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:1138,TotalVariants=9851866,VariantsNotRead=0,HarmonisedVariants=9851866,VariantsNotHarmonised=0,SwitchedAlleles=9851866,TotalControls=84303,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_5106.vcf.gz --id UKB-b:1138 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_5106.txt.gz --cohort_controls 84303 --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": "2019-09-12T23:11:54.297756",
    "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-1138/mac_discard.txt -Oz /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-1138/UKB-b-1138_raw.vcf.gz; Date=Thu Oct 17 12:25:17 2019",
    "bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-1138/ukb-b-1138.vcf.gz; Date=Sun May 10 11:25:36 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-1138/UKB-b-1138_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-1138/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:40:18 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-1138/UKB-b-1138_data.vcf.gz ...
Read summary statistics for 8887262 SNPs.
Dropped 8243 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, 1286829 SNPs remain.
After merging with regression SNP LD, 1286829 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.063 (0.0063)
Lambda GC: 1.1114
Mean Chi^2: 1.117
Intercept: 1.0131 (0.0065)
Ratio: 0.1122 (0.0558)
Analysis finished at Thu Oct 17 14:41:53 2019
Total time elapsed: 1.0m:34.92s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9474,
    "inflation_factor": 1.0966,
    "mean_EFFECT": 0.0001,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 3,
    "n_p_sig": 8,
    "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": 89248,
    "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": 1286829,
    "ldsc_nsnp_merge_regression_ld": 1286829,
    "ldsc_observed_scale_h2_beta": 0.063,
    "ldsc_observed_scale_h2_se": 0.0063,
    "ldsc_intercept_beta": 1.0131,
    "ldsc_intercept_se": 0.0065,
    "ldsc_lambda_gc": 1.1114,
    "ldsc_mean_chisq": 1.117,
    "ldsc_ratio": 0.112
}

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 8879058 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 8887262 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 8.646363e+00 5.759728e+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 7.877882e+07 5.634575e+07 828.0000000 3.241416e+07 6.933239e+07 1.145492e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 6.950000e-05 1.573430e-02 -0.1630530 -6.337600e-03 5.580000e-05 6.449300e-03 1.732560e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.257700e-02 8.992900e-03 0.0047024 5.588700e-03 8.453300e-03 1.719670e-02 1.076020e-01 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.875775e-01 2.919745e-01 0.0000000 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 4.875748e-01 2.919491e-01 0.0000000 2.317556e-01 4.830417e-01 7.403167e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.229324e-01 2.588377e-01 0.0041520 2.190300e-02 1.054090e-01 3.518930e-01 9.958480e-01 ▇▂▁▁▁
numeric AF_reference 89248 0.9899578 NA NA NA NA NA NA NA 2.230286e-01 2.507842e-01 0.0000000 1.936900e-02 1.224040e-01 3.500400e-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 0.0019430 0.0086975 0.8200001 0.8232245 0.623328 0.7821490 NA
1 54676 rs2462492 C T -0.0197694 0.0086385 0.0219999 0.0221071 0.399068 NA NA
1 86028 rs114608975 T C 0.0011750 0.0136826 0.9299999 0.9315654 0.104087 0.0277556 NA
1 91536 rs6702460 G T -0.0071554 0.0084803 0.4000000 0.3987956 0.455590 0.4207270 NA
1 234313 rs8179466 C T 0.0120196 0.0165913 0.4700002 0.4687883 0.074857 NA NA
1 534192 rs6680723 C T 0.0068291 0.0097040 0.4799997 0.4815939 0.240530 NA NA
1 546697 rs12025928 A G 0.0172701 0.0120159 0.1499999 0.1506411 0.912604 NA NA
1 693731 rs12238997 A G -0.0004899 0.0080605 0.9500000 0.9515407 0.117886 0.1417730 NA
1 705882 rs72631875 G A -0.0091313 0.0118420 0.4400003 0.4406495 0.067380 0.0315495 NA
1 706368 rs55727773 A G 0.0007035 0.0059785 0.9100000 0.9063333 0.514497 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A -0.0029172 0.0072438 0.6899999 0.6871519 0.137315 0.2052720 NA
22 51219387 rs9616832 T C -0.0010746 0.0094554 0.9100000 0.9095176 0.072445 0.0654952 NA
22 51219704 rs147475742 G A 0.0025490 0.0125654 0.8400000 0.8392486 0.041844 0.0473243 NA
22 51221190 rs369304721 G A -0.0039192 0.0126433 0.7600007 0.7565762 0.049027 NA NA
22 51221731 rs115055839 T C -0.0022683 0.0094574 0.8100000 0.8104519 0.071981 0.0625000 NA
22 51222100 rs114553188 G T -0.0082990 0.0110258 0.4500005 0.4516351 0.054759 0.0880591 NA
22 51223637 rs375798137 G A -0.0084365 0.0110827 0.4500005 0.4465183 0.054381 0.0788738 NA
22 51229805 rs9616985 T C -0.0027709 0.0094939 0.7700005 0.7703970 0.071827 0.0730831 NA
22 51232488 rs376461333 A G 0.0006060 0.0223201 0.9800000 0.9783380 0.020127 NA NA
22 51237063 rs3896457 T C 0.0076542 0.0057600 0.1800002 0.1838959 0.298658 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623328 ES:SE:LP:AF:ID  0.00194302:0.00869751:0.0861861:0.623328:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.399068 ES:SE:LP:AF:ID  -0.0197694:0.0086385:1.65758:0.399068:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.104087 ES:SE:LP:AF:ID  0.001175:0.0136826:0.0315171:0.104087:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.45559  ES:SE:LP:AF:ID  -0.00715545:0.00848029:0.39794:0.45559:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074857 ES:SE:LP:AF:ID  0.0120196:0.0165913:0.327902:0.074857:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.24053  ES:SE:LP:AF:ID  0.00682912:0.00970402:0.318759:0.24053:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.912604 ES:SE:LP:AF:ID  0.0172701:0.0120159:0.823909:0.912604:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.117886 ES:SE:LP:AF:ID  -0.000489851:0.00806046:0.0222764:0.117886:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.06738  ES:SE:LP:AF:ID  -0.00913133:0.011842:0.356547:0.06738:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.514497 ES:SE:LP:AF:ID  0.000703461:0.00597852:0.0409586:0.514497:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033641 ES:SE:LP:AF:ID  -0.0146518:0.0149445:0.481486:0.033641:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037343 ES:SE:LP:AF:ID  -0.0142216:0.0135802:0.537602:0.037343:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037429 ES:SE:LP:AF:ID  -0.014016:0.013537:0.522879:0.037429:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.037126 ES:SE:LP:AF:ID  -0.0136841:0.0136301:0.49485:0.037126:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016858 ES:SE:LP:AF:ID  -0.0127991:0.0209344:0.267606:0.016858:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037677 ES:SE:LP:AF:ID  -0.0126907:0.01348:0.455932:0.037677:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037773 ES:SE:LP:AF:ID  -0.0135068:0.0134358:0.508638:0.037773:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.10073  ES:SE:LP:AF:ID  0.0106873:0.00993622:0.552842:0.10073:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.957986 ES:SE:LP:AF:ID  0.0152618:0.0129136:0.619789:0.957986:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031745 ES:SE:LP:AF:ID  -0.0337643:0.0236976:0.823909:0.031745:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052556 ES:SE:LP:AF:ID  0.0262035:0.0190881:0.769551:0.052556:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.037261 ES:SE:LP:AF:ID  -0.0122492:0.0135228:0.431798:0.037261:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.03756  ES:SE:LP:AF:ID  -0.0125839:0.0134127:0.455932:0.03756:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.840764 ES:SE:LP:AF:ID  0.00278278:0.0069836:0.161151:0.840764:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056161 ES:SE:LP:AF:ID  0.00387623:0.0113688:0.136677:0.056161:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.123753 ES:SE:LP:AF:ID  0.00221231:0.0076557:0.113509:0.123753:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025786 ES:SE:LP:AF:ID  0.0471073:0.0188553:1.92082:0.025786:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122943 ES:SE:LP:AF:ID  0.00171997:0.00766007:0.0861861:0.122943:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.133622 ES:SE:LP:AF:ID  -0.000415007:0.00755398:0.0177288:0.133622:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.01134  ES:SE:LP:AF:ID  -0.0111264:0.0272327:0.167491:0.01134:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.006117 ES:SE:LP:AF:ID  0.0283575:0.0341223:0.387216:0.006117:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.037535 ES:SE:LP:AF:ID  -0.0124246:0.0132657:0.455932:0.037535:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.8367   ES:SE:LP:AF:ID  0.004676:0.00675887:0.309804:0.8367:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.836251 ES:SE:LP:AF:ID  0.00550599:0.00675052:0.387216:0.836251:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.868092 ES:SE:LP:AF:ID  0.00134139:0.00724462:0.0705811:0.868092:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.131595 ES:SE:LP:AF:ID  -0.00220677:0.00725864:0.119186:0.131595:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037984 ES:SE:LP:AF:ID  -0.0115789:0.0130557:0.420216:0.037984:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038243 ES:SE:LP:AF:ID  -0.0114699:0.0129732:0.420216:0.038243:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.867391 ES:SE:LP:AF:ID  0.00193375:0.00722966:0.102373:0.867391:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.867486 ES:SE:LP:AF:ID  0.00205604:0.00723327:0.107905:0.867486:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038174 ES:SE:LP:AF:ID  -0.0121709:0.0130271:0.455932:0.038174:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.86738  ES:SE:LP:AF:ID  0.00196734:0.00722909:0.102373:0.86738:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005063 ES:SE:LP:AF:ID  0.00163085:0.0376357:0.0132283:0.005063:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.00503  ES:SE:LP:AF:ID  0.000981799:0.0377425:0.00877392:0.00503:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.835803 ES:SE:LP:AF:ID  0.0049388:0.00673629:0.337242:0.835803:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038191 ES:SE:LP:AF:ID  -0.0126777:0.0130446:0.481486:0.038191:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.836415 ES:SE:LP:AF:ID  0.00464974:0.00675484:0.309804:0.836415:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013196 ES:SE:LP:AF:ID  0.00789532:0.024253:0.130768:0.013196:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005531 ES:SE:LP:AF:ID  -0.0327488:0.0365421:0.431798:0.005531:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.837734 ES:SE:LP:AF:ID  0.00303048:0.00684831:0.180456:0.837734:rs3131965