IEU GWAS QC Report

name	value
id	ieu-a-1018
pmid	27089181
year	2016
mr	1
note	ALSPAC; TEDS and 23andMe excluded (ALSPAC=5649; 23andME=93454; TEDS=2143)
trait	Subjective well being
category	Risk factor
subcategory	Psychiatric / neurological
population	European
sex	Males and females
ncase	NULL
ncontrol	NULL
sample_size	197174
nsnp	2264177
sd	NULL
priority	3
author	Okbay
group_name	public
access	public
build	HG19/GRCh37

name	value
gwas_id	ieu-a-1018
bcf_file	ieu-a-1018.vcf.gz
qc_metrics_file	qc_metrics.json
metadata	metadata.json
refdata	1kg_v3_nomult.bcf
compile_time	2020-05-09 22:29 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/ieu-a-import/processed/ieu-a-1018/ieu-a-1018.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/ieu-a-import/processed/ieu-a-1018/ldsc.txt \
--w-ld-chr /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/ 

Beginning analysis at Wed Feb  5 09:21:15 2020
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ieu-a-import/processed/ieu-a-1018/ieu-a-1018.vcf.gz ...
Read summary statistics for 2263928 SNPs.
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, 1105888 SNPs remain.
After merging with regression SNP LD, 1105888 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0377 (0.0032)
Lambda GC: 1.1301
Mean Chi^2: 1.149
Intercept: 1.0038 (0.0075)
Ratio: 0.0256 (0.0503)
Analysis finished at Wed Feb  5 09:21:41 2020
Total time elapsed: 25.77s

{
    "af_correlation": 0.9183,
    "inflation_factor": 1.1391,
    "mean_EFFECT": -0,
    "n": 197164,
    "n_snps": 2263928,
    "n_clumped_hits": 1,
    "n_p_sig": 14,
    "n_mono": 0,
    "n_ns": 0,
    "n_mac": 0,
    "is_snpid_unique": true,
    "n_miss_EFFECT": 0,
    "n_miss_SE": 0,
    "n_miss_PVAL": 0,
    "n_miss_AF": 0,
    "n_miss_AF_reference": 15037,
    "n_est": 195588.0655,
    "ratio_se_n": 0.996,
    "mean_diff": -4.6801e-07,
    "ratio_diff": 0.5335,
    "sd_y_est1": 1.0196,
    "sd_y_est2": 1.0155,
    "r2_sum1": 0.0002,
    "r2_sum2": 0.0002,
    "r2_sum3": 0.0002,
    "r2_sum4": 0.0002,
    "ldsc_nsnp_merge_refpanel_ld": 1105888,
    "ldsc_nsnp_merge_regression_ld": 1105888,
    "ldsc_observed_scale_h2_beta": 0.0377,
    "ldsc_observed_scale_h2_se": 0.0032,
    "ldsc_intercept_beta": 1.0038,
    "ldsc_intercept_se": 0.0075,
    "ldsc_lambda_gc": 1.1301,
    "ldsc_mean_chisq": 1.149,
    "ldsc_ratio": 0.0255
}

name	value
name	value
af_correlation	FALSE
inflation_factor	FALSE
n	FALSE
is_snpid_non_unique	FALSE
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	1.000000	3	34	0	2263928	0	NA	NA	NA	NA	NA	NA	NA	NA
character	REF	0	1.000000	1	1	0	4	0	NA	NA	NA	NA	NA	NA	NA	NA
character	ALT	0	1.000000	1	1	0	4	0	NA	NA	NA	NA	NA	NA	NA	NA
numeric	CHROM	0	1.000000	NA	NA	NA	NA	NA	8.623502e+00	5.658804e+00	1.00000e+00	4.000000e+00	8.000000e+00	1.200000e+01	2.200000e+01	▇▅▅▂▂
numeric	POS	0	1.000000	NA	NA	NA	NA	NA	7.883976e+07	5.560505e+07	1.15230e+04	3.280921e+07	7.041973e+07	1.144224e+08	2.492107e+08	▇▇▅▂▁
numeric	EFFECT	0	1.000000	NA	NA	NA	NA	NA	-2.670000e-05	5.068700e-03	-4.95650e-02	-3.052100e-03	-2.130000e-05	2.983400e-03	4.987180e-02	▁▁▇▁▁
numeric	SE	0	1.000000	NA	NA	NA	NA	NA	4.497300e-03	1.608400e-03	3.18500e-03	3.382700e-03	3.843800e-03	4.971500e-03	1.285000e-02	▇▂▁▁▁
numeric	PVAL	0	1.000000	NA	NA	NA	NA	NA	4.785754e-01	2.943086e-01	0.00000e+00	2.184002e-01	4.716002e-01	7.333005e-01	1.000000e+00	▇▇▇▆▆
numeric	PVAL_ztest	0	1.000000	NA	NA	NA	NA	NA	4.785708e-01	2.943082e-01	0.00000e+00	2.183231e-01	4.715251e-01	7.331038e-01	1.000000e+00	▇▇▆▆▆
numeric	AF	0	1.000000	NA	NA	NA	NA	NA	3.723611e-01	2.602597e-01	3.11000e-02	1.462000e-01	3.131000e-01	5.650000e-01	9.685000e-01	▇▅▃▃▂
numeric	AF_reference	15037	0.993358	NA	NA	NA	NA	NA	3.729425e-01	2.479638e-01	1.99700e-04	1.649360e-01	3.168930e-01	5.539140e-01	9.956070e-01	▇▇▅▃▂
numeric	N	0	1.000000	NA	NA	NA	NA	NA	1.858425e+05	1.536059e+04	1.00005e+05	1.828150e+05	1.911720e+05	1.963770e+05	1.971640e+05	▁▁▁▁▇

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	752566	rs3094315	G	A	-0.0010416	0.0052876	0.8438000	0.8438274	0.8357	0.7182510	130248
1	754192	rs3131968	A	G	-0.0057597	0.0054337	0.2891998	0.2891449	0.8609	0.6785140	141415
1	777122	rs2980319	A	T	0.0045673	0.0057162	0.4240001	0.4242904	0.8661	0.7472040	131948
1	779322	rs4040617	A	G	-0.0050763	0.0059722	0.3953002	0.3953247	0.1326	0.2264380	121883
1	780785	rs2977612	T	A	0.0031168	0.0056362	0.5803995	0.5802635	0.8614	0.6693290	131833
1	785989	rs2980300	T	C	0.0040114	0.0055560	0.4702004	0.4702949	0.8565	0.6269970	131784
1	798801	rs12132517	G	A	0.0175438	0.0075849	0.0207300	0.0207225	0.0861	0.0740815	110452
1	798959	rs11240777	G	A	0.0138124	0.0048652	0.0045300	0.0045254	0.2099	0.4099440	127371
1	846864	rs950122	G	C	-0.0064969	0.0055625	0.2429003	0.2428070	0.1870	0.2228430	106293
1	882033	rs2272756	G	A	-0.0058095	0.0050386	0.2491002	0.2489110	0.2460	0.1367810	106180

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
22	51162059	rs10451	G	A	0.0007307	0.0036175	0.8400000	0.8399167	0.3076	0.3865810	179391
22	51163138	rs715586	C	T	-0.0047492	0.0049113	0.3335001	0.3335447	0.1466	0.0902556	165686
22	51165664	rs8137951	G	A	-0.0000477	0.0036718	0.9895000	0.9896278	0.3065	0.4063500	174472
22	51171497	rs2301584	G	A	-0.0120105	0.0046953	0.0105300	0.0105279	0.1743	0.2533950	157589
22	51171693	rs756638	G	A	0.0025076	0.0041516	0.5457001	0.5458434	0.2672	0.3049120	148154
22	51173542	rs4824147	T	C	-0.0005823	0.0097055	0.9522001	0.9521556	0.9484	0.9812300	108466
22	51175626	rs3810648	A	G	0.0104493	0.0075500	0.1664999	0.1663572	0.0580	0.1084270	160543
22	51178090	rs2285395	G	A	0.0152591	0.0080269	0.0573099	0.0573023	0.0542	0.0666933	151383
22	51196164	rs8136603	A	T	0.0121166	0.0083390	0.1461999	0.1462236	0.0564	0.1427720	135105
22	51212875	rs2238837	A	C	-0.0022357	0.0042184	0.5959996	0.5961122	0.3486	0.3724040	123738

1   752566  rs3094315   G   A   .   PASS    AF=0.8357   ES:SE:LP:AF:SS:ID   -0.00104165:0.00528756:0.0737605:0.8357:130248:rs3094315
1   754192  rs3131968   A   G   .   PASS    AF=0.8609   ES:SE:LP:AF:SS:ID   -0.00575974:0.00543372:0.538802:0.8609:141415:rs3131968
1   777122  rs2980319   A   T   .   PASS    AF=0.8661   ES:SE:LP:AF:SS:ID   0.00456726:0.00571622:0.372634:0.8661:131948:rs2980319
1   779322  rs4040617   A   G   .   PASS    AF=0.1326   ES:SE:LP:AF:SS:ID   -0.00507634:0.00597216:0.403073:0.1326:121883:rs4040617
1   780785  rs2977612   T   A   .   PASS    AF=0.8614   ES:SE:LP:AF:SS:ID   0.00311684:0.00563624:0.236273:0.8614:131833:rs2977612
1   785989  rs2980300   T   C   .   PASS    AF=0.8565   ES:SE:LP:AF:SS:ID   0.00401145:0.00555603:0.327717:0.8565:131784:rs2980300
1   798801  rs12132517  G   A   .   PASS    AF=0.0861   ES:SE:LP:AF:SS:ID   0.0175438:0.00758486:1.6834:0.0861:110452:rs12132517
1   798959  rs11240777  G   A   .   PASS    AF=0.2099   ES:SE:LP:AF:SS:ID   0.0138124:0.00486522:2.3439:0.2099:127371:rs11240777
1   846864  rs950122    G   C   .   PASS    AF=0.187    ES:SE:LP:AF:SS:ID   -0.00649695:0.00556246:0.614572:0.187:106293:rs950122
1   882033  rs2272756   G   A   .   PASS    AF=0.246    ES:SE:LP:AF:SS:ID   -0.00580951:0.00503861:0.603626:0.246:106180:rs2272756
1   962210  rs3128126   A   G   .   PASS    AF=0.4092   ES:SE:LP:AF:SS:ID   -0.00376451:0.00398783:0.461929:0.4092:130053:rs3128126
1   990380  rs3121561   C   T   .   PASS    AF=0.2912   ES:SE:LP:AF:SS:ID   -0.00459273:0.00415256:0.570894:0.2912:140483:rs3121561
1   998501  rs3813193   G   C   .   PASS    AF=0.1654   ES:SE:LP:AF:SS:ID   0.00374415:0.00481253:0.360016:0.1654:156390:rs3813193
1   1003629 rs4075116   C   T   .   PASS    AF=0.7284   ES:SE:LP:AF:SS:ID   0.00386263:0.0039135:0.489723:0.7284:165021:rs4075116
1   1005806 rs3934834   C   T   .   PASS    AF=0.1531   ES:SE:LP:AF:SS:ID   0.00101884:0.00478328:0.080399:0.1531:168543:rs3934834
1   1017170 rs3766193   C   G   .   PASS    AF=0.5513   ES:SE:LP:AF:SS:ID   0.00450661:0.00357951:0.681728:0.5513:157753:rs3766193
1   1017197 rs3766192   C   T   .   PASS    AF=0.5525   ES:SE:LP:AF:SS:ID   0.00406391:0.0035711:0.59329:0.5525:158577:rs3766192
1   1017587 rs3766191   C   T   .   PASS    AF=0.148    ES:SE:LP:AF:SS:ID   0.000194562:0.00498877:0.0136762:0.148:159324:rs3766191
1   1018562 rs9442371   C   T   .   PASS    AF=0.5635   ES:SE:LP:AF:SS:ID   0.00369679:0.00342613:0.552222:0.5635:173175:rs9442371
1   1018704 rs9442372   A   G   .   PASS    AF=0.5658   ES:SE:LP:AF:SS:ID   0.00388866:0.00342915:0.590574:0.5658:173079:rs9442372
1   1021346 rs10907177  A   G   .   PASS    AF=0.1534   ES:SE:LP:AF:SS:ID   -5.2977e-05:0.00481609:0.00375109:0.1534:165988:rs10907177
1   1021415 rs3737728   A   G   .   PASS    AF=0.7195   ES:SE:LP:AF:SS:ID   0.00360317:0.00386192:0.455064:0.7195:166112:rs3737728
1   1021695 rs9442398   A   G   .   PASS    AF=0.7219   ES:SE:LP:AF:SS:ID   0.00439015:0.00382751:0.599462:0.7219:170004:rs9442398
1   1022037 rs6701114   C   T   .   PASS    AF=0.5603   ES:SE:LP:AF:SS:ID   0.0043551:0.00355518:0.656198:0.5603:160572:rs6701114
1   1026707 rs4074137   C   A   .   PASS    AF=0.6021   ES:SE:LP:AF:SS:ID   0.00766495:0.003739:1.39383:0.6021:149285:rs4074137
1   1030565 rs6687776   C   T   .   PASS    AF=0.1596   ES:SE:LP:AF:SS:ID   -0.00421784:0.00460463:0.44418:0.1596:175817:rs6687776
1   1030633 rs6678318   G   A   .   PASS    AF=0.1587   ES:SE:LP:AF:SS:ID   -0.00503298:0.00477512:0.534766:0.1587:164238:rs6678318
1   1031540 rs9651273   A   G   .   PASS    AF=0.728    ES:SE:LP:AF:SS:ID   -0.00111962:0.00419333:0.102483:0.728:143599:rs9651273
1   1036959 rs11579015  T   C   .   PASS    AF=0.1081   ES:SE:LP:AF:SS:ID   -0.00979313:0.00546492:1.13602:0.1081:173644:rs11579015
1   1040026 rs6671356   T   C   .   PASS    AF=0.1367   ES:SE:LP:AF:SS:ID   -0.00404548:0.00497599:0.380698:0.1367:171112:rs6671356
1   1046164 rs6666280   C   T   .   PASS    AF=0.1343   ES:SE:LP:AF:SS:ID   -0.00417709:0.00489121:0.405718:0.1343:179760:rs6666280
1   1048955 rs4970405   A   G   .   PASS    AF=0.1041   ES:SE:LP:AF:SS:ID   -0.00952975:0.00534478:1.12761:0.1041:187672:rs4970405
1   1049950 rs12726255  A   G   .   PASS    AF=0.1356   ES:SE:LP:AF:SS:ID   -0.00524057:0.0048167:0.557991:0.1356:183864:rs12726255
1   1053452 rs4970409   G   A   .   PASS    AF=0.1079   ES:SE:LP:AF:SS:ID   -0.010525:0.00534807:1.30892:0.1079:181610:rs4970409
1   1060174 rs7548798   C   T   .   PASS    AF=0.2918   ES:SE:LP:AF:SS:ID   0.00205312:0.00397122:0.218101:0.2918:153419:rs7548798
1   1060608 rs17160824  G   A   .   PASS    AF=0.1125   ES:SE:LP:AF:SS:ID   -0.0111909:0.00528372:1.46674:0.1125:179378:rs17160824
1   1061115 rs17160826  T   C   .   PASS    AF=0.1084   ES:SE:LP:AF:SS:ID   -0.0106096:0.00533684:1.32938:0.1084:181636:rs17160826
1   1061166 rs11807848  T   C   .   PASS    AF=0.4019   ES:SE:LP:AF:SS:ID   -0.00360699:0.00364711:0.49147:0.4019:156379:rs11807848
1   1062638 rs9442373   C   A   .   PASS    AF=0.5523   ES:SE:LP:AF:SS:ID   0.00377828:0.00361212:0.52959:0.5523:154983:rs9442373
1   1064535 rs6682475   G   C   .   PASS    AF=0.7351   ES:SE:LP:AF:SS:ID   0.000406865:0.00419448:0.0347983:0.7351:145944:rs6682475
1   1064979 rs2298217   C   T   .   PASS    AF=0.1477   ES:SE:LP:AF:SS:ID   -0.00870289:0.00480292:1.15459:0.1477:172181:rs2298217
1   1066029 rs12145826  G   A   .   PASS    AF=0.0686   ES:SE:LP:AF:SS:ID   -0.0074276:0.00834562:0.427593:0.0686:112355:rs12145826
1   1066403 rs10907182  T   C   .   PASS    AF=0.5945   ES:SE:LP:AF:SS:ID   0.00599046:0.00385734:0.919013:0.5945:139396:rs10907182
1   1071118 rs10907183  G   C   .   PASS    AF=0.6063   ES:SE:LP:AF:SS:ID   0.00500114:0.00387686:0.705313:0.6063:139366:rs10907183
1   1077064 rs4970357   C   A   .   PASS    AF=0.9092   ES:SE:LP:AF:SS:ID   0.00266982:0.00662487:0.162917:0.9092:137997:rs4970357
1   1079198 rs11260603  T   C   .   PASS    AF=0.2544   ES:SE:LP:AF:SS:ID   0.000479354:0.00479354:0.0361178:0.2544:114719:rs11260603
1   1087683 rs9442380   T   C   .   PASS    AF=0.9237   ES:SE:LP:AF:SS:ID   -0.000628838:0.00668976:0.0336705:0.9237:158523:rs9442380
1   1089262 rs4970358   A   G   .   PASS    AF=0.9578   ES:SE:LP:AF:SS:ID   -0.0134005:0.0106522:0.680894:0.9578:109019:rs4970358
1   1094738 rs4970362   A   G   .   PASS    AF=0.6359   ES:SE:LP:AF:SS:ID   0.00135093:0.00420851:0.126215:0.6359:121928:rs4970362
1   1097335 rs9442385   T   G   .   PASS    AF=0.9308   ES:SE:LP:AF:SS:ID   -0.00530626:0.00675097:0.364617:0.9308:170324:rs9442385

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