IEU GWAS information

Trait: Anti-Toxoplasma gondii IgG levels.

name	value
id	EBI-a-GCST006349
trait	Anti-Toxoplasma gondii IgG levels
note
pmid	30053915
year	2018
author	Scepanovic P
population	European
sample_size	559
build	HG19/GRCh37
group_name	public
access	public
nsnp	5278042
mr	1
batch	EBI-a

Report information

name	value
gwas_id	ebi-a-GCST006349
bcf_file	ebi-a-GCST006349.vcf.gz
qc_metrics_file	qc_metrics.json
metadata	metadata.json
refdata	1kg_v3_nomult.bcf
compile_time	2020-05-07 20:51 BST
build_version	run_igd-58cc472-2020-05-06

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

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    "gwas_harmonisation_command": "--json /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST006349/EBI-a-GCST006349_data.json --ref /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/QC/genomes/hg38/hg38.fa; 1.1.1",
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    "bcftools_annotateCommand": "annotate -a /mnt/storage/home/gh13047/mr-eve/vcf-reference-datasets/dbsnp/dbsnp.v153.b37.vcf.gz -c ID -o /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST006349/EBI-a-GCST006349.vcf.gz -O z /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST006349/EBI-a-GCST006349_data.vcf.gz; Date=Sat Oct 26 21:54:26 2019",
    "bcftools_viewVersion": "1.9-74-g6af271c+htslib-1.9-64-g226b4a8",
    "bcftools_viewCommand": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ebi-a-GCST006349/ebi-a-GCST006349.vcf.gz; Date=Thu May  7 20:46:25 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/dev/ebi_gwas_import/processed/EBI-a-GCST006349/EBI-a-GCST006349.vcf.gz \
--ref-ld-chr /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST006349/ldsc.txt \
--snplist /data/ref/snplist.gz \
--w-ld-chr /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/ 

Beginning analysis at Sat Oct 26 22:18:23 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST006349/EBI-a-GCST006349.vcf.gz ...
and extracting SNPs specified in /data/ref/snplist.gz ...
Traceback (most recent call last):
  File "./ldsc/ldsc.py", line 647, in <module>
    sumstats.estimate_h2(args, log)
  File "/mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/igd-hpc-pipeline/resources/gwas_processing/ldsc/ldscore/sumstats.py", line 330, in estimate_h2
    args, log, args.h2)
  File "/mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/igd-hpc-pipeline/resources/gwas_processing/ldsc/ldscore/sumstats.py", line 246, in _read_ld_sumstats
    sumstats = _read_sumstats(args, log, fh, alleles=alleles, dropna=dropna)
  File "/mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/igd-hpc-pipeline/resources/gwas_processing/ldsc/ldscore/sumstats.py", line 165, in _read_sumstats
    sumstats = ps.sumstats(fh, alleles=alleles, dropna=dropna, slh=args.snplist)
  File "/mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/igd-hpc-pipeline/resources/gwas_processing/ldsc/ldscore/parse.py", line 85, in sumstats
    x = read_vcf(fh, alleles, slh)
  File "/mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/igd-hpc-pipeline/resources/gwas_processing/ldsc/ldscore/parse.py", line 161, in read_vcf
    with gzip.open(slh) as f:
  File "/mnt/storage/home/gh13047/mr-eve/conda/ldsc/lib/python2.7/gzip.py", line 34, in open
    return GzipFile(filename, mode, compresslevel)
  File "/mnt/storage/home/gh13047/mr-eve/conda/ldsc/lib/python2.7/gzip.py", line 94, in __init__
    fileobj = self.myfileobj = __builtin__.open(filename, mode or 'rb')
IOError: [Errno 2] No such file or directory: '/data/ref/snplist.gz'

Analysis finished at Sat Oct 26 22:18:59 2019
Total time elapsed: 35.95s

QC metrics

Metrics

{
    "af_correlation": 0.9149,
    "inflation_factor": 0.9993,
    "mean_EFFECT": 0.0002,
    "n": "-Inf",
    "n_snps": 5278042,
    "n_clumped_hits": 0,
    "n_p_sig": 0,
    "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": 0,
    "n_miss_AF_reference": 35252,
    "n_est": "NA",
    "ratio_se_n": "NA",
    "mean_diff": "NaN",
    "ratio_diff": "NaN",
    "sd_y_est1": "NA",
    "sd_y_est2": "NA",
    "r2_sum1": 0,
    "r2_sum2": 0,
    "r2_sum3": 0,
    "r2_sum4": 0,
    "ldsc_nsnp_merge_refpanel_ld": "NA",
    "ldsc_nsnp_merge_regression_ld": "NA",
    "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	FALSE
inflation_factor	FALSE
n	TRUE
is_snpid_non_unique	TRUE
mean_EFFECT_nonfinite	FALSE
mean_EFFECT_05	FALSE
mean_EFFECT_01	FALSE
mean_chisq	TRUE
n_p_sig	FALSE
miss_EFFECT	FALSE
miss_SE	FALSE
miss_PVAL	FALSE
ldsc_ratio	TRUE
ldsc_intercept_beta	TRUE
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	5265316	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	5265346	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	8.638495e+00	5.738675e+00	1.0000000	4.000000e+00	8.000000e+00	1.300000e+01	2.200000e+01	<U+2587><U+2585><U+2585><U+2582><U+2582>
numeric	POS	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	7.879344e+07	5.645854e+07	828.0000000	3.228537e+07	6.932693e+07	1.146312e+08	2.492385e+08	<U+2587><U+2586><U+2585><U+2582><U+2581>
numeric	EFFECT	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	2.093000e-04	3.243720e-02	-0.2224160	-1.990670e-02	6.470000e-05	2.026920e-02	2.238270e-01	<U+2581><U+2581><U+2587><U+2581><U+2581>
numeric	SE	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	3.128100e-02	8.355300e-03	0.0211973	2.441000e-02	2.816330e-02	3.629110e-02	6.599920e-02	<U+2587><U+2583><U+2582><U+2581><U+2581>
numeric	PVAL	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	5.002126e-01	2.891036e-01	0.0000003	2.497261e-01	5.001554e-01	7.511449e-01	1.000000e+00	<U+2587><U+2587><U+2587><U+2587><U+2587>
numeric	PVAL_ztest	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	4.999233e-01	2.892777e-01	0.0000002	2.492245e-01	4.998723e-01	7.510245e-01	9.999998e-01	<U+2587><U+2587><U+2587><U+2587><U+2587>
numeric	AF	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	3.367593e-01	2.458807e-01	0.0332136	1.265710e-01	2.657090e-01	5.044880e-01	9.649910e-01	<U+2587><U+2585><U+2583><U+2582><U+2582>
numeric	AF_reference	35252	0.9933049	NA	NA	NA	NA	NA	NA	NA	3.319970e-01	2.396594e-01	0.0001997	1.343850e-01	2.693690e-01	4.936100e-01	9.998000e-01	<U+2587><U+2586><U+2583><U+2582><U+2581>

Head and tail

CHROM	POS	ID	REF	ALT	EFFECT	SE	PVAL	PVAL_ztest	AF	AF_reference	N
1	729679	rs4951859	C	G	0.0024990	0.0329805	0.9396290	0.9396017	0.8554760	0.639976	NA
1	731718	rs142557973	T	C	-0.0114658	0.0368317	0.7556888	0.7555707	0.1023340	0.154353	NA
1	734349	rs141242758	T	C	-0.0111877	0.0378494	0.7676584	0.7675473	0.0996409	0.152556	NA
1	736289	rs79010578	T	A	0.0026124	0.0352837	0.9410049	0.9409778	0.1220830	0.139577	NA
1	752566	rs3094315	G	A	0.0089665	0.0335958	0.7896525	0.7895519	0.8590660	0.718251	NA
1	752721	rs3131972	A	G	0.0089665	0.0335958	0.7896525	0.7895519	0.8590660	0.653355	NA
1	753405	rs3115860	C	A	0.0249311	0.0360823	0.4898894	0.4895964	0.8877920	0.751797	NA
1	753541	rs2073813	G	A	-0.0309440	0.0364145	0.3958221	0.3954522	0.1086180	0.301917	NA
1	754182	rs3131969	A	G	0.0249311	0.0360823	0.4898894	0.4895964	0.8877920	0.678514	NA
1	754192	rs3131968	A	G	0.0249311	0.0360823	0.4898894	0.4895964	0.8877920	0.678514	NA

CHROM	POS	ID	REF	ALT	EFFECT	SE	PVAL	PVAL_ztest	AF	AF_reference	N
22	51217954	rs9616974	G	A	0.0137114	0.0464500	0.7679642	0.7678516	0.0673250	0.0621006	NA
22	51218224	rs9616975	C	A	0.0137114	0.0464500	0.7679642	0.7678516	0.0673250	0.0619010	NA
22	51218377	rs2519461	G	C	0.0203174	0.0462020	0.6602904	0.6601171	0.0682226	0.0826677	NA
22	51219006	rs28729663	G	A	-0.0018674	0.0339212	0.9561169	0.9560973	0.1346500	0.2052720	NA
22	51219387	rs9616832	T	C	0.0137114	0.0464500	0.7679642	0.7678516	0.0673250	0.0654952	NA
22	51221731	rs115055839	T	C	0.0137114	0.0464500	0.7679642	0.7678516	0.0673250	0.0625000	NA
22	51222100	rs114553188	G	T	-0.0267135	0.0483601	0.5809075	0.5806831	0.0610413	0.0880591	NA
22	51223637	rs375798137	G	A	-0.0267135	0.0483601	0.5809075	0.5806831	0.0610413	0.0788738	NA
22	51229805	rs9616985	T	C	0.0205693	0.0466972	0.6597615	0.6595877	0.0664273	0.0730831	NA
22	51237063	rs3896457	T	C	0.0043362	0.0252030	0.8634619	0.8633990	0.2701970	0.2050720	NA

bcf preview

1   729679  rs4951859   C   G   .   PASS    AF=0.855476 ES:SE:LP:AF:ID  0.00249895:0.0329805:0.0270436:0.855476:rs4951859
1   731718  rs58276399  T   C   .   PASS    AF=0.102334 ES:SE:LP:AF:ID  -0.0114658:0.0368317:0.121657:0.102334:rs58276399
1   734349  rs141242758 T   C   .   PASS    AF=0.0996409    ES:SE:LP:AF:ID  -0.0111877:0.0378494:0.114832:0.0996409:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.122083 ES:SE:LP:AF:ID  0.00261244:0.0352837:0.0264081:0.122083:rs79010578
1   752566  rs3094315   G   A   .   PASS    AF=0.859066 ES:SE:LP:AF:ID  0.00896647:0.0335958:0.102564:0.859066:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.859066 ES:SE:LP:AF:ID  0.00896647:0.0335958:0.102564:0.859066:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.887792 ES:SE:LP:AF:ID  0.0249311:0.0360823:0.309902:0.887792:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.108618 ES:SE:LP:AF:ID  -0.030944:0.0364145:0.4025:0.108618:rs2073813
1   754182  rs3131969   A   G   .   PASS    AF=0.887792 ES:SE:LP:AF:ID  0.0249311:0.0360823:0.309902:0.887792:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.887792 ES:SE:LP:AF:ID  0.0249311:0.0360823:0.309902:0.887792:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.887792 ES:SE:LP:AF:ID  0.0249311:0.0360823:0.309902:0.887792:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.859964 ES:SE:LP:AF:ID  0.00981648:0.0336434:0.113191:0.859964:rs3115859
1   754964  rs3131966   C   T   .   PASS    AF=0.859964 ES:SE:LP:AF:ID  0.00981648:0.0336434:0.113191:0.859964:rs3131966
1   755775  rs3131965   A   G   .   PASS    AF=0.861759 ES:SE:LP:AF:ID  0.00800567:0.0337693:0.0900745:0.861759:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.887792 ES:SE:LP:AF:ID  0.0249311:0.0360823:0.309902:0.887792:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.887792 ES:SE:LP:AF:ID  0.0249311:0.0360823:0.309902:0.887792:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.887792 ES:SE:LP:AF:ID  0.0249311:0.0360823:0.309902:0.887792:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.887792 ES:SE:LP:AF:ID  0.0249311:0.0360823:0.309902:0.887792:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.887792 ES:SE:LP:AF:ID  0.0249311:0.0360823:0.309902:0.887792:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.887792 ES:SE:LP:AF:ID  0.0249311:0.0360823:0.309902:0.887792:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.887792 ES:SE:LP:AF:ID  0.0249311:0.0360823:0.309902:0.887792:rs3131954
1   760912  rs1048488   C   T   .   PASS    AF=0.861759 ES:SE:LP:AF:ID  0.00716648:0.0340471:0.0791647:0.861759:rs1048488
1   761147  rs3115850   T   C   .   PASS    AF=0.861759 ES:SE:LP:AF:ID  0.00716648:0.0340471:0.0791647:0.861759:rs3115850
1   761732  rs2286139   C   T   .   PASS    AF=0.884201 ES:SE:LP:AF:ID  0.0245811:0.035725:0.308298:0.884201:rs2286139
1   766007  rs61768174  A   C   .   PASS    AF=0.0861759    ES:SE:LP:AF:ID  -0.0217129:0.0400646:0.230568:0.0861759:rs61768174
1   768253  rs2977608   A   C   .   PASS    AF=0.780969 ES:SE:LP:AF:ID  0.0148627:0.028583:0.219478:0.780969:rs2977608
1   769223  rs60320384  C   G   .   PASS    AF=0.106822 ES:SE:LP:AF:ID  -0.0263039:0.0366029:0.325437:0.106822:rs60320384
1   771823  rs2977605   T   C   .   PASS    AF=0.889587 ES:SE:LP:AF:ID  0.0264896:0.0362566:0.332243:0.889587:rs2977605
1   771967  rs59066358  G   A   .   PASS    AF=0.106822 ES:SE:LP:AF:ID  -0.0263039:0.0366029:0.325437:0.106822:rs59066358
1   772755  rs2905039   A   C   .   PASS    AF=0.889587 ES:SE:LP:AF:ID  0.0264896:0.0362566:0.332243:0.889587:rs2905039
1   777122  rs2980319   A   T   .   PASS    AF=0.890485 ES:SE:LP:AF:ID  0.0282518:0.0363397:0.359285:0.890485:rs2980319
1   778745  rs1055606   A   G   .   PASS    AF=0.105925 ES:SE:LP:AF:ID  -0.0281013:0.0366898:0.352563:0.105925:rs1055606
1   779322  rs4040617   A   G   .   PASS    AF=0.106822 ES:SE:LP:AF:ID  -0.0317559:0.0365878:0.41363:0.106822:rs4040617
1   780785  rs2977612   T   A   .   PASS    AF=0.886894 ES:SE:LP:AF:ID  0.0326003:0.0356881:0.442024:0.886894:rs2977612
1   781845  rs61768199  A   G   .   PASS    AF=0.0834829    ES:SE:LP:AF:ID  -0.0178773:0.0404666:0.181231:0.0834829:rs61768199
1   785050  rs2905062   G   A   .   PASS    AF=0.888689 ES:SE:LP:AF:ID  0.0339539:0.0361748:0.45799:0.888689:rs2905062
1   785989  rs2980300   T   C   .   PASS    AF=0.888689 ES:SE:LP:AF:ID  0.0339539:0.0361748:0.45799:0.888689:rs2980300
1   787606  rs3863622   G   T   .   PASS    AF=0.106822 ES:SE:LP:AF:ID  -0.0317559:0.0365878:0.41363:0.106822:rs3863622
1   787685  rs2905054   G   T   .   PASS    AF=0.885099 ES:SE:LP:AF:ID  0.0296815:0.0355433:0.393581:0.885099:rs2905054
1   787844  rs2905053   C   T   .   PASS    AF=0.890485 ES:SE:LP:AF:ID  0.0282518:0.0363397:0.359285:0.890485:rs2905053
1   790465  rs61768207  G   A   .   PASS    AF=0.0816876    ES:SE:LP:AF:ID  -0.0357956:0.0411588:0.414712:0.0816876:rs61768207
1   791191  rs111818025 G   A   .   PASS    AF=0.106822 ES:SE:LP:AF:ID  -0.0317559:0.0365878:0.41363:0.106822:rs111818025
1   791853  rs6684487   G   A   .   PASS    AF=0.0924596    ES:SE:LP:AF:ID  0.0206454:0.0407197:0.213001:0.0924596:rs6684487
1   794332  rs12127425  G   A   .   PASS    AF=0.0816876    ES:SE:LP:AF:ID  0.0219393:0.0432766:0.212973:0.0816876:rs12127425
1   795222  rs12131377  C   G   .   PASS    AF=0.0816876    ES:SE:LP:AF:ID  0.0219393:0.0432766:0.212973:0.0816876:rs12131377
1   796100  rs12132398  C   T   .   PASS    AF=0.0816876    ES:SE:LP:AF:ID  0.0219393:0.0432766:0.212973:0.0816876:rs12132398
1   796375  rs12083781  T   C   .   PASS    AF=0.105027 ES:SE:LP:AF:ID  -0.0398523:0.0368137:0.553638:0.105027:rs12083781
1   797281  rs76631953  G   C   .   PASS    AF=0.0816876    ES:SE:LP:AF:ID  0.0219393:0.0432766:0.212973:0.0816876:rs76631953
1   797325  rs111739932 T   C   .   PASS    AF=0.0816876    ES:SE:LP:AF:ID  0.0219393:0.0432766:0.212973:0.0816876:rs111739932
1   797440  rs58013264  T   C   .   PASS    AF=0.105027 ES:SE:LP:AF:ID  -0.0398523:0.0368137:0.553638:0.105027:rs58013264

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bcf preview