IEU GWAS QC Report

name	value
id	ieu-a-1121
pmid	28346442
year	2017
mr	1
note	NULL
trait	High grade serous ovarian cancer
category	Disease
subcategory	Cancer
population	European
sex	Females
ncase	13037
ncontrol	40941
sample_size	53978
nsnp	0
sd	NULL
priority	1
author	Phelan
group_name	public
access	public
build	HG19/GRCh37

name	value
gwas_id	ieu-a-1121
bcf_file	ieu-a-1121.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

$qq_plot$

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-1121/ieu-a-1121.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-1121/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 16:13:26 2020
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ieu-a-import/processed/ieu-a-1121/ieu-a-1121.vcf.gz ...
Read summary statistics for 15635757 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, 1218951 SNPs remain.
After merging with regression SNP LD, 1218951 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0602 (0.0133)
Lambda GC: 1.0604
Mean Chi^2: 1.0871
Intercept: 1.0243 (0.0069)
Ratio: 0.2784 (0.0786)
Analysis finished at Tue Feb  4 16:16:03 2020
Total time elapsed: 2.0m:36.99s

{
    "af_correlation": 0.9604,
    "inflation_factor": 1.0552,
    "mean_EFFECT": -0.2376,
    "n": 53978,
    "n_snps": 15635757,
    "n_clumped_hits": 16,
    "n_p_sig": 4156,
    "n_mono": 0,
    "n_ns": 786352,
    "n_mac": 269927,
    "is_snpid_unique": false,
    "n_miss_EFFECT": 0,
    "n_miss_SE": 0,
    "n_miss_PVAL": 0,
    "n_miss_AF": 5006,
    "n_miss_AF_reference": 162878,
    "n_est": 51651.4528,
    "ratio_se_n": 0.9782,
    "mean_diff": 0.2467,
    "ratio_diff": 2.7103,
    "sd_y_est1": 2.8548,
    "sd_y_est2": 2.7926,
    "r2_sum1": 0.1401,
    "r2_sum2": 0.0172,
    "r2_sum3": 0.018,
    "r2_sum4": 0.021,
    "ldsc_nsnp_merge_refpanel_ld": 1218951,
    "ldsc_nsnp_merge_regression_ld": 1218951,
    "ldsc_observed_scale_h2_beta": 0.0602,
    "ldsc_observed_scale_h2_se": 0.0133,
    "ldsc_intercept_beta": 1.0243,
    "ldsc_intercept_se": 0.0069,
    "ldsc_lambda_gc": 1.0604,
    "ldsc_mean_chisq": 1.0871,
    "ldsc_ratio": 0.279
}

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	TRUE
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

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	47	0	15635755	0	NA	NA	NA	NA	NA	NA	NA	NA
character	REF	0	1.0000000	1	101	0	37882	0	NA	NA	NA	NA	NA	NA	NA	NA
character	ALT	0	1.0000000	1	105	0	18193	0	NA	NA	NA	NA	NA	NA	NA	NA
numeric	CHROM	0	1.0000000	NA	NA	NA	NA	NA	8.958477e+00	6.312051e+00	1.0000e+00	4.000000e+00	7.000000e+00	1.400000e+01	2.400000e+01	▇▆▂▃▂
numeric	POS	0	1.0000000	NA	NA	NA	NA	NA	7.932522e+07	5.676282e+07	5.6000e+01	3.254872e+07	6.974966e+07	1.158559e+08	2.492402e+08	▇▆▅▂▁
numeric	EFFECT	0	1.0000000	NA	NA	NA	NA	NA	-2.376291e-01	2.406417e+00	-2.0410e+01	-4.081000e-02	-2.197000e-04	3.887000e-02	2.037000e+01	▁▁▇▁▁
numeric	SE	0	1.0000000	NA	NA	NA	NA	NA	8.231001e-01	6.108504e+00	1.5820e-03	2.208000e-02	6.466000e-02	2.513000e-01	1.000000e+03	▇▁▁▁▁
numeric	PVAL	0	1.0000000	NA	NA	NA	NA	NA	4.915288e-01	2.905065e-01	0.0000e+00	2.375998e-01	4.884004e-01	7.434008e-01	1.000000e+00	▇▇▇▇▇
numeric	PVAL_ztest	0	1.0000000	NA	NA	NA	NA	NA	4.915523e-01	2.904962e-01	0.0000e+00	2.376748e-01	4.883844e-01	7.433909e-01	1.000000e+00	▇▇▇▇▇
numeric	AF	5006	0.9996798	NA	NA	NA	NA	NA	1.534350e-01	2.447970e-01	0.0000e+00	1.995000e-03	1.989000e-02	2.061000e-01	9.999980e-01	▇▁▁▁▁
numeric	AF_reference	162878	0.9895830	NA	NA	NA	NA	NA	1.531725e-01	2.322675e-01	0.0000e+00	2.595800e-03	3.055110e-02	2.122600e-01	1.000000e+00	▇▁▁▁▁
numeric	N	0	1.0000000	NA	NA	NA	NA	NA	5.397800e+04	0.000000e+00	5.3978e+04	5.397800e+04	5.397800e+04	5.397800e+04	5.397800e+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	10616	rs376342519	CCGCCGTTGCAAAGGCGCGCCG	C	0.03490	0.11470	0.7609007	0.7609207	0.991000	0.9930110	53978
1	14933	rs199856693	G	A	0.03889	0.07369	0.5976006	0.5976719	0.047970	0.0283546	53978
1	15774	rs374029747	G	A	-0.12340	0.18690	0.5088995	0.5090959	0.006579	0.0119808	53978
1	16949	rs199745162	A	C	-0.11620	0.10220	0.2555001	0.2555440	0.016750	0.0139776	53978
1	51479	rs116400033	T	A	-0.01086	0.03159	0.7309994	0.7310120	0.232100	0.1281950	53978
1	52185	rs201374420	TTAA	T	0.53560	1.16000	0.6442998	0.6442792	0.005882	0.0053914	53978
1	54353	rs140052487	C	A	-0.74990	0.50550	0.1380000	0.1379465	0.001339	0.0089856	53978
1	54490	rs141149254	G	A	-0.01122	0.03554	0.7522999	0.7522298	0.166100	0.0960463	53978
1	55164	rs3091274	C	A	0.06106	0.10690	0.5677995	0.5678722	0.980700	0.9233230	53978
1	55326	rs3107975	T	C	0.02660	0.20440	0.8965000	0.8964580	0.028260	0.0459265	53978

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	154926843	rs192521690	T	C	-0.126500	0.34920	0.7171995	0.7171603	0.002532	0.0007947	53978
23	154927185	rs185685661	T	C	-0.003081	0.03367	0.9271000	0.9270907	0.139300	0.1796030	53978
23	154927199	rs645904	C	T	0.003126	0.01820	0.8635999	0.8636276	0.248000	0.3674170	53978
23	154927581	rs644138	G	A	-0.006215	0.01757	0.7235008	0.7235428	0.299900	0.4635760	53978
23	154928151	rs144607509	C	T	0.625300	0.80860	0.4393999	0.4393378	0.018780	0.0084768	53978
23	154928909	rs148015245	C	T	-5.230000	2.23300	0.0191801	0.0191735	0.004206	0.0007947	53978
23	154929412	rs557132	C	T	0.002876	0.01818	0.8743000	0.8743025	0.250400	0.3568210	53978
23	154929952	rs4012982	CAA	C	0.007709	0.02639	0.7702009	0.7701962	0.250900	0.3165560	53978
23	154930230	rs781880	A	G	0.002489	0.01821	0.8913000	0.8912813	0.249000	0.3618540	53978
24	13537468	rs7203107	A	G	0.234000	0.11460	0.0411604	0.0411630	0.985590	NA	53978

1   10616   rs376342519 CCGCCGTTGCAAAGGCGCGCCG  C   .   PASS    AF=0.991    ES:SE:LP:AF:SS:ID   0.0349:0.1147:0.118672:0.991:53978:rs376342519
1   14933   rs199856693 G   A   .   PASS    AF=0.04797  ES:SE:LP:AF:SS:ID   0.03889:0.07369:0.223589:0.04797:53978:rs199856693
1   15774   rs374029747 G   A   .   PASS    AF=0.006579 ES:SE:LP:AF:SS:ID   -0.1234:0.1869:0.293368:0.006579:53978:rs374029747
1   16949   rs199745162 A   C   .   PASS    AF=0.01675  ES:SE:LP:AF:SS:ID   -0.1162:0.1022:0.592609:0.01675:53978:rs199745162
1   51479   rs116400033 T   A   .   PASS    AF=0.2321   ES:SE:LP:AF:SS:ID   -0.01086:0.03159:0.136083:0.2321:53978:rs116400033
1   52185   rs201374420 TTAA    T   .   PASS    AF=0.005882 ES:SE:LP:AF:SS:ID   0.5356:1.16:0.190912:0.005882:53978:rs201374420
1   54353   rs140052487 C   A   .   PASS    AF=0.001339 ES:SE:LP:AF:SS:ID   -0.7499:0.5055:0.860121:0.001339:53978:rs140052487
1   54490   rs141149254 G   A   .   PASS    AF=0.1661   ES:SE:LP:AF:SS:ID   -0.01122:0.03554:0.123609:0.1661:53978:rs141149254
1   55164   rs3091274   C   A   .   PASS    AF=0.9807   ES:SE:LP:AF:SS:ID   0.06106:0.1069:0.245805:0.9807:53978:rs3091274
1   55326   rs3107975   T   C   .   PASS    AF=0.02826  ES:SE:LP:AF:SS:ID   0.0266:0.2044:0.0474497:0.02826:53978:rs3107975
1   55545   rs28396308  C   T   .   PASS    AF=0.2248   ES:SE:LP:AF:SS:ID   0.03886:0.03473:0.579549:0.2248:53978:rs28396308
1   57183   rs368339209 A   G   .   PASS    AF=0.0007041    ES:SE:LP:AF:SS:ID   -0.2126:0.6065:0.139123:0.0007041:53978:rs368339209
1   57292   rs201418760 C   T   .   PASS    AF=0.02393  ES:SE:LP:AF:SS:ID   0.02247:0.2013:0.040434:0.02393:53978:rs201418760
1   58814   rs114420996 G   A   .   PASS    AF=0.1003   ES:SE:LP:AF:SS:ID   0.04123:0.04913:0.396531:0.1003:53978:rs114420996
1   61543   rs201849102 T   C   .   PASS    AF=0.0003079    ES:SE:LP:AF:SS:ID   -1.309:1.511:0.412963:0.0003079:53978:rs201849102
1   61743   rs184286948 G   C   .   PASS    AF=0.009155 ES:SE:LP:AF:SS:ID   0.09065:0.1453:0.273599:0.009155:53978:rs184286948
1   61920   rs62637820  G   A   .   PASS    AF=0.02945  ES:SE:LP:AF:SS:ID   0.09099:0.08392:0.555643:0.02945:53978:rs62637820
1   63093   rs200092917 G   A   .   PASS    AF=0.0207   ES:SE:LP:AF:SS:ID   0.2107:0.1072:1.30759:0.0207:53978:rs200092917
1   64649   rs181431124 A   C   .   PASS    AF=0.02715  ES:SE:LP:AF:SS:ID   0.1248:0.0915:0.762959:0.02715:53978:rs181431124
1   66219   rs181028663 A   T   .   PASS    AF=0.01624  ES:SE:LP:AF:SS:ID   0.063:0.1158:0.231806:0.01624:53978:rs181028663
1   68082   rs367789441 T   C   .   PASS    AF=0.06526  ES:SE:LP:AF:SS:ID   -0.03789:0.05447:0.312739:0.06526:53978:rs367789441
1   68596   rs372212855 T   G   .   PASS    AF=0.003797 ES:SE:LP:AF:SS:ID   -0.3325:0.2195:0.88706:0.003797:53978:rs372212855
1   69428   rs140739101 T   G   .   PASS    AF=0.04215  ES:SE:LP:AF:SS:ID   -0.09426:0.06959:0.755475:0.04215:53978:rs140739101
1   69534   rs190717287 T   C   .   PASS    AF=0.0003164    ES:SE:LP:AF:SS:ID   -1.265:0.9312:0.758703:0.0003164:53978:rs190717287
1   69761   rs200505207 A   T   .   PASS    AF=0.06821  ES:SE:LP:AF:SS:ID   -0.03409:0.05295:0.284331:0.06821:53978:rs200505207
1   69897   rs200676709 T   C   .   PASS    AF=0.7661   ES:SE:LP:AF:SS:ID   -0.02085:0.03419:0.266001:0.7661:53978:rs200676709
1   72297   rs200651397 G   GTAT    .   PASS    AF=0.006341 ES:SE:LP:AF:SS:ID   -0.06657:0.1908:0.138406:0.006341:53978:rs200651397
1   74790   rs13328700  C   G   .   PASS    AF=0.03421  ES:SE:LP:AF:SS:ID   0.06374:0.06936:0.445996:0.03421:53978:rs13328700
1   74792   rs13328684  G   A   .   PASS    AF=0.03421  ES:SE:LP:AF:SS:ID   0.06374:0.06936:0.445996:0.03421:53978:rs13328684
1   76854   rs367666799 A   G   .   PASS    AF=0.06856  ES:SE:LP:AF:SS:ID   -0.03686:0.05907:0.273599:0.06856:53978:rs367666799
1   78942   rs372315362 C   G   .   PASS    AF=0.01116  ES:SE:LP:AF:SS:ID   -2.338:1.251:1.21021:0.01116:53978:rs372315362
1   80454   rs144226842 G   C   .   PASS    AF=0.0004404    ES:SE:LP:AF:SS:ID   -20.13:21.55:0.455436:0.0004404:53978:rs144226842
1   81590   rs202072409 AC  A   .   PASS    AF=0.004955 ES:SE:LP:AF:SS:ID   1.196:0.9898:0.644357:0.004955:53978:rs202072409
1   82163   rs139113303 G   A   .   PASS    AF=0.06791  ES:SE:LP:AF:SS:ID   -0.03982:0.05948:0.298259:0.06791:53978:rs139113303
1   82609   rs149189449 C   G   .   PASS    AF=0.06818  ES:SE:LP:AF:SS:ID   -0.03689:0.05931:0.27254:0.06818:53978:rs149189449
1   83514   rs201754587 C   T   .   PASS    AF=0.3501   ES:SE:LP:AF:SS:ID   0.001985:0.03137:0.022505:0.3501:53978:rs201754587
1   84139   rs183605470 A   T   .   PASS    AF=0.02132  ES:SE:LP:AF:SS:ID   -0.1017:0.1146:0.426548:0.02132:53978:rs183605470
1   86028   rs114608975 T   C   .   PASS    AF=0.04742  ES:SE:LP:AF:SS:ID   -0.03854:0.06659:0.249723:0.04742:53978:rs114608975
1   86065   rs116504101 G   C   .   PASS    AF=0.06995  ES:SE:LP:AF:SS:ID   -0.03729:0.0588:0.279014:0.06995:53978:rs116504101
1   86331   rs115209712 A   G   .   PASS    AF=0.1018   ES:SE:LP:AF:SS:ID   0.09207:0.04824:1.24941:0.1018:53978:rs115209712
1   87021   rs188486692 T   C   .   PASS    AF=0.00763  ES:SE:LP:AF:SS:ID   0.1007:0.1547:0.288108:0.00763:53978:rs188486692
1   87360   rs180907504 C   T   .   PASS    AF=0.01853  ES:SE:LP:AF:SS:ID   0.04605:0.09713:0.196953:0.01853:53978:rs180907504
1   87409   rs139490478 C   T   .   PASS    AF=0.07088  ES:SE:LP:AF:SS:ID   -0.04254:0.05829:0.33208:0.07088:53978:rs139490478
1   88169   rs940550    C   T   .   PASS    AF=0.2022   ES:SE:LP:AF:SS:ID   0.05078:0.03333:0.894149:0.2022:53978:rs940550
1   88172   rs940551    G   A   .   PASS    AF=0.06423  ES:SE:LP:AF:SS:ID   -0.06005:0.05532:0.556424:0.06423:53978:rs940551
1   88177   rs143215837 G   C   .   PASS    AF=0.06377  ES:SE:LP:AF:SS:ID   -0.05466:0.0556:0.487316:0.06377:53978:rs143215837
1   88188   rs148331237 C   A   .   PASS    AF=0.007142 ES:SE:LP:AF:SS:ID   -0.1546:0.157:0.488651:0.007142:53978:rs148331237
1   88236   rs186918018 C   T   .   PASS    AF=0.01279  ES:SE:LP:AF:SS:ID   0.01523:0.246:0.0220022:0.01279:53978:rs186918018
1   88316   rs113759966 G   A   .   PASS    AF=0.06376  ES:SE:LP:AF:SS:ID   -0.05522:0.0558:0.49174:0.06376:53978:rs113759966
1   88338   rs55700207  G   A   .   PASS    AF=0.08209  ES:SE:LP:AF:SS:ID   0.05008:0.05396:0.451733:0.08209:53978:rs55700207

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