IEU GWAS QC Report

name	value
id	UKB-b-17612
trait	Heel bone ultrasound T-score, manual entry
note	77: Output from GWAS pipeline using Phesant derived variables from UKBiobank
year	2018
author	Ben Elsworth
population	European
sex	Males and Females
sample_size	40613
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-17612
bcf_file	ukb-b-17612.vcf.gz
qc_metrics_file	qc_metrics.json
metadata	metadata.json
refdata	1kg_v3_nomult.bcf
compile_time	2020-05-10 10:16 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/public/UKB-b-17612/UKB-b-17612_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-17612/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:40:19 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-17612/UKB-b-17612_data.vcf.gz ...
Read summary statistics for 7939558 SNPs.
Dropped 6016 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, 1281557 SNPs remain.
After merging with regression SNP LD, 1281557 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.222 (0.0233)
Lambda GC: 1.136
Mean Chi^2: 1.2045
Intercept: 1.0226 (0.0081)
Ratio: 0.1105 (0.0397)
Analysis finished at Thu Oct 17 14:41:47 2019
Total time elapsed: 1.0m:28.33s

{
    "af_correlation": 0.9425,
    "inflation_factor": 1.0966,
    "mean_EFFECT": -0,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 40,
    "n_p_sig": 3218,
    "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": 74070,
    "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": 1281557,
    "ldsc_nsnp_merge_regression_ld": 1281557,
    "ldsc_observed_scale_h2_beta": 0.222,
    "ldsc_observed_scale_h2_se": 0.0233,
    "ldsc_intercept_beta": 1.0226,
    "ldsc_intercept_se": 0.0081,
    "ldsc_lambda_gc": 1.136,
    "ldsc_mean_chisq": 1.2045,
    "ldsc_ratio": 0.1105
}

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	TRUE
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	7933569	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	7939558	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	8.660405e+00	5.763588e+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	7.870876e+07	5.642285e+07	828.0000000	3.225644e+07	6.918309e+07	1.145575e+08	2.492385e+08	▇▆▅▂▁
numeric	EFFECT	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	-3.130000e-05	1.740170e-02	-0.2139300	-8.266300e-03	-3.510000e-05	8.197000e-03	2.193700e-01	▁▁▇▁▁
numeric	SE	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	1.436030e-02	8.531100e-03	0.0065982	7.643800e-03	1.059230e-02	1.884580e-02	7.986820e-02	▇▂▁▁▁
numeric	PVAL	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	4.815331e-01	2.940974e-01	0.0000000	2.200002e-01	4.799997e-01	7.400005e-01	1.000000e+00	▇▇▇▆▇
numeric	PVAL_ztest	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	4.815325e-01	2.940705e-01	0.0000000	2.225855e-01	4.754247e-01	7.363206e-01	9.999999e-01	▇▇▇▇▇
numeric	AF	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	2.471889e-01	2.607850e-01	0.0086180	3.650920e-02	1.388410e-01	3.914740e-01	9.913820e-01	▇▂▂▁▁
numeric	AF_reference	74070	0.9906708	NA	NA	NA	NA	NA	NA	NA	2.463905e-01	2.526037e-01	0.0000000	3.873800e-02	1.541530e-01	3.865810e-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	0.0057343	0.0121244	0.6400000	0.6362456	0.623882	0.7821490	NA
1	54676	rs2462492	C	T	-0.0258659	0.0120430	0.0320000	0.0317300	0.401861	NA	NA
1	86028	rs114608975	T	C	0.0224525	0.0190655	0.2399999	0.2389359	0.103927	0.0277556	NA
1	91536	rs6702460	G	T	-0.0231761	0.0117603	0.0490004	0.0487575	0.458825	0.4207270	NA
1	234313	rs8179466	C	T	-0.0125732	0.0235111	0.5900000	0.5928039	0.073683	NA	NA
1	534192	rs6680723	C	T	-0.0108264	0.0134245	0.4199997	0.4199743	0.242155	NA	NA
1	546697	rs12025928	A	G	0.0126268	0.0171312	0.4600002	0.4610831	0.915728	NA	NA
1	693731	rs12238997	A	G	0.0037680	0.0115185	0.7400005	0.7435740	0.112708	0.1417730	NA
1	705882	rs72631875	G	A	0.0126150	0.0169824	0.4600002	0.4575859	0.064258	0.0315495	NA
1	706368	rs55727773	A	G	-0.0052628	0.0084232	0.5300002	0.5321051	0.513756	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	0.0065226	0.0101148	0.5199996	0.5190211	0.139274	0.2052720	NA
22	51219387	rs9616832	T	C	-0.0071289	0.0130219	0.5800000	0.5840680	0.076170	0.0654952	NA
22	51219704	rs147475742	G	A	0.0082442	0.0176357	0.6400000	0.6401625	0.042286	0.0473243	NA
22	51221190	rs369304721	G	A	0.0103299	0.0175284	0.5600000	0.5556442	0.051070	NA	NA
22	51221731	rs115055839	T	C	-0.0053914	0.0130224	0.6800001	0.6788687	0.075698	0.0625000	NA
22	51222100	rs114553188	G	T	0.0231699	0.0156380	0.1400000	0.1384359	0.053592	0.0880591	NA
22	51223637	rs375798137	G	A	0.0236239	0.0157111	0.1299999	0.1326731	0.053223	0.0788738	NA
22	51229805	rs9616985	T	C	-0.0046211	0.0130702	0.7199992	0.7236681	0.075449	0.0730831	NA
22	51232488	rs376461333	A	G	0.0676434	0.0311478	0.0299999	0.0298790	0.019783	NA	NA
22	51237063	rs3896457	T	C	-0.0171287	0.0080584	0.0340001	0.0335381	0.300040	0.2050720	NA

1   49298   rs10399793  T   C   .   PASS    AF=0.623882 ES:SE:LP:AF:ID  0.00573429:0.0121244:0.19382:0.623882:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.401861 ES:SE:LP:AF:ID  -0.0258659:0.012043:1.49485:0.401861:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103927 ES:SE:LP:AF:ID  0.0224525:0.0190655:0.619789:0.103927:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.458825 ES:SE:LP:AF:ID  -0.0231761:0.0117603:1.3098:0.458825:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.073683 ES:SE:LP:AF:ID  -0.0125732:0.0235111:0.229148:0.073683:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.242155 ES:SE:LP:AF:ID  -0.0108264:0.0134245:0.376751:0.242155:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.915728 ES:SE:LP:AF:ID  0.0126268:0.0171312:0.337242:0.915728:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.112708 ES:SE:LP:AF:ID  0.00376798:0.0115185:0.130768:0.112708:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.064258 ES:SE:LP:AF:ID  0.012615:0.0169824:0.337242:0.064258:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.513756 ES:SE:LP:AF:ID  -0.00526278:0.0084232:0.275724:0.513756:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.031782 ES:SE:LP:AF:ID  -0.0207366:0.0215835:0.468521:0.031782:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.035201 ES:SE:LP:AF:ID  -0.0190964:0.0196217:0.481486:0.035201:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.035252 ES:SE:LP:AF:ID  -0.0187146:0.0195614:0.468521:0.035252:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.035033 ES:SE:LP:AF:ID  -0.0164194:0.0196828:0.39794:0.035033:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016372 ES:SE:LP:AF:ID  -0.00606877:0.0298335:0.0757207:0.016372:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.035597 ES:SE:LP:AF:ID  -0.0172705:0.0194563:0.431798:0.035597:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.035636 ES:SE:LP:AF:ID  -0.0169707:0.019404:0.420216:0.035636:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.1009   ES:SE:LP:AF:ID  0.00553697:0.0138865:0.161151:0.1009:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.96074  ES:SE:LP:AF:ID  0.0144444:0.0187225:0.356547:0.96074:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031119 ES:SE:LP:AF:ID  -0.0731728:0.0333471:1.55284:0.031119:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.05433  ES:SE:LP:AF:ID  0.00719149:0.026278:0.107905:0.05433:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.035163 ES:SE:LP:AF:ID  -0.0168783:0.0195347:0.408935:0.035163:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.035499 ES:SE:LP:AF:ID  -0.0177028:0.0193398:0.443698:0.035499:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.848068 ES:SE:LP:AF:ID  -0.00378562:0.00996253:0.154902:0.848068:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.054239 ES:SE:LP:AF:ID  0.0107781:0.0161448:0.30103:0.054239:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.118715 ES:SE:LP:AF:ID  0.0073998:0.0109068:0.30103:0.118715:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.026176 ES:SE:LP:AF:ID  -0.0335906:0.0261843:0.69897:0.026176:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.117985 ES:SE:LP:AF:ID  0.00731215:0.0109164:0.30103:0.117985:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.127457 ES:SE:LP:AF:ID  0.00486381:0.0107844:0.187087:0.127457:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011719 ES:SE:LP:AF:ID  -0.0177252:0.0371445:0.200659:0.011719:rs181876450
1   752478  rs146277091 G   A   .   PASS    AF=0.035231 ES:SE:LP:AF:ID  -0.0125579:0.0191929:0.29243:0.035231:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.844081 ES:SE:LP:AF:ID  -0.000659209:0.00965761:0.0222764:0.844081:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.843762 ES:SE:LP:AF:ID  -0.0011284:0.00964746:0.0409586:0.843762:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.873824 ES:SE:LP:AF:ID  -0.0021583:0.0103551:0.0809219:0.873824:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.125756 ES:SE:LP:AF:ID  0.00343329:0.010384:0.130768:0.125756:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.035873 ES:SE:LP:AF:ID  -0.0136644:0.0188261:0.327902:0.035873:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.036099 ES:SE:LP:AF:ID  -0.0138624:0.0187091:0.337242:0.036099:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.873233 ES:SE:LP:AF:ID  -0.00270864:0.0103362:0.102373:0.873233:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.873396 ES:SE:LP:AF:ID  -0.0025174:0.0103426:0.091515:0.873396:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.036056 ES:SE:LP:AF:ID  -0.0134777:0.0187955:0.327902:0.036056:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.87325  ES:SE:LP:AF:ID  -0.00282664:0.0103357:0.107905:0.87325:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.843261 ES:SE:LP:AF:ID  -0.000799745:0.00962459:0.0315171:0.843261:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.036051 ES:SE:LP:AF:ID  -0.0124909:0.018826:0.29243:0.036051:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.843845 ES:SE:LP:AF:ID  -0.000701327:0.00965161:0.0268721:0.843845:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.014003 ES:SE:LP:AF:ID  -0.0445477:0.0330737:0.744727:0.014003:rs181660517
1   755775  rs3131965   A   G   .   PASS    AF=0.844672 ES:SE:LP:AF:ID  -0.00149702:0.00978078:0.0555173:0.844672:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.873462 ES:SE:LP:AF:ID  -0.00189243:0.010323:0.0705811:0.873462:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.872965 ES:SE:LP:AF:ID  -0.00206046:0.0102961:0.0757207:0.872965:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.872051 ES:SE:LP:AF:ID  -0.00199703:0.0102785:0.0705811:0.872051:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.873174 ES:SE:LP:AF:ID  -0.00153597:0.0103066:0.0555173:0.873174:rs4951929

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