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IEU GWAS information

Trait: Non-cancer illness code self-reported: psoriasis.

name	value
id	UKB-a-100
trait	Non-cancer illness code self-reported: psoriasis
year	2017
author	Neale
population	European
sex	Males and Females
sample_size	337159
ncase	3871
ncontrol	333288
units	SD
build	HG19/GRCh37
group_name	public
access	public
nsnp	10894596
mr	1
batch	UKB-a
sd	NULL
priority	1
consortium	Neale Lab

Report information

name	value
gwas_id	ukb-a-100
bcf_file	ukb-a-100.vcf.gz
qc_metrics_file	qc_metrics.json
metadata	metadata.json
refdata	1kg_v3_nomult.bcf
compile_time	2020-05-10 08:38 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

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-a-import/processed/ukb-a-100/ukb-a-100.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-a-import/processed/ukb-a-100/ldsc.txt \
--w-ld-chr /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/ 

Beginning analysis at Sun Feb 16 01:54:06 2020
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-a-import/processed/ukb-a-100/ukb-a-100.vcf.gz ...
Read summary statistics for 10877936 SNPs.
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, 1281483 SNPs remain.
After merging with regression SNP LD, 1281483 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0071 (0.0017)
Lambda GC: 1.0386
Mean Chi^2: 1.0588
Intercept: 1.0118 (0.007)
Ratio: 0.2015 (0.1194)
Analysis finished at Sun Feb 16 01:55:55 2020
Total time elapsed: 1.0m:49.28s

QC metrics

Metrics

{
    "af_correlation": 0.9533,
    "inflation_factor": 1.0294,
    "mean_EFFECT": 6.132e-06,
    "n": 337159,
    "n_snps": 10877936,
    "n_clumped_hits": 18,
    "n_p_sig": 13018,
    "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": 356865,
    "n_est": 335661.2196,
    "ratio_se_n": 0.9978,
    "mean_diff": 0.0001,
    "ratio_diff": 44.1924,
    "sd_y_est1": 0.1072,
    "sd_y_est2": 0.107,
    "r2_sum1": 0.0001,
    "r2_sum2": 0.0092,
    "r2_sum3": 0.0093,
    "r2_sum4": 0.0092,
    "ldsc_nsnp_merge_refpanel_ld": 1281483,
    "ldsc_nsnp_merge_regression_ld": 1281483,
    "ldsc_observed_scale_h2_beta": 0.0071,
    "ldsc_observed_scale_h2_se": 0.0017,
    "ldsc_intercept_beta": 1.0118,
    "ldsc_intercept_se": 0.007,
    "ldsc_lambda_gc": 1.0386,
    "ldsc_mean_chisq": 1.0588,
    "ldsc_ratio": 0.2007
}

Flags

name	value
af_correlation	FALSE
inflation_factor	FALSE
n	FALSE
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

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	58	0	10877936	0	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
character	ALT	0	1.0000000	1	1	0	4	0	NA	NA	NA	NA	NA	NA	NA	NA
numeric	CHROM	0	1.0000000	NA	NA	NA	NA	NA	8.598638e+00	5.745666e+00	1.00000e+00	4.000000e+00	8.000000e+00	1.300000e+01	2.200000e+01	▇▅▃▂▂
numeric	POS	0	1.0000000	NA	NA	NA	NA	NA	7.911383e+07	5.624931e+07	8.28000e+02	3.287294e+07	6.988889e+07	1.148258e+08	2.492251e+08	▇▆▅▂▁
numeric	EFFECT	0	1.0000000	NA	NA	NA	NA	NA	6.100000e-06	1.577900e-03	-1.80359e-02	-4.353000e-04	-8.700000e-06	4.129000e-04	3.444450e-02	▁▇▁▁▁
numeric	SE	0	1.0000000	NA	NA	NA	NA	NA	1.124700e-03	1.065300e-03	2.58000e-04	3.187000e-04	6.121000e-04	1.635600e-03	5.863100e-03	▇▂▁▁▁
numeric	PVAL	0	1.0000000	NA	NA	NA	NA	NA	4.951485e-01	2.903503e-01	0.00000e+00	2.431840e-01	4.937715e-01	7.465209e-01	1.000000e+00	▇▇▇▇▇
numeric	PVAL_ztest	0	1.0000000	NA	NA	NA	NA	NA	4.951480e-01	2.903506e-01	0.00000e+00	2.431838e-01	4.937707e-01	7.465207e-01	1.000000e+00	▇▇▇▇▇
numeric	AF	0	1.0000000	NA	NA	NA	NA	NA	1.820865e-01	2.503832e-01	1.00090e-03	6.878700e-03	5.191440e-02	2.739880e-01	9.989990e-01	▇▂▁▁▁
numeric	AF_reference	356865	0.9671937	NA	NA	NA	NA	NA	1.877709e-01	2.439191e-01	0.00000e+00	5.191700e-03	7.468050e-02	2.873400e-01	1.000000e+00	▇▂▁▁▁
numeric	N	0	1.0000000	NA	NA	NA	NA	NA	3.371590e+05	0.000000e+00	3.37159e+05	3.371590e+05	3.371590e+05	3.371590e+05	3.371590e+05	▁▁▇▁▁

Head and tail

CHROM	POS	ID	REF	ALT	EFFECT	SE	PVAL	PVAL_ztest	AF	AF_reference	N
1	693731	rs12238997	A	G	-0.0009536	0.0004307	0.0268250	0.0268246	0.1169260	0.1417730	337159
1	717587	rs144155419	G	A	-0.0028312	0.0011569	0.0144016	0.0144005	0.0143862	0.0045926	337159
1	730087	rs148120343	T	C	-0.0007352	0.0006002	0.2206028	0.2206017	0.0554733	0.0127796	337159
1	731718	rs142557973	T	C	-0.0008886	0.0004086	0.0296640	0.0296636	0.1216580	0.1543530	337159
1	734349	rs141242758	T	C	-0.0008641	0.0004089	0.0345693	0.0345682	0.1215130	0.1525560	337159
1	740284	rs61770167	C	T	-0.0014552	0.0018612	0.4343002	0.4343000	0.0058556	0.0023962	337159
1	742813	rs112573343	C	T	-0.0006365	0.0035123	0.8561990	0.8561989	0.0015525	0.1030350	337159
1	753405	rs3115860	C	A	0.0007579	0.0003885	0.0510423	0.0510422	0.8706760	0.7517970	337159
1	753541	rs2073813	G	A	-0.0007601	0.0003894	0.0509577	0.0509572	0.1288330	0.3019170	337159
1	754182	rs3131969	A	G	0.0007666	0.0003880	0.0482025	0.0482015	0.8703180	0.6785140	337159

CHROM	POS	ID	REF	ALT	EFFECT	SE	PVAL	PVAL_ztest	AF	AF_reference	N
22	51219387	rs9616832	T	C	-0.0002530	0.0005003	0.6130445	0.6130445	0.0729806	0.0654952	337159
22	51219704	rs147475742	G	A	-0.0006485	0.0006707	0.3336038	0.3336030	0.0418225	0.0473243	337159
22	51219766	rs182321900	C	T	-0.0000445	0.0034480	0.9897070	0.9897066	0.0014546	NA	337159
22	51220088	rs566371895	G	A	-0.0006549	0.0022553	0.7715090	0.7715090	0.0039843	0.0003994	337159
22	51220146	rs868950473	C	T	0.0003733	0.0033964	0.9124801	0.9124803	0.0015139	NA	337159
22	51221731	rs115055839	T	C	-0.0002442	0.0005006	0.6257574	0.6257570	0.0728101	0.0625000	337159
22	51223637	rs375798137	G	A	-0.0012939	0.0005897	0.0282351	0.0282340	0.0540289	0.0788738	337159
22	51226692	rs150189434	G	A	-0.0001894	0.0035264	0.9571599	0.9571601	0.0013983	0.0155751	337159
22	51229805	rs9616985	T	C	-0.0002810	0.0005025	0.5760856	0.5760854	0.0727671	0.0730831	337159
22	51237063	rs3896457	T	C	0.0005193	0.0003058	0.0894911	0.0894903	0.2968410	0.2050720	337159

bcf preview

1   693731  rs12238997  A   G   .   PASS    AF=0.116926 ES:SE:LP:AF:SS:ID   -0.00095364:0.00043072:1.57146:0.116926:337159:rs12238997
1   717587  rs144155419 G   A   .   PASS    AF=0.0143862    ES:SE:LP:AF:SS:ID   -0.00283119:0.00115695:1.84159:0.0143862:337159:rs144155419
1   730087  rs148120343 T   C   .   PASS    AF=0.0554733    ES:SE:LP:AF:SS:ID   -0.000735198:0.000600196:0.656389:0.0554733:337159:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.121658 ES:SE:LP:AF:SS:ID   -0.000888579:0.000408626:1.52777:0.121658:337159:rs58276399
1   734349  rs141242758 T   C   .   PASS    AF=0.121513 ES:SE:LP:AF:SS:ID   -0.000864077:0.00040886:1.46131:0.121513:337159:rs141242758
1   740284  rs61770167  C   T   .   PASS    AF=0.00585559   ES:SE:LP:AF:SS:ID   -0.00145518:0.00186119:0.36221:0.00585559:337159:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.0015525    ES:SE:LP:AF:SS:ID   -0.000636481:0.0035123:0.0674253:0.0015525:337159:rs112573343
1   753405  rs3115860   C   A   .   PASS    AF=0.870676 ES:SE:LP:AF:SS:ID   0.000757932:0.000388459:1.29207:0.870676:337159:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.128833 ES:SE:LP:AF:SS:ID   -0.000760105:0.00038943:1.29279:0.128833:337159:rs2073813
1   754182  rs3131969   A   G   .   PASS    AF=0.870318 ES:SE:LP:AF:SS:ID   0.000766623:0.000388048:1.31693:0.870318:337159:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.870409 ES:SE:LP:AF:SS:ID   0.000772608:0.000388232:1.33176:0.870409:337159:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.870037 ES:SE:LP:AF:SS:ID   0.000767408:0.000388033:1.31908:0.870037:337159:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.00465304   ES:SE:LP:AF:SS:ID   -0.000789025:0.00197981:0.161002:0.00465304:337159:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.00464711   ES:SE:LP:AF:SS:ID   -0.000762486:0.00198521:0.154334:0.00464711:337159:rs142682604
1   755435  rs184270342 T   G   .   PASS    AF=0.00590897   ES:SE:LP:AF:SS:ID   -0.00122875:0.00193325:0.279803:0.00590897:337159:rs184270342
1   755890  rs3115858   A   T   .   PASS    AF=0.870329 ES:SE:LP:AF:SS:ID   0.000768563:0.00038736:1.32564:0.870329:337159:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.87002  ES:SE:LP:AF:SS:ID   0.000780088:0.000386431:1.36132:0.87002:337159:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.869421 ES:SE:LP:AF:SS:ID   0.000749189:0.000386061:1.28143:0.869421:337159:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.870219 ES:SE:LP:AF:SS:ID   0.000767846:0.000386751:1.32695:0.870219:337159:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.870219 ES:SE:LP:AF:SS:ID   0.000768132:0.000386778:1.32756:0.870219:337159:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.870222 ES:SE:LP:AF:SS:ID   0.000767264:0.000386791:1.3252:0.870222:337159:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.870396 ES:SE:LP:AF:SS:ID   0.000770544:0.000387783:1.32867:0.870396:337159:rs3131954
1   761732  rs2286139   C   T   .   PASS    AF=0.867743 ES:SE:LP:AF:SS:ID   0.000764434:0.000385933:1.3222:0.867743:337159:rs2286139
1   766007  rs61768174  A   C   .   PASS    AF=0.106958 ES:SE:LP:AF:SS:ID   -0.000667226:0.000429722:0.91902:0.106958:337159:rs61768174
1   768253  rs2977608   A   C   .   PASS    AF=0.763932 ES:SE:LP:AF:SS:ID   0.000619643:0.000306228:1.36627:0.763932:337159:rs2977608
1   768448  rs12562034  G   A   .   PASS    AF=0.105257 ES:SE:LP:AF:SS:ID   -0.000245768:0.000422742:0.251042:0.105257:337159:rs12562034
1   768819  rs12562811  C   T   .   PASS    AF=0.00692766   ES:SE:LP:AF:SS:ID   -0.000703678:0.00157052:0.184347:0.00692766:337159:rs12562811
1   769223  rs60320384  C   G   .   PASS    AF=0.128651 ES:SE:LP:AF:SS:ID   -0.000808839:0.00038885:1.42575:0.128651:337159:rs60320384
1   769224  rs141644775 G   A   .   PASS    AF=0.00143239   ES:SE:LP:AF:SS:ID   0.00230789:0.00365371:0.277686:0.00143239:337159:rs141644775
1   770181  rs146076599 A   G   .   PASS    AF=0.00907328   ES:SE:LP:AF:SS:ID   -0.00415526:0.00149013:2.27611:0.00907328:337159:rs146076599
1   770377  rs112563271 A   T   .   PASS    AF=0.00692321   ES:SE:LP:AF:SS:ID   -0.000710231:0.00157734:0.18541:0.00692321:337159:rs112563271
1   771823  rs2977605   T   C   .   PASS    AF=0.869934 ES:SE:LP:AF:SS:ID   0.000790672:0.000387126:1.38604:0.869934:337159:rs2977605
1   771967  rs59066358  G   A   .   PASS    AF=0.128709 ES:SE:LP:AF:SS:ID   -0.000782751:0.000388674:1.35634:0.128709:337159:rs59066358
1   772755  rs2905039   A   C   .   PASS    AF=0.870112 ES:SE:LP:AF:SS:ID   0.000801891:0.000387144:1.41644:0.870112:337159:rs2905039
1   774736  rs28830877  A   C   .   PASS    AF=0.998797 ES:SE:LP:AF:SS:ID   0.000185172:0.00402187:0.0162488:0.998797:337159:rs28830877
1   776556  rs151160018 C   T   .   PASS    AF=0.00851871   ES:SE:LP:AF:SS:ID   -0.00245095:0.00145285:1.03808:0.00851871:337159:rs151160018
1   777122  rs2980319   A   T   .   PASS    AF=0.871122 ES:SE:LP:AF:SS:ID   0.000809557:0.000387802:1.43369:0.871122:337159:rs2980319
1   777232  rs112618790 C   T   .   PASS    AF=0.0961319    ES:SE:LP:AF:SS:ID   -0.000152686:0.00044774:0.134842:0.0961319:337159:rs112618790
1   778745  rs1055606   A   G   .   PASS    AF=0.127871 ES:SE:LP:AF:SS:ID   -0.000818083:0.000389076:1.44979:0.127871:337159:rs1055606
1   779322  rs4040617   A   G   .   PASS    AF=0.127984 ES:SE:LP:AF:SS:ID   -0.000821273:0.00038855:1.46164:0.127984:337159:rs4040617
1   780785  rs2977612   T   A   .   PASS    AF=0.870455 ES:SE:LP:AF:SS:ID   0.000783098:0.000387167:1.36541:0.870455:337159:rs2977612
1   781367  rs149821290 A   C   .   PASS    AF=0.00989772   ES:SE:LP:AF:SS:ID   -0.00141293:0.00137795:0.51544:0.00989772:337159:rs149821290
1   781845  rs61768199  A   G   .   PASS    AF=0.1044   ES:SE:LP:AF:SS:ID   -0.00079013:0.000435376:1.15769:0.1044:337159:rs61768199
1   782721  rs185280546 G   A   .   PASS    AF=0.0143713    ES:SE:LP:AF:SS:ID   -3.88421e-05:0.00117845:0.0115722:0.0143713:337159:rs185280546
1   782981  rs6594026   C   T   .   PASS    AF=0.128338 ES:SE:LP:AF:SS:ID   -0.000807154:0.000388763:1.42165:0.128338:337159:rs6594026
1   783193  rs145767270 G   C   .   PASS    AF=0.0143639    ES:SE:LP:AF:SS:ID   -5.15772e-05:0.00118345:0.015366:0.0143639:337159:rs145767270
1   783194  rs138555831 G   T   .   PASS    AF=0.0143713    ES:SE:LP:AF:SS:ID   -4.29152e-05:0.0011833:0.0127499:0.0143713:337159:rs138555831
1   783711  rs184266993 G   A   .   PASS    AF=0.00541372   ES:SE:LP:AF:SS:ID   -0.00335037:0.0018919:1.11589:0.00541372:337159:rs184266993
1   785050  rs2905062   G   A   .   PASS    AF=0.87014  ES:SE:LP:AF:SS:ID   0.000834365:0.000387225:1.50607:0.87014:337159:rs2905062
1   785989  rs2980300   T   C   .   PASS    AF=0.87009  ES:SE:LP:AF:SS:ID   0.000835412:0.000387471:1.50753:0.87009:337159:rs2980300