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:19398,TotalVariants=9851866,VariantsNotRead=0,HarmonisedVariants=9851866,VariantsNotHarmonised=0,SwitchedAlleles=9851866,TotalControls=452766,TotalCases=8407,StudyType=CaseControl>",
    "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_41245_1370.vcf.gz --id UKB-b:19398 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_41245_1370.txt.gz --cohort_cases 8407 --cohort_controls 452766 --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": "2019-09-12T19:14:02.776555",
    "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-19398/mac_discard.txt -Oz /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-19398/UKB-b-19398_raw.vcf.gz; Date=Thu Oct 17 12:37:45 2019",
    "bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-19398/ukb-b-19398.vcf.gz; Date=Sun May 10 10:38:06 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-19398/UKB-b-19398_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-19398/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:41:15 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-19398/UKB-b-19398_data.vcf.gz ...
Read summary statistics for 5697574 SNPs.
Dropped 2313 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, 1191308 SNPs remain.
After merging with regression SNP LD, 1191308 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0032 (0.0013)
Lambda GC: 1.0617
Mean Chi^2: 1.0566
Intercept: 1.027 (0.0072)
Ratio: 0.4766 (0.1269)
Analysis finished at Thu Oct 17 14:42:22 2019
Total time elapsed: 1.0m:7.66s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.92,
    "inflation_factor": 1.0475,
    "mean_EFFECT": 1.2256e-06,
    "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": 50645,
    "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": 1191308,
    "ldsc_nsnp_merge_regression_ld": 1191308,
    "ldsc_observed_scale_h2_beta": 0.0032,
    "ldsc_observed_scale_h2_se": 0.0013,
    "ldsc_intercept_beta": 1.027,
    "ldsc_intercept_se": 0.0072,
    "ldsc_lambda_gc": 1.0617,
    "ldsc_mean_chisq": 1.0566,
    "ldsc_ratio": 0.477
}

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 5695277 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 5697574 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 8.669818e+00 5.762193e+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 7.859989e+07 5.655654e+07 828.0000000 3.198913e+07 6.903570e+07 1.145444e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 1.200000e-06 4.190000e-04 -0.0029672 -2.567000e-04 -6.000000e-07 2.569000e-04 3.665800e-03 ▁▂▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 3.929000e-04 1.175000e-04 0.0002688 2.953000e-04 3.482000e-04 4.630000e-04 1.282500e-03 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.916452e-01 2.904636e-01 0.0000003 2.399999e-01 4.899999e-01 7.400005e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.916439e-01 2.904372e-01 0.0000003 2.379742e-01 4.880592e-01 7.428899e-01 9.999999e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 3.234601e-01 2.498220e-01 0.0416320 1.105600e-01 2.461310e-01 4.899218e-01 9.583680e-01 ▇▃▂▂▁
numeric AF_reference 50645 0.9911111 NA NA NA NA NA NA NA 3.191645e-01 2.437607e-01 0.0000000 1.186100e-01 2.523960e-01 4.800320e-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 -0.0004152 0.0004947 0.4000000 0.4013181 0.623776 0.7821490 NA
1 54676 rs2462492 C T 0.0005071 0.0004902 0.2999998 0.3008618 0.400406 NA NA
1 86028 rs114608975 T C -0.0000121 0.0007837 0.9900000 0.9876866 0.103553 0.0277556 NA
1 91536 rs6702460 G T -0.0001762 0.0004826 0.7099994 0.7149716 0.456859 0.4207270 NA
1 234313 rs8179466 C T 0.0010624 0.0009515 0.2599998 0.2641615 0.074511 NA NA
1 534192 rs6680723 C T -0.0003360 0.0005513 0.5400003 0.5421927 0.240938 NA NA
1 546697 rs12025928 A G 0.0009858 0.0006877 0.1499999 0.1517155 0.913474 NA NA
1 693731 rs12238997 A G -0.0009868 0.0004620 0.0329997 0.0326870 0.116317 0.1417730 NA
1 705882 rs72631875 G A -0.0001160 0.0006769 0.8600001 0.8639631 0.067303 0.0315495 NA
1 706368 rs55727773 A G 0.0005618 0.0003422 0.1000000 0.1006721 0.515676 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51218377 rs2519461 G C -0.0005250 0.0005348 0.3300000 0.3261933 0.073605 0.0826677 NA
22 51219006 rs28729663 G A -0.0000647 0.0004128 0.8800001 0.8754859 0.137948 0.2052720 NA
22 51219387 rs9616832 T C -0.0005468 0.0005358 0.3100002 0.3074820 0.073729 0.0654952 NA
22 51219704 rs147475742 G A -0.0002858 0.0007179 0.6899999 0.6905447 0.041959 0.0473243 NA
22 51221190 rs369304721 G A -0.0007340 0.0007168 0.3100002 0.3058171 0.049727 NA NA
22 51221731 rs115055839 T C -0.0005512 0.0005362 0.2999998 0.3039511 0.073219 0.0625000 NA
22 51222100 rs114553188 G T 0.0005751 0.0006312 0.3599996 0.3621972 0.054469 0.0880591 NA
22 51223637 rs375798137 G A 0.0005428 0.0006343 0.3900004 0.3921190 0.054098 0.0788738 NA
22 51229805 rs9616985 T C -0.0005399 0.0005381 0.3200000 0.3157311 0.073057 0.0730831 NA
22 51237063 rs3896457 T C 0.0008345 0.0003291 0.0109999 0.0112325 0.297978 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623776 ES:SE:LP:AF:ID  -0.000415164:0.000494673:0.39794:0.623776:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400406 ES:SE:LP:AF:ID  0.000507137:0.000490183:0.522879:0.400406:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103553 ES:SE:LP:AF:ID  -1.20944e-05:0.000783664:0.00436481:0.103553:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456859 ES:SE:LP:AF:ID  -0.000176227:0.000482566:0.148742:0.456859:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074511 ES:SE:LP:AF:ID  0.00106243:0.000951479:0.585027:0.074511:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240938 ES:SE:LP:AF:ID  -0.000335989:0.000551253:0.267606:0.240938:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913474 ES:SE:LP:AF:ID  0.000985801:0.000687689:0.823909:0.913474:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116317 ES:SE:LP:AF:ID  -0.000986785:0.000461998:1.48149:0.116317:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067303 ES:SE:LP:AF:ID  -0.000115975:0.000676904:0.0655015:0.067303:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515676 ES:SE:LP:AF:ID  0.000561798:0.000342225:1:0.515676:rs12029736
1   722670  rs116030099 T   C   .   PASS    AF=0.101207 ES:SE:LP:AF:ID  4.10486e-05:0.000564592:0.0268721:0.101207:rs116030099
1   725060  rs865924913 A   T   .   PASS    AF=0.053269 ES:SE:LP:AF:ID  -0.00136379:0.00107896:0.677781:0.053269:rs865924913
1   729679  rs4951859   C   G   .   PASS    AF=0.843202 ES:SE:LP:AF:ID  0.000702949:0.000400359:1.10237:0.843202:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055913 ES:SE:LP:AF:ID  -2.32737e-05:0.00064826:0.0132283:0.055913:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122309 ES:SE:LP:AF:ID  -0.000700635:0.000438236:0.958607:0.122309:rs58276399
1   734349  rs141242758 T   C   .   PASS    AF=0.121551 ES:SE:LP:AF:ID  -0.00070552:0.00043842:0.958607:0.121551:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.13233  ES:SE:LP:AF:ID  -0.000656976:0.000432119:0.886057:0.13233:rs79010578
1   752566  rs3094315   G   A   .   PASS    AF=0.838942 ES:SE:LP:AF:ID  0.00062716:0.000387718:0.958607:0.838942:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838569 ES:SE:LP:AF:ID  0.000577446:0.000387299:0.853872:0.838569:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869778 ES:SE:LP:AF:ID  0.000544954:0.000415595:0.721246:0.869778:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129875 ES:SE:LP:AF:ID  -0.00045968:0.000416436:0.568636:0.129875:rs2073813
1   754182  rs3131969   A   G   .   PASS    AF=0.869118 ES:SE:LP:AF:ID  0.000485215:0.000414777:0.619789:0.869118:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869216 ES:SE:LP:AF:ID  0.000479954:0.00041494:0.60206:0.869216:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.869122 ES:SE:LP:AF:ID  0.000493091:0.00041477:0.638272:0.869122:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.838022 ES:SE:LP:AF:ID  0.000605065:0.000386224:0.920819:0.838022:rs3115859
1   754964  rs3131966   C   T   .   PASS    AF=0.838652 ES:SE:LP:AF:ID  0.000579488:0.000387309:0.886057:0.838652:rs3131966
1   755775  rs3131965   A   G   .   PASS    AF=0.839768 ES:SE:LP:AF:ID  0.000651092:0.00039255:1.01323:0.839768:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.869403 ES:SE:LP:AF:ID  0.00055499:0.000414293:0.744727:0.869403:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.868953 ES:SE:LP:AF:ID  0.000523651:0.000413255:0.677781:0.868953:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.867903 ES:SE:LP:AF:ID  0.000453998:0.000412457:0.568636:0.867903:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.869095 ES:SE:LP:AF:ID  0.000543551:0.000413591:0.721246:0.869095:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.869103 ES:SE:LP:AF:ID  0.00054391:0.000413623:0.721246:0.869103:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.869111 ES:SE:LP:AF:ID  0.00054367:0.000413633:0.721246:0.869111:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.869587 ES:SE:LP:AF:ID  0.00054108:0.000414765:0.721246:0.869587:rs3131954
1   760912  rs1048488   C   T   .   PASS    AF=0.838302 ES:SE:LP:AF:ID  0.000666501:0.000385489:1.07572:0.838302:rs1048488
1   761147  rs3115850   T   C   .   PASS    AF=0.838423 ES:SE:LP:AF:ID  0.000658104:0.000385762:1.05552:0.838423:rs3115850
1   761732  rs2286139   C   T   .   PASS    AF=0.862258 ES:SE:LP:AF:ID  0.000528241:0.000412131:0.69897:0.862258:rs2286139
1   763394  rs3115847   G   A   .   PASS    AF=0.706763 ES:SE:LP:AF:ID  0.000501008:0.000401218:0.677781:0.706763:rs3115847
1   766007  rs61768174  A   C   .   PASS    AF=0.105124 ES:SE:LP:AF:ID  -0.00033464:0.00046223:0.327902:0.105124:rs61768174
1   768253  rs2977608   A   C   .   PASS    AF=0.761345 ES:SE:LP:AF:ID  0.000311599:0.000327488:0.468521:0.761345:rs2977608
1   768448  rs12562034  G   A   .   PASS    AF=0.106452 ES:SE:LP:AF:ID  -9.3167e-06:0.000451404:0.00877392:0.106452:rs12562034
1   769223  rs60320384  C   G   .   PASS    AF=0.129572 ES:SE:LP:AF:ID  -0.000446318:0.0004162:0.552842:0.129572:rs60320384
1   771823  rs2977605   T   C   .   PASS    AF=0.868914 ES:SE:LP:AF:ID  0.000475562:0.000413979:0.60206:0.868914:rs2977605
1   771967  rs59066358  G   A   .   PASS    AF=0.129673 ES:SE:LP:AF:ID  -0.000474987:0.00041593:0.60206:0.129673:rs59066358
1   772755  rs2905039   A   C   .   PASS    AF=0.868925 ES:SE:LP:AF:ID  0.0004709:0.000413989:0.585027:0.868925:rs2905039
1   776546  rs12124819  A   G   .   PASS    AF=0.265363 ES:SE:LP:AF:ID  -0.000436867:0.000365774:0.638272:0.265363:rs12124819
1   777122  rs2980319   A   T   .   PASS    AF=0.870043 ES:SE:LP:AF:ID  0.000565562:0.00041483:0.769551:0.870043:rs2980319
1   777232  rs112618790 C   T   .   PASS    AF=0.095113 ES:SE:LP:AF:ID  5.8739e-05:0.000480826:0.0457575:0.095113:rs112618790
1   778745  rs1055606   A   G   .   PASS    AF=0.128574 ES:SE:LP:AF:ID  -0.000569335:0.000416463:0.769551:0.128574:rs1055606
1   779322  rs4040617   A   G   .   PASS    AF=0.128872 ES:SE:LP:AF:ID  -0.000582814:0.000415756:0.79588:0.128872:rs4040617