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:19438,TotalVariants=9851866,VariantsNotRead=0,HarmonisedVariants=9851866,VariantsNotHarmonised=0,SwitchedAlleles=9851866,TotalControls=461005,TotalCases=1928,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_20004_1463.vcf.gz --id UKB-b:19438 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20004_1463.txt.gz --cohort_cases 1928 --cohort_controls 461005 --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": "2019-09-13T01:16:00.648353",
    "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-19438/mac_discard.txt -Oz /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-19438/UKB-b-19438_raw.vcf.gz; Date=Thu Oct 17 12:37:51 2019",
    "bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-19438/ukb-b-19438.vcf.gz; Date=Sat May  9 17:21:13 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-19438/UKB-b-19438_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-19438/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:41:22 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-19438/UKB-b-19438_data.vcf.gz ...
Read summary statistics for 3105625 SNPs.
Dropped 427 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, 770560 SNPs remain.
After merging with regression SNP LD, 770560 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0025 (0.0012)
Lambda GC: 1.0056
Mean Chi^2: 1.0191
Intercept: 0.9932 (0.0086)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Thu Oct 17 14:42:04 2019
Total time elapsed: 42.07s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.8251,
    "inflation_factor": 1,
    "mean_EFFECT": -6.8954e-07,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 1,
    "n_p_sig": 11,
    "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": 24812,
    "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": 770560,
    "ldsc_nsnp_merge_regression_ld": 770560,
    "ldsc_observed_scale_h2_beta": 0.0025,
    "ldsc_observed_scale_h2_se": 0.0012,
    "ldsc_intercept_beta": 0.9932,
    "ldsc_intercept_se": 0.0086,
    "ldsc_lambda_gc": 1.0056,
    "ldsc_mean_chisq": 1.0191,
    "ldsc_ratio": -0.356
}
 

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 3105201 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 3105625 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 8.664724e+00 5.772589e+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 7.854971e+07 5.670705e+07 828.0000000 3.159016e+07 6.894302e+07 1.147956e+08 2.492013e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -7.000000e-07 1.488000e-04 -0.0008162 -9.940000e-05 -5.000000e-07 9.840000e-05 9.188000e-04 ▁▂▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.470000e-04 1.200000e-05 0.0001292 1.366000e-04 1.433000e-04 1.555000e-04 3.123000e-04 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.983520e-01 2.902156e-01 0.0000000 2.500000e-01 5.000000e-01 7.499995e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.983513e-01 2.901875e-01 0.0000000 2.459597e-01 4.990076e-01 7.501742e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 4.308907e-01 1.796756e-01 0.1815360 2.731390e-01 3.985440e-01 5.708520e-01 8.184640e-01 ▇▆▅▃▃
numeric AF_reference 24812 0.9920106 NA NA NA NA NA NA NA 4.153626e-01 1.944281e-01 0.0000000 2.585860e-01 3.903750e-01 5.581070e-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 -0.0002174 0.0002377 0.3599996 0.3604784 0.623763 0.7821490 NA
1 54676 rs2462492 C T -0.0001581 0.0002355 0.5000000 0.5020893 0.400401 NA NA
1 91536 rs6702460 G T -0.0003272 0.0002319 0.1600000 0.1581836 0.456851 0.4207270 NA
1 534192 rs6680723 C T 0.0002184 0.0002649 0.4100001 0.4095720 0.240960 NA NA
1 706368 rs55727773 A G -0.0000318 0.0001644 0.8499999 0.8468715 0.515650 0.2751600 NA
1 763394 rs369924889 G A 0.0000138 0.0001928 0.9400001 0.9428875 0.706753 0.6176120 NA
1 768253 rs2977608 A C -0.0000847 0.0001573 0.5900000 0.5902831 0.761304 0.4894170 NA
1 776546 rs12124819 A G 0.0000999 0.0001757 0.5700002 0.5698567 0.265390 0.0756789 NA
1 798400 rs10900604 A G 0.0000655 0.0001678 0.6999999 0.6964495 0.206580 0.4105430 NA
1 798959 rs11240777 G A 0.0000660 0.0001679 0.6899999 0.6942310 0.206409 0.4099440 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51180501 rs5770999 T C 0.0000793 0.0001547 0.6100002 0.6084901 0.713658 0.6369810 NA
22 51181919 rs9616825 G C 0.0001402 0.0001540 0.3599996 0.3626349 0.695471 0.6194090 NA
22 51182485 rs6009961 A G 0.0000584 0.0001552 0.7099994 0.7067152 0.715505 0.6383790 NA
22 51186143 rs2879914 T C -0.0000055 0.0001439 0.9699999 0.9696425 0.381826 0.2733630 NA
22 51186228 rs3865766 C T -0.0000112 0.0001403 0.9400001 0.9364306 0.451063 0.4532750 NA
22 51197266 rs61290853 A G 0.0000788 0.0001448 0.5900000 0.5863876 0.386333 0.4229230 NA
22 51198027 rs34939255 A G 0.0000342 0.0001640 0.8400000 0.8350131 0.254557 0.0984425 NA
22 51211106 rs9628250 T C -0.0000151 0.0001626 0.9299999 0.9259438 0.271547 0.1671330 NA
22 51212875 rs2238837 A C -0.0000281 0.0001545 0.8600001 0.8557359 0.331455 0.3724040 NA
22 51237063 rs3896457 T C -0.0000051 0.0001581 0.9699999 0.9742437 0.297971 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623763 ES:SE:LP:AF:ID  -0.000217373:0.000237708:0.443698:0.623763:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400401 ES:SE:LP:AF:ID  -0.000158065:0.000235494:0.30103:0.400401:rs2462492
1   91536   rs6702460   G   T   .   PASS    AF=0.456851 ES:SE:LP:AF:ID  -0.000327225:0.000231876:0.79588:0.456851:rs6702460
1   534192  rs6680723   C   T   .   PASS    AF=0.24096  ES:SE:LP:AF:ID  0.000218416:0.00026486:0.387216:0.24096:rs6680723
1   706368  rs12029736  A   G   .   PASS    AF=0.51565  ES:SE:LP:AF:ID  -3.17518e-05:0.000164422:0.0705811:0.51565:rs12029736
1   763394  rs3115847   G   A   .   PASS    AF=0.706753 ES:SE:LP:AF:ID  1.38102e-05:0.000192769:0.0268721:0.706753:rs3115847
1   768253  rs2977608   A   C   .   PASS    AF=0.761304 ES:SE:LP:AF:ID  -8.47127e-05:0.000157334:0.229148:0.761304:rs2977608
1   776546  rs12124819  A   G   .   PASS    AF=0.26539  ES:SE:LP:AF:ID  9.98693e-05:0.000175745:0.244125:0.26539:rs12124819
1   798400  rs10900604  A   G   .   PASS    AF=0.20658  ES:SE:LP:AF:ID  6.54797e-05:0.000167846:0.154902:0.20658:rs10900604
1   798959  rs11240777  G   A   .   PASS    AF=0.206409 ES:SE:LP:AF:ID  6.60119e-05:0.000167918:0.161151:0.206409:rs11240777
1   808631  rs11240779  G   A   .   PASS    AF=0.772626 ES:SE:LP:AF:ID  7.69449e-05:0.000159735:0.200659:0.772626:rs11240779
1   808928  rs11240780  C   T   .   PASS    AF=0.772854 ES:SE:LP:AF:ID  7.34629e-05:0.000160004:0.187087:0.772854:rs11240780
1   814495  rs74461805  C   A   .   PASS    AF=0.340397 ES:SE:LP:AF:ID  -0.000164741:0.000225455:0.337242:0.340397:rs74461805
1   830181  rs28444699  A   G   .   PASS    AF=0.697259 ES:SE:LP:AF:ID  5.79993e-05:0.000150836:0.154902:0.697259:rs28444699
1   831489  rs4970385   C   T   .   PASS    AF=0.705403 ES:SE:LP:AF:ID  3.14227e-06:0.000148107:0.00877392:0.705403:rs4970385
1   831909  rs9697642   C   T   .   PASS    AF=0.705448 ES:SE:LP:AF:ID  4.60502e-06:0.000148102:0.00877392:0.705448:rs9697642
1   832066  rs9697380   G   C   .   PASS    AF=0.705634 ES:SE:LP:AF:ID  1.84504e-06:0.000148109:0.00436481:0.705634:rs9697380
1   832318  rs4500250   C   A   .   PASS    AF=0.705662 ES:SE:LP:AF:ID  1.25194e-06:0.000148124:0.00436481:0.705662:rs4500250
1   832398  rs4553118   T   C   .   PASS    AF=0.730134 ES:SE:LP:AF:ID  3.34154e-05:0.000152161:0.0809219:0.730134:rs4553118
1   832918  rs28765502  T   C   .   PASS    AF=0.294371 ES:SE:LP:AF:ID  6.71649e-07:0.000148118:-0:0.294371:rs28765502
1   833223  rs13303211  C   T   .   PASS    AF=0.236696 ES:SE:LP:AF:ID  -5.90963e-05:0.000157695:0.148742:0.236696:rs13303211
1   833302  rs28752186  C   T   .   PASS    AF=0.236684 ES:SE:LP:AF:ID  -6.03373e-05:0.000157696:0.154902:0.236684:rs28752186
1   833641  rs28594623  T   C   .   PASS    AF=0.239743 ES:SE:LP:AF:ID  -7.21367e-05:0.000157191:0.187087:0.239743:rs28594623
1   833824  rs28484835  T   C   .   PASS    AF=0.236686 ES:SE:LP:AF:ID  -6.03383e-05:0.000157695:0.154902:0.236686:rs28484835
1   833927  rs28593608  T   C   .   PASS    AF=0.212416 ES:SE:LP:AF:ID  -0.000103991:0.000163902:0.275724:0.212416:rs28593608
1   834198  rs28385272  T   C   .   PASS    AF=0.212311 ES:SE:LP:AF:ID  -9.57477e-05:0.000163931:0.251812:0.212311:rs28385272
1   834832  rs4411087   G   C   .   PASS    AF=0.237171 ES:SE:LP:AF:ID  -4.86925e-05:0.000157574:0.119186:0.237171:rs4411087
1   834928  rs4422949   A   G   .   PASS    AF=0.212956 ES:SE:LP:AF:ID  -9.94577e-05:0.000163698:0.267606:0.212956:rs4422949
1   834999  rs28570054  G   A   .   PASS    AF=0.212918 ES:SE:LP:AF:ID  -9.99678e-05:0.000163732:0.267606:0.212918:rs28570054
1   835499  rs4422948   A   G   .   PASS    AF=0.241155 ES:SE:LP:AF:ID  -9.09879e-05:0.000156474:0.251812:0.241155:rs4422948
1   836529  rs28731045  C   G   .   PASS    AF=0.213538 ES:SE:LP:AF:ID  -0.000103333:0.00016349:0.275724:0.213538:rs28731045
1   836896  rs28705752  T   C   .   PASS    AF=0.269503 ES:SE:LP:AF:ID  -0.000156691:0.000150986:0.522879:0.269503:rs28705752
1   836924  rs72890788  G   A   .   PASS    AF=0.213508 ES:SE:LP:AF:ID  -9.9634e-05:0.00016351:0.267606:0.213508:rs72890788
1   838387  rs4970384   T   C   .   PASS    AF=0.214583 ES:SE:LP:AF:ID  -9.03957e-05:0.000163195:0.236572:0.214583:rs4970384
1   838555  rs4970383   C   A   .   PASS    AF=0.246197 ES:SE:LP:AF:ID  -0.000189384:0.000155399:0.657577:0.246197:rs4970383
1   839103  rs28562941  A   G   .   PASS    AF=0.27001  ES:SE:LP:AF:ID  -0.000161387:0.000151093:0.537602:0.27001:rs28562941
1   840753  rs4970382   T   C   .   PASS    AF=0.400106 ES:SE:LP:AF:ID  -2.51684e-05:0.000136617:0.0705811:0.400106:rs4970382
1   841085  rs1574243   C   G   .   PASS    AF=0.237094 ES:SE:LP:AF:ID  -0.000208983:0.000158686:0.721246:0.237094:rs1574243
1   842013  rs7419119   T   G   .   PASS    AF=0.215384 ES:SE:LP:AF:ID  -0.000135502:0.000163304:0.387216:0.215384:rs7419119
1   842362  rs28540380  C   T   .   PASS    AF=0.235321 ES:SE:LP:AF:ID  -1.9204e-05:0.00016106:0.0409586:0.235321:rs28540380
1   843405  rs11516185  A   G   .   PASS    AF=0.362599 ES:SE:LP:AF:ID  0.000295004:0.000169594:1.08619:0.362599:rs11516185
1   845283  rs7366404   G   T   .   PASS    AF=0.814502 ES:SE:LP:AF:ID  -0.000106095:0.000172761:0.267606:0.814502:rs7366404
1   845635  rs117086422 C   T   .   PASS    AF=0.20543  ES:SE:LP:AF:ID  -0.000160723:0.000166081:0.481486:0.20543:rs117086422
1   845938  rs57760052  G   A   .   PASS    AF=0.210865 ES:SE:LP:AF:ID  -0.000153666:0.000164483:0.455932:0.210865:rs57760052
1   846078  rs28612348  C   T   .   PASS    AF=0.196786 ES:SE:LP:AF:ID  -0.000113037:0.000168626:0.30103:0.196786:rs28612348
1   846338  rs4970334   A   G   .   PASS    AF=0.813832 ES:SE:LP:AF:ID  -0.000121062:0.000172614:0.318759:0.813832:rs4970334
1   846398  rs58781670  G   A   .   PASS    AF=0.204448 ES:SE:LP:AF:ID  -0.000160285:0.000166649:0.468521:0.204448:rs58781670
1   846489  rs4970333   T   C   .   PASS    AF=0.813925 ES:SE:LP:AF:ID  -0.000120371:0.000172733:0.309804:0.813925:rs4970333
1   846808  rs4475691   C   T   .   PASS    AF=0.198378 ES:SE:LP:AF:ID  -0.000124954:0.000168148:0.337242:0.198378:rs4475691
1   846864  rs950122    G   C   .   PASS    AF=0.198107 ES:SE:LP:AF:ID  -0.000132391:0.000168052:0.366532:0.198107:rs950122