IEU GWAS QC Report

name	value
id	UKB-b-3027
trait	Diagnoses - secondary ICD10: K31.7 Polyp of stomach and duodenum
note	41204#K317: Output from GWAS pipeline using Phesant derived variables from UKBiobank
year	2018
author	Ben Elsworth
population	European
sex	Males and Females
sample_size	463010
ncase	1233
ncontrol	461777
units	SD
build	HG19/GRCh37
group_name	public
access	public
nsnp	9851867
mr	1
category	Binary
batch	UKB-b
sd	NULL
priority	1
consortium	MRC-IEU

name	value
gwas_id	ukb-b-3027
bcf_file	ukb-b-3027.vcf.gz
qc_metrics_file	qc_metrics.json
metadata	metadata.json
refdata	1kg_v3_nomult.bcf
compile_time	2020-05-10 08:58 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/public/UKB-b-3027/UKB-b-3027_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-3027/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:43:15 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-3027/UKB-b-3027_data.vcf.gz ...
Read summary statistics for 1971010 SNPs.
Dropped 190 SNPs with duplicated rs numbers.
Reading reference panel LD Score from ../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 ../reference/eur_w_ld_chr/[1-22] ...
Read regression weight LD Scores for 1290028 SNPs.
After merging with reference panel LD, 505377 SNPs remain.
After merging with regression SNP LD, 505377 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0017 (0.0013)
Lambda GC: 1.0155
Mean Chi^2: 1.0111
Intercept: 0.9924 (0.0104)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Thu Oct 17 14:43:44 2019
Total time elapsed: 29.29s

{
    "af_correlation": 0.6906,
    "inflation_factor": 1,
    "mean_EFFECT": -3.448e-07,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 0,
    "n_p_sig": 0,
    "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": 15643,
    "n_est": "NA",
    "ratio_se_n": "NA",
    "mean_diff": "NaN",
    "ratio_diff": "NaN",
    "sd_y_est1": "NaN",
    "sd_y_est2": "NA",
    "r2_sum1": 0,
    "r2_sum2": 0,
    "r2_sum3": 0,
    "r2_sum4": 0,
    "ldsc_nsnp_merge_refpanel_ld": 505377,
    "ldsc_nsnp_merge_regression_ld": 505377,
    "ldsc_observed_scale_h2_beta": 0.0017,
    "ldsc_observed_scale_h2_se": 0.0013,
    "ldsc_intercept_beta": 0.9924,
    "ldsc_intercept_se": 0.0104,
    "ldsc_lambda_gc": 1.0155,
    "ldsc_mean_chisq": 1.0111,
    "ldsc_ratio": -0.6847
}

name	value
name	value
af_correlation	TRUE
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	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	logical.mean	logical.count	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	logical.mean	logical.count	numeric.mean	numeric.sd	numeric.p0	numeric.p25	numeric.p50	numeric.p75	numeric.p100	numeric.hist
character	ID	0	1.0000000	4	58	0	1970822	0	NA	NA	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	NA	NA
character	ALT	0	1.0000000	1	1	0	4	0	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
logical	N	1971010	0.0000000	NA	NA	NA	NA	NA	NaN	:	NA	NA	NA	NA	NA	NA	NA	NA
numeric	CHROM	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	8.650238e+00	5.762960e+00	1.0000000	4.000000e+00	8.000000e+00	1.300000e+01	2.200000e+01	▇▅▅▂▂
numeric	POS	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	7.873926e+07	5.665054e+07	5687.0000000	3.185507e+07	6.927208e+07	1.148744e+08	2.491722e+08	▇▆▅▂▁
numeric	EFFECT	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	-3.000000e-07	1.123000e-04	-0.0006017	-7.620000e-05	-6.000000e-07	7.530000e-05	5.630000e-04	▁▁▇▂▁
numeric	SE	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	1.114000e-04	4.100000e-06	0.0001033	1.082000e-04	1.104000e-04	1.140000e-04	2.120000e-04	▇▁▁▁▁
numeric	PVAL	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	4.973517e-01	2.891162e-01	0.0000002	2.500000e-01	5.000000e-01	7.499995e-01	1.000000e+00	▇▇▇▇▇
numeric	PVAL_ztest	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	4.973516e-01	2.890915e-01	0.0000002	2.464164e-01	4.964381e-01	7.473201e-01	9.999985e-01	▇▇▇▇▇
numeric	AF	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	4.700286e-01	1.236018e-01	0.2838610	3.614660e-01	4.551600e-01	5.713310e-01	7.161390e-01	▇▆▆▅▅
numeric	AF_reference	15643	0.9920635	NA	NA	NA	NA	NA	NA	NA	4.496177e-01	1.595155e-01	0.0001997	3.268770e-01	4.404950e-01	5.652960e-01	1.000000e+00	▁▇▇▃▁

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	49298	rs200943160	T	C	-0.0001367	0.0001902	0.4700002	0.4723573	0.623765	0.782149	NA
1	54676	rs2462492	C	T	-0.0000772	0.0001884	0.6800001	0.6818421	0.400401	NA	NA
1	91536	rs6702460	G	T	-0.0000911	0.0001855	0.6200004	0.6234082	0.456846	0.420727	NA
1	706368	rs55727773	A	G	-0.0000276	0.0001316	0.8300000	0.8335852	0.515645	0.275160	NA
1	763394	rs369924889	G	A	-0.0000285	0.0001542	0.8499999	0.8531740	0.706753	0.617612	NA
1	814495	rs74461805	C	A	0.0004398	0.0001804	0.0150000	0.0147693	0.340396	NA	NA
1	830181	rs28444699	A	G	0.0000126	0.0001207	0.9199999	0.9166707	0.697255	0.691294	NA
1	831489	rs4970385	C	T	0.0000083	0.0001185	0.9400001	0.9439784	0.705397	0.649161	NA
1	831909	rs9697642	C	T	0.0000073	0.0001185	0.9500000	0.9510018	0.705442	0.648562	NA
1	832066	rs9697380	G	C	0.0000059	0.0001185	0.9599999	0.9603027	0.705627	0.664337	NA

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	51165664	rs8137951	G	A	0.0000577	0.0001166	0.6200004	0.6208583	0.301546	0.406350	NA
22	51174048	rs9628245	G	C	0.0000725	0.0001222	0.5500004	0.5531292	0.380135	0.433107	NA
22	51180501	rs5770999	T	C	0.0001541	0.0001238	0.2099999	0.2132206	0.713656	0.636981	NA
22	51181919	rs9616825	G	C	0.0000848	0.0001232	0.4899999	0.4910398	0.695470	0.619409	NA
22	51182485	rs6009961	A	G	0.0001478	0.0001242	0.2300001	0.2340241	0.715502	0.638379	NA
22	51186143	rs2879914	T	C	0.0000524	0.0001152	0.6499995	0.6493511	0.381825	0.273363	NA
22	51186228	rs3865766	C	T	0.0000081	0.0001123	0.9400001	0.9421919	0.451061	0.453275	NA
22	51197266	rs61290853	A	G	-0.0000501	0.0001159	0.6700003	0.6655646	0.386333	0.422923	NA
22	51212875	rs2238837	A	C	0.0000399	0.0001236	0.7499995	0.7469404	0.331457	0.372404	NA
22	51237063	rs3896457	T	C	-0.0000943	0.0001265	0.4600002	0.4558460	0.297974	0.205072	NA

1   49298   rs10399793  T   C   .   PASS    AF=0.623765 ES:SE:LP:AF:ID  -0.000136687:0.0001902:0.327902:0.623765:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400401 ES:SE:LP:AF:ID  -7.72466e-05:0.000188429:0.167491:0.400401:rs2462492
1   91536   rs6702460   G   T   .   PASS    AF=0.456846 ES:SE:LP:AF:ID  -9.11005e-05:0.000185531:0.207608:0.456846:rs6702460
1   706368  rs12029736  A   G   .   PASS    AF=0.515645 ES:SE:LP:AF:ID  -2.76413e-05:0.000131559:0.0809219:0.515645:rs12029736
1   763394  rs3115847   G   A   .   PASS    AF=0.706753 ES:SE:LP:AF:ID  -2.85454e-05:0.000154241:0.0705811:0.706753:rs3115847
1   814495  rs74461805  C   A   .   PASS    AF=0.340396 ES:SE:LP:AF:ID  0.000439791:0.000180391:1.82391:0.340396:rs74461805
1   830181  rs28444699  A   G   .   PASS    AF=0.697255 ES:SE:LP:AF:ID  1.26277e-05:0.000120691:0.0362122:0.697255:rs28444699
1   831489  rs4970385   C   T   .   PASS    AF=0.705397 ES:SE:LP:AF:ID  8.32747e-06:0.000118506:0.0268721:0.705397:rs4970385
1   831909  rs9697642   C   T   .   PASS    AF=0.705442 ES:SE:LP:AF:ID  7.28181e-06:0.000118502:0.0222764:0.705442:rs9697642
1   832066  rs9697380   G   C   .   PASS    AF=0.705627 ES:SE:LP:AF:ID  5.89858e-06:0.000118508:0.0177288:0.705627:rs9697380
1   832318  rs4500250   C   A   .   PASS    AF=0.705655 ES:SE:LP:AF:ID  5.34499e-06:0.00011852:0.0177288:0.705655:rs4500250
1   832918  rs28765502  T   C   .   PASS    AF=0.294377 ES:SE:LP:AF:ID  -5.93418e-06:0.000118515:0.0177288:0.294377:rs28765502
1   840753  rs4970382   T   C   .   PASS    AF=0.400124 ES:SE:LP:AF:ID  -0.000185787:0.000109312:1.05061:0.400124:rs4970382
1   843405  rs11516185  A   G   .   PASS    AF=0.362606 ES:SE:LP:AF:ID  5.1727e-05:0.000135695:0.154902:0.362606:rs11516185
1   850218  rs6664536   T   A   .   PASS    AF=0.590331 ES:SE:LP:AF:ID  4.31353e-05:0.000108993:0.161151:0.590331:rs6664536
1   850371  rs6679046   G   T   .   PASS    AF=0.603723 ES:SE:LP:AF:ID  3.55065e-05:0.000109605:0.124939:0.603723:rs6679046
1   850780  rs6657440   C   T   .   PASS    AF=0.603942 ES:SE:LP:AF:ID  3.44387e-05:0.00010959:0.124939:0.603942:rs6657440
1   852037  rs4970463   G   A   .   PASS    AF=0.589686 ES:SE:LP:AF:ID  4.51869e-05:0.00010917:0.167491:0.589686:rs4970463
1   852063  rs28436996  G   A   .   PASS    AF=0.589665 ES:SE:LP:AF:ID  4.68236e-05:0.000109122:0.173925:0.589665:rs28436996
1   852875  rs13303369  C   T   .   PASS    AF=0.607671 ES:SE:LP:AF:ID  4.06646e-05:0.000109834:0.148742:0.607671:rs13303369
1   853954  rs1806509   C   A   .   PASS    AF=0.607829 ES:SE:LP:AF:ID  3.90124e-05:0.000109849:0.142668:0.607829:rs1806509
1   854777  rs13303019  A   G   .   PASS    AF=0.610316 ES:SE:LP:AF:ID  2.65311e-05:0.000109957:0.091515:0.610316:rs13303019
1   854978  rs13303057  A   C   .   PASS    AF=0.603283 ES:SE:LP:AF:ID  3.54217e-05:0.000109632:0.124939:0.603283:rs13303057
1   855075  rs6673914   C   G   .   PASS    AF=0.610337 ES:SE:LP:AF:ID  2.68688e-05:0.000109959:0.091515:0.610337:rs6673914
1   856099  rs28534711  T   G   .   PASS    AF=0.389936 ES:SE:LP:AF:ID  -3.06877e-05:0.00010998:0.107905:0.389936:rs28534711
1   856108  rs28742275  A   G   .   PASS    AF=0.38992  ES:SE:LP:AF:ID  -3.04757e-05:0.000109986:0.107905:0.38992:rs28742275
1   856476  rs4040605   A   G   .   PASS    AF=0.350356 ES:SE:LP:AF:ID  -4.79331e-05:0.000112987:0.173925:0.350356:rs4040605
1   866893  rs2880024   T   C   .   PASS    AF=0.610552 ES:SE:LP:AF:ID  2.07534e-05:0.000110576:0.0705811:0.610552:rs2880024
1   868418  rs28546443  C   T   .   PASS    AF=0.297867 ES:SE:LP:AF:ID  2.17856e-06:0.000121491:0.00436481:0.297867:rs28546443
1   870645  rs28576697  T   C   .   PASS    AF=0.291285 ES:SE:LP:AF:ID  -2.18093e-06:0.000120525:0.00436481:0.291285:rs28576697
1   873558  rs1110052   G   T   .   PASS    AF=0.715251 ES:SE:LP:AF:ID  -4.44427e-05:0.000118826:0.148742:0.715251:rs1110052
1   875770  rs4970379   A   G   .   PASS    AF=0.600085 ES:SE:LP:AF:ID  2.03761e-05:0.000111496:0.0705811:0.600085:rs4970379
1   881627  rs2272757   G   A   .   PASS    AF=0.652393 ES:SE:LP:AF:ID  -1.32272e-06:0.00011263:0.00436481:0.652393:rs2272757
1   891059  rs13303065  C   T   .   PASS    AF=0.652432 ES:SE:LP:AF:ID  -1.65914e-06:0.000112612:0.00436481:0.652432:rs13303065
1   891945  rs13303106  A   G   .   PASS    AF=0.652494 ES:SE:LP:AF:ID  3.88636e-06:0.000112744:0.0132283:0.652494:rs13303106
1   903245  rs28690976  A   G   .   PASS    AF=0.566938 ES:SE:LP:AF:ID  -3.41171e-05:0.000111979:0.119186:0.566938:rs28690976
1   909073  rs3892467   C   T   .   PASS    AF=0.386681 ES:SE:LP:AF:ID  -0.000187006:0.000111675:1.02687:0.386681:rs3892467
1   909238  rs3829740   G   C   .   PASS    AF=0.571408 ES:SE:LP:AF:ID  -0.000154803:0.000108154:0.823909:0.571408:rs3829740
1   910394  rs28477686  C   T   .   PASS    AF=0.324458 ES:SE:LP:AF:ID  -5.78741e-05:0.000117226:0.207608:0.324458:rs28477686
1   912049  rs7367995   T   C   .   PASS    AF=0.585249 ES:SE:LP:AF:ID  2.4948e-05:0.00010925:0.0861861:0.585249:rs7367995
1   913889  rs2340596   G   A   .   PASS    AF=0.59921  ES:SE:LP:AF:ID  3.76155e-05:0.000109428:0.136677:0.59921:rs2340596
1   914333  rs13302979  C   G   .   PASS    AF=0.602516 ES:SE:LP:AF:ID  -1.47799e-05:0.000109759:0.05061:0.602516:rs13302979
1   914852  rs13303368  G   C   .   PASS    AF=0.600074 ES:SE:LP:AF:ID  3.33884e-05:0.000109548:0.119186:0.600074:rs13303368
1   914940  rs13303033  T   C   .   PASS    AF=0.584289 ES:SE:LP:AF:ID  5.98139e-05:0.000108934:0.236572:0.584289:rs13303033
1   916834  rs6694632   G   A   .   PASS    AF=0.589102 ES:SE:LP:AF:ID  4.60402e-05:0.000109101:0.173925:0.589102:rs6694632
1   918384  rs13303118  G   T   .   PASS    AF=0.584202 ES:SE:LP:AF:ID  5.55306e-05:0.000108883:0.21467:0.584202:rs13303118
1   918573  rs2341354   A   G   .   PASS    AF=0.589327 ES:SE:LP:AF:ID  4.80382e-05:0.000109017:0.180456:0.589327:rs2341354
1   919501  rs4970414   G   T   .   PASS    AF=0.583926 ES:SE:LP:AF:ID  0.00010883:0.000112741:0.481486:0.583926:rs4970414
1   921716  rs13303278  C   A   .   PASS    AF=0.567888 ES:SE:LP:AF:ID  0.000108406:0.000108792:0.49485:0.567888:rs13303278
1   924528  rs34712273  C   A   .   PASS    AF=0.578491 ES:SE:LP:AF:ID  0.000122813:0.000109104:0.585027:0.578491:rs34712273

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