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IEU GWAS information

Trait: Ever had known person concerned about, or recommend reduction of, alcohol consumption: No.

name	value
id	ukb-d-20405_0
trait	Ever had known person concerned about, or recommend reduction of, alcohol consumption: No
category	Binary
ncase	107474
ncontrol	10406
sample_size	117880
sex	Males and females
population	European
group_name	public
access	public
build	HG19/GRCh37
author	Neale lab
year	2018
mr	1
nsnp	13571660

Report information

name	value
gwas_id	ukb-d-20405_0
bcf_file	ukb-d-20405_0.vcf.gz
qc_metrics_file	qc_metrics.json
metadata	metadata.json
refdata	1kg_v3_nomult.bcf
compile_time	2020-05-10 23:12 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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}

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/ukb-d-import/processed/ukb-d-20405_0/ukb-d-20405_0.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/ukb-d-import/processed/ukb-d-20405_0/ldsc.txt \
--w-ld-chr /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/ 

Beginning analysis at Mon Nov 25 16:37:14 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-20405_0/ukb-d-20405_0.vcf.gz ...
Read summary statistics for 13571659 SNPs.
Dropped 12694 SNPs with duplicated rs numbers.
Reading reference panel LD Score from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/[1-22] ...
Read reference panel LD Scores for 1290028 SNPs.
Removing partitioned LD Scores with zero variance.
Reading regression weight LD Score from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/[1-22] ...
Read regression weight LD Scores for 1290028 SNPs.
After merging with reference panel LD, 1283502 SNPs remain.
After merging with regression SNP LD, 1283502 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0265 (0.0041)
Lambda GC: 1.0614
Mean Chi^2: 1.0709
Intercept: 1.0098 (0.0065)
Ratio: 0.1379 (0.0918)
Analysis finished at Mon Nov 25 16:39:32 2019
Total time elapsed: 2.0m:18.21s

QC metrics

Metrics

{
    "af_correlation": 0.9521,
    "inflation_factor": 1.0442,
    "mean_EFFECT": -4.8767e-07,
    "n": 117880,
    "n_snps": 13571659,
    "n_clumped_hits": 1,
    "n_p_sig": 8,
    "n_mono": 0,
    "n_ns": 1251349,
    "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": 564615,
    "n_est": 118302.9546,
    "ratio_se_n": 1.0018,
    "mean_diff": -1.584e-06,
    "ratio_diff": 4.6496,
    "sd_y_est1": 0.2812,
    "sd_y_est2": 0.2817,
    "r2_sum1": 0.0001,
    "r2_sum2": 0.0007,
    "r2_sum3": 0.0007,
    "r2_sum4": 0.0007,
    "ldsc_nsnp_merge_refpanel_ld": 1283502,
    "ldsc_nsnp_merge_regression_ld": 1283502,
    "ldsc_observed_scale_h2_beta": 0.0265,
    "ldsc_observed_scale_h2_se": 0.0041,
    "ldsc_intercept_beta": 1.0098,
    "ldsc_intercept_se": 0.0065,
    "ldsc_lambda_gc": 1.0614,
    "ldsc_mean_chisq": 1.0709,
    "ldsc_ratio": 0.1382
}

Flags

name	value
af_correlation	FALSE
inflation_factor	FALSE
n	FALSE
is_snpid_non_unique	TRUE
mean_EFFECT_nonfinite	FALSE
mean_EFFECT_05	FALSE
mean_EFFECT_01	FALSE
mean_chisq	FALSE
n_p_sig	FALSE
miss_EFFECT	FALSE
miss_SE	FALSE
miss_PVAL	FALSE
ldsc_ratio	FALSE
ldsc_intercept_beta	FALSE
n_clumped_hits	FALSE
r2_sum1	FALSE
r2_sum2	FALSE
r2_sum3	FALSE
r2_sum4	FALSE

Definitions

General metrics

af_correlation: Correlation coefficient between AF and AF_reference.
inflation_factor (lambda): Genomic inflation factor.
mean_EFFECT: Mean of EFFECT size.
n: Maximum value of reported sample size across all SNPs, \(n\).
n_clumped_hits: Number of clumped hits.
n_snps: Number of SNPs
n_p_sig: Number of SNPs with pvalue below 5e-8.
n_mono: Number of monomorphic (MAF == 1 or MAF == 0) SNPs.
n_ns: Number of SNPs with nonsense values:
- alleles other than A, C, G or T.
- P-values < 0 or > 1.
- negative or infinite standard errors (<= 0 or = Infinity).
- infinite beta estimates or allele frequencies < 0 or > 1.
n_mac: Number of cases where MAC (\(2 \times N \times MAF\)) is less than 6.
is_snpid_unique: true if the combination of ID REF ALT is unique and therefore no duplication in snpid.
n_miss_<*>: Number of NA observations for <*> column.

se_n metrics

n_est: Estimated sample size value, \(\widehat{n}\).
ratio_se_n: \(\texttt{ratio_se_n} = \frac{\sqrt{\widehat{n}}}{\sqrt{n}}\). We expect ratio_se_n to be 1. When it is not 1, it implies that the trait did not have a variance of 1, the reported sample size is wrong, or that the SNP-level effective sample sizes differ markedly from the reported sample size.
mean_diff: \(\texttt{mean_diff} = \sum_{j} \frac{\widehat{\beta_j^{std}} - \beta_j}{\texttt{n_snps}}\), mean difference between the standardised beta, predicted from P-values, and the observed beta. The difference should be very close to zero if trait has a variance of 1.
- \(\widehat{\beta_j^{std}} = \sqrt{\frac{{z}_j^2 / ({z}_j^2 + n -2)}{2 \times {MAF}_j \times (1 - {MAF}_j)}} \times sign({z}_j)\),
- \({z}_j = \frac{\beta_j}{{se}_j}\),
- and \(\beta_j\) is the reported effect size.
ratio_diff: \(\texttt{ratio_diff} = |\frac{\texttt{mean_diff}}{\texttt{mean_diff2}}|\), absolute ratio between the mean of diff and the mean of diff2 (expected difference between the standardised beta predicted from P-values, and the standardised beta derived from the observed beta divided by the predicted SD; NOT reported). The ratio should be close to 1. If different from 1, then implies that the betas are not in a standard deviation scale.
- \(\texttt{mean_diff2} = \sum_{j} \frac{\widehat{\beta_j^{std}} - \beta^{\prime}_j}{\texttt{n_snps}}\)
- \(\beta^{\prime}_j = \frac{\beta_j}{\widehat{\texttt{sd2}}_{y}}\)
sd_y_est1: The standard deviation for the trait inferred from the reported sample size, median standard errors for the SNP-trait assocations and SNP variances.
- \(\widehat{\texttt{sd1}}_{y} = \frac{\sqrt{n} \times median({se}_j)}{C}\),
- \(C = median(\frac{1}{\sqrt{2 \times {MAF}_j \times (1 - {MAF}_j)}})\),
- and \({se}_j\) is the reported standard error.
sd_y_est2: The standard deviation for the trait inferred from the reported sample size, Z statistics for the SNP-trait effects (beta/se) and allele frequency.
- \(\widehat{\texttt{sd2}}_{y} = median(\widehat{sd_j})\),
- \(\widehat{sd_j} = \frac{\beta_j}{\widehat{\beta_j^{std}}}\),

r2 metrics

Sum of variance explained, calculated from the clumped top hits sample.

r2_sum<*>: r2 statistics under various assumptions
- 1: \(r^2 = \sum_j{\frac{2 \times \beta_j^2 \times {MAF}_j \times (1 - {MAF}_j)}{\texttt{var1}}}\), \(\texttt{var1} = 1\).
- 2: \(r^2 = \sum_j{\frac{2 \times \beta_j^2 \times {MAF}_j \times (1 - {MAF}_j)}{\texttt{var2}}}\), \(\texttt{var2} = {\widehat{\texttt{sd1}}_{y}}^2\),
- 3: \(r^2 = \sum_j{\frac{2 \times \beta_j^2 \times {MAF}_j \times (1 - {MAF}_j)}{\texttt{var3}}}\), \(\texttt{var3} = {\widehat{\texttt{sd2}}_{y}}^2\),
- 4: \(r^2 = \sum_j{\frac{F_j}{F_j + n - 2}}\), \(F = \frac{\beta_j^2}{{se}_j^2}\).

LDSC metrics

Metrics from LD regression

ldsc_nsnp_merge_refpanel_ld: Number of remaining SNPs after merging with reference panel LD.
ldsc_nsnp_merge_regression_ld: Number of remaining SNPs after merging with regression SNP LD.
ldsc_observed_scale_h2_{beta,se} Coefficient value and SE for total observed scale h2.
ldsc_intercept_{beta,se}: Coefficient value and SE for intercept. Intercept is expected to be 1.
ldsc_lambda_gc: Lambda GC statistics.
ldsc_mean_chisq: Mean \(\chi^2\) statistics.
ldsc_ratio: \(\frac{\texttt{ldsc_intercept_beta} - 1}{\texttt{ldsc_mean_chisq} - 1}\), the proportion of the inflation in the mean \(\chi^2\) that the LD Score regression intercepts ascribes to causes other than polygenic heritability. The value of ratio should be close to zero, though in practice values of 0.1-0.2 are not uncommon, probably due to sample/reference LD Score mismatch or model misspecification (e.g., low LD variants have slightly higher \(h^2\) per SNP).

Flags

When a metric needs attention, the flag should return TRUE.

af_correlation: abs(af_correlation) < 0.7.
inflation_factor: inflation_factor > 1.2.
n: n (max reported sample size) < 10000.
is_snpid_non_unique: NOT is_snpid_unique.
mean_EFFECT_nonfinite: mean(EFFECT) is NA, NaN, or Inf.
mean_EFFECT_05: abs(mean(EFFECT)) > 0.5.
mean_EFFECT_01: abs(mean(EFFECT)) > 0.1.
mean_chisq: ldsc_mean_chisq > 1.3 or ldsc_mean_chisq < 0.7.
n_p_sig: n_p_sig > 1000.
miss_<*>: n_miss_<*> / n_snps > 0.01.
ldsc_ratio: ldsc_ratio > 0.5
ldsc_intercept_beta: ldsc_intercept_beta > 1.5
n_clumped_hits: n_clumped_hits > 1000
r2_sum<*>: r2_sum<*> > 0.5

Plots

Manhattan plot
- Red line: \(-log_{10}^{5 \times 10^{-8}}\)
- Blue line: \(-log_{10}^{5 \times 10^{-5}}\)
QQ plot
AF plot
P-Z plot
beta_std plot: Scatter plot between \(\widehat{\beta_j^{std}}\) and \(\beta_j\)

Diagnostics

Details

Summary stats

skim_type	skim_variable	n_missing	complete_rate	character.min	character.max	character.empty	character.n_unique	character.whitespace	numeric.mean	numeric.sd	numeric.p0	numeric.p25	numeric.p50	numeric.p75	numeric.p100	numeric.hist
character	ID	0	1.0000000	3	94	0	13559629	0	NA	NA	NA	NA	NA	NA	NA	NA
character	REF	0	1.0000000	1	100	0	57058	0	NA	NA	NA	NA	NA	NA	NA	NA
character	ALT	0	1.0000000	1	342	0	33331	0	NA	NA	NA	NA	NA	NA	NA	NA
numeric	CHROM	0	1.0000000	NA	NA	NA	NA	NA	9.051462e+00	6.185102e+00	1.00000e+00	4.000000e+00	8.000000e+00	1.300000e+01	2.300000e+01	▇▅▅▂▂
numeric	POS	0	1.0000000	NA	NA	NA	NA	NA	7.902482e+07	5.591574e+07	3.02000e+02	3.292784e+07	7.013846e+07	1.148582e+08	2.492309e+08	▇▆▅▂▁
numeric	EFFECT	0	1.0000000	NA	NA	NA	NA	NA	-5.000000e-07	6.757800e-03	-9.12548e-02	-1.807200e-03	1.430000e-05	1.851800e-03	7.481700e-02	▁▁▇▁▁
numeric	SE	0	1.0000000	NA	NA	NA	NA	NA	4.853100e-03	4.678000e-03	9.59100e-04	1.385100e-03	2.549100e-03	6.968400e-03	2.330960e-02	▇▂▁▁▁
numeric	PVAL	0	1.0000000	NA	NA	NA	NA	NA	4.930306e-01	2.908657e-01	0.00000e+00	2.393972e-01	4.906694e-01	7.452294e-01	1.000000e+00	▇▇▇▇▇
numeric	PVAL_ztest	0	1.0000000	NA	NA	NA	NA	NA	4.930292e-01	2.908665e-01	0.00000e+00	2.393942e-01	4.906684e-01	7.452291e-01	9.999999e-01	▇▇▇▇▇
numeric	AF	0	1.0000000	NA	NA	NA	NA	NA	1.909962e-01	2.544486e-01	1.00000e-03	7.483200e-03	6.026350e-02	2.956700e-01	9.990000e-01	▇▂▁▁▁
numeric	AF_reference	564615	0.9583975	NA	NA	NA	NA	NA	1.964716e-01	2.471286e-01	0.00000e+00	5.790700e-03	8.446490e-02	3.083070e-01	1.000000e+00	▇▂▁▁▁
numeric	N	0	1.0000000	NA	NA	NA	NA	NA	1.178800e+05	0.000000e+00	1.17880e+05	1.178800e+05	1.178800e+05	1.178800e+05	1.178800e+05	▁▁▇▁▁

Head and tail

CHROM	POS	ID	REF	ALT	EFFECT	SE	PVAL	PVAL_ztest	AF	AF_reference	N
1	692794	rs530212009	CA	C	0.0007687	0.0020170	0.7031241	0.7031225	0.1116960	0.1894970	117880
1	693731	rs12238997	A	G	0.0013037	0.0019101	0.4949086	0.4949083	0.1167130	0.1417730	117880
1	707522	rs371890604	G	C	0.0001385	0.0021433	0.9484781	0.9484781	0.0983344	0.1293930	117880
1	717587	rs144155419	G	A	0.0075622	0.0051365	0.1409610	0.1409582	0.0157581	0.0045926	117880
1	723329	rs189787166	A	T	-0.0201115	0.0149565	0.1787352	0.1787331	0.0017795	0.0003994	117880
1	730087	rs148120343	T	C	-0.0012257	0.0026583	0.6447272	0.6447276	0.0568842	0.0127796	117880
1	731718	rs142557973	T	C	0.0010189	0.0018123	0.5739618	0.5739591	0.1226360	0.1543530	117880
1	732032	rs61770163	A	C	0.0009044	0.0019316	0.6396406	0.6396402	0.1222430	0.1555510	117880
1	734349	rs141242758	T	C	0.0009436	0.0018133	0.6027802	0.6027792	0.1218910	0.1525560	117880
1	740284	rs61770167	C	T	-0.0048385	0.0082740	0.5586914	0.5586896	0.0058634	0.0023962	117880

CHROM	POS	ID	REF	ALT	EFFECT	SE	PVAL	PVAL_ztest	AF	AF_reference	N
23	154923311	rs141127553	C	T	-0.0023323	0.0021310	0.2737361	0.2737326	0.0555055	0.0309934	117880
23	154923374	rs111332691	T	A	0.0014219	0.0023228	0.5404481	0.5404478	0.0447871	0.0116556	117880
23	154925045	rs509981	C	T	-0.0009703	0.0011187	0.3857447	0.3857454	0.2446550	0.3634440	117880
23	154925895	rs538470	C	T	-0.0009011	0.0011440	0.4308965	0.4308940	0.2410780	0.3634440	117880
23	154927581	rs644138	G	A	-0.0014003	0.0010526	0.1834159	0.1834112	0.3005220	0.4635760	117880
23	154929412	rs557132	C	T	-0.0009677	0.0011190	0.3871426	0.3871411	0.2445300	0.3568210	117880
23	154929637	rs35185538	CT	C	-0.0015680	0.0011690	0.1798142	0.1798090	0.2287520	0.3011920	117880
23	154929952	rs4012982	CAA	C	-0.0004891	0.0011777	0.6779037	0.6779031	0.2386140	0.3165560	117880
23	154930230	rs781880	A	G	-0.0008369	0.0011187	0.4544185	0.4544168	0.2449530	0.3618540	117880
23	154930487	rs781879	T	A	-0.0016811	0.0038011	0.6582987	0.6582973	0.0196917	0.1263580	117880

bcf preview

1   692794  rs530212009 CA  C   .   PASS    AF=0.111696 ES:SE:LP:AF:SS:ID   0.000768689:0.00201698:0.152968:0.111696:117880:1_692794_CA_C
1   693731  rs12238997  A   G   .   PASS    AF=0.116713 ES:SE:LP:AF:SS:ID   0.00130366:0.00191006:0.305475:0.116713:117880:rs12238997
1   707522  rs371890604 G   C   .   PASS    AF=0.0983344    ES:SE:LP:AF:SS:ID   0.000138496:0.0021433:0.0229727:0.0983344:117880:rs371890604
1   717587  rs144155419 G   A   .   PASS    AF=0.0157581    ES:SE:LP:AF:SS:ID   0.00756218:0.00513654:0.850901:0.0157581:117880:rs144155419
1   723329  rs189787166 A   T   .   PASS    AF=0.00177954   ES:SE:LP:AF:SS:ID   -0.0201115:0.0149565:0.74779:0.00177954:117880:rs189787166
1   730087  rs148120343 T   C   .   PASS    AF=0.0568842    ES:SE:LP:AF:SS:ID   -0.00122573:0.00265828:0.190624:0.0568842:117880:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122636 ES:SE:LP:AF:SS:ID   0.00101893:0.0018123:0.241117:0.122636:117880:rs58276399
1   732032  rs61770163  A   C   .   PASS    AF=0.122243 ES:SE:LP:AF:SS:ID   0.00090436:0.00193156:0.194064:0.122243:117880:rs61770163
1   734349  rs141242758 T   C   .   PASS    AF=0.121891 ES:SE:LP:AF:SS:ID   0.000943635:0.00181326:0.219841:0.121891:117880:rs141242758
1   740284  rs61770167  C   T   .   PASS    AF=0.00586344   ES:SE:LP:AF:SS:ID   -0.00483855:0.00827401:0.252828:0.00586344:117880:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.00183407   ES:SE:LP:AF:SS:ID   -0.0106765:0.0158735:0.299986:0.00183407:117880:rs112573343
1   749963  rs529266287 T   TAA .   PASS    AF=0.869025 ES:SE:LP:AF:SS:ID   -0.00102892:0.00178669:0.248185:0.869025:117880:rs529266287
1   750230  rs190826124 G   C   .   PASS    AF=0.00158736   ES:SE:LP:AF:SS:ID   -0.0296513:0.0158762:1.20892:0.00158736:117880:rs190826124
1   751343  rs28544273  T   A   .   PASS    AF=0.123676 ES:SE:LP:AF:SS:ID   0.00158865:0.00177024:0.432391:0.123676:117880:rs28544273
1   751488  rs200141114 G   GA  .   PASS    AF=0.143312 ES:SE:LP:AF:SS:ID   0.000314954:0.00175077:0.0668996:0.143312:117880:rs200141114
1   751756  rs28527770  T   C   .   PASS    AF=0.123798 ES:SE:LP:AF:SS:ID   0.00149538:0.00176779:0.400546:0.123798:117880:rs28527770
1   753405  rs3115860   C   A   .   PASS    AF=0.869794 ES:SE:LP:AF:SS:ID   -0.00133032:0.00172157:0.356861:0.869794:117880:rs3115860
1   753425  rs3131970   T   C   .   PASS    AF=0.874573 ES:SE:LP:AF:SS:ID   -0.0013361:0.0017505:0.351343:0.874573:117880:rs3131970
1   753541  rs2073813   G   A   .   PASS    AF=0.129665 ES:SE:LP:AF:SS:ID   0.00121923:0.00172638:0.318717:0.129665:117880:rs2073813
1   754105  rs12184325  C   T   .   PASS    AF=0.0365244    ES:SE:LP:AF:SS:ID   -0.00572599:0.00313528:1.16873:0.0365244:117880:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869388 ES:SE:LP:AF:SS:ID   -0.00122492:0.00171994:0.322074:0.869388:117880:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869459 ES:SE:LP:AF:SS:ID   -0.00124392:0.00172057:0.328178:0.869459:117880:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.869387 ES:SE:LP:AF:SS:ID   -0.00123074:0.00171989:0.323997:0.869387:117880:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.00511644   ES:SE:LP:AF:SS:ID   0.00629113:0.00879899:0.323654:0.00511644:117880:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.0050855    ES:SE:LP:AF:SS:ID   0.00650951:0.00881842:0.336854:0.0050855:117880:rs142682604
1   755435  rs184270342 T   G   .   PASS    AF=0.00568415   ES:SE:LP:AF:SS:ID   0.00915284:0.0084796:0.5522:0.00568415:117880:rs184270342
1   755890  rs3115858   A   T   .   PASS    AF=0.869461 ES:SE:LP:AF:SS:ID   -0.0013353:0.00171674:0.359832:0.869461:117880:rs3115858
1   756434  rs61768170  G   C   .   PASS    AF=0.126942 ES:SE:LP:AF:SS:ID   0.00135385:0.0017524:0.356766:0.126942:117880:rs61768170
1   756604  rs3131962   A   G   .   PASS    AF=0.868981 ES:SE:LP:AF:SS:ID   -0.00134225:0.00171223:0.363422:0.868981:117880:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.868381 ES:SE:LP:AF:SS:ID   -0.00127213:0.00171157:0.33977:0.868381:117880:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.86917  ES:SE:LP:AF:SS:ID   -0.00135382:0.00171393:0.366944:0.86917:117880:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.869177 ES:SE:LP:AF:SS:ID   -0.00135508:0.00171405:0.367344:0.869177:117880:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.86918  ES:SE:LP:AF:SS:ID   -0.00135463:0.00171406:0.367187:0.86918:117880:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.869615 ES:SE:LP:AF:SS:ID   -0.00135814:0.00171845:0.367199:0.869615:117880:rs3131954
1   759293  rs10157329  T   A   .   PASS    AF=0.0995696    ES:SE:LP:AF:SS:ID   2.37006e-06:0.00199761:0.000411472:0.0995696:117880:rs10157329
1   759600  rs545998451 AGT A   .   PASS    AF=0.00653662   ES:SE:LP:AF:SS:ID   0.00797526:0.00770241:0.522195:0.00653662:117880:1_759600_AGT_A
1   759837  rs3115851   T   A   .   PASS    AF=0.873673 ES:SE:LP:AF:SS:ID   -0.00149094:0.00174477:0.405808:0.873673:117880:rs3115851
1   761732  rs2286139   C   T   .   PASS    AF=0.862961 ES:SE:LP:AF:SS:ID   -0.00083977:0.00171109:0.205106:0.862961:117880:rs2286139
1   761752  rs1057213   C   T   .   PASS    AF=0.868361 ES:SE:LP:AF:SS:ID   -0.00127732:0.00172757:0.337542:0.868361:117880:rs1057213
1   762273  rs3115849   G   A   .   PASS    AF=0.865372 ES:SE:LP:AF:SS:ID   -0.00159575:0.00172783:0.448892:0.865372:117880:rs3115849
1   762485  rs12095200  C   A   .   PASS    AF=0.0997403    ES:SE:LP:AF:SS:ID   0.00132247:0.00206471:0.282461:0.0997403:117880:rs12095200
1   762589  rs3115848   G   C   .   PASS    AF=0.870662 ES:SE:LP:AF:SS:ID   -0.00169336:0.00174824:0.477891:0.870662:117880:rs3115848
1   762592  rs3131950   C   G   .   PASS    AF=0.870662 ES:SE:LP:AF:SS:ID   -0.00169325:0.00174824:0.477849:0.870662:117880:rs3131950
1   762601  rs3131949   T   C   .   PASS    AF=0.87066  ES:SE:LP:AF:SS:ID   -0.00169149:0.00174824:0.477195:0.87066:117880:rs3131949
1   762632  rs3131948   T   A   .   PASS    AF=0.870982 ES:SE:LP:AF:SS:ID   -0.00154527:0.00174898:0.42371:0.870982:117880:rs3131948
1   764191  rs7515915   T   G   .   PASS    AF=0.126535 ES:SE:LP:AF:SS:ID   0.00152731:0.00175049:0.416875:0.126535:117880:rs7515915
1   766007  rs61768174  A   C   .   PASS    AF=0.106314 ES:SE:LP:AF:SS:ID   0.000315891:0.00190234:0.0614227:0.106314:117880:rs61768174
1   766105  rs2519015   T   A   .   PASS    AF=0.854398 ES:SE:LP:AF:SS:ID   -0.0016983:0.0017106:0.493759:0.854398:117880:rs2519015
1   767393  rs538667473 A   C   .   PASS    AF=0.00169182   ES:SE:LP:AF:SS:ID   -0.0185233:0.0150228:0.662397:0.00169182:117880:rs538667473
1   768116  rs376645387 A   AGTTTT  .   PASS    AF=0.83908  ES:SE:LP:AF:SS:ID   -0.00269922:0.00169491:0.953645:0.83908:117880:rs376645387