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--- ugli [2020/12/07 09:44] – [Quality Checks] sylvia
+++ ugli [2025/08/13 13:56] (current) – petra_vinke
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-====== UGLI release 1======
+====== UGLI ======
 UGLI is one of [[cohort|Lifelines]]' [[additional assessments]]. UGLI is the abbreviation for UMCG Genetics Lifelines Initiative. UGLI aims at facilitating and accelerating genetic data generation and data analysis and thereby scientific output through using the Lifelines genomics data.
@@ Line 5: / Line 5: @@
 ===== Background =====
 Genome-wide association (GWAS) data is highly valuable for biobanks such as Lifelines in identifying disease/trait associations, predicting future disease development and personalized treatment.\\
-To facilitate the generation, analysis and study of genetic data in Lifelines, the UGLI consortium was founded. UGLI brings together many groups and PIs within the UMCG, RUG and beyond that are interested in performing such research with Lifelines data. They have brought the funding together which led to the initial genotyping of a total of 38,030 Lifelines participants, including [[children]], as part of the [[http://glimdna.org|HUGE]] consortium in Rotterdam on the Infinium Global Screening Array® (GSA) MultiEthnic Disease Version 1.0. In combination with 15,400 samples already genotyped on the Cytochip ([[GWAS]]), a single quality controlled GWAS dataset of n~50,000 subjects will be made available to members of the UGLI consortium based on specific proposals approved by the UGLI steering committee and by Lifelines.\\
+To facilitate the generation, analysis and study of genetic data in Lifelines, the UGLI consortium was founded. UGLI brings together many groups and PIs within the UMCG, RUG and beyond that are interested in performing such research with Lifelines data. They have brought the funding together which led to the initial genotyping of a total of 38,030 + 29,366 Lifelines participants, including [[children]], as part of the [[http://glimdna.org|HUGE]] consortium in Rotterdam on the Infinium Global Screening Array® (GSA) MultiEthnic Disease Version 1.0 and the FinnGen Thermo Fisher Axiom® custom array (resp.). Together with 15,400 samples already genotyped on the CytoSNP array ([[GWAS]]), three quality controlled GWAS datasets with a combined sample size of n~80,000 subjects are available. Genotyping of the remaining Lifelines participants is still ongoing.\\
-The UGLI consortium is actively raising funding for the genotyping of additional samples. The genotyped additional samples are generally referred to as UGLI2. With additional funding of new UGLI members, the consortium will increase the number of genotyped Lifelines participants. These efforts will make Lifelines a more interesting partner for national and international collaborations as well as with non-academic partners that work on healthy ageing.
+\\
+\\
-===== Subcohort =====
+===== UGLI1 - GSA =====
-,030 Lifelines participants were selected for UGLI release 1 using the following criteria:
+,030 Lifelines participants were selected for UGLI1 using the following criteria:
   *  availability of isolated DNA-samples of adequate volume and concentration at Lifelines
   *  Caucasian-ancestry samples
-The genotype of 38,030 participants was assessed using the Infinium Global Screening Array® (GSA) MultiEthnic Disease Version 1.0. In the QC screening all genotyped samples were included, and the focuss of the QC of genetic
+The genotype of 38,030 participants was assessed using the Infinium Global Screening Array® (GSA) MultiEthnic Disease Version 1.0((https://www.illumina.com/content/dam/illumina-marketing/documents/products/datasheets/infinium-commercial-gsa-data-sheet-370-2016-016.pdf)). In the QC screening all genotyped samples were included, and the focuss of the QC of genetic
 markers was on the autosomes and chromosomes X (N=691,072 markers).
-A final set of 36,339 samples and 571,420 markers on autosomal and X chromosomes passed the QC steps described in QC_report_UGLI_R1.pdf.
+A final set of 36,339 samples and 548,029 markers on autosomal and X chromosomes passed the QC steps described in {{ {{ ::qc_report_ugli1_release_2_-v1.pdf }}.
-^ UGLI release 1 cohort - samples that passed QC           ||
+^ UGLI1 - GSA cohort - samples that passed QC           ||
 | Subgroup                                        | N       |
 | Total                                           | 36,339  |
@@ Line 26: / Line 26: @@
 | Age* 18-64                                      | 30,416  |
 | Age* >64                                        | 2,401   |
-Table 1: UGLI release 1 cohort information. These are samples that passed QC. Age at [[1a_visit_1|Baseline assessment first visit]]. *One participant did not visit during [[1a|Baseline]], but did visit during [[2a|2nd screening]]. Since participant was under 18 years of age at [[2a_visit_1|2nd screening visit 1]], this participant has been added to the children 8-17 group.
+Table 1: UGLI1 - GSA cohort information. These are samples that passed QC. Age at [[1a_visit_1|Baseline assessment first visit]]. *One participant did not visit during [[1a|Baseline]], but did visit during [[2a|2nd screening]]. Since participant was under 18 years of age at [[2a_visit_1|2nd screening visit 1]], this participant has been added to the children 8-17 group.
-==== Overlap between studies====
+{{:ugli_age_distribution.jpg?400|}}
-^ Study name  ^ N in UGLI1  ^
-| DAG1        | ~500        |
-| DAG3        | ~9000       |
-| GoNL        | 143*         |
-| GWAS4       | 938*        |
-Table 2: A number of participants in UGLI1 also participated in other studies, i.e. [[deep|DAG1]], [[dag3|DAG3]], [[dag2|DAG2]]/[[http://www.nlgenome.nl/|GoNL]] and [[gwas|GWAS4]]. In the second column the sample sizes that overlap between these studies and UGLI1 can be found. For DAG1 and DAG3 these are approximations. *To evaluate sample mix-ups, a subset of samples with genotype information from a different array were compared. For this N=606 [[gwas|GWAS4]] (CytoSNP 250k array) samples and 143 samples the Genome of the Netherlands ([[http://www.nlgenome.nl/|GoNL]]) project were used. See also section Quality Checks below.
-===== SNP Genotyping Array =====
-The Infinium Global Screening Array® (GSA) MultiEthnic Disease Version was used for SNP genotyping of the UGLI release 1 cohort. This array contains approximately 1,000,000 SNPs and combines multi-ethnic genome-wide content, curated clinical research variants, and quality control (QC) markers for precision medicine research((https://www.illumina.com/content/dam/illumina-marketing/documents/products/datasheets/infinium-commercial-gsa-data-sheet-370-2016-016.pdf)).
+==== Quality Checks ====
+An UGLI1 - GSA (release 2.0) Quality Control Report is available, describing in detail the QC steps that were taken during the quality control (QC) process of the first release of UGLI comprising the genotype of 38,030 participants
-===== Quality Checks =====
+assessed using the Infinium Global Screening Array® (GSA) MultiEthnic Disease Version 1.0. {{ :qc_report_ugli1_release_2_-v1.pdf |}}
-An UGLI (release 1.0) Quality Control Report is available, describing in detail the QC steps that were taken during the quality control (QC) process of the first release of UGLI comprising the genotype of 38,030 participants
-assessed using the Infinium Global Screening Array® (GSA) MultiEthnic Disease Version 1.0. In this QC screening all genotyped samples were included, but the focus was on QC of genetic markers on the autosomes and chromosomes X (N=691,072 markers).\\
-\\
-In brief, first translations and corrections specific from the GSA platform to a general
-context of usage were made; namely, strand harmonization and removal of duplicate markers within the
-array. Secondly, low quality samples and markers were carefully filtered with a two-steps
-procedure of call rate thresholding. Further possible genotyping errors were assessed at the
-marker level by detecting variants that deviated significantly from Hardy-Weinberg equilibrium
-(HW) and at the sample level by evaluating heterozygosity. Then evaluated samples mix-ups
-were evaluated in two levels: i) concordance of reported sex with sex derived from genotyping data from the X
-and Y chromosomes, and ii) concordance of reported family information (Lifelines pedigree) and
-thus of the expected genome sharing between relatives with the observed sharing from
-genotyped data (genetic kinship). Moreso, to further evaluate sample mix-ups the
-concordance of genotype calling among a subset of samples with genotype information from a
-different array were compared ([[gwas|GWAS4]]: CytoSNP 250k array, n=606, from the Lifelines GWAS data set) and whole genome sequence (WGS, n=143, from the Genome of the Netherlands ([[http://www.nlgenome.nl/|GoNL]]) project).
-Subsequently, Mendelian errors and further removal of SNPs that deviated from
-HW in unrelated individuals was ascertained. Finally, population stratification was inspected by a principle
-components analysis (PCA), incorporating samples from 1000 Genomes (1000G) and GoNL
-projects. These summarized steps are shown in Figure 1 in {{ :qc_report_ugli_r1.pdf |QC_report_UGLI_R1.pdf}}, where each step is annotated together with the required input and whether the step generates a graphical output or a report. The code and detailed description of the process can be found in: [[https://github.com/molgenis/GAP]].\\
-\\
-For a more detailed description of the QC steps: {{ :qc_report_ugli_r1.pdf |QC_report_UGLI_R1.pdf}}
-===== Releasing SNP Genotyping files =====
-====QCed genotype calls====
-A final set of 36,339 samples and 571,420 markers on autosomal and X chromosomes passing all QC steps described in {{ :qc_report_ugli_r1.pdf |QC_report_UGLI_R1.pdf}} will be released.
 ====Imputation====
-A final set of 36,339 samples and 571,420 markers on autosomal and X
+A final set of 36,339 samples and 548,029 markers on autosomal and X
-chromosomes passing all QC steps described in {{ :qc_report_ugli_r1.pdf |QC_report_UGLI_R1.pdf}} were used for genetic imputation. Genetic imputation was done through the Sanger imputation service
+chromosomes passing all QC steps described in {{ :qc_report_ugli1_release_2_-v1.pdf |}} were used for genetic imputation. Genetic imputation was done through the Sanger imputation service
 using the Haplotype Reference Consortium
 ( [[http://www.haplotype-reference-consortium.org]] ) panel. The dataset was formatted
 following the instructions from the Sanger webpage
 ( [[https://www.sanger.ac.uk/science/tools/sanger-imputation-service]] ).\\
-More details on imputation can found in {{ :qc_report_ugli_r1.pdf |QC_report_UGLI_R1.pdf}}
 ====SNP array intensity files====
 Raw intensity data from the GSA will be made available to the researchers.
-====Sex mismatches====
+\\
-In UGLI, samples for which biological sex does not match registrated sex will be excluded from the main dataset, but not completely neglected (as opposed to [[gwas|GWAS]]). These samples will still undergo quality check analyses and will be made available for analyses to the researchers.
+\\
+===== UGLI2 - Affymetrix =====
+As of March 2023, data of an additional 28,149 genotyped participants has been made available. Samples in this release, called UGLI2, were genotyped using the FinnGen Thermo Fisher Axiom® custom array.
+,366 participants were selected for UGLI 2 release and assessed using the pre mentioned array. All genotypes were included for QC screening, but the QC focussed on the the autosomes and chromosomes X for which there are N=617,715 and 22,346 markers available, respectively. A final set of 28,149 samples and 441,596 markers on autosomal and 18,450 X chromosomes markers passed the QC steps described in
+{{ :qc_report_ugli2_release_1_-v1.pdf |}}.
+^ UGLI2 - Affymetrix cohort - samples that passed QC           ||
+| Subgroup                                        | N       |
+| Total                                           | 28,149  |
+| Male                                            | TBA  |
+| Female                                          | TBA  |
+| Age* [[children|8-17]]                          | TBA  |
+| Age* 18-64                                      | TBA  |
+| Age* >64                                        | TBA   |
+Table 3: UGLI2 - Affymetrix cohort information. These are samples that passed QC.
+Please note that the array used for UGLI2 differs from the one used in UGLI1. Overlap in SNPs between these two arrays (GSA chip from Illumina=UGLI1 and FinnGen array from Affymetrix/ThermoFischer=UGLI2) is small, namely 1000-10000 SNPs.
+==== Quality Checks ====
+An UGLI2 - Affymetrix (release 2.0) Quality Control Report is available, describing in detail the QC steps that were taken during the quality control (QC) process of the second release of UGLI comprising the genotype of 29,366 participants assessed using the FinnGen Thermo Fisher Axiom® custom array. {{ :qc_report_ugli2_release_1_-v1.pdf |}}
+====Imputation====
+A final set of 28,149 samples and 460,136 markers on autosomal and X chromosomes passing all QC steps described above were used for genetic imputation. Genetic imputation was done through the Sanger imputation service using the Haplotype Reference Consortium (http://www.haplotype-reference-consortium.org) panel.
+====SNP array intensity files====
+Raw intensity data from the FinnGen Thermo Fisher Axiom® custom array will be made available to the researchers.
+\\
+\\
+===== UGLI3 =====
+TBA
+\\
+\\
+===== Overlap between studies=====
+^ Study name  ^ N in UGLI1  ^ N in UGLI2 ^
+| DEEP (DAG1)        | ~500        |
+| DAG3        | ~9000       |
+| GoNL        | 143         |
+| GWAS4       | 938        |
+Table 2: A number of participants in UGLI1 also participated in other studies, i.e. [[deep|DAG1]], [[dag3|DAG3]], [[dag2|DAG2]]/[[http://www.nlgenome.nl/|GoNL]] and [[gwas|GWAS4]]. In the second column the sample sizes that overlap between these studies and UGLI1 can be found. For DAG1 and DAG3 these are approximations.
+\\
+\\
 =====UGLI-data release=====
-UGLI data will be made available on the HPC (Linux environment) of the UMCG and therefore the data is only accessible to researchers who are working at or are affiliated with the University Medical Center Groningen (UMCG). The data will not be accessible through the Lifelines workspace. The applicant’s proposal will be reviewed by both Lifelines and the UGLI steering committee (UGLI SC).
+UGLI data is available on the HPC (Linux environment) of the UMCG. The data will not be accessible through the Lifelines workspace. The applicant’s proposal will be reviewed by both Lifelines and the UGLI steering committee (UGLI SC).
-UGLI consortium members receive temporary exclusive right to use the data for a period of 3 years, meaning that consortium members hold the first right to use the data should the non-UGLI member applicant’s proposed study overlap with a study from one of the UGLI consortium members. The 3-year period starts when UGLI-data is released on the UMCG cluster. After 3 years, UGLI consortium members must submit applications for the use of the data generated within UGLI via the regular Lifelines application procedure.
+Requesting UGLI data:
+The applicant applies via the regular Lifelines application procedure. This means the applicant submits the proposal together with the dataset order using our online catalogue (https://data-catalogue.lifelines.nl/). UGLI data cannot be selected through the online catalogue. The applicant can request UGLI data by stating this in the application form (Appendix: Request for Source Data (Not in catalogue).
-A non-UMCG researcher requesting UGLI data has three options:
-  * The applicant joins the UGLI consortium by paying €10.000. After joining the consortium you will receive an UMCG account so that you can access the UGLI data. This option holds the advantages that you will also gain the right of the 3 year embargo period, you will be able to submit multiple proposals (non-UGLI consortium members can only submit one proposal and get access to the data for 1 year). Furthermore, as an UGLI consortium member you will gain access to all the available Lifelines phenotype data and get full support from other UGLI consortium members. If the applicant is considering this option, please contact the UGLI contact person: Harold Snieder.
-  * The applicant establishes a partnership with an UGLI consortium member. The applicant and the UGLI consortium partner will discuss the terms of this partnership, i.e. co-autorship, costs for analyses. To gain access to the data for 1 year, costs will be involved which still have to be determined. Phenotypic data that has been requested specifically in your proposal will be made available together with access to the UGLI release folder.
-  * The applicant hires an in-house UMCG analyst who would be available to analyse the data. That UMCG analyst (or PhD student or postdoc) can have access to the data if the applicant’s application is approved. Extra costs may be involved and it needs to be clear to the applicant that Lifelines can not ensure that willing partners or willing analysts are available. This is up to the UGLI steering group. Furthermore, to gain access to the data for 1 year, costs will be involved which still have to be determined.  Phenotypic data that has been requested specifically in your proposal will be made available together with access to the UGLI release folder.
@@ Line 126: / Line 113: @@
 | PLINK    | PLINK is a command line program written in C/C++  |
+===== Publications with UGLI data =====
+  * Li et al. 2024 [[https://academic.oup.com/gpb/article/22/2/qzae031/7649324?login=false|Genome-wide Studies Reveal Genetic Risk Factors for Hepatic Fat Content]], Genomics, Proteomics & Bioinformatics, 22(2):qzae031
+  * Keaton et al. 2024 [[https://www.nature.com/articles/s41588-024-01714-w | Genome-wide analysis in over 1 million individuals of European ancestry yields improved polygenic risk scores for blood pressure traits]] Nature Genetics. 56(5):778-791
+  * Qiao et al. 2023 [[https://www.nature.com/articles/s41467-023-36013-1 | Estimation and implications of the genetic architecture of fasting and non-fasting blood glucose]] Nature Communications. 14(1):451
+  * Warmerdam et al. 2022 [[https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1010135 | Increased genetic contribution to wellbeing during the COVID-19 pandemic]] PLoS Genetics. 18(5):e1010135
+  * Nolte et al. 2017 [[https://www.nature.com/articles/ejhg201750 | Missing heritability: is the gap closing? An analysis of 32 complex traits in the Lifelines Cohort Study]] Eur J Hum Genet. 25(7):877-885