1.Xue_et_al_ukbEUR_SI_common_2024.txt.gz: Summary statistics of Smoking Initiation (SI).

2.Xue_et_al_ukbEUR_FS_common_2024.txt.gz: Summary statistics of Former Smoking (FS).

3.Xue_et_al_ukbEUR_CS_common_2024.txt.gz: Summary statistics of Current Smoking (CS).

4.Xue_et_al_ukbEUR_SC_common_2024.txt.gz: Summary statistics of Smoking Cessation (SC).

5.Xue_et_al_ukbEUR_AC_common_2024.txt.gz: Summary statistics of Alcohol Consumption (AC).

6.Xue_et_al_ukbEUR_TI_common_2024.txt.gz: Summary statistics of Tea Intake (TI).

7.Xue_et_al_ukbEUR_CI_common_2024.txt.gz: Summary statistics of Coffee Intake (CI).

8.Xue et al MR_SUB Commun Med 2024.pdf: Description of the dataset.

1.BrainMeta sQTLs: BrainMeta sQTL summary statistics (2,865 samples on 2,443 individuals).

2.BrainMeta eQTLs: BrainMeta eQTL summary statistics (2,865 samples on 2,443 individuals).

3.Qi_et_al_SMR_COLOC.tar.gz: SMR and COLOC analyses summary statistics for 12 brain-related phenotypes.

1.Xue et al AC MLC bias Nat Commun 2020.tar.gz: Summary statistics of genome-wide association of alcohol consumption.

2.Xue et al AC MLC bias Nat Commun 2020.pdf: Description of the dataset.

1.Adolphe_Xue_et_al_BCC_Genome_Med_2020.tar.gz: Summary statistics of basal cell carcinoma (BCC) GWAS.

2.Adolphe_Xue_et_al_BCC_Genome_Med_2020.pdf: Description of the dataset.

1.Xue_et_al_T2D_META_Nat_Commun_2018.gz: GWAS summary statistics of common variants.

2.Xue_et_al_T2D_META_Nat_Commun_2018.pdf: Description of the dataset.

3.Xue_et_al_T2D_META_rare_Nat_Commun_2018.gz: GWAS summary statistics of rare variants (added on 6 Feb 2022).

The summary-level GWAS data for 23 phenotypes were from GERA and UK Biobank. Each data set has been made available as a whitespace-separate table in GCTA-COJO format. Columns are SNP, the effect allele, the other allele, frequency of the effect allele, effect size, standard error, p-value and sample size.

1.GERA data: Details of quality controls of the genotyped and imputed data can be found in Zhu et al. (2018 Nat. Commun.). The individual-level ICD-9 codes were classified into 22 common diseases. We added an additional trait ‘Disease Count’ (a count of the number of diseases affecting each individual) as a crude measure of general health status of each individual.

2.UK Biobank data: Details of quality controls of the genotyped and imputed data can be found in Zhu et al. (2018 Nat. Commun.). Individual-level ICD-10 codes were available in the UKB data. To match the diseases in GERA, we classified the phenotypes into 22 common diseases by projecting the ICD-10 codes to the classifications of ICD-9 codes in GERA taking into account the self-reported disease status. Note that we did not perform the association analysis for dermatophytosis because the number of cases was too small. We only performed the association analyses on a subset of SNPs (in common with the top associated SNPs for the risk factors) for insomnia, iron deficiency anemias, macular degeneration, peripheral vascular disease and acute reaction to stress.

1.LDSCORE_release_July2015.tar.gz: per-SNP and per-segment LD scores calculated from 44,126 unrelated indivduals and ~17M imputed variants. Columns are SNP, per-SNP LD score, and per-segment LD score.

2.GWAS_summary_release_July2015.tar.gz, GWAS summary data. Columns are SNP, the coded allele, effect size, and standard error.