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IEU GWAS information

Trait: Ever had refractive laser eye surgery: No.

name	value
id	ukb-d-5325_0
trait	Ever had refractive laser eye surgery: No
category	Binary
ncase	4085
ncontrol	1634
sample_size	5719
sex	Males and females
population	European
group_name	public
access	public
build	HG19/GRCh37
author	Neale lab
year	2018
mr	1
nsnp	11690177

Report information

name	value
gwas_id	ukb-d-5325_0
bcf_file	ukb-d-5325_0.vcf.gz
qc_metrics_file	qc_metrics.json
metadata	metadata.json
refdata	1kg_v3_nomult.bcf
compile_time	2020-05-10 10:10 BST
build_version	run_igd-8f4f4c9-2020-05-09

Summary

Summary {data-width=650}

Manhattan plot

manhattan_plot

QQ plot

qq_plot

AF plot

$af_plot$

af_plot

P-Z plot

pz_plot

beta_std plot

beta_std_plot

Metadata

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}

LDSC

*********************************************************************
* 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/ukb-d-import/processed/ukb-d-5325_0/ukb-d-5325_0.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/ukb-d-import/processed/ukb-d-5325_0/ldsc.txt \
--w-ld-chr /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/ 

Beginning analysis at Mon Nov 25 17:06:15 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-5325_0/ukb-d-5325_0.vcf.gz ...
Read summary statistics for 11690176 SNPs.
Dropped 10052 SNPs with duplicated rs numbers.
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, 1282793 SNPs remain.
After merging with regression SNP LD, 1282793 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0271 (0.0694)
Lambda GC: 1.0076
Mean Chi^2: 1.0067
Intercept: 1.0036 (0.006)
Ratio: 0.5414 (0.9)
Analysis finished at Mon Nov 25 17:08:11 2019
Total time elapsed: 1.0m:56.05s

QC metrics

Metrics

{
    "af_correlation": 0.9481,
    "inflation_factor": 1.0066,
    "mean_EFFECT": -0.0001,
    "n": 5719,
    "n_snps": 11690176,
    "n_clumped_hits": 1,
    "n_p_sig": 2,
    "n_mono": 0,
    "n_ns": 1170429,
    "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": 259399,
    "n_est": 5761.0307,
    "ratio_se_n": 1.0037,
    "mean_diff": -0.0001,
    "ratio_diff": 16.2896,
    "sd_y_est1": 0.4354,
    "sd_y_est2": 0.437,
    "r2_sum1": 0.0012,
    "r2_sum2": 0.0061,
    "r2_sum3": 0.006,
    "r2_sum4": 0.0058,
    "ldsc_nsnp_merge_refpanel_ld": 1282793,
    "ldsc_nsnp_merge_regression_ld": 1282793,
    "ldsc_observed_scale_h2_beta": 0.0271,
    "ldsc_observed_scale_h2_se": 0.0694,
    "ldsc_intercept_beta": 1.0036,
    "ldsc_intercept_se": 0.006,
    "ldsc_lambda_gc": 1.0076,
    "ldsc_mean_chisq": 1.0067,
    "ldsc_ratio": 0.5373
}

Flags

name	value
af_correlation	FALSE
inflation_factor	FALSE
n	TRUE
is_snpid_non_unique	TRUE
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	TRUE
ldsc_intercept_beta	FALSE
n_clumped_hits	FALSE
r2_sum1	FALSE
r2_sum2	FALSE
r2_sum3	FALSE
r2_sum4	FALSE

Definitions

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 $\beta_j$

Diagnostics

Details

Summary stats

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	3	94	0	11680735	0	NA	NA	NA	NA	NA	NA	NA	NA
character	REF	0	1.0000000	1	100	0	52671	0	NA	NA	NA	NA	NA	NA	NA	NA
character	ALT	0	1.0000000	1	342	0	31906	0	NA	NA	NA	NA	NA	NA	NA	NA
numeric	CHROM	0	1.0000000	NA	NA	NA	NA	NA	9.071024e+00	6.183775e+00	1.0000000	4.000000e+00	8.000000e+00	1.300000e+01	2.300000e+01	▇▅▅▂▂
numeric	POS	0	1.0000000	NA	NA	NA	NA	NA	7.887033e+07	5.599515e+07	302.0000000	3.266314e+07	6.988376e+07	1.147531e+08	2.492309e+08	▇▆▅▂▁
numeric	EFFECT	0	1.0000000	NA	NA	NA	NA	NA	-9.870000e-05	2.958020e-02	-0.3624460	-1.052630e-02	1.320000e-05	1.059550e-02	3.369580e-01	▁▁▇▁▁
numeric	SE	0	1.0000000	NA	NA	NA	NA	NA	2.313830e-02	1.841780e-02	0.0066086	9.297700e-03	1.436690e-02	3.163650e-02	1.104000e-01	▇▂▁▁▁
numeric	PVAL	0	1.0000000	NA	NA	NA	NA	NA	4.989328e-01	2.890149e-01	0.0000000	2.475899e-01	4.985951e-01	7.494972e-01	1.000000e+00	▇▇▇▇▇
numeric	PVAL_ztest	0	1.0000000	NA	NA	NA	NA	NA	4.989048e-01	2.890317e-01	0.0000000	2.475414e-01	4.985681e-01	7.494856e-01	9.999999e-01	▇▇▇▇▇
numeric	AF	0	1.0000000	NA	NA	NA	NA	NA	2.201138e-01	2.602575e-01	0.0030600	1.772250e-02	1.001920e-01	3.510740e-01	9.969400e-01	▇▂▁▁▁
numeric	AF_reference	259399	0.9778105	NA	NA	NA	NA	NA	2.210527e-01	2.515966e-01	0.0000000	1.457670e-02	1.192090e-01	3.518370e-01	1.000000e+00	▇▂▂▁▁
numeric	N	0	1.0000000	NA	NA	NA	NA	NA	5.719000e+03	0.000000e+00	5719.0000000	5.719000e+03	5.719000e+03	5.719000e+03	5.719000e+03	▁▁▇▁▁

Head and tail

CHROM	POS	ID	REF	ALT	EFFECT	SE	PVAL	PVAL_ztest	AF	AF_reference	N
1	692794	rs530212009	CA	C	-0.0001917	0.0140746	0.9891340	0.9891333	0.1138240	0.1894970	5719
1	693731	rs12238997	A	G	0.0070466	0.0135420	0.6028413	0.6028196	0.1160130	0.1417730	5719
1	707522	rs371890604	G	C	-0.0008855	0.0150860	0.9531960	0.9531937	0.0990976	0.1293930	5719
1	717587	rs144155419	G	A	0.0230190	0.0391372	0.5564485	0.5564238	0.0137193	0.0045926	5719
1	730087	rs148120343	T	C	0.0080996	0.0184786	0.6611683	0.6611520	0.0585695	0.0127796	5719
1	731718	rs142557973	T	C	0.0030267	0.0128111	0.8132411	0.8132334	0.1224010	0.1543530	5719
1	732032	rs61770163	A	C	-0.0023671	0.0136987	0.8628150	0.8628089	0.1219710	0.1555510	5719
1	734349	rs141242758	T	C	0.0026745	0.0128207	0.8347611	0.8347537	0.1216620	0.1525560	5719
1	740284	rs61770167	C	T	-0.0422679	0.0502857	0.4006333	0.4005972	0.0074146	0.0023962	5719
1	749963	rs529266287	T	TAA	-0.0032792	0.0127195	0.7965650	0.7965550	0.8709570	0.7641770	5719

CHROM	POS	ID	REF	ALT	EFFECT	SE	PVAL	PVAL_ztest	AF	AF_reference	N
23	154923311	rs141127553	C	T	-0.0118149	0.0143348	0.4098557	0.4098197	0.0574919	0.0309934	5719
23	154923374	rs111332691	T	A	0.0088058	0.0153725	0.5667819	0.5667602	0.0479979	0.0116556	5719
23	154925045	rs509981	C	T	-0.0106200	0.0078440	0.1758199	0.1757674	0.2414960	0.3634440	5719
23	154925895	rs538470	C	T	-0.0081163	0.0080376	0.3126367	0.3125946	0.2380400	0.3634440	5719
23	154927581	rs644138	G	A	-0.0122230	0.0073411	0.0959666	0.0959118	0.2992670	0.4635760	5719
23	154929412	rs557132	C	T	-0.0109956	0.0078497	0.1613389	0.1612833	0.2413590	0.3568210	5719
23	154929637	rs35185538	CT	C	-0.0111084	0.0082144	0.1763291	0.1762752	0.2262820	0.3011920	5719
23	154929952	rs4012982	CAA	C	-0.0116793	0.0082968	0.1592799	0.1592262	0.2341500	0.3165560	5719
23	154930230	rs781880	A	G	-0.0103948	0.0078461	0.1852802	0.1852276	0.2420540	0.3618540	5719
23	154930487	rs781879	T	A	-0.0671333	0.0260272	0.0099234	0.0098986	0.0196661	0.1263580	5719

bcf preview

1   692794  rs530212009 CA  C   .   PASS    AF=0.113824 ES:SE:LP:AF:SS:ID   -0.000191693:0.0140746:0.00474487:0.113824:5719:1_692794_CA_C
1   693731  rs12238997  A   G   .   PASS    AF=0.116013 ES:SE:LP:AF:SS:ID   0.00704658:0.013542:0.219797:0.116013:5719:rs12238997
1   707522  rs371890604 G   C   .   PASS    AF=0.0990976    ES:SE:LP:AF:SS:ID   -0.000885498:0.015086:0.0208178:0.0990976:5719:rs371890604
1   717587  rs144155419 G   A   .   PASS    AF=0.0137193    ES:SE:LP:AF:SS:ID   0.023019:0.0391372:0.254575:0.0137193:5719:rs144155419
1   730087  rs148120343 T   C   .   PASS    AF=0.0585695    ES:SE:LP:AF:SS:ID   0.0080996:0.0184786:0.179688:0.0585695:5719:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122401 ES:SE:LP:AF:SS:ID   0.00302671:0.0128111:0.0897807:0.122401:5719:rs58276399
1   732032  rs61770163  A   C   .   PASS    AF=0.121971 ES:SE:LP:AF:SS:ID   -0.00236713:0.0136987:0.0640823:0.121971:5719:rs61770163
1   734349  rs141242758 T   C   .   PASS    AF=0.121662 ES:SE:LP:AF:SS:ID   0.00267451:0.0128207:0.0784378:0.121662:5719:rs141242758
1   740284  rs61770167  C   T   .   PASS    AF=0.00741457   ES:SE:LP:AF:SS:ID   -0.0422679:0.0502857:0.397253:0.00741457:5719:rs61770167
1   749963  rs529266287 T   TAA .   PASS    AF=0.870957 ES:SE:LP:AF:SS:ID   -0.00327919:0.0127195:0.0987788:0.870957:5719:rs529266287
1   751343  rs28544273  T   A   .   PASS    AF=0.121639 ES:SE:LP:AF:SS:ID   0.00416408:0.012586:0.130317:0.121639:5719:rs28544273
1   751488  rs200141114 G   GA  .   PASS    AF=0.141418 ES:SE:LP:AF:SS:ID   -0.00478544:0.0125292:0.153343:0.141418:5719:rs200141114
1   751756  rs28527770  T   C   .   PASS    AF=0.121759 ES:SE:LP:AF:SS:ID   0.00392142:0.0125642:0.122071:0.121759:5719:rs28527770
1   753405  rs3115860   C   A   .   PASS    AF=0.87174  ES:SE:LP:AF:SS:ID   -0.00137998:0.0122595:0.0407773:0.87174:5719:rs3115860
1   753425  rs3131970   T   C   .   PASS    AF=0.877726 ES:SE:LP:AF:SS:ID   -0.00340586:0.012481:0.105156:0.877726:5719:rs3131970
1   753541  rs2073813   G   A   .   PASS    AF=0.128329 ES:SE:LP:AF:SS:ID   0.000686816:0.0122563:0.0198539:0.128329:5719:rs2073813
1   754105  rs12184325  C   T   .   PASS    AF=0.0368071    ES:SE:LP:AF:SS:ID   -0.0535753:0.0219498:1.83313:0.0368071:5719:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.871439 ES:SE:LP:AF:SS:ID   -0.00129222:0.0122444:0.0381259:0.871439:5719:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.871612 ES:SE:LP:AF:SS:ID   -0.000886514:0.0122506:0.0258044:0.871612:5719:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.871443 ES:SE:LP:AF:SS:ID   -0.00126152:0.0122442:0.0371843:0.871443:5719:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.00446396   ES:SE:LP:AF:SS:ID   -0.0654248:0.0664748:0.488042:0.00446396:5719:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.00443996   ES:SE:LP:AF:SS:ID   -0.0654013:0.0666247:0.486355:0.00443996:5719:rs142682604
1   755435  rs184270342 T   G   .   PASS    AF=0.00551824   ES:SE:LP:AF:SS:ID   -0.0426502:0.0611498:0.313778:0.00551824:5719:rs184270342
1   755890  rs3115858   A   T   .   PASS    AF=0.871574 ES:SE:LP:AF:SS:ID   -0.000828959:0.012226:0.0241341:0.871574:5719:rs3115858
1   756434  rs61768170  G   C   .   PASS    AF=0.125065 ES:SE:LP:AF:SS:ID   -0.000631078:0.0124749:0.0178844:0.125065:5719:rs61768170
1   756604  rs3131962   A   G   .   PASS    AF=0.871044 ES:SE:LP:AF:SS:ID   -0.000859634:0.012195:0.0251177:0.871044:5719:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.870569 ES:SE:LP:AF:SS:ID   -0.00133473:0.0121763:0.0396639:0.870569:5719:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.871429 ES:SE:LP:AF:SS:ID   -0.000750808:0.0122115:0.0218304:0.871429:5719:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.871434 ES:SE:LP:AF:SS:ID   -0.000741599:0.0122123:0.0215547:0.871434:5719:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.871436 ES:SE:LP:AF:SS:ID   -0.000754646:0.0122122:0.0219433:0.871436:5719:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.871832 ES:SE:LP:AF:SS:ID   -0.000898198:0.0122445:0.0261677:0.871832:5719:rs3131954
1   759293  rs10157329  T   A   .   PASS    AF=0.0993774    ES:SE:LP:AF:SS:ID   0.000376671:0.0141714:0.00930781:0.0993774:5719:rs10157329
1   759600  rs545998451 AGT A   .   PASS    AF=0.00671652   ES:SE:LP:AF:SS:ID   -0.0761897:0.0527945:0.826706:0.00671652:5719:1_759600_AGT_A
1   759837  rs3115851   T   A   .   PASS    AF=0.876855 ES:SE:LP:AF:SS:ID   -0.0028264:0.012457:0.0859129:0.876855:5719:rs3115851
1   761732  rs2286139   C   T   .   PASS    AF=0.865308 ES:SE:LP:AF:SS:ID   0.00117817:0.0121731:0.0348449:0.865308:5719:rs2286139
1   761752  rs1057213   C   T   .   PASS    AF=0.870343 ES:SE:LP:AF:SS:ID   0.00022727:0.0123199:0.00643901:0.870343:5719:rs1057213
1   762273  rs3115849   G   A   .   PASS    AF=0.8667   ES:SE:LP:AF:SS:ID   -0.000935597:0.0122925:0.0271804:0.8667:5719:rs3115849
1   762485  rs12095200  C   A   .   PASS    AF=0.0966894    ES:SE:LP:AF:SS:ID   0.00696341:0.0147979:0.195202:0.0966894:5719:rs12095200
1   762589  rs3115848   G   C   .   PASS    AF=0.872497 ES:SE:LP:AF:SS:ID   -0.001538:0.0124325:0.0450097:0.872497:5719:rs3115848
1   762592  rs3131950   C   G   .   PASS    AF=0.872497 ES:SE:LP:AF:SS:ID   -0.001538:0.0124325:0.0450097:0.872497:5719:rs3131950
1   762601  rs3131949   T   C   .   PASS    AF=0.872493 ES:SE:LP:AF:SS:ID   -0.00154479:0.0124326:0.0452179:0.872493:5719:rs3131949
1   762632  rs3131948   T   A   .   PASS    AF=0.873202 ES:SE:LP:AF:SS:ID   -0.00227007:0.0124618:0.0677993:0.873202:5719:rs3131948
1   764191  rs7515915   T   G   .   PASS    AF=0.12448  ES:SE:LP:AF:SS:ID   0.000613322:0.0124603:0.0173923:0.12448:5719:rs7515915
1   766007  rs61768174  A   C   .   PASS    AF=0.107419 ES:SE:LP:AF:SS:ID   -0.000149481:0.0133728:0.00389041:0.107419:5719:rs61768174
1   766105  rs2519015   T   A   .   PASS    AF=0.857652 ES:SE:LP:AF:SS:ID   -0.000981995:0.0121968:0.0288021:0.857652:5719:rs2519015
1   768116  rs376645387 A   AGTTTT  .   PASS    AF=0.838448 ES:SE:LP:AF:SS:ID   -0.0065941:0.0118795:0.237427:0.838448:5719:rs376645387
1   768253  rs2977608   A   C   .   PASS    AF=0.7626   ES:SE:LP:AF:SS:ID   -0.00200155:0.00960412:0.0783551:0.7626:5719:rs2977608
1   768448  rs12562034  G   A   .   PASS    AF=0.107449 ES:SE:LP:AF:SS:ID   0.00312915:0.0130543:0.09121:0.107449:5719:rs12562034
1   768819  rs12562811  C   T   .   PASS    AF=0.00694074   ES:SE:LP:AF:SS:ID   0.0220137:0.0521116:0.172163:0.00694074:5719:rs12562811
1   769138  rs59306077  CAT C   .   PASS    AF=0.128341 ES:SE:LP:AF:SS:ID   -0.00128585:0.0122611:0.0378765:0.128341:5719:rs762168062