IEU GWAS QC Report

name	value
id	ieu-a-996
pmid	26482879
year	2014
mr	1
note	23andME excluded
trait	Eczema
category	Disease
subcategory	Autoimmune / inflammatory
population	European
sex	Males and females
ncase	10788
ncontrol	30047
sample_size	40835
nsnp	11059641
sd	NULL
priority	1
author	Paternoster
group_name	public
access	public
build	HG19/GRCh37

name	value
gwas_id	ieu-a-996
bcf_file	ieu-a-996.vcf.gz
qc_metrics_file	qc_metrics.json
metadata	metadata.json
refdata	1kg_v3_nomult.bcf
compile_time	2020-05-09 23:36 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/dev/ieu-a-import/processed/ieu-a-996/ieu-a-996.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-996/ldsc.txt \
--w-ld-chr /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/ 

Beginning analysis at Tue Feb  4 11:56:39 2020
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ieu-a-import/processed/ieu-a-996/ieu-a-996.vcf.gz ...
Read summary statistics for 9965795 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, 1289604 SNPs remain.
After merging with regression SNP LD, 1289604 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0659 (0.0163)
Lambda GC: 1.0512
Mean Chi^2: 1.08
Intercept: 1.0284 (0.0066)
Ratio: 0.3555 (0.083)
Analysis finished at Tue Feb  4 11:58:04 2020
Total time elapsed: 1.0m:25.44s

{
    "af_correlation": 0.946,
    "inflation_factor": 1.0447,
    "mean_EFFECT": 0.0057,
    "n": 40835,
    "n_snps": 9965795,
    "n_clumped_hits": 12,
    "n_p_sig": 721,
    "n_mono": 0,
    "n_ns": 0,
    "n_mac": 3,
    "is_snpid_unique": true,
    "n_miss_EFFECT": 0,
    "n_miss_SE": 0,
    "n_miss_PVAL": 0,
    "n_miss_AF": 0,
    "n_miss_AF_reference": 290038,
    "n_est": 36370.2006,
    "ratio_se_n": 0.9437,
    "mean_diff": -0.0038,
    "ratio_diff": 14.8661,
    "sd_y_est1": 2.8298,
    "sd_y_est2": 2.6706,
    "r2_sum1": 0.088,
    "r2_sum2": 0.011,
    "r2_sum3": 0.0123,
    "r2_sum4": 0.014,
    "ldsc_nsnp_merge_refpanel_ld": 1289604,
    "ldsc_nsnp_merge_regression_ld": 1289604,
    "ldsc_observed_scale_h2_beta": 0.0659,
    "ldsc_observed_scale_h2_se": 0.0163,
    "ldsc_intercept_beta": 1.0284,
    "ldsc_intercept_se": 0.0066,
    "ldsc_lambda_gc": 1.0512,
    "ldsc_mean_chisq": 1.08,
    "ldsc_ratio": 0.355
}

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	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	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	9965793	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	8.680186e+00	5.777235e+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	7.866053e+07	5.642987e+07	828.0000000	3.236759e+07	6.916933e+07	1.144637e+08	2.492297e+08	▇▆▅▂▁
numeric	EFFECT	0	1.0000000	NA	NA	NA	NA	NA	5.689100e-03	4.453580e-01	-261.1540000	-2.217400e-02	2.462000e-03	3.015670e-02	3.052700e+01	▁▁▁▁▇
numeric	SE	0	1.0000000	NA	NA	NA	NA	NA	9.575830e-02	6.248778e-01	0.0164146	2.041670e-02	3.455590e-02	8.772550e-02	3.368330e+02	▇▁▁▁▁
numeric	PVAL	0	1.0000000	NA	NA	NA	NA	NA	4.928946e-01	2.910629e-01	0.0000000	2.391509e-01	4.905745e-01	7.453684e-01	1.000000e+00	▇▇▇▇▇
numeric	PVAL_ztest	0	1.0000000	NA	NA	NA	NA	NA	4.928945e-01	2.910637e-01	0.0000000	2.391717e-01	4.905587e-01	7.453610e-01	1.000000e+00	▇▇▇▇▇
numeric	AF	0	1.0000000	NA	NA	NA	NA	NA	2.094925e-01	2.425788e-01	0.0003190	2.124400e-02	9.259300e-02	3.358830e-01	9.952650e-01	▇▂▁▁▁
numeric	AF_reference	290038	0.9708967	NA	NA	NA	NA	NA	2.195667e-01	2.526678e-01	0.0000000	1.837060e-02	1.140180e-01	3.428510e-01	1.000000e+00	▇▂▁▁▁
numeric	N	0	1.0000000	NA	NA	NA	NA	NA	3.020600e+04	1.335767e+04	304.0000000	2.787800e+04	3.486700e+04	4.053000e+04	4.083500e+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	55164	rs3091274	C	A	0.0639668	0.349420	0.8547311	0.8547465	0.957143	0.9233230	856
1	55299	rs10399749	C	T	-0.0022154	0.190480	0.9907160	0.9907201	0.182289	NA	856
1	55326	rs3107975	T	C	0.3412300	0.316440	0.2808640	0.2808819	0.035449	0.0459265	856
1	55394	rs2949420	T	A	-0.4503410	0.528760	0.3943846	0.3943847	0.014356	NA	856
1	55850	rs191890754	C	G	0.0739417	0.294474	0.8017331	0.8017387	0.012673	NA	3470
1	66176	rs28552463	T	A	-1.1014000	0.634620	0.0826514	0.0826475	0.011975	NA	856
1	66219	rs181028663	A	T	1.1094000	0.749560	0.1388430	0.1388550	0.010238	0.0203674	856
1	77961	rs78385339	G	A	0.5305700	0.290820	0.0681020	0.0680927	0.067717	NA	856
1	84002	rs28850140	G	A	-0.0452317	0.104808	0.6660733	0.6660560	0.173270	NA	3470
1	85063	rs187802690	T	C	-0.3224700	0.427930	0.4511241	0.4511148	0.030280	NA	856

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	51224635	rs143053851	G	A	0.1886580	0.1567970	0.2288790	0.2288993	0.043084	NA	4945
22	51224718	rs189454695	T	G	0.0444723	0.0765311	0.5611927	0.5611724	0.135809	NA	7883
22	51227891	rs6010091	G	A	-0.0040492	0.0354251	0.9089709	0.9089977	0.372510	0.4039540	15798
22	51229805	rs9616985	T	C	-0.0257292	0.0621591	0.6789488	0.6789291	0.079470	0.0730831	15798
22	51229855	rs144549712	G	A	-0.0438631	0.0815574	0.5907232	0.5907022	0.093923	0.1160140	9087
22	51234799	rs191117135	G	A	0.1813100	0.6048700	0.7643706	0.7643676	0.010701	0.0059904	1307
22	51237063	rs3896457	T	C	0.0115154	0.0450824	0.7983840	0.7983910	0.249514	0.2050720	12763
22	51238249	rs149733995	A	C	-0.0717990	0.0819438	0.3809211	0.3809224	0.072168	NA	10394
23	50040891	rs192267219	C	T	-0.1058840	0.1732570	0.5411292	0.5411080	0.977139	0.0222517	6754
23	100784211	rs188350543	C	A	0.0084324	0.0283846	0.7664115	0.7664067	0.739636	0.6498010	20007

1   55164   rs3091274   C   A   .   PASS    AF=0.957143 ES:SE:LP:AF:SS:ID   0.0639668:0.34942:0.0681705:0.957143:856:rs3091274
1   55299   rs10399749  C   T   .   PASS    AF=0.182289 ES:SE:LP:AF:SS:ID   -0.00221545:0.19048:0.00405082:0.182289:856:rs10399749
1   55326   rs3107975   T   C   .   PASS    AF=0.035449 ES:SE:LP:AF:SS:ID   0.34123:0.31644:0.551504:0.035449:856:rs3107975
1   55394   rs2949420   T   A   .   PASS    AF=0.014356 ES:SE:LP:AF:SS:ID   -0.450341:0.52876:0.40408:0.014356:856:rs2949420
1   55850   rs191890754 C   G   .   PASS    AF=0.012673 ES:SE:LP:AF:SS:ID   0.0739417:0.294474:0.0959702:0.012673:3470:rs191890754
1   66176   rs28552463  T   A   .   PASS    AF=0.011975 ES:SE:LP:AF:SS:ID   -1.1014:0.63462:1.08275:0.011975:856:rs28552463
1   66219   rs181028663 A   T   .   PASS    AF=0.010238 ES:SE:LP:AF:SS:ID   1.1094:0.74956:0.857476:0.010238:856:rs181028663
1   77961   rs78385339  G   A   .   PASS    AF=0.067717 ES:SE:LP:AF:SS:ID   0.53057:0.29082:1.16684:0.067717:856:rs78385339
1   84002   rs28850140  G   A   .   PASS    AF=0.17327  ES:SE:LP:AF:SS:ID   -0.0452317:0.104808:0.176478:0.17327:3470:rs28850140
1   85063   rs187802690 T   C   .   PASS    AF=0.03028  ES:SE:LP:AF:SS:ID   -0.32247:0.42793:0.345704:0.03028:856:rs187802690
1   87021   rs188486692 T   C   .   PASS    AF=0.027216 ES:SE:LP:AF:SS:ID   -0.0527343:0.223121:0.0898256:0.027216:3470:rs188486692
1   99687   rs139153227 C   T   .   PASS    AF=0.056542 ES:SE:LP:AF:SS:ID   -0.0736552:0.180382:0.165548:0.056542:3470:rs139153227
1   115746  rs147538909 C   T   .   PASS    AF=0.040325 ES:SE:LP:AF:SS:ID   -0.948019:0.383749:1.86915:0.040325:856:rs147538909
1   120705  rs4117992   T   C   .   PASS    AF=0.957408 ES:SE:LP:AF:SS:ID   -0.10109:0.34124:0.115177:0.957408:856:rs4117992
1   126113  rs79114531  C   A   .   PASS    AF=0.986805 ES:SE:LP:AF:SS:ID   -0.20747:0.53256:0.156848:0.986805:856:rs79114531
1   135203  rs147502335 G   A   .   PASS    AF=0.034675 ES:SE:LP:AF:SS:ID   -0.311631:0.40406:0.355987:0.034675:856:rs147502335
1   168373  rs183198872 C   T   .   PASS    AF=0.044838 ES:SE:LP:AF:SS:ID   -0.0400327:0.165975:0.0918384:0.044838:4326:rs183198872
1   168964  rs187450123 A   G   .   PASS    AF=0.02437  ES:SE:LP:AF:SS:ID   -0.264559:0.49346:0.227759:0.02437:856:rs187450123
1   230433  rs190807317 T   C   .   PASS    AF=0.010111 ES:SE:LP:AF:SS:ID   -1.5508:0.737781:1.44886:0.010111:856:rs190807317
1   249276  rs115018998 T   C   .   PASS    AF=0.010143 ES:SE:LP:AF:SS:ID   0.0425047:0.49841:0.0305762:0.010143:2511:rs115018998
1   526057  rs141459107 T   C   .   PASS    AF=0.012903 ES:SE:LP:AF:SS:ID   -0.398191:0.59215:0.299889:0.012903:1307:rs141459107
1   526970  rs60791385  T   C   .   PASS    AF=0.012306 ES:SE:LP:AF:SS:ID   1.389:0.68542:1.36925:0.012306:856:rs60791385
1   527186  rs111227194 A   G   .   PASS    AF=0.017086 ES:SE:LP:AF:SS:ID   -1.0201:0.797502:0.697161:0.017086:1631:rs111227194
1   527207  rs112901516 A   G   .   PASS    AF=0.015506 ES:SE:LP:AF:SS:ID   -1.5205:0.985749:0.910275:0.015506:1631:rs112901516
1   533198  rs78497331  C   T   .   PASS    AF=0.041151 ES:SE:LP:AF:SS:ID   -0.305001:0.38631:0.36672:0.041151:856:rs78497331
1   540975  rs80246094  G   A   .   PASS    AF=0.087192 ES:SE:LP:AF:SS:ID   -0.3509:0.26254:0.741487:0.087192:856:rs80246094
1   600478  rs114979547 G   A   .   PASS    AF=0.089717 ES:SE:LP:AF:SS:ID   -0.14461:0.25903:0.239066:0.089717:856:rs114979547
1   601141  rs140845213 C   T   .   PASS    AF=0.098554 ES:SE:LP:AF:SS:ID   0.0239499:0.25132:0.0343008:0.098554:856:rs140845213
1   603331  rs186375515 T   C   .   PASS    AF=0.012097 ES:SE:LP:AF:SS:ID   -0.6185:0.63673:0.479709:0.012097:612:rs186375515
1   645605  rs185127847 T   A   .   PASS    AF=0.04293  ES:SE:LP:AF:SS:ID   0.384977:0.193064:1.33568:0.04293:3367:rs185127847
1   662622  rs61769339  G   A   .   PASS    AF=0.113796 ES:SE:LP:AF:SS:ID   -0.0156072:0.0613263:0.0973934:0.113796:11006:rs61769339
1   676127  rs150820983 C   T   .   PASS    AF=0.022678 ES:SE:LP:AF:SS:ID   -0.221676:0.309322:0.324586:0.022678:3130:rs150820983
1   693625  rs190214723 T   C   .   PASS    AF=0.040483 ES:SE:LP:AF:SS:ID   -0.0839241:0.192287:0.178795:0.040483:3986:rs190214723
1   701835  rs189800799 T   C   .   PASS    AF=0.025763 ES:SE:LP:AF:SS:ID   0.0267917:0.220697:0.0441401:0.025763:5713:rs189800799
1   704637  rs142559957 G   A   .   PASS    AF=0.074505 ES:SE:LP:AF:SS:ID   0.0560353:0.1253:0.183926:0.074505:3470:rs142559957
1   705882  rs72631875  G   A   .   PASS    AF=0.069887 ES:SE:LP:AF:SS:ID   0.0605512:0.161448:0.150191:0.069887:3718:rs72631875
1   712871  rs4311830   T   C   .   PASS    AF=0.019615 ES:SE:LP:AF:SS:ID   -0.366245:0.418651:0.418313:0.019615:1475:rs4311830
1   712930  rs28457007  T   C   .   PASS    AF=0.02637  ES:SE:LP:AF:SS:ID   -0.0505144:0.21307:0.090128:0.02637:5713:rs28457007
1   714019  rs114983708 A   G   .   PASS    AF=0.043374 ES:SE:LP:AF:SS:ID   0.0443144:0.2953:0.0551745:0.043374:856:rs114983708
1   714310  rs141232927 C   G   .   PASS    AF=0.018196 ES:SE:LP:AF:SS:ID   -0.155718:0.286041:0.231959:0.018196:5025:rs141232927
1   714427  rs12028261  G   A   .   PASS    AF=0.981626 ES:SE:LP:AF:SS:ID   0.40101:0.54061:0.338912:0.981626:856:rs12028261
1   714596  rs149887893 T   C   .   PASS    AF=0.027038 ES:SE:LP:AF:SS:ID   0.0747731:0.248051:0.117429:0.027038:4406:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.039114 ES:SE:LP:AF:SS:ID   -0.0342613:0.138819:0.0941734:0.039114:6332:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.035262 ES:SE:LP:AF:SS:ID   0.0450748:0.159398:0.109387:0.035262:5713:rs12184277
1   716041  rs149301622 G   A   .   PASS    AF=0.034258 ES:SE:LP:AF:SS:ID   0.0376271:0.162721:0.087712:0.034258:5713:rs149301622
1   717485  rs12184279  C   A   .   PASS    AF=0.033217 ES:SE:LP:AF:SS:ID   0.0347581:0.165261:0.0791434:0.033217:5713:rs12184279
1   718505  rs181440659 G   A   .   PASS    AF=0.013199 ES:SE:LP:AF:SS:ID   -0.126922:0.321661:0.159162:0.013199:3986:rs181440659
1   719914  rs187772768 C   G   .   PASS    AF=0.039751 ES:SE:LP:AF:SS:ID   0.0239311:0.139263:0.0637145:0.039751:5713:rs187772768
1   720381  rs116801199 G   T   .   PASS    AF=0.040785 ES:SE:LP:AF:SS:ID   0.00487888:0.135256:0.0126855:0.040785:5713:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.042801 ES:SE:LP:AF:SS:ID   0.0069944:0.127792:0.0193909:0.042801:5713:rs12565286

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