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IEU GWAS information

Trait: IgE levels .

name	value
id	EBI-a-GCST006358
trait	IgE levels
note
pmid	30053915
year	2018
author	Scepanovic P
population	European
sample_size	1000
build	HG19/GRCh37
group_name	public
access	public
nsnp	5278042
mr	1
batch	EBI-a

Report information

name	value
gwas_id	ebi-a-GCST006358
bcf_file	ebi-a-GCST006358.vcf.gz
qc_metrics_file	qc_metrics.json
metadata	metadata.json
refdata	1kg_v3_nomult.bcf
compile_time	2020-05-10 08:07 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.2": "<ID=EZ,Number=A,Type=Float,Description=\"Z-score provided if it was used to derive the EFFECT and SE fields\">",
    "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\">",
    "FORMAT.5": "<ID=NC,Number=A,Type=Float,Description=\"Number of cases used to estimate genetic effect\">",
    "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.\">",
    "INFO.2": "<ID=SwappedAlleles,Number=0,Type=Flag,Description=\"The REF and the ALT alleles have been swapped in liftover due to changes in the reference. It is possible that not all INFO annotations reflect this swap, and in the genotypes, only the GT, PL, and AD fields have been modified. You should check the TAGS_TO_REVERSE parameter that was used during the LiftOver to be sure.\">",
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    "META.4": "<ID=TotalControls,Number=1,Type=Integer,Description=\"Total number of controls in the association study\">",
    "META.5": "<ID=TotalVariants,Number=1,Type=Integer,Description=\"Total number of variants in input\">",
    "META.6": "<ID=VariantsNotHarmonised,Number=1,Type=Integer,Description=\"Total number of variants that could not be harmonised\">",
    "META.7": "<ID=VariantsNotRead,Number=1,Type=Integer,Description=\"Number of variants that could not be read\">",
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    "file_date": "2019-10-26T21:49:20.746333",
    "gwas_harmonisation_command": "--json /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST006358/EBI-a-GCST006358_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-GCST006358/EBI-a-GCST006358.vcf.gz -O z /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST006358/EBI-a-GCST006358_data.vcf.gz; Date=Sat Oct 26 22:00:05 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-GCST006358/ebi-a-GCST006358.vcf.gz; Date=Sun May 10 07:58:29 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-GCST006358/EBI-a-GCST006358.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-GCST006358/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 22:22:50 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ebi_gwas_import/processed/EBI-a-GCST006358/EBI-a-GCST006358.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 22:23:25 2019
Total time elapsed: 34.85s

QC metrics

Metrics

{
    "af_correlation": 0.9164,
    "inflation_factor": 1.0045,
    "mean_EFFECT": -0.0001,
    "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": "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	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	▇▅▅▂▂
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	▇▆▅▂▁
numeric	EFFECT	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	-7.640000e-05	3.791880e-02	-0.2586050	-2.370650e-02	-1.162000e-04	2.333590e-02	2.811720e-01	▁▁▇▁▁
numeric	SE	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	3.650900e-02	9.637800e-03	0.0251986	2.849460e-02	3.290910e-02	4.235320e-02	6.624440e-02	▇▃▂▂▁
numeric	PVAL	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	4.991635e-01	2.890274e-01	0.0000001	2.492294e-01	4.990344e-01	7.489210e-01	1.000000e+00	▇▇▇▇▇
numeric	PVAL_ztest	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	4.990021e-01	2.891242e-01	0.0000001	2.489507e-01	4.988757e-01	7.488524e-01	1.000000e+00	▇▇▇▇▇
numeric	AF	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	3.367567e-01	2.455678e-01	0.0500527	1.262580e-01	2.655940e-01	5.045270e-01	9.499470e-01	▇▃▂▂▂
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	▇▆▃▂▁

Head and tail

CHROM	POS	ID	REF	ALT	EFFECT	SE	PVAL	PVAL_ztest	AF	AF_reference	N
1	729679	rs4951859	C	G	-0.0220384	0.0386636	0.5688045	0.5686751	0.852616	0.639976	NA
1	731718	rs142557973	T	C	0.0102407	0.0434871	0.8138789	0.8138298	0.105634	0.154353	NA
1	734349	rs141242758	T	C	0.0186230	0.0442747	0.6741212	0.6740297	0.103119	0.152556	NA
1	736289	rs79010578	T	A	0.0443115	0.0420134	0.2918213	0.2915629	0.121227	0.139577	NA
1	752566	rs3094315	G	A	-0.0162810	0.0391272	0.6774247	0.6773336	0.860161	0.718251	NA
1	752721	rs3131972	A	G	-0.0162810	0.0391272	0.6774247	0.6773336	0.860161	0.653355	NA
1	753405	rs3115860	C	A	-0.0152836	0.0421508	0.7169865	0.7169087	0.886318	0.751797	NA
1	753541	rs2073813	G	A	0.0198878	0.0429120	0.6431407	0.6430379	0.110161	0.301917	NA
1	754182	rs3131969	A	G	-0.0140347	0.0421003	0.7389295	0.7388600	0.885815	0.678514	NA
1	754192	rs3131968	A	G	-0.0140347	0.0421003	0.7389295	0.7388600	0.885815	0.678514	NA

CHROM	POS	ID	REF	ALT	EFFECT	SE	PVAL	PVAL_ztest	AF	AF_reference	N
22	51217954	rs9616974	G	A	0.0613591	0.0529438	0.2467579	0.2464775	0.0729376	0.0621006	NA
22	51218224	rs9616975	C	A	0.0613591	0.0529438	0.2467579	0.2464775	0.0729376	0.0619010	NA
22	51218377	rs2519461	G	C	0.0588576	0.0528063	0.2652962	0.2650243	0.0734406	0.0826677	NA
22	51219006	rs28729663	G	A	0.0337548	0.0391471	0.3887577	0.3885470	0.1423540	0.2052720	NA
22	51219387	rs9616832	T	C	0.0613591	0.0529438	0.2467579	0.2464775	0.0729376	0.0654952	NA
22	51221731	rs115055839	T	C	0.0613591	0.0529438	0.2467579	0.2464775	0.0729376	0.0625000	NA
22	51222100	rs114553188	G	T	0.0245164	0.0578302	0.6717041	0.6716113	0.0588531	0.0880591	NA
22	51223637	rs375798137	G	A	0.0207562	0.0576324	0.7188131	0.7187364	0.0593561	0.0788738	NA
22	51229805	rs9616985	T	C	0.0624923	0.0530900	0.2394391	0.2391551	0.0724346	0.0730831	NA
22	51237063	rs3896457	T	C	0.0094006	0.0295157	0.7501774	0.7501101	0.2756540	0.2050720	NA

bcf preview

1   729679  rs4951859   C   G   .   PASS    AF=0.852616 ES:SE:LP:AF:ID  -0.0220384:0.0386636:0.245037:0.852616:rs4951859
1   731718  rs58276399  T   C   .   PASS    AF=0.105634 ES:SE:LP:AF:ID  0.0102407:0.0434871:0.0894402:0.105634:rs58276399
1   734349  rs141242758 T   C   .   PASS    AF=0.103119 ES:SE:LP:AF:ID  0.018623:0.0442747:0.171262:0.103119:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.121227 ES:SE:LP:AF:ID  0.0443115:0.0420134:0.534883:0.121227:rs79010578
1   752566  rs3094315   G   A   .   PASS    AF=0.860161 ES:SE:LP:AF:ID  -0.016281:0.0391272:0.169139:0.860161:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.860161 ES:SE:LP:AF:ID  -0.016281:0.0391272:0.169139:0.860161:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.886318 ES:SE:LP:AF:ID  -0.0152836:0.0421508:0.144489:0.886318:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.110161 ES:SE:LP:AF:ID  0.0198878:0.042912:0.191694:0.110161:rs2073813
1   754182  rs3131969   A   G   .   PASS    AF=0.885815 ES:SE:LP:AF:ID  -0.0140347:0.0421003:0.131397:0.885815:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.885815 ES:SE:LP:AF:ID  -0.0140347:0.0421003:0.131397:0.885815:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.886318 ES:SE:LP:AF:ID  -0.0152836:0.0421508:0.144489:0.886318:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.86167  ES:SE:LP:AF:ID  -0.0169708:0.0393523:0.176279:0.86167:rs3115859
1   754964  rs3131966   C   T   .   PASS    AF=0.86167  ES:SE:LP:AF:ID  -0.0169708:0.0393523:0.176279:0.86167:rs3131966
1   755775  rs3131965   A   G   .   PASS    AF=0.864185 ES:SE:LP:AF:ID  -0.0169161:0.039516:0.174778:0.864185:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.886318 ES:SE:LP:AF:ID  -0.0152836:0.0421508:0.144489:0.886318:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.886318 ES:SE:LP:AF:ID  -0.0152836:0.0421508:0.144489:0.886318:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.884306 ES:SE:LP:AF:ID  -0.0174494:0.0416272:0.170584:0.884306:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.886318 ES:SE:LP:AF:ID  -0.0152836:0.0421508:0.144489:0.886318:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.886318 ES:SE:LP:AF:ID  -0.0152836:0.0421508:0.144489:0.886318:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.886318 ES:SE:LP:AF:ID  -0.0152836:0.0421508:0.144489:0.886318:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.886318 ES:SE:LP:AF:ID  -0.0152836:0.0421508:0.144489:0.886318:rs3131954
1   760912  rs1048488   C   T   .   PASS    AF=0.863682 ES:SE:LP:AF:ID  -0.030817:0.0396306:0.359531:0.863682:rs1048488
1   761147  rs3115850   T   C   .   PASS    AF=0.863682 ES:SE:LP:AF:ID  -0.030817:0.0396306:0.359531:0.863682:rs3115850
1   761732  rs2286139   C   T   .   PASS    AF=0.881791 ES:SE:LP:AF:ID  -0.0200059:0.0415283:0.200593:0.881791:rs2286139
1   766007  rs61768174  A   C   .   PASS    AF=0.0885312    ES:SE:LP:AF:ID  0.0287698:0.0464815:0.270761:0.0885312:rs61768174
1   768253  rs2977608   A   C   .   PASS    AF=0.776157 ES:SE:LP:AF:ID  -0.025891:0.0329083:0.364908:0.776157:rs2977608
1   769223  rs60320384  C   G   .   PASS    AF=0.107646 ES:SE:LP:AF:ID  0.030774:0.043152:0.322467:0.107646:rs60320384
1   771823  rs2977605   T   C   .   PASS    AF=0.887324 ES:SE:LP:AF:ID  -0.0206812:0.0421945:0.204715:0.887324:rs2977605
1   771967  rs59066358  G   A   .   PASS    AF=0.108652 ES:SE:LP:AF:ID  0.0325192:0.0430307:0.346789:0.108652:rs59066358
1   772755  rs2905039   A   C   .   PASS    AF=0.887324 ES:SE:LP:AF:ID  -0.0206812:0.0421945:0.204715:0.887324:rs2905039
1   777122  rs2980319   A   T   .   PASS    AF=0.889336 ES:SE:LP:AF:ID  -0.0180586:0.0424316:0.173604:0.889336:rs2980319
1   778745  rs1055606   A   G   .   PASS    AF=0.10664  ES:SE:LP:AF:ID  0.0299264:0.0432822:0.310282:0.10664:rs1055606
1   779322  rs4040617   A   G   .   PASS    AF=0.107143 ES:SE:LP:AF:ID  0.0266093:0.0432203:0.26901:0.107143:rs4040617
1   780785  rs2977612   T   A   .   PASS    AF=0.885815 ES:SE:LP:AF:ID  -0.0089239:0.0414801:0.0810763:0.885815:rs2977612
1   781845  rs61768199  A   G   .   PASS    AF=0.0850101    ES:SE:LP:AF:ID  0.0342708:0.0476901:0.325553:0.0850101:rs61768199
1   785050  rs2905062   G   A   .   PASS    AF=0.885312 ES:SE:LP:AF:ID  0.00194598:0.0415749:0.0165194:0.885312:rs2905062
1   785989  rs2980300   T   C   .   PASS    AF=0.885312 ES:SE:LP:AF:ID  0.00194598:0.0415749:0.0165194:0.885312:rs2980300
1   787606  rs3863622   G   T   .   PASS    AF=0.10664  ES:SE:LP:AF:ID  0.0244545:0.0432813:0.242457:0.10664:rs3863622
1   787685  rs2905054   G   T   .   PASS    AF=0.881791 ES:SE:LP:AF:ID  -0.00314925:0.0410218:0.0274167:0.881791:rs2905054
1   787844  rs2905053   C   T   .   PASS    AF=0.887324 ES:SE:LP:AF:ID  -0.00836424:0.0419641:0.0746601:0.887324:rs2905053
1   790465  rs61768207  G   A   .   PASS    AF=0.084507 ES:SE:LP:AF:ID  0.0375443:0.0480665:0.361575:0.084507:rs61768207
1   791191  rs111818025 G   A   .   PASS    AF=0.10664  ES:SE:LP:AF:ID  0.0293544:0.0432775:0.302988:0.10664:rs111818025
1   791853  rs6684487   G   A   .   PASS    AF=0.0940644    ES:SE:LP:AF:ID  0.0476338:0.047514:0.49985:0.0940644:rs6684487
1   794332  rs12127425  G   A   .   PASS    AF=0.0870221    ES:SE:LP:AF:ID  0.0388271:0.0489492:0.368714:0.0870221:rs12127425
1   795222  rs12131377  C   G   .   PASS    AF=0.0855131    ES:SE:LP:AF:ID  0.0389522:0.0492726:0.367139:0.0855131:rs12131377
1   796100  rs12132398  C   T   .   PASS    AF=0.0865191    ES:SE:LP:AF:ID  0.0406785:0.0490759:0.390013:0.0865191:rs12132398
1   796375  rs12083781  T   C   .   PASS    AF=0.109155 ES:SE:LP:AF:ID  0.0442182:0.0424116:0.526672:0.109155:rs12083781
1   797281  rs76631953  G   C   .   PASS    AF=0.0860161    ES:SE:LP:AF:ID  0.0368896:0.0491709:0.343621:0.0860161:rs76631953
1   797325  rs111739932 T   C   .   PASS    AF=0.0860161    ES:SE:LP:AF:ID  0.0368896:0.0491709:0.343621:0.0860161:rs111739932
1   797440  rs58013264  T   C   .   PASS    AF=0.108652 ES:SE:LP:AF:ID  0.0402123:0.042468:0.463531:0.108652:rs58013264