IEU GWAS QC Report

name	value
id	UKB-b-16872
trait	Round of numeric memory test
note	4260: Output from GWAS pipeline using Phesant derived variables from UKBiobank
year	2018
author	Ben Elsworth
population	European
sex	Males and Females
sample_size	48080
units	SD
build	HG19/GRCh37
group_name	public
access	public
nsnp	9851867
mr	1
category	Categorical Ordered
batch	UKB-b
sd	NULL
priority	1
consortium	MRC-IEU

name	value
gwas_id	ukb-b-16872
bcf_file	ukb-b-16872.vcf.gz
qc_metrics_file	qc_metrics.json
metadata	metadata.json
refdata	1kg_v3_nomult.bcf
compile_time	2020-05-10 08:51 BST
build_version	run_igd-8f4f4c9-2020-05-09

manhattan_plot

qq_plot

$af_plot$

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/public/UKB-b-16872/UKB-b-16872_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-16872/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:45:20 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-16872/UKB-b-16872_data.vcf.gz ...
Read summary statistics for 8181367 SNPs.
Dropped 6476 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, 1283604 SNPs remain.
After merging with regression SNP LD, 1283604 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.1176 (0.0105)
Lambda GC: 1.1198
Mean Chi^2: 1.1252
Intercept: 1.0159 (0.0064)
Ratio: 0.1271 (0.0514)
Analysis finished at Thu Oct 17 14:46:38 2019
Total time elapsed: 1.0m:17.71s

{
    "af_correlation": 0.9438,
    "inflation_factor": 1.0966,
    "mean_EFFECT": -0.0001,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 1,
    "n_p_sig": 1,
    "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": 76703,
    "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": 1283604,
    "ldsc_nsnp_merge_regression_ld": 1283604,
    "ldsc_observed_scale_h2_beta": 0.1176,
    "ldsc_observed_scale_h2_se": 0.0105,
    "ldsc_intercept_beta": 1.0159,
    "ldsc_intercept_se": 0.0064,
    "ldsc_lambda_gc": 1.1198,
    "ldsc_mean_chisq": 1.1252,
    "ldsc_ratio": 0.127
}

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	8174920	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	8181367	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	8.657401e+00	5.763061e+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	7.871586e+07	5.639154e+07	828.0000000	3.230464e+07	6.919470e+07	1.145251e+08	2.492385e+08	▇▆▅▂▁
numeric	EFFECT	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	-8.940000e-05	1.429260e-02	-0.1410490	-6.598500e-03	-3.420000e-05	6.470400e-03	1.551080e-01	▁▁▇▁▁
numeric	SE	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	1.179960e-02	7.387500e-03	0.0051551	6.004400e-03	8.499300e-03	1.564820e-02	7.621400e-02	▇▂▁▁▁
numeric	PVAL	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	4.847001e-01	2.924388e-01	0.0000000	2.300001e-01	4.799997e-01	7.400005e-01	1.000000e+00	▇▇▇▆▇
numeric	PVAL_ztest	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	4.846999e-01	2.924132e-01	0.0000000	2.274545e-01	4.792372e-01	7.378281e-01	9.999999e-01	▇▇▇▇▇
numeric	AF	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	2.405467e-01	2.604955e-01	0.0072800	3.210800e-02	1.295700e-01	3.811985e-01	9.927200e-01	▇▂▂▁▁
numeric	AF_reference	76703	0.9906247	NA	NA	NA	NA	NA	NA	NA	2.399371e-01	2.523480e-01	0.0000000	3.274760e-02	1.453670e-01	3.767970e-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	0.0138653	0.0094827	0.1400000	0.1436960	0.624244	0.7821490	NA
1	54676	rs2462492	C	T	-0.0082090	0.0094162	0.3800004	0.3833209	0.399988	NA	NA
1	86028	rs114608975	T	C	0.0026036	0.0151585	0.8600001	0.8636280	0.103098	0.0277556	NA
1	91536	rs6702460	G	T	0.0020460	0.0093145	0.8300000	0.8261386	0.457331	0.4207270	NA
1	234313	rs8179466	C	T	0.0161705	0.0186851	0.3900004	0.3868071	0.073997	NA	NA
1	534192	rs6680723	C	T	0.0009259	0.0106127	0.9299999	0.9304737	0.242654	NA	NA
1	546697	rs12025928	A	G	0.0052538	0.0131833	0.6899999	0.6902457	0.913261	NA	NA
1	693731	rs12238997	A	G	0.0009222	0.0088981	0.9199999	0.9174537	0.115538	0.1417730	NA
1	705882	rs72631875	G	A	-0.0219350	0.0129938	0.0909997	0.0913896	0.067561	0.0315495	NA
1	706368	rs55727773	A	G	0.0046605	0.0065736	0.4799997	0.4783455	0.515693	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	0.0061428	0.0079998	0.4400003	0.4425600	0.136376	0.2052720	NA
22	51219387	rs9616832	T	C	-0.0017299	0.0103542	0.8700001	0.8673147	0.072823	0.0654952	NA
22	51219704	rs147475742	G	A	-0.0095062	0.0139145	0.4899999	0.4944894	0.041298	0.0473243	NA
22	51221190	rs369304721	G	A	0.0070680	0.0138307	0.6100002	0.6093251	0.049075	NA	NA
22	51221731	rs115055839	T	C	-0.0028471	0.0103681	0.7800007	0.7836214	0.072224	0.0625000	NA
22	51222100	rs114553188	G	T	0.0162255	0.0121896	0.1800002	0.1831582	0.054089	0.0880591	NA
22	51223637	rs375798137	G	A	0.0168468	0.0122480	0.1700000	0.1689847	0.053721	0.0788738	NA
22	51229805	rs9616985	T	C	-0.0028998	0.0104073	0.7800007	0.7805255	0.072051	0.0730831	NA
22	51232488	rs376461333	A	G	0.0245469	0.0241124	0.3100002	0.3086686	0.020369	NA	NA
22	51237063	rs3896457	T	C	0.0014729	0.0063238	0.8200001	0.8158294	0.296402	0.2050720	NA

1   49298   rs10399793  T   C   .   PASS    AF=0.624244 ES:SE:LP:AF:ID  0.0138653:0.00948272:0.853872:0.624244:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.399988 ES:SE:LP:AF:ID  -0.00820902:0.00941624:0.420216:0.399988:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103098 ES:SE:LP:AF:ID  0.00260359:0.0151585:0.0655015:0.103098:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.457331 ES:SE:LP:AF:ID  0.002046:0.00931454:0.0809219:0.457331:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.073997 ES:SE:LP:AF:ID  0.0161705:0.0186851:0.408935:0.073997:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.242654 ES:SE:LP:AF:ID  0.000925946:0.0106127:0.0315171:0.242654:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913261 ES:SE:LP:AF:ID  0.00525383:0.0131833:0.161151:0.913261:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.115538 ES:SE:LP:AF:ID  0.000922214:0.0088981:0.0362122:0.115538:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067561 ES:SE:LP:AF:ID  -0.021935:0.0129938:1.04096:0.067561:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515693 ES:SE:LP:AF:ID  0.00466046:0.0065736:0.318759:0.515693:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.0333   ES:SE:LP:AF:ID  -0.0178556:0.0165082:0.552842:0.0333:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037167 ES:SE:LP:AF:ID  -0.0192977:0.0149229:0.69897:0.037167:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037399 ES:SE:LP:AF:ID  -0.0194798:0.0148394:0.721246:0.037399:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.037002 ES:SE:LP:AF:ID  -0.0191943:0.0149691:0.69897:0.037002:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.015849 ES:SE:LP:AF:ID  0.0202861:0.0237665:0.408935:0.015849:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037572 ES:SE:LP:AF:ID  -0.0186777:0.0147967:0.677781:0.037572:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037693 ES:SE:LP:AF:ID  -0.0188044:0.0147436:0.69897:0.037693:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.102061 ES:SE:LP:AF:ID  0.0107304:0.0107481:0.49485:0.102061:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958813 ES:SE:LP:AF:ID  0.0217461:0.0143075:0.886057:0.958813:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031428 ES:SE:LP:AF:ID  -0.0171048:0.02615:0.29243:0.031428:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052766 ES:SE:LP:AF:ID  0.0087705:0.0210743:0.167491:0.052766:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.0372   ES:SE:LP:AF:ID  -0.0205776:0.0148376:0.769551:0.0372:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.03753  ES:SE:LP:AF:ID  -0.0211659:0.0146993:0.823909:0.03753:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.843749 ES:SE:LP:AF:ID  0.00371694:0.00768689:0.200659:0.843749:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055998 ES:SE:LP:AF:ID  0.0189208:0.0124465:0.886057:0.055998:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.121416 ES:SE:LP:AF:ID  -0.000227439:0.00844074:0.00877392:0.121416:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.0255   ES:SE:LP:AF:ID  -0.0266701:0.0208115:0.69897:0.0255:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.120687 ES:SE:LP:AF:ID  8.13866e-05:0.00844428:0.00436481:0.120687:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132846 ES:SE:LP:AF:ID  -0.00583639:0.00827105:0.318759:0.132846:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011222 ES:SE:LP:AF:ID  -0.0279601:0.0301628:0.455932:0.011222:rs181876450
1   752478  rs146277091 G   A   .   PASS    AF=0.03746  ES:SE:LP:AF:ID  -0.0220798:0.0145618:0.886057:0.03746:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.839224 ES:SE:LP:AF:ID  0.004185:0.00745202:0.244125:0.839224:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838946 ES:SE:LP:AF:ID  0.00456485:0.00744372:0.267606:0.838946:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.870669 ES:SE:LP:AF:ID  0.00263039:0.00800358:0.130768:0.870669:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129072 ES:SE:LP:AF:ID  -0.00203781:0.00801719:0.09691:0.129072:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.038054 ES:SE:LP:AF:ID  -0.0220611:0.0143036:0.920819:0.038054:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038322 ES:SE:LP:AF:ID  -0.022402:0.0142073:0.958607:0.038322:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.870055 ES:SE:LP:AF:ID  0.00323726:0.00798698:0.161151:0.870055:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.870168 ES:SE:LP:AF:ID  0.00322095:0.00799024:0.161151:0.870168:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038189 ES:SE:LP:AF:ID  -0.0220291:0.0142844:0.920819:0.038189:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.870046 ES:SE:LP:AF:ID  0.00336354:0.0079864:0.173925:0.870046:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.838319 ES:SE:LP:AF:ID  0.00501582:0.00741828:0.30103:0.838319:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038176 ES:SE:LP:AF:ID  -0.0215392:0.0143067:0.886057:0.038176:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.839035 ES:SE:LP:AF:ID  0.00479397:0.00744177:0.283997:0.839035:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013302 ES:SE:LP:AF:ID  -0.0355489:0.0266283:0.744727:0.013302:rs181660517
1   755775  rs3131965   A   G   .   PASS    AF=0.84016  ES:SE:LP:AF:ID  0.00569058:0.00754515:0.346787:0.84016:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.870264 ES:SE:LP:AF:ID  0.00288734:0.00797715:0.142668:0.870264:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.869769 ES:SE:LP:AF:ID  0.00301553:0.00795519:0.154902:0.869769:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.868798 ES:SE:LP:AF:ID  0.00318924:0.00794017:0.161151:0.868798:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.869925 ES:SE:LP:AF:ID  0.00303803:0.00796206:0.154902:0.869925:rs4951929

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