GWAS summary statistics for 9 quantitative phenotypes from the UK Biobank (5-fold cross-validation)

This dataset contains GWAS summary statistics for 9 quantitative phenotypes from the UK Biobank.

The dataset is designed to enable systematic PRS analyses with 5-fold cross validation. For each phenotype and fold, we provide GWAS summary statistics for the training, validation, and test sets. The validation summary statistics can be used for model selection/tuning. The test summary statistics can be used to evaluate PRS models via pseudo-validation metrics. Association testing for all phenotypes and samples was done with plink2.

The phenotypes included in this dataset are:

HEIGHT: Standing height (Data-Field: 50)

BMI: Body mass index (Data-Field: 21001)

WC: Waist circumference (Data-Field: 48)

HC: Hip circumference (Data-Field: 49)

BW: Birth weight (Data-Field: 20022)

FVC: Forced vital capacity (Data-Field: 3062)

FEV1: Forced expiratory volume in 1-second (Data-Field: 3063)

HDL: HDL cholesterol (Data-Field: 30760)

LDL: LDL cholesterol (Data-Field: 30780)

To allow users to assess PRS performance as a function of sample size, we also provide subsampled training GWAS summary statistics. This is done by taking the training samples and randomly selecting (without replacement) a subset of them for conducting association testing. The training sample sizes are:

N = 5000

N = 10000

N = 20000

N = 40000

N = 80000

N = 160000

Full training set (sample size varies by phenotype).

NOTE: Due to the smaller overall sample size for the Birth weight phenotype, we do not include training data for the `N=160000` setting.

The folder structure of the GWAS data for each phenotype is as follows:

train

N_5000

 fold_1

chr_1.PHENO1.glm.linear

chr_2.PHENO1.glm.linear

...

fold_2

fold_3

...

N_10000

N_20000

N_40000

N_80000

N_160000

full

validation

fold_1

chr_1.PHENO1.glm.linear

chr_2.PHENO1.glm.linear

...

fold_2

fold_3

...

test

fold_1

fold_2

fold_3

...

For more details about the GWAS study, Quality Control (QC) criteria, or other information, please consult our publication:

Zabad, S., Gravel, S., & Li, Y. (2023). Fast and accurate Bayesian polygenic risk modeling with variational inference. The American Journal of Human Genetics, 110(5), 741–761. https://doi.org/10.1016/j.ajhg.2023.03.009

If you use this data in your work, please cite the publication above.