IEU GWAS QC Report

name	value
id	ieu-a-1085
pmid	27455348
year	2016
mr	1
note	Results from linear mixed model
trait	Amyotrophic lateral sclerosis
category	Disease
subcategory	Psychiatric / neurological
population	European
sex	Males and females
ncase	12577
ncontrol	23475
sample_size	36052
nsnp	7740345
sd	NULL
priority	1
author	van Rheenen
group_name	public
access	public
build	HG19/GRCh37

name	value
gwas_id	ieu-a-1085
bcf_file	ieu-a-1085.vcf.gz
qc_metrics_file	qc_metrics.json
metadata	metadata.json
refdata	1kg_v3_nomult.bcf
compile_time	2020-05-10 06:55 BST
build_version	run_igd-8f4f4c9-2020-05-09

manhattan_plot

qq_plot

$af_plot$

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/dev/ieu-a-import/processed/ieu-a-1085/ieu-a-1085.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/ieu-a-import/processed/ieu-a-1085/ldsc.txt \
--w-ld-chr /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/ 

Beginning analysis at Wed Feb  5 09:21:13 2020
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ieu-a-import/processed/ieu-a-1085/ieu-a-1085.vcf.gz ...
Read summary statistics for 7481913 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, 1234259 SNPs remain.
After merging with regression SNP LD, 1234259 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0379 (0.0159)
Lambda GC: 1.1183
Mean Chi^2: 1.1298
Intercept: 1.1024 (0.0078)
Ratio: 0.7893 (0.0602)
Analysis finished at Wed Feb  5 09:22:29 2020
Total time elapsed: 1.0m:16.48s

{
    "af_correlation": 0.932,
    "inflation_factor": 1.1172,
    "mean_EFFECT": 0,
    "n": 36052,
    "n_snps": 7481913,
    "n_clumped_hits": 7,
    "n_p_sig": 153,
    "n_mono": 0,
    "n_ns": 503785,
    "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": 351700,
    "n_est": 35376.8989,
    "ratio_se_n": 0.9906,
    "mean_diff": 0,
    "ratio_diff": 16.9064,
    "sd_y_est1": 0.4671,
    "sd_y_est2": 0.4627,
    "r2_sum1": 0.0019,
    "r2_sum2": 0.0087,
    "r2_sum3": 0.0088,
    "r2_sum4": 0.0087,
    "ldsc_nsnp_merge_refpanel_ld": 1234259,
    "ldsc_nsnp_merge_regression_ld": 1234259,
    "ldsc_observed_scale_h2_beta": 0.0379,
    "ldsc_observed_scale_h2_se": 0.0159,
    "ldsc_intercept_beta": 1.1024,
    "ldsc_intercept_se": 0.0078,
    "ldsc_lambda_gc": 1.1183,
    "ldsc_mean_chisq": 1.1298,
    "ldsc_ratio": 0.7889
}

name	value
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	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

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	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	numeric.mean	numeric.sd	numeric.p0	numeric.p25	numeric.p50	numeric.p75	numeric.p100	numeric.hist
character	ID	0	1.0000000	3	59	0	7481912	0	NA	NA	NA	NA	NA	NA	NA	NA
character	REF	0	1.0000000	1	44	0	19630	0	NA	NA	NA	NA	NA	NA	NA	NA
character	ALT	0	1.0000000	1	40	0	9815	0	NA	NA	NA	NA	NA	NA	NA	NA
numeric	CHROM	0	1.0000000	NA	NA	NA	NA	NA	8.681703e+00	5.775558e+00	1.00000e+00	4.000000e+00	8.000000e+00	1.300000e+01	2.300000e+01	▇▅▅▂▂
numeric	POS	0	1.0000000	NA	NA	NA	NA	NA	7.847136e+07	5.667005e+07	3.02000e+02	3.178035e+07	6.891596e+07	1.144324e+08	2.492391e+08	▇▆▅▂▁
numeric	EFFECT	0	1.0000000	NA	NA	NA	NA	NA	1.140000e-05	6.601900e-03	-6.62175e-02	-3.620500e-03	-6.500000e-06	3.575100e-03	1.180090e-01	▁▇▁▁▁
numeric	SE	0	1.0000000	NA	NA	NA	NA	NA	5.711200e-03	2.566900e-03	3.33060e-03	3.725100e-03	4.660300e-03	6.965500e-03	2.308620e-02	▇▂▁▁▁
numeric	PVAL	0	1.0000000	NA	NA	NA	NA	NA	4.822477e-01	2.936359e-01	0.00000e+00	2.231038e-01	4.758905e-01	7.367820e-01	1.000000e+00	▇▇▇▇▇
numeric	PVAL_ztest	0	1.0000000	NA	NA	NA	NA	NA	4.822477e-01	2.936359e-01	0.00000e+00	2.231041e-01	4.758899e-01	7.367817e-01	9.999996e-01	▇▇▇▇▇
numeric	AF	0	1.0000000	NA	NA	NA	NA	NA	2.913287e-01	2.592639e-01	1.20090e-02	7.335100e-02	2.010460e-01	4.539820e-01	9.870730e-01	▇▃▂▂▁
numeric	AF_reference	351700	0.9529933	NA	NA	NA	NA	NA	2.885181e-01	2.504129e-01	0.00000e+00	8.286740e-02	2.112620e-01	4.452880e-01	1.000000e+00	▇▃▂▂▁
numeric	N	0	1.0000000	NA	NA	NA	NA	NA	3.605200e+04	0.000000e+00	3.60520e+04	3.605200e+04	3.605200e+04	3.605200e+04	3.605200e+04	▁▁▇▁▁

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	10583	rs58108140	G	A	0.0034029	0.0077076	0.6588552	0.6588545	0.158153	NA	36052
1	51479	rs116400033	T	A	0.0108971	0.0072451	0.1325669	0.1325657	0.183122	0.1281950	36052
1	54676	rs2462492	C	T	-0.0159222	0.0079343	0.0447765	0.0447757	0.153919	NA	36052
1	55299	rs10399749	C	T	-0.0076824	0.0073605	0.2966060	0.2966055	0.177563	NA	36052
1	61987	rs76735897	A	G	-0.0122878	0.0062170	0.0480994	0.0480996	0.303514	NA	36052
1	61989	rs77573425	G	C	-0.0122958	0.0062170	0.0479535	0.0479539	0.303512	NA	36052
1	66162	rs62639105	A	T	-0.0094384	0.0060367	0.1179351	0.1179350	0.329955	NA	36052
1	73841	rs143773730	C	T	-0.0009135	0.0074509	0.9024231	0.9024227	0.176353	NA	36052
1	82163	rs139113303	G	A	0.0143658	0.0106687	0.1781312	0.1781294	0.074582	0.0327476	36052
1	82609	rs149189449	C	G	0.0143658	0.0106687	0.1781312	0.1781294	0.074582	0.0327476	36052

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	51224600	rs187908482	G	A	0.0056764	0.0061457	0.3556706	0.3556710	0.133329	0.0387380	36052
22	51224718	rs189454695	T	G	0.0000889	0.0049530	0.9856830	0.9856833	0.231318	NA	36052
22	51227891	rs6010091	G	A	0.0007610	0.0039750	0.8481741	0.8481742	0.406363	0.4039540	36052
22	51229805	rs9616985	T	C	0.0072660	0.0077363	0.3476250	0.3476255	0.069207	0.0730831	36052
22	51229855	rs144549712	G	A	-0.0029729	0.0055087	0.5894215	0.5894226	0.148164	0.1160140	36052
22	51236013	rs200507571	A	AT	0.0003262	0.0044760	0.9418989	0.9418993	0.263895	0.1487620	36052
22	51237063	rs3896457	T	C	-0.0028892	0.0043315	0.5047508	0.5047521	0.291447	0.2050720	36052
22	51238249	rs149733995	A	C	0.0077582	0.0078375	0.3222337	0.3222343	0.070647	NA	36052
23	57596530	rs36177845	A	AT	-0.0018928	0.0034645	0.5848305	0.5848311	0.558975	NA	36052
23	100784211	rs188350543	C	A	0.0042025	0.0041203	0.3077471	0.3077464	0.748382	0.6498010	36052

1   10583   rs58108140  G   A   .   PASS    AF=0.158153 ES:SE:LP:AF:SS:ID   0.00340286:0.00770758:0.18121:0.158153:36052:rs58108140
1   51479   rs116400033 T   A   .   PASS    AF=0.183122 ES:SE:LP:AF:SS:ID   0.0108971:0.00724512:0.877565:0.183122:36052:rs116400033
1   54676   rs2462492   C   T   .   PASS    AF=0.153919 ES:SE:LP:AF:SS:ID   -0.0159222:0.0079343:1.34895:0.153919:36052:rs2462492
1   55299   rs10399749  C   T   .   PASS    AF=0.177563 ES:SE:LP:AF:SS:ID   -0.00768241:0.00736046:0.52782:0.177563:36052:rs10399749
1   61987   rs76735897  A   G   .   PASS    AF=0.303514 ES:SE:LP:AF:SS:ID   -0.0122878:0.00621699:1.31786:0.303514:36052:rs76735897
1   61989   rs77573425  G   C   .   PASS    AF=0.303512 ES:SE:LP:AF:SS:ID   -0.0122958:0.00621698:1.31918:0.303512:36052:rs77573425
1   66162   rs62639105  A   T   .   PASS    AF=0.329955 ES:SE:LP:AF:SS:ID   -0.00943835:0.00603668:0.928357:0.329955:36052:rs62639105
1   73841   rs143773730 C   T   .   PASS    AF=0.176353 ES:SE:LP:AF:SS:ID   -0.000913492:0.00745091:0.0445898:0.176353:36052:rs143773730
1   82163   rs139113303 G   A   .   PASS    AF=0.074582 ES:SE:LP:AF:SS:ID   0.0143658:0.0106687:0.74926:0.074582:36052:rs139113303
1   82609   rs149189449 C   G   .   PASS    AF=0.074582 ES:SE:LP:AF:SS:ID   0.0143658:0.0106687:0.74926:0.074582:36052:rs149189449
1   86028   rs114608975 T   C   .   PASS    AF=0.094621 ES:SE:LP:AF:SS:ID   0.00823631:0.00958433:0.408773:0.094621:36052:rs114608975
1   86065   rs116504101 G   C   .   PASS    AF=0.094629 ES:SE:LP:AF:SS:ID   0.00812642:0.00958437:0.401754:0.094629:36052:rs116504101
1   86303   rs2949417   G   T   .   PASS    AF=0.08279  ES:SE:LP:AF:SS:ID   -0.0187542:0.0102669:1.16909:0.08279:36052:rs2949417
1   86331   rs115209712 A   G   .   PASS    AF=0.082575 ES:SE:LP:AF:SS:ID   -0.018644:0.0102796:1.15662:0.082575:36052:rs115209712
1   87409   rs139490478 C   T   .   PASS    AF=0.094644 ES:SE:LP:AF:SS:ID   0.00800137:0.00958452:0.393814:0.094644:36052:rs139490478
1   88172   rs940551    G   A   .   PASS    AF=0.065736 ES:SE:LP:AF:SS:ID   0.0052302:0.0114257:0.189011:0.065736:36052:rs940551
1   88177   rs143215837 G   C   .   PASS    AF=0.065633 ES:SE:LP:AF:SS:ID   0.00495241:0.0114327:0.177255:0.065633:36052:rs143215837
1   88316   rs113759966 G   A   .   PASS    AF=0.065947 ES:SE:LP:AF:SS:ID   0.00602148:0.0114101:0.223528:0.065947:36052:rs113759966
1   88338   rs55700207  G   A   .   PASS    AF=0.089174 ES:SE:LP:AF:SS:ID   0.00260613:0.00989929:0.101085:0.089174:36052:rs55700207
1   88710   rs186575039 C   G   .   PASS    AF=0.094391 ES:SE:LP:AF:SS:ID   0.00948938:0.00960366:0.490658:0.094391:36052:rs186575039
1   89946   rs138808727 A   T   .   PASS    AF=0.15244  ES:SE:LP:AF:SS:ID   0.00507945:0.00797513:0.280517:0.15244:36052:rs138808727
1   91190   rs143856811 G   A   .   PASS    AF=0.093758 ES:SE:LP:AF:SS:ID   0.00868046:0.0096412:0.43423:0.093758:36052:rs143856811
1   92858   rs147061536 G   T   .   PASS    AF=0.157743 ES:SE:LP:AF:SS:ID   0.00444316:0.00780078:0.244916:0.157743:36052:rs147061536
1   98929   rs12184306  A   G   .   PASS    AF=0.080661 ES:SE:LP:AF:SS:ID   -0.0253613:0.0103126:1.85627:0.080661:36052:rs12184306
1   98974   rs12184307  A   G   .   PASS    AF=0.079988 ES:SE:LP:AF:SS:ID   -0.0258108:0.0103781:1.89005:0.079988:36052:rs12184307
1   120983  rs182468771 C   T   .   PASS    AF=0.084269 ES:SE:LP:AF:SS:ID   -0.00283142:0.00990956:0.110649:0.084269:36052:rs182468771
1   121009  rs1851943   C   T   .   PASS    AF=0.131238 ES:SE:LP:AF:SS:ID   0.000678637:0.00827994:0.0293384:0.131238:36052:rs1851943
1   137825  rs147252685 G   A   .   PASS    AF=0.17979  ES:SE:LP:AF:SS:ID   0.00937777:0.00724441:0.708855:0.17979:36052:rs147252685
1   162463  rs143399298 T   C   .   PASS    AF=0.08252  ES:SE:LP:AF:SS:ID   -0.0241424:0.0103218:1.71361:0.08252:36052:rs143399298
1   234432  rs8179455   A   G   .   PASS    AF=0.074895 ES:SE:LP:AF:SS:ID   0.0028993:0.0105153:0.106371:0.074895:36052:rs8179455
1   249275  rs200079338 GT  G   .   PASS    AF=0.865096 ES:SE:LP:AF:SS:ID   0.00665704:0.00841985:0.367385:0.865096:36052:rs200079338
1   251627  rs72502741  AC  A   .   PASS    AF=0.140351 ES:SE:LP:AF:SS:ID   -0.0107968:0.0083215:0.711136:0.140351:36052:rs72502741
1   255923  rs199745078 G   GTC .   PASS    AF=0.266223 ES:SE:LP:AF:SS:ID   -0.00711638:0.00641974:0.572451:0.266223:36052:rs199745078
1   540975  rs80246094  G   A   .   PASS    AF=0.086384 ES:SE:LP:AF:SS:ID   -0.000129917:0.00997436:0.0045368:0.086384:36052:rs80246094
1   662613  rs151190501 G   A   .   PASS    AF=0.022293 ES:SE:LP:AF:SS:ID   -0.0115643:0.0174474:0.294603:0.022293:36052:rs151190501
1   662622  rs61769339  G   A   .   PASS    AF=0.10955  ES:SE:LP:AF:SS:ID   0.0229681:0.00737058:2.73705:0.10955:36052:rs61769339
1   691541  rs201999089 AT  A   .   PASS    AF=0.10754  ES:SE:LP:AF:SS:ID   -0.00322526:0.00860772:0.150035:0.10754:36052:rs201999089
1   693625  rs190214723 T   C   .   PASS    AF=0.044048 ES:SE:LP:AF:SS:ID   0.000789312:0.0131881:0.0212376:0.044048:36052:rs190214723
1   704637  rs142559957 G   A   .   PASS    AF=0.052775 ES:SE:LP:AF:SS:ID   -0.00158721:0.0111474:0.0521861:0.052775:36052:rs142559957
1   718505  rs181440659 G   A   .   PASS    AF=0.03238  ES:SE:LP:AF:SS:ID   -0.00429824:0.0136:0.123801:0.03238:36052:rs181440659
1   722670  rs116030099 T   C   .   PASS    AF=0.101886 ES:SE:LP:AF:SS:ID   -0.00599479:0.00818109:0.333759:0.101886:36052:rs116030099
1   729679  rs4951859   C   G   .   PASS    AF=0.832886 ES:SE:LP:AF:SS:ID   -0.00640064:0.00593554:0.55149:0.832886:36052:rs4951859
1   732809  rs12131618  T   C   .   PASS    AF=0.083236 ES:SE:LP:AF:SS:ID   0.0143285:0.00777994:1.18365:0.083236:36052:rs12131618
1   734349  rs141242758 T   C   .   PASS    AF=0.132061 ES:SE:LP:AF:SS:ID   0.0155058:0.00644472:1.79238:0.132061:36052:rs141242758
1   734462  rs12564807  G   A   .   PASS    AF=0.88634  ES:SE:LP:AF:SS:ID   0.0138319:0.00874023:0.944916:0.88634:36052:rs12564807
1   735682  rs138174321 G   C   .   PASS    AF=0.04706  ES:SE:LP:AF:SS:ID   -0.0252399:0.0129359:1.29209:0.04706:36052:rs138174321
1   736289  rs79010578  T   A   .   PASS    AF=0.134639 ES:SE:LP:AF:SS:ID   0.00511974:0.00649569:0.365932:0.134639:36052:rs79010578
1   742990  rs202009397 C   T   .   PASS    AF=0.070901 ES:SE:LP:AF:SS:ID   0.0253763:0.00893355:2.34646:0.070901:36052:rs202009397
1   743021  rs3964475   T   C   .   PASS    AF=0.277884 ES:SE:LP:AF:SS:ID   -0.00464113:0.0056138:0.388928:0.277884:36052:rs3964475
1   743420  rs201920982 G   A   .   PASS    AF=0.049864 ES:SE:LP:AF:SS:ID   0.0206885:0.0103565:1.33955:0.049864:36052:rs201920982

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