Summary
Summary {data-width=650}
Manhattan plot
manhattan_plot
QQ plot
qq_plot
AF plot
af_plot
P-Z plot
pz_plot
beta_std plot
beta_std_plot
Metadata
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"gwas_harmonisation_command": "--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/vcf_09_19b/bgzip_vcf/data.batch_20088_347.vcf.gz --id UKB-b:15071 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20088_347.txt.gz --cohort_cases 1111 --cohort_controls 63838 --ref /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/human_g1k_v37.fasta --json /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/ukb_gwas.json; 1.1.1",
"file_date": "2019-09-13T11:45:04.554547",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-15071/ukb-b-15071.vcf.gz; Date=Sun May 10 08:29:00 2020"
}
LDSC
*********************************************************************
* 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-15071/UKB-b-15071_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-15071/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:43:53 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-15071/UKB-b-15071_data.vcf.gz ...
Read summary statistics for 1664943 SNPs.
Dropped 157 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, 429637 SNPs remain.
After merging with regression SNP LD, 429637 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: -0.0045 (0.0086)
Lambda GC: 1.0009
Mean Chi^2: 0.9958
Intercept: 1.0027 (0.0103)
Ratio: NA (mean chi^2 < 1)
Analysis finished at Thu Oct 17 14:44:21 2019
Total time elapsed: 28.1sQC metrics
Metrics
Metrics
{
"af_correlation": 0.6301,
"inflation_factor": 1,
"mean_EFFECT": 2.0124e-06,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 0,
"n_p_sig": 0,
"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": 13185,
"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": 429637,
"ldsc_nsnp_merge_regression_ld": 429637,
"ldsc_observed_scale_h2_beta": "NA",
"ldsc_observed_scale_h2_se": "NA",
"ldsc_intercept_beta": 1.0027,
"ldsc_intercept_se": 0.0103,
"ldsc_lambda_gc": 1.0009,
"ldsc_mean_chisq": 0.9958,
"ldsc_ratio": -0.6429
}
Flags
| name | value |
|---|---|
| af_correlation | TRUE |
| 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 | FALSE |
| ldsc_intercept_beta | FALSE |
| n_clumped_hits | FALSE |
| r2_sum1 | FALSE |
| r2_sum2 | FALSE |
| r2_sum3 | FALSE |
| r2_sum4 | FALSE |
Definitions
General metrics
af_correlation: Correlation coefficient betweenAFandAF_reference.inflation_factor(lambda): Genomic inflation factor.mean_EFFECT: Mean ofEFFECTsize.n: Maximum value of reported sample size across all SNPs, \(n\).n_clumped_hits: Number of clumped hits.n_snps: Number of SNPsn_p_sig: Number of SNPs with pvalue below5e-8.n_mono: Number of monomorphic (MAF == 1orMAF == 0) SNPs.n_ns: Number of SNPs with nonsense values:- alleles other than
A, C, G or T. - P-values
< 0or> 1. - negative or infinite standard errors (
<= 0or= Infinity). - infinite beta estimates or allele frequencies
< 0or> 1.
- alleles other than
n_mac: Number of cases whereMAC(\(2 \times N \times MAF\)) is less than6.is_snpid_unique:trueif the combination ofIDREFALTis unique and therefore no duplication in snpid.n_miss_<*>: Number ofNAobservations 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 expectratio_se_nto 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 ofdiffand the mean ofdiff2(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<*>:r2statistics under various assumptions1: \(r^2 = \sum_j{\frac{2 \times \beta_j^2 \times {MAF}_j \times (1 - {MAF}_j)}{\texttt{var1}}}\), \(\texttt{var1} = 1\).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: NOTis_snpid_unique.mean_EFFECT_nonfinite:mean(EFFECT)isNA,NaN, orInf.mean_EFFECT_05:abs(mean(EFFECT))> 0.5.mean_EFFECT_01:abs(mean(EFFECT))> 0.1.mean_chisq:ldsc_mean_chisq> 1.3 orldsc_mean_chisq< 0.7.n_p_sig:n_p_sig> 1000.miss_<*>:n_miss_<*>/n_snps> 0.01.ldsc_ratio:ldsc_ratio> 0.5ldsc_intercept_beta:ldsc_intercept_beta> 1.5n_clumped_hits:n_clumped_hits> 1000r2_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 \(\beta_j\)
Diagnostics
Details
Summary stats
| 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 | 4 | 58 | 0 | 1664788 | 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 | 1664943 | 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 | 8.658368e+00 | 5.764267e+00 | 1.00000e+00 | 4.000000e+00 | 8.000000e+00 | 1.300000e+01 | 2.200000e+01 | ▇▅▅▂▂ |
| numeric | POS | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 7.860275e+07 | 5.656953e+07 | 1.23330e+04 | 3.177707e+07 | 6.919960e+07 | 1.145830e+08 | 2.491722e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.000000e-06 | 7.409000e-04 | -3.53390e-03 | -5.011000e-04 | -4.000000e-07 | 5.035000e-04 | 3.802900e-03 | ▁▂▇▂▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 7.408000e-04 | 2.230000e-05 | 6.94600e-04 | 7.251000e-04 | 7.357000e-04 | 7.529000e-04 | 1.416900e-03 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.988581e-01 | 2.880412e-01 | 3.40000e-06 | 2.500000e-01 | 5.000000e-01 | 7.499995e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.988625e-01 | 2.880152e-01 | 3.40000e-06 | 2.494401e-01 | 4.974114e-01 | 7.476439e-01 | 9.999990e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.784107e-01 | 1.061728e-01 | 3.15032e-01 | 3.853080e-01 | 4.674470e-01 | 5.660500e-01 | 6.849680e-01 | ▇▇▆▅▅ |
| numeric | AF_reference | 13185 | 0.9920808 | NA | NA | NA | NA | NA | NA | NA | 4.572283e-01 | 1.500625e-01 | 1.99700e-04 | 3.438500e-01 | 4.502800e-01 | 5.648960e-01 | 1.000000e+00 | ▁▆▇▃▁ |
Head and tail
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 49298 | rs200943160 | T | C | -0.0003222 | 0.0012802 | 0.8000000 | 0.8012780 | 0.623812 | 0.782149 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0003669 | 0.0012765 | 0.7700005 | 0.7737763 | 0.399144 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0015083 | 0.0012555 | 0.2300001 | 0.2296083 | 0.455916 | 0.420727 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0006489 | 0.0008850 | 0.4600002 | 0.4634598 | 0.513304 | 0.275160 | NA |
| 1 | 814495 | rs74461805 | C | A | 0.0002610 | 0.0012120 | 0.8300000 | 0.8295071 | 0.340108 | NA | NA |
| 1 | 840753 | rs4970382 | T | C | 0.0012278 | 0.0007350 | 0.0949992 | 0.0948135 | 0.400406 | 0.468850 | NA |
| 1 | 843405 | rs11516185 | A | G | -0.0011190 | 0.0009144 | 0.2200002 | 0.2210538 | 0.362367 | 0.375399 | NA |
| 1 | 850218 | rs6664536 | T | A | -0.0007288 | 0.0007310 | 0.3200000 | 0.3187772 | 0.589315 | 0.345248 | NA |
| 1 | 850371 | rs6679046 | G | T | -0.0011926 | 0.0007345 | 0.1000000 | 0.1044293 | 0.603035 | 0.508786 | NA |
| 1 | 850780 | rs6657440 | C | T | -0.0012108 | 0.0007345 | 0.0990011 | 0.0992506 | 0.603381 | 0.560304 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51158499 | rs8136930 | T | G | -0.0006908 | 0.0007211 | 0.3400001 | 0.3380936 | 0.459892 | 0.544728 | NA |
| 22 | 51161019 | rs5770994 | C | T | 0.0007270 | 0.0007185 | 0.3100002 | 0.3116156 | 0.483487 | 0.425719 | NA |
| 22 | 51163039 | rs715584 | G | T | -0.0008575 | 0.0007294 | 0.2399999 | 0.2397188 | 0.425020 | 0.473642 | NA |
| 22 | 51164109 | rs5770995 | G | C | -0.0009787 | 0.0007258 | 0.1800002 | 0.1775168 | 0.450784 | 0.510982 | NA |
| 22 | 51164115 | rs5770996 | C | T | -0.0008252 | 0.0007254 | 0.2599998 | 0.2552772 | 0.454945 | 0.514776 | NA |
| 22 | 51174048 | rs9628245 | G | C | -0.0007857 | 0.0008222 | 0.3400001 | 0.3393238 | 0.377819 | 0.433107 | NA |
| 22 | 51186143 | rs2879914 | T | C | -0.0016691 | 0.0007749 | 0.0309999 | 0.0312453 | 0.380077 | 0.273363 | NA |
| 22 | 51186228 | rs3865766 | C | T | -0.0011618 | 0.0007548 | 0.1199999 | 0.1237293 | 0.449547 | 0.453275 | NA |
| 22 | 51197266 | rs61290853 | A | G | -0.0008972 | 0.0007778 | 0.2500000 | 0.2486900 | 0.386693 | 0.422923 | NA |
| 22 | 51212875 | rs2238837 | A | C | -0.0023516 | 0.0008318 | 0.0047000 | 0.0046985 | 0.331351 | 0.372404 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623812 ES:SE:LP:AF:ID -0.000322218:0.0012802:0.09691:0.623812:rs10399793
1 54676 rs2462492 C T . PASS AF=0.399144 ES:SE:LP:AF:ID -0.00036691:0.00127648:0.113509:0.399144:rs2462492
1 91536 rs6702460 G T . PASS AF=0.455916 ES:SE:LP:AF:ID -0.00150833:0.00125551:0.638272:0.455916:rs6702460
1 706368 rs12029736 A G . PASS AF=0.513304 ES:SE:LP:AF:ID -0.000648876:0.000885038:0.337242:0.513304:rs12029736
1 814495 rs74461805 C A . PASS AF=0.340108 ES:SE:LP:AF:ID 0.000260978:0.00121197:0.0809219:0.340108:rs74461805
1 840753 rs4970382 T C . PASS AF=0.400406 ES:SE:LP:AF:ID 0.00122779:0.000734968:1.02228:0.400406:rs4970382
1 843405 rs11516185 A G . PASS AF=0.362367 ES:SE:LP:AF:ID -0.00111901:0.000914425:0.657577:0.362367:rs11516185
1 850218 rs6664536 T A . PASS AF=0.589315 ES:SE:LP:AF:ID -0.000728771:0.000730983:0.49485:0.589315:rs6664536
1 850371 rs6679046 G T . PASS AF=0.603035 ES:SE:LP:AF:ID -0.00119261:0.000734479:1:0.603035:rs6679046
1 850780 rs6657440 C T . PASS AF=0.603381 ES:SE:LP:AF:ID -0.00121081:0.000734493:1.00436:0.603381:rs6657440
1 852037 rs4970463 G A . PASS AF=0.588673 ES:SE:LP:AF:ID -0.000759454:0.000732026:0.522879:0.588673:rs4970463
1 852063 rs28436996 G A . PASS AF=0.588649 ES:SE:LP:AF:ID -0.000723289:0.000731696:0.49485:0.588649:rs28436996
1 852875 rs13303369 C T . PASS AF=0.606715 ES:SE:LP:AF:ID -0.00102208:0.000735701:0.79588:0.606715:rs13303369
1 853954 rs1806509 C A . PASS AF=0.60688 ES:SE:LP:AF:ID -0.00102905:0.000735778:0.79588:0.60688:rs1806509
1 854777 rs13303019 A G . PASS AF=0.609504 ES:SE:LP:AF:ID -0.00108199:0.000736513:0.853872:0.609504:rs13303019
1 854978 rs13303057 A C . PASS AF=0.602505 ES:SE:LP:AF:ID -0.0011658:0.000734608:0.958607:0.602505:rs13303057
1 855075 rs6673914 C G . PASS AF=0.609473 ES:SE:LP:AF:ID -0.00108206:0.000736416:0.853872:0.609473:rs6673914
1 856099 rs28534711 T G . PASS AF=0.390835 ES:SE:LP:AF:ID 0.00106187:0.000736715:0.823909:0.390835:rs28534711
1 856108 rs28742275 A G . PASS AF=0.390777 ES:SE:LP:AF:ID 0.00106423:0.000736791:0.823909:0.390777:rs28742275
1 856476 rs4040605 A G . PASS AF=0.352504 ES:SE:LP:AF:ID 0.00105551:0.000756295:0.79588:0.352504:rs4040605
1 866893 rs2880024 T C . PASS AF=0.608083 ES:SE:LP:AF:ID -7.16185e-05:0.000743231:0.0362122:0.608083:rs2880024
1 875770 rs4970379 A G . PASS AF=0.598464 ES:SE:LP:AF:ID 0.000110623:0.000748789:0.0555173:0.598464:rs4970379
1 881627 rs2272757 G A . PASS AF=0.65048 ES:SE:LP:AF:ID -0.000188503:0.000756414:0.09691:0.65048:rs2272757
1 891059 rs13303065 C T . PASS AF=0.650626 ES:SE:LP:AF:ID -9.57775e-05:0.00075634:0.0457575:0.650626:rs13303065
1 891945 rs13303106 A G . PASS AF=0.650689 ES:SE:LP:AF:ID -0.000100663:0.000757205:0.05061:0.650689:rs13303106
1 903245 rs28690976 A G . PASS AF=0.565133 ES:SE:LP:AF:ID -0.000463193:0.000751896:0.267606:0.565133:rs28690976
1 909073 rs3892467 C T . PASS AF=0.386217 ES:SE:LP:AF:ID 0.00135586:0.000750324:1.14874:0.386217:rs3892467
1 909238 rs3829740 G C . PASS AF=0.572164 ES:SE:LP:AF:ID 0.000757074:0.000725898:0.522879:0.572164:rs3829740
1 910394 rs28477686 C T . PASS AF=0.322663 ES:SE:LP:AF:ID -0.00136838:0.000788469:1.08092:0.322663:rs28477686
1 912049 rs7367995 T C . PASS AF=0.585242 ES:SE:LP:AF:ID 0.000879402:0.000732936:0.638272:0.585242:rs7367995
1 913889 rs2340596 G A . PASS AF=0.600025 ES:SE:LP:AF:ID 0.000790239:0.000734393:0.552842:0.600025:rs2340596
1 914333 rs13302979 C G . PASS AF=0.60338 ES:SE:LP:AF:ID 0.000856366:0.000736885:0.60206:0.60338:rs13302979
1 914852 rs13303368 G C . PASS AF=0.60101 ES:SE:LP:AF:ID 0.000788993:0.000735528:0.552842:0.60101:rs13303368
1 914940 rs13303033 T C . PASS AF=0.585327 ES:SE:LP:AF:ID 0.000893336:0.000731869:0.657577:0.585327:rs13303033
1 916834 rs6694632 G A . PASS AF=0.590116 ES:SE:LP:AF:ID 0.000939599:0.000732875:0.69897:0.590116:rs6694632
1 918384 rs13303118 G T . PASS AF=0.585329 ES:SE:LP:AF:ID 0.000778424:0.000731595:0.537602:0.585329:rs13303118
1 918573 rs2341354 A G . PASS AF=0.590348 ES:SE:LP:AF:ID 0.000901198:0.00073236:0.657577:0.590348:rs2341354
1 919501 rs4970414 G T . PASS AF=0.583818 ES:SE:LP:AF:ID 0.000195935:0.000759412:0.09691:0.583818:rs4970414
1 921716 rs13303278 C A . PASS AF=0.569359 ES:SE:LP:AF:ID 0.000619763:0.000733029:0.39794:0.569359:rs13303278
1 924528 rs34712273 C A . PASS AF=0.579293 ES:SE:LP:AF:ID 0.00057206:0.000735866:0.356547:0.579293:rs34712273
1 930533 rs3128110 C G . PASS AF=0.384463 ES:SE:LP:AF:ID -0.0006924:0.00074576:0.455932:0.384463:rs3128110
1 930567 rs3121574 A G . PASS AF=0.384502 ES:SE:LP:AF:ID -0.000695678:0.000745788:0.455932:0.384502:rs3121574
1 930751 rs3128111 C G . PASS AF=0.383433 ES:SE:LP:AF:ID -0.000722801:0.000746456:0.481486:0.383433:rs3128111
1 931166 rs2710880 A G . PASS AF=0.385119 ES:SE:LP:AF:ID -0.000694705:0.000745975:0.455932:0.385119:rs2710880
1 931362 rs2799060 G A . PASS AF=0.383925 ES:SE:LP:AF:ID -0.000731953:0.000746501:0.481486:0.383925:rs2799060
1 933790 rs9442392 G A . PASS AF=0.579341 ES:SE:LP:AF:ID 0.000437967:0.00073519:0.259637:0.579341:rs9442392
1 936111 rs1936360 C T . PASS AF=0.574447 ES:SE:LP:AF:ID 0.000242893:0.000735239:0.130768:0.574447:rs1936360
1 940005 rs2799056 A G . PASS AF=0.396767 ES:SE:LP:AF:ID -0.000687318:0.000743705:0.443698:0.396767:rs2799056
1 940096 rs4503294 C T . PASS AF=0.56692 ES:SE:LP:AF:ID 0.000455002:0.000733018:0.275724:0.56692:rs4503294
1 941284 rs3128116 C T . PASS AF=0.39488 ES:SE:LP:AF:ID -0.000662551:0.000743665:0.431798:0.39488:rs3128116