IEU GWAS QC Report

name	value
id	ieu-a-9
pmid	23202125
year	2013
mr	1
note	NULL
trait	Coronary heart disease
category	Disease
subcategory	Cardiovascular
population	Mixed
sex	Males and females
ncase	63746
ncontrol	130681
sample_size	194427
nsnp	79129
sd	NULL
priority	1
author	Deloukas
group_name	public
access	public
build	HG19/GRCh37

name	value
gwas_id	ieu-a-9
bcf_file	ieu-a-9.vcf.gz
qc_metrics_file	qc_metrics.json
metadata	metadata.json
refdata	1kg_v3_nomult.bcf
compile_time	2020-05-09 15:43 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-9/ieu-a-9.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-9/ldsc.txt \
--w-ld-chr /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/ 

Beginning analysis at Wed Feb  5 11:33:08 2020
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ieu-a-import/processed/ieu-a-9/ieu-a-9.vcf.gz ...
Read summary statistics for 76933 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, 51681 SNPs remain.
After merging with regression SNP LD, 51681 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: 0.2129 (0.0496)
Lambda GC: 1.3162
Mean Chi^2: 1.5048
Intercept: 1.077 (0.0338)
Ratio: 0.1525 (0.0669)
Analysis finished at Wed Feb  5 11:33:17 2020
Total time elapsed: 9.15s

{
    "af_correlation": 0.9306,
    "inflation_factor": 1.3123,
    "mean_EFFECT": 0.0005,
    "n": 107418,
    "n_snps": 76933,
    "n_clumped_hits": 10,
    "n_p_sig": 109,
    "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": 501,
    "n_est": 102952.1511,
    "ratio_se_n": 0.979,
    "mean_diff": -0.0003,
    "ratio_diff": 237.1447,
    "sd_y_est1": 2.6477,
    "sd_y_est2": 2.592,
    "r2_sum1": 0.0447,
    "r2_sum2": 0.0064,
    "r2_sum3": 0.0067,
    "r2_sum4": 0.0063,
    "ldsc_nsnp_merge_refpanel_ld": 51681,
    "ldsc_nsnp_merge_regression_ld": 51681,
    "ldsc_observed_scale_h2_beta": 0.2129,
    "ldsc_observed_scale_h2_se": 0.0496,
    "ldsc_intercept_beta": 1.077,
    "ldsc_intercept_se": 0.0338,
    "ldsc_lambda_gc": 1.3162,
    "ldsc_mean_chisq": 1.5048,
    "ldsc_ratio": 0.1525
}

name	value
name	value
af_correlation	FALSE
inflation_factor	TRUE
n	FALSE
is_snpid_non_unique	FALSE
mean_EFFECT_nonfinite	FALSE
mean_EFFECT_05	FALSE
mean_EFFECT_01	FALSE
mean_chisq	TRUE
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	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	22	0	76933	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	8.553664e+00	5.571715e+00	1.00000e+00	4.000000e+00	8.000000e+00	1.200000e+01	2.200000e+01	▇▆▅▃▂
numeric	POS	0	1.0000000	NA	NA	NA	NA	NA	7.599609e+07	5.523722e+07	4.56530e+04	2.894011e+07	6.453400e+07	1.120635e+08	2.491546e+08	▇▆▃▂▁
numeric	EFFECT	0	1.0000000	NA	NA	NA	NA	NA	5.488000e-04	2.106180e-02	-1.48845e-01	-1.114900e-02	4.460000e-04	1.222400e-02	1.994100e-01	▁▃▇▁▁
numeric	SE	0	1.0000000	NA	NA	NA	NA	NA	1.656470e-02	6.963700e-03	1.08120e-02	1.183300e-02	1.390800e-02	1.858600e-02	1.038830e-01	▇▁▁▁▁
numeric	PVAL	0	1.0000000	NA	NA	NA	NA	NA	4.550781e-01	3.001037e-01	0.00000e+00	1.862332e-01	4.397279e-01	7.145934e-01	9.999820e-01	▇▆▆▆▅
numeric	PVAL_ztest	0	1.0000000	NA	NA	NA	NA	NA	4.550649e-01	3.001045e-01	0.00000e+00	1.862373e-01	4.396935e-01	7.145217e-01	1.000000e+00	▇▆▆▆▅
numeric	AF	0	1.0000000	NA	NA	NA	NA	NA	3.465507e-01	2.596724e-01	1.03020e-02	1.242660e-01	2.775850e-01	5.257970e-01	9.886510e-01	▇▅▃▂▂
numeric	AF_reference	501	0.9934878	NA	NA	NA	NA	NA	3.468647e-01	2.527169e-01	9.98400e-04	1.367810e-01	2.807510e-01	5.181710e-01	9.964060e-01	▇▆▃▂▂
numeric	N	0	1.0000000	NA	NA	NA	NA	NA	1.017710e+05	8.534607e+03	3.97180e+04	1.005170e+05	1.052130e+05	1.070290e+05	1.074180e+05	▁▁▁▁▇

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	1706136	rs6603811	T	C	-0.005703	0.018393	0.7565158	0.7565126	0.916669	0.7466050	102472
1	1706160	rs7531583	A	G	-0.010088	0.012280	0.4113287	0.4113625	0.732961	0.5832670	106922
1	1708801	rs12044597	A	G	-0.023707	0.011330	0.0364259	0.0364019	0.481177	0.3602240	102702
1	1723031	rs9660180	G	A	-0.017866	0.012300	0.1463420	0.1463570	0.480706	0.3580270	92359
1	1812688	rs6603803	A	G	-0.019260	0.011335	0.0892997	0.0892887	0.480130	0.3606230	103550
1	1823922	rs6688000	A	G	-0.005784	0.018682	0.7568887	0.7568630	0.902267	0.7651760	103707
1	2069172	rs425277	C	T	-0.020675	0.012189	0.0898442	0.0898477	0.279653	0.2252400	105073
1	2180524	rs260513	G	A	-0.022537	0.013042	0.0839847	0.0839824	0.454835	0.4191290	69960
1	2182342	rs10797416	T	C	-0.036504	0.011511	0.0015260	0.0015180	0.383879	0.2486020	105190
1	2184855	rs12385717	G	C	0.026027	0.021742	0.2312352	0.2312738	0.067124	0.0613019	101895

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	50588313	rs12484907	G	A	-0.019081	0.021606	0.3771551	0.3771637	0.074848	0.0814696	102817
22	50661869	rs5771242	T	C	0.004795	0.013569	0.7237874	0.7238043	0.763453	0.8512380	100234
22	50858813	rs7410608	G	C	0.010613	0.011649	0.3622547	0.3622610	0.357772	0.4916130	100085
22	50873694	rs2273259	T	C	0.014193	0.013765	0.3024851	0.3024970	0.347396	0.4802320	76193
22	51015674	rs3213446	C	T	0.004544	0.021834	0.8351250	0.8351386	0.057046	0.1859030	98772
22	51018911	rs2269382	C	T	0.000636	0.023589	0.9784850	0.9784903	0.051347	0.1222040	91326
22	51062832	rs8142033	G	A	0.008647	0.019318	0.6544538	0.6544321	0.095726	0.1773160	100607
22	51163138	rs715586	C	T	0.014420	0.016630	0.3858886	0.3858830	0.148083	0.0902556	95129
22	51165664	rs8137951	G	A	0.018693	0.012073	0.1215189	0.1215426	0.313417	0.4063500	101063
22	51178090	rs2285395	G	A	0.021418	0.025456	0.4001410	0.4001388	0.064712	0.0666933	97693

1   1706136 rs6603811   T   C   .   PASS    AF=0.916669 ES:SE:LP:AF:SS:ID   -0.005703:0.018393:0.121182:0.916669:102472:rs6603811
1   1706160 rs7531583   A   G   .   PASS    AF=0.732961 ES:SE:LP:AF:SS:ID   -0.010088:0.01228:0.385811:0.732961:106922:rs7531583
1   1708801 rs12044597  A   G   .   PASS    AF=0.481177 ES:SE:LP:AF:SS:ID   -0.023707:0.01133:1.43859:0.481177:102702:rs12044597
1   1723031 rs9660180   G   A   .   PASS    AF=0.480706 ES:SE:LP:AF:SS:ID   -0.017866:0.0123:0.834631:0.480706:92359:rs9660180
1   1812688 rs6603803   A   G   .   PASS    AF=0.48013  ES:SE:LP:AF:SS:ID   -0.01926:0.011335:1.04915:0.48013:103550:rs6603803
1   1823922 rs6688000   A   G   .   PASS    AF=0.902267 ES:SE:LP:AF:SS:ID   -0.005784:0.018682:0.120968:0.902267:103707:rs6688000
1   2069172 rs425277    C   T   .   PASS    AF=0.279653 ES:SE:LP:AF:SS:ID   -0.020675:0.012189:1.04651:0.279653:105073:rs425277
1   2180524 rs260513    G   A   .   PASS    AF=0.454835 ES:SE:LP:AF:SS:ID   -0.022537:0.013042:1.0758:0.454835:69960:rs260513
1   2182342 rs10797416  T   C   .   PASS    AF=0.383879 ES:SE:LP:AF:SS:ID   -0.036504:0.011511:2.81645:0.383879:105190:rs10797416
1   2184855 rs12385717  G   C   .   PASS    AF=0.067124 ES:SE:LP:AF:SS:ID   0.026027:0.021742:0.635946:0.067124:101895:rs12385717
1   2224645 rs2645091   A   G   .   PASS    AF=0.844979 ES:SE:LP:AF:SS:ID   -0.008355:0.015216:0.234341:0.844979:104081:rs2645091
1   2280661 rs2055204   G   A   .   PASS    AF=0.554667 ES:SE:LP:AF:SS:ID   -0.003397:0.011345:0.116553:0.554667:100866:rs2055204
1   2291680 rs2840542   T   C   .   PASS    AF=0.804127 ES:SE:LP:AF:SS:ID   -0.021078:0.013963:0.882275:0.804127:105093:rs2840542
1   2490942 rs2281852   C   A   .   PASS    AF=0.493853 ES:SE:LP:AF:SS:ID   0.013811:0.011251:0.658314:0.493853:103593:rs2281852
1   2723345 rs4648360   C   T   .   PASS    AF=0.429605 ES:SE:LP:AF:SS:ID   -0.010552:0.011598:0.440177:0.429605:102005:rs4648360
1   2725952 rs7538063   C   T   .   PASS    AF=0.167641 ES:SE:LP:AF:SS:ID   -0.003199:0.015248:0.0789339:0.167641:93055:rs7538063
1   2839757 rs4648432   A   C   .   PASS    AF=0.275602 ES:SE:LP:AF:SS:ID   -0.004086:0.012139:0.132882:0.275602:105430:rs4648432
1   2842270 rs2045331   G   A   .   PASS    AF=0.281395 ES:SE:LP:AF:SS:ID   -0.006242:0.012142:0.21664:0.281395:105443:rs2045331
1   2904056 rs10797373  G   A   .   PASS    AF=0.676313 ES:SE:LP:AF:SS:ID   0.024131:0.014042:1.06689:0.676313:65904:rs10797373
1   2996196 rs7525173   C   G   .   PASS    AF=0.528517 ES:SE:LP:AF:SS:ID   -0.03125:0.012825:1.82854:0.528517:86667:rs7525173
1   3089849 rs17390062  T   C   .   PASS    AF=0.099318 ES:SE:LP:AF:SS:ID   -0.062093:0.023127:2.13829:0.099318:90616:rs17390062
1   3103826 rs4233024   C   T   .   PASS    AF=0.860065 ES:SE:LP:AF:SS:ID   0.015461:0.018121:0.405017:0.860065:68587:rs4233024
1   3105276 rs2817138   T   G   .   PASS    AF=0.863697 ES:SE:LP:AF:SS:ID   0.010506:0.015309:0.307528:0.863697:105041:rs2817138
1   3144068 rs10158583  G   A   .   PASS    AF=0.080834 ES:SE:LP:AF:SS:ID   -0.006947:0.01907:0.145305:0.080834:92775:rs10158583
1   3144745 rs1553288   C   T   .   PASS    AF=0.07014  ES:SE:LP:AF:SS:ID   -0.01345:0.019976:0.30036:0.07014:101908:rs1553288
1   3197747 rs10492940  C   A   .   PASS    AF=0.1364   ES:SE:LP:AF:SS:ID   -0.001132:0.016648:0.024208:0.1364:103741:rs10492940
1   3209631 rs4648376   G   A   .   PASS    AF=0.552998 ES:SE:LP:AF:SS:ID   0.006799:0.011091:0.26769:0.552998:104734:rs4648376
1   3209767 rs2500294   C   T   .   PASS    AF=0.564631 ES:SE:LP:AF:SS:ID   0.001176:0.011121:0.0382131:0.564631:104718:rs2500294
1   3214170 rs7553849   T   C   .   PASS    AF=0.768712 ES:SE:LP:AF:SS:ID   -0.002031:0.013439:0.0555854:0.768712:98586:rs7553849
1   3229185 rs12743017  G   A   .   PASS    AF=0.092478 ES:SE:LP:AF:SS:ID   0.001227:0.020394:0.0213644:0.092478:102072:rs12743017
1   3308034 rs2493275   G   A   .   PASS    AF=0.096886 ES:SE:LP:AF:SS:ID   0.002855:0.019977:0.0523918:0.096886:105187:rs2493275
1   3328659 rs2493292   C   T   .   PASS    AF=0.131784 ES:SE:LP:AF:SS:ID   0.033189:0.016625:1.33813:0.131784:104050:rs2493292
1   3684321 rs1181871   A   G   .   PASS    AF=0.568872 ES:SE:LP:AF:SS:ID   0.020171:0.011517:1.09763:0.568872:103671:rs1181871
1   3691727 rs1175549   A   C   .   PASS    AF=0.234088 ES:SE:LP:AF:SS:ID   0.008186:0.013144:0.27291:0.234088:105217:rs1175549
1   3765424 rs10492943  G   T   .   PASS    AF=0.15856  ES:SE:LP:AF:SS:ID   -0.029387:0.015333:1.25731:0.15856:106799:rs10492943
1   4039896 rs11587331  C   T   .   PASS    AF=0.565714 ES:SE:LP:AF:SS:ID   -0.001518:0.010958:0.0507135:0.565714:102719:rs11587331
1   4080885 rs4378174   C   T   .   PASS    AF=0.143027 ES:SE:LP:AF:SS:ID   -0.002217:0.015742:0.0516086:0.143027:102544:rs4378174
1   4116604 rs3856168   T   C   .   PASS    AF=0.917108 ES:SE:LP:AF:SS:ID   0.0227:0.018916:0.638108:0.917108:105557:rs3856168
1   4122464 rs10799180  T   C   .   PASS    AF=0.85731  ES:SE:LP:AF:SS:ID   0.017747:0.016131:0.566659:0.85731:95214:rs10799180
1   4272139 rs12402543  T   A   .   PASS    AF=0.157244 ES:SE:LP:AF:SS:ID   -0.005922:0.016107:0.14684:0.157244:105526:rs12402543
1   4367810 rs17448695  C   T   .   PASS    AF=0.084644 ES:SE:LP:AF:SS:ID   0.005679:0.022592:0.0960873:0.084644:97967:rs17448695
1   4407985 rs523634    A   G   .   PASS    AF=0.228002 ES:SE:LP:AF:SS:ID   0.019702:0.012849:0.902462:0.228002:107245:rs523634
1   4444706 rs349413    C   A   .   PASS    AF=0.737873 ES:SE:LP:AF:SS:ID   -0.010374:0.01238:0.395716:0.737873:103647:rs349413
1   4546183 rs350173    C   T   .   PASS    AF=0.082671 ES:SE:LP:AF:SS:ID   -0.01227:0.019933:0.269065:0.082671:100341:rs350173
1   4551679 rs780614    T   C   .   PASS    AF=0.234786 ES:SE:LP:AF:SS:ID   0.011729:0.013074:0.43224:0.234786:106289:rs780614
1   4573099 rs4480324   A   G   .   PASS    AF=0.704196 ES:SE:LP:AF:SS:ID   0.004576:0.012251:0.14949:0.704196:106973:rs4480324
1   4640902 rs1024139   T   C   .   PASS    AF=0.188085 ES:SE:LP:AF:SS:ID   0.015822:0.014097:0.582191:0.188085:98543:rs1024139
1   4641799 rs241224    G   T   .   PASS    AF=0.925445 ES:SE:LP:AF:SS:ID   -0.015434:0.022093:0.314433:0.925445:106954:rs241224
1   4666494 rs164780    C   G   .   PASS    AF=0.689663 ES:SE:LP:AF:SS:ID   -0.012391:0.012941:0.470703:0.689663:96882:rs164780
1   4672755 rs6703429   T   C   .   PASS    AF=0.269433 ES:SE:LP:AF:SS:ID   -0.030312:0.015394:1.31012:0.269433:81144:rs6703429

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