The iPSYCH2015 Case-Cohort sample: updated directions for unravelling genetic and environmental architectures of severe mental disorders

Bybjerg‐Grauholm, Jonas; Pedersen, Carsten Bøcker; Bækvad‐Hansen, Marie; Adamsen, Dea; Hansen, Christine Søholm; Agerbo, Esben; Grove, Jakob; Als, Thomas Damm; Schork, Andrew J.; Buil, Alfonso; Mors, Ole; Nordentoft, Merete; Werge, Thomas; Boerglum, Anders; Hougaard, David M.; Mortensen, Preben Bo

doi:10.1101/2020.11.30.20237768

Cited by 56 publications

(94 citation statements)

References 49 publications

(49 reference statements)

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“…Indeed, a number of powerful alternatives exist for deriving PRSs using individual-level data. 20 , 21 , 22 , 23 , 24 , 25 Until recently, most individual-level datasets have been small, especially in comparison to sample sizes achieved in GWAS meta-analyses, but cheaper genotyping has led to the generation of large genetic datasets (e.g., iPSYCH for psychiatric disorders 26 , 27 and UK Biobank for a multitude of complex traits 28 ). Therefore, researchers often have access to large individual-level genetic data as well as large published GWAS summary statistics.…”

Section: Introductionmentioning

confidence: 99%

Leveraging both individual-level genetic data and GWAS summary statistics increases polygenic prediction

Albiñana

Grove

McGrath

et al. 2021

The American Journal of Human Genetics

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Summary The accuracy of polygenic risk scores (PRSs) to predict complex diseases increases with the training sample size. PRSs are generally derived based on summary statistics from large meta-analyses of multiple genome-wide association studies (GWASs). However, it is now common for researchers to have access to large individual-level data as well, such as the UK Biobank data. To the best of our knowledge, it has not yet been explored how best to combine both types of data (summary statistics and individual-level data) to optimize polygenic prediction. The most widely used approach to combine data is the meta-analysis of GWAS summary statistics (meta-GWAS), but we show that it does not always provide the most accurate PRS. Through simulations and using 12 real case-control and quantitative traits from both iPSYCH and UK Biobank along with external GWAS summary statistics, we compare meta-GWAS with two alternative data-combining approaches, stacked clumping and thresholding (SCT) and meta-PRS. We find that, when large individual-level data are available, the linear combination of PRSs (meta-PRS) is both a simple alternative to meta-GWAS and often more accurate.

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Section: Introductionmentioning

confidence: 99%

Leveraging both individual-level genetic data and GWAS summary statistics increases polygenic prediction

Albiñana

Grove

McGrath

et al. 2021

The American Journal of Human Genetics

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“…For the variants, we use 1,040,096 HapMap3 variants used in the LD reference provided in Privé et al . (2020b) and that were also present in the iPSYCH2015 data (Bybjerg-Grauholm et al . 2020) with imputation INFO score larger than 0.6.…”

Section: Methodsmentioning

confidence: 68%

Section: Methodsmentioning

confidence: 99%

“…We first restrict to UKBB variants with MAF > 0.01 and INFO > 0.6. We then compile frequencies and imputation INFO scores from other datasets, iPSYCH and summary statistics for breast cancer, prostate cancer, coronary artery disease and type-1 diabetes (Bybjerg-Grauholm et al 2020;Michailidou et al 2017;Schumacher et al 2018;Nikpay et al 2015;Censin et al 2017). We restrict to variants with a mean INFO > 0.5 in these other datasets, and also compute the median frequency.…”

Section: Additional Data: 8m+ Variantsmentioning

confidence: 99%

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High-resolution portability of 245 polygenic scores when derived and applied in the same cohort

Privé

Aschard

Folkersen³

et al. 2021

Preprint

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The low portability of polygenic scores (PGS) across global populations is a major concern that must be addressed before PGS can be used for everyone in the clinic. Indeed, prediction accuracy has been shown to decay as a function of the genetic distance between the training and test cohorts. However, such cohorts differ not only in their genetic distance but also in their geographical distance and their data collection and assaying, conflating multiple factors. In this study, we examine the extent to which PGS are transferable between ancestries by deriving polygenic scores for 240 curated traits from the UK Biobank data and applying them in eight ancestry groups from the same cohort. By restricting both training and testing to the UK Biobank data, we reduce the risk of environmental and genotyping confounding from using different cohorts. We define the eight ancestry groups at a high-resolution, country-specific level, based on a simple, robust and effective method that we introduce here. We then apply two different predictive methods to derive polygenic scores for all 240 phenotypes, and show a systematic and dramatic reduction in portability of PGS trained in the inferred ancestral UK population and applied to the inferred ancestral Polish - Italian - Iranian - Indian - Chinese - Caribbean - Nigerian populations, respectively. These analyses, performed at a finer scale than the usual continental scale, demonstrate that prediction already drops off within European ancestries and reduces globally in proportion to PC distance, even when all individuals reside in the same country and are genotyped and phenotyped as part of the same cohort. Our study provides high-resolution and robust insights into the PGS portability problem and offers clues towards possible solutions.

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“…The iPSYCH cohort has recently received a second wave of genotyped individuals, increasing the number of genotyped individuals from ~80k to ~143k 68 . The two iPSYCH waves have been imputed separately with the Ricopili pipeline 69 .…”

Section: Gwas In Ipsychmentioning

confidence: 99%

Accounting for age-of-onset and family history improves power in genome-wide association studies

Agerbo

Plana‐Ripoll

et al. 2021

Preprint

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Genome-wide association studies (GWAS) have revolutionized human genetics, allowing researchers to identify thousands of disease-related genes and possible drug targets. However, case-control status does not account for the fact that not all controls may have lived through their period of risk for the disorder of interest. This can be quantified by examining the age-of-onset distribution and the age of the controls or the age-of-onset for cases. The age-of-onset distribution may also depend on information such as sex and birth year. In addition, family history is not routinely included in the assessment of control status. Here we present LT-FH++, an extension of the liability threshold model conditioned on family history (LT-FH), that jointly accounts for age-of-onset and sex, as well as family history. Using simulations, we show that, when family history and the age-of-onset distribution are available, the proposed approach yields large power gains over both LT-FH and genome-wide association study by proxy (GWAX). We applied our method to four psychiatric disorders available in the iPSYCH data, and to mortality in the UK Biobank, finding 20 genome-wide significant associations with LT-FH++, compared to 10 for LT-FH and 8 for a standard case-control GWAS. As more genetic data with linked electronic health records become available to researchers, we expect methods that account for additional health information, such as LT-FH++, to become even more beneficial.

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The iPSYCH2015 Case-Cohort sample: updated directions for unravelling genetic and environmental architectures of severe mental disorders

Cited by 56 publications

References 49 publications

Leveraging both individual-level genetic data and GWAS summary statistics increases polygenic prediction

Leveraging both individual-level genetic data and GWAS summary statistics increases polygenic prediction

High-resolution portability of 245 polygenic scores when derived and applied in the same cohort

Accounting for age-of-onset and family history improves power in genome-wide association studies

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