IEU GWAS QC Report

name	value
id	UKB-b-9324
trait	Number of trend entries
note	6038: Output from GWAS pipeline using Phesant derived variables from UKBiobank
year	2018
author	Ben Elsworth
population	European
sex	Males and Females
sample_size	68426
units	SD
build	HG19/GRCh37
group_name	public
access	public
nsnp	9851867
mr	1
category	Continuous
batch	UKB-b
sd	NULL
priority	1
consortium	MRC-IEU

name	value
gwas_id	ukb-b-9324
bcf_file	ukb-b-9324.vcf.gz
qc_metrics_file	qc_metrics.json
metadata	metadata.json
refdata	1kg_v3_nomult.bcf
compile_time	2020-05-10 00:13 BST
build_version	run_igd-8f4f4c9-2020-05-09

manhattan_plot

qq_plot

af_plot

pz_plot

beta_std_plot

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    "FORMAT.5": "<ID=EZ,Number=A,Type=Float,Description=\"Z-score provided if it was used to derive the EFFECT and SE fields\">",
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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-9324/UKB-b-9324_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-9324/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:42:26 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-9324/UKB-b-9324_data.vcf.gz ...
Read summary statistics for 8696309 SNPs.
Dropped 7568 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, 1286068 SNPs remain.
After merging with regression SNP LD, 1286068 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0345 (0.0067)
Lambda GC: 1.0512
Mean Chi^2: 1.0567
Intercept: 1.0106 (0.0058)
Ratio: 0.1868 (0.1014)
Analysis finished at Thu Oct 17 14:43:50 2019
Total time elapsed: 1.0m:23.89s

{
    "af_correlation": 0.9467,
    "inflation_factor": 1.0475,
    "mean_EFFECT": -0,
    "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": 84418,
    "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": 1286068,
    "ldsc_nsnp_merge_regression_ld": 1286068,
    "ldsc_observed_scale_h2_beta": 0.0345,
    "ldsc_observed_scale_h2_se": 0.0067,
    "ldsc_intercept_beta": 1.0106,
    "ldsc_intercept_se": 0.0058,
    "ldsc_lambda_gc": 1.0512,
    "ldsc_mean_chisq": 1.0567,
    "ldsc_ratio": 0.1869
}

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	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	3	58	0	8688776	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	8696309	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	8.649415e+00	5.760862e+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	7.877090e+07	5.635988e+07	828.0000000	3.239313e+07	6.929985e+07	1.145659e+08	2.492385e+08	▇▆▅▂▁
numeric	EFFECT	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	-2.590000e-05	1.544610e-02	-0.1666280	-6.402700e-03	-1.700000e-06	6.397500e-03	1.884560e-01	▁▁▇▁▁
numeric	SE	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	1.258770e-02	8.698000e-03	0.0049193	5.809000e-03	8.623000e-03	1.707220e-02	8.857360e-02	▇▂▁▁▁
numeric	PVAL	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	4.938973e-01	2.905415e-01	0.0000001	2.399999e-01	4.899999e-01	7.499995e-01	1.000000e+00	▇▇▇▇▇
numeric	PVAL_ztest	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	4.938968e-01	2.905172e-01	0.0000001	2.408001e-01	4.917751e-01	7.455923e-01	9.999995e-01	▇▇▇▇▇
numeric	AF	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	2.274263e-01	2.593105e-01	0.0051160	2.430900e-02	1.115450e-01	3.595750e-01	9.948840e-01	▇▂▁▁▁
numeric	AF_reference	84418	0.9902927	NA	NA	NA	NA	NA	NA	NA	2.272634e-01	2.512995e-01	0.0000000	2.216450e-02	1.281950e-01	3.568290e-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	-0.0090210	0.0090885	0.3200000	0.3209191	0.623987	0.7821490	NA
1	54676	rs2462492	C	T	-0.0029462	0.0090326	0.7400005	0.7442932	0.399180	NA	NA
1	86028	rs114608975	T	C	-0.0150026	0.0143168	0.2900000	0.2946839	0.104002	0.0277556	NA
1	91536	rs6702460	G	T	-0.0078936	0.0088864	0.3700002	0.3743905	0.456263	0.4207270	NA
1	234313	rs8179466	C	T	0.0046917	0.0173702	0.7899998	0.7870829	0.074922	NA	NA
1	534192	rs6680723	C	T	0.0063885	0.0101572	0.5300002	0.5293748	0.240514	NA	NA
1	546697	rs12025928	A	G	0.0045374	0.0126054	0.7199992	0.7188806	0.912581	NA	NA
1	693731	rs12238997	A	G	0.0019708	0.0084578	0.8200001	0.8157457	0.117125	0.1417730	NA
1	705882	rs72631875	G	A	-0.0078308	0.0123252	0.5300002	0.5251994	0.067985	0.0315495	NA
1	706368	rs55727773	A	G	-0.0020663	0.0062634	0.7400005	0.7414738	0.513999	0.2751600	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	51219006	rs28729663	G	A	0.0063402	0.0075731	0.4000000	0.4024791	0.136759	0.2052720	NA
22	51219387	rs9616832	T	C	0.0112498	0.0098849	0.2599998	0.2550893	0.072276	0.0654952	NA
22	51219704	rs147475742	G	A	0.0100170	0.0132253	0.4500005	0.4488031	0.041093	0.0473243	NA
22	51221190	rs369304721	G	A	0.0108441	0.0132766	0.4100001	0.4140525	0.048575	NA	NA
22	51221731	rs115055839	T	C	0.0104290	0.0098873	0.2900000	0.2915211	0.071785	0.0625000	NA
22	51222100	rs114553188	G	T	0.0021965	0.0115418	0.8499999	0.8490683	0.054300	0.0880591	NA
22	51223637	rs375798137	G	A	0.0020786	0.0116020	0.8600001	0.8578144	0.053911	0.0788738	NA
22	51229805	rs9616985	T	C	0.0101249	0.0099230	0.3100002	0.3075621	0.071663	0.0730831	NA
22	51232488	rs376461333	A	G	0.0029448	0.0234498	0.9000000	0.9000653	0.019937	NA	NA
22	51237063	rs3896457	T	C	0.0015761	0.0060219	0.7899998	0.7935292	0.297836	0.2050720	NA

1   49298   rs10399793  T   C   .   PASS    AF=0.623987 ES:SE:LP:AF:ID  -0.00902099:0.00908851:0.49485:0.623987:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.39918  ES:SE:LP:AF:ID  -0.00294618:0.00903256:0.130768:0.39918:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.104002 ES:SE:LP:AF:ID  -0.0150026:0.0143168:0.537602:0.104002:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456263 ES:SE:LP:AF:ID  -0.00789357:0.00888636:0.431798:0.456263:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074922 ES:SE:LP:AF:ID  0.0046917:0.0173702:0.102373:0.074922:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240514 ES:SE:LP:AF:ID  0.00638848:0.0101572:0.275724:0.240514:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.912581 ES:SE:LP:AF:ID  0.00453738:0.0126054:0.142668:0.912581:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.117125 ES:SE:LP:AF:ID  0.00197085:0.00845785:0.0861861:0.117125:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067985 ES:SE:LP:AF:ID  -0.00783083:0.0123252:0.275724:0.067985:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.513999 ES:SE:LP:AF:ID  -0.0020663:0.00626337:0.130768:0.513999:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033768 ES:SE:LP:AF:ID  -0.000298151:0.0155808:0.00877392:0.033768:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037466 ES:SE:LP:AF:ID  0.00571382:0.0141672:0.161151:0.037466:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037558 ES:SE:LP:AF:ID  0.00637096:0.0141179:0.187087:0.037558:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.037242 ES:SE:LP:AF:ID  0.0062158:0.0142196:0.180456:0.037242:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016297 ES:SE:LP:AF:ID  0.00866896:0.0222941:0.154902:0.016297:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037839 ES:SE:LP:AF:ID  0.0067914:0.0140558:0.200659:0.037839:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.03792  ES:SE:LP:AF:ID  0.0048095:0.0140122:0.136677:0.03792:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101533 ES:SE:LP:AF:ID  -0.00237514:0.010346:0.0861861:0.101533:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.957853 ES:SE:LP:AF:ID  -0.0166909:0.013484:0.657577:0.957853:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031543 ES:SE:LP:AF:ID  0.0511495:0.0250845:1.38722:0.031543:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052627 ES:SE:LP:AF:ID  0.0193978:0.0199098:0.481486:0.052627:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.037373 ES:SE:LP:AF:ID  0.00700552:0.0141109:0.207608:0.037373:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037703 ES:SE:LP:AF:ID  0.00542671:0.0139888:0.154902:0.037703:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.841145 ES:SE:LP:AF:ID  -0.00373701:0.00730981:0.21467:0.841145:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.05603  ES:SE:LP:AF:ID  -0.0168572:0.0118942:0.79588:0.05603:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.123133 ES:SE:LP:AF:ID  -1.38673e-06:0.00802236:-0:0.123133:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025594 ES:SE:LP:AF:ID  -0.0380992:0.0198359:1.25964:0.025594:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122315 ES:SE:LP:AF:ID  -0.000876148:0.00802819:0.0409586:0.122315:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.133576 ES:SE:LP:AF:ID  0.000993646:0.00789602:0.0457575:0.133576:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011128 ES:SE:LP:AF:ID  -0.0149597:0.0288316:0.221849:0.011128:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.006311 ES:SE:LP:AF:ID  0.0639938:0.0351626:1.16115:0.006311:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.037638 ES:SE:LP:AF:ID  0.00489153:0.0138448:0.142668:0.037638:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.837051 ES:SE:LP:AF:ID  -0.00346683:0.00707428:0.207608:0.837051:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.836575 ES:SE:LP:AF:ID  -0.00283915:0.00706523:0.161151:0.836575:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.868582 ES:SE:LP:AF:ID  -0.00347154:0.00759027:0.187087:0.868582:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.131122 ES:SE:LP:AF:ID  0.00116577:0.00760545:0.0555173:0.131122:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.038062 ES:SE:LP:AF:ID  0.00422057:0.013637:0.119186:0.038062:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038304 ES:SE:LP:AF:ID  0.00289274:0.0135542:0.0809219:0.038304:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.867862 ES:SE:LP:AF:ID  -0.00314873:0.00757503:0.167491:0.867862:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.867967 ES:SE:LP:AF:ID  -0.00317164:0.00757891:0.167491:0.867967:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038284 ES:SE:LP:AF:ID  0.00356137:0.0136037:0.102373:0.038284:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.867883 ES:SE:LP:AF:ID  -0.00306192:0.00757485:0.161151:0.867883:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005276 ES:SE:LP:AF:ID  -0.0200618:0.0383742:0.221849:0.005276:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005249 ES:SE:LP:AF:ID  -0.020282:0.0384677:0.221849:0.005249:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.836137 ES:SE:LP:AF:ID  -0.00329483:0.00705229:0.19382:0.836137:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038303 ES:SE:LP:AF:ID  0.00401356:0.0136223:0.113509:0.038303:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.836768 ES:SE:LP:AF:ID  -0.00365163:0.00707173:0.21467:0.836768:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.012842 ES:SE:LP:AF:ID  0.00527624:0.0256955:0.0757207:0.012842:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005538 ES:SE:LP:AF:ID  0.00122117:0.0382709:0.0132283:0.005538:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.838175 ES:SE:LP:AF:ID  -0.00373027:0.00717248:0.221849:0.838175:rs3131965

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