IEU GWAS QC Report

name	value
id	ieu-a-1024
pmid	21833088
year	2011
mr	1
note	NULL
trait	Multiple sclerosis
category	Disease
subcategory	Autoimmune / inflammatory
population	European
sex	Males and females
ncase	9722
ncontrol	17376
sample_size	27098
nsnp	465435
sd	NULL
priority	1
author	Sawcer S
group_name	public
access	public
build	HG19/GRCh37

name	value
gwas_id	ieu-a-1024
bcf_file	ieu-a-1024.vcf.gz
qc_metrics_file	qc_metrics.json
metadata	metadata.json
refdata	1kg_v3_nomult.bcf
compile_time	2020-05-10 00:24 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-1024/ieu-a-1024.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-1024/ldsc.txt \
--w-ld-chr /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/ 

Beginning analysis at Tue Feb  4 12:43:08 2020
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ieu-a-import/processed/ieu-a-1024/ieu-a-1024.vcf.gz ...
Read summary statistics for 464939 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, 454156 SNPs remain.
After merging with regression SNP LD, 454156 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0496 (0.0262)
Lambda GC: 1.0367
Mean Chi^2: 1.084
Intercept: 1.0598 (0.0085)
Ratio: 0.712 (0.101)
Analysis finished at Tue Feb  4 12:43:21 2020
Total time elapsed: 12.53s

{
    "af_correlation": 0.9112,
    "inflation_factor": 1.0446,
    "mean_EFFECT": -0.0004,
    "n": 27098,
    "n_snps": 464939,
    "n_clumped_hits": 26,
    "n_p_sig": 496,
    "n_mono": 0,
    "n_ns": 0,
    "n_mac": 4,
    "is_snpid_unique": true,
    "n_miss_EFFECT": 0,
    "n_miss_SE": 0,
    "n_miss_PVAL": 0,
    "n_miss_AF": 1,
    "n_miss_AF_reference": 2678,
    "n_est": 26625.7696,
    "ratio_se_n": 0.9912,
    "mean_diff": 0.0002,
    "ratio_diff": 14.1396,
    "sd_y_est1": 2.1156,
    "sd_y_est2": 2.0971,
    "r2_sum1": 0.3108,
    "r2_sum2": 0.0694,
    "r2_sum3": 0.0707,
    "r2_sum4": 0.0807,
    "ldsc_nsnp_merge_refpanel_ld": 454156,
    "ldsc_nsnp_merge_regression_ld": 454156,
    "ldsc_observed_scale_h2_beta": 0.0496,
    "ldsc_observed_scale_h2_se": 0.0262,
    "ldsc_intercept_beta": 1.0598,
    "ldsc_intercept_se": 0.0085,
    "ldsc_lambda_gc": 1.0367,
    "ldsc_mean_chisq": 1.084,
    "ldsc_ratio": 0.7119
}

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	TRUE
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	30	0	464939	0	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
character	ALT	0	1.0000000	1	1	0	4	0	NA	NA	NA	NA	NA	NA	NA	NA
numeric	CHROM	0	1.0000000	NA	NA	NA	NA	NA	8.838297e+00	5.804750e+00	1.0000e+00	4.000000e+00	8.000000e+00	1.300000e+01	2.300000e+01	▇▅▅▃▂
numeric	POS	0	1.0000000	NA	NA	NA	NA	NA	7.793116e+07	5.676715e+07	3.3012e+04	3.088170e+07	6.859557e+07	1.145533e+08	2.492026e+08	▇▆▅▂▁
numeric	EFFECT	0	1.0000000	NA	NA	NA	NA	NA	-4.228000e-04	3.029770e-02	-1.3150e+00	-1.592000e-02	-1.388000e-04	1.555000e-02	9.851000e-01	▁▁▇▁▁
numeric	SE	0	1.0000000	NA	NA	NA	NA	NA	2.467630e-02	1.249880e-02	1.6320e-02	1.912000e-02	2.126000e-02	2.640000e-02	7.757000e-01	▇▁▁▁▁
numeric	PVAL	0	1.0000000	NA	NA	NA	NA	NA	4.919875e-01	2.919297e-01	0.0000e+00	2.375002e-01	4.906005e-01	7.451007e-01	1.000000e+00	▇▇▇▇▇
numeric	PVAL_ztest	0	1.0000000	NA	NA	NA	NA	NA	4.919873e-01	2.919296e-01	0.0000e+00	2.374601e-01	4.905786e-01	7.451217e-01	9.999992e-01	▇▇▇▇▇
numeric	AF	1	0.9999978	NA	NA	NA	NA	NA	3.742757e-01	2.557038e-01	1.0000e-04	1.548000e-01	3.195500e-01	5.628375e-01	9.999000e-01	▇▆▅▃▂
numeric	AF_reference	2678	0.9942401	NA	NA	NA	NA	NA	3.756917e-01	2.424312e-01	1.9970e-04	1.727240e-01	3.236820e-01	5.527160e-01	9.974040e-01	▇▇▅▃▂
numeric	N	0	1.0000000	NA	NA	NA	NA	NA	2.709800e+04	0.000000e+00	2.7098e+04	2.709800e+04	2.709800e+04	2.709800e+04	2.709800e+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	768448	rs12562034	G	A	-0.024010	0.02807	0.3922997	0.3923510	0.10330	0.191893	27098
1	1005806	rs3934834	C	T	0.003846	0.02360	0.8706001	0.8705451	0.13890	0.223442	27098
1	1018704	rs9442372	A	G	0.012120	0.01777	0.4954000	0.4952083	0.57150	0.611022	27098
1	1021415	rs3737728	A	G	0.027630	0.01929	0.1520999	0.1520441	0.71330	0.812700	27098
1	1030565	rs6687776	C	T	0.004449	0.02384	0.8520000	0.8519593	0.15290	0.306709	27098
1	1031540	rs9651273	A	G	-0.012380	0.01968	0.5294001	0.5293065	0.72000	0.881989	27098
1	1048955	rs4970405	A	G	-0.031690	0.02897	0.2741000	0.2740031	0.10245	0.110623	27098
1	1049950	rs12726255	A	G	-0.011760	0.02564	0.6463995	0.6464795	0.13415	0.289736	27098
1	1061166	rs11807848	T	C	0.001567	0.01833	0.9319000	0.9318732	0.40825	0.329673	27098
1	1062638	rs9442373	C	A	-0.004163	0.01816	0.8187000	0.8186823	0.55715	0.574281	27098

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	51105556	rs9616812	C	T	-0.0054860	0.01846	0.7663003	0.7663267	0.47605	0.3628190	27098
22	51109992	rs9628185	T	C	0.0003403	0.01844	0.9853000	0.9852763	0.47755	0.4053510	27098
22	51121521	rs7284093	C	T	0.0351500	0.02399	0.1428999	0.1428680	0.15965	0.0485224	27098
22	51150473	rs5770820	G	A	0.0195900	0.02058	0.3412000	0.3411502	0.22630	0.2462060	27098
22	51151724	rs6010061	C	T	0.0085810	0.01812	0.6359003	0.6358101	0.38995	0.6098240	27098
22	51163138	rs715586	C	T	-0.0030030	0.02469	0.9031999	0.9031935	0.14860	0.0902556	27098
22	51165664	rs8137951	G	A	0.0158800	0.01926	0.4095001	0.4096517	0.29905	0.4063500	27098
22	51171693	rs756638	G	A	-0.0019670	0.01958	0.9199999	0.9199794	0.28930	0.3049120	27098
22	51175626	rs3810648	A	G	-0.0501000	0.03590	0.1629000	0.1628521	0.06100	0.1084270	27098
23	51666786	rs14115	A	G	0.0183300	0.03706	0.6209005	0.6208801	0.04875	NA	27098

1   768448  rs12562034  G   A   .   PASS    AF=0.1033   ES:SE:LP:AF:SS:ID   -0.02401:0.02807:0.406382:0.1033:27098:rs12562034
1   1005806 rs3934834   C   T   .   PASS    AF=0.1389   ES:SE:LP:AF:SS:ID   0.003846:0.0236:0.0601813:0.1389:27098:rs3934834
1   1018704 rs9442372   A   G   .   PASS    AF=0.5715   ES:SE:LP:AF:SS:ID   0.01212:0.01777:0.305044:0.5715:27098:rs9442372
1   1021415 rs3737728   A   G   .   PASS    AF=0.7133   ES:SE:LP:AF:SS:ID   0.02763:0.01929:0.817871:0.7133:27098:rs3737728
1   1030565 rs6687776   C   T   .   PASS    AF=0.1529   ES:SE:LP:AF:SS:ID   0.004449:0.02384:0.0695604:0.1529:27098:rs6687776
1   1031540 rs9651273   A   G   .   PASS    AF=0.72 ES:SE:LP:AF:SS:ID   -0.01238:0.01968:0.276216:0.72:27098:rs9651273
1   1048955 rs4970405   A   G   .   PASS    AF=0.10245  ES:SE:LP:AF:SS:ID   -0.03169:0.02897:0.562091:0.10245:27098:rs4970405
1   1049950 rs12726255  A   G   .   PASS    AF=0.13415  ES:SE:LP:AF:SS:ID   -0.01176:0.02564:0.189499:0.13415:27098:rs12726255
1   1061166 rs11807848  T   C   .   PASS    AF=0.40825  ES:SE:LP:AF:SS:ID   0.001567:0.01833:0.0306307:0.40825:27098:rs11807848
1   1062638 rs9442373   C   A   .   PASS    AF=0.55715  ES:SE:LP:AF:SS:ID   -0.004163:0.01816:0.0868752:0.55715:27098:rs9442373
1   1064979 rs2298217   C   T   .   PASS    AF=0.1456   ES:SE:LP:AF:SS:ID   -0.01441:0.02474:0.251657:0.1456:27098:rs2298217
1   1066029 rs12145826  G   A   .   PASS    AF=0.0714   ES:SE:LP:AF:SS:ID   0.02522:0.03255:0.358129:0.0714:27098:rs12145826
1   1087683 rs9442380   T   C   .   PASS    AF=0.9323   ES:SE:LP:AF:SS:ID   0.06926:0.03399:1.3808:0.9323:27098:rs9442380
1   1090557 rs7553429   A   C   .   PASS    AF=0.026    ES:SE:LP:AF:SS:ID   0.04694:0.04974:0.461678:0.026:27098:rs7553429
1   1094738 rs4970362   A   G   .   PASS    AF=0.6419   ES:SE:LP:AF:SS:ID   0.03031:0.01817:1.02072:0.6419:27098:rs4970362
1   1099342 rs9660710   A   C   .   PASS    AF=0.93475  ES:SE:LP:AF:SS:ID   0.08096:0.03454:1.71919:0.93475:27098:rs9660710
1   1106473 rs4970420   G   A   .   PASS    AF=0.18115  ES:SE:LP:AF:SS:ID   0.001675:0.02203:0.0271494:0.18115:27098:rs4970420
1   1119858 rs1320565   C   T   .   PASS    AF=0.0742   ES:SE:LP:AF:SS:ID   0.01151:0.03189:0.143815:0.0742:27098:rs1320565
1   1135242 rs9729550   A   C   .   PASS    AF=0.26105  ES:SE:LP:AF:SS:ID   0.01675:0.01997:0.396206:0.26105:27098:rs9729550
1   1156131 rs2887286   T   C   .   PASS    AF=0.1725   ES:SE:LP:AF:SS:ID   0.005611:0.02363:0.0902836:0.1725:27098:rs2887286
1   1172907 rs715643    C   T   .   PASS    AF=0.05495  ES:SE:LP:AF:SS:ID   0.03231:0.03696:0.417937:0.05495:27098:rs715643
1   1176597 rs6675798   T   C   .   PASS    AF=0.0969   ES:SE:LP:AF:SS:ID   0.02254:0.02949:0.352031:0.0969:27098:rs6675798
1   1192515 rs7524470   A   G   .   PASS    AF=0.03895  ES:SE:LP:AF:SS:ID   0.03354:0.04728:0.32039:0.03895:27098:rs7524470
1   1194804 rs11804831  T   C   .   PASS    AF=0.1816   ES:SE:LP:AF:SS:ID   0.03146:0.02213:0.809108:0.1816:27098:rs11804831
1   1211292 rs6685064   C   T   .   PASS    AF=0.06485  ES:SE:LP:AF:SS:ID   0.01129:0.03437:0.129304:0.06485:27098:rs6685064
1   1314015 rs2649588   C   T   .   PASS    AF=0.93435  ES:SE:LP:AF:SS:ID   -0.02728:0.03298:0.389233:0.93435:27098:rs2649588
1   1425700 rs819980    T   C   .   PASS    AF=0.0753   ES:SE:LP:AF:SS:ID   -0.005013:0.02981:0.0622816:0.0753:27098:rs819980
1   1462766 rs9439462   C   T   .   PASS    AF=0.0332   ES:SE:LP:AF:SS:ID   -0.0228:0.04204:0.230844:0.0332:27098:rs9439462
1   1478180 rs3766178   T   C   .   PASS    AF=0.285    ES:SE:LP:AF:SS:ID   0.0008849:0.01952:0.0160131:0.285:27098:rs3766178
1   1485984 rs2031709   C   A   .   PASS    AF=0.0192   ES:SE:LP:AF:SS:ID   0.05961:0.06072:0.486383:0.0192:27098:rs2031709
1   1486834 rs3128342   C   A   .   PASS    AF=0.43305  ES:SE:LP:AF:SS:ID   -0.005414:0.01745:0.121248:0.43305:27098:rs3128342
1   1493727 rs880051    G   A   .   PASS    AF=0.24005  ES:SE:LP:AF:SS:ID   -0.0003967:0.02056:0.00674017:0.24005:27098:rs880051
1   1497824 rs2296716   C   T   .   PASS    AF=0.12135  ES:SE:LP:AF:SS:ID   0.004194:0.02616:0.059135:0.12135:27098:rs2296716
1   1505255 rs6603793   C   T   .   PASS    AF=0.32015  ES:SE:LP:AF:SS:ID   0.01471:0.01894:0.359121:0.32015:27098:rs6603793
1   1509034 rs7520996   T   C   .   PASS    AF=0.29855  ES:SE:LP:AF:SS:ID   0.01117:0.0193:0.249569:0.29855:27098:rs7520996
1   1706160 rs7531583   A   G   .   PASS    AF=0.7761   ES:SE:LP:AF:SS:ID   -0.03384:0.02058:0.999566:0.7761:27098:rs7531583
1   1745726 rs16825336  G   A   .   PASS    AF=0.09145  ES:SE:LP:AF:SS:ID   0.04283:0.02796:0.901356:0.09145:27098:rs16825336
1   1748734 rs2180311   T   C   .   PASS    AF=0.5043   ES:SE:LP:AF:SS:ID   -0.02126:0.01739:0.654626:0.5043:27098:rs2180311
1   1781220 rs6681938   T   C   .   PASS    AF=0.31085  ES:SE:LP:AF:SS:ID   -0.03053:0.01884:0.978397:0.31085:27098:rs6681938
1   1793111 rs10907192  A   G   .   PASS    AF=0.95105  ES:SE:LP:AF:SS:ID   -0.0113:0.03807:0.115488:0.95105:27098:rs10907192
1   1801034 rs4648592   G   A   .   PASS    AF=0.27185  ES:SE:LP:AF:SS:ID   -0.001318:0.02005:0.0233749:0.27185:27098:rs4648592
1   1810090 rs7525092   C   T   .   PASS    AF=0.2732   ES:SE:LP:AF:SS:ID   -0.002082:0.02001:0.0375833:0.2732:27098:rs7525092
1   1844046 rs2474460   C   T   .   PASS    AF=0.4962   ES:SE:LP:AF:SS:ID   -0.02742:0.01735:0.943095:0.4962:27098:rs2474460
1   1873625 rs12758705  G   A   .   PASS    AF=0.26505  ES:SE:LP:AF:SS:ID   0.007691:0.02015:0.15323:0.26505:27098:rs12758705
1   1874581 rs2803329   A   G   .   PASS    AF=0.80375  ES:SE:LP:AF:SS:ID   -0.02301:0.02184:0.534617:0.80375:27098:rs2803329
1   1888193 rs3820011   C   A   .   PASS    AF=0.27525  ES:SE:LP:AF:SS:ID   -0.01159:0.01995:0.250805:0.27525:27098:rs3820011
1   1892325 rs2803291   T   C   .   PASS    AF=0.8061   ES:SE:LP:AF:SS:ID   -0.00731:0.02282:0.125634:0.8061:27098:rs2803291
1   2024064 rs2459994   C   T   .   PASS    AF=0.1641   ES:SE:LP:AF:SS:ID   0.003037:0.02277:0.0487111:0.1641:27098:rs2459994
1   2026361 rs12755035  G   A   .   PASS    AF=0.13345  ES:SE:LP:AF:SS:ID   0.00839:0.02636:0.124765:0.13345:27098:rs12755035
1   2026749 rs884080    A   G   .   PASS    AF=0.4517   ES:SE:LP:AF:SS:ID   -0.005972:0.01763:0.13389:0.4517:27098:rs884080

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