IEU GWAS QC Report

name	value
author	Manousaki et al.
consortium	NA
subcategory	NA
category	NA
build	HG19/GRCh37
population	European
trait	25 hydroxyvitamin D level
sex	Males and Females
group_name	public
year	2020
nsnp	16668957
unit	log transformed serum 25OHD
id	ieu-b-4808
ontology	NA
sample_size	441291

name	value
gwas_id	ieu-b-4808
bcf_file	ieu-b-4808.vcf.gz
qc_metrics_file	qc_metrics.json
metadata	metadata.json
refdata	1kg_v3_nomult.bcf
compile_time	2021-12-12 16:38 UTC
build_version	master-74213fc-2021-03-26

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/602af46e-ca4e-4a28-a866-9e19e368ff62/call-ldsc/inputs/-261044566/ieu-b-4808.vcf.gz \
--ref-ld-chr /data/ref/eur_w_ld_chr/ \
--out /data/igd/ieu-b-4808/ldsc.txt \
--w-ld-chr /data/ref/eur_w_ld_chr/ 

Beginning analysis at Sun Dec 12 16:15:12 2021
Reading summary statistics from /data/cromwell-executions/qc/602af46e-ca4e-4a28-a866-9e19e368ff62/call-ldsc/inputs/-261044566/ieu-b-4808.vcf.gz ...
Read summary statistics for 0 SNPs.
Reading reference panel LD Score from /data/ref/eur_w_ld_chr/[1-22] ... (ldscore_fromlist)
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] ... (ldscore_fromlist)
Read regression weight LD Scores for 1290028 SNPs.
Traceback (most recent call last):
  File "./ldsc/ldsc.py", line 647, in <module>
    sumstats.estimate_h2(args, log)
  File "/ldsc/ldscore/sumstats.py", line 330, in estimate_h2
    args, log, args.h2)
  File "/ldsc/ldscore/sumstats.py", line 252, in _read_ld_sumstats
    sumstats = _merge_and_log(ref_ld, sumstats, 'reference panel LD', log)
  File "/ldsc/ldscore/sumstats.py", line 238, in _merge_and_log
    raise ValueError(msg.format(N=len(sumstats), F=noun))
ValueError: After merging with reference panel LD, 0 SNPs remain.

Analysis finished at Sun Dec 12 16:16:47 2021
Total time elapsed: 1.0m:34.99s

{
    "af_correlation": 0.9569,
    "inflation_factor": 1.2263,
    "mean_EFFECT": -0.0008,
    "n": "-Inf",
    "n_snps": 16667396,
    "n_clumped_hits": 177,
    "n_p_sig": 21470,
    "n_mono": 0,
    "n_ns": 0,
    "n_mac": 0,
    "is_snpid_unique": false,
    "n_miss_EFFECT": 0,
    "n_miss_SE": 0,
    "n_miss_PVAL": 0,
    "n_miss_AF": 0,
    "n_miss_AF_reference": 1296465,
    "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": 0,
    "ldsc_nsnp_merge_regression_ld": "NA",
    "ldsc_observed_scale_h2_beta": "NA",
    "ldsc_observed_scale_h2_se": "NA",
    "ldsc_intercept_beta": "NA",
    "ldsc_intercept_se": "NA",
    "ldsc_lambda_gc": "NA",
    "ldsc_mean_chisq": "NA",
    "ldsc_ratio": "NA"
}

name	value
name	value
af_correlation	FALSE
inflation_factor	TRUE
n	TRUE
is_snpid_non_unique	TRUE
mean_EFFECT_nonfinite	FALSE
mean_EFFECT_05	FALSE
mean_EFFECT_01	FALSE
mean_chisq	NA
n_p_sig	TRUE
miss_EFFECT	FALSE
miss_SE	FALSE
miss_PVAL	FALSE
ldsc_ratio	NA
ldsc_intercept_beta	NA
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	25322	0.9984807	3	83	0	16550596	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	16667396	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	9.179572e+00	6.270297e+00	1.000000	4.000000e+00	8.000000e+00	1.400000e+01	2.300000e+01	▇▅▅▂▂
numeric	POS	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	7.837179e+07	5.594343e+07	173.000000	3.245213e+07	6.917073e+07	1.142711e+08	2.492405e+08	▇▆▅▂▁
numeric	EFFECT	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	-8.122000e-04	6.103970e-02	-3.690900	-5.442000e-03	-3.000000e-05	5.280000e-03	2.954200e+00	▁▁▇▁▁
numeric	SE	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	2.444710e-02	5.107460e-02	0.001663	2.785000e-03	8.538000e-03	2.464800e-02	1.084250e+00	▇▁▁▁▁
numeric	PVAL	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	4.660796e-01	2.988263e-01	0.000000	1.984440e-01	4.551201e-01	7.253306e-01	1.000000e+00	▇▆▆▆▆
numeric	PVAL_ztest	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	4.660792e-01	2.988275e-01	0.000000	1.984615e-01	4.551091e-01	7.253186e-01	1.000000e+00	▇▆▆▆▆
numeric	AF	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	1.463673e-01	2.361993e-01	0.001001	3.657000e-03	1.920200e-02	1.883140e-01	9.989990e-01	▇▁▁▁▁
numeric	AF_reference	1296465	0.9222155	NA	NA	NA	NA	NA	NA	NA	1.554455e-01	2.344998e-01	0.000000	1.397800e-03	3.254790e-02	2.196490e-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	10611	rs189107123	C	G	0.169663	0.201960	0.4008630	0.4008623	0.006000	NA	NA
1	11008	rs575272151	C	G	-0.005260	0.005196	0.3113838	0.3113864	0.085911	0.0880591	NA
1	11012	rs544419019	C	G	-0.005260	0.005196	0.3113838	0.3113864	0.085911	0.0880591	NA
1	13110	rs540538026	G	A	-0.001721	0.006875	0.8023911	0.8023343	0.059067	0.0267572	NA
1	13116	rs62635286	T	G	0.006826	0.004089	0.0950780	0.0950467	0.189051	0.0970447	NA
1	13118	rs200579949	A	G	0.006826	0.004089	0.0950780	0.0950467	0.189051	0.0970447	NA
1	13273	rs531730856	G	C	-0.002931	0.004779	0.5396585	0.5396726	0.134204	0.0950479	NA
1	13302	rs75241669	C	T	0.191902	0.106340	0.0711443	0.0711361	0.016696	NA	NA
1	13327	rs2691329	G	C	0.107822	0.182447	0.5545568	0.5545358	0.008000	NA	NA
1	13453	rs568927457	T	C	-0.035525	0.018407	0.0536266	0.0536103	0.006537	0.0007987	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	155235969	rs1373573212	A	G	-0.049894	0.037808	0.1869270	0.1869460	0.420109	NA	NA
23	155236031	rs868811290	T	C	0.108090	0.130717	0.4082949	0.4082933	0.024174	NA	NA
23	155236075	rs3093521	G	C	-0.071810	0.039885	0.0718026	0.0717934	0.269558	0.4608630	NA
23	155236723	rs3093526	A	G	-0.024484	0.053983	0.6501746	0.6501526	0.117248	0.3574280	NA
23	155238906	rs1222075132	G	C	0.115680	0.203266	0.5693063	0.5692839	0.123550	NA	NA
23	155238933	rs1569479045	G	A	-0.136820	0.066157	0.0386492	0.0386296	0.099296	NA	NA
23	155239499	rs142104921	G	A	-0.093948	0.171622	0.5841186	0.5840955	0.016808	0.0385383	NA
23	155239824	rs966180348	A	G	-0.088240	0.054007	0.1022860	0.1022878	0.119244	0.1419730	NA
23	155239827	rs966180348	A	G	-0.088267	0.054038	0.1023781	0.1023797	0.119129	0.1044330	NA
23	155259571	rs1569483582	T	G	-0.049280	0.072900	0.4990603	0.4990441	0.215299	NA	NA

1   10611   rs189107123 C   G   .   PASS    AF=0.006    ES:SE:LP:AF:ID  0.169663:0.20196:0.397004:0.006:rs189107123
1   11008   rs575272151 C   G   .   PASS    AF=0.085911 ES:SE:LP:AF:ID  -0.00526:0.005196:0.506704:0.085911:rs575272151
1   11012   rs544419019 C   G   .   PASS    AF=0.085911 ES:SE:LP:AF:ID  -0.00526:0.005196:0.506704:0.085911:rs544419019
1   13110   rs540538026 G   A   .   PASS    AF=0.059067 ES:SE:LP:AF:ID  -0.001721:0.006875:0.0956139:0.059067:rs540538026
1   13116   rs62635286  T   G   .   PASS    AF=0.189051 ES:SE:LP:AF:ID  0.006826:0.004089:1.02192:0.189051:rs62635286
1   13118   rs62028691  A   G   .   PASS    AF=0.189051 ES:SE:LP:AF:ID  0.006826:0.004089:1.02192:0.189051:rs62028691
1   13273   rs531730856 G   C   .   PASS    AF=0.134204 ES:SE:LP:AF:ID  -0.002931:0.004779:0.267881:0.134204:rs531730856
1   13302   rs75241669  C   T   .   PASS    AF=0.016696 ES:SE:LP:AF:ID  0.191902:0.10634:1.14786:0.016696:rs75241669
1   13327   rs2691329   G   C   .   PASS    AF=0.008    ES:SE:LP:AF:ID  0.107822:0.182447:0.256054:0.008:rs2691329
1   13453   rs568927457 T   C   .   PASS    AF=0.006537 ES:SE:LP:AF:ID  -0.035525:0.018407:1.27062:0.006537:rs568927457
1   13483   rs554760071 G   C   .   PASS    AF=0.00507  ES:SE:LP:AF:ID  -0.030491:0.020183:0.883243:0.00507:rs554760071
1   13494   rs1272445563    A   G   .   PASS    AF=0.00354  ES:SE:LP:AF:ID  -0.002991:0.029202:0.0369648:0.00354:rs1272445563
1   13550   rs554008981 G   A   .   PASS    AF=0.004862 ES:SE:LP:AF:ID  -0.000306:0.02429:0.0043863:0.004862:rs554008981
1   14464   rs546169444 A   T   .   PASS    AF=0.157708 ES:SE:LP:AF:ID  -0.003366:0.004333:0.359204:0.157708:rs546169444
1   14599   rs707680    T   A   .   PASS    AF=0.191236 ES:SE:LP:AF:ID  0.003549:0.003963:0.431245:0.191236:rs707680
1   14604   rs1418508701    A   G   .   PASS    AF=0.191236 ES:SE:LP:AF:ID  0.003549:0.003963:0.431245:0.191236:rs1418508701
1   14930   rs6682385   A   G   .   PASS    AF=0.473967 ES:SE:LP:AF:ID  -0.005685:0.00307:1.19349:0.473967:rs6682385
1   14933   rs199856693 G   A   .   PASS    AF=0.045283 ES:SE:LP:AF:ID  0.003832:0.00753:0.214092:0.045283:rs199856693
1   15211   rs3982632   T   G   .   PASS    AF=0.741782 ES:SE:LP:AF:ID  0.001996:0.003557:0.240467:0.741782:rs3982632
1   15245   rs576044687 C   T   .   PASS    AF=0.001154 ES:SE:LP:AF:ID  -0.049388:0.046037:0.547691:0.001154:rs576044687
1   15274   rs1328799397    A   G   .   PASS    AF=0.2731   ES:SE:LP:AF:ID  0.0915:0.077234:0.626884:0.2731:rs1328799397
1   15585   rs533630043 G   A   .   PASS    AF=0.008253 ES:SE:LP:AF:ID  0.014619:0.018063:0.378461:0.008253:rs533630043
1   15644   rs564003018 G   A   .   PASS    AF=0.004346 ES:SE:LP:AF:ID  0.01493:0.026705:0.239483:0.004346:rs564003018
1   15774   rs374029747 G   A   .   PASS    AF=0.007197 ES:SE:LP:AF:ID  -0.001898:0.021898:0.0310932:0.007197:rs374029747
1   15777   rs2691317   A   G   .   PASS    AF=0.016961 ES:SE:LP:AF:ID  0.010743:0.013271:0.378586:0.016961:rs2691317
1   15820   rs1316988498    G   T   .   PASS    AF=0.274605 ES:SE:LP:AF:ID  0.002746:0.003651:0.344935:0.274605:rs1316988498
1   16142   rs548165136 G   A   .   PASS    AF=0.003919 ES:SE:LP:AF:ID  0.003671:0.026395:0.0509239:0.003919:rs548165136
1   16949   rs199745162 A   C   .   PASS    AF=0.018842 ES:SE:LP:AF:ID  -0.012106:0.010894:0.574412:0.018842:rs199745162
1   18643   rs564023708 G   A   .   PASS    AF=0.006378 ES:SE:LP:AF:ID  0.017419:0.01925:0.437086:0.006378:rs564023708
1   18849   rs533090414 C   G   .   PASS    AF=0.975628 ES:SE:LP:AF:ID  -0.003531:0.009201:0.154168:0.975628:rs533090414
1   30923   rs1165072081    G   T   .   PASS    AF=0.903804 ES:SE:LP:AF:ID  0.003349:0.00551:0.264864:0.903804:rs1165072081
1   47159   rs540662756 T   C   .   PASS    AF=0.062958 ES:SE:LP:AF:ID  -0.003422:0.00646:0.224498:0.062958:rs540662756
1   49298   rs10399793  T   C   .   PASS    AF=0.623687 ES:SE:LP:AF:ID  -0.001167:0.003603:0.127205:0.623687:rs10399793
1   49315   rs1231347385    T   A   .   PASS    AF=0.001239 ES:SE:LP:AF:ID  -0.093502:0.049014:1.24834:0.001239:rs1231347385
1   49318   rs1231347385    A   G   .   PASS    AF=0.001409 ES:SE:LP:AF:ID  -0.013395:0.036889:0.144761:0.001409:rs1231347385
1   49343   rs553572247 T   C   .   PASS    AF=0.001583 ES:SE:LP:AF:ID  -0.013811:0.039279:0.139579:0.001583:rs553572247
1   49554   rs539322794 A   G   .   PASS    AF=0.092862 ES:SE:LP:AF:ID  0.013562:0.005552:1.83553:0.092862:rs539322794
1   50891   rs542415070 T   C   .   PASS    AF=0.004574 ES:SE:LP:AF:ID  -0.00651:0.024423:0.102481:0.004574:rs542415070
1   51047   rs559500163 A   T   .   PASS    AF=0.001661 ES:SE:LP:AF:ID  -0.021678:0.038598:0.240798:0.001661:rs559500163
1   51049   rs528344458 A   C   .   PASS    AF=0.001661 ES:SE:LP:AF:ID  -0.021678:0.038598:0.240798:0.001661:rs528344458
1   51050   rs551668143 A   T   .   PASS    AF=0.001661 ES:SE:LP:AF:ID  -0.021678:0.038598:0.240798:0.001661:rs551668143
1   51053   rs565211799 G   T   .   PASS    AF=0.001661 ES:SE:LP:AF:ID  -0.021678:0.038598:0.240798:0.001661:rs565211799
1   51479   rs116400033 T   A   .   PASS    AF=0.214672 ES:SE:LP:AF:ID  -0.006182:0.003804:0.982599:0.214672:rs116400033
1   51762   rs1359003408    A   G   .   PASS    AF=0.009287 ES:SE:LP:AF:ID  -0.027131:0.016473:1.00194:0.009287:rs1359003408
1   51765   rs1359003408    C   G   .   PASS    AF=0.009116 ES:SE:LP:AF:ID  -0.027339:0.016522:1.00889:0.009116:rs1359003408
1   52058   rs1199236530    G   C   .   PASS    AF=0.023739 ES:SE:LP:AF:ID  0.028986:0.083489:0.137592:0.023739:rs1199236530
1   52144   rs190291950 T   A   .   PASS    AF=0.005    ES:SE:LP:AF:ID  0.224572:0.229834:0.483457:0.005:rs190291950
1   52238   rs2691277   T   G   .   PASS    AF=0.974185 ES:SE:LP:AF:ID  -0.008595:0.010349:0.391178:0.974185:rs2691277
1   52253   rs530867301 C   G   .   PASS    AF=0.005155 ES:SE:LP:AF:ID  0.007616:0.022367:0.134615:0.005155:rs530867301
1   54354   rs569165477 C   T   .   PASS    AF=0.002191 ES:SE:LP:AF:ID  -0.015675:0.028751:0.232373:0.002191:rs569165477

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