IEU GWAS QC Report

name	value
id	UKB-b-15918
trait	Treatment speciality of consultant (recoded): Dermatology
note	41246#1390: Output from GWAS pipeline using Phesant derived variables from UKBiobank
year	2018
author	Ben Elsworth
population	European
sex	Males and Females
sample_size	444587
ncase	8914
ncontrol	435673
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-15918
bcf_file	ukb-b-15918.vcf.gz
qc_metrics_file	qc_metrics.json
metadata	metadata.json
refdata	1kg_v3_nomult.bcf
compile_time	2020-05-10 10:56 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-15918/UKB-b-15918_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-15918/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:44:35 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-15918/UKB-b-15918_data.vcf.gz ...
Read summary statistics for 5782811 SNPs.
Dropped 2451 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, 1198505 SNPs remain.
After merging with regression SNP LD, 1198505 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0032 (0.0016)
Lambda GC: 1.0612
Mean Chi^2: 1.0701
Intercept: 1.0403 (0.008)
Ratio: 0.5749 (0.1143)
Analysis finished at Thu Oct 17 14:45:43 2019
Total time elapsed: 1.0m:8.65s

{
    "af_correlation": 0.9213,
    "inflation_factor": 1.0475,
    "mean_EFFECT": 4.7628e-07,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 5,
    "n_p_sig": 131,
    "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": 51674,
    "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": 1198505,
    "ldsc_nsnp_merge_regression_ld": 1198505,
    "ldsc_observed_scale_h2_beta": 0.0032,
    "ldsc_observed_scale_h2_se": 0.0016,
    "ldsc_intercept_beta": 1.0403,
    "ldsc_intercept_se": 0.008,
    "ldsc_lambda_gc": 1.0612,
    "ldsc_mean_chisq": 1.0701,
    "ldsc_ratio": 0.5749
}

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	3	58	0	5780376	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	5782811	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	8.670417e+00	5.762395e+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	7.859678e+07	5.654471e+07	828.0000000	3.199487e+07	6.903686e+07	1.145161e+08	2.492385e+08	▇▆▅▂▁
numeric	EFFECT	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	5.000000e-07	4.599000e-04	-0.0034537	-2.772000e-04	-5.000000e-07	2.782000e-04	4.195200e-03	▁▂▇▁▁
numeric	SE	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	4.243000e-04	1.312000e-04	0.0002869	3.157000e-04	3.741000e-04	5.021000e-04	1.375900e-03	▇▂▁▁▁
numeric	PVAL	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	4.891067e-01	2.914282e-01	0.0000000	2.300001e-01	4.899999e-01	7.400005e-01	1.000000e+00	▇▇▇▇▇
numeric	PVAL_ztest	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	4.891078e-01	2.914026e-01	0.0000000	2.340707e-01	4.853826e-01	7.415745e-01	9.999998e-01	▇▇▇▇▇
numeric	AF	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	3.200923e-01	2.508819e-01	0.0392650	1.066230e-01	2.415740e-01	4.863110e-01	9.607350e-01	▇▃▂▂▁
numeric	AF_reference	51674	0.9910642	NA	NA	NA	NA	NA	NA	NA	3.160371e-01	2.445653e-01	0.0000000	1.150160e-01	2.480030e-01	4.762380e-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	0.0000032	0.0005278	1.0000000	0.9952070	0.623746	0.7821490	NA
1	54676	rs2462492	C	T	-0.0000987	0.0005230	0.8499999	0.8502486	0.400427	NA	NA
1	86028	rs114608975	T	C	0.0003733	0.0008361	0.6600001	0.6552140	0.103538	0.0277556	NA
1	91536	rs6702460	G	T	-0.0001371	0.0005148	0.7899998	0.7899632	0.456859	0.4207270	NA
1	234313	rs8179466	C	T	-0.0007273	0.0010158	0.4700002	0.4739739	0.074445	NA	NA
1	534192	rs6680723	C	T	-0.0004262	0.0005881	0.4700002	0.4686909	0.240915	NA	NA
1	546697	rs12025928	A	G	-0.0007356	0.0007338	0.3200000	0.3161209	0.913487	NA	NA
1	693731	rs12238997	A	G	-0.0000694	0.0004930	0.8900000	0.8880508	0.116345	0.1417730	NA
1	705882	rs72631875	G	A	-0.0012189	0.0007223	0.0920005	0.0915080	0.067324	0.0315495	NA
1	706368	rs55727773	A	G	-0.0003344	0.0003652	0.3599996	0.3597380	0.515712	0.2751600	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	51218377	rs2519461	G	C	-0.0003712	0.0005703	0.5199996	0.5151482	0.073669	0.0826677	NA
22	51219006	rs28729663	G	A	-0.0000623	0.0004403	0.8900000	0.8875541	0.138042	0.2052720	NA
22	51219387	rs9616832	T	C	-0.0002951	0.0005714	0.6100002	0.6055284	0.073796	0.0654952	NA
22	51219704	rs147475742	G	A	-0.0006444	0.0007658	0.4000000	0.4000423	0.041981	0.0473243	NA
22	51221190	rs369304721	G	A	-0.0007308	0.0007644	0.3400001	0.3390304	0.049776	NA	NA
22	51221731	rs115055839	T	C	-0.0003650	0.0005718	0.5199996	0.5232522	0.073291	0.0625000	NA
22	51222100	rs114553188	G	T	0.0004357	0.0006732	0.5199996	0.5175750	0.054522	0.0880591	NA
22	51223637	rs375798137	G	A	0.0004606	0.0006765	0.5000000	0.4959614	0.054150	0.0788738	NA
22	51229805	rs9616985	T	C	-0.0003432	0.0005739	0.5500004	0.5498848	0.073125	0.0730831	NA
22	51237063	rs3896457	T	C	-0.0002901	0.0003511	0.4100001	0.4087673	0.298132	0.2050720	NA

1   49298   rs10399793  T   C   .   PASS    AF=0.623746 ES:SE:LP:AF:ID  3.17067e-06:0.000527814:-0:0.623746:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400427 ES:SE:LP:AF:ID  -9.87336e-05:0.00052295:0.0705811:0.400427:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103538 ES:SE:LP:AF:ID  0.000373346:0.000836104:0.180456:0.103538:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456859 ES:SE:LP:AF:ID  -0.000137124:0.00051481:0.102373:0.456859:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074445 ES:SE:LP:AF:ID  -0.00072732:0.00101577:0.327902:0.074445:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240915 ES:SE:LP:AF:ID  -0.000426174:0.000588142:0.327902:0.240915:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913487 ES:SE:LP:AF:ID  -0.000735586:0.00073378:0.49485:0.913487:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116345 ES:SE:LP:AF:ID  -6.94045e-05:0.000493031:0.05061:0.116345:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067324 ES:SE:LP:AF:ID  -0.00121887:0.000722295:1.03621:0.067324:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515712 ES:SE:LP:AF:ID  -0.000334438:0.000365161:0.443698:0.515712:rs12029736
1   722670  rs116030099 T   C   .   PASS    AF=0.101251 ES:SE:LP:AF:ID  0.000187373:0.000602367:0.119186:0.101251:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.95906  ES:SE:LP:AF:ID  0.000285868:0.000797201:0.142668:0.95906:rs2977670
1   725060  rs865924913 A   T   .   PASS    AF=0.053321 ES:SE:LP:AF:ID  -0.000830678:0.00114981:0.327902:0.053321:rs865924913
1   729679  rs4951859   C   G   .   PASS    AF=0.843143 ES:SE:LP:AF:ID  0.000252922:0.000427224:0.259637:0.843143:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055906 ES:SE:LP:AF:ID  -0.000258002:0.000691771:0.148742:0.055906:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122331 ES:SE:LP:AF:ID  -0.000115512:0.000467628:0.09691:0.122331:rs58276399
1   734349  rs141242758 T   C   .   PASS    AF=0.121573 ES:SE:LP:AF:ID  -0.000132312:0.000467821:0.107905:0.121573:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132396 ES:SE:LP:AF:ID  -0.000184044:0.00046102:0.161151:0.132396:rs79010578
1   752566  rs3094315   G   A   .   PASS    AF=0.838836 ES:SE:LP:AF:ID  7.37357e-05:0.000413638:0.0655015:0.838836:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838457 ES:SE:LP:AF:ID  7.69344e-05:0.000413176:0.0705811:0.838457:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869716 ES:SE:LP:AF:ID  -5.0926e-06:0.000443382:0.00436481:0.869716:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129951 ES:SE:LP:AF:ID  7.00572e-05:0.000444262:0.0604807:0.129951:rs2073813
1   754182  rs3131969   A   G   .   PASS    AF=0.869055 ES:SE:LP:AF:ID  -2.24945e-05:0.0004425:0.0177288:0.869055:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869156 ES:SE:LP:AF:ID  7.88583e-06:0.000442681:0.00436481:0.869156:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.869058 ES:SE:LP:AF:ID  -2.11159e-05:0.000442493:0.0177288:0.869058:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.837906 ES:SE:LP:AF:ID  6.63682e-05:0.000412026:0.0604807:0.837906:rs3115859
1   754964  rs3131966   C   T   .   PASS    AF=0.838537 ES:SE:LP:AF:ID  4.6871e-05:0.000413183:0.0409586:0.838537:rs3131966
1   755775  rs3131965   A   G   .   PASS    AF=0.839659 ES:SE:LP:AF:ID  5.25871e-05:0.000418798:0.0457575:0.839659:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.869344 ES:SE:LP:AF:ID  -9.44915e-06:0.000442005:0.00877392:0.869344:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.868896 ES:SE:LP:AF:ID  -1.92641e-05:0.000440913:0.0132283:0.868896:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.867838 ES:SE:LP:AF:ID  -0.000129016:0.000440037:0.113509:0.867838:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.869036 ES:SE:LP:AF:ID  -1.53368e-05:0.000441266:0.0132283:0.869036:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.869044 ES:SE:LP:AF:ID  -1.59203e-05:0.000441299:0.0132283:0.869044:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.869052 ES:SE:LP:AF:ID  -1.65882e-05:0.00044131:0.0132283:0.869052:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.869527 ES:SE:LP:AF:ID  -1.21228e-05:0.000442507:0.00877392:0.869527:rs3131954
1   760912  rs1048488   C   T   .   PASS    AF=0.838193 ES:SE:LP:AF:ID  4.71538e-05:0.000411262:0.0409586:0.838193:rs1048488
1   761147  rs3115850   T   C   .   PASS    AF=0.838312 ES:SE:LP:AF:ID  4.21338e-05:0.000411553:0.0362122:0.838312:rs3115850
1   761732  rs2286139   C   T   .   PASS    AF=0.862191 ES:SE:LP:AF:ID  -0.000126234:0.000439723:0.113509:0.862191:rs2286139
1   763394  rs3115847   G   A   .   PASS    AF=0.706667 ES:SE:LP:AF:ID  -0.000102872:0.000428075:0.091515:0.706667:rs3115847
1   766007  rs61768174  A   C   .   PASS    AF=0.105151 ES:SE:LP:AF:ID  -2.48568e-05:0.000493197:0.0177288:0.105151:rs61768174
1   768253  rs2977608   A   C   .   PASS    AF=0.76131  ES:SE:LP:AF:ID  -0.00034838:0.000349518:0.49485:0.76131:rs2977608
1   768448  rs12562034  G   A   .   PASS    AF=0.106427 ES:SE:LP:AF:ID  0.0005069:0.000481827:0.537602:0.106427:rs12562034
1   769223  rs60320384  C   G   .   PASS    AF=0.129638 ES:SE:LP:AF:ID  4.7488e-05:0.000444049:0.0409586:0.129638:rs60320384
1   771823  rs2977605   T   C   .   PASS    AF=0.868855 ES:SE:LP:AF:ID  -3.9112e-05:0.000441681:0.0315171:0.868855:rs2977605
1   771967  rs59066358  G   A   .   PASS    AF=0.129739 ES:SE:LP:AF:ID  4.98332e-05:0.00044376:0.0409586:0.129739:rs59066358
1   772755  rs2905039   A   C   .   PASS    AF=0.868865 ES:SE:LP:AF:ID  -4.36701e-05:0.00044169:0.0362122:0.868865:rs2905039
1   776546  rs12124819  A   G   .   PASS    AF=0.265291 ES:SE:LP:AF:ID  0.00032864:0.000390359:0.39794:0.265291:rs12124819
1   777122  rs2980319   A   T   .   PASS    AF=0.870012 ES:SE:LP:AF:ID  -9.84472e-05:0.000442621:0.0861861:0.870012:rs2980319
1   777232  rs112618790 C   T   .   PASS    AF=0.09511  ES:SE:LP:AF:ID  0.00040447:0.000513144:0.366532:0.09511:rs112618790
1   778745  rs1055606   A   G   .   PASS    AF=0.128609 ES:SE:LP:AF:ID  0.000107654:0.000444368:0.091515:0.128609:rs1055606

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