IEU GWAS QC Report

name	value
id	ukb-d-22611_0
trait	Workplace had a lot of cigarette smoke from other people smoking: Rarely/never
category	Binary
ncase	77294
ncontrol	13422
sample_size	90716
sex	Males and females
population	European
group_name	public
access	public
build	HG19/GRCh37
author	Neale lab
year	2018
mr	1
nsnp	13566864

name	value
gwas_id	ukb-d-22611_0
bcf_file	ukb-d-22611_0.vcf.gz
qc_metrics_file	qc_metrics.json
metadata	metadata.json
refdata	1kg_v3_nomult.bcf
compile_time	2020-05-10 11:33 BST
build_version	run_igd-8f4f4c9-2020-05-09

manhattan_plot

qq_plot

af_plot

pz_plot

beta_std_plot

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}

*********************************************************************
* 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-22611_0/ukb-d-22611_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-22611_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 16:34:12 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-22611_0/ukb-d-22611_0.vcf.gz ...
Read summary statistics for 13566863 SNPs.
Dropped 12681 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, 1283503 SNPs remain.
After merging with regression SNP LD, 1283503 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0119 (0.0046)
Lambda GC: 1.0244
Mean Chi^2: 1.0288
Intercept: 1.0079 (0.0057)
Ratio: 0.2733 (0.1966)
Analysis finished at Mon Nov 25 16:36:57 2019
Total time elapsed: 2.0m:44.64s

{
    "af_correlation": 0.9521,
    "inflation_factor": 1.0168,
    "mean_EFFECT": -3.7189e-06,
    "n": 89714,
    "n_snps": 13566863,
    "n_clumped_hits": 0,
    "n_p_sig": 1,
    "n_mono": 0,
    "n_ns": 1251230,
    "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": 563191,
    "n_est": 90036.2474,
    "ratio_se_n": 1.0018,
    "mean_diff": -9.4984e-06,
    "ratio_diff": 3.5294,
    "sd_y_est1": 0.3527,
    "sd_y_est2": 0.3533,
    "r2_sum1": 0,
    "r2_sum2": 0,
    "r2_sum3": 0,
    "r2_sum4": 0,
    "ldsc_nsnp_merge_refpanel_ld": 1283503,
    "ldsc_nsnp_merge_regression_ld": 1283503,
    "ldsc_observed_scale_h2_beta": 0.0119,
    "ldsc_observed_scale_h2_se": 0.0046,
    "ldsc_intercept_beta": 1.0079,
    "ldsc_intercept_se": 0.0057,
    "ldsc_lambda_gc": 1.0244,
    "ldsc_mean_chisq": 1.0288,
    "ldsc_ratio": 0.2743
}

name	value
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

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 $\beta_j$

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
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	13554846	0	NA	NA	NA	NA	NA	NA	NA	NA
character	REF	0	1.0000000	1	100	0	57044	0	NA	NA	NA	NA	NA	NA	NA	NA
character	ALT	0	1.0000000	1	342	0	33332	0	NA	NA	NA	NA	NA	NA	NA	NA
numeric	CHROM	0	1.0000000	NA	NA	NA	NA	NA	9.051329e+00	6.184968e+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	7.902489e+07	5.591644e+07	3.02000e+02	3.292812e+07	7.013467e+07	1.148620e+08	2.492309e+08	▇▆▅▂▁
numeric	EFFECT	0	1.0000000	NA	NA	NA	NA	NA	-3.700000e-06	9.685200e-03	-1.23795e-01	-2.555900e-03	1.710000e-05	2.612200e-03	1.025320e-01	▁▁▇▁▁
numeric	SE	0	1.0000000	NA	NA	NA	NA	NA	6.968400e-03	6.715600e-03	1.37360e-03	1.991000e-03	3.662200e-03	1.000610e-02	3.383630e-02	▇▂▁▁▁
numeric	PVAL	0	1.0000000	NA	NA	NA	NA	NA	4.969576e-01	2.898275e-01	0.00000e+00	2.446628e-01	4.964151e-01	7.484176e-01	1.000000e+00	▇▇▇▇▇
numeric	PVAL_ztest	0	1.0000000	NA	NA	NA	NA	NA	4.969558e-01	2.898285e-01	0.00000e+00	2.446595e-01	4.964124e-01	7.484177e-01	1.000000e+00	▇▇▇▇▇
numeric	AF	0	1.0000000	NA	NA	NA	NA	NA	1.910684e-01	2.544751e-01	1.00000e-03	7.499400e-03	6.030120e-02	2.957645e-01	9.990000e-01	▇▂▁▁▁
numeric	AF_reference	563191	0.9584878	NA	NA	NA	NA	NA	1.965245e-01	2.471463e-01	0.00000e+00	5.790700e-03	8.446490e-02	3.085060e-01	1.000000e+00	▇▂▁▁▁
numeric	N	0	1.0000000	NA	NA	NA	NA	NA	8.971400e+04	0.000000e+00	8.97140e+04	8.971400e+04	8.971400e+04	8.971400e+04	8.971400e+04	▁▁▇▁▁

CHROM	POS	ID	REF	ALT	EFFECT	SE	PVAL	PVAL_ztest	AF	AF_reference	N
CHROM	POS	ID	REF	ALT	EFFECT	SE	PVAL	PVAL_ztest	AF	AF_reference	N
1	692794	rs530212009	CA	C	0.0020187	0.0029046	0.4870584	0.4870560	0.1116230	0.1894970	89714
1	693731	rs12238997	A	G	0.0027915	0.0027513	0.3103002	0.3102982	0.1166320	0.1417730	89714
1	707522	rs371890604	G	C	0.0030644	0.0030927	0.3217548	0.3217513	0.0980012	0.1293930	89714
1	717587	rs144155419	G	A	-0.0017244	0.0073603	0.8147711	0.8147703	0.0158424	0.0045926	89714
1	723329	rs189787166	A	T	-0.0479839	0.0216444	0.0266312	0.0266286	0.0017596	0.0003994	89714
1	730087	rs148120343	T	C	0.0057075	0.0038150	0.1346421	0.1346386	0.0570389	0.0127796	89714
1	731718	rs142557973	T	C	0.0028308	0.0026108	0.2782403	0.2782365	0.1225270	0.1543530	89714
1	732032	rs61770163	A	C	0.0017954	0.0027840	0.5190008	0.5189999	0.1221950	0.1555510	89714
1	734349	rs141242758	T	C	0.0028898	0.0026127	0.2687089	0.2687062	0.1217590	0.1525560	89714
1	740284	rs61770167	C	T	0.0161706	0.0120280	0.1788191	0.1788149	0.0057274	0.0023962	89714

CHROM	POS	ID	REF	ALT	EFFECT	SE	PVAL	PVAL_ztest	AF	AF_reference	N
CHROM	POS	ID	REF	ALT	EFFECT	SE	PVAL	PVAL_ztest	AF	AF_reference	N
23	154923311	rs141127553	C	T	0.0048942	0.0030569	0.1093810	0.1093777	0.0552893	0.0309934	89714
23	154923374	rs111332691	T	A	-0.0021648	0.0033244	0.5149286	0.5149262	0.0448982	0.0116556	89714
23	154925045	rs509981	C	T	0.0008731	0.0016026	0.5858872	0.5858849	0.2447410	0.3634440	89714
23	154925895	rs538470	C	T	0.0012593	0.0016382	0.4420862	0.4420858	0.2412510	0.3634440	89714
23	154927581	rs644138	G	A	0.0020163	0.0015074	0.1810369	0.1810330	0.3004360	0.4635760	89714
23	154929412	rs557132	C	T	0.0008961	0.0016030	0.5761745	0.5761729	0.2446020	0.3568210	89714
23	154929637	rs35185538	CT	C	0.0010365	0.0016737	0.5357288	0.5357281	0.2289830	0.3011920	89714
23	154929952	rs4012982	CAA	C	0.0007643	0.0016872	0.6505355	0.6505360	0.2384720	0.3165560	89714
23	154930230	rs781880	A	G	0.0008541	0.0016025	0.5940664	0.5940666	0.2450560	0.3618540	89714
23	154930487	rs781879	T	A	-0.0025545	0.0054316	0.6381430	0.6381422	0.0196698	0.1263580	89714

1   692794  rs530212009 CA  C   .   PASS    AF=0.111623 ES:SE:LP:AF:SS:ID   0.00201868:0.00290458:0.312419:0.111623:89714:1_692794_CA_C
1   693731  rs12238997  A   G   .   PASS    AF=0.116632 ES:SE:LP:AF:SS:ID   0.00279149:0.00275133:0.508218:0.116632:89714:rs12238997
1   707522  rs371890604 G   C   .   PASS    AF=0.0980012    ES:SE:LP:AF:SS:ID   0.00306442:0.00309267:0.492475:0.0980012:89714:rs371890604
1   717587  rs144155419 G   A   .   PASS    AF=0.0158424    ES:SE:LP:AF:SS:ID   -0.00172435:0.00736032:0.0889644:0.0158424:89714:rs144155419
1   723329  rs189787166 A   T   .   PASS    AF=0.00175956   ES:SE:LP:AF:SS:ID   -0.0479839:0.0216444:1.57461:0.00175956:89714:rs189787166
1   730087  rs148120343 T   C   .   PASS    AF=0.0570389    ES:SE:LP:AF:SS:ID   0.00570749:0.00381502:0.870819:0.0570389:89714:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122527 ES:SE:LP:AF:SS:ID   0.00283083:0.00261077:0.55558:0.122527:89714:rs58276399
1   732032  rs61770163  A   C   .   PASS    AF=0.122195 ES:SE:LP:AF:SS:ID   0.0017954:0.00278405:0.284832:0.122195:89714:rs61770163
1   734349  rs141242758 T   C   .   PASS    AF=0.121759 ES:SE:LP:AF:SS:ID   0.00288978:0.00261271:0.570718:0.121759:89714:rs141242758
1   740284  rs61770167  C   T   .   PASS    AF=0.00572742   ES:SE:LP:AF:SS:ID   0.0161706:0.012028:0.747586:0.00572742:89714:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.00183221   ES:SE:LP:AF:SS:ID   -0.0181797:0.0229046:0.369204:0.00183221:89714:rs112573343
1   749963  rs529266287 T   TAA .   PASS    AF=0.868769 ES:SE:LP:AF:SS:ID   -0.00199002:0.00257039:0.357725:0.868769:89714:rs529266287
1   750230  rs190826124 G   C   .   PASS    AF=0.00162108   ES:SE:LP:AF:SS:ID   0.00533221:0.022573:0.0897695:0.00162108:89714:rs190826124
1   751343  rs28544273  T   A   .   PASS    AF=0.123875 ES:SE:LP:AF:SS:ID   0.00181851:0.00254806:0.322918:0.123875:89714:rs28544273
1   751488  rs200141114 G   GA  .   PASS    AF=0.143391 ES:SE:LP:AF:SS:ID   0.00241736:0.0025229:0.471106:0.143391:89714:rs200141114
1   751756  rs28527770  T   C   .   PASS    AF=0.124012 ES:SE:LP:AF:SS:ID   0.0018562:0.00254436:0.331916:0.124012:89714:rs28527770
1   753405  rs3115860   C   A   .   PASS    AF=0.869705 ES:SE:LP:AF:SS:ID   -0.00164997:0.00247916:0.296098:0.869705:89714:rs3115860
1   753425  rs3131970   T   C   .   PASS    AF=0.874365 ES:SE:LP:AF:SS:ID   -0.00204303:0.00251947:0.379419:0.874365:89714:rs3131970
1   753541  rs2073813   G   A   .   PASS    AF=0.129721 ES:SE:LP:AF:SS:ID   0.00178967:0.0024869:0.326289:0.129721:89714:rs2073813
1   754105  rs12184325  C   T   .   PASS    AF=0.035825 ES:SE:LP:AF:SS:ID   -0.00930288:0.00454711:1.38966:0.035825:89714:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869349 ES:SE:LP:AF:SS:ID   -0.00169612:0.0024771:0.306693:0.869349:89714:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869427 ES:SE:LP:AF:SS:ID   -0.00173222:0.0024782:0.314649:0.869427:89714:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.869345 ES:SE:LP:AF:SS:ID   -0.0016929:0.002477:0.305987:0.869345:89714:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.00533064   ES:SE:LP:AF:SS:ID   -0.0107387:0.0123674:0.414283:0.00533064:89714:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.00529572   ES:SE:LP:AF:SS:ID   -0.0108233:0.0123982:0.417162:0.00529572:89714:rs142682604
1   755435  rs184270342 T   G   .   PASS    AF=0.00567704   ES:SE:LP:AF:SS:ID   -0.031813:0.012191:2.0425:0.00567704:89714:rs184270342
1   755890  rs3115858   A   T   .   PASS    AF=0.869328 ES:SE:LP:AF:SS:ID   -0.00184333:0.00247186:0.341191:0.869328:89714:rs3115858
1   756434  rs61768170  G   C   .   PASS    AF=0.127114 ES:SE:LP:AF:SS:ID   0.00186276:0.00252319:0.336903:0.127114:89714:rs61768170
1   756604  rs3131962   A   G   .   PASS    AF=0.86885  ES:SE:LP:AF:SS:ID   -0.00172139:0.00246541:0.314218:0.86885:89714:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.868235 ES:SE:LP:AF:SS:ID   -0.00197106:0.00246384:0.372926:0.868235:89714:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.869004 ES:SE:LP:AF:SS:ID   -0.00169718:0.00246767:0.308387:0.869004:89714:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.86901  ES:SE:LP:AF:SS:ID   -0.00169658:0.00246784:0.308225:0.86901:89714:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.869012 ES:SE:LP:AF:SS:ID   -0.00169467:0.00246786:0.307792:0.869012:89714:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.869497 ES:SE:LP:AF:SS:ID   -0.00177977:0.0024745:0.326064:0.869497:89714:rs3131954
1   759293  rs10157329  T   A   .   PASS    AF=0.0992799    ES:SE:LP:AF:SS:ID   0.00274427:0.00288055:0.467564:0.0992799:89714:rs10157329
1   759600  rs545998451 AGT A   .   PASS    AF=0.00652131   ES:SE:LP:AF:SS:ID   -0.0237302:0.0110996:1.48778:0.00652131:89714:1_759600_AGT_A
1   759837  rs3115851   T   A   .   PASS    AF=0.873488 ES:SE:LP:AF:SS:ID   -0.00171809:0.00251114:0.306396:0.873488:89714:rs3115851
1   761732  rs2286139   C   T   .   PASS    AF=0.862845 ES:SE:LP:AF:SS:ID   -0.00153244:0.00246314:0.272586:0.862845:89714:rs2286139
1   761752  rs1057213   C   T   .   PASS    AF=0.86828  ES:SE:LP:AF:SS:ID   -0.00169843:0.00248758:0.305608:0.86828:89714:rs1057213
1   762273  rs3115849   G   A   .   PASS    AF=0.865153 ES:SE:LP:AF:SS:ID   -0.00158686:0.002488:0.280997:0.865153:89714:rs3115849
1   762485  rs12095200  C   A   .   PASS    AF=0.0996835    ES:SE:LP:AF:SS:ID   0.000993256:0.00297825:0.1315:0.0996835:89714:rs12095200
1   762589  rs3115848   G   C   .   PASS    AF=0.870449 ES:SE:LP:AF:SS:ID   -0.00132462:0.00251642:0.222849:0.870449:89714:rs3115848
1   762592  rs3131950   C   G   .   PASS    AF=0.870449 ES:SE:LP:AF:SS:ID   -0.00132456:0.00251642:0.222838:0.870449:89714:rs3131950
1   762601  rs3131949   T   C   .   PASS    AF=0.870446 ES:SE:LP:AF:SS:ID   -0.00132485:0.00251643:0.222894:0.870446:89714:rs3131949
1   762632  rs3131948   T   A   .   PASS    AF=0.870814 ES:SE:LP:AF:SS:ID   -0.00132075:0.00251762:0.22195:0.870814:89714:rs3131948
1   764191  rs7515915   T   G   .   PASS    AF=0.12671  ES:SE:LP:AF:SS:ID   0.00171085:0.0025204:0.303412:0.12671:89714:rs7515915
1   766007  rs61768174  A   C   .   PASS    AF=0.106163 ES:SE:LP:AF:SS:ID   0.00333753:0.00274014:0.651265:0.106163:89714:rs61768174
1   766105  rs2519015   T   A   .   PASS    AF=0.854165 ES:SE:LP:AF:SS:ID   -0.00156787:0.00246228:0.280433:0.854165:89714:rs2519015
1   767393  rs538667473 A   C   .   PASS    AF=0.00167172   ES:SE:LP:AF:SS:ID   -0.0451346:0.0217114:1.42442:0.00167172:89714:rs538667473
1   768116  rs376645387 A   AGTTTT  .   PASS    AF=0.839232 ES:SE:LP:AF:SS:ID   0.00440387:0.00243524:1.15151:0.839232:89714:rs376645387

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