IEU GWAS QC Report

name	value
covariates	gender
ncase	3769
unit	log odds ratio
sex	Males and Females
build	HG19/GRCh37
nsnp	4867068
pmid	30531953
imputation_panel	HRC
year	2018
ncontrol	29677
id	ieu-b-9
qc_prior_to_upload	converted z-score to beta and SE
group_name	public
doi	10.1038/s41467-018-07524-z
subcategory	Psychiatric / neurological
study_design	Case-control study
priority	1
population	Mixed
coverage	whole genome
author	Abou-Khalil B
sample_size	33446
category	Binary
mr	1
trait	generalized epilepsy, all documented cases
consortium	The International League Against Epilepsy Consortium on Complex Epilepsies

name	value
gwas_id	ieu-b-9
bcf_file	ieu-b-9.vcf.gz
qc_metrics_file	qc_metrics.json
metadata	metadata.json
refdata	1kg_v3_nomult.bcf
compile_time	2021-02-24 15:45 UTC
build_version	master-2dbfcc4-2020-10-23

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 /data/cromwell-executions/qc/8df62438-7698-4906-a791-eb7c23c12153/call-ldsc/inputs/-674580005/ieu-b-9.vcf.gz \
--ref-ld-chr /data/ref/eur_w_ld_chr/ \
--out /data/igd/ieu-b-9/ldsc.txt \
--w-ld-chr /data/ref/eur_w_ld_chr/ 

Beginning analysis at Fri Jun 26 15:26:18 2020
Reading summary statistics from /data/cromwell-executions/qc/8df62438-7698-4906-a791-eb7c23c12153/call-ldsc/inputs/-674580005/ieu-b-9.vcf.gz ...
Read summary statistics for 4867055 SNPs.
Dropped 11484 SNPs with duplicated rs numbers.
Reading reference panel LD Score from /data/ref/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 /data/ref/eur_w_ld_chr/[1-22] ...
Read regression weight LD Scores for 1290028 SNPs.
After merging with reference panel LD, 928180 SNPs remain.
After merging with regression SNP LD, 928180 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.262 (0.0248)
Lambda GC: 1.2597
Mean Chi^2: 1.2926
Intercept: 1.0983 (0.011)
Ratio: 0.336 (0.0378)
Analysis finished at Fri Jun 26 15:27:51 2020
Total time elapsed: 1.0m:33.53s

{
    "af_correlation": 0.9333,
    "inflation_factor": 1.2455,
    "mean_EFFECT": -0.0012,
    "n": "-Inf",
    "n_snps": 4867068,
    "n_clumped_hits": 11,
    "n_p_sig": 279,
    "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": 29192,
    "n_est": "NA",
    "ratio_se_n": "NA",
    "mean_diff": "NaN",
    "ratio_diff": "NaN",
    "sd_y_est1": "NA",
    "sd_y_est2": "NA",
    "r2_sum1": 0,
    "r2_sum2": 0,
    "r2_sum3": 0,
    "r2_sum4": 0,
    "ldsc_nsnp_merge_refpanel_ld": 928180,
    "ldsc_nsnp_merge_regression_ld": 928180,
    "ldsc_observed_scale_h2_beta": 0.262,
    "ldsc_observed_scale_h2_se": 0.0248,
    "ldsc_intercept_beta": 1.0983,
    "ldsc_intercept_se": 0.011,
    "ldsc_lambda_gc": 1.2597,
    "ldsc_mean_chisq": 1.2926,
    "ldsc_ratio": 0.336
}

name	value
name	value
af_correlation	FALSE
inflation_factor	TRUE
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	1	0.9999998	3	35	0	4867065	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	4867068	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	8.640583e+00	5.868190e+00	1.00000e+00	4.000000e+00	8.000000e+00	1.200000e+01	2.30000e+01	▇▅▅▂▂
numeric	POS	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	7.979938e+07	5.501269e+07	3.30120e+04	3.479013e+07	7.127838e+07	1.144224e+08	2.49219e+08	▇▇▅▂▁
numeric	EFFECT	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	-1.225600e-03	2.386810e-02	-2.31175e-01	-1.431490e-02	-1.124600e-03	1.206160e-02	2.46288e-01	▁▁▇▁▁
numeric	SE	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	1.977680e-02	9.326500e-03	1.23560e-02	1.326020e-02	1.601340e-02	2.265310e-02	1.44980e-01	▇▁▁▁▁
numeric	PVAL	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	4.656309e-01	2.976145e-01	0.00000e+00	2.000000e-01	4.516001e-01	7.225002e-01	1.00000e+00	▇▆▆▆▆
numeric	PVAL_ztest	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	4.656397e-01	2.976167e-01	0.00000e+00	1.998423e-01	4.514507e-01	7.225894e-01	1.00000e+00	▇▆▆▆▆
numeric	AF	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	3.061288e-01	2.580667e-01	1.90000e-03	8.900000e-02	2.245000e-01	4.753000e-01	9.90000e-01	▇▃▂▂▁
numeric	AF_reference	29192	0.9940021	NA	NA	NA	NA	NA	NA	NA	3.078942e-01	2.470854e-01	1.99700e-04	1.038340e-01	2.366210e-01	4.682510e-01	1.00000e+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	1029805	rs6689308	A	G	0.0161984	0.0166137	0.3297000	0.3295590	0.1708	0.315695	NA
1	1031973	rs9651270	C	T	0.0190694	0.0172887	0.2700001	0.2700281	0.1610	0.310703	NA
1	1033596	rs6604964	T	C	0.0190775	0.0172960	0.2700001	0.2700269	0.1609	0.311701	NA
1	1033670	rs6604966	T	C	0.0198978	0.0173024	0.2500000	0.2501429	0.1609	0.315895	NA
1	1033680	rs6604967	T	A	0.0194859	0.0173054	0.2599998	0.2601650	0.1608	0.311701	NA
1	1033994	rs6698368	C	T	0.0198962	0.0173011	0.2500000	0.2501454	0.1609	0.311502	NA
1	1034200	rs77977351	T	C	0.0198981	0.0173027	0.2500000	0.2501440	0.1609	0.311502	NA
1	1036860	rs11579922	A	C	0.0169433	0.0189099	0.3700002	0.3702519	0.1278	0.282748	NA
1	1036959	rs11579015	T	C	0.0027191	0.0197038	0.8900000	0.8902402	0.1136	0.156949	NA
1	1037303	rs11260592	T	C	0.0157483	0.0179365	0.3800004	0.3799421	0.1410	0.282548	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
23	139277416	rs435268	G	A	0.0201347	0.0125999	0.1100001	0.1100419	0.4850	0.650861	NA
23	139279657	rs369604	G	A	0.0216341	0.0124836	0.0830902	0.0830954	0.4416	0.442384	NA
23	139279671	rs382435	A	G	0.0262823	0.0125994	0.0369999	0.0369790	0.4437	0.435762	NA
23	139279881	rs414554	C	G	0.0200616	0.0125542	0.1100001	0.1100432	0.4933	0.655894	NA
23	139282091	rs426349	C	T	0.0214735	0.0125870	0.0879995	0.0880069	0.4825	0.471788	NA
23	139282915	rs419307	C	T	0.0200721	0.0124439	0.1067999	0.1067429	0.4844	0.539868	NA
23	139284404	rs430026	G	A	0.0176423	0.0128588	0.1700000	0.1700628	0.4169	0.308609	NA
23	139287243	rs412811	T	C	0.0213195	0.0126902	0.0929994	0.0929579	0.4470	0.460397	NA
23	139288359	rs367697	C	T	0.0182255	0.0124491	0.1432000	0.1431936	0.4801	0.488477	NA
23	139289049	rs387193	A	G	0.0228496	0.0127013	0.0719996	0.0720192	0.4452	0.450331	NA

1   1029805 rs891281851 A   G   .   PASS    AF=0.1708   ES:SE:LP:AF:ID  0.0161984:0.0166137:0.481881:0.1708:rs891281851
1   1031973 rs9651270   C   T   .   PASS    AF=0.161    ES:SE:LP:AF:ID  0.0190694:0.0172887:0.568636:0.161:rs9651270
1   1033596 rs6604964   T   C   .   PASS    AF=0.1609   ES:SE:LP:AF:ID  0.0190775:0.017296:0.568636:0.1609:rs6604964
1   1033670 rs1370950991    T   C   .   PASS    AF=0.1609   ES:SE:LP:AF:ID  0.0198978:0.0173024:0.60206:0.1609:rs1370950991
1   1033680 rs1370950991    T   A   .   PASS    AF=0.1608   ES:SE:LP:AF:ID  0.0194859:0.0173054:0.585027:0.1608:rs1370950991
1   1033994 rs6698368   C   T   .   PASS    AF=0.1609   ES:SE:LP:AF:ID  0.0198962:0.0173011:0.60206:0.1609:rs6698368
1   1034200 rs77977351  T   C   .   PASS    AF=0.1609   ES:SE:LP:AF:ID  0.0198981:0.0173027:0.60206:0.1609:rs77977351
1   1036860 rs11579922  A   C   .   PASS    AF=0.1278   ES:SE:LP:AF:ID  0.0169433:0.0189099:0.431798:0.1278:rs11579922
1   1036959 rs1162868282    T   C   .   PASS    AF=0.1136   ES:SE:LP:AF:ID  0.00271913:0.0197038:0.05061:0.1136:rs1162868282
1   1037303 rs11260592  T   C   .   PASS    AF=0.141    ES:SE:LP:AF:ID  0.0157483:0.0179365:0.420216:0.141:rs11260592
1   1037313 rs11260593  A   G   .   PASS    AF=0.1475   ES:SE:LP:AF:ID  0.0199417:0.0174468:0.596879:0.1475:rs11260593
1   1037367 rs11260594  G   A   .   PASS    AF=0.141    ES:SE:LP:AF:ID  0.0157502:0.0179388:0.420216:0.141:rs11260594
1   1038088 rs66622470  G   C   .   PASS    AF=0.1411   ES:SE:LP:AF:ID  0.0164062:0.0179302:0.443698:0.1411:rs66622470
1   1039268 rs9329410   T   C   .   PASS    AF=0.1413   ES:SE:LP:AF:ID  0.0163933:0.0179161:0.443698:0.1413:rs9329410
1   1039817 rs1205065516    A   G   .   PASS    AF=0.1412   ES:SE:LP:AF:ID  0.0163989:0.0179223:0.443698:0.1412:rs1205065516
1   1040026 rs6671356   T   C   .   PASS    AF=0.1416   ES:SE:LP:AF:ID  0.0157091:0.0178919:0.420216:0.1416:rs6671356
1   1040472 rs6664124   C   T   .   PASS    AF=0.1415   ES:SE:LP:AF:ID  0.0157146:0.0178982:0.420216:0.1415:rs6664124
1   1040794 rs6687681   G   A   .   PASS    AF=0.1414   ES:SE:LP:AF:ID  0.0153981:0.0179047:0.408935:0.1414:rs6687681
1   1040824 rs6656379   T   C   .   PASS    AF=0.1481   ES:SE:LP:AF:ID  0.0206935:0.0174041:0.630042:0.1481:rs6656379
1   1040985 rs6697379   C   G   .   PASS    AF=0.1414   ES:SE:LP:AF:ID  0.0157198:0.0179041:0.420216:0.1414:rs6697379
1   1041700 rs6604968   A   G   .   PASS    AF=0.1416   ES:SE:LP:AF:ID  0.0153934:0.0178993:0.408935:0.1416:rs6604968
1   1041786 rs6604969   T   C   .   PASS    AF=0.1416   ES:SE:LP:AF:ID  0.015392:0.0178977:0.408935:0.1416:rs6604969
1   1042483 rs12733365  C   T   .   PASS    AF=0.1415   ES:SE:LP:AF:ID  0.0160393:0.017901:0.431798:0.1415:rs12733365
1   1042527 rs1486993720    G   C   .   PASS    AF=0.1122   ES:SE:LP:AF:ID  0.00787112:0.0197271:0.161403:0.1122:rs1486993720
1   1042673 rs897825316 C   T   .   PASS    AF=0.1426   ES:SE:LP:AF:ID  0.0157605:0.0179504:0.420216:0.1426:rs897825316
1   1042927 rs4970354   G   T   .   PASS    AF=0.1414   ES:SE:LP:AF:ID  0.0157195:0.0179037:0.420216:0.1414:rs4970354
1   1043053 rs4970355   A   G   .   PASS    AF=0.1414   ES:SE:LP:AF:ID  0.0160432:0.0179053:0.431798:0.1414:rs4970355
1   1045473 rs11586034  G   A   .   PASS    AF=0.1116   ES:SE:LP:AF:ID  0.00472771:0.0197812:0.0911398:0.1116:rs11586034
1   1046073 rs11590188  C   A   .   PASS    AF=0.1397   ES:SE:LP:AF:ID  0.0115836:0.0180149:0.283997:0.1397:rs11590188
1   1046164 rs386627439 C   T   .   PASS    AF=0.1414   ES:SE:LP:AF:ID  0.0163828:0.0179047:0.443698:0.1414:rs386627439
1   1046717 rs34820586  G   C   .   PASS    AF=0.1126   ES:SE:LP:AF:ID  0.00858634:0.0196934:0.178486:0.1126:rs34820586
1   1046861 rs12723165  G   A   .   PASS    AF=0.1126   ES:SE:LP:AF:ID  0.00858587:0.0196924:0.178486:0.1126:rs12723165
1   1047374 rs12743678  T   A   .   PASS    AF=0.1472   ES:SE:LP:AF:ID  0.0218634:0.0174628:0.676335:0.1472:rs12743678
1   1048501 rs7518814   G   A   .   PASS    AF=0.1397   ES:SE:LP:AF:ID  0.0115838:0.0180152:0.283997:0.1397:rs7518814
1   1048955 rs4970405   A   G   .   PASS    AF=0.1041   ES:SE:LP:AF:ID  0.0159036:0.0203632:0.36181:0.1041:rs4970405
1   1048989 rs4970406   A   G   .   PASS    AF=0.1142   ES:SE:LP:AF:ID  0.00348271:0.0197881:0.0655015:0.1142:rs4970406
1   1049083 rs4970407   C   A   .   PASS    AF=0.1134   ES:SE:LP:AF:ID  0.00325171:0.0198275:0.0604807:0.1134:rs4970407
1   1049950 rs12726255  A   G   .   PASS    AF=0.1459   ES:SE:LP:AF:ID  0.00585346:0.017578:0.131121:0.1459:rs12726255
1   1052946 rs12755848  G   T   .   PASS    AF=0.1116   ES:SE:LP:AF:ID  0.00761164:0.019822:0.154406:0.1116:rs12755848
1   1053452 rs4970409   G   A   .   PASS    AF=0.1115   ES:SE:LP:AF:ID  0.00675988:0.0198237:0.134659:0.1115:rs4970409
1   1053670 rs4970410   G   A   .   PASS    AF=0.1381   ES:SE:LP:AF:ID  -0.00136299:0.0181731:0.0268721:0.1381:rs4970410
1   1053724 rs4970411   A   G   .   PASS    AF=0.1377   ES:SE:LP:AF:ID  0.00136511:0.0182015:0.0268721:0.1377:rs4970411
1   1054552 rs12567697  G   A   .   PASS    AF=0.1105   ES:SE:LP:AF:ID  0.00627138:0.0199091:0.12332:0.1105:rs12567697
1   1054893 rs4970412   T   C   .   PASS    AF=0.1442   ES:SE:LP:AF:ID  0.00429952:0.0176935:0.0924812:0.1442:rs4970412
1   1055653 rs34808604  C   G   .   PASS    AF=0.1107   ES:SE:LP:AF:ID  0.00386035:0.0198987:0.0724757:0.1107:rs34808604
1   1055797 rs76744376  A   G   .   PASS    AF=0.112    ES:SE:LP:AF:ID  0.00275796:0.0199852:0.05061:0.112:rs76744376
1   1055901 rs74865318  C   A   .   PASS    AF=0.1107   ES:SE:LP:AF:ID  0.00409914:0.0198987:0.0775341:0.1107:rs74865318
1   1056269 rs71628940  C   T   .   PASS    AF=0.112    ES:SE:LP:AF:ID  0.00225845:0.0199863:0.0409586:0.112:rs71628940
1   1056348 rs66868065  G   A   .   PASS    AF=0.137    ES:SE:LP:AF:ID  -0.00321239:0.0182522:0.0655015:0.137:rs66868065
1   1056715 rs7538862   C   T   .   PASS    AF=0.1441   ES:SE:LP:AF:ID  0.00529617:0.0177129:0.116509:0.1441:rs7538862

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