Summary

Summary {data-width=650}

Manhattan plot

manhattan_plot

manhattan_plot

QQ plot

qq_plot

qq_plot

AF plot

af_plot

af_plot

P-Z plot

pz_plot

pz_plot

beta_std plot

beta_std_plot

beta_std_plot

Metadata

{
    "fileformat": "VCFv4.2",
    "FILTER": "<ID=PASS,Description=\"All filters passed\">",
    "INFO": "<ID=AF,Number=A,Type=Float,Description=\"Allele Frequency\">",
    "FORMAT": "<ID=ES,Number=A,Type=Float,Description=\"Effect size estimate relative to the alternative allele\">",
    "FORMAT.1": "<ID=SE,Number=A,Type=Float,Description=\"Standard error of effect size estimate\">",
    "FORMAT.2": "<ID=LP,Number=A,Type=Float,Description=\"-log10 p-value for effect estimate\">",
    "FORMAT.3": "<ID=AF,Number=A,Type=Float,Description=\"Alternate allele frequency in the association study\">",
    "FORMAT.4": "<ID=SS,Number=A,Type=Float,Description=\"Sample size used to estimate genetic effect\">",
    "FORMAT.5": "<ID=EZ,Number=A,Type=Float,Description=\"Z-score provided if it was used to derive the EFFECT and SE fields\">",
    "FORMAT.6": "<ID=SI,Number=A,Type=Float,Description=\"Accuracy score of summary data imputation\">",
    "FORMAT.7": "<ID=NC,Number=A,Type=Float,Description=\"Number of cases used to estimate genetic effect\">",
    "FORMAT.8": "<ID=ID,Number=1,Type=String,Description=\"Study variant identifier\">",
    "META": "<ID=TotalVariants,Number=1,Type=Integer,Description=\"Total number of variants in input\">",
    "META.1": "<ID=VariantsNotRead,Number=1,Type=Integer,Description=\"Number of variants that could not be read\">",
    "META.2": "<ID=HarmonisedVariants,Number=1,Type=Integer,Description=\"Total number of harmonised variants\">",
    "META.3": "<ID=VariantsNotHarmonised,Number=1,Type=Integer,Description=\"Total number of variants that could not be harmonised\">",
    "META.4": "<ID=SwitchedAlleles,Number=1,Type=Integer,Description=\"Total number of variants strand switched\">",
    "META.5": "<ID=TotalControls,Number=1,Type=Integer,Description=\"Total number of controls in the association study\">",
    "META.6": "<ID=TotalCases,Number=1,Type=Integer,Description=\"Total number of cases in the association study\">",
    "META.7": "<ID=StudyType,Number=1,Type=String,Description=\"Type of GWAS study [Continuous or CaseControl]\">",
    "SAMPLE": "<ID=UKB-b:9991,TotalVariants=9851866,VariantsNotRead=0,HarmonisedVariants=9851866,VariantsNotHarmonised=0,SwitchedAlleles=9851866,TotalControls=46945,StudyType=Continuous>",
    "contig": "<ID=1,length=249250621,assembly=GRCh37>",
    "contig.1": "<ID=2,length=243199373,assembly=GRCh37>",
    "contig.2": "<ID=3,length=198022430,assembly=GRCh37>",
    "contig.3": "<ID=4,length=191154276,assembly=GRCh37>",
    "contig.4": "<ID=5,length=180915260,assembly=GRCh37>",
    "contig.5": "<ID=6,length=171115067,assembly=GRCh37>",
    "contig.6": "<ID=7,length=159138663,assembly=GRCh37>",
    "contig.7": "<ID=8,length=146364022,assembly=GRCh37>",
    "contig.8": "<ID=9,length=141213431,assembly=GRCh37>",
    "contig.9": "<ID=10,length=135534747,assembly=GRCh37>",
    "contig.10": "<ID=11,length=135006516,assembly=GRCh37>",
    "contig.11": "<ID=12,length=133851895,assembly=GRCh37>",
    "contig.12": "<ID=13,length=115169878,assembly=GRCh37>",
    "contig.13": "<ID=14,length=107349540,assembly=GRCh37>",
    "contig.14": "<ID=15,length=102531392,assembly=GRCh37>",
    "contig.15": "<ID=16,length=90354753,assembly=GRCh37>",
    "contig.16": "<ID=17,length=81195210,assembly=GRCh37>",
    "contig.17": "<ID=18,length=78077248,assembly=GRCh37>",
    "contig.18": "<ID=19,length=59128983,assembly=GRCh37>",
    "contig.19": "<ID=20,length=63025520,assembly=GRCh37>",
    "contig.20": "<ID=21,length=48129895,assembly=GRCh37>",
    "contig.21": "<ID=22,length=51304566,assembly=GRCh37>",
    "contig.22": "<ID=X,length=155270560,assembly=GRCh37>",
    "contig.23": "<ID=Y,length=59373566,assembly=GRCh37>",
    "contig.24": "<ID=MT,length=16569,assembly=GRCh37>",
    "contig.25": "<ID=GL000207.1,length=4262,assembly=GRCh37>",
    "contig.26": "<ID=GL000226.1,length=15008,assembly=GRCh37>",
    "contig.27": "<ID=GL000229.1,length=19913,assembly=GRCh37>",
    "contig.28": "<ID=GL000231.1,length=27386,assembly=GRCh37>",
    "contig.29": "<ID=GL000210.1,length=27682,assembly=GRCh37>",
    "contig.30": "<ID=GL000239.1,length=33824,assembly=GRCh37>",
    "contig.31": "<ID=GL000235.1,length=34474,assembly=GRCh37>",
    "contig.32": "<ID=GL000201.1,length=36148,assembly=GRCh37>",
    "contig.33": "<ID=GL000247.1,length=36422,assembly=GRCh37>",
    "contig.34": "<ID=GL000245.1,length=36651,assembly=GRCh37>",
    "contig.35": "<ID=GL000197.1,length=37175,assembly=GRCh37>",
    "contig.36": "<ID=GL000203.1,length=37498,assembly=GRCh37>",
    "contig.37": "<ID=GL000246.1,length=38154,assembly=GRCh37>",
    "contig.38": "<ID=GL000249.1,length=38502,assembly=GRCh37>",
    "contig.39": "<ID=GL000196.1,length=38914,assembly=GRCh37>",
    "contig.40": "<ID=GL000248.1,length=39786,assembly=GRCh37>",
    "contig.41": "<ID=GL000244.1,length=39929,assembly=GRCh37>",
    "contig.42": "<ID=GL000238.1,length=39939,assembly=GRCh37>",
    "contig.43": "<ID=GL000202.1,length=40103,assembly=GRCh37>",
    "contig.44": "<ID=GL000234.1,length=40531,assembly=GRCh37>",
    "contig.45": "<ID=GL000232.1,length=40652,assembly=GRCh37>",
    "contig.46": "<ID=GL000206.1,length=41001,assembly=GRCh37>",
    "contig.47": "<ID=GL000240.1,length=41933,assembly=GRCh37>",
    "contig.48": "<ID=GL000236.1,length=41934,assembly=GRCh37>",
    "contig.49": "<ID=GL000241.1,length=42152,assembly=GRCh37>",
    "contig.50": "<ID=GL000243.1,length=43341,assembly=GRCh37>",
    "contig.51": "<ID=GL000242.1,length=43523,assembly=GRCh37>",
    "contig.52": "<ID=GL000230.1,length=43691,assembly=GRCh37>",
    "contig.53": "<ID=GL000237.1,length=45867,assembly=GRCh37>",
    "contig.54": "<ID=GL000233.1,length=45941,assembly=GRCh37>",
    "contig.55": "<ID=GL000204.1,length=81310,assembly=GRCh37>",
    "contig.56": "<ID=GL000198.1,length=90085,assembly=GRCh37>",
    "contig.57": "<ID=GL000208.1,length=92689,assembly=GRCh37>",
    "contig.58": "<ID=GL000191.1,length=106433,assembly=GRCh37>",
    "contig.59": "<ID=GL000227.1,length=128374,assembly=GRCh37>",
    "contig.60": "<ID=GL000228.1,length=129120,assembly=GRCh37>",
    "contig.61": "<ID=GL000214.1,length=137718,assembly=GRCh37>",
    "contig.62": "<ID=GL000221.1,length=155397,assembly=GRCh37>",
    "contig.63": "<ID=GL000209.1,length=159169,assembly=GRCh37>",
    "contig.64": "<ID=GL000218.1,length=161147,assembly=GRCh37>",
    "contig.65": "<ID=GL000220.1,length=161802,assembly=GRCh37>",
    "contig.66": "<ID=GL000213.1,length=164239,assembly=GRCh37>",
    "contig.67": "<ID=GL000211.1,length=166566,assembly=GRCh37>",
    "contig.68": "<ID=GL000199.1,length=169874,assembly=GRCh37>",
    "contig.69": "<ID=GL000217.1,length=172149,assembly=GRCh37>",
    "contig.70": "<ID=GL000216.1,length=172294,assembly=GRCh37>",
    "contig.71": "<ID=GL000215.1,length=172545,assembly=GRCh37>",
    "contig.72": "<ID=GL000205.1,length=174588,assembly=GRCh37>",
    "contig.73": "<ID=GL000219.1,length=179198,assembly=GRCh37>",
    "contig.74": "<ID=GL000224.1,length=179693,assembly=GRCh37>",
    "contig.75": "<ID=GL000223.1,length=180455,assembly=GRCh37>",
    "contig.76": "<ID=GL000195.1,length=182896,assembly=GRCh37>",
    "contig.77": "<ID=GL000212.1,length=186858,assembly=GRCh37>",
    "contig.78": "<ID=GL000222.1,length=186861,assembly=GRCh37>",
    "contig.79": "<ID=GL000200.1,length=187035,assembly=GRCh37>",
    "contig.80": "<ID=GL000193.1,length=189789,assembly=GRCh37>",
    "contig.81": "<ID=GL000194.1,length=191469,assembly=GRCh37>",
    "contig.82": "<ID=GL000225.1,length=211173,assembly=GRCh37>",
    "contig.83": "<ID=GL000192.1,length=547496,assembly=GRCh37>",
    "gwas_harmonisation_command": "--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/vcf_09_19b/bgzip_vcf/data.batch_991.vcf.gz --id UKB-b:9991 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_991.txt.gz --cohort_controls 46945 --ref /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/human_g1k_v37.fasta --json /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/ukb_gwas.json; 1.1.1",
    "file_date": "2019-09-13T02:01:12.234882",
    "bcftools_viewVersion": "1.9-74-g6af271c+htslib-1.9-64-g226b4a8",
    "bcftools_viewCommand": "view -T ^/mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-9991/mac_discard.txt -Oz /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-9991/UKB-b-9991_raw.vcf.gz; Date=Thu Oct 17 12:23:20 2019",
    "bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-9991/ukb-b-9991.vcf.gz; Date=Sun May 10 07:15:29 2020"
}

LDSC

*********************************************************************
* LD Score Regression (LDSC)
* Version 1.0.1
* (C) 2014-2019 Brendan Bulik-Sullivan and Hilary Finucane
* Broad Institute of MIT and Harvard / MIT Department of Mathematics
* GNU General Public License v3
*********************************************************************
Call: 
./ldsc.py \
--h2 /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-9991/UKB-b-9991_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-9991/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:43:04 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-9991/UKB-b-9991_data.vcf.gz ...
Read summary statistics for 8149325 SNPs.
Dropped 6401 SNPs with duplicated rs numbers.
Reading reference panel LD Score from ../reference/eur_w_ld_chr/[1-22] ...
Read reference panel LD Scores for 1290028 SNPs.
Removing partitioned LD Scores with zero variance.
Reading regression weight LD Score from ../reference/eur_w_ld_chr/[1-22] ...
Read regression weight LD Scores for 1290028 SNPs.
After merging with reference panel LD, 1283541 SNPs remain.
After merging with regression SNP LD, 1283541 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0514 (0.0095)
Lambda GC: 1.044
Mean Chi^2: 1.0513
Intercept: 1.0038 (0.0062)
Ratio: 0.0738 (0.1209)
Analysis finished at Thu Oct 17 14:44:20 2019
Total time elapsed: 1.0m:16.0s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9438,
    "inflation_factor": 1.0475,
    "mean_EFFECT": -0,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 0,
    "n_p_sig": 0,
    "n_mono": 0,
    "n_ns": 0,
    "n_mac": 0,
    "is_snpid_unique": true,
    "n_miss_EFFECT": 0,
    "n_miss_SE": 0,
    "n_miss_PVAL": 0,
    "n_miss_AF": 0,
    "n_miss_AF_reference": 76321,
    "n_est": "NA",
    "ratio_se_n": "NA",
    "mean_diff": "NaN",
    "ratio_diff": "NaN",
    "sd_y_est1": "NaN",
    "sd_y_est2": "NA",
    "r2_sum1": 0,
    "r2_sum2": 0,
    "r2_sum3": 0,
    "r2_sum4": 0,
    "ldsc_nsnp_merge_refpanel_ld": 1283541,
    "ldsc_nsnp_merge_regression_ld": 1283541,
    "ldsc_observed_scale_h2_beta": 0.0514,
    "ldsc_observed_scale_h2_se": 0.0095,
    "ldsc_intercept_beta": 1.0038,
    "ldsc_intercept_se": 0.0062,
    "ldsc_lambda_gc": 1.044,
    "ldsc_mean_chisq": 1.0513,
    "ldsc_ratio": 0.0741
}

Flags

name value
af_correlation FALSE
inflation_factor FALSE
n TRUE
is_snpid_non_unique FALSE
mean_EFFECT_nonfinite FALSE
mean_EFFECT_05 FALSE
mean_EFFECT_01 FALSE
mean_chisq FALSE
n_p_sig FALSE
miss_EFFECT FALSE
miss_SE FALSE
miss_PVAL FALSE
ldsc_ratio FALSE
ldsc_intercept_beta FALSE
n_clumped_hits FALSE
r2_sum1 FALSE
r2_sum2 FALSE
r2_sum3 FALSE
r2_sum4 FALSE

Definitions

General metrics

  • af_correlation: Correlation coefficient between AF and AF_reference.
  • inflation_factor (lambda): Genomic inflation factor.
  • mean_EFFECT: Mean of EFFECT size.
  • n: Maximum value of reported sample size across all SNPs, \(n\).
  • n_clumped_hits: Number of clumped hits.
  • n_snps: Number of SNPs
  • n_p_sig: Number of SNPs with pvalue below 5e-8.
  • n_mono: Number of monomorphic (MAF == 1 or MAF == 0) SNPs.
  • n_ns: Number of SNPs with nonsense values:
    • alleles other than A, C, G or T.
    • P-values < 0 or > 1.
    • negative or infinite standard errors (<= 0 or = Infinity).
    • infinite beta estimates or allele frequencies < 0 or > 1.
  • n_mac: Number of cases where MAC (\(2 \times N \times MAF\)) is less than 6.
  • is_snpid_unique: true if the combination of ID REF ALT is unique and therefore no duplication in snpid.
  • n_miss_<*>: Number of NA observations for <*> column.

se_n metrics

  • n_est: Estimated sample size value, \(\widehat{n}\).
  • ratio_se_n: \(\texttt{ratio_se_n} = \frac{\sqrt{\widehat{n}}}{\sqrt{n}}\). We expect ratio_se_n to be 1. When it is not 1, it implies that the trait did not have a variance of 1, the reported sample size is wrong, or that the SNP-level effective sample sizes differ markedly from the reported sample size.
  • mean_diff: \(\texttt{mean_diff} = \sum_{j} \frac{\widehat{\beta_j^{std}} - \beta_j}{\texttt{n_snps}}\), mean difference between the standardised beta, predicted from P-values, and the observed beta. The difference should be very close to zero if trait has a variance of 1.
    • \(\widehat{\beta_j^{std}} = \sqrt{\frac{{z}_j^2 / ({z}_j^2 + n -2)}{2 \times {MAF}_j \times (1 - {MAF}_j)}} \times sign({z}_j)\),
    • \({z}_j = \frac{\beta_j}{{se}_j}\),
    • and \(\beta_j\) is the reported effect size.
  • ratio_diff: \(\texttt{ratio_diff} = |\frac{\texttt{mean_diff}}{\texttt{mean_diff2}}|\), absolute ratio between the mean of diff and the mean of diff2 (expected difference between the standardised beta predicted from P-values, and the standardised beta derived from the observed beta divided by the predicted SD; NOT reported). The ratio should be close to 1. If different from 1, then implies that the betas are not in a standard deviation scale.
    • \(\texttt{mean_diff2} = \sum_{j} \frac{\widehat{\beta_j^{std}} - \beta^{\prime}_j}{\texttt{n_snps}}\)
    • \(\beta^{\prime}_j = \frac{\beta_j}{\widehat{\texttt{sd2}}_{y}}\)
  • sd_y_est1: The standard deviation for the trait inferred from the reported sample size, median standard errors for the SNP-trait assocations and SNP variances.
    • \(\widehat{\texttt{sd1}}_{y} = \frac{\sqrt{n} \times median({se}_j)}{C}\),
    • \(C = median(\frac{1}{\sqrt{2 \times {MAF}_j \times (1 - {MAF}_j)}})\),
    • and \({se}_j\) is the reported standard error.
  • sd_y_est2: The standard deviation for the trait inferred from the reported sample size, Z statistics for the SNP-trait effects (beta/se) and allele frequency.
    • \(\widehat{\texttt{sd2}}_{y} = median(\widehat{sd_j})\),
    • \(\widehat{sd_j} = \frac{\beta_j}{\widehat{\beta_j^{std}}}\),

r2 metrics

Sum of variance explained, calculated from the clumped top hits sample.

  • r2_sum<*>: r2 statistics under various assumptions
    • 1: \(r^2 = \sum_j{\frac{2 \times \beta_j^2 \times {MAF}_j \times (1 - {MAF}_j)}{\texttt{var1}}}\), \(\texttt{var1} = 1\).
    • 2: \(r^2 = \sum_j{\frac{2 \times \beta_j^2 \times {MAF}_j \times (1 - {MAF}_j)}{\texttt{var2}}}\), \(\texttt{var2} = {\widehat{\texttt{sd1}}_{y}}^2\),
    • 3: \(r^2 = \sum_j{\frac{2 \times \beta_j^2 \times {MAF}_j \times (1 - {MAF}_j)}{\texttt{var3}}}\), \(\texttt{var3} = {\widehat{\texttt{sd2}}_{y}}^2\),
    • 4: \(r^2 = \sum_j{\frac{F_j}{F_j + n - 2}}\), \(F = \frac{\beta_j^2}{{se}_j^2}\).

LDSC metrics

Metrics from LD regression

  • ldsc_nsnp_merge_refpanel_ld: Number of remaining SNPs after merging with reference panel LD.
  • ldsc_nsnp_merge_regression_ld: Number of remaining SNPs after merging with regression SNP LD.
  • ldsc_observed_scale_h2_{beta,se} Coefficient value and SE for total observed scale h2.
  • ldsc_intercept_{beta,se}: Coefficient value and SE for intercept. Intercept is expected to be 1.
  • ldsc_lambda_gc: Lambda GC statistics.
  • ldsc_mean_chisq: Mean \(\chi^2\) statistics.
  • ldsc_ratio: \(\frac{\texttt{ldsc_intercept_beta} - 1}{\texttt{ldsc_mean_chisq} - 1}\), the proportion of the inflation in the mean \(\chi^2\) that the LD Score regression intercepts ascribes to causes other than polygenic heritability. The value of ratio should be close to zero, though in practice values of 0.1-0.2 are not uncommon, probably due to sample/reference LD Score mismatch or model misspecification (e.g., low LD variants have slightly higher \(h^2\) per SNP).

Flags

When a metric needs attention, the flag should return TRUE.

  • af_correlation: abs(af_correlation) < 0.7.
  • inflation_factor: inflation_factor > 1.2.
  • n: n (max reported sample size) < 10000.
  • is_snpid_non_unique: NOT is_snpid_unique.
  • mean_EFFECT_nonfinite: mean(EFFECT) is NA, NaN, or Inf.
  • mean_EFFECT_05: abs(mean(EFFECT)) > 0.5.
  • mean_EFFECT_01: abs(mean(EFFECT)) > 0.1.
  • mean_chisq: ldsc_mean_chisq > 1.3 or ldsc_mean_chisq < 0.7.
  • n_p_sig: n_p_sig > 1000.
  • miss_<*>: n_miss_<*> / n_snps > 0.01.
  • ldsc_ratio: ldsc_ratio > 0.5
  • ldsc_intercept_beta: ldsc_intercept_beta > 1.5
  • n_clumped_hits: n_clumped_hits > 1000
  • r2_sum<*>: r2_sum<*> > 0.5

Plots

  • Manhattan plot
    • Red line: \(-log_{10}^{5 \times 10^{-8}}\)
    • Blue line: \(-log_{10}^{5 \times 10^{-5}}\)
  • QQ plot
  • AF plot
  • P-Z plot
  • beta_std plot: Scatter plot between \(\widehat{\beta_j^{std}}\) and \(\beta_j\)

Diagnostics

Details

Summary stats

skim_type skim_variable n_missing complete_rate character.min character.max character.empty character.n_unique character.whitespace logical.mean logical.count numeric.mean numeric.sd numeric.p0 numeric.p25 numeric.p50 numeric.p75 numeric.p100 numeric.hist
character ID 0 1.0000000 3 58 0 8142954 0 NA NA NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 1 0 4 0 NA NA NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 1 0 4 0 NA NA NA NA NA NA NA NA NA NA
logical N 8149325 0.0000000 NA NA NA NA NA NaN : NA NA NA NA NA NA NA NA
numeric CHROM 0 1.0000000 NA NA NA NA NA NA NA 8.658812e+00 5.763361e+00 1.0000000 4.000000e+00 8.000000e+00 1.300000e+01 2.200000e+01 ▇▅▅▂▂
numeric POS 0 1.0000000 NA NA NA NA NA NA NA 7.871053e+07 5.639528e+07 828.0000000 3.229161e+07 6.918761e+07 1.145307e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -1.300000e-05 1.921710e-02 -0.2285580 -8.751000e-03 -3.480000e-05 8.683400e-03 1.976090e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.618240e-02 1.005820e-02 0.0071274 8.288300e-03 1.169450e-02 2.142930e-02 8.715030e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.938748e-01 2.907033e-01 0.0000009 2.399999e-01 4.899999e-01 7.499995e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.938749e-01 2.906785e-01 0.0000009 2.406112e-01 4.917907e-01 7.456595e-01 9.999999e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.414363e-01 2.605246e-01 0.0074560 3.267800e-02 1.308360e-01 3.826580e-01 9.925440e-01 ▇▂▂▁▁
numeric AF_reference 76321 0.9906347 NA NA NA NA NA NA NA 2.408184e-01 2.523859e-01 0.0000000 3.354630e-02 1.465650e-01 3.779950e-01 1.000000e+00 ▇▂▂▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 49298 rs200943160 T C 0.0118321 0.0130960 0.3700002 0.3662661 0.624187 0.7821490 NA
1 54676 rs2462492 C T 0.0042915 0.0129644 0.7400005 0.7406268 0.400478 NA NA
1 86028 rs114608975 T C -0.0032304 0.0209355 0.8800001 0.8773693 0.102852 0.0277556 NA
1 91536 rs6702460 G T 0.0060511 0.0127945 0.6400000 0.6362510 0.457609 0.4207270 NA
1 234313 rs8179466 C T -0.0070487 0.0249952 0.7800007 0.7779413 0.075037 NA NA
1 534192 rs6680723 C T 0.0274090 0.0146255 0.0610000 0.0609228 0.240629 NA NA
1 546697 rs12025928 A G -0.0141213 0.0181071 0.4400003 0.4354636 0.912963 NA NA
1 693731 rs12238997 A G 0.0194748 0.0122046 0.1100001 0.1105573 0.117157 0.1417730 NA
1 705882 rs72631875 G A -0.0019429 0.0179924 0.9100000 0.9140084 0.067282 0.0315495 NA
1 706368 rs55727773 A G -0.0190208 0.0090677 0.0359998 0.0359375 0.515056 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A -0.0038929 0.0109621 0.7199992 0.7224998 0.136992 0.2052720 NA
22 51219387 rs9616832 T C -0.0155667 0.0142700 0.2800000 0.2753305 0.073305 0.0654952 NA
22 51219704 rs147475742 G A -0.0180262 0.0191572 0.3500000 0.3467243 0.041529 0.0473243 NA
22 51221190 rs369304721 G A -0.0135279 0.0190530 0.4799997 0.4776953 0.049525 NA NA
22 51221731 rs115055839 T C -0.0167837 0.0142705 0.2399999 0.2395503 0.072910 0.0625000 NA
22 51222100 rs114553188 G T 0.0163086 0.0167773 0.3300000 0.3310190 0.053863 0.0880591 NA
22 51223637 rs375798137 G A 0.0167597 0.0168583 0.3200000 0.3201492 0.053490 0.0788738 NA
22 51229805 rs9616985 T C -0.0169905 0.0143268 0.2399999 0.2356522 0.072701 0.0730831 NA
22 51232488 rs376461333 A G -0.0016709 0.0336074 0.9599999 0.9603462 0.019878 NA NA
22 51237063 rs3896457 T C -0.0011542 0.0087160 0.8900000 0.8946537 0.297950 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.624187 ES:SE:LP:AF:ID  0.0118321:0.013096:0.431798:0.624187:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400478 ES:SE:LP:AF:ID  0.00429152:0.0129644:0.130768:0.400478:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.102852 ES:SE:LP:AF:ID  -0.00323045:0.0209355:0.0555173:0.102852:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.457609 ES:SE:LP:AF:ID  0.00605112:0.0127945:0.19382:0.457609:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.075037 ES:SE:LP:AF:ID  -0.00704872:0.0249952:0.107905:0.075037:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240629 ES:SE:LP:AF:ID  0.027409:0.0146255:1.21467:0.240629:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.912963 ES:SE:LP:AF:ID  -0.0141213:0.0181071:0.356547:0.912963:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.117157 ES:SE:LP:AF:ID  0.0194748:0.0122046:0.958607:0.117157:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067282 ES:SE:LP:AF:ID  -0.00194289:0.0179924:0.0409586:0.067282:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515056 ES:SE:LP:AF:ID  -0.0190208:0.00906774:1.4437:0.515056:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.032968 ES:SE:LP:AF:ID  0.0212337:0.0228879:0.455932:0.032968:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036652 ES:SE:LP:AF:ID  0.0242104:0.020767:0.619789:0.036652:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036749 ES:SE:LP:AF:ID  0.0216213:0.0207024:0.522879:0.036749:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036482 ES:SE:LP:AF:ID  0.0247897:0.0208297:0.638272:0.036482:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016466 ES:SE:LP:AF:ID  0.0271109:0.0320222:0.39794:0.016466:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037011 ES:SE:LP:AF:ID  0.0261605:0.020609:0.69897:0.037011:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037114 ES:SE:LP:AF:ID  0.0269146:0.0205357:0.721246:0.037114:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101415 ES:SE:LP:AF:ID  -0.00712678:0.0149974:0.200659:0.101415:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959078 ES:SE:LP:AF:ID  -0.0287199:0.0198207:0.823909:0.959078:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031571 ES:SE:LP:AF:ID  -0.0313504:0.0359435:0.420216:0.031571:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053226 ES:SE:LP:AF:ID  0.0112049:0.0285493:0.161151:0.053226:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036623 ES:SE:LP:AF:ID  0.0248693:0.0206719:0.638272:0.036623:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036972 ES:SE:LP:AF:ID  0.0225763:0.0204681:0.568636:0.036972:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.842677 ES:SE:LP:AF:ID  -0.0252123:0.0106031:1.76955:0.842677:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055855 ES:SE:LP:AF:ID  0.0187371:0.0172217:0.552842:0.055855:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122733 ES:SE:LP:AF:ID  0.0203527:0.0115997:1.10237:0.122733:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025036 ES:SE:LP:AF:ID  -0.020936:0.0288825:0.327902:0.025036:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.121972 ES:SE:LP:AF:ID  0.0211441:0.0116031:1.16749:0.121972:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132788 ES:SE:LP:AF:ID  0.0232321:0.0114686:1.36653:0.132788:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011323 ES:SE:LP:AF:ID  0.00173527:0.0410662:0.0132283:0.011323:rs181876450
1   752478  rs146277091 G   A   .   PASS    AF=0.036971 ES:SE:LP:AF:ID  0.0253624:0.0202227:0.677781:0.036971:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.837808 ES:SE:LP:AF:ID  -0.0262948:0.0102658:2:0.837808:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.837448 ES:SE:LP:AF:ID  -0.0257462:0.0102564:1.92082:0.837448:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.868737 ES:SE:LP:AF:ID  -0.0229052:0.0109998:1.4318:0.868737:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.130863 ES:SE:LP:AF:ID  0.0232124:0.0110236:1.45593:0.130863:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037406 ES:SE:LP:AF:ID  0.0242175:0.0199123:0.657577:0.037406:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.03764  ES:SE:LP:AF:ID  0.0232301:0.019793:0.619789:0.03764:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.868108 ES:SE:LP:AF:ID  -0.02245:0.0109793:1.38722:0.868108:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.868164 ES:SE:LP:AF:ID  -0.0222562:0.010981:1.36653:0.868164:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037609 ES:SE:LP:AF:ID  0.0254577:0.0198752:0.69897:0.037609:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.868097 ES:SE:LP:AF:ID  -0.0223669:0.0109783:1.37675:0.868097:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.836835 ES:SE:LP:AF:ID  -0.0260751:0.0102235:1.95861:0.836835:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037617 ES:SE:LP:AF:ID  0.0247358:0.0198989:0.677781:0.037617:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.83752  ES:SE:LP:AF:ID  -0.025736:0.0102515:1.92082:0.83752:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.014019 ES:SE:LP:AF:ID  -0.0395757:0.0354253:0.585027:0.014019:rs181660517
1   755775  rs3131965   A   G   .   PASS    AF=0.838707 ES:SE:LP:AF:ID  -0.0253586:0.0103914:1.82391:0.838707:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.868248 ES:SE:LP:AF:ID  -0.022756:0.0109627:1.42022:0.868248:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.867749 ES:SE:LP:AF:ID  -0.0226416:0.010935:1.42022:0.867749:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.866822 ES:SE:LP:AF:ID  -0.0221103:0.0109093:1.36653:0.866822:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.867902 ES:SE:LP:AF:ID  -0.0227716:0.0109421:1.4318:0.867902:rs4951929