IEU GWAS QC Report

name	value
id	ieu-a-13
pmid	26192919
year	2015
mr	1
note	NULL
trait	Crohn’s disease
category	Disease
subcategory	Autoimmune / inflammatory
population	Indian
sex	Males and females
ncase	184
ncontrol	990
sample_size	1174
nsnp	154590
sd	NULL
priority	1
author	Liu
group_name	public
access	public
build	HG19/GRCh37

name	value
gwas_id	ieu-a-13
bcf_file	ieu-a-13.vcf.gz
qc_metrics_file	qc_metrics.json
metadata	metadata.json
refdata	1kg_v3_nomult.bcf
compile_time	2020-05-10 07:18 BST
build_version	run_igd-8f4f4c9-2020-05-09

manhattan_plot

qq_plot

$af_plot$

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-13/ieu-a-13.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-13/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 11:50:08 2020
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ieu-a-import/processed/ieu-a-13/ieu-a-13.vcf.gz ...
Read summary statistics for 121777 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, 50448 SNPs remain.
After merging with regression SNP LD, 50448 SNPs remain.
WARNING: number of SNPs less than 200k; this is almost always bad.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 1.8473 (1.243)
Lambda GC: 1.1072
Mean Chi^2: 1.0955
Intercept: 1.0487 (0.0213)
Ratio: 0.5104 (0.2228)
Analysis finished at Tue Feb  4 11:50:18 2020
Total time elapsed: 9.75s

{
    "af_correlation": 0.7936,
    "inflation_factor": 1.1506,
    "mean_EFFECT": -0.0492,
    "n": 1174,
    "n_snps": 121777,
    "n_clumped_hits": 1,
    "n_p_sig": 1,
    "n_mono": 0,
    "n_ns": 0,
    "n_mac": 3282,
    "is_snpid_unique": true,
    "n_miss_EFFECT": 0,
    "n_miss_SE": 0,
    "n_miss_PVAL": 0,
    "n_miss_AF": 288,
    "n_miss_AF_reference": 811,
    "n_est": 1175.4105,
    "ratio_se_n": 1.0006,
    "mean_diff": 0.057,
    "ratio_diff": 2.6531,
    "sd_y_est1": 2.7479,
    "sd_y_est2": 2.7495,
    "r2_sum1": 0.1126,
    "r2_sum2": 0.0149,
    "r2_sum3": 0.0149,
    "r2_sum4": 0.032,
    "ldsc_nsnp_merge_refpanel_ld": 50448,
    "ldsc_nsnp_merge_regression_ld": 50448,
    "ldsc_observed_scale_h2_beta": 1.8473,
    "ldsc_observed_scale_h2_se": 1.243,
    "ldsc_intercept_beta": 1.0487,
    "ldsc_intercept_se": 0.0213,
    "ldsc_lambda_gc": 1.1072,
    "ldsc_mean_chisq": 1.0955,
    "ldsc_ratio": 0.5099
}

name	value
name	value
af_correlation	FALSE
inflation_factor	FALSE
n	TRUE
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	5	25	0	121777	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	8.097966e+00	5.774464e+00	1.00000e+00	3.000000e+00	6.000000e+00	1.200000e+01	2.200000e+01	▇▅▃▂▂
numeric	POS	0	1.0000000	NA	NA	NA	NA	NA	7.895711e+07	5.781914e+07	6.34110e+04	3.286898e+07	6.219831e+07	1.176130e+08	2.492107e+08	▇▅▃▂▁
numeric	EFFECT	0	1.0000000	NA	NA	NA	NA	NA	-4.915560e-02	1.045244e+01	-8.48770e+02	-1.289380e-01	3.027000e-04	1.230180e-01	8.487700e+02	▁▁▇▁▁
numeric	SE	0	1.0000000	NA	NA	NA	NA	NA	4.234232e-01	4.820506e+00	1.06893e-01	1.220790e-01	1.514490e-01	2.396430e-01	3.856660e+02	▇▁▁▁▁
numeric	PVAL	0	1.0000000	NA	NA	NA	NA	NA	4.789084e-01	2.924754e-01	0.00000e+00	2.242962e-01	4.693826e-01	7.316746e-01	9.999800e-01	▇▇▇▆▆
numeric	PVAL_ztest	0	1.0000000	NA	NA	NA	NA	NA	4.791804e-01	2.922354e-01	0.00000e+00	2.248497e-01	4.696120e-01	7.316953e-01	9.999804e-01	▇▇▇▆▆
numeric	AF	288	0.9976350	NA	NA	NA	NA	NA	3.188701e-01	2.862978e-01	5.05000e-04	7.533000e-02	2.278000e-01	5.121000e-01	9.994950e-01	▇▃▂▂▂
numeric	AF_reference	811	0.9933403	NA	NA	NA	NA	NA	2.907768e-01	2.567245e-01	1.99700e-04	7.907350e-02	2.122600e-01	4.512780e-01	1.000000e+00	▇▃▂▂▁
numeric	N	0	1.0000000	NA	NA	NA	NA	NA	1.174000e+03	0.000000e+00	1.17400e+03	1.174000e+03	1.174000e+03	1.174000e+03	1.174000e+03	▁▁▇▁▁

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	1118275	rs61733845	C	T	0.0313315	0.139612	0.8224341	0.8224318	0.2242	0.185703	1174
1	1120431	rs1320571	G	A	-0.1410810	0.145387	0.3314691	0.3318559	0.2091	0.185304	1174
1	1135242	rs9729550	A	C	0.0007017	0.115197	0.9951400	0.9951402	0.4141	0.551917	1174
1	1140435	rs1815606	G	T	-0.0467251	0.114448	0.6830288	0.6830792	0.6354	0.712061	1174
1	1163804	rs7515488	C	T	0.0793085	0.151160	0.5997137	0.5998155	0.1591	0.186901	1174
1	1165310	rs11260562	G	A	-0.1304980	0.226966	0.5652089	0.5653135	0.0697	0.101837	1174
1	1173611	rs6697886	G	A	0.0357525	0.156017	0.8187309	0.8187463	0.1510	0.220647	1174
1	1186502	rs6603785	A	T	-0.0636855	0.158657	0.6880928	0.6881230	0.1542	0.246206	1174
1	1194804	rs11804831	T	C	-0.1232210	0.114300	0.2804581	0.2810119	0.6162	0.685903	1174
1	1218086	rs6603788	C	T	-0.0618022	0.120508	0.6079656	0.6080582	0.3520	0.469449	1174

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	50960682	rs140524	C	T	-0.0641224	0.138687	0.6437763	0.6438279	0.20860	0.230232	1174
22	50966914	rs470119	T	C	0.1396130	0.120833	0.2473882	0.2479180	0.65000	0.554712	1174
22	50971752	rs131794	A	C	0.3268860	0.191481	0.0869801	0.0877949	0.86920	0.832668	1174
22	50988193	rs131779	A	G	-0.0649348	0.113493	0.5671462	0.5672217	0.56460	0.580272	1174
22	50999182	rs140518	C	T	0.2150090	0.137409	0.1169160	0.1176445	0.74700	0.765575	1174
22	51078251	rs4040041	C	T	-0.0700269	0.116928	0.5490881	0.5492469	0.52020	0.466653	1174
22	51094926	rs9616810	C	T	-0.0456348	0.129471	0.7244560	0.7244849	0.27270	0.222444	1174
22	51105556	rs9616812	C	T	-0.0288849	0.116676	0.8044590	0.8044710	0.35300	0.362819	1174
22	51109992	rs9628185	T	C	-0.0397972	0.116840	0.7333664	0.7333951	0.36250	0.405351	1174
22	51156666	rs9628187	C	T	0.0035748	0.206057	0.9861590	0.9861587	0.07879	0.129992	1174

1   1118275 rs61733845  C   T   .   PASS    AF=0.2242   ES:SE:LP:AF:SS:ID   0.0313315:0.139612:0.0848989:0.2242:1174:rs61733845
1   1120431 rs1320571   G   A   .   PASS    AF=0.2091   ES:SE:LP:AF:SS:ID   -0.141081:0.145387:0.479557:0.2091:1174:rs1320571
1   1135242 rs9729550   A   C   .   PASS    AF=0.4141   ES:SE:LP:AF:SS:ID   0.000701652:0.115197:0.00211582:0.4141:1174:rs9729550
1   1140435 rs1815606   G   T   .   PASS    AF=0.6354   ES:SE:LP:AF:SS:ID   -0.0467251:0.114448:0.165561:0.6354:1174:rs1815606
1   1163804 rs7515488   C   T   .   PASS    AF=0.1591   ES:SE:LP:AF:SS:ID   0.0793085:0.15116:0.222056:0.1591:1174:rs7515488
1   1165310 rs11260562  G   A   .   PASS    AF=0.0697   ES:SE:LP:AF:SS:ID   -0.130498:0.226966:0.247791:0.0697:1174:rs11260562
1   1173611 rs6697886   G   A   .   PASS    AF=0.151    ES:SE:LP:AF:SS:ID   0.0357525:0.156017:0.0868588:0.151:1174:rs6697886
1   1186502 rs6603785   A   T   .   PASS    AF=0.1542   ES:SE:LP:AF:SS:ID   -0.0636855:0.158657:0.162353:0.1542:1174:rs6603785
1   1194804 rs11804831  T   C   .   PASS    AF=0.6162   ES:SE:LP:AF:SS:ID   -0.123221:0.1143:0.552132:0.6162:1174:rs11804831
1   1218086 rs6603788   C   T   .   PASS    AF=0.352    ES:SE:LP:AF:SS:ID   -0.0618022:0.120508:0.216121:0.352:1174:rs6603788
1   1227897 rs3737721   A   G   .   PASS    AF=0.1899   ES:SE:LP:AF:SS:ID   0.063492:0.139505:0.187752:0.1899:1174:rs3737721
1   1231656 rs1749951   G   A   .   PASS    AF=0.09394  ES:SE:LP:AF:SS:ID   -0.153868:0.205429:0.343348:0.09394:1174:rs1749951
1   1233941 rs1739855   T   C   .   PASS    AF=0.3414   ES:SE:LP:AF:SS:ID   -0.0421974:0.118658:0.141411:0.3414:1174:rs1739855
1   1241529 rs1536168   A   G   .   PASS    AF=0.8862   ES:SE:LP:AF:SS:ID   0.126131:0.186866:0.301488:0.8862:1174:rs1536168
1   1242468 rs2274264   G   A   .   PASS    AF=0.1874   ES:SE:LP:AF:SS:ID   0.0770219:0.14016:0.234623:0.1874:1174:rs2274264
1   1247494 rs12103 T   C   .   PASS    AF=0.4056   ES:SE:LP:AF:SS:ID   0.0290177:0.11467:0.0967993:0.4056:1174:rs12103
1   1249187 rs12142199  G   A   .   PASS    AF=0.4045   ES:SE:LP:AF:SS:ID   0.0222403:0.114873:0.0723874:0.4045:1174:rs12142199
1   1254255 rs62623580  G   A   .   PASS    AF=0.03283  ES:SE:LP:AF:SS:ID   -0.231482:0.339288:0.305483:0.03283:1174:rs62623580
1   1335790 rs1240708   A   G   .   PASS    AF=0.5919   ES:SE:LP:AF:SS:ID   0.00203915:0.115252:0.00617418:0.5919:1174:rs1240708
1   1493727 rs880051    G   A   .   PASS    AF=0.5875   ES:SE:LP:AF:SS:ID   0.170322:0.118841:0.820626:0.5875:1174:rs880051
1   1497824 rs2296716   C   T   .   PASS    AF=0.2692   ES:SE:LP:AF:SS:ID   0.131827:0.128806:0.514575:0.2692:1174:rs2296716
1   1611995 rs4074196   A   G   .   PASS    AF=0.4364   ES:SE:LP:AF:SS:ID   -0.187926:0.117095:0.968139:0.4364:1174:rs4074196
1   1706160 rs7531583   A   G   .   PASS    AF=0.5141   ES:SE:LP:AF:SS:ID   -0.0430598:0.114623:0.150497:0.5141:1174:rs7531583
1   1721479 rs2272908   C   T   .   PASS    AF=0.3551   ES:SE:LP:AF:SS:ID   -0.0753667:0.119844:0.276305:0.3551:1174:rs2272908
1   1723031 rs9660180   G   A   .   PASS    AF=0.3551   ES:SE:LP:AF:SS:ID   -0.0753667:0.119844:0.276305:0.3551:1174:rs9660180
1   1781220 rs6681938   T   C   .   PASS    AF=0.1914   ES:SE:LP:AF:SS:ID   -0.126804:0.151011:0.397068:0.1914:1174:rs6681938
1   1838516 rs2377037   C   A   .   PASS    AF=0.1556   ES:SE:LP:AF:SS:ID   0.0136718:0.158765:0.0308754:0.1556:1174:rs2377037
1   1840038 rs2474461   T   C   .   PASS    AF=0.7796   ES:SE:LP:AF:SS:ID   0.0916481:0.141043:0.287657:0.7796:1174:rs2474461
1   1853288 rs1884454   G   T   .   PASS    AF=0.1545   ES:SE:LP:AF:SS:ID   0.118924:0.154128:0.356453:0.1545:1174:rs1884454
1   1855319 rs2295362   C   T   .   PASS    AF=0.2702   ES:SE:LP:AF:SS:ID   -0.195612:0.134333:0.839772:0.2702:1174:rs2295362
1   1871337 rs16824543  G   A   .   PASS    AF=0.2712   ES:SE:LP:AF:SS:ID   -0.207201:0.13481:0.908154:0.2712:1174:rs16824543
1   1873625 rs12758705  G   A   .   PASS    AF=0.1556   ES:SE:LP:AF:SS:ID   0.111469:0.153263:0.330864:0.1556:1174:rs12758705
1   1881070 rs4648596   G   A   .   PASS    AF=0.1934   ES:SE:LP:AF:SS:ID   -0.295945:0.160328:1.19268:0.1934:1174:rs4648596
1   1888193 rs3820011   C   A   .   PASS    AF=0.1881   ES:SE:LP:AF:SS:ID   -0.0601878:0.149905:0.162391:0.1881:1174:rs3820011
1   2024064 rs2459994   C   T   .   PASS    AF=0.4083   ES:SE:LP:AF:SS:ID   0.0837102:0.114978:0.331173:0.4083:1174:rs2459994
1   2146966 rs7512482   T   C   .   PASS    AF=0.2227   ES:SE:LP:AF:SS:ID   0.249984:0.130086:1.26801:0.2227:1174:rs7512482
1   2202774 rs6673129   C   T   .   PASS    AF=0.1581   ES:SE:LP:AF:SS:ID   -0.193744:0.169554:0.597302:0.1581:1174:rs6673129
1   2229478 rs12562937  C   T   .   PASS    AF=0.2056   ES:SE:LP:AF:SS:ID   -0.145496:0.146847:0.492969:0.2056:1174:rs12562937
1   2283896 rs2840528   A   G   .   PASS    AF=0.4707   ES:SE:LP:AF:SS:ID   -0.0166789:0.114028:0.0536917:0.4707:1174:rs2840528
1   2290143 rs34587196  G   A   .   PASS    AF=0.00202  ES:SE:LP:AF:SS:ID   0.538631:1.0386:0.218988:0.00202:1174:rs34587196
1   2404256 rs2494626   C   T   .   PASS    AF=0.2465   ES:SE:LP:AF:SS:ID   0.0398802:0.132602:0.117143:0.2465:1174:rs2494626
1   2407781 rs78504402  C   T   .   PASS    AF=0.04444  ES:SE:LP:AF:SS:ID   -0.170896:0.295473:0.24957:0.04444:1174:rs78504402
1   2408471 rs115996655 G   A   .   PASS    AF=0.00202  ES:SE:LP:AF:SS:ID   0.492824:1.05238:0.194149:0.00202:1174:rs115996655
1   2408834 rs11588930  G   A   .   PASS    AF=0.02172  ES:SE:LP:AF:SS:ID   -0.310587:0.44224:0.316665:0.02172:1174:rs11588930
1   2409892 rs12727342  A   G   .   PASS    AF=0.652    ES:SE:LP:AF:SS:ID   0.107589:0.115981:0.451899:0.652:1174:rs12727342
1   2410789 rs11799501  C   T   .   PASS    AF=0.6081   ES:SE:LP:AF:SS:ID   0.122264:0.114411:0.545494:0.6081:1174:rs11799501
1   2412279 rs4648638   A   T   .   PASS    AF=0.3696   ES:SE:LP:AF:SS:ID   -0.0844071:0.114082:0.338021:0.3696:1174:rs4648638
1   2412293 rs12731186  C   T   .   PASS    AF=0.09495  ES:SE:LP:AF:SS:ID   0.139155:0.181915:0.352541:0.09495:1174:rs12731186
1   2413166 rs115810747 A   G   .   PASS    AF=0.008081 ES:SE:LP:AF:SS:ID   0.750356:0.48945:0.904228:0.008081:1174:rs115810747
1   2413464 rs114424485 G   C   .   PASS    AF=0.001515 ES:SE:LP:AF:SS:ID   0.748963:1.10313:0.303628:0.001515:1174:rs114424485

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