukb_sqc_v2.txt to application-specific .fam file 3_make_sample_qc_table.py - make application-specific qc key table 4_build_pipelines.py - set up block of phenotypes to be analyzed 5_make_variant_annotation_vds.py - create sites-only VDS of all SNPs 6_filter_gwas_variants.py - filter sites-only VDS to QC'ed SNPs 7_run_linreg3.py - run association with linreg3 command 8_export_results.py - export results to .tsv file Manhattan_plot.R - create quick Manhattan plot in R from summary stat file PHESANT_pipeline.pdf - diagram of PHESANT phenotype curation strategy QQ_plot.R - create quick QQ plot in R from summary stat file run_single_phenotype.py - run a single phenotype through Hail on the Google cloud platform Phenotypes and applications

Phenotype Collection Strategy

Collect as many phenotypes as possible from collaborating UK Biobank applications Ensure all applications permit GWAS analysis and public release of summary statistics Add participating Neale Lab members to all applications

Phenotype Curation Strategy

Auto-curation of phenotypes using PHESANT: Source repository: https://github.com/MRCIEU/PHESANT Customized PHESANT repository: https://github.com/astheeggeggs/PHESANT Transformed phenotypes are written to disk Altered .log file for easier creation of downstream plotting and summary files Removed additional analysis and plotting functionality not required for our purposes Altered the variable-list file variable-info/outcome_info_final.tsv to restrict to the phenotypes we wish to analyze - see 'EXCLUDED' column Added data codings tables from UK biobank data showcase to allow mapping from original encoding in phenotype file (e.g. 1 = 'bungalow', 2 = 'flat' etc) Added additional scripts for creation of simple test plots and summary tables PHESANT phenotype curation strategy

Example PHESANT script

Rscript phenomeScan.r \ --phenofile="../../ukb[XXXX].tsv" \ --variablelistfile="../variable-info/outcome_info_final.tsv" \ --datacodingfile="../variable-info/data-coding-ordinal-info.txt" \ --userId="userId" \ --resDir="../../" \ --log="ukb[XXXX]_output" \

generates an output file. Finally, run get_hists_and_notes followed by include_PHESANT_reassignment_names in summarise_phenoypes.r to generate a summary file of the phenotypes for analysis. Note: the sample QC file ukb[XXXX]_qc.tsv and participant withdrawal list w[XXXX]_20170726.csv for each application is required to ensure that numbers in the summary file reflect the QC pipeline described below.

Phenotype output Output file: PHESANT ukb[XXXX]_output.tsv (full phenotype file with rows=sample ID and columns=phenotype ID) Summary file: ukb[XXXX]_phenosummary_final.tsv row number - numeric field matching UK Biobank data showcase field Field - short description of phenotype N.non.missing - number of non-missing QC positive samples N.missing - number of missing QC positive samples N.cases - number of QC positive samples responding affirmatively to phenotype designation (NA if quantitative) N.controls - number of QC positive samples responding negatively to phenotype designation (NA if quantitative) Notes - extended description of phenotype PHESANT.notes - categorizations of phenotype by PHESANT algorithm PHESANT.reassignments - changes to phenotype values by PHESANT Example phenosummary_final.tsv file Field N.non.missing N.missing N.cases N.controls Notes PHESANT.notes PHESANT.reassignments 46 Hand grip strength (left) 335821 1378 NA NA Left grip strength 46_0|| INTEGER || CONTINUOUS || IRNT || 47 Hand grip strength (right) 335842 1357 NA NA Right grip strength 47_0|| INTEGER || CONTINUOUS || IRNT || 20116_1 Smoking status: Previous 336024 1175 118419 217605 current/past smoking status 20116_0 || CAT-SINGLE || CAT-SINGLE-BINARY-VAR: 1 || Inc(>=10): 1(173099) || Phenotype to Genotype linking The main link files from application to UK Biobank imputed dataset are the .fam and .sample file Both files have the same IDs The .fam file is ordered the same way as the ukb_sqc_v2.txt file The .sample file is ordered the same way as the .bgen file R Script for linking application-specific .fam file and ukb_sqc_v2.txt file

Current list of participating UK Biobank Applications

Completed associations

18597 - "Methodological extensions to estimate genetic heritability and shared risk factors for phenotypes of the UK Biobank" Primary investigators: Benjamin Neale / Verneri Anttila 11898 - "Genetic studies of anthropometric traits and methods for analysis of multiple phenotypes" Primary investigator: Joel Hirschhorn

Ongoing analyses

24983 - "Generating effective therapeutic hypotheses from genomic and hospital linkage data" Primary investigator: Manuel Rivas 11425 - "The Social Science Genetic Association Consortium" Primary investigator: Daniel Benjamin 32568 - "Phenomewide Heritability Analysis" Primary investigator: Jordan Smoller Sample and Variant QC Sample QC

Primary sample QC parameters for GWAS from ukb_sqc_v2.txt file:

in.Phasing.Input.chr1_22==1 in.white.British.ancestry.subset==1 used.in.pca.calculation==1 excess.relatives==0 putative.sex.chromosome.aneuploidy==0

Additional QC parameters

Samples withdrawn un UK Biobank update = 8 Samples redacted = 3 ([-3,-2,-1] in the sample ID)

Pre/post QC sample counts

Imputed samples removed from QC file = 151180 Imputed samples retained in QC file = 337199 NOTE: all samples retained are in the .bgen files NOTE: The ukb_sqc_v2.txt file has more samples than the .bgen files, but the same number of samples as the application specific .sample file

Description of inclusion parameters:

het.missing.outliers (0/1) (no/yes) Indicates samples identified as outliers in heterozygosity and missing rates, which indicates poor-quality genotypes for these samples. putative.sex.chromosome.aneuploidy (0/1) (no/yes) Indicates samples identified as putatively carrying sex chromosome configurations that are not either XX or XY. These were identified by looking at average log2Ratios for Y and X chromosomes. See genotype QC documentation for details. in.kinship.table (0/1) (no/yes) Indicates samples which have at least one relative among the set of genotyped individuals. These are exactly the set of samples that appear in the kinship table. See genotype QC documentation for details. excluded.from.kinship.inference (0/1) (no/yes) Indicates samples which were excluded from the kinship inference procedure. See genotype QC documentation for details. excess.relatives (0/1) (no/yes) Indicates samples which have more than 10 putative third-degree relatives in the kinship table. in.white.British.ancestry.subset (0/1) (no/yes) Indicates samples who self-reported 'White British' and have very similar genetic ancestry based on a principal components analysis of the genotypes. See genotype QC documentation for details. used.in.pca.calculation (0/1) (no/yes) Indicates samples which were used to compute principal components. All samples with genotype data were subsequently projected on to each of the 40 computed components. See genotype QC documentation for details. in.Phasing.Input.chr1_22 (0/1) (no/yes) Indicates sample was in the input for phasing of chr1-chr22. in.Phasing.Input.chrX (0/1) (no/yes) Indicates sample was in the input for phasing of chrX. in.Phasing.Input.chrXY (0/1) (no/yes) Indicates sample was in the input for phasing of chrXY. Quick descriptives of categories: table(in.Phasing.Input.chr1_22) 0 1 969 487409 table(het.missing.outliers) 0 487409 table(putative.sex.chromosome.aneuploidy) 0 1 486757 652 table(in.kinship.table) 0 1 339678 147731 table(excluded.from.kinship.inference) 0 1 487400 9 table(excess.relatives) 0 1 487221 188 table(in.white.British.ancestry.subset) 0 1 78437 408972 table(used.in.pca.calculation) 0 1 80190 407219 Total samples in imputed BGEN files: 487409 Withdrawn samples (remove): 8 Redacted samples (remove): 3 In white, British ancestry subset (keep): 408972 Used in PCA calculation (keep): 407219 Marked as having excess relatives (remove): 188 Marked as sex chromosome aneuploidies (remove): 652 Samples remaining: 337199 Genotype QC

Primary genotype QC parameters for inclusion to GWAS from ukb_sqc_v2.txt file

Autosomal SNPs SNPs present in HRC imputation file: ../imputed/resources/HRC/HRC.r1-1.GRCh37.wgs.mac5.sites.tab pHWE > 1e-10 in 337199 QC positive samples call rate > 0.95 in 337199 QC positive samples UKBB INFO score > 0.8 AF > 0.001 and AF < 0.999 (AF = alternate allele frequency in 337199 QC positive samples) NOTE: UKBB INFO score available in per chromosome files: ukb_mfi_chr*_v2.txt Quick descriptive of genotype inclusion counts: QC positive sample subset (n = 337199): - Total Variants: 92,693,895 - in HRC imputation: 39,131,578 - w/ pHWE > 1e-10: 92,471,744 - w/ call rate > 95%: 92,693,895 - w/ 0.001 < MAF < 0.999: 16,047,294 - w/ UKBB INFO score > 0.8: 29,447,617 - w/ all of above: 10,894,596 Association in Hail

SNP association is performed on UKBB imputed dosage BGEN files using hail on a google cloud platform.

Hail scalable genetic analyses Google Cloud Platform cluster computation and data storage Cloud Tools script submission to compute clusters

This allows us to take advantage of large cluster computing and parallel processing via Apache Spark

Association model To expedite our initial GWAS, we implemented a linear regression model on all phenotypes Linear regression model covariates were inferred sex and the first 10 PCs (taken directly from ukb_sqc_v2.txt) Hail's [linreg3] command allowed us to run associations on blocks of phenotypes with the same missingness structure at the same time to speed up analysis However, memory constraints limited our blocks to: 110 phenotypes when the missingness structure of all phenotypes is the same 37 phenotypes when each phenotype has a different missingness structure A critical consideration in designing the linreg3 pipeline (step 7 below) was to avoid commands that would invoke shuffles in Spark The following modifications were made to the Spark configuration while running linreg3 (step 7 below): spark.memory.storageFraction=0.5,spark.memory.fraction=0.4,spark.broadcast.blockSize=128m,spark.dynamicAllocation.enabled=false,spark.executor.memory=38g,spark.executor.cores=8 Workflow for running associations in Hail for phenotypes across multiple applications: Merge chromosome-specific ukb_mfi_chr*_v2.txt files into one ukb_mfi_v2.tsv file with a chromosome field Merge ukb_sqc_v2.txt sample QC file with application-specific sample IDs using application-specific .fam files Create application-specific Hail keytables with application-specific sample ID, inferred sex (from ukb_sqc_v2.txt) and the first 10 PCs (also from ukb_sqc_v2.txt), subset to the set of 337,199 samples outlined above Build application-specific 'pipelines', where each pipeline is defined by a Hail keytable with (for the subset of 337,199 samples): application-specific sample ID inferred sex PCs 1-10 A block of phenotypes capable of being run through linreg3 in one pass of reading in BGEN -> writing out results keytable Create a sites-only VDS for all 92,693,895 sites in the imputed data with: rsid info score (from ukb_mfi_v2.tsv) flag indicating if site is in HRC 1.1 release (HRC.r1-1.GRCh37.wgs.mac5.sites.tab file from http://www.haplotype-reference-consortium.org/site) variant QC metrics from Hail's variant_qc() method, calculated on the subset of 337,199 samples Create a filtered sites-only VDS with 10,894,596 variants, that satisfy the criteria: HRC site info score > 0.8 0.001 < AF < 0.999 pHWE > 1e-10 callRate > 0.95 Run associations using linreg3, where for each pipeline (see step 4) in each application, we: Read in the BGENs Use the filtered sites-only VDS created in step (6) to filter variants Annotate samples with the pipeline-specific keytable Call the linreg3 command once for each group of phenotypes in the pipeline with the same missingness structure Write out association results to a Hail keytable Export a separate tsv results file (described below) and LDSC summary stat file for each phenotype in each pipelines across all applications Scripts associated with each step in the workflow above: 1_merge_mfi.sh 2_fam_sqc_merge.R 3_make_sample_qc_table.py 4_build_pipelines.py 5_make_variant_annotation_vds.py 6_filter_gwas_variants.py 7_run_linreg3.py 8_export_results.py Single phenotype association workflow

Use the script run_single_phenotype.py to run an association on a single phenotype using Hail v0.1, using the same set of 337,199 UK Biobank samples and 10,894,596 variants described above. The script requires 6 parameters:

--bgens - Google storage path to UKBB .bgen file(s) (can use "*" to regex match). --sample - Google storage path to application-specific .sample file. --fam - Google storage path to headerless, application-specific .fam file. --withdrawn - Google storage path to headerless, application-specific list of sample IDs withdrawn from the study. --phenotype - Google storage path to two-column, headerless tsv file where the first column is application-specific sample IDs and the second column is the phenotype (quantitative or 1/0 for case/control; "NA" to denote missing). --working-directory - Google storage path to working directory to write results and intermediate files.

Here's an example of running this script using the cloudtools interface:

cluster submit my-cluster run_single_phenotypes.py --args " --bgens gs:///ukb_imp_chr*_v2.bgen --sample gs://mybucket/myapplication.sample --fam gs://mybucket/myapplication.fam --withdrawn gs://mybucket/myapplication_withdrawn.txt --phenotype gs://mybucket/myphenotype.tsv --working-directory gs://mybucket/ "

This script will write a file variants.results.tsv to the specified --working-directory, with the format outlined below in the "SNP summary stat file" section.

The script expects the --phenotype file to be a two-column tsv file with no header, where the first column contains the application-specific sample ID and the second column contains the phenotype of interest (either quantitative or 1/0 for case/control, with missing values coded as "NA").

Summary stat output

NOTES:

Variants are listed in the form CHR:POS:REF:ALT, where the ALT allele is the effect allele in the model The ALT allele is NOT always the minor allele, but the non-reference allele as stated in the UK Biobank ukb_mfi_chr*_v2.txt and our variants.tsv file

QCed SNP information file (variants.tsv)

variant (hg19) [CHROM:POS:REF:ALT] rsid info (UKBB INFO score) AF (QC positive alternate allele frequency) pHWE callRate Example SNP information: variant rsid info AF pHWE callRate 10:61334:G:A rs183305313 8.46690e-01 4.82208e-03 4.77873e-03 1.00000e+00 10:69083:C:T rs35418599 8.01306e-01 7.77212e-01 7.74927e-01 9.99961e-01 10:90127:C:T rs185642176 9.84897e-01 8.62740e-02 2.77123e-01 1.00000e+00 10:94263:C:A rs184120752 9.57328e-01 2.51928e-02 8.26055e-02 1.00000e+00 10:94426:C:T rs10904045 9.90536e-01 3.96792e-01 3.54267e-01 1.00000e+00 10:94538:C:T rs189409193 8.24138e-01 5.50862e-03 9.37271e-01 1.00000e+00

SNP summary stat file ([FieldID]_assoc.tsv.gz)

variant (hg19) [CHROM:POS:REF:ALT] rsid nCompleteSamples (non-missing samples) AC (non-missing sample allele count) ytx (case/control = dosage weighted alternate allele count in cases; quantitative = dosage weighted mean trait value among alternate allele carriers) beta se pval Example SNP summary stat: variant rsid nCompleteSamples AC ytx beta se tstat pval 5:43888254:C:T rs13184706 953 4.17176e+01 5.64980e+01 -1.11569e-01 8.01312e-02 -1.39233e+00 1.64152e-01 5:43888493:C:T rs58824264 953 9.03529e+00 1.30706e+01 -3.42168e-02 1.68596e-01 -2.02951e-01 8.39217e-01 5:43888556:T:C rs72762387 953 4.86235e+01 7.81804e+01 1.31571e-01 7.44976e-02 1.76611e+00 7.77023e-02 5:43888648:C:T rs115032754 953 3.77647e+01 5.40039e+01 -5.98780e-02 8.59590e-02 -6.96588e-01 4.86233e-01 5:43888690:C:G rs147555725 953 5.87843e+00 9.80000e+00 1.98330e-01 2.11226e-01 9.38946e-01 3.48000e-01 5:43888838:G:C rs13185925 953 7.21765e+01 1.04306e+02 -2.46341e-02 6.00665e-02 -4.10113e-01 6.81816e-01 Quick Manhattan and QQ plot

R Scripts to generate basic Manhattan and QQ plots on a single summary stat file

Manhattan_plot.R Creates manhattan plot on SNPs with p < .001 Example usage: ## generic example: Rscript Manhattan_plot.R [summary stat file] [output name] ## actual example: Rscript Manhattan_plot.R 2443.assoc.tsv.gz /home/user/graphs/2443 ## generates: /home/user/graphs/2443_Manhattan.png file QQ_plot.R Creates QQ plot on all SNPs Requires > 1 Gb memory to read in summary stat file Example usage: ## generic example: Rscript QQ_plot.R [summary stat file] [output name] ## actual example: Rscript QQ_plot.R 2443.assoc.tsv.gz /home/user/graphs/2443 ## generates: /home/user/graphs/2443_QQ.png file UK Biobank Updates

July 27th, 2017 - Errors in imputation identified

Imputation of non-HRC SNPs was mis-mapped. Email details below:

Dear researcher, We have identified a problem with the UK Biobank imputed data and which has come to light following discussion via the UKB-GENETICS mail list. This problem relates to the imputed data and does not affect the genotyped data from the Affymetrix array. The genetic data was imputed using two different reference panels. The Haplotype Reference Consortium (HRC) panel was used as first choice option, but for SNPs not in that reference panel the UK10K + 1000 Genomes panel was used. The problem arose in the second set of imputed data from the UK10K + 1000 Genomes panel. The genotypes at these SNPs are imputed correctly, but have not been recorded as having the correct genome position in the files. We have established that the imputed data from the HRC panel is not affected and has the correct positions. This is about ~40M sites and will include the majority of the common SNPs i.e. sites most likely to show genetic associations. These sites are readily identified since the HRC site list is public http://www.haplotype-reference-consortium.org/site The problem is not easy to fix post-hoc, so we intend to re-impute the data from the UK10K + 1000 Genomes panel and re-release the imputed data. For now we recommend that researchers focus exclusively on SNPs in the HRC panel, or work with the directly genotyped data until the new release is available. We will progress the re-imputation as quickly as we can and expect to release a new version of the imputed files ideally in September. We will send more details about this data release and confirm timelines in due course. We can only apologise that this error was not identified during the QA review and do not underestimate the frustration this will cause for the research community. If you have any questions about this issue please send them to the UKB-GENETICS mail list to https://www.jiscmail.ac.uk/cgi-bin/webadmin?A0=UKB-GENETICS

Kind regards, UK Biobank & the Access Team

July 26th, 2017 - Samples withdrawn from UK Biobank

23 samples have withdrawn consent for use of their data 8 samples listed in imputed data sample file 6 samples identified as being part of our QC positive sample set

Dear Researcher, As you are aware, participants are free to withdraw from UK Biobank at any time and request that their data no longer be used. Since our last review, some participants involved with Application [XXXX] have requested that their data should no longer be used. We have attached a file containing the anonymised IDs of these participants and any others who have withdrawn previously. Please remove the corresponding records from your unpublished analyses. Note that it is possible that this list contains IDs which you have never received as they may have withdrawn before your final dataset was generated.

Regards, The UK Biobank Access Team

【本文地址】

UK

UK

今日新闻

推荐新闻