IEU GWAS QC Report

name	value
doi	10.1038/s41588-019-0397-8
priority	1
sample_size	51710
nsnp	13413244
id	ieu-b-41
unit	log odds ratio
pmid	31043756
trait	bipolar disorder
year	2019
population	European
study_design	Meta-analysis of case-control studies
consortium	Bipolar Disorder Working Group of the Psychiatric Genomics Consortium
sex	Males and Females
group_name	public
ncase	20352
author	Stahl, E
mr	1
ncontrol	31358
covariates	ancestry PCs
category	Binary
subcategory	Psychiatric / neurological
imputation_panel	1000 Genomes
coverage	whole genome
build	HG19/GRCh37

name	value
gwas_id	ieu-b-41
bcf_file	ieu-b-41.vcf.gz
qc_metrics_file	qc_metrics.json
metadata	metadata.json
refdata	1kg_v3_nomult.bcf
compile_time	2021-02-25 01:50 UTC
build_version	master-2dbfcc4-2020-10-23

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 /data/cromwell-executions/qc/17bc5d34-80eb-4d56-ba46-26e48a8cd3cf/call-ldsc/inputs/562856219/ieu-b-41.vcf.gz \
--ref-ld-chr /data/ref/eur_w_ld_chr/ \
--out /data/igd/ieu-b-41/ldsc.txt \
--w-ld-chr /data/ref/eur_w_ld_chr/ 

Beginning analysis at Fri Aug 21 23:03:01 2020
Reading summary statistics from /data/cromwell-executions/qc/17bc5d34-80eb-4d56-ba46-26e48a8cd3cf/call-ldsc/inputs/562856219/ieu-b-41.vcf.gz ...
Read summary statistics for 12382194 SNPs.
Dropped 99953 SNPs with duplicated rs numbers.
Reading reference panel LD Score from /data/ref/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 /data/ref/eur_w_ld_chr/[1-22] ...
Read regression weight LD Scores for 1290028 SNPs.
After merging with reference panel LD, 1220685 SNPs remain.
After merging with regression SNP LD, 1220685 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.3459 (0.0156)
Lambda GC: 1.3034
Mean Chi^2: 1.3637
Intercept: 1.0194 (0.0077)
Ratio: 0.0534 (0.0212)
Analysis finished at Fri Aug 21 23:04:53 2020
Total time elapsed: 1.0m:52.27s

{
    "af_correlation": 0.9559,
    "inflation_factor": 1.134,
    "mean_EFFECT": 0.0088,
    "n": 51710,
    "n_snps": 12382417,
    "n_clumped_hits": 16,
    "n_p_sig": 240,
    "n_mono": 1,
    "n_ns": 0,
    "n_mac": 263227,
    "is_snpid_unique": false,
    "n_miss_EFFECT": 0,
    "n_miss_SE": 0,
    "n_miss_PVAL": 0,
    "n_miss_AF": 0,
    "n_miss_AF_reference": 402769,
    "n_est": 50417.7817,
    "ratio_se_n": 0.9874,
    "mean_diff": "-Inf",
    "ratio_diff": "NaN",
    "sd_y_est1": 2.2993,
    "sd_y_est2": 2.2703,
    "r2_sum1": 0.0492,
    "r2_sum2": 0.0093,
    "r2_sum3": 0.0095,
    "r2_sum4": 0.0105,
    "ldsc_nsnp_merge_refpanel_ld": 1220685,
    "ldsc_nsnp_merge_regression_ld": 1220685,
    "ldsc_observed_scale_h2_beta": 0.3459,
    "ldsc_observed_scale_h2_se": 0.0156,
    "ldsc_intercept_beta": 1.0194,
    "ldsc_intercept_se": 0.0077,
    "ldsc_lambda_gc": 1.3034,
    "ldsc_mean_chisq": 1.3637,
    "ldsc_ratio": 0.0533
}

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	TRUE
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	110	0.9999911	3	59	0	12357171	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	8.666664e+00	5.773755e+00	1.0000	4.000000e+00	8.000000e+00	1.300000e+01	2.200000e+01	▇▅▃▂▂
numeric	POS	0	1.0000000	NA	NA	NA	NA	NA	7.853619e+07	5.633116e+07	80.0000	3.234684e+07	6.892030e+07	1.142068e+08	2.492395e+08	▇▆▅▂▁
numeric	EFFECT	0	1.0000000	NA	NA	NA	NA	NA	8.819600e-03	5.006294e-01	-45.7722	-2.369900e-02	1.898000e-03	3.069600e-02	1.121650e+02	▁▇▁▁▁
numeric	SE	0	1.0000000	NA	NA	NA	NA	NA	6.110674e-01	2.855158e+01	0.0131	1.710000e-02	3.540000e-02	1.118000e-01	2.557000e+03	▇▁▁▁▁
numeric	PVAL	0	1.0000000	NA	NA	NA	NA	NA	4.797090e-01	2.941236e-01	0.0000	2.205002e-01	4.726003e-01	7.339999e-01	1.000000e+00	▇▇▇▆▆
numeric	PVAL_ztest	0	1.0000000	NA	NA	NA	NA	NA	4.797085e-01	2.941254e-01	0.0000	2.205338e-01	4.726531e-01	7.339817e-01	1.000000e+00	▇▇▇▆▆
numeric	AF	0	1.0000000	NA	NA	NA	NA	NA	1.801139e-01	2.508572e-01	0.0000	8.161400e-03	4.706390e-02	2.654260e-01	9.996540e-01	▇▂▁▁▁
numeric	AF_reference	402769	0.9674725	NA	NA	NA	NA	NA	1.850055e-01	2.454395e-01	0.0000	6.589500e-03	6.749200e-02	2.773560e-01	1.000000e+00	▇▂▁▁▁
numeric	N	0	1.0000000	NA	NA	NA	NA	NA	4.094042e+04	1.876209e+04	260.0000	3.868200e+04	5.171000e+04	5.171000e+04	5.171000e+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	10611	rs189107123	C	G	0.025502	0.1016	0.8019000	0.8018111	0.0160000	NA	51710
1	13302	rs75241669	C	T	-0.015398	0.0373	0.6802006	0.6797422	0.1170000	NA	51710
1	13327	rs2691329	G	C	-0.019101	0.0689	0.7813003	0.7816051	0.0335458	NA	51710
1	13980	rs2592732	T	C	-0.040403	0.0881	0.6462998	0.6465187	0.0186064	NA	51710
1	30923	rs806731	G	T	0.005803	0.0281	0.8361000	0.8363906	0.7530000	0.8724040	51710
1	51476	rs2531249	T	C	-0.319900	0.1446	0.0269799	0.0269453	0.0076064	NA	49357
1	51479	rs116400033	T	A	-0.007599	0.0294	0.7949999	0.7960447	0.1962130	0.1281950	51710
1	52058	rs1199236530	G	C	-0.080700	0.0597	0.1765001	0.1764525	0.0417458	NA	51710
1	52144	rs190291950	T	A	-0.180204	0.1183	0.1278000	0.1276887	0.0116851	0.0009984	51050
1	52238	rs2691277	T	G	0.088099	0.0546	0.1065999	0.1066284	0.9449080	0.9217250	51710

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	51233300	rs9616839	C	T	0.013400	0.0203	0.5084005	0.5091906	0.2933940	0.3146960	51710
22	51234048	rs141330630	T	C	-0.257398	0.6227	0.6794007	0.6793444	0.0060000	0.0095847	4232
22	51234159	rs8138356	T	A	0.106196	0.1173	0.3654004	0.3652870	0.0082698	0.0215655	46524
22	51234677	rs189571549	A	C	0.068397	0.1131	0.5453006	0.5453464	0.0083936	0.0227636	47470
22	51234799	rs191117135	G	A	0.171303	0.1142	0.1335999	0.1336076	0.0081447	0.0059904	51238
22	51237063	rs3896457	T	C	0.028297	0.0212	0.1813001	0.1819536	0.2511810	0.2050720	51710
22	51237069	rs183661957	T	C	0.170397	0.5900	0.7727002	0.7727279	0.0050000	0.0029952	3141
22	51238249	rs149733995	A	C	-0.009396	0.0347	0.7869008	0.7865617	0.0756064	NA	51710
22	51242613	rs140611932	A	C	0.173199	0.5901	0.7691995	0.7691340	0.0050000	NA	3141
22	51243297	rs1555938782	A	T	0.007004	0.0297	0.8140000	0.8135685	0.1400000	NA	51710

1   10611   rs189107123 C   G   .   PASS    AF=0.016    ES:SE:LP:AF:SS:NC:ID    0.025502:0.1016:0.0958798:0.016:51710:20352:rs189107123
1   13302   rs75241669  C   T   .   PASS    AF=0.117    ES:SE:LP:AF:SS:NC:ID    -0.015398:0.0373:0.167363:0.117:51710:20352:rs75241669
1   13327   rs2691329   G   C   .   PASS    AF=0.0335458    ES:SE:LP:AF:SS:NC:ID    -0.019101:0.0689:0.107182:0.0335458:51710:20352:rs2691329
1   13980   rs2592732   T   C   .   PASS    AF=0.0186064    ES:SE:LP:AF:SS:NC:ID    -0.040403:0.0881:0.189566:0.0186064:51710:20352:rs2592732
1   30923   rs1165072081    G   T   .   PASS    AF=0.753    ES:SE:LP:AF:SS:NC:ID    0.005803:0.0281:0.0777418:0.753:51710:20352:rs1165072081
1   51476   rs2531249   T   C   .   PASS    AF=0.00760642   ES:SE:LP:AF:SS:NC:ID    -0.3199:0.1446:1.56896:0.00760642:49357:19237:rs2531249
1   51479   rs116400033 T   A   .   PASS    AF=0.196213 ES:SE:LP:AF:SS:NC:ID    -0.007599:0.0294:0.0996329:0.196213:51710:20352:rs116400033
1   52058   rs1199236530    G   C   .   PASS    AF=0.0417458    ES:SE:LP:AF:SS:NC:ID    -0.0807:0.0597:0.753255:0.0417458:51710:20352:rs1199236530
1   52144   rs190291950 T   A   .   PASS    AF=0.0116851    ES:SE:LP:AF:SS:NC:ID    -0.180204:0.1183:0.893469:0.0116851:51050:20171:rs190291950
1   52238   rs2691277   T   G   .   PASS    AF=0.944908 ES:SE:LP:AF:SS:NC:ID    0.088099:0.0546:0.972243:0.944908:51710:20352:rs2691277
1   54421   rs146477069 A   G   .   PASS    AF=0.0536064    ES:SE:LP:AF:SS:NC:ID    0.004902:0.0537:0.032452:0.0536064:51710:20352:rs146477069
1   54490   rs141149254 G   A   .   PASS    AF=0.148606 ES:SE:LP:AF:SS:NC:ID    0.006797:0.0326:0.0780016:0.148606:51710:20352:rs141149254
1   54676   rs2462492   C   T   .   PASS    AF=0.176394 ES:SE:LP:AF:SS:NC:ID    -0.029295:0.0307:0.468393:0.176394:51710:20352:rs2462492
1   54753   rs1472236223    T   G   .   PASS    AF=0.027    ES:SE:LP:AF:SS:NC:ID    -0.0618:0.0765:0.377372:0.027:51710:20352:rs1472236223
1   55164   rs3091274   C   A   .   PASS    AF=0.951394 ES:SE:LP:AF:SS:NC:ID    0.058495:0.0585:0.497983:0.951394:51710:20352:rs3091274
1   55299   rs10399749  C   T   .   PASS    AF=0.173787 ES:SE:LP:AF:SS:NC:ID    0.032496:0.0306:0.540306:0.173787:51710:20352:rs10399749
1   55326   rs3107975   T   C   .   PASS    AF=0.021    ES:SE:LP:AF:SS:NC:ID    -0.023599:0.0718:0.129538:0.021:51710:20352:rs3107975
1   55394   rs2949420   T   A   .   PASS    AF=0.0133032    ES:SE:LP:AF:SS:NC:ID    -0.065702:0.0995:0.293368:0.0133032:51710:20352:rs2949420
1   55816   rs3020699   G   A   .   PASS    AF=0.00802301   ES:SE:LP:AF:SS:NC:ID    -0.205205:0.143:0.820448:0.00802301:45373:18371:rs3020699
1   55850   rs191890754 C   G   .   PASS    AF=0.00760642   ES:SE:LP:AF:SS:NC:ID    -0.182097:0.171:0.542118:0.00760642:25915:9678:rs191890754
1   55852   rs184233019 G   C   .   PASS    AF=0.00673598   ES:SE:LP:AF:SS:NC:ID    -0.114401:0.524:0.0823895:0.00673598:4063:1370:rs184233019
1   57952   rs2691334   A   C   .   PASS    AF=0.911857 ES:SE:LP:AF:SS:NC:ID    0.041197:0.0436:0.463063:0.911857:51710:20352:rs2691334
1   58814   rs114420996 G   A   .   PASS    AF=0.0855394    ES:SE:LP:AF:SS:NC:ID    0.014002:0.0418:0.131767:0.0855394:51710:20352:rs114420996
1   59040   rs62637815  T   C   .   PASS    AF=0.064    ES:SE:LP:AF:SS:NC:ID    -0.007502:0.0512:0.0540393:0.064:51710:20352:rs62637815
1   60726   rs2531295   C   A   .   PASS    AF=0.0497883    ES:SE:LP:AF:SS:NC:ID    -0.041103:0.0559:0.334982:0.0497883:51710:20352:rs2531295
1   61442   rs2531261   A   G   .   PASS    AF=0.981197 ES:SE:LP:AF:SS:NC:ID    0.032399:0.1005:0.126738:0.981197:51710:20352:rs2531261
1   61462   rs56992750  T   A   .   PASS    AF=0.00735382   ES:SE:LP:AF:SS:NC:ID    0.159803:0.1926:0.390619:0.00735382:36442:14248:rs56992750
1   61987   rs76735897  A   G   .   PASS    AF=0.347213 ES:SE:LP:AF:SS:NC:ID    -0.045604:0.025:1.16845:0.347213:51710:20352:rs76735897
1   61989   rs77573425  G   C   .   PASS    AF=0.347213 ES:SE:LP:AF:SS:NC:ID    -0.042803:0.0249:1.06485:0.347213:51710:20352:rs77573425
1   63671   rs80011619  G   A   .   PASS    AF=0.146787 ES:SE:LP:AF:SS:NC:ID    0.023297:0.0332:0.316233:0.146787:51710:20352:rs80011619
1   64649   rs181431124 A   C   .   PASS    AF=0.0186064    ES:SE:LP:AF:SS:NC:ID    -0.001099:0.0888:0.00423322:0.0186064:51710:20352:rs181431124
1   66162   rs201684885 A   T   .   PASS    AF=0.333    ES:SE:LP:AF:SS:NC:ID    -0.017899:0.0253:0.319211:0.333:51710:20352:rs201684885
1   66176   rs1491257206    T   A   .   PASS    AF=0.00757297   ES:SE:LP:AF:SS:NC:ID    0.102196:0.1786:0.246187:0.00757297:44414:18177:rs1491257206
1   66219   rs1557435148    A   T   .   PASS    AF=0.016    ES:SE:LP:AF:SS:NC:ID    -0.058703:0.0994:0.255629:0.016:51710:20352:rs1557435148
1   66331   rs1557435418    A   C   .   PASS    AF=0.006    ES:SE:LP:AF:SS:NC:ID    0.205205:0.2868:0.323947:0.006:21630:8219:rs1557435418
1   66442   rs1227875005    T   A   .   PASS    AF=0.0196639    ES:SE:LP:AF:SS:NC:ID    0.021898:0.0916:0.090765:0.0196639:51710:20352:rs1227875005
1   66457   rs1227875005    T   A   .   PASS    AF=0.00905161   ES:SE:LP:AF:SS:NC:ID    -0.054805:0.1368:0.162096:0.00905161:51710:20352:rs1227875005
1   66507   rs1268339504    T   A   .   PASS    AF=0.0981245    ES:SE:LP:AF:SS:NC:ID    0.006399:0.0396:0.0598821:0.0981245:51710:20352:rs1268339504
1   67181   rs28503582  A   G   .   PASS    AF=0.009    ES:SE:LP:AF:SS:NC:ID    0.196696:0.1549:0.690157:0.009:38067:14843:rs28503582
1   69511   rs2691305   A   G   .   PASS    AF=0.675394 ES:SE:LP:AF:SS:NC:ID    0.033598:0.0249:0.748849:0.675394:51710:20352:rs2691305
1   73841   rs143773730 C   T   .   PASS    AF=0.234819 ES:SE:LP:AF:SS:NC:ID    -0.059803:0.0279:1.49336:0.234819:51710:20352:rs143773730
1   77462   rs2462497   G   A   .   PASS    AF=0.0744606    ES:SE:LP:AF:SS:NC:ID    0.025502:0.0476:0.227019:0.0744606:51710:20352:rs2462497
1   77874   rs62641297  G   A   .   PASS    AF=0.0554703    ES:SE:LP:AF:SS:NC:ID    -0.064805:0.051:0.691863:0.0554703:51710:20352:rs62641297
1   77961   rs78385339  G   A   .   PASS    AF=0.0542394    ES:SE:LP:AF:SS:NC:ID    0.064701:0.0555:0.611721:0.0542394:51710:20352:rs78385339
1   79033   rs2462495   A   G   .   PASS    AF=0.957708 ES:SE:LP:AF:SS:NC:ID    0.064304:0.0609:0.535212:0.957708:51710:20352:rs2462495
1   79050   rs2949413   G   T   .   PASS    AF=0.923181 ES:SE:LP:AF:SS:NC:ID    0.080104:0.0449:1.1272:0.923181:51710:20352:rs2949413
1   79137   rs143777184 A   T   .   PASS    AF=0.007    ES:SE:LP:AF:SS:NC:ID    0.363901:0.334:0.559248:0.007:14237:5197:rs143777184
1   79772   rs147215883 C   G   .   PASS    AF=0.0842128    ES:SE:LP:AF:SS:NC:ID    -0.062205:0.0444:0.791827:0.0842128:51710:20352:rs147215883
1   82163   rs139113303 G   A   .   PASS    AF=0.0593595    ES:SE:LP:AF:SS:NC:ID    0.117801:0.049:1.78941:0.0593595:51710:20352:rs139113303
1   82249   rs1851945   A   G   .   PASS    AF=0.0376064    ES:SE:LP:AF:SS:NC:ID    -0.046397:0.0651:0.322211:0.0376064:51710:20352:rs1851945

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