IEU GWAS QC Report

name	value
id	UKB-b-17992
trait	Diagnoses - main ICD10: I84.2 Internal haemorrhoids without complication
note	41202#I842: Output from GWAS pipeline using Phesant derived variables from UKBiobank
year	2018
author	Ben Elsworth
population	European
sex	Males and Females
sample_size	463010
ncase	1461
ncontrol	461549
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-17992
bcf_file	ukb-b-17992.vcf.gz
qc_metrics_file	qc_metrics.json
metadata	metadata.json
refdata	1kg_v3_nomult.bcf
compile_time	2020-05-09 19:28 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-17992/UKB-b-17992_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-17992/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:40:18 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-17992/UKB-b-17992_data.vcf.gz ...
Read summary statistics for 2432518 SNPs.
Dropped 280 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, 616806 SNPs remain.
After merging with regression SNP LD, 616806 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0005 (0.0013)
Lambda GC: 1.0107
Mean Chi^2: 1.0222
Intercept: 1.0165 (0.0103)
Ratio: 0.7432 (0.4637)
Analysis finished at Thu Oct 17 14:40:52 2019
Total time elapsed: 33.51s

{
    "af_correlation": 0.76,
    "inflation_factor": 1,
    "mean_EFFECT": 6.3121e-07,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 0,
    "n_p_sig": 0,
    "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": 19265,
    "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": 616806,
    "ldsc_nsnp_merge_regression_ld": 616806,
    "ldsc_observed_scale_h2_beta": 0.0005,
    "ldsc_observed_scale_h2_se": 0.0013,
    "ldsc_intercept_beta": 1.0165,
    "ldsc_intercept_se": 0.0103,
    "ldsc_lambda_gc": 1.0107,
    "ldsc_mean_chisq": 1.0222,
    "ldsc_ratio": 0.7432
}

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	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	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	4	58	0	2432241	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	2432518	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	8.652717e+00	5.766806e+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	7.857872e+07	5.663337e+07	5687.0000000	3.170297e+07	6.899896e+07	1.147658e+08	2.491722e+08	▇▆▅▂▁
numeric	EFFECT	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	6.000000e-07	1.250000e-04	-0.0007472	-8.290000e-05	4.000000e-07	8.410000e-05	6.841000e-04	▁▁▇▂▁
numeric	SE	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	1.236000e-04	6.400000e-06	0.0001125	1.182000e-04	1.218000e-04	1.279000e-04	2.436000e-04	▇▁▁▁▁
numeric	PVAL	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	4.978308e-01	2.894519e-01	0.0000012	2.500000e-01	5.000000e-01	7.499995e-01	1.000000e+00	▇▇▇▇▇
numeric	PVAL_ztest	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	4.978322e-01	2.894233e-01	0.0000012	2.468313e-01	4.980775e-01	7.482144e-01	9.999997e-01	▇▇▇▇▇
numeric	AF	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	4.557194e-01	1.482557e-01	0.2395620	3.252930e-01	4.345425e-01	5.750230e-01	7.604380e-01	▇▆▅▅▃
numeric	AF_reference	19265	0.9920802	NA	NA	NA	NA	NA	NA	NA	4.370793e-01	1.742663e-01	0.0001997	2.999200e-01	4.225240e-01	5.644970e-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	-0.0001804	0.0002070	0.3800004	0.3833898	0.623765	0.7821490	NA
1	54676	rs2462492	C	T	0.0003330	0.0002051	0.1000000	0.1044603	0.400401	NA	NA
1	91536	rs6702460	G	T	0.0002969	0.0002019	0.1400000	0.1414359	0.456846	0.4207270	NA
1	534192	rs6680723	C	T	-0.0002144	0.0002307	0.3500000	0.3525082	0.240959	NA	NA
1	706368	rs55727773	A	G	-0.0000353	0.0001432	0.8100000	0.8050944	0.515645	0.2751600	NA
1	763394	rs369924889	G	A	0.0002578	0.0001679	0.1199999	0.1246252	0.706753	0.6176120	NA
1	776546	rs12124819	A	G	0.0001480	0.0001530	0.3300000	0.3335486	0.265385	0.0756789	NA
1	814495	rs74461805	C	A	-0.0002531	0.0001963	0.2000000	0.1972746	0.340396	NA	NA
1	830181	rs28444699	A	G	-0.0000197	0.0001314	0.8800001	0.8808886	0.697255	0.6912940	NA
1	831489	rs4970385	C	T	0.0000154	0.0001290	0.9100000	0.9050107	0.705397	0.6491610	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	51180501	rs5770999	T	C	0.0001807	0.0001348	0.1800002	0.1799586	0.713656	0.6369810	NA
22	51181919	rs9616825	G	C	0.0001763	0.0001341	0.1900002	0.1884682	0.695470	0.6194090	NA
22	51182485	rs6009961	A	G	0.0001709	0.0001352	0.2099999	0.2060742	0.715502	0.6383790	NA
22	51186143	rs2879914	T	C	0.0001315	0.0001254	0.2900000	0.2940248	0.381825	0.2733630	NA
22	51186228	rs3865766	C	T	0.0000661	0.0001222	0.5900000	0.5887709	0.451061	0.4532750	NA
22	51197266	rs61290853	A	G	0.0000389	0.0001261	0.7600007	0.7575324	0.386333	0.4229230	NA
22	51198027	rs34939255	A	G	0.0000425	0.0001428	0.7700005	0.7659652	0.254562	0.0984425	NA
22	51211106	rs9628250	T	C	0.0001084	0.0001416	0.4400003	0.4437083	0.271547	0.1671330	NA
22	51212875	rs2238837	A	C	0.0001028	0.0001345	0.4400003	0.4447887	0.331457	0.3724040	NA
22	51237063	rs3896457	T	C	0.0001984	0.0001377	0.1499999	0.1495555	0.297974	0.2050720	NA

1   49298   rs10399793  T   C   .   PASS    AF=0.623765 ES:SE:LP:AF:ID  -0.000180443:0.000207009:0.420216:0.623765:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400401 ES:SE:LP:AF:ID  0.000332972:0.000205082:1:0.400401:rs2462492
1   91536   rs6702460   G   T   .   PASS    AF=0.456846 ES:SE:LP:AF:ID  0.000296928:0.000201928:0.853872:0.456846:rs6702460
1   534192  rs6680723   C   T   .   PASS    AF=0.240959 ES:SE:LP:AF:ID  -0.000214449:0.000230656:0.455932:0.240959:rs6680723
1   706368  rs12029736  A   G   .   PASS    AF=0.515645 ES:SE:LP:AF:ID  -3.53325e-05:0.000143186:0.091515:0.515645:rs12029736
1   763394  rs3115847   G   A   .   PASS    AF=0.706753 ES:SE:LP:AF:ID  0.000257792:0.000167872:0.920819:0.706753:rs3115847
1   776546  rs12124819  A   G   .   PASS    AF=0.265385 ES:SE:LP:AF:ID  0.000147997:0.000153049:0.481486:0.265385:rs12124819
1   814495  rs74461805  C   A   .   PASS    AF=0.340396 ES:SE:LP:AF:ID  -0.000253143:0.000196333:0.69897:0.340396:rs74461805
1   830181  rs28444699  A   G   .   PASS    AF=0.697255 ES:SE:LP:AF:ID  -1.96829e-05:0.000131357:0.0555173:0.697255:rs28444699
1   831489  rs4970385   C   T   .   PASS    AF=0.705397 ES:SE:LP:AF:ID  1.53917e-05:0.00012898:0.0409586:0.705397:rs4970385
1   831909  rs9697642   C   T   .   PASS    AF=0.705442 ES:SE:LP:AF:ID  1.18933e-05:0.000128976:0.0315171:0.705442:rs9697642
1   832066  rs9697380   G   C   .   PASS    AF=0.705627 ES:SE:LP:AF:ID  9.90814e-06:0.000128981:0.0268721:0.705627:rs9697380
1   832318  rs4500250   C   A   .   PASS    AF=0.705655 ES:SE:LP:AF:ID  1.16476e-05:0.000128995:0.0315171:0.705655:rs4500250
1   832398  rs4553118   T   C   .   PASS    AF=0.730129 ES:SE:LP:AF:ID  -2.62582e-06:0.00013251:0.00877392:0.730129:rs4553118
1   832918  rs28765502  T   C   .   PASS    AF=0.294377 ES:SE:LP:AF:ID  -1.24181e-05:0.000128989:0.0362122:0.294377:rs28765502
1   833641  rs28594623  T   C   .   PASS    AF=0.23975  ES:SE:LP:AF:ID  -1.13875e-05:0.00013689:0.0315171:0.23975:rs28594623
1   835499  rs4422948   A   G   .   PASS    AF=0.241162 ES:SE:LP:AF:ID  2.95417e-06:0.000136266:0.00877392:0.241162:rs4422948
1   836896  rs28705752  T   C   .   PASS    AF=0.269511 ES:SE:LP:AF:ID  -1.07471e-05:0.000131486:0.0315171:0.269511:rs28705752
1   838555  rs4970383   C   A   .   PASS    AF=0.246204 ES:SE:LP:AF:ID  2.06074e-07:0.000135329:-0:0.246204:rs4970383
1   839103  rs28562941  A   G   .   PASS    AF=0.270019 ES:SE:LP:AF:ID  -2.68756e-05:0.000131579:0.0757207:0.270019:rs28562941
1   840753  rs4970382   T   C   .   PASS    AF=0.400124 ES:SE:LP:AF:ID  -1.78946e-06:0.000118973:0.00436481:0.400124:rs4970382
1   843405  rs11516185  A   G   .   PASS    AF=0.362606 ES:SE:LP:AF:ID  0.000170153:0.000147688:0.60206:0.362606:rs11516185
1   850218  rs6664536   T   A   .   PASS    AF=0.590331 ES:SE:LP:AF:ID  1.00061e-06:0.000118626:0.00436481:0.590331:rs6664536
1   850371  rs6679046   G   T   .   PASS    AF=0.603723 ES:SE:LP:AF:ID  -2.02234e-06:0.000119292:0.00436481:0.603723:rs6679046
1   850780  rs6657440   C   T   .   PASS    AF=0.603942 ES:SE:LP:AF:ID  -4.66527e-06:0.000119275:0.0132283:0.603942:rs6657440
1   852037  rs4970463   G   A   .   PASS    AF=0.589686 ES:SE:LP:AF:ID  -1.29815e-06:0.000118819:0.00436481:0.589686:rs4970463
1   852063  rs28436996  G   A   .   PASS    AF=0.589665 ES:SE:LP:AF:ID  1.99768e-06:0.000118765:0.00436481:0.589665:rs28436996
1   852875  rs13303369  C   T   .   PASS    AF=0.607671 ES:SE:LP:AF:ID  -8.61181e-07:0.000119541:0.00436481:0.607671:rs13303369
1   853954  rs1806509   C   A   .   PASS    AF=0.607829 ES:SE:LP:AF:ID  3.8451e-06:0.000119558:0.0132283:0.607829:rs1806509
1   854777  rs13303019  A   G   .   PASS    AF=0.610316 ES:SE:LP:AF:ID  -6.76902e-06:0.000119675:0.0222764:0.610316:rs13303019
1   854978  rs13303057  A   C   .   PASS    AF=0.603283 ES:SE:LP:AF:ID  4.14901e-06:0.000119321:0.0132283:0.603283:rs13303057
1   855075  rs6673914   C   G   .   PASS    AF=0.610337 ES:SE:LP:AF:ID  -7.96776e-06:0.000119678:0.0222764:0.610337:rs6673914
1   856099  rs28534711  T   G   .   PASS    AF=0.389936 ES:SE:LP:AF:ID  -4.36647e-07:0.0001197:-0:0.389936:rs28534711
1   856108  rs28742275  A   G   .   PASS    AF=0.38992  ES:SE:LP:AF:ID  -2.66322e-07:0.000119706:-0:0.38992:rs28742275
1   856476  rs4040605   A   G   .   PASS    AF=0.350356 ES:SE:LP:AF:ID  3.83701e-05:0.000122972:0.119186:0.350356:rs4040605
1   864938  rs2340587   G   A   .   PASS    AF=0.760006 ES:SE:LP:AF:ID  -5.91246e-05:0.000136899:0.173925:0.760006:rs2340587
1   866893  rs2880024   T   C   .   PASS    AF=0.610552 ES:SE:LP:AF:ID  -4.20238e-05:0.000120349:0.136677:0.610552:rs2880024
1   868418  rs28546443  C   T   .   PASS    AF=0.297867 ES:SE:LP:AF:ID  1.17223e-05:0.000132229:0.0315171:0.297867:rs28546443
1   870645  rs28576697  T   C   .   PASS    AF=0.291285 ES:SE:LP:AF:ID  -5.47319e-05:0.000131176:0.167491:0.291285:rs28576697
1   871334  rs4072383   G   T   .   PASS    AF=0.72062  ES:SE:LP:AF:ID  8.11579e-05:0.000130363:0.275724:0.72062:rs4072383
1   872352  rs1806780   G   C   .   PASS    AF=0.267527 ES:SE:LP:AF:ID  -9.81696e-05:0.00013213:0.337242:0.267527:rs1806780
1   873558  rs1110052   G   T   .   PASS    AF=0.715251 ES:SE:LP:AF:ID  5.63648e-05:0.000129328:0.180456:0.715251:rs1110052
1   875770  rs4970379   A   G   .   PASS    AF=0.600085 ES:SE:LP:AF:ID  -5.80486e-06:0.000121349:0.0177288:0.600085:rs4970379
1   877147  rs114982608 G   A   .   PASS    AF=0.242966 ES:SE:LP:AF:ID  -0.000163128:0.000137438:0.619789:0.242966:rs114982608
1   881627  rs2272757   G   A   .   PASS    AF=0.652393 ES:SE:LP:AF:ID  -6.81926e-05:0.000122584:0.236572:0.652393:rs2272757
1   882033  rs2272756   G   A   .   PASS    AF=0.243918 ES:SE:LP:AF:ID  -8.17239e-05:0.000136527:0.259637:0.243918:rs2272756
1   890104  rs28631199  G   A   .   PASS    AF=0.246771 ES:SE:LP:AF:ID  -0.000119059:0.00013599:0.420216:0.246771:rs28631199
1   891059  rs13303065  C   T   .   PASS    AF=0.652432 ES:SE:LP:AF:ID  -6.87098e-05:0.000122565:0.236572:0.652432:rs13303065
1   891945  rs13303106  A   G   .   PASS    AF=0.652494 ES:SE:LP:AF:ID  -6.64576e-05:0.000122708:0.229148:0.652494:rs13303106
1   900505  rs28705211  G   C   .   PASS    AF=0.271763 ES:SE:LP:AF:ID  -0.000133162:0.000132656:0.49485:0.271763:rs28705211

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