IEU GWAS QC Report

name	value
id	UKB-b-15527
trait	Operation code: squint correction
note	20004#1491: Output from GWAS pipeline using Phesant derived variables from UKBiobank
year	2018
author	Ben Elsworth
population	European
sex	Males and Females
sample_size	462933
ncase	6117
ncontrol	456816
units	SD
build	HG19/GRCh37
group_name	public
access	public
nsnp	9851867
mr	1
category	Binary
batch	UKB-b
sd	NULL
priority	1
consortium	MRC-IEU

name	value
gwas_id	ukb-b-15527
bcf_file	ukb-b-15527.vcf.gz
qc_metrics_file	qc_metrics.json
metadata	metadata.json
refdata	1kg_v3_nomult.bcf
compile_time	2020-05-09 16:46 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-15527/UKB-b-15527_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-15527/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:44:10 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-15527/UKB-b-15527_data.vcf.gz ...
Read summary statistics for 5221988 SNPs.
Dropped 1650 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, 1143046 SNPs remain.
After merging with regression SNP LD, 1143046 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0138 (0.0014)
Lambda GC: 1.1362
Mean Chi^2: 1.1506
Intercept: 1.0178 (0.0088)
Ratio: 0.1179 (0.0585)
Analysis finished at Thu Oct 17 14:45:15 2019
Total time elapsed: 1.0m:5.15s

{
    "af_correlation": 0.9112,
    "inflation_factor": 1.1474,
    "mean_EFFECT": 6.1412e-08,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 9,
    "n_p_sig": 224,
    "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": 45065,
    "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": 1143046,
    "ldsc_nsnp_merge_regression_ld": 1143046,
    "ldsc_observed_scale_h2_beta": 0.0138,
    "ldsc_observed_scale_h2_se": 0.0014,
    "ldsc_intercept_beta": 1.0178,
    "ldsc_intercept_se": 0.0088,
    "ldsc_lambda_gc": 1.1362,
    "ldsc_mean_chisq": 1.1506,
    "ldsc_ratio": 0.1182
}

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	5220349	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	5221988	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	8.672788e+00	5.763855e+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	7.852909e+07	5.659719e+07	828.0000000	3.190509e+07	6.890535e+07	1.144823e+08	2.492385e+08	▇▆▅▂▁
numeric	EFFECT	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	1.000000e-07	3.446000e-04	-0.0028002	-2.176000e-04	-1.100000e-06	2.158000e-04	2.654200e-03	▁▁▇▁▁
numeric	SE	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	3.146000e-04	7.740000e-05	0.0002290	2.494000e-04	2.863000e-04	3.629000e-04	1.052200e-03	▇▂▁▁▁
numeric	PVAL	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	4.798311e-01	2.938624e-01	0.0000000	2.200002e-01	4.700002e-01	7.300002e-01	1.000000e+00	▇▇▇▆▇
numeric	PVAL_ztest	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	4.798320e-01	2.938380e-01	0.0000000	2.205038e-01	4.730280e-01	7.340339e-01	9.999998e-01	▇▇▇▆▆
numeric	AF	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	3.424626e-01	2.424412e-01	0.0572180	1.340990e-01	2.732060e-01	5.091920e-01	9.427820e-01	▇▃▃▂▂
numeric	AF_reference	45065	0.9913701	NA	NA	NA	NA	NA	NA	NA	3.366373e-01	2.381447e-01	0.0000000	1.401760e-01	2.767570e-01	4.988020e-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	-0.0003593	0.0004214	0.3900004	0.3939440	0.623763	0.7821490	NA
1	54676	rs2462492	C	T	0.0001178	0.0004175	0.7800007	0.7778112	0.400401	NA	NA
1	86028	rs114608975	T	C	0.0000464	0.0006675	0.9400001	0.9445732	0.103556	0.0277556	NA
1	91536	rs6702460	G	T	-0.0003062	0.0004111	0.4600002	0.4564180	0.456851	0.4207270	NA
1	234313	rs8179466	C	T	-0.0001344	0.0008106	0.8700001	0.8682758	0.074508	NA	NA
1	534192	rs6680723	C	T	0.0004961	0.0004696	0.2900000	0.2907186	0.240960	NA	NA
1	546697	rs12025928	A	G	0.0006731	0.0005858	0.2500000	0.2505489	0.913473	NA	NA
1	693731	rs12238997	A	G	-0.0008051	0.0003935	0.0409996	0.0407785	0.116325	0.1417730	NA
1	705882	rs72631875	G	A	-0.0007271	0.0005767	0.2099999	0.2073532	0.067285	0.0315495	NA
1	706368	rs55727773	A	G	0.0001544	0.0002915	0.5999997	0.5963802	0.515650	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	51213613	rs34726907	C	T	0.0006814	0.0003608	0.0589997	0.0589768	0.127816	0.1727240	NA
22	51216564	rs9616970	T	C	0.0006832	0.0003593	0.0569994	0.0572433	0.128330	0.1563500	NA
22	51217954	rs9616974	G	A	0.0012418	0.0004559	0.0064999	0.0064568	0.073314	0.0621006	NA
22	51218224	rs9616975	C	A	0.0012441	0.0004561	0.0064000	0.0063805	0.073336	0.0619010	NA
22	51218377	rs2519461	G	C	0.0011952	0.0004556	0.0087000	0.0087026	0.073625	0.0826677	NA
22	51219006	rs28729663	G	A	0.0006442	0.0003517	0.0669993	0.0669918	0.137953	0.2052720	NA
22	51219387	rs9616832	T	C	0.0012715	0.0004565	0.0053001	0.0053471	0.073747	0.0654952	NA
22	51221731	rs115055839	T	C	0.0012518	0.0004568	0.0061000	0.0061333	0.073238	0.0625000	NA
22	51229805	rs9616985	T	C	0.0012778	0.0004584	0.0053001	0.0053149	0.073073	0.0730831	NA
22	51237063	rs3896457	T	C	-0.0000551	0.0002804	0.8400000	0.8443245	0.297971	0.2050720	NA

1   49298   rs10399793  T   C   .   PASS    AF=0.623763 ES:SE:LP:AF:ID  -0.000359272:0.00042144:0.408935:0.623763:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400401 ES:SE:LP:AF:ID  0.000117811:0.000417514:0.107905:0.400401:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103556 ES:SE:LP:AF:ID  4.64083e-05:0.000667523:0.0268721:0.103556:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456851 ES:SE:LP:AF:ID  -0.00030617:0.0004111:0.337242:0.456851:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074508 ES:SE:LP:AF:ID  -0.000134433:0.000810575:0.0604807:0.074508:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.24096  ES:SE:LP:AF:ID  0.000496131:0.000469578:0.537602:0.24096:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913473 ES:SE:LP:AF:ID  0.000673123:0.000585825:0.60206:0.913473:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116325 ES:SE:LP:AF:ID  -0.000805076:0.000393531:1.38722:0.116325:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067285 ES:SE:LP:AF:ID  -0.000727115:0.000576674:0.677781:0.067285:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.51565  ES:SE:LP:AF:ID  0.000154387:0.000291509:0.221849:0.51565:rs12029736
1   722670  rs116030099 T   C   .   PASS    AF=0.101199 ES:SE:LP:AF:ID  -3.81939e-05:0.000480971:0.0268721:0.101199:rs116030099
1   729679  rs4951859   C   G   .   PASS    AF=0.843212 ES:SE:LP:AF:ID  0.000832805:0.000341045:1.82391:0.843212:rs4951859
1   731718  rs58276399  T   C   .   PASS    AF=0.122307 ES:SE:LP:AF:ID  -0.000770063:0.000373303:1.40894:0.122307:rs58276399
1   734349  rs141242758 T   C   .   PASS    AF=0.121549 ES:SE:LP:AF:ID  -0.000769512:0.00037346:1.40894:0.121549:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.13233  ES:SE:LP:AF:ID  -0.00034647:0.000368082:0.455932:0.13233:rs79010578
1   752566  rs3094315   G   A   .   PASS    AF=0.838951 ES:SE:LP:AF:ID  0.000675127:0.000330278:1.38722:0.838951:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.83858  ES:SE:LP:AF:ID  0.000689593:0.000329922:1.4318:0.83858:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869781 ES:SE:LP:AF:ID  0.00063784:0.00035402:1.14267:0.869781:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129871 ES:SE:LP:AF:ID  -0.000639083:0.000354744:1.14267:0.129871:rs2073813
1   754182  rs3131969   A   G   .   PASS    AF=0.869123 ES:SE:LP:AF:ID  0.000655558:0.000353326:1.19382:0.869123:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869221 ES:SE:LP:AF:ID  0.000645109:0.000353466:1.16749:0.869221:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.869126 ES:SE:LP:AF:ID  0.000654343:0.000353319:1.19382:0.869126:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.838033 ES:SE:LP:AF:ID  0.000668401:0.000329006:1.37675:0.838033:rs3115859
1   754964  rs3131966   C   T   .   PASS    AF=0.838664 ES:SE:LP:AF:ID  0.000665868:0.000329931:1.35655:0.838664:rs3131966
1   755775  rs3131965   A   G   .   PASS    AF=0.839777 ES:SE:LP:AF:ID  0.000665321:0.000334393:1.3279:0.839777:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.869405 ES:SE:LP:AF:ID  0.000621626:0.000352911:1.10791:0.869405:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.868952 ES:SE:LP:AF:ID  0.000600834:0.000352023:1.05552:0.868952:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.867905 ES:SE:LP:AF:ID  0.00064327:0.000351348:1.17393:0.867905:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.869095 ES:SE:LP:AF:ID  0.000623978:0.000352311:1.11351:0.869095:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.869104 ES:SE:LP:AF:ID  0.000623624:0.000352338:1.11351:0.869104:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.869112 ES:SE:LP:AF:ID  0.000624078:0.000352346:1.11351:0.869112:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.869589 ES:SE:LP:AF:ID  0.000626438:0.000353313:1.11919:0.869589:rs3131954
1   760912  rs1048488   C   T   .   PASS    AF=0.838313 ES:SE:LP:AF:ID  0.000653488:0.000328382:1.3279:0.838313:rs1048488
1   761147  rs3115850   T   C   .   PASS    AF=0.838434 ES:SE:LP:AF:ID  0.000654757:0.000328614:1.33724:0.838434:rs3115850
1   761732  rs2286139   C   T   .   PASS    AF=0.862261 ES:SE:LP:AF:ID  0.000663271:0.000351072:1.22915:0.862261:rs2286139
1   763394  rs3115847   G   A   .   PASS    AF=0.706753 ES:SE:LP:AF:ID  0.000355329:0.000341766:0.522879:0.706753:rs3115847
1   766007  rs61768174  A   C   .   PASS    AF=0.105142 ES:SE:LP:AF:ID  -0.000278886:0.000393711:0.318759:0.105142:rs61768174
1   768253  rs2977608   A   C   .   PASS    AF=0.761304 ES:SE:LP:AF:ID  0.000389346:0.000278942:0.79588:0.761304:rs2977608
1   768448  rs12562034  G   A   .   PASS    AF=0.106488 ES:SE:LP:AF:ID  2.522e-05:0.000384467:0.0222764:0.106488:rs12562034
1   769223  rs60320384  C   G   .   PASS    AF=0.129576 ES:SE:LP:AF:ID  -0.00060753:0.000354533:1.06048:0.129576:rs60320384
1   771823  rs2977605   T   C   .   PASS    AF=0.868911 ES:SE:LP:AF:ID  0.000641866:0.000352639:1.16115:0.868911:rs2977605
1   771967  rs59066358  G   A   .   PASS    AF=0.129677 ES:SE:LP:AF:ID  -0.000608366:0.000354304:1.0655:0.129677:rs59066358
1   772755  rs2905039   A   C   .   PASS    AF=0.868921 ES:SE:LP:AF:ID  0.000644288:0.000352646:1.16749:0.868921:rs2905039
1   776546  rs12124819  A   G   .   PASS    AF=0.26539  ES:SE:LP:AF:ID  0.000461178:0.000311583:0.853872:0.26539:rs12124819
1   777122  rs2980319   A   T   .   PASS    AF=0.870044 ES:SE:LP:AF:ID  0.00064394:0.000353365:1.16749:0.870044:rs2980319
1   777232  rs112618790 C   T   .   PASS    AF=0.095139 ES:SE:LP:AF:ID  -5.77241e-05:0.000409536:0.05061:0.095139:rs112618790
1   778745  rs1055606   A   G   .   PASS    AF=0.128576 ES:SE:LP:AF:ID  -0.00059214:0.00035476:1.02228:0.128576:rs1055606
1   779322  rs4040617   A   G   .   PASS    AF=0.128873 ES:SE:LP:AF:ID  -0.000597881:0.000354158:1.04096:0.128873:rs4040617
1   780785  rs2977612   T   A   .   PASS    AF=0.868786 ES:SE:LP:AF:ID  0.000679432:0.000352423:1.26761:0.868786:rs2977612
1   781845  rs61768199  A   G   .   PASS    AF=0.10187  ES:SE:LP:AF:ID  -0.000298902:0.000399329:0.346787:0.10187:rs61768199

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