**Sample size**: 198 cases vs. 395 controls**Genotyping array**: Infinium Asian Screening Array**Sample QC**: We excluded samples with low genotyping call rates (call rate < 98%) and in close genetic relation (PI_HAT > 0.175). We included samples of the estimated East Asian ancestry.**Variant QC**: We excluded variants with (1) genotyping call rate < 98%, (2) P value for Hardy–Weinberg equilibrium < 1.0 × 10−6, and (3) minor allele count < 5, or (4) > 10% frequency difference with the imputation reference panel.**Phasing and imputation**: shapeit2 and minimac3**Imputation reference**: Combined panel of WGS data from the BioBank Japan project (N=1,037) and 1KGP p3v5 ALL (N=2,504).**Post-imputation QC**: We here report results of whole-genome imputed variants with Rsq > 0.7.**Association test**: Logistic regression was performed with age, sex, and top 20 principal components as covariates by using PLINK2 software.

File name | Descriptions |
---|---|

GWASsummary_PAP_Japanese_SakaueNatCommun2020.autosome.txt.gz | Results for 12,153,232 autosomal variants |

GWASsummary_PAP_Japanese_SakaueNatCommun2020.chrX.txt.gz | Results for 242,876 X-chromosomal variants (males, females, and meta-analysis of males and females) |

# | column name | Descriptions |
---|---|---|

1 | SNP | marker name (CHR:POS:REF:ALT) |

2 | CHR | chromosome |

3 | POS | position (hg19) |

4 | A1 | effect allele (ALT) |

5 | A2 | other allele (REF) |

6 | N_CASE | number of cases |

7 | N_CONTROL | number of controls |

8 | FREQ_A1_CASE | effect allele frequency in cases |

9 | FREQ_A1_CONTROL | effect allele frequency in controls |

10 | RSQ | imputation Rsq value |

11 | BETA | beta for effect allele |

12 | SE | standard error of beta for effect allele |

13 | P | P value |

# | column name | Descriptions |
---|---|---|

1 | SNP | marker name (CHR:POS:REF:ALT) |

2 | CHR | chromosome |

3 | POS | position (hg19) |

4 | A1 | effect allele (ALT) |

5 | A2 | other allele (REF) |

6 | M_N_CASE | number of cases in males |

7 | M_N_CONTROL | number of controls in males |

8 | M_FREQ_A1_CASE | effect allele frequency in cases in males |

9 | M_FREQ_A1_CONTROL | effect allele frequency in controls in males |

10 | M_RSQ | imputation Rsq value in males |

11 | MBETA | beta for effect allele in males |

12 | MSE | standard error of beta for effect allele in males |

13 | MP | P value in males |

14 | F_N_CASE | number of cases in females |

15 | F_N_CONTROL | number of controls in females |

16 | F_FREQ_A1_CASE | effect allele frequency in cases in females |

17 | F_FREQ_A1_CONTROL | effect allele frequency in controls in females |

18 | F_RSQ | imputation Rsq value in females |

19 | FBETA | beta for effect allele in females |

20 | FSE | standard error of beta for effect allele in females |

21 | FP | P value in females |

22 | BETA | beta for effect allele in meta-analysis of sexes |

23 | SE | standard error of beta for effect allele in meta-analysis of sexes |

24 | P | P value in meta-analysis of sexes |

If you use these summary statistics, please cite the following paper;

Sakaue S et al. Genetic determinants of risk in autoimmune pulmonary alveolar proteinosis. *Nat Commun* 2020.