1,723,063 research outputs found
Recontacting biobank participants to collect lifestyle, behavioural and cognitive information via online questionnaires : lessons from a pilot study within FinnGen
Full text linkObjectives To recontact biobank participants and collect cognitive, behavioural and lifestyle information via a secure online platform. Design Biobank-based recontacting pilot study. Setting Three Finnish biobanks (Helsinki, Auria, Tampere) recruiting participants from February 2021 to July 2021. Participants All eligible invitees were enrolled in FinnGen by their biobanks (Helsinki, Auria, Tampere), had available genetic data and were >18 years old. Individuals with severe neuropsychiatric disease or cognitive or physical disabilities were excluded. Lastly, 5995 participants were selected based on their polygenic score for cognitive abilities and invited to the study. Among invitees, 1115 had successfully participated and completed the study questionnaire(s). Outcome measures The primary outcome was the participation rate among study invitees. Secondary outcomes included questionnaire completion rate, quality of data collected and comparison of participation rate boosting strategies. Results The overall participation rate was 18.6% among all invitees and 23.1% among individuals aged 18-69. A second reminder letter yielded an additional 9.7% participation rate in those who did not respond to the first invitation. Recontacting participants via an online healthcare portal yielded lower participation than recontacting via physical letter. The completion rate of the questionnaire and cognitive tests was high (92% and 85%, respectively), and measurements were overall reliable among participants. For example, the correlation (r) between self-reported body mass index and that collected by the biobanks was 0.92. Conclusion In summary, this pilot suggests that recontacting FinnGen participants with the goal to collect a wide range of cognitive, behavioural and lifestyle information without additional engagement results in a low participation rate, but with reliable data. We suggest that such information be collected at enrolment, if possible, rather than via post hoc recontacting.Peer reviewe
Reweighting FinnGen using Iterative Proportional Fitting method
No full textThe increasing demand for comprehensive datasets to address complex diseases has resulted in a widespread popularity of biobank-based research. However, the collection of biobank-level data may be susceptible to biases when fundamental aspects of study design, such as sampling approach, are overlooked. FinnGen is a large-scale cohort study aiming to improve diagnoses and prevent diseases through genetic research by combining biobank data with registry data.However, FinnGen’s hospital-based recruitment strategy makes FinnGen suffer from selection bias and thus epidemiologically less representative of its sampling population. In this study, we examine the profound impact of selection bias in FinnGen. We use well-established epidemiological methods and leverage representative data on the Finnish population to try and correct for the bias. By comparing key demographic characteristics and association statistics of interest between FinnGen and a comprehensive registry-based study, FinRegistry, we highlight the extent to which selection bias within FinnGen results in distorted association estimates and a dataset that is highly non - representative of its underlying population. In response to these findings, we estimate Iterative Proportional Fitting (IPF) weights to estimate association statistics that are representative of the true sampling population of FinnGen and unaffected by selection bias. By comparing weighted associations estimated in the FinnGen with associations estimated using FinRegistry data, we infer that the use of our IPF weights mitigates volunteer bias in FinnGen
Genetic Loci Associated With Periodontitis: The FinnGen Study Based on National Health Registers
No full textAimTo perform a genome-wide association study (GWAS) for periodontitis in the FinnGen cohort, as genetic factors contribute to periodontitis.Materials and MethodsWe included nearly 250,000 Finnish individuals who had visited a dentist in the public healthcare sector for a clinical oral examination. We designed three periodontitis phenotypes based on diagnosis and procedure codes and CPI indexes in national health registers.ResultsWe identified 11 independent genetic loci associated with periodontitis, among which 6 were common and novel. A locus near the FST gene was associated with two phenotypes, whereas other lead SNPs were located near ARL15, MFHAS1, DEFB130A and APOE. Additionally, all phenotypes in the discovery and replication cohorts were associated with genetic variations in the HLA region. Furthermore, imputed HLA allele frequencies identified independent associations between HLA-DRB1, HLA-DPB1 and HLA-DQA1 and periodontitis. Based on single-cell RNA sequencing, the expression of genes near our lead SNPs across all three phenotypes was particularly enriched in gingival cell lineages important in the pathogenesis of periodontitis. Phenotypical and genetic correlations revealed associations between periodontitis and bacterial diseases, as well as autoimmune and cardiometabolic phenotypes.ConclusionsOur GWAS suggests that genetic variation contributing to immune dysregulation is involved in the pathogenesis of periodontitis, which has considerable genetic similarity with other complex traits.Peer reviewe
Finngen disease associations for rs148726219.
No full textFinngen metadata (data freeze 3) for rs148726219 showing associations with Alzheimer’s disease and dementia. (PDF)</p
Guidelines for clinicians and scientists to make most out of FinnGen genome and digital health care data - The FinnGen Analyst Handbook
Full text linkThe FinnGen Analyst Handbook is an electronic guidebook aiming to provide FinnGen researchers with all the guidelines, knowledge, and helpful tips they need when analysing, interpreting, and making discoveries with the FinnGen data. FinnGen Analyst Handbook provides detailed instructions for conducting genome-wide association study (GWAS) and medical register-based analysis aiming to reveal associations between conditions and the genome.
FinnGen, started in 2017, is a public-private research project funded by Business Finland and 13 pharmaceutical companies. FinnGen’s host organization is the Institute for Molecular Medicine Finland, FIMM, University of Helsinki. The aim of FinnGen study is to improve human health through genetic research and lead to improvements in diagnostics and new therapeutic targets for treating numerous human diseases.
FinnGen project combines genome data from 500,000 Finnish biobank participants with a longitudinal lifetime spanning health registry data aiming to provide comprehensive data for research of various human diseases. By finding associations between genetic factors and health outcomes FinnGen project aims to provide novel medically and therapeutically relevant insights. Being one of the biggest Biobank projects worldwide FinnGen provides a world-class resource for future research.
This Master’s Thesis work was to write documentation for FinnGen Analyst Handbook. This thesis gives a report about the Analyst Handbook and its writing process. In addition, one example of the entire workflow for GWAS using Analyst Handbook instructions, FinnGen custom-made tools, and R coding is provided
Kidney stone disease GWAS meta-analysis- FinnGen & UK Biobank
No full textA fixed-effects meta-analysis of kidney stone disease was undertaken using UK Biobank and FinnGen kidney stone GWAS summary statistics for autosomes and the X-chromosome. FinnGen r8 GWAS data are publicly available for the phenotype N14 calculus of kidney and ureter comprising 8597 cases and 333,128 controls. Information on sample phenotyping, genotyping, and GWAS in the FinnGen sample has been previously described. SNPs with MAF 75%) were excluded. The resultant summary statistics were used to perform MR analyses.</p
SAIGE pipelines in FinnGen
No full textsaige-pipelines
Running GWAS
How to run SAIGE GWAS with Cromwell
This in an example scenario creating new phenotypes in R7 and running those
Create a covariate/phenotype file that contains your phenotypes. E.g. get gs://r7_data/pheno/R7_COV_PHENO_V2.txt.gz, add phenotypes to that (cases 1, controls 0, everyone else NA), and upload the new file to a bucket
Create a text file with your new phenotypes one per line, e.g.
my_phenos.txt
PHENO1
PHENO2
and upload the file to a bucket.
Clone this repo git clone https://github.com/FINNGEN/saige-pipelines
Cromwell requires subworkflows be zipped: cd saige-pipelines/wdl/gwas/ && zip saige_sub saige_sub.wdl saige_summary.wdl
Change saige.null.phenofile in saige.json to the file from step 1
Change saige.phenolistfile in saige.json to the file from step 2
6.1. Use "saige.traitType": "binary" or "saige.traitType": "quantitative" depending on whether your traits are case/control or continuous
6.2. Use "saige.analysisType": "additive" or "saige.analysisType": "recessive", "saige.analysisType": "dominant" or "saige.analysisType": "het" - additive being regular GWAS.
Connect to Cromwell server
gcloud compute ssh cromwell-fg-1 --project finngen-refinery-dev --zone europe-west1-b -- -fN -L localhost:5000:localhost:80
Submit workflow
8.1. Using the web interface
8.1.1 Go to http://0.0.0.0:5000 with your browser
8.1.2 Click /api/workflows/{version}
8.1.3 Choose wdl/gwas/saige.wdl as workflowSource
8.1.4 Choose the edited wdl/gwas/saige.json as workflowInputs
8.1.5 Choose wdl/gwas/saige_sub.zip as workflowDependencies
8.1.6 Execute
8.2. Or with https://github.com/FINNGEN/CromwellInteract
Use the given workflow id to look at timing diagram or to get metadata
http://0.0.0.0:5000/api/workflows/v1/WORKFLOW_ID/timing http://0.0.0.0:5000/api/workflows/v1/WORKFLOW_ID/metadata
Logs and results go under
gs://fg-cromwell/saige/WORKFLOW_ID, plots gs://fg-cromwell/saige/WORKFLOW_ID/call-test_combine/shard-*/**/*.png, summary stats and tabix indexes gs://fg-cromwell/saige/WORKFLOW_ID/call-test_combine/shard-*/**/*.gz*
Docker file creation for R6 GWAS
Same image used for R7 GWAS
git clone https://github.com/FINNGEN/saige-pipelines
cd saige-pipelines
git clone https://github.com/weizhouUMICH/SAIGE -b finngen_r6_jk
docker build -t gcr.io/finngen-refinery-dev/saige:0.39.1-TAG -f docker/Dockerfile_SAIGE_GWAS .
Conditional analysis for genomewide significant regions.
wdl/saige_conditional_full.wdl and corresponding .json scan for genomewide significant regions and then performs conditional analysis on those regions, adding significant variants as covariate and iterating on that until no significant variants are left.
If you want to run conditional analysis without scanning for gw-sig loci from results files, you can use saige_conditional.wdl/.json directly. It needs configuration file which can be greated using scripts/generate_conditional_analysis_config.py. See scripts/generate_conditional_analysis_config_examples.sh for example commands.
Output files
PHENOTYPE.REGION.independent.snps files
Summary of top snp conditional statistics after conditioning
Columns:
SNPID variant
BETA original beta
SE original se
p.value original se
BETA_cond beta after conditioning
SE_cond se after conditioning
p.value_cond p-value after conditioning
Conditioned_on variants used in conditioning
PHENOTYPE.REGION_n files
Summary statistics of all snps in the region after conditioning on n snps.
The condition snp corresponds to the line in .independent.snps file
Columns:
CHR
POS
rsid
SNPID
Allele1
Allele2
AC_Allele2
AF_Allele2
imputationInfo
N
BETA
SE
Tstat
p.value original p-value using SPA estimator (you want this p-value)
p.value.NA original p-value using normal approximation (don’t use!)
Is.SPA.converge did the model converge
varT original t statistic
varTstat original variance of t-statistic
Tstat_cond t-statistic after conditioning
p.value_cond p-value after conditioning
varT_cond t statistic variance after conditioning
BETA_cond beta after conditioning
SE_cond standard error after conditionin
MR results of exposures on urolithiasis from the FinnGen.
No full textMR results of exposures on urolithiasis from the FinnGen.</p
Mendelian randomization highlights insomnia as a risk factor for pain diagnoses
Full text linkStudy Objective: Insomnia has been linked to acute and chronic pain conditions; however, it is unclear whether such relationships are causal. Recently, a large number of genetic variants have been discovered for both insomnia and pain through genome-wide association studies (GWASs) providing a unique opportunity to examine the evidence for causal relationships through the use of the Mendelian randomization paradigm. Methods: To elucidate the causality between insomnia and pain, we performed bidirectional Mendelian randomization analysis in FinnGen, where clinically diagnosed ICD-10 categories of pain had been evaluated. In addition, we used measures of self-reported insomnia symptoms. We used endpoints for pain in the FinnGen Release 5 (R5) (N = 218,379), and a non-overlapping sample for insomnia (UK Biobank (UKBB) and 23andMe, N = 1,331,010 or UKBB alone N = 453,379). We assessed the robustness of results through conventional Mendelian randomization sensitivity analyses. Results: Genetic liability to insomnia symptoms increased the odds of reporting pain (odds ratio (OR) [95% confidence interval (CI)] = 1.47 [1.38-1.58], p = 4.12 x 10(-28)). Manifested pain had a small effect on increased risk for insomnia (OR [95% CI] = 1.04 [1.01-1.07], p < 0.05). Results were consistent in sensitivity analyses. Conclusions: Our findings support a bidirectional causal relationship between insomnia and pain. These data support a further clinical investigation into the utility of insomnia treatment as a strategy for pain management and vice versa.Peer reviewe
Genetic liability to sedentary behaviour and cardiovascular disease incidence in the FinnGen and HUNT cohorts
No full textObjective Energy-saving sedentary behaviour may be an evolutionarily selected trait that is no longer advantageous. We investigated the associations between genetic liability to sedentary behaviour and the incidence of the most common cardiovascular disease (CVD).
Methods We constructed and validated a genome-wide polygenic score for leisure screen time (PGS LST) as a measure of genetic liability to sedentary behaviour. We performed survival analyses between higher PGS LST and register-based CVDs using the FinnGen cohort (N=293 250–333 012). Replication and exploratory analyses were conducted in an independent Norwegian Trøndelag Health Study (HUNT) cohort (N=35 289).
Results In FinnGen, each SD increase in PGS LST was associated with a higher risk of incident CVD (HR: 1.05 (95% CI 1.05 to 1.06)) (168 770 cases over 17 101 133 person-years). The magnitudes of association for the three most common CVDs were 1.09 ((95% CI 1.08 to 1.09), 1.06 ((95% CI 1.05 to 1.07) and 1.05 ((95% CI 1.04 to 1.06) for hypertensive disease, ischaemic heart disease and cerebrovascular disease, respectively. Those in the top decile of PGS LST had 21%, 35%, 26% and 19% higher risk of any CVD, hypertensive disease, ischaemic heart disease and cerebrovascular disease, respectively, than those in the bottom decile. Associations were replicated in HUNT and remained independent of covariates (socioeconomic status, body mass index and smoking) except for cerebrovascular disease. Besides direct effects, reduced physical activity served as a potential mediating pathway for the observed associations.
Conclusions We found that genetic liability to sedentary behaviour is associated with incident CVD, although effect sizes with current PGS remained small. These findings suggest that genetic liability to sedentary behaviour is an under-recognised driver of common CVDs.peerReviewe
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