Summary

Summary {data-width=650}

Manhattan plot

manhattan_plot

QQ plot

qq_plot

AF plot

af_plot

P-Z plot

pz_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=ieu-b-5111,TotalVariants=7605137,VariantsNotRead=0,HarmonisedVariants=7605137,VariantsNotHarmonised=0,SwitchedAlleles=7605137,NormalisedVariants=0,TotalControls=312463,TotalCases=39543,StudyType=CaseControl>",
    "contig": "<ID=1,length=249250621,assembly=HG19/GRCh37>",
    "contig.1": "<ID=2,length=243199373,assembly=HG19/GRCh37>",
    "contig.2": "<ID=3,length=198022430,assembly=HG19/GRCh37>",
    "contig.3": "<ID=4,length=191154276,assembly=HG19/GRCh37>",
    "contig.4": "<ID=5,length=180915260,assembly=HG19/GRCh37>",
    "contig.5": "<ID=6,length=171115067,assembly=HG19/GRCh37>",
    "contig.6": "<ID=7,length=159138663,assembly=HG19/GRCh37>",
    "contig.7": "<ID=8,length=146364022,assembly=HG19/GRCh37>",
    "contig.8": "<ID=9,length=141213431,assembly=HG19/GRCh37>",
    "contig.9": "<ID=10,length=135534747,assembly=HG19/GRCh37>",
    "contig.10": "<ID=11,length=135006516,assembly=HG19/GRCh37>",
    "contig.11": "<ID=12,length=133851895,assembly=HG19/GRCh37>",
    "contig.12": "<ID=13,length=115169878,assembly=HG19/GRCh37>",
    "contig.13": "<ID=14,length=107349540,assembly=HG19/GRCh37>",
    "contig.14": "<ID=15,length=102531392,assembly=HG19/GRCh37>",
    "contig.15": "<ID=16,length=90354753,assembly=HG19/GRCh37>",
    "contig.16": "<ID=17,length=81195210,assembly=HG19/GRCh37>",
    "contig.17": "<ID=18,length=78077248,assembly=HG19/GRCh37>",
    "contig.18": "<ID=19,length=59128983,assembly=HG19/GRCh37>",
    "contig.19": "<ID=20,length=63025520,assembly=HG19/GRCh37>",
    "contig.20": "<ID=21,length=48129895,assembly=HG19/GRCh37>",
    "contig.21": "<ID=22,length=51304566,assembly=HG19/GRCh37>",
    "contig.22": "<ID=X,length=155270560,assembly=HG19/GRCh37>",
    "contig.23": "<ID=Y,length=59373566,assembly=HG19/GRCh37>",
    "contig.24": "<ID=MT,length=16569,assembly=HG19/GRCh37>",
    "contig.25": "<ID=GL000207.1,length=4262,assembly=HG19/GRCh37>",
    "contig.26": "<ID=GL000226.1,length=15008,assembly=HG19/GRCh37>",
    "contig.27": "<ID=GL000229.1,length=19913,assembly=HG19/GRCh37>",
    "contig.28": "<ID=GL000231.1,length=27386,assembly=HG19/GRCh37>",
    "contig.29": "<ID=GL000210.1,length=27682,assembly=HG19/GRCh37>",
    "contig.30": "<ID=GL000239.1,length=33824,assembly=HG19/GRCh37>",
    "contig.31": "<ID=GL000235.1,length=34474,assembly=HG19/GRCh37>",
    "contig.32": "<ID=GL000201.1,length=36148,assembly=HG19/GRCh37>",
    "contig.33": "<ID=GL000247.1,length=36422,assembly=HG19/GRCh37>",
    "contig.34": "<ID=GL000245.1,length=36651,assembly=HG19/GRCh37>",
    "contig.35": "<ID=GL000197.1,length=37175,assembly=HG19/GRCh37>",
    "contig.36": "<ID=GL000203.1,length=37498,assembly=HG19/GRCh37>",
    "contig.37": "<ID=GL000246.1,length=38154,assembly=HG19/GRCh37>",
    "contig.38": "<ID=GL000249.1,length=38502,assembly=HG19/GRCh37>",
    "contig.39": "<ID=GL000196.1,length=38914,assembly=HG19/GRCh37>",
    "contig.40": "<ID=GL000248.1,length=39786,assembly=HG19/GRCh37>",
    "contig.41": "<ID=GL000244.1,length=39929,assembly=HG19/GRCh37>",
    "contig.42": "<ID=GL000238.1,length=39939,assembly=HG19/GRCh37>",
    "contig.43": "<ID=GL000202.1,length=40103,assembly=HG19/GRCh37>",
    "contig.44": "<ID=GL000234.1,length=40531,assembly=HG19/GRCh37>",
    "contig.45": "<ID=GL000232.1,length=40652,assembly=HG19/GRCh37>",
    "contig.46": "<ID=GL000206.1,length=41001,assembly=HG19/GRCh37>",
    "contig.47": "<ID=GL000240.1,length=41933,assembly=HG19/GRCh37>",
    "contig.48": "<ID=GL000236.1,length=41934,assembly=HG19/GRCh37>",
    "contig.49": "<ID=GL000241.1,length=42152,assembly=HG19/GRCh37>",
    "contig.50": "<ID=GL000243.1,length=43341,assembly=HG19/GRCh37>",
    "contig.51": "<ID=GL000242.1,length=43523,assembly=HG19/GRCh37>",
    "contig.52": "<ID=GL000230.1,length=43691,assembly=HG19/GRCh37>",
    "contig.53": "<ID=GL000237.1,length=45867,assembly=HG19/GRCh37>",
    "contig.54": "<ID=GL000233.1,length=45941,assembly=HG19/GRCh37>",
    "contig.55": "<ID=GL000204.1,length=81310,assembly=HG19/GRCh37>",
    "contig.56": "<ID=GL000198.1,length=90085,assembly=HG19/GRCh37>",
    "contig.57": "<ID=GL000208.1,length=92689,assembly=HG19/GRCh37>",
    "contig.58": "<ID=GL000191.1,length=106433,assembly=HG19/GRCh37>",
    "contig.59": "<ID=GL000227.1,length=128374,assembly=HG19/GRCh37>",
    "contig.60": "<ID=GL000228.1,length=129120,assembly=HG19/GRCh37>",
    "contig.61": "<ID=GL000214.1,length=137718,assembly=HG19/GRCh37>",
    "contig.62": "<ID=GL000221.1,length=155397,assembly=HG19/GRCh37>",
    "contig.63": "<ID=GL000209.1,length=159169,assembly=HG19/GRCh37>",
    "contig.64": "<ID=GL000218.1,length=161147,assembly=HG19/GRCh37>",
    "contig.65": "<ID=GL000220.1,length=161802,assembly=HG19/GRCh37>",
    "contig.66": "<ID=GL000213.1,length=164239,assembly=HG19/GRCh37>",
    "contig.67": "<ID=GL000211.1,length=166566,assembly=HG19/GRCh37>",
    "contig.68": "<ID=GL000199.1,length=169874,assembly=HG19/GRCh37>",
    "contig.69": "<ID=GL000217.1,length=172149,assembly=HG19/GRCh37>",
    "contig.70": "<ID=GL000216.1,length=172294,assembly=HG19/GRCh37>",
    "contig.71": "<ID=GL000215.1,length=172545,assembly=HG19/GRCh37>",
    "contig.72": "<ID=GL000205.1,length=174588,assembly=HG19/GRCh37>",
    "contig.73": "<ID=GL000219.1,length=179198,assembly=HG19/GRCh37>",
    "contig.74": "<ID=GL000224.1,length=179693,assembly=HG19/GRCh37>",
    "contig.75": "<ID=GL000223.1,length=180455,assembly=HG19/GRCh37>",
    "contig.76": "<ID=GL000195.1,length=182896,assembly=HG19/GRCh37>",
    "contig.77": "<ID=GL000212.1,length=186858,assembly=HG19/GRCh37>",
    "contig.78": "<ID=GL000222.1,length=186861,assembly=HG19/GRCh37>",
    "contig.79": "<ID=GL000200.1,length=187035,assembly=HG19/GRCh37>",
    "contig.80": "<ID=GL000193.1,length=189789,assembly=HG19/GRCh37>",
    "contig.81": "<ID=GL000194.1,length=191469,assembly=HG19/GRCh37>",
    "contig.82": "<ID=GL000225.1,length=211173,assembly=HG19/GRCh37>",
    "contig.83": "<ID=GL000192.1,length=547496,assembly=HG19/GRCh37>",
    "Gwas2VCF_command": "--data /data/cromwell-executions/qc/16477d41-8e8c-459b-b328-e346df42e7c7/call-vcf/inputs/-261021478/upload.txt.gz --id ieu-b-5111 --json /data/cromwell-executions/qc/16477d41-8e8c-459b-b328-e346df42e7c7/call-vcf/inputs/-261021478/ieu-b-5111_data.json --ref /data/cromwell-executions/qc/16477d41-8e8c-459b-b328-e346df42e7c7/call-vcf/inputs/1899004205/human_g1k_v37.fasta --dbsnp /data/cromwell-executions/qc/16477d41-8e8c-459b-b328-e346df42e7c7/call-vcf/inputs/-307190728/dbsnp.v153.b37.vcf.gz --out /data/igd/ieu-b-5111/ieu-b-5111.vcf.gz --alias alias.txt --cohort_cases 39543 --cohort_controls 312463; 1.3.0",
    "file_date": "2023-08-28T20:26:43.233187",
    "bcftools_viewVersion": "1.9+htslib-1.9",
    "bcftools_viewCommand": "view -h /data/cromwell-executions/qc/16477d41-8e8c-459b-b328-e346df42e7c7/call-report/inputs/-261021478/ieu-b-5111.vcf.gz; Date=Mon Aug 28 20:47:12 2023"
}

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 /data/cromwell-executions/qc/16477d41-8e8c-459b-b328-e346df42e7c7/call-ldsc/inputs/-261021478/ieu-b-5111.vcf.gz \
--ref-ld-chr /data/ref/eur_w_ld_chr/ \
--out /data/igd/ieu-b-5111/ldsc.txt \
--w-ld-chr /data/ref/eur_w_ld_chr/ 

Beginning analysis at Mon Aug 28 20:40:13 2023
Reading summary statistics from /data/cromwell-executions/qc/16477d41-8e8c-459b-b328-e346df42e7c7/call-ldsc/inputs/-261021478/ieu-b-5111.vcf.gz ...
Read summary statistics for 7605036 SNPs.
Dropped 26529 SNPs with duplicated rs numbers.
Reading reference panel LD Score from /data/ref/eur_w_ld_chr/[1-22] ... (ldscore_fromlist)
Read reference panel LD Scores for 1290028 SNPs.
Removing partitioned LD Scores with zero variance.
Reading regression weight LD Score from /data/ref/eur_w_ld_chr/[1-22] ... (ldscore_fromlist)
Read regression weight LD Scores for 1290028 SNPs.
After merging with reference panel LD, 1212790 SNPs remain.
After merging with regression SNP LD, 1212790 SNPs remain.
Using two-step estimator with cutoff at 30.
Traceback (most recent call last):
  File "./ldsc/ldsc.py", line 647, in <module>
    sumstats.estimate_h2(args, log)
  File "/ldsc/ldscore/sumstats.py", line 363, in estimate_h2
    twostep=args.two_step, old_weights=old_weights)
  File "/ldsc/ldscore/regressions.py", line 346, in __init__
    slow=slow, step1_ii=step1_ii, old_weights=old_weights)
  File "/ldsc/ldscore/regressions.py", line 190, in __init__
    x1, yp1, update_func1, n_blocks, slow=slow, w=initial_w1)
  File "/ldsc/ldscore/irwls.py", line 58, in __init__
    n, p = jk._check_shape(x, y)
  File "/ldsc/ldscore/jackknife.py", line 32, in _check_shape
    raise ValueError('More dimensions than datapoints.')
ValueError: More dimensions than datapoints.

Analysis finished at Mon Aug 28 20:41:16 2023
Total time elapsed: 1.0m:3.13s

QC metrics

Metrics

Metrics

{
    "af_correlation": "NA",
    "inflation_factor": 1.3602,
    "mean_EFFECT": -0.9992,
    "n": "-Inf",
    "n_snps": 7605137,
    "n_clumped_hits": 45,
    "n_p_sig": 3091,
    "n_mono": 0,
    "n_ns": 0,
    "n_mac": 0,
    "is_snpid_unique": false,
    "n_miss_EFFECT": 0,
    "n_miss_SE": 0,
    "n_miss_PVAL": 0,
    "n_miss_AF": 7605137,
    "n_miss_AF_reference": 74695,
    "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": 1212790,
    "ldsc_nsnp_merge_regression_ld": 1212790,
    "ldsc_observed_scale_h2_beta": "NA",
    "ldsc_observed_scale_h2_se": "NA",
    "ldsc_intercept_beta": "NA",
    "ldsc_intercept_se": "NA",
    "ldsc_lambda_gc": "NA",
    "ldsc_mean_chisq": "NA",
    "ldsc_ratio": "NA"
}

Flags

name value
af_correlation NA
inflation_factor TRUE
n TRUE
is_snpid_non_unique TRUE
mean_EFFECT_nonfinite FALSE
mean_EFFECT_05 TRUE
mean_EFFECT_01 TRUE
mean_chisq NA
n_p_sig TRUE
miss_EFFECT FALSE
miss_SE FALSE
miss_PVAL FALSE
ldsc_ratio NA
ldsc_intercept_beta NA
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 69 0.9999909 3 58 0 7600322 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 AF 7605137 0.0000000 NA NA NA NA NA NaN : NA NA NA NA NA NA NA NA
logical N 7605137 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 8.659080e+00 5.762495e+00 1.00000 4.000000e+00 8.000000e+00 1.300000e+01 2.200000e+01 ▇▅▅▂▂
numeric POS 0 1.0000000 NA NA NA NA NA NA NA 7.869335e+07 5.645057e+07 828.00000 3.220139e+07 6.915497e+07 1.146046e+08 2.492225e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -9.992005e-01 2.532270e-02 -1.51589 -1.011670e+00 -9.993000e-01 -9.867900e-01 -6.131800e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.953750e-02 1.240000e-02 0.00880 1.040000e-02 1.410000e-02 2.460000e-02 1.619000e-01 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.495368e-01 3.019331e-01 0.00000 1.752000e-01 4.315002e-01 7.105997e-01 1.000000e+00 ▇▆▅▅▅
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 0.000000e+00 1.000000e-07 0.00000 0.000000e+00 0.000000e+00 0.000000e+00 1.522000e-04 ▇▁▁▁▁
numeric AF_reference 74695 0.9901783 NA NA NA NA NA NA NA 2.564199e-01 2.526606e-01 0.00000 4.912140e-02 1.675320e-01 4.007590e-01 1.000000e+00 ▇▃▂▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 546697 rs12025928 A G -1.02286 0.0431 0.6004004 0 NA NA NA
1 693731 rs12238997 A G -1.00000 0.0197 0.9996000 0 NA 0.1417730 NA
1 705882 rs72631875 G A -1.02460 0.0471 0.6053994 0 NA 0.0315495 NA
1 706368 rs55727773 A G -0.99561 0.0189 0.8162000 0 NA 0.2751600 NA
1 717587 rs144155419 G A -0.99074 0.0717 0.8967001 0 NA 0.0045927 NA
1 722670 rs116030099 T C -1.00531 0.0310 0.8631999 0 NA 0.0413339 NA
1 723891 rs2977670 G C -0.98030 0.0502 0.6909999 0 NA 0.7799520 NA
1 729679 rs4951859 C G -0.99551 0.0166 0.7881992 0 NA 0.6399760 NA
1 730087 rs148120343 T C -1.02470 0.0292 0.4043002 0 NA 0.0127796 NA
1 731718 rs142557973 T C -1.00260 0.0189 0.8910999 0 NA 0.1543530 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219387 rs9616832 T C -1.03365 0.0232 0.1540001 0 NA 0.0654952 NA
22 51219704 rs147475742 G A -1.05295 0.0304 0.0902194 0 NA 0.0473243 NA
22 51220517 rs9616980 C G -1.09210 0.0784 0.2616002 0 NA 0.0027955 NA
22 51221190 rs369304721 G A -1.03272 0.0301 0.2853002 0 NA NA NA
22 51221731 rs115055839 T C -1.03645 0.0234 0.1262999 0 NA 0.0625000 NA
22 51222100 rs114553188 G T -1.06908 0.0250 0.0076231 0 NA 0.0880591 NA
22 51223637 rs375798137 G A -1.07509 0.0271 0.0075480 0 NA 0.0788738 NA
22 51229805 rs9616985 T C -1.03552 0.0237 0.1401000 0 NA 0.0730831 NA
22 51232488 rs376461333 A G -1.09374 0.0468 0.0555098 0 NA NA NA
22 51237063 rs3896457 T C -0.97893 0.0134 0.1131001 0 NA 0.2050720 NA

bcf preview

1   546697  rs12025928  A   G   .   PASS    .   ES:SE:LP:ID -1.02286:0.0431:0.221559:rs12025928
1   693731  rs12238997  A   G   .   PASS    .   ES:SE:LP:ID -1:0.0197:0.000173753:rs12238997
1   705882  rs72631875  G   A   .   PASS    .   ES:SE:LP:ID -1.0246:0.0471:0.217958:rs72631875
1   706368  rs963699400 A   G   .   PASS    .   ES:SE:LP:ID -0.99561:0.0189:0.0882034:rs963699400
1   717587  rs144155419 G   A   .   PASS    .   ES:SE:LP:ID -0.99074:0.0717:0.0473528:rs144155419
1   722670  rs116030099 T   C   .   PASS    .   ES:SE:LP:ID -1.00531:0.031:0.0638886:rs116030099
1   723891  rs2977670   G   C   .   PASS    .   ES:SE:LP:ID -0.9803:0.0502:0.160522:rs2977670
1   729679  rs4951859   C   G   .   PASS    .   ES:SE:LP:ID -0.99551:0.0166:0.103364:rs4951859
1   730087  rs148120343 T   C   .   PASS    .   ES:SE:LP:ID -1.0247:0.0292:0.393296:rs148120343
1   731718  rs58276399  T   C   .   PASS    .   ES:SE:LP:ID -1.0026:0.0189:0.0500736:rs58276399
1   732989  rs369030935 C   T   .   PASS    .   ES:SE:LP:ID -0.99253:0.0636:0.0430636:rs369030935
1   734349  rs141242758 T   C   .   PASS    .   ES:SE:LP:ID -1.0007:0.0176:0.0138555:rs141242758
1   736289  rs1254887344    T   A   .   PASS    .   ES:SE:LP:ID -0.9986:0.0192:0.0253502:rs1254887344
1   752566  rs3094315   G   A   .   PASS    .   ES:SE:LP:ID -0.9992:0.0153:0.0176835:rs3094315
1   752721  rs3131972   A   G   .   PASS    .   ES:SE:LP:ID -0.99621:0.0151:0.0968557:rs3131972
1   753405  rs3115860   C   A   .   PASS    .   ES:SE:LP:ID -0.99352:0.0161:0.162412:rs3115860
1   753541  rs1388595942    G   A   .   PASS    .   ES:SE:LP:ID -1.00321:0.0161:0.0747911:rs1388595942
1   754182  rs3131969   A   G   .   PASS    .   ES:SE:LP:ID -0.99531:0.0161:0.113115:rs3131969
1   754192  rs3131968   A   G   .   PASS    .   ES:SE:LP:ID -0.99541:0.016:0.112833:rs3131968
1   754334  rs3131967   T   C   .   PASS    .   ES:SE:LP:ID -0.99541:0.016:0.112045:rs3131967
1   754503  rs3115859   G   A   .   PASS    .   ES:SE:LP:ID -0.9971:0.0151:0.0728864:rs3115859
1   754964  rs3131966   C   T   .   PASS    .   ES:SE:LP:ID -0.99641:0.0153:0.0893756:rs3131966
1   755775  rs3131965   A   G   .   PASS    .   ES:SE:LP:ID -0.99541:0.0165:0.108463:rs3131965
1   755890  rs1280367067    A   T   .   PASS    .   ES:SE:LP:ID -0.9989:0.0169:0.0221393:rs1280367067
1   756604  rs3131962   A   G   .   PASS    .   ES:SE:LP:ID -1.0001:0.016:0.00270101:rs3131962
1   757640  rs3115853   G   A   .   PASS    .   ES:SE:LP:ID -0.98827:0.017:0.314258:rs3115853
1   757734  rs1557551770    C   T   .   PASS    .   ES:SE:LP:ID -1.0009:0.016:0.0209983:rs1557551770
1   757936  rs1360886751    C   A   .   PASS    .   ES:SE:LP:ID -0.99332:0.0172:0.157017:rs1360886751
1   758144  rs3131956   A   G   .   PASS    .   ES:SE:LP:ID -0.99422:0.0162:0.142728:rs3131956
1   758626  rs3131954   C   T   .   PASS    .   ES:SE:LP:ID -0.99561:0.0162:0.105186:rs3131954
1   760912  rs1048488   C   T   .   PASS    .   ES:SE:LP:ID -1:0.0153:0.00113064:rs1048488
1   760998  rs148828841 C   A   .   PASS    .   ES:SE:LP:ID -0.9686:0.0591:0.229737:rs148828841
1   761147  rs3115850   T   C   .   PASS    .   ES:SE:LP:ID -1.0002:0.0155:0.00370728:rs3115850
1   761732  rs2286139   C   T   .   PASS    .   ES:SE:LP:ID -0.98946:0.0158:0.29895:rs2286139
1   763394  rs3115847   G   A   .   PASS    .   ES:SE:LP:ID -0.97902:0.0192:0.570086:rs3115847
1   766007  rs61768174  A   C   .   PASS    .   ES:SE:LP:ID -0.9975:0.0181:0.0502198:rs61768174
1   768253  rs2977608   A   C   .   PASS    .   ES:SE:LP:ID -0.9989:0.0131:0.0300718:rs2977608
1   768448  rs12562034  G   A   .   PASS    .   ES:SE:LP:ID -0.99193:0.0212:0.153168:rs12562034
1   769223  rs60320384  C   G   .   PASS    .   ES:SE:LP:ID -0.9998:0.0161:0.00326948:rs60320384
1   771823  rs2977605   T   C   .   PASS    .   ES:SE:LP:ID -0.99312:0.0162:0.173148:rs2977605
1   771967  rs59066358  G   A   .   PASS    .   ES:SE:LP:ID -1.0001:0.0162:0.00283213:rs59066358
1   772755  rs2905039   A   C   .   PASS    .   ES:SE:LP:ID -0.9989:0.0158:0.0242925:rs2905039
1   773106  rs115616822 G   A   .   PASS    .   ES:SE:LP:ID -0.94895:0.0946:0.237171:rs115616822
1   774760  rs1387547637    C   T   .   PASS    .   ES:SE:LP:ID -1.1116:0.0882:0.637706:rs1387547637
1   776546  rs12124819  A   G   .   PASS    .   ES:SE:LP:ID -1.00592:0.0183:0.126796:rs12124819
1   777122  rs2980319   A   T   .   PASS    .   ES:SE:LP:ID -0.9994:0.0159:0.0123784:rs2980319
1   777232  rs112618790 C   T   .   PASS    .   ES:SE:LP:ID -0.99084:0.0225:0.166343:rs112618790
1   778745  rs1055606   A   G   .   PASS    .   ES:SE:LP:ID -0.9988:0.0161:0.0255804:rs1055606
1   779322  rs4040617   A   G   .   PASS    .   ES:SE:LP:ID -0.9982:0.016:0.0418663:rs4040617
1   780785  rs2977612   T   A   .   PASS    .   ES:SE:LP:ID -0.99511:0.0158:0.120904:rs2977612