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:16026,TotalVariants=9851866,VariantsNotRead=0,HarmonisedVariants=9851866,VariantsNotHarmonised=0,SwitchedAlleles=9851866,TotalControls=99479,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_20156.vcf.gz --id UKB-b:16026 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20156.txt.gz --cohort_controls 99479 --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": "2019-09-13T11:53:22.253490",
    "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-16026/mac_discard.txt -Oz /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-16026/UKB-b-16026_raw.vcf.gz; Date=Thu Oct 17 12:32:49 2019",
    "bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-16026/ukb-b-16026.vcf.gz; Date=Sun May 10 05:02:08 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-16026/UKB-b-16026_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-16026/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:44:40 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-16026/UKB-b-16026_data.vcf.gz ...
Read summary statistics for 9015040 SNPs.
Dropped 8782 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, 1287172 SNPs remain.
After merging with regression SNP LD, 1287172 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.069 (0.0052)
Lambda GC: 1.1268
Mean Chi^2: 1.1417
Intercept: 1.006 (0.0064)
Ratio: 0.0425 (0.0449)
Analysis finished at Thu Oct 17 14:46:19 2019
Total time elapsed: 1.0m:38.92s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9477,
    "inflation_factor": 1.0966,
    "mean_EFFECT": 0.0001,
    "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": 94022,
    "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": 1287172,
    "ldsc_nsnp_merge_regression_ld": 1287172,
    "ldsc_observed_scale_h2_beta": 0.069,
    "ldsc_observed_scale_h2_se": 0.0052,
    "ldsc_intercept_beta": 1.006,
    "ldsc_intercept_se": 0.0064,
    "ldsc_lambda_gc": 1.1268,
    "ldsc_mean_chisq": 1.1417,
    "ldsc_ratio": 0.0423
}

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 9006299 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 9015040 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 8.643111e+00 5.758162e+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 7.878883e+07 5.633838e+07 828.0000000 3.243485e+07 6.934964e+07 1.145370e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 5.100000e-05 1.508310e-02 -0.1930800 -5.975600e-03 4.330000e-05 6.051000e-03 1.738340e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.188570e-02 8.732000e-03 0.0043115 5.136900e-03 7.875400e-03 1.630120e-02 9.797660e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.845765e-01 2.928905e-01 0.0000001 2.300001e-01 4.799997e-01 7.400005e-01 1.000000e+00 ▇▇▇▆▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.845785e-01 2.928661e-01 0.0000001 2.272903e-01 4.789297e-01 7.381498e-01 9.999999e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.199863e-01 2.585244e-01 0.0035190 2.038700e-02 1.011850e-01 3.469780e-01 9.964810e-01 ▇▂▁▁▁
numeric AF_reference 94022 0.9895705 NA NA NA NA NA NA NA 2.202924e-01 2.504155e-01 0.0000000 1.757190e-02 1.186100e-01 3.454470e-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 -0.0000921 0.0079126 0.9900000 0.9907171 0.624010 0.7821490 NA
1 54676 rs2462492 C T -0.0023361 0.0078538 0.7700005 0.7661189 0.399529 NA NA
1 86028 rs114608975 T C 0.0048690 0.0125670 0.6999999 0.6984257 0.103465 0.0277556 NA
1 91536 rs6702460 G T 0.0031480 0.0077306 0.6800001 0.6838484 0.456311 0.4207270 NA
1 234313 rs8179466 C T 0.0059720 0.0153496 0.6999999 0.6972282 0.074073 NA NA
1 534192 rs6680723 C T -0.0060441 0.0088070 0.4899999 0.4925352 0.241114 NA NA
1 546697 rs12025928 A G -0.0171218 0.0109929 0.1199999 0.1193441 0.913088 NA NA
1 693731 rs12238997 A G 0.0092095 0.0073938 0.2099999 0.2129225 0.116996 0.1417730 NA
1 705882 rs72631875 G A 0.0092636 0.0107662 0.3900004 0.3895521 0.067861 0.0315495 NA
1 706368 rs55727773 A G -0.0010947 0.0054848 0.8400000 0.8418063 0.515618 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0049367 0.0066512 0.4600002 0.4579455 0.136655 0.2052720 NA
22 51219387 rs9616832 T C 0.0069596 0.0086462 0.4199997 0.4208639 0.072655 0.0654952 NA
22 51219704 rs147475742 G A 0.0062073 0.0116167 0.5900000 0.5931068 0.041170 0.0473243 NA
22 51221190 rs369304721 G A 0.0069852 0.0115419 0.5500004 0.5450425 0.049237 NA NA
22 51221731 rs115055839 T C 0.0074538 0.0086553 0.3900004 0.3891369 0.072114 0.0625000 NA
22 51222100 rs114553188 G T 0.0050225 0.0101523 0.6200004 0.6207989 0.054347 0.0880591 NA
22 51223637 rs375798137 G A 0.0042834 0.0102059 0.6700003 0.6747022 0.053972 0.0788738 NA
22 51229805 rs9616985 T C 0.0083870 0.0086856 0.3300000 0.3342309 0.071952 0.0730831 NA
22 51232488 rs376461333 A G -0.0127843 0.0205373 0.5300002 0.5336186 0.019798 NA NA
22 51237063 rs3896457 T C 0.0013422 0.0052605 0.8000000 0.7986163 0.298170 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.62401  ES:SE:LP:AF:ID  -9.20598e-05:0.00791258:0.00436481:0.62401:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.399529 ES:SE:LP:AF:ID  -0.00233614:0.00785375:0.113509:0.399529:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103465 ES:SE:LP:AF:ID  0.00486904:0.012567:0.154902:0.103465:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456311 ES:SE:LP:AF:ID  0.00314803:0.00773059:0.167491:0.456311:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074073 ES:SE:LP:AF:ID  0.00597199:0.0153496:0.154902:0.074073:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.241114 ES:SE:LP:AF:ID  -0.00604406:0.00880696:0.309804:0.241114:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913088 ES:SE:LP:AF:ID  -0.0171218:0.0109929:0.920819:0.913088:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116996 ES:SE:LP:AF:ID  0.00920948:0.00739379:0.677781:0.116996:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067861 ES:SE:LP:AF:ID  0.00926356:0.0107662:0.408935:0.067861:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515618 ES:SE:LP:AF:ID  -0.00109467:0.00548477:0.0757207:0.515618:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.032881 ES:SE:LP:AF:ID  0.0132671:0.0138585:0.468521:0.032881:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036521 ES:SE:LP:AF:ID  0.00804015:0.0125736:0.283997:0.036521:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036625 ES:SE:LP:AF:ID  0.00886208:0.0125282:0.318759:0.036625:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036319 ES:SE:LP:AF:ID  0.0093811:0.0126224:0.337242:0.036319:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016661 ES:SE:LP:AF:ID  -0.000254357:0.0192443:0.00436481:0.016661:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.03686  ES:SE:LP:AF:ID  0.00792715:0.0124805:0.275724:0.03686:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.036982 ES:SE:LP:AF:ID  0.00920709:0.0124365:0.337242:0.036982:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.102106 ES:SE:LP:AF:ID  0.00145661:0.00899341:0.0604807:0.102106:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959391 ES:SE:LP:AF:ID  -0.0123026:0.0120207:0.508638:0.959391:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031174 ES:SE:LP:AF:ID  0.0422606:0.0220333:1.25964:0.031174:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053286 ES:SE:LP:AF:ID  -0.0311314:0.0172646:1.14874:0.053286:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036474 ES:SE:LP:AF:ID  0.0102634:0.0125136:0.387216:0.036474:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036756 ES:SE:LP:AF:ID  0.00979032:0.0124063:0.366532:0.036756:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.842904 ES:SE:LP:AF:ID  -0.00830823:0.00642138:0.69897:0.842904:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.05611  ES:SE:LP:AF:ID  0.00146055:0.0103824:0.05061:0.05611:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122989 ES:SE:LP:AF:ID  0.00572131:0.00702034:0.376751:0.122989:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.024755 ES:SE:LP:AF:ID  -0.0160342:0.0175912:0.443698:0.024755:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122224 ES:SE:LP:AF:ID  0.00635881:0.00702428:0.431798:0.122224:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.13268  ES:SE:LP:AF:ID  0.00891323:0.00692316:0.69897:0.13268:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011093 ES:SE:LP:AF:ID  0.0185415:0.0253478:0.337242:0.011093:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005556 ES:SE:LP:AF:ID  -0.00708181:0.0330895:0.0809219:0.005556:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.036678 ES:SE:LP:AF:ID  0.00878896:0.0122787:0.327902:0.036678:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.838471 ES:SE:LP:AF:ID  -0.0073859:0.00621896:0.638272:0.838471:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838107 ES:SE:LP:AF:ID  -0.00740802:0.0062121:0.638272:0.838107:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869005 ES:SE:LP:AF:ID  -0.00546241:0.00665817:0.387216:0.869005:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.130661 ES:SE:LP:AF:ID  0.00479896:0.00667225:0.327902:0.130661:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037183 ES:SE:LP:AF:ID  0.0124297:0.012073:0.522879:0.037183:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037431 ES:SE:LP:AF:ID  0.0120232:0.0119946:0.49485:0.037431:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.868339 ES:SE:LP:AF:ID  -0.00538898:0.00664551:0.376751:0.868339:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.868434 ES:SE:LP:AF:ID  -0.00572856:0.00664858:0.408935:0.868434:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037384 ES:SE:LP:AF:ID  0.0110807:0.0120492:0.443698:0.037384:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.868343 ES:SE:LP:AF:ID  -0.00536676:0.00664529:0.376751:0.868343:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005306 ES:SE:LP:AF:ID  -0.0331357:0.0334585:0.49485:0.005306:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005277 ES:SE:LP:AF:ID  -0.0340888:0.0335172:0.508638:0.005277:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.83751  ES:SE:LP:AF:ID  -0.00717589:0.0061945:0.60206:0.83751:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037419 ES:SE:LP:AF:ID  0.0103319:0.0120631:0.408935:0.037419:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838092 ES:SE:LP:AF:ID  -0.00684873:0.00621093:0.568636:0.838092:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013728 ES:SE:LP:AF:ID  0.0255565:0.0216661:0.619789:0.013728:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005406 ES:SE:LP:AF:ID  -0.00210142:0.0337898:0.0222764:0.005406:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.839278 ES:SE:LP:AF:ID  -0.00685347:0.00629503:0.552842:0.839278:rs3131965