IEU GWAS QC Report

name	value
id	ieu-a-966
pmid	24880342
year	2014
mr	1
note	NULL
trait	Lung cancer
category	Disease
subcategory	Cancer
population	European
sex	Males and females
ncase	11348
ncontrol	15861
sample_size	27209
nsnp	8945893
sd	NULL
priority	1
author	Wang Y
group_name	public
access	public
build	HG19/GRCh37

name	value
gwas_id	ieu-a-966
bcf_file	ieu-a-966.vcf.gz
qc_metrics_file	qc_metrics.json
metadata	metadata.json
refdata	1kg_v3_nomult.bcf
compile_time	2020-05-10 06:34 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-966/ieu-a-966.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-966/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 16:05:45 2020
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ieu-a-import/processed/ieu-a-966/ieu-a-966.vcf.gz ...
Read summary statistics for 8827545 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, 1237082 SNPs remain.
After merging with regression SNP LD, 1237082 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0715 (0.0274)
Lambda GC: 1.1136
Mean Chi^2: 1.1333
Intercept: 1.0945 (0.008)
Ratio: 0.7087 (0.0602)
Analysis finished at Tue Feb  4 16:07:13 2020
Total time elapsed: 1.0m:27.96s

{
    "af_correlation": 0.9506,
    "inflation_factor": 1.0433,
    "mean_EFFECT": 0.002,
    "n": 27209,
    "n_snps": 8827545,
    "n_clumped_hits": 5,
    "n_p_sig": 668,
    "n_mono": 0,
    "n_ns": 0,
    "n_mac": 53,
    "is_snpid_unique": true,
    "n_miss_EFFECT": 0,
    "n_miss_SE": 0,
    "n_miss_PVAL": 0,
    "n_miss_AF": 0,
    "n_miss_AF_reference": 174026,
    "n_est": 26786.2123,
    "ratio_se_n": 0.9922,
    "mean_diff": -0.006,
    "ratio_diff": 1.2156,
    "sd_y_est1": 2.1107,
    "sd_y_est2": 2.0942,
    "r2_sum1": 0.0854,
    "r2_sum2": 0.0192,
    "r2_sum3": 0.0195,
    "r2_sum4": 0.015,
    "ldsc_nsnp_merge_refpanel_ld": 1237082,
    "ldsc_nsnp_merge_regression_ld": 1237082,
    "ldsc_observed_scale_h2_beta": 0.0715,
    "ldsc_observed_scale_h2_se": 0.0274,
    "ldsc_intercept_beta": 1.0945,
    "ldsc_intercept_se": 0.008,
    "ldsc_lambda_gc": 1.1136,
    "ldsc_mean_chisq": 1.1333,
    "ldsc_ratio": 0.7089
}

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	1.000000	3	58	0	8827545	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	8.542993e+00	5.696804e+00	1.00000e+00	4.000000e+00	8.000000e+00	1.200000e+01	2.300000e+01	▇▅▅▂▂
numeric	POS	0	1.000000	NA	NA	NA	NA	NA	7.950493e+07	5.583567e+07	1.31330e+04	3.368842e+07	7.068938e+07	1.147806e+08	2.492183e+08	▇▇▅▂▁
numeric	EFFECT	0	1.000000	NA	NA	NA	NA	NA	2.021400e-03	1.005592e-01	-2.51766e+00	-2.433350e-02	3.829000e-04	2.534610e-02	3.764280e+00	▁▆▇▁▁
numeric	SE	0	1.000000	NA	NA	NA	NA	NA	5.715970e-02	7.171410e-02	1.54490e-02	2.087300e-02	3.122500e-02	6.786800e-02	2.051760e+01	▇▁▁▁▁
numeric	PVAL	0	1.000000	NA	NA	NA	NA	NA	4.928142e-01	2.909463e-01	0.00000e+00	2.383828e-01	4.908649e-01	7.451694e-01	1.000000e+00	▇▇▇▇▇
numeric	PVAL_ztest	0	1.000000	NA	NA	NA	NA	NA	4.756948e-01	2.945654e-01	0.00000e+00	2.163852e-01	4.665978e-01	7.304438e-01	1.000000e+00	▇▇▆▆▆
numeric	AF	0	1.000000	NA	NA	NA	NA	NA	2.196449e-01	2.591293e-01	6.26000e-05	1.952250e-02	1.045440e-01	3.380880e-01	9.998750e-01	▇▂▁▁▁
numeric	AF_reference	174026	0.980286	NA	NA	NA	NA	NA	2.236081e-01	2.515816e-01	0.00000e+00	2.336260e-02	1.222040e-01	3.452480e-01	1.000000e+00	▇▂▁▁▁
numeric	N	0	1.000000	NA	NA	NA	NA	NA	2.720900e+04	0.000000e+00	2.72090e+04	2.720900e+04	2.720900e+04	2.720900e+04	2.720900e+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	996248	rs28479311	C	T	-0.0421927	0.027652	0.1552891	0.1270482	0.153315	0.106629	27209
1	998501	rs3813193	G	C	-0.0397028	0.025365	0.1428749	0.1175221	0.168632	0.206669	27209
1	1002387	rs74048003	A	G	-0.0402524	0.024547	0.1246979	0.1010448	0.231683	0.244609	27209
1	1002539	rs60442576	G	A	-0.0429827	0.024427	0.1003571	0.0784690	0.236591	0.343051	27209
1	1004957	rs4073176	G	A	0.0515323	0.019370	0.0133079	0.0078044	0.575145	0.550319	27209
1	1004980	rs4073177	G	A	0.0509523	0.019375	0.0143489	0.0085436	0.574737	0.550319	27209
1	1005806	rs3934834	C	T	-0.0356137	0.022663	0.1385109	0.1160789	0.162134	0.223442	27209
1	1006223	rs9442394	G	A	0.0484038	0.019249	0.0188951	0.0119163	0.571111	0.547125	27209
1	1006990	rs4326571	G	A	0.0369275	0.021452	0.1046779	0.0851780	0.717331	0.816294	27209
1	1007203	rs4633229	A	G	0.0436970	0.024533	0.0965562	0.0748876	0.771093	0.841254	27209

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	51178607	rs6010067	G	C	-0.0127996	0.068332	0.8633570	0.8514138	0.0239328	0.0814696	27209
22	51178756	rs141844965	A	T	0.1718570	0.122002	0.1341090	0.1589414	0.0083250	0.0059904	27209
22	51179000	rs2283675	C	A	0.0002940	0.023614	0.9902770	0.9900679	0.3125400	0.5127800	27209
22	51179770	rs2283676	G	A	0.0071752	0.026653	0.7917389	0.7877696	0.2455510	0.3945690	27209
22	51180501	rs5770999	T	C	0.0130407	0.024177	0.6034176	0.5896215	0.6887920	0.6369810	27209
22	51180878	rs6010069	A	G	0.0325679	0.034858	0.3508431	0.3501481	0.1389130	0.1589460	27209
22	51180934	rs9616824	C	T	-0.0266346	0.033118	0.4492499	0.4212622	0.1269510	0.1689300	27209
22	51180959	rs6010070	A	G	0.0090836	0.035104	0.8009371	0.7958180	0.1346950	0.2056710	27209
22	51182399	rs56238942	A	G	-0.0095706	0.039872	0.8194550	0.8103036	0.0768871	0.0992412	27209
23	51866774	rs150899468	C	A	0.1269550	0.158170	0.5662810	0.4221776	0.9975970	0.9965560	27209

1   996248  rs28479311  C   T   .   PASS    AF=0.153315 ES:SE:LP:AF:SS:ID   -0.0421927:0.027652:0.808859:0.153315:27209:rs28479311
1   998501  rs3813193   G   C   .   PASS    AF=0.168632 ES:SE:LP:AF:SS:ID   -0.0397028:0.025365:0.845044:0.168632:27209:rs3813193
1   1002387 rs74048003  A   G   .   PASS    AF=0.231683 ES:SE:LP:AF:SS:ID   -0.0402524:0.024547:0.904141:0.231683:27209:rs74048003
1   1002539 rs60442576  G   A   .   PASS    AF=0.236591 ES:SE:LP:AF:SS:ID   -0.0429827:0.024427:0.998452:0.236591:27209:rs60442576
1   1004957 rs4073176   G   A   .   PASS    AF=0.575145 ES:SE:LP:AF:SS:ID   0.0515323:0.01937:1.87589:0.575145:27209:rs4073176
1   1004980 rs4073177   G   A   .   PASS    AF=0.574737 ES:SE:LP:AF:SS:ID   0.0509523:0.019375:1.84318:0.574737:27209:rs4073177
1   1005806 rs3934834   C   T   .   PASS    AF=0.162134 ES:SE:LP:AF:SS:ID   -0.0356137:0.022663:0.858516:0.162134:27209:rs3934834
1   1006223 rs9442394   G   A   .   PASS    AF=0.571111 ES:SE:LP:AF:SS:ID   0.0484038:0.019249:1.72365:0.571111:27209:rs9442394
1   1006990 rs4326571   G   A   .   PASS    AF=0.717331 ES:SE:LP:AF:SS:ID   0.0369275:0.021452:0.980145:0.717331:27209:rs4326571
1   1007203 rs4633229   A   G   .   PASS    AF=0.771093 ES:SE:LP:AF:SS:ID   0.043697:0.024533:1.01522:0.771093:27209:rs4633229
1   1007222 rs71628928  G   T   .   PASS    AF=0.171881 ES:SE:LP:AF:SS:ID   -0.0241114:0.023978:0.470866:0.171881:27209:rs71628928
1   1007432 rs4333796   G   A   .   PASS    AF=0.552182 ES:SE:LP:AF:SS:ID   0.0553732:0.018722:2.21531:0.552182:27209:rs4333796
1   1009234 rs9442366   T   C   .   PASS    AF=0.552086 ES:SE:LP:AF:SS:ID   0.0510502:0.01864:1.96944:0.552086:27209:rs9442366
1   1009478 rs9442367   G   C   .   PASS    AF=0.717736 ES:SE:LP:AF:SS:ID   0.0365841:0.021441:0.96803:0.717736:27209:rs9442367
1   1009823 rs12752391  G   A   .   PASS    AF=0.166319 ES:SE:LP:AF:SS:ID   -0.0309872:0.023163:0.687499:0.166319:27209:rs12752391
1   1010717 rs9442368   C   T   .   PASS    AF=0.557687 ES:SE:LP:AF:SS:ID   0.0501256:0.018721:1.9017:0.557687:27209:rs9442368
1   1012706 rs78632925  T   C   .   PASS    AF=0.444176 ES:SE:LP:AF:SS:ID   -0.0358676:0.022517:0.875346:0.444176:27209:rs78632925
1   1014836 rs12401605  A   G   .   PASS    AF=0.525769 ES:SE:LP:AF:SS:ID   0.0419664:0.018631:1.46657:0.525769:27209:rs12401605
1   1015126 rs36027499  A   G   .   PASS    AF=0.545    ES:SE:LP:AF:SS:ID   0.0477239:0.018219:1.84485:0.545:27209:rs36027499
1   1015257 rs9442369   A   G   .   PASS    AF=0.54227  ES:SE:LP:AF:SS:ID   0.0478466:0.018322:1.83505:0.54227:27209:rs9442369
1   1015551 rs9442370   C   T   .   PASS    AF=0.534143 ES:SE:LP:AF:SS:ID   0.0428198:0.018691:1.50358:0.534143:27209:rs9442370
1   1015716 rs61290587  G   A   .   PASS    AF=0.174792 ES:SE:LP:AF:SS:ID   -0.0193072:0.024365:0.348384:0.174792:27209:rs61290587
1   1015817 rs12746483  G   A   .   PASS    AF=0.707924 ES:SE:LP:AF:SS:ID   0.0349134:0.019975:1.00775:0.707924:27209:rs12746483
1   1017197 rs3766192   C   T   .   PASS    AF=0.534764 ES:SE:LP:AF:SS:ID   0.0440846:0.018003:1.66782:0.534764:27209:rs3766192
1   1017341 rs1133647   G   T   .   PASS    AF=0.70929  ES:SE:LP:AF:SS:ID   0.035161:0.019513:1.05452:0.70929:27209:rs1133647
1   1017587 rs3766191   C   T   .   PASS    AF=0.16163  ES:SE:LP:AF:SS:ID   -0.0256214:0.024179:0.503545:0.16163:27209:rs3766191
1   1018144 rs9442395   T   C   .   PASS    AF=0.54575  ES:SE:LP:AF:SS:ID   0.0442278:0.017749:1.71489:0.54575:27209:rs9442395
1   1018562 rs9442371   C   T   .   PASS    AF=0.5454   ES:SE:LP:AF:SS:ID   0.0440438:0.01775:1.70391:0.5454:27209:rs9442371
1   1018704 rs9442372   A   G   .   PASS    AF=0.547002 ES:SE:LP:AF:SS:ID   0.0447746:0.017688:1.75681:0.547002:27209:rs9442372
1   1019175 rs2298215   C   G   .   PASS    AF=0.7062   ES:SE:LP:AF:SS:ID   0.0448342:0.019338:1.52522:0.7062:27209:rs2298215
1   1019180 rs9442396   T   C   .   PASS    AF=0.555473 ES:SE:LP:AF:SS:ID   0.0477113:0.017965:1.88686:0.555473:27209:rs9442396
1   1020406 rs9442397   T   C   .   PASS    AF=0.710077 ES:SE:LP:AF:SS:ID   0.046794:0.019192:1.64595:0.710077:27209:rs9442397
1   1021346 rs10907177  A   G   .   PASS    AF=0.169669 ES:SE:LP:AF:SS:ID   -0.0101453:0.025261:0.155913:0.169669:27209:rs10907177
1   1021415 rs3737728   A   G   .   PASS    AF=0.713377 ES:SE:LP:AF:SS:ID   0.033745:0.018863:1.04656:0.713377:27209:rs3737728
1   1021583 rs10907178  A   C   .   PASS    AF=0.163747 ES:SE:LP:AF:SS:ID   -0.0150862:0.025136:0.248371:0.163747:27209:rs10907178
1   1021695 rs9442398   A   G   .   PASS    AF=0.707997 ES:SE:LP:AF:SS:ID   0.0464702:0.019193:1.62873:0.707997:27209:rs9442398
1   1022037 rs6701114   C   T   .   PASS    AF=0.550963 ES:SE:LP:AF:SS:ID   0.0464294:0.018002:1.80405:0.550963:27209:rs6701114
1   1023145 rs3737727   G   A   .   PASS    AF=0.154294 ES:SE:LP:AF:SS:ID   -0.0188689:0.025415:0.320761:0.154294:27209:rs3737727
1   1023310 rs11260589  T   C   .   PASS    AF=0.160316 ES:SE:LP:AF:SS:ID   -0.011244:0.026477:0.165766:0.160316:27209:rs11260589
1   1023788 rs12132100  C   T   .   PASS    AF=0.15309  ES:SE:LP:AF:SS:ID   -0.0130619:0.026182:0.19955:0.15309:27209:rs12132100
1   1026707 rs4074137   C   A   .   PASS    AF=0.600511 ES:SE:LP:AF:SS:ID   0.0189504:0.019322:0.461768:0.600511:27209:rs4074137
1   1026801 rs4562563   T   A   .   PASS    AF=0.614106 ES:SE:LP:AF:SS:ID   0.0122578:0.019428:0.266706:0.614106:27209:rs4562563
1   1027070 rs11260590  G   A   .   PASS    AF=0.110149 ES:SE:LP:AF:SS:ID   0.0487111:0.03326:0.865272:0.110149:27209:rs11260590
1   1027845 rs74048006  T   G   .   PASS    AF=0.163537 ES:SE:LP:AF:SS:ID   0.00727348:0.024191:0.114929:0.163537:27209:rs74048006
1   1027846 rs76994018  T   C   .   PASS    AF=0.163537 ES:SE:LP:AF:SS:ID   0.00727249:0.024191:0.114917:0.163537:27209:rs76994018
1   1028259 rs12077244  C   T   .   PASS    AF=0.109835 ES:SE:LP:AF:SS:ID   0.0426887:0.032354:0.740053:0.109835:27209:rs12077244
1   1029805 rs6689308   A   G   .   PASS    AF=0.1656   ES:SE:LP:AF:SS:ID   0.00625738:0.023753:0.0992528:0.1656:27209:rs6689308
1   1030374 rs12731175  G   A   .   PASS    AF=0.054412 ES:SE:LP:AF:SS:ID   -0.0715948:0.040494:0.938061:0.054412:27209:rs12731175
1   1030565 rs6687776   C   T   .   PASS    AF=0.16565  ES:SE:LP:AF:SS:ID   0.00481837:0.023636:0.0749939:0.16565:27209:rs6687776
1   1030633 rs6678318   G   A   .   PASS    AF=0.165562 ES:SE:LP:AF:SS:ID   0.00569177:0.023696:0.0896633:0.165562:27209:rs6678318

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