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IEU GWAS information

Trait: Years of cough on most days.

name	value
id	ukb-d-22503_irnt
trait	Years of cough on most days
category	Continuous
sample_size	12593
sex	Males and females
population	European
group_name	public
access	public
build	HG19/GRCh37
author	Neale lab
year	2018
mr	1
nsnp	13495482

Report information

name	value
gwas_id	ukb-d-22503_irnt
bcf_file	ukb-d-22503_irnt.vcf.gz
qc_metrics_file	qc_metrics.json
metadata	metadata.json
refdata	1kg_v3_nomult.bcf
compile_time	2020-05-09 16:08 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

P-Z plot

pz_plot

beta_std 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-22503_irnt/ukb-d-22503_irnt.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-22503_irnt/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 14:50:48 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-22503_irnt/ukb-d-22503_irnt.vcf.gz ...
Read summary statistics for 13495481 SNPs.
Dropped 12572 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, 1283471 SNPs remain.
After merging with regression SNP LD, 1283471 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0351 (0.0371)
Lambda GC: 1.0051
Mean Chi^2: 1.0089
Intercept: 1.0002 (0.0065)
Ratio: 0.0248 (0.7262)
Analysis finished at Mon Nov 25 14:53:32 2019
Total time elapsed: 2.0m:43.74s

QC metrics

Metrics

{
    "af_correlation": 0.9519,
    "inflation_factor": 1.0011,
    "mean_EFFECT": -0.0001,
    "n": 12593,
    "n_snps": 13495481,
    "n_clumped_hits": 0,
    "n_p_sig": 0,
    "n_mono": 0,
    "n_ns": 1248628,
    "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": 546257,
    "n_est": 12684.597,
    "ratio_se_n": 1.0036,
    "mean_diff": -2.846e-06,
    "ratio_diff": 0.8788,
    "sd_y_est1": 1.0022,
    "sd_y_est2": 1.0058,
    "r2_sum1": 0,
    "r2_sum2": 0,
    "r2_sum3": 0,
    "r2_sum4": 0,
    "ldsc_nsnp_merge_refpanel_ld": 1283471,
    "ldsc_nsnp_merge_regression_ld": 1283471,
    "ldsc_observed_scale_h2_beta": 0.0351,
    "ldsc_observed_scale_h2_se": 0.0371,
    "ldsc_intercept_beta": 1.0002,
    "ldsc_intercept_se": 0.0065,
    "ldsc_lambda_gc": 1.0051,
    "ldsc_mean_chisq": 1.0089,
    "ldsc_ratio": 0.0225
}

Flags

name	value
af_correlation	FALSE
inflation_factor	FALSE
n	FALSE
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	FALSE
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	1.000000	3	94	0	13483572	0	NA	NA	NA	NA	NA	NA	NA	NA
character	REF	0	1.000000	1	100	0	56900	0	NA	NA	NA	NA	NA	NA	NA	NA
character	ALT	0	1.000000	1	342	0	33297	0	NA	NA	NA	NA	NA	NA	NA	NA
numeric	CHROM	0	1.000000	NA	NA	NA	NA	NA	9.052156e+00	6.184716e+00	1.00000e+00	4.000000e+00	8.000000e+00	1.300000e+01	2.300000e+01	▇▅▅▂▂
numeric	POS	0	1.000000	NA	NA	NA	NA	NA	7.901658e+07	5.592039e+07	3.02000e+02	3.291431e+07	7.012077e+07	1.148465e+08	2.492309e+08	▇▆▅▂▁
numeric	EFFECT	0	1.000000	NA	NA	NA	NA	NA	-6.750000e-05	7.218270e-02	-9.38262e-01	-1.928440e-02	-1.010000e-05	1.919190e-02	8.972020e-01	▁▁▇▁▁
numeric	SE	0	1.000000	NA	NA	NA	NA	NA	5.215000e-02	5.013000e-02	1.02754e-02	1.507260e-02	2.752810e-02	7.469100e-02	2.826350e-01	▇▂▁▁▁
numeric	PVAL	0	1.000000	NA	NA	NA	NA	NA	4.998659e-01	2.887336e-01	6.00000e-07	2.496060e-01	4.997629e-01	7.500324e-01	1.000000e+00	▇▇▇▇▇
numeric	PVAL_ztest	0	1.000000	NA	NA	NA	NA	NA	4.998532e-01	2.887412e-01	6.00000e-07	2.495842e-01	4.997497e-01	7.500281e-01	9.999999e-01	▇▇▇▇▇
numeric	AF	0	1.000000	NA	NA	NA	NA	NA	1.920174e-01	2.547100e-01	1.00010e-03	7.743000e-03	6.142430e-02	2.976590e-01	9.990000e-01	▇▂▁▁▁
numeric	AF_reference	546257	0.959523	NA	NA	NA	NA	NA	1.972536e-01	2.473154e-01	0.00000e+00	5.790700e-03	8.546330e-02	3.099040e-01	1.000000e+00	▇▂▁▁▁
numeric	N	0	1.000000	NA	NA	NA	NA	NA	1.259300e+04	0.000000e+00	1.25930e+04	1.259300e+04	1.259300e+04	1.259300e+04	1.259300e+04	▁▁▇▁▁

Head and tail

CHROM	POS	ID	REF	ALT	EFFECT	SE	PVAL	PVAL_ztest	AF	AF_reference	N
1	692794	rs530212009	CA	C	-0.0094323	0.0222354	0.6714258	0.6714179	0.1096180	0.1894970	12593
1	693731	rs12238997	A	G	-0.0150395	0.0209613	0.4730859	0.4730725	0.1154650	0.1417730	12593
1	707522	rs371890604	G	C	-0.0175827	0.0238622	0.4612305	0.4612177	0.0959143	0.1293930	12593
1	717587	rs144155419	G	A	0.0641512	0.0564257	0.2555948	0.2555740	0.0156701	0.0045926	12593
1	723329	rs189787166	A	T	-0.2954580	0.1798680	0.1004840	0.1004579	0.0015012	0.0003994	12593
1	730087	rs148120343	T	C	-0.0242118	0.0293057	0.4087182	0.4087017	0.0557569	0.0127796	12593
1	731718	rs142557973	T	C	-0.0179761	0.0199514	0.3676097	0.3675913	0.1210360	0.1543530	12593
1	732032	rs61770163	A	C	-0.0093998	0.0214225	0.6608273	0.6608208	0.1199460	0.1555510	12593
1	734349	rs141242758	T	C	-0.0161663	0.0199562	0.4179054	0.4178890	0.1203390	0.1525560	12593
1	740284	rs61770167	C	T	-0.1360220	0.0934881	0.1457048	0.1456787	0.0053601	0.0023962	12593

CHROM	POS	ID	REF	ALT	EFFECT	SE	PVAL	PVAL_ztest	AF	AF_reference	N
23	154923311	rs141127553	C	T	-0.0259320	0.0236712	0.2733141	0.2732939	0.0532076	0.0309934	12593
23	154923374	rs111332691	T	A	0.0230548	0.0244740	0.3462031	0.3461865	0.0462956	0.0116556	12593
23	154925045	rs509981	C	T	0.0129038	0.0121368	0.2877120	0.2876930	0.2398690	0.3634440	12593
23	154925895	rs538470	C	T	0.0102225	0.0124143	0.4102721	0.4102547	0.2363080	0.3634440	12593
23	154927581	rs644138	G	A	0.0050195	0.0114104	0.6600123	0.6600054	0.2935930	0.4635760	12593
23	154929412	rs557132	C	T	0.0127852	0.0121386	0.2922382	0.2922181	0.2397160	0.3568210	12593
23	154929637	rs35185538	CT	C	0.0116666	0.0126302	0.3556559	0.3556391	0.2265670	0.3011920	12593
23	154929952	rs4012982	CAA	C	0.0100238	0.0127811	0.4329004	0.4328832	0.2333990	0.3165560	12593
23	154930230	rs781880	A	G	0.0113605	0.0121363	0.3492553	0.3492340	0.2402240	0.3618540	12593
23	154930487	rs781879	T	A	0.0678389	0.0416274	0.1031970	0.1031714	0.0190870	0.1263580	12593

bcf preview

1   692794  rs530212009 CA  C   .   PASS    AF=0.109618 ES:SE:LP:AF:SS:ID   -0.00943232:0.0222354:0.173002:0.109618:12593:1_692794_CA_C
1   693731  rs12238997  A   G   .   PASS    AF=0.115465 ES:SE:LP:AF:SS:ID   -0.0150395:0.0209613:0.32506:0.115465:12593:rs12238997
1   707522  rs371890604 G   C   .   PASS    AF=0.0959143    ES:SE:LP:AF:SS:ID   -0.0175827:0.0238622:0.336082:0.0959143:12593:rs371890604
1   717587  rs144155419 G   A   .   PASS    AF=0.0156701    ES:SE:LP:AF:SS:ID   0.0641512:0.0564257:0.592448:0.0156701:12593:rs144155419
1   723329  rs189787166 A   T   .   PASS    AF=0.00150115   ES:SE:LP:AF:SS:ID   -0.295458:0.179868:0.997903:0.00150115:12593:rs189787166
1   730087  rs148120343 T   C   .   PASS    AF=0.0557569    ES:SE:LP:AF:SS:ID   -0.0242118:0.0293057:0.388576:0.0557569:12593:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.121036 ES:SE:LP:AF:SS:ID   -0.0179761:0.0199514:0.434613:0.121036:12593:rs58276399
1   732032  rs61770163  A   C   .   PASS    AF=0.119946 ES:SE:LP:AF:SS:ID   -0.00939977:0.0214225:0.179912:0.119946:12593:rs61770163
1   734349  rs141242758 T   C   .   PASS    AF=0.120339 ES:SE:LP:AF:SS:ID   -0.0161663:0.0199562:0.378922:0.120339:12593:rs141242758
1   740284  rs61770167  C   T   .   PASS    AF=0.00536012   ES:SE:LP:AF:SS:ID   -0.136022:0.0934881:0.836526:0.00536012:12593:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.00163879   ES:SE:LP:AF:SS:ID   -0.0925629:0.179665:0.217223:0.00163879:12593:rs112573343
1   749963  rs529266287 T   TAA .   PASS    AF=0.871333 ES:SE:LP:AF:SS:ID   0.00454129:0.0197688:0.0870805:0.871333:12593:rs529266287
1   750230  rs190826124 G   C   .   PASS    AF=0.00129733   ES:SE:LP:AF:SS:ID   -0.0570236:0.18827:0.118054:0.00129733:12593:rs190826124
1   751343  rs28544273  T   A   .   PASS    AF=0.122069 ES:SE:LP:AF:SS:ID   -0.00435552:0.0195308:0.084319:0.122069:12593:rs28544273
1   751488  rs200141114 G   GA  .   PASS    AF=0.142387 ES:SE:LP:AF:SS:ID   0.00289746:0.0192143:0.0554492:0.142387:12593:rs200141114
1   751756  rs28527770  T   C   .   PASS    AF=0.122239 ES:SE:LP:AF:SS:ID   -0.00466758:0.0194976:0.0910836:0.122239:12593:rs28527770
1   753405  rs3115860   C   A   .   PASS    AF=0.871744 ES:SE:LP:AF:SS:ID   0.00996679:0.0189918:0.22204:0.871744:12593:rs3115860
1   753425  rs3131970   T   C   .   PASS    AF=0.876639 ES:SE:LP:AF:SS:ID   0.00493997:0.0193286:0.0978442:0.876639:12593:rs3131970
1   753541  rs2073813   G   A   .   PASS    AF=0.128023 ES:SE:LP:AF:SS:ID   -0.00772302:0.0190315:0.164376:0.128023:12593:rs2073813
1   754105  rs12184325  C   T   .   PASS    AF=0.0371635    ES:SE:LP:AF:SS:ID   0.0111032:0.0339557:0.128613:0.0371635:12593:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.87143  ES:SE:LP:AF:SS:ID   0.00997114:0.0189797:0.222324:0.87143:12593:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.871521 ES:SE:LP:AF:SS:ID   0.00939941:0.0189856:0.207223:0.871521:12593:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.871433 ES:SE:LP:AF:SS:ID   0.00986191:0.0189787:0.219445:0.871433:12593:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.00537227   ES:SE:LP:AF:SS:ID   0.0628646:0.0939071:0.298233:0.00537227:12593:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.00533785   ES:SE:LP:AF:SS:ID   0.0614811:0.0941409:0.289274:0.00533785:12593:rs142682604
1   755435  rs184270342 T   G   .   PASS    AF=0.00553373   ES:SE:LP:AF:SS:ID   -0.0846922:0.0936856:0.436507:0.00553373:12593:rs184270342
1   755890  rs3115858   A   T   .   PASS    AF=0.871553 ES:SE:LP:AF:SS:ID   0.00873092:0.018946:0.190489:0.871553:12593:rs3115858
1   756434  rs61768170  G   C   .   PASS    AF=0.125146 ES:SE:LP:AF:SS:ID   -0.00491554:0.0193398:0.0972522:0.125146:12593:rs61768170
1   756604  rs3131962   A   G   .   PASS    AF=0.871159 ES:SE:LP:AF:SS:ID   0.00892516:0.0189017:0.195996:0.871159:12593:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.87054  ES:SE:LP:AF:SS:ID   0.00897206:0.0188991:0.197237:0.87054:12593:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.871271 ES:SE:LP:AF:SS:ID   0.0085381:0.0189192:0.185894:0.871271:12593:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.871273 ES:SE:LP:AF:SS:ID   0.0085261:0.0189205:0.185575:0.871273:12593:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.871273 ES:SE:LP:AF:SS:ID   0.00854223:0.0189205:0.185985:0.871273:12593:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.871704 ES:SE:LP:AF:SS:ID   0.00896066:0.0189675:0.196111:0.871704:12593:rs3131954
1   759293  rs10157329  T   A   .   PASS    AF=0.0976149    ES:SE:LP:AF:SS:ID   -0.0198977:0.022137:0.433266:0.0976149:12593:rs10157329
1   759600  rs545998451 AGT A   .   PASS    AF=0.0063982    ES:SE:LP:AF:SS:ID   -0.0824966:0.0845054:0.482844:0.0063982:12593:1_759600_AGT_A
1   759837  rs3115851   T   A   .   PASS    AF=0.875739 ES:SE:LP:AF:SS:ID   0.00635422:0.0192722:0.129815:0.875739:12593:rs3115851
1   761732  rs2286139   C   T   .   PASS    AF=0.864628 ES:SE:LP:AF:SS:ID   0.00926396:0.0188928:0.204886:0.864628:12593:rs2286139
1   761752  rs1057213   C   T   .   PASS    AF=0.870467 ES:SE:LP:AF:SS:ID   0.0103489:0.0190816:0.230928:0.870467:12593:rs1057213
1   762273  rs3115849   G   A   .   PASS    AF=0.867566 ES:SE:LP:AF:SS:ID   0.00927691:0.0190802:0.202851:0.867566:12593:rs3115849
1   762485  rs12095200  C   A   .   PASS    AF=0.098398 ES:SE:LP:AF:SS:ID   -0.0098822:0.0227873:0.177482:0.098398:12593:rs12095200
1   762589  rs3115848   G   C   .   PASS    AF=0.872425 ES:SE:LP:AF:SS:ID   0.00625571:0.0192951:0.127388:0.872425:12593:rs3115848
1   762592  rs3131950   C   G   .   PASS    AF=0.872425 ES:SE:LP:AF:SS:ID   0.00625571:0.0192951:0.127388:0.872425:12593:rs3131950
1   762601  rs3131949   T   C   .   PASS    AF=0.872423 ES:SE:LP:AF:SS:ID   0.00625212:0.0192952:0.127305:0.872423:12593:rs3131949
1   762632  rs3131948   T   A   .   PASS    AF=0.872786 ES:SE:LP:AF:SS:ID   0.00524378:0.0193042:0.104631:0.872786:12593:rs3131948
1   764191  rs7515915   T   G   .   PASS    AF=0.124867 ES:SE:LP:AF:SS:ID   -0.00519369:0.0193071:0.103512:0.124867:12593:rs7515915
1   766007  rs61768174  A   C   .   PASS    AF=0.10445  ES:SE:LP:AF:SS:ID   -0.0189452:0.0210118:0.435024:0.10445:12593:rs61768174
1   766105  rs2519015   T   A   .   PASS    AF=0.856414 ES:SE:LP:AF:SS:ID   0.00760538:0.0188753:0.163038:0.856414:12593:rs2519015
1   767393  rs538667473 A   C   .   PASS    AF=0.00142719   ES:SE:LP:AF:SS:ID   -0.295094:0.180041:0.994682:0.00142719:12593:rs538667473
1   768116  rs376645387 A   AGTTTT  .   PASS    AF=0.841604 ES:SE:LP:AF:SS:ID   -0.0236392:0.0186422:0.688659:0.841604:12593:rs376645387