IEU GWAS QC Report

name	value
id	ieu-a-25
pmid	26551672
year	2015
mr	1
note	forward strand
trait	Type 2 diabetes
category	Disease
subcategory	Diabetes
population	Mixed
sex	Males and females
ncase	27206
ncontrol	57574
sample_size	84780
nsnp	45944
sd	NULL
priority	4
author	Gaulton
group_name	public
access	public
build	HG19/GRCh37

name	value
gwas_id	ieu-a-25
bcf_file	ieu-a-25.vcf.gz
qc_metrics_file	qc_metrics.json
metadata	metadata.json
refdata	1kg_v3_nomult.bcf
compile_time	2020-05-10 14:18 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/ieu-a-import/processed/ieu-a-25/ieu-a-25.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-25/ldsc.txt \
--w-ld-chr /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/ 

Beginning analysis at Wed Feb  5 11:42:09 2020
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ieu-a-import/processed/ieu-a-25/ieu-a-25.vcf.gz ...
Read summary statistics for 42089 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, 5149 SNPs remain.
After merging with regression SNP LD, 5149 SNPs remain.
WARNING: number of SNPs less than 200k; this is almost always bad.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 5.5793 (1.1749)
Lambda GC: 5.3016
Mean Chi^2: 8.949
Intercept: 1.4518 (0.2706)
Ratio: 0.0568 (0.034)
Analysis finished at Wed Feb  5 11:42:18 2020
Total time elapsed: 8.82s

{
    "af_correlation": 0.9473,
    "inflation_factor": 2.2655,
    "mean_EFFECT": 0.0024,
    "n": 73903.1,
    "n_snps": 42089,
    "n_clumped_hits": 27,
    "n_p_sig": 1384,
    "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": 31,
    "n_miss_AF_reference": 1052,
    "n_est": 72930.6204,
    "ratio_se_n": 0.9934,
    "mean_diff": -0.0012,
    "ratio_diff": 4.9088,
    "sd_y_est1": 2.4804,
    "sd_y_est2": 2.464,
    "r2_sum1": 0.1591,
    "r2_sum2": 0.0259,
    "r2_sum3": 0.0262,
    "r2_sum4": 0.0269,
    "ldsc_nsnp_merge_refpanel_ld": 5149,
    "ldsc_nsnp_merge_regression_ld": 5149,
    "ldsc_observed_scale_h2_beta": 5.5793,
    "ldsc_observed_scale_h2_se": 1.1749,
    "ldsc_intercept_beta": 1.4518,
    "ldsc_intercept_se": 0.2706,
    "ldsc_lambda_gc": 5.3016,
    "ldsc_mean_chisq": 8.949,
    "ldsc_ratio": 0.0568
}

name	value
name	value
af_correlation	FALSE
inflation_factor	TRUE
n	FALSE
is_snpid_non_unique	FALSE
mean_EFFECT_nonfinite	FALSE
mean_EFFECT_05	FALSE
mean_EFFECT_01	FALSE
mean_chisq	TRUE
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	5	58	0	42089	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	8.947231e+00	5.387551e+00	1.000000e+00	4.000000e+00	7.000000e+00	1.200000e+01	2.000000e+01	▇▇▇▃▃
numeric	POS	0	1.0000000	NA	NA	NA	NA	NA	4.920317e+07	4.677479e+07	2.443938e+06	1.726418e+07	2.821981e+07	6.233321e+07	2.271907e+08	▇▃▂▁▁
numeric	EFFECT	0	1.0000000	NA	NA	NA	NA	NA	2.436300e-03	8.674340e-02	-7.390760e-01	-3.536710e-02	1.998000e-03	4.018180e-02	9.050280e-01	▁▁▇▁▁
numeric	SE	0	1.0000000	NA	NA	NA	NA	NA	5.759650e-02	5.740080e-02	1.242220e-02	1.580560e-02	3.218140e-02	7.917560e-02	4.256580e-01	▇▂▁▁▁
numeric	PVAL	0	1.0000000	NA	NA	NA	NA	NA	3.680900e-01	3.186724e-01	0.000000e+00	5.399950e-02	3.100002e-01	6.400000e-01	1.000000e+00	▇▃▃▃▃
numeric	PVAL_ztest	0	1.0000000	NA	NA	NA	NA	NA	3.681309e-01	3.187322e-01	0.000000e+00	5.430650e-02	3.067689e-01	6.438721e-01	1.000000e+00	▇▃▃▃▃
numeric	AF	31	0.9992635	NA	NA	NA	NA	NA	1.677616e-01	2.341735e-01	1.000000e-03	8.000000e-03	4.500000e-02	2.560000e-01	9.990000e-01	▇▂▁▁▁
numeric	AF_reference	1052	0.9750053	NA	NA	NA	NA	NA	1.814399e-01	2.401072e-01	0.000000e+00	5.990400e-03	6.549520e-02	2.815500e-01	1.000000e+00	▇▂▁▁▁
numeric	N	0	1.0000000	NA	NA	NA	NA	NA	7.238018e+04	3.396033e+03	5.912810e+04	7.340060e+04	7.390220e+04	7.390230e+04	7.390310e+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	120437718	rs2793823	G	A	0.0592119	0.0185129	0.0014000	0.0013819	0.132	0.3306710	73902.3
1	120437884	rs2641348	A	G	0.0648510	0.0200746	0.0011000	0.0012357	0.110	0.1958870	73902.3
1	120438577	rs147294252	G	A	0.1239860	0.1084960	0.2500000	0.2531340	0.006	0.0013978	67530.5
1	120439109	rs6668119	G	C	0.0685928	0.0202474	0.0007300	0.0007047	0.106	0.1930910	73902.3
1	120440029	rs2027447	T	C	0.0676586	0.0200183	0.0006800	0.0007253	0.110	0.1958870	73902.3
1	120441885	rs76686822	C	T	0.0119286	0.0733156	0.8700001	0.8707530	0.010	0.0143770	73902.4
1	120441998	rs2487568	A	G	0.0601539	0.0184955	0.0012000	0.0011445	0.133	0.3955670	73902.3
1	120442257	rs2487569	A	T	0.0573251	0.0238317	0.0160000	0.0161542	0.088	0.2799520	73901.9
1	120443424	rs180671497	C	A	0.0601539	0.0998053	0.5500004	0.5467000	0.006	0.0011981	68337.2
1	120443540	rs2641346	C	T	0.0667236	0.0200370	0.0008400	0.0008684	0.110	0.1950880	73902.3

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
20	43056149	rs6031598	G	T	0.0089597	0.0128905	0.4799997	0.4870143	0.610	0.4670530	73902.3
20	43056562	rs3212208	T	C	-0.0285875	0.0225638	0.2099999	0.2051685	0.088	0.1591450	73902.3
20	43056584	rs181422893	G	A	-0.0148886	0.0973840	0.8800001	0.8784886	0.005	0.0057907	72055.1
20	43056732	rs6031600	A	G	0.3729420	0.1667590	0.0250000	0.0253247	0.002	0.0057907	61986.2
20	43057480	rs3818247	G	T	-0.0324672	0.0133385	0.0150000	0.0149290	0.348	0.4934110	73902.3
20	43057820	rs6031601	C	A	-0.0009995	0.0129935	0.9500000	0.9386847	0.611	0.5087860	73902.3
20	43057831	rs112600410	A	G	-0.3611650	0.1746860	0.0389996	0.0386862	0.003	0.0085863	61574.2
20	43058012	rs74173201	A	C	-0.0207825	0.0249810	0.4100001	0.4054472	0.069	0.0597045	73902.3
20	43058018	rs3746574	T	C	0.0039920	0.0127069	0.7499995	0.7533983	0.517	0.3801920	73901.3
20	43058096	rs3746575	C	G	-0.0009995	0.0129935	0.9199999	0.9386847	0.607	0.4988020	73901.4

1   120437718   rs2793823   G   A   .   PASS    AF=0.132    ES:SE:LP:AF:SS:ID   0.0592119:0.0185129:2.85387:0.132:73902.3:rs2793823
1   120437884   rs2641348   A   G   .   PASS    AF=0.11 ES:SE:LP:AF:SS:ID   0.064851:0.0200746:2.95861:0.11:73902.3:rs2641348
1   120438577   rs147294252 G   A   .   PASS    AF=0.006    ES:SE:LP:AF:SS:ID   0.123986:0.108496:0.60206:0.006:67530.5:rs147294252
1   120439109   rs6668119   G   C   .   PASS    AF=0.106    ES:SE:LP:AF:SS:ID   0.0685928:0.0202474:3.13668:0.106:73902.3:rs6668119
1   120440029   rs2027447   T   C   .   PASS    AF=0.11 ES:SE:LP:AF:SS:ID   0.0676586:0.0200183:3.16749:0.11:73902.3:rs2027447
1   120441885   rs76686822  C   T   .   PASS    AF=0.01 ES:SE:LP:AF:SS:ID   0.0119286:0.0733156:0.0604807:0.01:73902.4:rs76686822
1   120441998   rs2487568   A   G   .   PASS    AF=0.133    ES:SE:LP:AF:SS:ID   0.0601539:0.0184955:2.92082:0.133:73902.3:rs2487568
1   120442257   rs2487569   A   T   .   PASS    AF=0.088    ES:SE:LP:AF:SS:ID   0.0573251:0.0238317:1.79588:0.088:73901.9:rs2487569
1   120443424   rs180671497 C   A   .   PASS    AF=0.006    ES:SE:LP:AF:SS:ID   0.0601539:0.0998053:0.259637:0.006:68337.2:rs180671497
1   120443540   rs2641346   C   T   .   PASS    AF=0.11 ES:SE:LP:AF:SS:ID   0.0667236:0.020037:3.07572:0.11:73902.3:rs2641346
1   120443702   rs2793826   G   A   .   PASS    AF=0.133    ES:SE:LP:AF:SS:ID   0.0544882:0.0183502:2.50864:0.133:73902.3:rs2793826
1   120444009   rs115640286 C   T   .   PASS    AF=0.04 ES:SE:LP:AF:SS:ID   -0.0246926:0.0385181:0.283997:0.04:73902.2:rs115640286
1   120444402   rs78188788  A   G   .   PASS    AF=0.008    ES:SE:LP:AF:SS:ID   0.0353671:0.0799261:0.187087:0.008:69643.2:rs78188788
1   120444648   rs112142442 T   C   .   PASS    AF=0.004    ES:SE:LP:AF:SS:ID   0.160417:0.106363:0.886057:0.004:69375.4:rs112142442
1   120445032   rs74115387  A   G   .   PASS    AF=0.021    ES:SE:LP:AF:SS:ID   0.00299551:0.0437226:0.0222764:0.021:73902:rs74115387
1   120445902   rs7517669   A   G   .   PASS    AF=0.002    ES:SE:LP:AF:SS:ID   0.116004:0.178911:0.283997:0.002:59522.4:rs7517669
1   120446063   rs2641352   T   C   .   PASS    AF=0.11 ES:SE:LP:AF:SS:ID   0.0676586:0.019789:3.11919:0.11:73902.3:rs2641352
1   120446201   rs74115388  T   C   .   PASS    AF=0.021    ES:SE:LP:AF:SS:ID   -0.00796817:0.0442459:0.0655015:0.021:73902.3:rs74115388
1   120446294   rs115764389 C   G   .   PASS    AF=0.142    ES:SE:LP:AF:SS:ID   -0.0237165:0.0181848:0.721246:0.142:73902.3:rs115764389
1   120447463   rs2493398   G   A   .   PASS    AF=0.131    ES:SE:LP:AF:SS:ID   0.0573251:0.0187801:2.69897:0.131:73902.3:rs2493398
1   120448697   rs12071541  A   G   .   PASS    AF=0.002    ES:SE:LP:AF:SS:ID   -0.0382587:0.179254:0.0809219:0.002:64213.2:rs12071541
1   120448749   rs2641338   G   A   .   PASS    AF=0.13 ES:SE:LP:AF:SS:ID   0.0573251:0.0185479:2.65758:0.13:73902.5:rs2641338
1   120448805   rs2793827   A   T   .   PASS    AF=0.129    ES:SE:LP:AF:SS:ID   0.0563803:0.0187979:2.56864:0.129:73902.1:rs2793827
1   120450282   rs74115390  G   T   .   PASS    AF=0.021    ES:SE:LP:AF:SS:ID   0.0009995:0.0435324:0.00436481:0.021:73902.1:rs74115390
1   120451190   rs3897478   T   C   .   PASS    AF=0.108    ES:SE:LP:AF:SS:ID   0.0657877:0.0200558:3:0.108:73902.3:rs3897478
1   120451209   rs74115392  T   C   .   PASS    AF=0.021    ES:SE:LP:AF:SS:ID   0.0009995:0.0438101:0.00436481:0.021:73902.2:rs74115392
1   120451352   rs947273    C   A   .   PASS    AF=0.131    ES:SE:LP:AF:SS:ID   0.0573251:0.0187801:2.69897:0.131:73902.3:rs947273
1   120452676   rs17024486  G   C   .   PASS    AF=0.021    ES:SE:LP:AF:SS:ID   0.0009995:0.0438101:0.00436481:0.021:73902.3:rs17024486
1   120453618   rs74115396  G   A   .   PASS    AF=0.021    ES:SE:LP:AF:SS:ID   0.0009995:0.0435324:0.00436481:0.021:73902.3:rs74115396
1   120454086   rs17024487  A   G   .   PASS    AF=0.021    ES:SE:LP:AF:SS:ID   -0.00399202:0.0441453:0.0315171:0.021:73902.3:rs17024487
1   120455095   rs1043964   T   C   .   PASS    AF=0.022    ES:SE:LP:AF:SS:ID   0.00697561:0.0435053:0.0655015:0.022:73902.3:rs1043964
1   120455310   rs699779    A   G   .   PASS    AF=0.108    ES:SE:LP:AF:SS:ID   0.0667236:0.0202853:3.01773:0.108:73902.3:rs699779
1   120455441   rs699780    A   G   .   PASS    AF=0.131    ES:SE:LP:AF:SS:ID   0.0573251:0.0185479:2.69897:0.131:73902.3:rs699780
1   120455586   rs835576    T   C   .   PASS    AF=0.108    ES:SE:LP:AF:SS:ID   0.0657877:0.0200558:3:0.108:73902.3:rs835576
1   120456563   rs835575    G   T   .   PASS    AF=0.108    ES:SE:LP:AF:SS:ID   0.0676586:0.0202663:3.08092:0.108:73902.3:rs835575
1   120457501   rs7527186   C   T   .   PASS    AF=0.021    ES:SE:LP:AF:SS:ID   -0.00299551:0.0439561:0.0222764:0.021:73902.2:rs7527186
1   120458004   rs6685892   A   T   .   PASS    AF=0.108    ES:SE:LP:AF:SS:ID   0.064851:0.0203233:2.88606:0.108:73902.3:rs6685892
1   120458924   rs3795666   G   A   .   PASS    AF=0.021    ES:SE:LP:AF:SS:ID   -0.00598207:0.044334:0.0457575:0.021:73902.3:rs3795666
1   120459433   rs41277624  G   A   .   PASS    AF=0.051    ES:SE:LP:AF:SS:ID   0.0344014:0.0283327:0.638272:0.051:73902.3:rs41277624
1   120459721   rs144654514 G   A   .   PASS    AF=0.004    ES:SE:LP:AF:SS:ID   -0.225541:0.141874:0.958607:0.004:60163.4:rs144654514
1   120460634   rs3897840   A   G   .   PASS    AF=0.002    ES:SE:LP:AF:SS:ID   0.0353671:0.178738:0.0757207:0.002:59522.3:rs3897840
1   120460916   rs10494233  A   C   .   PASS    AF=0.021    ES:SE:LP:AF:SS:ID   -0.00399202:0.0441453:0.0315171:0.021:73902.3:rs10494233
1   120461253   rs2793829   C   T   .   PASS    AF=0.107    ES:SE:LP:AF:SS:ID   0.0685928:0.0202474:3.14874:0.107:73902.3:rs2793829
1   120461536   rs17258558  T   A   .   PASS    AF=0.029    ES:SE:LP:AF:SS:ID   -0.0256677:0.03825:0.30103:0.029:73902.3:rs17258558
1   120461629   rs17024508  G   A   .   PASS    AF=0.021    ES:SE:LP:AF:SS:ID   -0.00399202:0.0439122:0.0315171:0.021:73902.3:rs17024508
1   120462468   rs56342935  C   T   .   PASS    AF=0.021    ES:SE:LP:AF:SS:ID   -0.00299551:0.0441894:0.0268721:0.021:73902.2:rs56342935
1   120463044   rs41306195  C   T   .   PASS    AF=0.079    ES:SE:LP:AF:SS:ID   -0.0478373:0.0240588:1.35655:0.079:73902.3:rs41306195
1   120463230   rs835574    C   T   .   PASS    AF=0.131    ES:SE:LP:AF:SS:ID   0.0592119:0.0185129:2.79588:0.131:73902.3:rs835574
1   120463398   rs12117337  C   T   .   PASS    AF=0.021    ES:SE:LP:AF:SS:ID   -0.0639133:0.0516246:0.657577:0.021:72853.7:rs12117337
1   120464165   rs835573    C   T   .   PASS    AF=0.129    ES:SE:LP:AF:SS:ID   0.0592119:0.0187447:2.85387:0.129:73902.3:rs835573

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