IEU GWAS QC Report

name	value
covariates	gender
ncase	9671
unit	log odds ratio
sex	Males and Females
build	HG19/GRCh37
nsnp	4862782
pmid	30531953
imputation_panel	HRC
year	2018
ncontrol	29677
id	ieu-b-10
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	39348
category	Binary
mr	1
trait	focal epilepsy, all documented cases
consortium	The International League Against Epilepsy Consortium on Complex Epilepsies

name	value
gwas_id	ieu-b-10
bcf_file	ieu-b-10.vcf.gz
qc_metrics_file	qc_metrics.json
metadata	metadata.json
refdata	1kg_v3_nomult.bcf
compile_time	2021-02-25 04:32 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/51a78191-9487-491f-bad1-ae9e123740e7/call-ldsc/inputs/562856125/ieu-b-10.vcf.gz \
--ref-ld-chr /data/ref/eur_w_ld_chr/ \
--out /data/igd/ieu-b-10/ldsc.txt \
--w-ld-chr /data/ref/eur_w_ld_chr/ 

Beginning analysis at Fri Jun 26 15:25:34 2020
Reading summary statistics from /data/cromwell-executions/qc/51a78191-9487-491f-bad1-ae9e123740e7/call-ldsc/inputs/562856125/ieu-b-10.vcf.gz ...
Read summary statistics for 4862769 SNPs.
Dropped 11434 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, 930582 SNPs remain.
After merging with regression SNP LD, 930582 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0314 (0.0173)
Lambda GC: 1.194
Mean Chi^2: 1.1913
Intercept: 1.1639 (0.0101)
Ratio: 0.8568 (0.0528)
Analysis finished at Fri Jun 26 15:28:10 2020
Total time elapsed: 2.0m:36.81s

{
    "af_correlation": 0.9402,
    "inflation_factor": 1.1909,
    "mean_EFFECT": 0.0007,
    "n": "-Inf",
    "n_snps": 4862782,
    "n_clumped_hits": 1,
    "n_p_sig": 4,
    "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": 28996,
    "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": 930582,
    "ldsc_nsnp_merge_regression_ld": 930582,
    "ldsc_observed_scale_h2_beta": 0.0314,
    "ldsc_observed_scale_h2_se": 0.0173,
    "ldsc_intercept_beta": 1.1639,
    "ldsc_intercept_se": 0.0101,
    "ldsc_lambda_gc": 1.194,
    "ldsc_mean_chisq": 1.1913,
    "ldsc_ratio": 0.8568
}

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	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	4862779	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	4862782	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	8.638433e+00	5.865020e+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	7.980983e+07	5.499637e+07	3.30120e+04	3.481541e+07	7.129924e+07	1.144377e+08	2.49219e+08	▇▇▅▂▁
numeric	EFFECT	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	6.963000e-04	1.619080e-02	-1.51322e-01	-8.257000e-03	4.211000e-04	9.229200e-03	1.92876e-01	▁▂▇▁▁
numeric	SE	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	1.348290e-02	6.379600e-03	8.38360e-03	9.049900e-03	1.092100e-02	1.542740e-02	8.75774e-02	▇▁▁▁▁
numeric	PVAL	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	4.730882e-01	2.956290e-01	0.00000e+00	2.099999e-01	4.617001e-01	7.300002e-01	1.00000e+00	▇▆▆▆▆
numeric	PVAL_ztest	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	4.730932e-01	2.956302e-01	0.00000e+00	2.098431e-01	4.617308e-01	7.300942e-01	1.00000e+00	▇▇▆▆▆
numeric	AF	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	3.062228e-01	2.567318e-01	2.60000e-03	9.060000e-02	2.251000e-01	4.741000e-01	9.90000e-01	▇▃▂▂▁
numeric	AF_reference	28996	0.9940372	NA	NA	NA	NA	NA	NA	NA	3.074937e-01	2.470186e-01	1.99700e-04	1.034350e-01	2.360220e-01	4.676520e-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	-0.0050973	0.0113023	0.6520997	0.6519907	0.1685	0.3156950	NA
1	1030565	rs6687776	C	T	-0.0023476	0.0112865	0.8352000	0.8352284	0.1684	0.3067090	NA
1	1030633	rs6678318	G	A	-0.0022920	0.0112907	0.8395000	0.8391359	0.1683	0.3065100	NA
1	1031973	rs9651270	C	T	-0.0015743	0.0116615	0.8923000	0.8926114	0.1611	0.3107030	NA
1	1033596	rs6604964	T	C	-0.0014353	0.0116692	0.9022000	0.9021065	0.1609	0.3117010	NA
1	1033670	rs6604966	T	C	-0.0024406	0.0116777	0.8342000	0.8344482	0.1608	0.3158950	NA
1	1033680	rs6604967	T	A	-0.0025221	0.0116765	0.8292000	0.8289873	0.1608	0.3117010	NA
1	1033994	rs6698368	C	T	-0.0025916	0.0116739	0.8243000	0.8243143	0.1609	0.3115020	NA
1	1034200	rs77977351	T	C	-0.0021481	0.0116742	0.8540001	0.8540130	0.1609	0.3115020	NA
1	1036601	rs72910156	C	T	0.0654638	0.0285868	0.0219999	0.0220213	0.0242	0.0399361	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	139314056	rs5955415	C	G	0.0065677	0.0149265	0.6600001	0.6599371	0.0969	0.125033	NA
23	139314798	rs62609008	C	A	0.0076393	0.0149791	0.6100002	0.6100515	0.0961	0.106755	NA
23	139315107	rs62609010	T	A	0.0082786	0.0149703	0.5800000	0.5802637	0.0962	0.107020	NA
23	139316769	rs73230748	T	C	0.0096035	0.0149354	0.5199996	0.5202229	0.0967	0.121060	NA
23	139317055	rs62609011	C	A	0.0096027	0.0149343	0.5199996	0.5202247	0.0967	0.116026	NA
23	139317165	rs28877369	C	G	0.0078335	0.0149495	0.5999997	0.6002789	0.0965	0.116291	NA
23	139317337	rs55757628	A	G	0.0072163	0.0149717	0.6300007	0.6298069	0.0962	0.107285	NA
23	139319949	rs140616281	A	G	0.0082804	0.0149735	0.5800000	0.5802623	0.0962	0.116291	NA
23	139320136	rs76621315	C	G	0.0082547	0.0149272	0.5800000	0.5802644	0.0968	0.114437	NA
23	139325995	rs62609013	G	A	0.0080563	0.0149467	0.5900000	0.5898869	0.0965	0.101987	NA

1   1029805 rs891281851 A   G   .   PASS    AF=0.1685   ES:SE:LP:AF:ID  -0.00509732:0.0113023:0.185686:0.1685:rs891281851
1   1030565 rs6687776   C   T   .   PASS    AF=0.1684   ES:SE:LP:AF:ID  -0.0023476:0.0112865:0.0782095:0.1684:rs6687776
1   1030633 rs6678318   G   A   .   PASS    AF=0.1683   ES:SE:LP:AF:ID  -0.002292:0.0112907:0.0759793:0.1683:rs6678318
1   1031973 rs9651270   C   T   .   PASS    AF=0.1611   ES:SE:LP:AF:ID  -0.00157431:0.0116615:0.0494891:0.1611:rs9651270
1   1033596 rs6604964   T   C   .   PASS    AF=0.1609   ES:SE:LP:AF:ID  -0.00143532:0.0116692:0.0446972:0.1609:rs6604964
1   1033670 rs1370950991    T   C   .   PASS    AF=0.1608   ES:SE:LP:AF:ID  -0.00244064:0.0116777:0.0787298:0.1608:rs1370950991
1   1033680 rs1370950991    T   A   .   PASS    AF=0.1608   ES:SE:LP:AF:ID  -0.00252213:0.0116765:0.0813407:0.1608:rs1370950991
1   1033994 rs6698368   C   T   .   PASS    AF=0.1609   ES:SE:LP:AF:ID  -0.0025916:0.0116739:0.0839147:0.1609:rs6698368
1   1034200 rs77977351  T   C   .   PASS    AF=0.1609   ES:SE:LP:AF:ID  -0.00214806:0.0116742:0.0685421:0.1609:rs77977351
1   1036601 rs72910156  C   T   .   PASS    AF=0.0242   ES:SE:LP:AF:ID  0.0654638:0.0285868:1.65758:0.0242:rs72910156
1   1036860 rs11579922  A   C   .   PASS    AF=0.1304   ES:SE:LP:AF:ID  -0.017295:0.0126796:0.762959:0.1304:rs11579922
1   1036959 rs1162868282    T   C   .   PASS    AF=0.1169   ES:SE:LP:AF:ID  -0.026296:0.0131546:1.34046:0.1169:rs1162868282
1   1037303 rs11260592  T   C   .   PASS    AF=0.1432   ES:SE:LP:AF:ID  -0.00978826:0.0120545:0.37976:0.1432:rs11260592
1   1037313 rs11260593  A   G   .   PASS    AF=0.1474   ES:SE:LP:AF:ID  -0.00804109:0.0118426:0.303818:0.1474:rs11260593
1   1037367 rs11260594  G   A   .   PASS    AF=0.1432   ES:SE:LP:AF:ID  -0.00977758:0.0120562:0.37976:0.1432:rs11260594
1   1038088 rs66622470  G   C   .   PASS    AF=0.1433   ES:SE:LP:AF:ID  -0.0101104:0.0120506:0.396314:0.1433:rs66622470
1   1039098 rs11260595  C   A   .   PASS    AF=0.0246   ES:SE:LP:AF:ID  0.0622853:0.0283502:1.55284:0.0246:rs11260595
1   1039268 rs9329410   T   C   .   PASS    AF=0.1434   ES:SE:LP:AF:ID  -0.0106113:0.0120446:0.421934:0.1434:rs9329410
1   1039817 rs1205065516    A   G   .   PASS    AF=0.1433   ES:SE:LP:AF:ID  -0.0111339:0.0120497:0.449283:0.1433:rs1205065516
1   1040026 rs6671356   T   C   .   PASS    AF=0.1437   ES:SE:LP:AF:ID  -0.0108017:0.0120287:0.433091:0.1437:rs6671356
1   1040472 rs6664124   C   T   .   PASS    AF=0.1435   ES:SE:LP:AF:ID  -0.0116281:0.0120373:0.476384:0.1435:rs6664124
1   1040794 rs6687681   G   A   .   PASS    AF=0.1435   ES:SE:LP:AF:ID  -0.0119178:0.0120382:0.492144:0.1435:rs6687681
1   1040824 rs6656379   T   C   .   PASS    AF=0.1478   ES:SE:LP:AF:ID  -0.010013:0.0118217:0.4011:0.1478:rs6656379
1   1040985 rs6697379   C   G   .   PASS    AF=0.1435   ES:SE:LP:AF:ID  -0.0116768:0.0120379:0.478862:0.1435:rs6697379
1   1041700 rs6604968   A   G   .   PASS    AF=0.1436   ES:SE:LP:AF:ID  -0.0120871:0.012039:0.501001:0.1436:rs6604968
1   1041786 rs6604969   T   C   .   PASS    AF=0.1436   ES:SE:LP:AF:ID  -0.0130651:0.0120415:0.556268:0.1436:rs6604969
1   1042483 rs12733365  C   T   .   PASS    AF=0.1435   ES:SE:LP:AF:ID  -0.0125691:0.0120394:0.528122:0.1435:rs12733365
1   1042527 rs1486993720    G   C   .   PASS    AF=0.1158   ES:SE:LP:AF:ID  -0.0251581:0.0132202:1.24397:0.1158:rs1486993720
1   1042673 rs897825316 C   T   .   PASS    AF=0.1445   ES:SE:LP:AF:ID  -0.0130381:0.0120723:0.552687:0.1445:rs897825316
1   1042927 rs4970354   G   T   .   PASS    AF=0.1435   ES:SE:LP:AF:ID  -0.0125672:0.0120376:0.527975:0.1435:rs4970354
1   1043053 rs4970355   A   G   .   PASS    AF=0.1434   ES:SE:LP:AF:ID  -0.0122709:0.0120421:0.510886:0.1434:rs4970355
1   1045473 rs11586034  G   A   .   PASS    AF=0.1153   ES:SE:LP:AF:ID  -0.0260186:0.0132478:1.30487:0.1153:rs11586034
1   1046073 rs11590188  C   A   .   PASS    AF=0.1418   ES:SE:LP:AF:ID  -0.0128713:0.0121085:0.540758:0.1418:rs11590188
1   1046164 rs386627439 C   T   .   PASS    AF=0.1434   ES:SE:LP:AF:ID  -0.0125237:0.012042:0.525056:0.1434:rs386627439
1   1046717 rs34820586  G   C   .   PASS    AF=0.1162   ES:SE:LP:AF:ID  -0.0267244:0.0131972:1.36785:0.1162:rs34820586
1   1046861 rs12723165  G   A   .   PASS    AF=0.1162   ES:SE:LP:AF:ID  -0.0271588:0.0131967:1.4022:0.1162:rs12723165
1   1047374 rs12743678  T   A   .   PASS    AF=0.147    ES:SE:LP:AF:ID  -0.0109789:0.0118562:0.450506:0.147:rs12743678
1   1048501 rs7518814   G   A   .   PASS    AF=0.1418   ES:SE:LP:AF:ID  -0.0128105:0.0121082:0.537752:0.1418:rs7518814
1   1048955 rs4970405   A   G   .   PASS    AF=0.1057   ES:SE:LP:AF:ID  -0.034906:0.0137534:1.9535:0.1057:rs4970405
1   1048989 rs4970406   A   G   .   PASS    AF=0.1179   ES:SE:LP:AF:ID  -0.0226921:0.0132084:1.06697:0.1179:rs4970406
1   1049083 rs4970407   C   A   .   PASS    AF=0.117    ES:SE:LP:AF:ID  -0.0236713:0.013239:1.1323:0.117:rs4970407
1   1049950 rs12726255  A   G   .   PASS    AF=0.1468   ES:SE:LP:AF:ID  -0.005082:0.0118738:0.174899:0.1468:rs12726255
1   1052946 rs12755848  G   T   .   PASS    AF=0.1153   ES:SE:LP:AF:ID  -0.0239559:0.013272:1.14783:0.1153:rs12755848
1   1053452 rs4970409   G   A   .   PASS    AF=0.1152   ES:SE:LP:AF:ID  -0.024132:0.0132739:1.1604:0.1152:rs4970409
1   1053670 rs4970410   G   A   .   PASS    AF=0.1414   ES:SE:LP:AF:ID  -0.00462848:0.0121482:0.152859:0.1414:rs4970410
1   1053724 rs4970411   A   G   .   PASS    AF=0.141    ES:SE:LP:AF:ID  -0.00500123:0.0121685:0.166789:0.141:rs4970411
1   1054552 rs12567697  G   A   .   PASS    AF=0.1143   ES:SE:LP:AF:ID  -0.0237692:0.0133236:1.12808:0.1143:rs12567697
1   1054893 rs4970412   T   C   .   PASS    AF=0.1453   ES:SE:LP:AF:ID  -0.00432446:0.011946:0.14442:0.1453:rs4970412
1   1055653 rs34808604  C   G   .   PASS    AF=0.1145   ES:SE:LP:AF:ID  -0.0238627:0.0133163:1.13632:0.1145:rs34808604
1   1055797 rs76744376  A   G   .   PASS    AF=0.1157   ES:SE:LP:AF:ID  -0.0216763:0.0133393:0.982132:0.1157:rs76744376

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