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IEU GWAS information

Trait: Bone mineral density (paediatric, total body less head).

name	value
id	EBI-a-GCST004772
trait	Bone mineral density (paediatric, total body less head)
note
pmid	28743860
year	2017
author	Medina-Gomez C
population	Mixed
sample_size	8327
build	HG19/GRCh37
group_name	public
access	public
nsnp	2272477
mr	1
batch	EBI-a

Report information

name	value
gwas_id	ebi-a-GCST004772
bcf_file	ebi-a-GCST004772.vcf.gz
qc_metrics_file	qc_metrics.json
metadata	metadata.json
refdata	1kg_v3_nomult.bcf
compile_time	2020-05-10 01:59 BST
build_version	run_igd-8f4f4c9-2020-05-09

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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    "FORMAT.3": "<ID=ID,Number=1,Type=String,Description=\"Study variant identifier\">",
    "FORMAT.4": "<ID=LP,Number=A,Type=Float,Description=\"-log10 p-value for effect estimate\">",
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    "FORMAT.6": "<ID=SE,Number=A,Type=Float,Description=\"Standard error of effect size estimate\">",
    "FORMAT.7": "<ID=SI,Number=A,Type=Float,Description=\"Accuracy score of summary data imputation\">",
    "FORMAT.8": "<ID=SS,Number=A,Type=Float,Description=\"Sample size used to estimate genetic effect\">",
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    "INFO.1": "<ID=ReverseComplementedAlleles,Number=0,Type=Flag,Description=\"The REF and the ALT alleles have been reverse complemented in liftover since the mapping from the previous reference to the current one was on the negative strand.\">",
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    "file_date": "2019-10-27T07:19:15.177902",
    "gwas_harmonisation_command": "--json /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST004772/EBI-a-GCST004772_data.json --ref /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/QC/genomes/hg38/hg38.fa; 1.1.1",
    "reference": "file:/mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/QC/genomes/b37/human_g1k_v37.fasta",
    "bcftools_annotateVersion": "1.9-74-g6af271c+htslib-1.9-64-g226b4a8",
    "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-GCST004772/EBI-a-GCST004772.vcf.gz -O z /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST004772/EBI-a-GCST004772_data.vcf.gz; Date=Sun Oct 27 07:23:25 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-GCST004772/ebi-a-GCST004772.vcf.gz; Date=Sun May 10 01:56:14 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-GCST004772/EBI-a-GCST004772.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-GCST004772/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 Sun Oct 27 07:45:51 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST004772/EBI-a-GCST004772.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 Sun Oct 27 07:46:06 2019
Total time elapsed: 15.49s

QC metrics

Metrics

{
    "af_correlation": 0.937,
    "inflation_factor": 1.0433,
    "mean_EFFECT": 0.0001,
    "n": "-Inf",
    "n_snps": 2272474,
    "n_clumped_hits": 4,
    "n_p_sig": 91,
    "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": 132477,
    "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": "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	42	0	2149038	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	2266184	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	8.178545e+00	5.766661e+00	1.00000e+00	3.000000e+00	7.000000e+00	1.200000e+01	2.300000e+01	▇▅▅▂▁
numeric	POS	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	7.964869e+07	5.585911e+07	1.15230e+04	3.321000e+07	7.113542e+07	1.151876e+08	2.492107e+08	▇▇▅▂▁
numeric	EFFECT	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	1.349000e-04	2.060220e-02	-3.02464e-01	-1.212250e-02	6.950000e-05	1.232160e-02	3.805700e-01	▁▁▇▁▁
numeric	SE	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	1.899310e-02	7.148400e-03	1.37572e-02	1.442220e-02	1.632050e-02	2.072610e-02	1.230430e-01	▇▁▁▁▁
numeric	PVAL	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	4.926025e-01	2.914473e-01	0.00000e+00	2.377850e-01	4.908632e-01	7.452174e-01	9.999998e-01	▇▇▇▇▇
numeric	PVAL_ztest	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	4.924154e-01	2.914861e-01	0.00000e+00	2.375832e-01	4.906079e-01	7.450372e-01	9.999997e-01	▇▇▇▇▇
numeric	AF	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	3.752874e-01	2.525968e-01	5.02272e-02	1.541300e-01	3.162670e-01	5.627603e-01	9.497260e-01	▇▅▃▂▂
numeric	AF_reference	132477	0.9415418	NA	NA	NA	NA	NA	NA	NA	3.761291e-01	2.450486e-01	1.99700e-04	1.693290e-01	3.188900e-01	5.557110e-01	1.000000e+00	▇▇▅▃▂

Head and tail

CHROM	POS	ID	REF	ALT	EFFECT	SE	PVAL	PVAL_ztest	AF	AF_reference	N
1	750235	rs12138618	G	A	0.0348500	0.0367444	0.3375833	0.3429035	0.0783669	NA	NA
1	750235	rs12138618	G	A	0.0348500	0.0367444	0.3375833	0.3429035	0.0783669	NA	NA
1	752566	rs3094315	G	A	0.0627649	0.0239912	0.0095352	0.0088924	0.7716100	0.718251	NA
1	752566	rs3094315	G	A	0.0627649	0.0239912	0.0095352	0.0088924	0.7716100	NA	NA
1	754192	rs3131968	A	G	0.0482606	0.0235457	0.0380785	0.0403980	0.7698570	0.678514	NA
1	754192	rs3131968	A	G	0.0482606	0.0235457	0.0380785	0.0403980	0.7698570	NA	NA
1	768448	rs12562034	G	A	0.0038307	0.0308363	0.9001194	0.9011351	0.1039750	0.191893	NA
1	768448	rs12562034	G	A	0.0038307	0.0308363	0.9001194	0.9011351	0.1039750	NA	NA
1	777122	rs2980319	A	T	0.0648531	0.0240287	0.0069767	0.0069551	0.7831690	0.747204	NA
1	777122	rs2980319	A	T	0.0648531	0.0240287	0.0069767	0.0069551	0.7831690	NA	NA

CHROM	POS	ID	REF	ALT	EFFECT	SE	PVAL	PVAL_ztest	AF	AF_reference	N
22	51178090	rs2285395	G	A	-0.0102641	0.0389471	0.7923262	0.7921348	0.0609379	0.0666933	NA
22	51183017	rs58651371	C	T	-0.0267779	0.0159681	0.1012596	0.0935499	0.2703310	0.4137380	NA
22	51196164	rs8136603	A	T	-0.0044727	0.0280517	0.8616131	0.8733192	0.0802782	0.1427720	NA
22	51212875	rs2238837	A	C	0.0174938	0.0186258	0.3577643	0.3476157	0.3954860	0.3724040	NA
22	51216564	rs9616970	T	C	-0.0296358	0.0278083	0.2894343	0.2865512	0.1231530	0.1563500	NA
22	51217134	rs117417021	A	G	0.0168027	0.0182738	0.3686074	0.3578358	0.4417210	0.2671730	NA
22	51222100	rs114553188	G	T	-0.0029184	0.0388496	0.9392865	0.9401185	0.0611948	0.0880591	NA
22	51223637	rs375798137	G	A	-0.0033544	0.0390835	0.9324177	0.9316037	0.0578453	0.0788738	NA
22	51229805	rs9616985	T	C	-0.0356029	0.0375695	0.3395963	0.3433054	0.0581993	0.0730831	NA
23	118495837	rs12882977	G	A	-0.0114565	0.0138874	0.4136833	0.4093963	0.4621990	0.2307280	NA

bcf preview

1   750235  rs12138618  G   A   .   PASS    AF=0.0783669    ES:SE:LP:AF:ID  0.03485:0.0367444:0.471619:0.0783669:rs12138618
1   750235  rs12138618  G   A   .   PASS    AF=0.0783669    ES:SE:LP:AF:ID  0.03485:0.0367444:0.471619:0.0783669:rs12138618
1   752566  rs3094315   G   A   .   PASS    AF=0.77161  ES:SE:LP:AF:ID  0.0627649:0.0239912:2.02067:0.77161:rs3094315
1   752566  rs3094315   G   A   .   PASS    AF=0.77161  ES:SE:LP:AF:ID  0.0627649:0.0239912:2.02067:0.77161:rs3094315
1   754192  rs3131968   A   G   .   PASS    AF=0.769857 ES:SE:LP:AF:ID  0.0482606:0.0235457:1.41932:0.769857:rs3131968
1   754192  rs3131968   A   G   .   PASS    AF=0.769857 ES:SE:LP:AF:ID  0.0482606:0.0235457:1.41932:0.769857:rs3131968
1   768448  rs12562034  G   A   .   PASS    AF=0.103975 ES:SE:LP:AF:ID  0.00383072:0.0308363:0.0456999:0.103975:rs12562034
1   768448  rs12562034  G   A   .   PASS    AF=0.103975 ES:SE:LP:AF:ID  0.00383072:0.0308363:0.0456999:0.103975:rs12562034
1   777122  rs2980319   A   T   .   PASS    AF=0.783169 ES:SE:LP:AF:ID  0.0648531:0.0240287:2.15635:0.783169:rs2980319
1   777122  rs2980319   A   T   .   PASS    AF=0.783169 ES:SE:LP:AF:ID  0.0648531:0.0240287:2.15635:0.783169:rs2980319
1   779322  rs4040617   A   G   .   PASS    AF=0.209005 ES:SE:LP:AF:ID  -0.057922:0.0243678:1.75557:0.209005:rs4040617
1   779322  rs4040617   A   G   .   PASS    AF=0.209005 ES:SE:LP:AF:ID  -0.057922:0.0243678:1.75557:0.209005:rs4040617
1   785050  rs2905062   G   A   .   PASS    AF=0.751303 ES:SE:LP:AF:ID  0.0331457:0.0223009:0.866134:0.751303:rs2905062
1   785050  rs2905062   G   A   .   PASS    AF=0.751303 ES:SE:LP:AF:ID  0.0331457:0.0223009:0.866134:0.751303:rs2905062
1   785989  rs2980300   T   C   .   PASS    AF=0.748103 ES:SE:LP:AF:ID  0.0366881:0.0220844:1.00075:0.748103:rs2980300
1   785989  rs2980300   T   C   .   PASS    AF=0.748103 ES:SE:LP:AF:ID  0.0366881:0.0220844:1.00075:0.748103:rs2980300
1   798026  rs4951864   C   T   .   PASS    AF=0.904449 ES:SE:LP:AF:ID  -0.0133437:0.0320294:0.17037:0.904449:rs4951864
1   798026  rs4951864   C   T   .   PASS    AF=0.904449 ES:SE:LP:AF:ID  -0.0133437:0.0320294:0.17037:0.904449:rs4951864
1   798801  rs12132517  G   A   .   PASS    AF=0.0948258    ES:SE:LP:AF:ID  0.0103614:0.0320119:0.132514:0.0948258:rs12132517
1   798959  rs11240777  G   A   .   PASS    AF=0.245647 ES:SE:LP:AF:ID  -0.0742383:0.0493696:0.830079:0.245647:rs11240777
1   798959  rs11240777  G   A   .   PASS    AF=0.245647 ES:SE:LP:AF:ID  -0.0742383:0.0493696:0.830079:0.245647:rs11240777
1   962210  rs3128126   A   G   .   PASS    AF=0.406653 ES:SE:LP:AF:ID  -0.0310902:0.0148095:1.38811:0.406653:rs3128126
1   962210  rs3128126   A   G   .   PASS    AF=0.406653 ES:SE:LP:AF:ID  -0.0310902:0.0148095:1.38811:0.406653:rs3128126
1   990380  rs3121561   C   T   .   PASS    AF=0.249532 ES:SE:LP:AF:ID  -0.0216945:0.0162595:0.663544:0.249532:rs3121561
1   990380  rs3121561   C   T   .   PASS    AF=0.249532 ES:SE:LP:AF:ID  -0.0216945:0.0162595:0.663544:0.249532:rs3121561
1   998501  rs3813193   G   C   .   PASS    AF=0.159535 ES:SE:LP:AF:ID  -0.0269364:0.0194201:0.766416:0.159535:rs3813193
1   998501  rs3813193   G   C   .   PASS    AF=0.159535 ES:SE:LP:AF:ID  -0.0269364:0.0194201:0.766416:0.159535:rs3813193
1   1003629 rs4075116   C   T   .   PASS    AF=0.729794 ES:SE:LP:AF:ID  0.014884:0.0155164:0.453213:0.729794:rs4075116
1   1003629 rs4075116   C   T   .   PASS    AF=0.729794 ES:SE:LP:AF:ID  0.014884:0.0155164:0.453213:0.729794:rs4075116
1   1005806 rs3934834   C   T   .   PASS    AF=0.156184 ES:SE:LP:AF:ID  -0.0160008:0.0192712:0.388916:0.156184:rs3934834
1   1005806 rs3934834   C   T   .   PASS    AF=0.156184 ES:SE:LP:AF:ID  -0.0160008:0.0192712:0.388916:0.156184:rs3934834
1   1017170 rs3766193   C   G   .   PASS    AF=0.583504 ES:SE:LP:AF:ID  0.01263:0.0140606:0.426964:0.583504:rs3766193
1   1017170 rs3766193   C   G   .   PASS    AF=0.583504 ES:SE:LP:AF:ID  0.01263:0.0140606:0.426964:0.583504:rs3766193
1   1017197 rs3766192   C   T   .   PASS    AF=0.566086 ES:SE:LP:AF:ID  0.021564:0.0139961:0.89418:0.566086:rs3766192
1   1017197 rs3766192   C   T   .   PASS    AF=0.566086 ES:SE:LP:AF:ID  0.021564:0.0139961:0.89418:0.566086:rs3766192
1   1017587 rs3766191   C   T   .   PASS    AF=0.145397 ES:SE:LP:AF:ID  -0.018319:0.0196476:0.450125:0.145397:rs3766191
1   1017587 rs3766191   C   T   .   PASS    AF=0.145397 ES:SE:LP:AF:ID  -0.018319:0.0196476:0.450125:0.145397:rs3766191
1   1018562 rs9442371   C   T   .   PASS    AF=0.566461 ES:SE:LP:AF:ID  0.0233078:0.0139975:1.00119:0.566461:rs9442371
1   1018562 rs9442371   C   T   .   PASS    AF=0.566461 ES:SE:LP:AF:ID  0.0233078:0.0139975:1.00119:0.566461:rs9442371
1   1018704 rs9442372   A   G   .   PASS    AF=0.5916   ES:SE:LP:AF:ID  0.0119602:0.0140776:0.399149:0.5916:rs9442372
1   1018704 rs9442372   A   G   .   PASS    AF=0.5916   ES:SE:LP:AF:ID  0.0119602:0.0140776:0.399149:0.5916:rs9442372
1   1021346 rs10907177  A   G   .   PASS    AF=0.144537 ES:SE:LP:AF:ID  -0.0387237:0.0203286:1.23887:0.144537:rs10907177
1   1021346 rs10907177  A   G   .   PASS    AF=0.144537 ES:SE:LP:AF:ID  -0.0387237:0.0203286:1.23887:0.144537:rs10907177
1   1021415 rs3737728   A   G   .   PASS    AF=0.75021  ES:SE:LP:AF:ID  0.0101531:0.0158521:0.283915:0.75021:rs3737728
1   1021415 rs3737728   A   G   .   PASS    AF=0.75021  ES:SE:LP:AF:ID  0.0101531:0.0158521:0.283915:0.75021:rs3737728
1   1021583 rs10907178  A   C   .   PASS    AF=0.149181 ES:SE:LP:AF:ID  -0.0232158:0.0269206:0.418355:0.149181:rs10907178
1   1021695 rs9442398   A   G   .   PASS    AF=0.725319 ES:SE:LP:AF:ID  0.00266737:0.0154049:0.0665341:0.725319:rs9442398
1   1021695 rs9442398   A   G   .   PASS    AF=0.725319 ES:SE:LP:AF:ID  0.00266737:0.0154049:0.0665341:0.725319:rs9442398
1   1022037 rs6701114   C   T   .   PASS    AF=0.575493 ES:SE:LP:AF:ID  0.0181371:0.0140196:0.700881:0.575493:rs6701114
1   1022037 rs6701114   C   T   .   PASS    AF=0.575493 ES:SE:LP:AF:ID  0.0181371:0.0140196:0.700881:0.575493:rs6701114