IEU GWAS QC Report

name	value
id	ukb-d-5012
trait	FI8 : chained arithmetic
category	Binary
ncase	49086
ncontrol	26606
sample_size	75692
sex	Males and females
population	European
group_name	public
access	public
build	HG19/GRCh37
author	Neale lab
year	2018
mr	1
nsnp	13560286

name	value
gwas_id	ukb-d-5012
bcf_file	ukb-d-5012.vcf.gz
qc_metrics_file	qc_metrics.json
metadata	metadata.json
refdata	1kg_v3_nomult.bcf
compile_time	2020-05-10 23:24 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/ukb-d-import/processed/ukb-d-5012/ukb-d-5012.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-d-import/processed/ukb-d-5012/ldsc.txt \
--w-ld-chr /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/ 

Beginning analysis at Mon Nov 25 17:20:28 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-5012/ukb-d-5012.vcf.gz ...
Read summary statistics for 13560285 SNPs.
Dropped 12681 SNPs with duplicated rs numbers.
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, 1283500 SNPs remain.
After merging with regression SNP LD, 1283500 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.1036 (0.0082)
Lambda GC: 1.1379
Mean Chi^2: 1.1575
Intercept: 1.0044 (0.007)
Ratio: 0.0279 (0.0448)
Analysis finished at Mon Nov 25 17:22:43 2019
Total time elapsed: 2.0m:14.52s

{
    "af_correlation": 0.9521,
    "inflation_factor": 1.0818,
    "mean_EFFECT": -0,
    "n": 75692,
    "n_snps": 13560285,
    "n_clumped_hits": 4,
    "n_p_sig": 79,
    "n_mono": 0,
    "n_ns": 1251011,
    "n_mac": 0,
    "is_snpid_unique": false,
    "n_miss_EFFECT": 0,
    "n_miss_SE": 0,
    "n_miss_PVAL": 0,
    "n_miss_AF": 0,
    "n_miss_AF_reference": 561141,
    "n_est": 76013.8246,
    "ratio_se_n": 1.0021,
    "mean_diff": -0.0001,
    "ratio_diff": 41.3318,
    "sd_y_est1": 0.4739,
    "sd_y_est2": 0.4749,
    "r2_sum1": 0.0004,
    "r2_sum2": 0.0018,
    "r2_sum3": 0.0018,
    "r2_sum4": 0.0018,
    "ldsc_nsnp_merge_refpanel_ld": 1283500,
    "ldsc_nsnp_merge_regression_ld": 1283500,
    "ldsc_observed_scale_h2_beta": 0.1036,
    "ldsc_observed_scale_h2_se": 0.0082,
    "ldsc_intercept_beta": 1.0044,
    "ldsc_intercept_se": 0.007,
    "ldsc_lambda_gc": 1.1379,
    "ldsc_mean_chisq": 1.1575,
    "ldsc_ratio": 0.0279
}

name	value
name	value
af_correlation	FALSE
inflation_factor	FALSE
n	FALSE
is_snpid_non_unique	TRUE
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	94	0	13548267	0	NA	NA	NA	NA	NA	NA	NA	NA
character	REF	0	1.0000000	1	100	0	57042	0	NA	NA	NA	NA	NA	NA	NA	NA
character	ALT	0	1.0000000	1	342	0	33328	0	NA	NA	NA	NA	NA	NA	NA	NA
numeric	CHROM	0	1.0000000	NA	NA	NA	NA	NA	9.051635e+00	6.184905e+00	1.000000	4.000000e+00	8.000000e+00	1.300000e+01	2.300000e+01	▇▅▅▂▂
numeric	POS	0	1.0000000	NA	NA	NA	NA	NA	7.902396e+07	5.591667e+07	302.000000	3.292773e+07	7.013545e+07	1.148551e+08	2.492309e+08	▇▆▅▂▁
numeric	EFFECT	0	1.0000000	NA	NA	NA	NA	NA	-4.770000e-05	1.425130e-02	-0.174012	-3.951500e-03	-9.300000e-06	3.914000e-03	1.609700e-01	▁▁▇▁▁
numeric	SE	0	1.0000000	NA	NA	NA	NA	NA	1.018100e-02	9.807600e-03	0.002002	2.911700e-03	5.351600e-03	1.461530e-02	4.973850e-02	▇▂▁▁▁
numeric	PVAL	0	1.0000000	NA	NA	NA	NA	NA	4.871489e-01	2.926428e-01	0.000000	2.302778e-01	4.829676e-01	7.408256e-01	1.000000e+00	▇▇▇▇▇
numeric	PVAL_ztest	0	1.0000000	NA	NA	NA	NA	NA	4.871468e-01	2.926441e-01	0.000000	2.302739e-01	4.829652e-01	7.408249e-01	9.999999e-01	▇▇▇▇▇
numeric	AF	0	1.0000000	NA	NA	NA	NA	NA	1.911583e-01	2.544857e-01	0.001000	7.530600e-03	6.041590e-02	2.959770e-01	9.990000e-01	▇▂▁▁▁
numeric	AF_reference	561141	0.9586188	NA	NA	NA	NA	NA	1.965855e-01	2.471594e-01	0.000000	5.790700e-03	8.466450e-02	3.085060e-01	1.000000e+00	▇▂▁▁▁
numeric	N	0	1.0000000	NA	NA	NA	NA	NA	7.569200e+04	0.000000e+00	75692.000000	7.569200e+04	7.569200e+04	7.569200e+04	7.569200e+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	692794	rs530212009	CA	C	-0.0022908	0.0042518	0.5900407	0.5900394	0.1123220	0.1894970	75692
1	693731	rs12238997	A	G	-0.0022860	0.0040217	0.5697574	0.5697561	0.1176250	0.1417730	75692
1	707522	rs371890604	G	C	-0.0024068	0.0045155	0.5940254	0.5940233	0.0986233	0.1293930	75692
1	717587	rs144155419	G	A	0.0044302	0.0108823	0.6839337	0.6839315	0.0156471	0.0045926	75692
1	723329	rs189787166	A	T	0.0111077	0.0318330	0.7271366	0.7271369	0.0017292	0.0003994	75692
1	730087	rs148120343	T	C	-0.0037710	0.0055981	0.5005552	0.5005535	0.0573192	0.0127796	75692
1	731718	rs142557973	T	C	-0.0028162	0.0038205	0.4610436	0.4610417	0.1234080	0.1543530	75692
1	732032	rs61770163	A	C	-0.0026629	0.0040696	0.5128921	0.5128895	0.1227430	0.1555510	75692
1	734349	rs141242758	T	C	-0.0029600	0.0038233	0.4388064	0.4388029	0.1225940	0.1525560	75692
1	740284	rs61770167	C	T	-0.0117095	0.0171197	0.4939921	0.4939890	0.0060204	0.0023962	75692

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
23	154923311	rs141127553	C	T	0.0028547	0.0044673	0.5228137	0.5228121	0.0550520	0.0309934	75692
23	154923374	rs111332691	T	A	-0.0060358	0.0048684	0.2150552	0.2150517	0.0445027	0.0116556	75692
23	154925045	rs509981	C	T	-0.0004440	0.0023415	0.8496119	0.8496119	0.2435720	0.3634440	75692
23	154925895	rs538470	C	T	-0.0006132	0.0023934	0.7977900	0.7977891	0.2400870	0.3634440	75692
23	154927581	rs644138	G	A	0.0003198	0.0022032	0.8845930	0.8845926	0.2990340	0.4635760	75692
23	154929412	rs557132	C	T	-0.0005002	0.0023425	0.8309250	0.8309249	0.2433900	0.3568210	75692
23	154929637	rs35185538	CT	C	-0.0002479	0.0024427	0.9191500	0.9191500	0.2277940	0.3011920	75692
23	154929952	rs4012982	CAA	C	-0.0001849	0.0024638	0.9401830	0.9401824	0.2376030	0.3165560	75692
23	154930230	rs781880	A	G	-0.0005363	0.0023421	0.8188970	0.8188964	0.2438310	0.3618540	75692
23	154930487	rs781879	T	A	0.0149259	0.0081058	0.0655692	0.0655665	0.0190614	0.1263580	75692

1   692794  rs530212009 CA  C   .   PASS    AF=0.112322 ES:SE:LP:AF:SS:ID   -0.00229078:0.0042518:0.229118:0.112322:75692:1_692794_CA_C
1   693731  rs12238997  A   G   .   PASS    AF=0.117625 ES:SE:LP:AF:SS:ID   -0.002286:0.00402174:0.24431:0.117625:75692:rs12238997
1   707522  rs371890604 G   C   .   PASS    AF=0.0986233    ES:SE:LP:AF:SS:ID   -0.00240685:0.00451554:0.226195:0.0986233:75692:rs371890604
1   717587  rs144155419 G   A   .   PASS    AF=0.0156471    ES:SE:LP:AF:SS:ID   0.00443023:0.0108823:0.164986:0.0156471:75692:rs144155419
1   723329  rs189787166 A   T   .   PASS    AF=0.00172917   ES:SE:LP:AF:SS:ID   0.0111077:0.031833:0.138384:0.00172917:75692:rs189787166
1   730087  rs148120343 T   C   .   PASS    AF=0.0573192    ES:SE:LP:AF:SS:ID   -0.00377098:0.00559809:0.300548:0.0573192:75692:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.123408 ES:SE:LP:AF:SS:ID   -0.00281623:0.00382052:0.336258:0.123408:75692:rs58276399
1   732032  rs61770163  A   C   .   PASS    AF=0.122743 ES:SE:LP:AF:SS:ID   -0.00266289:0.00406955:0.289974:0.122743:75692:rs61770163
1   734349  rs141242758 T   C   .   PASS    AF=0.122594 ES:SE:LP:AF:SS:ID   -0.00296003:0.00382326:0.357727:0.122594:75692:rs141242758
1   740284  rs61770167  C   T   .   PASS    AF=0.0060204    ES:SE:LP:AF:SS:ID   -0.0117095:0.0171197:0.30628:0.0060204:75692:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.00178375   ES:SE:LP:AF:SS:ID   0.0221226:0.0345825:0.282025:0.00178375:75692:rs112573343
1   749963  rs529266287 T   TAA .   PASS    AF=0.868143 ES:SE:LP:AF:SS:ID   0.00355743:0.00377183:0.461423:0.868143:75692:rs529266287
1   750230  rs190826124 G   C   .   PASS    AF=0.00161654   ES:SE:LP:AF:SS:ID   -0.0353787:0.0330865:0.545236:0.00161654:75692:rs190826124
1   751343  rs28544273  T   A   .   PASS    AF=0.12446  ES:SE:LP:AF:SS:ID   -0.00386841:0.00373463:0.522462:0.12446:75692:rs28544273
1   751488  rs200141114 G   GA  .   PASS    AF=0.144111 ES:SE:LP:AF:SS:ID   -0.00394747:0.00369948:0.543696:0.144111:75692:rs200141114
1   751756  rs28527770  T   C   .   PASS    AF=0.124607 ES:SE:LP:AF:SS:ID   -0.0039011:0.00372871:0.529504:0.124607:75692:rs28527770
1   753405  rs3115860   C   A   .   PASS    AF=0.869209 ES:SE:LP:AF:SS:ID   0.00369148:0.00363255:0.509303:0.869209:75692:rs3115860
1   753425  rs3131970   T   C   .   PASS    AF=0.873939 ES:SE:LP:AF:SS:ID   0.0041707:0.00369266:0.587188:0.873939:75692:rs3131970
1   753541  rs2073813   G   A   .   PASS    AF=0.130444 ES:SE:LP:AF:SS:ID   -0.00336286:0.00363997:0.44909:0.130444:75692:rs2073813
1   754105  rs12184325  C   T   .   PASS    AF=0.0368599    ES:SE:LP:AF:SS:ID   -0.0102758:0.00656701:0.929441:0.0368599:75692:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.868773 ES:SE:LP:AF:SS:ID   0.00353683:0.00362847:0.481893:0.868773:75692:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.868908 ES:SE:LP:AF:SS:ID   0.00343028:0.00363058:0.462497:0.868908:75692:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.868773 ES:SE:LP:AF:SS:ID   0.00358265:0.0036283:0.490205:0.868773:75692:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.00512101   ES:SE:LP:AF:SS:ID   -0.0249113:0.0185363:0.7472:0.00512101:75692:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.0050863    ES:SE:LP:AF:SS:ID   -0.0255588:0.0185899:0.771666:0.0050863:75692:rs142682604
1   755435  rs184270342 T   G   .   PASS    AF=0.0053082    ES:SE:LP:AF:SS:ID   0.00393689:0.0185823:0.0797645:0.0053082:75692:rs184270342
1   755890  rs3115858   A   T   .   PASS    AF=0.868919 ES:SE:LP:AF:SS:ID   0.00354001:0.00362302:0.483426:0.868919:75692:rs3115858
1   756434  rs61768170  G   C   .   PASS    AF=0.127628 ES:SE:LP:AF:SS:ID   -0.00348389:0.00369734:0.460851:0.127628:75692:rs61768170
1   756604  rs3131962   A   G   .   PASS    AF=0.86848  ES:SE:LP:AF:SS:ID   0.00317447:0.00361435:0.42046:0.86848:75692:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.867615 ES:SE:LP:AF:SS:ID   0.00313734:0.00360891:0.414914:0.867615:75692:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.868642 ES:SE:LP:AF:SS:ID   0.00338282:0.00361747:0.456278:0.868642:75692:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.868649 ES:SE:LP:AF:SS:ID   0.00339008:0.00361772:0.457519:0.868649:75692:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.868657 ES:SE:LP:AF:SS:ID   0.00337875:0.00361784:0.455495:0.868657:75692:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.869075 ES:SE:LP:AF:SS:ID   0.00359805:0.003627:0.493234:0.869075:75692:rs3131954
1   759293  rs10157329  T   A   .   PASS    AF=0.100202 ES:SE:LP:AF:SS:ID   -0.00252737:0.00420691:0.261221:0.100202:75692:rs10157329
1   759600  rs545998451 AGT A   .   PASS    AF=0.0060957    ES:SE:LP:AF:SS:ID   -0.000785704:0.0168672:0.0164427:0.0060957:75692:1_759600_AGT_A
1   759837  rs3115851   T   A   .   PASS    AF=0.873067 ES:SE:LP:AF:SS:ID   0.00348242:0.00368159:0.463188:0.873067:75692:rs3115851
1   761732  rs2286139   C   T   .   PASS    AF=0.862343 ES:SE:LP:AF:SS:ID   0.00432962:0.00361079:0.637327:0.862343:75692:rs2286139
1   761752  rs1057213   C   T   .   PASS    AF=0.867857 ES:SE:LP:AF:SS:ID   0.00320818:0.00364554:0.421536:0.867857:75692:rs1057213
1   762273  rs3115849   G   A   .   PASS    AF=0.864642 ES:SE:LP:AF:SS:ID   0.00410084:0.0036449:0.584102:0.864642:75692:rs3115849
1   762485  rs12095200  C   A   .   PASS    AF=0.0997611    ES:SE:LP:AF:SS:ID   -0.00548046:0.00437043:0.678089:0.0997611:75692:rs12095200
1   762589  rs3115848   G   C   .   PASS    AF=0.869955 ES:SE:LP:AF:SS:ID   0.00431001:0.00368685:0.615471:0.869955:75692:rs3115848
1   762592  rs3131950   C   G   .   PASS    AF=0.869954 ES:SE:LP:AF:SS:ID   0.00430972:0.00368685:0.615414:0.869954:75692:rs3131950
1   762601  rs3131949   T   C   .   PASS    AF=0.869958 ES:SE:LP:AF:SS:ID   0.0043272:0.00368713:0.618775:0.869958:75692:rs3131949
1   762632  rs3131948   T   A   .   PASS    AF=0.870303 ES:SE:LP:AF:SS:ID   0.00466047:0.00368908:0.685122:0.870303:75692:rs3131948
1   764191  rs7515915   T   G   .   PASS    AF=0.127192 ES:SE:LP:AF:SS:ID   -0.00358811:0.00369235:0.47995:0.127192:75692:rs7515915
1   766007  rs61768174  A   C   .   PASS    AF=0.106883 ES:SE:LP:AF:SS:ID   -0.0029375:0.00400862:0.333778:0.106883:75692:rs61768174
1   766105  rs2519015   T   A   .   PASS    AF=0.853559 ES:SE:LP:AF:SS:ID   0.0026537:0.00360928:0.335176:0.853559:75692:rs2519015
1   767393  rs538667473 A   C   .   PASS    AF=0.00161685   ES:SE:LP:AF:SS:ID   0.000649009:0.0321843:0.00704418:0.00161685:75692:rs538667473
1   768116  rs376645387 A   AGTTTT  .   PASS    AF=0.837428 ES:SE:LP:AF:SS:ID   -0.00139953:0.00354993:0.159014:0.837428:75692:rs376645387

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