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_20090_375.vcf.gz --id UKB-b:3530 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20090_375.txt.gz --cohort_cases 1024 --cohort_controls 63925 --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": "2019-09-13T11:43:16.653118",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-3530/ukb-b-3530.vcf.gz; Date=Fri May 8 04:50:22 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-3530/UKB-b-3530_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-3530/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:43:44 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-3530/UKB-b-3530_data.vcf.gz ...
Read summary statistics for 1410238 SNPs.
Dropped 117 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, 365071 SNPs remain.
After merging with regression SNP LD, 365071 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: -0.002 (0.0092)
Lambda GC: 0.977
Mean Chi^2: 0.9906
Intercept: 0.9937 (0.0122)
Ratio: NA (mean chi^2 < 1)
Analysis finished at Thu Oct 17 14:44:09 2019
Total time elapsed: 24.42sQC metrics
Metrics
Metrics
{
"af_correlation": 0.5692,
"inflation_factor": 0.9541,
"mean_EFFECT": 3.9497e-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": 11228,
"n_est": "NA",
"ratio_se_n": "NA",
"mean_diff": "NaN",
"ratio_diff": "NaN",
"sd_y_est1": "NA",
"sd_y_est2": "NA",
"r2_sum1": 0,
"r2_sum2": 0,
"r2_sum3": 0,
"r2_sum4": 0,
"ldsc_nsnp_merge_refpanel_ld": 365071,
"ldsc_nsnp_merge_regression_ld": 365071,
"ldsc_observed_scale_h2_beta": "NA",
"ldsc_observed_scale_h2_se": "NA",
"ldsc_intercept_beta": 0.9937,
"ldsc_intercept_se": 0.0122,
"ldsc_lambda_gc": 0.977,
"ldsc_mean_chisq": 0.9906,
"ldsc_ratio": 0.6702
}
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 | TRUE |
| 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 | 1410123 | 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 | 1410238 | 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 | 8.676938e+00 | 5.769330e+00 | 1.00000e+00 | 4.000000e+00 | 8.000000e+00 | 1.300000e+01 | 2.200000e+01 | <U+2587><U+2585><U+2585><U+2582><U+2582> |
| numeric | POS | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 7.854483e+07 | 5.657288e+07 | 1.23330e+04 | 3.163420e+07 | 6.903347e+07 | 1.145140e+08 | 2.491722e+08 | <U+2587><U+2586><U+2585><U+2582><U+2581> |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.900000e-06 | 7.027000e-04 | -3.26800e-03 | -4.652000e-04 | 6.300000e-06 | 4.764000e-04 | 3.296400e-03 | <U+2581><U+2582><U+2587><U+2582><U+2581> |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 7.069000e-04 | 1.830000e-05 | 6.67500e-04 | 6.957000e-04 | 7.032000e-04 | 7.151000e-04 | 1.321100e-03 | <U+2587><U+2581><U+2581><U+2581><U+2581> |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 5.032307e-01 | 2.887577e-01 | 2.00000e-06 | 2.500000e-01 | 5.099998e-01 | 7.499995e-01 | 1.000000e+00 | <U+2587><U+2587><U+2587><U+2587><U+2587> |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 5.032294e-01 | 2.887374e-01 | 2.00000e-06 | 2.531783e-01 | 5.053496e-01 | 7.534325e-01 | 9.999992e-01 | <U+2587><U+2587><U+2587><U+2587><U+2587> |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.838413e-01 | 9.089530e-02 | 3.41797e-01 | 4.039350e-01 | 4.758185e-01 | 5.594120e-01 | 6.582030e-01 | <U+2587><U+2587><U+2586><U+2586><U+2585> |
| numeric | AF_reference | 11228 | 0.9920382 | NA | NA | NA | NA | NA | NA | NA | 4.617929e-01 | 1.427620e-01 | 1.99700e-04 | 3.554310e-01 | 4.562700e-01 | 5.636980e-01 | 1.000000e+00 | <U+2581><U+2586><U+2587><U+2583><U+2581> |
Head and tail
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 49298 | rs200943160 | T | C | 0.0000703 | 0.0012303 | 0.9500000 | 0.9544194 | 0.623812 | 0.782149 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0013594 | 0.0012267 | 0.2700001 | 0.2677919 | 0.399144 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0008029 | 0.0012065 | 0.5099998 | 0.5057375 | 0.455916 | 0.420727 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0007827 | 0.0008505 | 0.3599996 | 0.3574437 | 0.513304 | 0.275160 | NA |
| 1 | 840753 | rs4970382 | T | C | 0.0008300 | 0.0007063 | 0.2399999 | 0.2399625 | 0.400406 | 0.468850 | NA |
| 1 | 843405 | rs11516185 | A | G | -0.0002227 | 0.0008788 | 0.8000000 | 0.7999314 | 0.362367 | 0.375399 | NA |
| 1 | 850218 | rs6664536 | T | A | 0.0006809 | 0.0007025 | 0.3300000 | 0.3324070 | 0.589315 | 0.345248 | NA |
| 1 | 850371 | rs6679046 | G | T | 0.0006528 | 0.0007058 | 0.3599996 | 0.3550623 | 0.603035 | 0.508786 | NA |
| 1 | 850780 | rs6657440 | C | T | 0.0006261 | 0.0007058 | 0.3800004 | 0.3750742 | 0.603381 | 0.560304 | NA |
| 1 | 852037 | rs4970463 | G | A | 0.0006103 | 0.0007035 | 0.3900004 | 0.3856409 | 0.588673 | 0.345048 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51158017 | rs6010065 | G | C | -0.0001688 | 0.0006925 | 0.8100000 | 0.8074638 | 0.459676 | 0.547524 | NA |
| 22 | 51158499 | rs8136930 | T | G | -0.0002327 | 0.0006930 | 0.7400005 | 0.7370668 | 0.459892 | 0.544728 | NA |
| 22 | 51161019 | rs5770994 | C | T | -0.0002500 | 0.0006905 | 0.7199992 | 0.7173455 | 0.483487 | 0.425719 | NA |
| 22 | 51163039 | rs715584 | G | T | -0.0003667 | 0.0007009 | 0.5999997 | 0.6008625 | 0.425020 | 0.473642 | NA |
| 22 | 51164109 | rs5770995 | G | C | -0.0003757 | 0.0006975 | 0.5900000 | 0.5901263 | 0.450784 | 0.510982 | NA |
| 22 | 51164115 | rs5770996 | C | T | -0.0003610 | 0.0006971 | 0.5999997 | 0.6045942 | 0.454945 | 0.514776 | NA |
| 22 | 51174048 | rs9628245 | G | C | 0.0000537 | 0.0007902 | 0.9500000 | 0.9458253 | 0.377819 | 0.433107 | NA |
| 22 | 51186143 | rs2879914 | T | C | -0.0012556 | 0.0007447 | 0.0920005 | 0.0917697 | 0.380077 | 0.273363 | NA |
| 22 | 51186228 | rs3865766 | C | T | -0.0004617 | 0.0007253 | 0.5199996 | 0.5243940 | 0.449547 | 0.453275 | NA |
| 22 | 51197266 | rs61290853 | A | G | -0.0004686 | 0.0007475 | 0.5300002 | 0.5306815 | 0.386693 | 0.422923 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623812 ES:SE:LP:AF:ID 7.03191e-05:0.00123026:0.0222764:0.623812:rs10399793
1 54676 rs2462492 C T . PASS AF=0.399144 ES:SE:LP:AF:ID 0.00135936:0.00122668:0.568636:0.399144:rs2462492
1 91536 rs6702460 G T . PASS AF=0.455916 ES:SE:LP:AF:ID 0.000802933:0.00120653:0.29243:0.455916:rs6702460
1 706368 rs12029736 A G . PASS AF=0.513304 ES:SE:LP:AF:ID -0.000782683:0.000850514:0.443698:0.513304:rs12029736
1 840753 rs4970382 T C . PASS AF=0.400406 ES:SE:LP:AF:ID 0.000829957:0.000706298:0.619789:0.400406:rs4970382
1 843405 rs11516185 A G . PASS AF=0.362367 ES:SE:LP:AF:ID -0.000222708:0.000878755:0.09691:0.362367:rs11516185
1 850218 rs6664536 T A . PASS AF=0.589315 ES:SE:LP:AF:ID 0.000680887:0.000702469:0.481486:0.589315:rs6664536
1 850371 rs6679046 G T . PASS AF=0.603035 ES:SE:LP:AF:ID 0.000652761:0.000705829:0.443698:0.603035:rs6679046
1 850780 rs6657440 C T . PASS AF=0.603381 ES:SE:LP:AF:ID 0.000626088:0.000705842:0.420216:0.603381:rs6657440
1 852037 rs4970463 G A . PASS AF=0.588673 ES:SE:LP:AF:ID 0.000610296:0.000703471:0.408935:0.588673:rs4970463
1 852063 rs28436996 G A . PASS AF=0.588649 ES:SE:LP:AF:ID 0.000625301:0.000703154:0.431798:0.588649:rs28436996
1 852875 rs13303369 C T . PASS AF=0.606715 ES:SE:LP:AF:ID 0.000617231:0.000707003:0.420216:0.606715:rs13303369
1 853954 rs1806509 C A . PASS AF=0.60688 ES:SE:LP:AF:ID 0.000596929:0.000707076:0.39794:0.60688:rs1806509
1 854777 rs13303019 A G . PASS AF=0.609504 ES:SE:LP:AF:ID 0.000556978:0.000707783:0.366532:0.609504:rs13303019
1 854978 rs13303057 A C . PASS AF=0.602505 ES:SE:LP:AF:ID 0.000681091:0.000705952:0.481486:0.602505:rs13303057
1 855075 rs6673914 C G . PASS AF=0.609473 ES:SE:LP:AF:ID 0.000561518:0.00070769:0.366532:0.609473:rs6673914
1 856099 rs28534711 T G . PASS AF=0.390835 ES:SE:LP:AF:ID -0.000515595:0.000707977:0.327902:0.390835:rs28534711
1 856108 rs28742275 A G . PASS AF=0.390777 ES:SE:LP:AF:ID -0.000517199:0.00070805:0.327902:0.390777:rs28742275
1 856476 rs4040605 A G . PASS AF=0.352504 ES:SE:LP:AF:ID -0.000454474:0.000726793:0.275724:0.352504:rs4040605
1 866893 rs2880024 T C . PASS AF=0.608083 ES:SE:LP:AF:ID -6.59198e-05:0.000714239:0.0315171:0.608083:rs2880024
1 875770 rs4970379 A G . PASS AF=0.598464 ES:SE:LP:AF:ID -2.86668e-05:0.00071958:0.0132283:0.598464:rs4970379
1 881627 rs2272757 G A . PASS AF=0.65048 ES:SE:LP:AF:ID -0.000343163:0.000726907:0.19382:0.65048:rs2272757
1 891059 rs13303065 C T . PASS AF=0.650626 ES:SE:LP:AF:ID -0.000320079:0.000726836:0.180456:0.650626:rs13303065
1 891945 rs13303106 A G . PASS AF=0.650689 ES:SE:LP:AF:ID -0.000339782:0.000727668:0.19382:0.650689:rs13303106
1 903245 rs28690976 A G . PASS AF=0.565133 ES:SE:LP:AF:ID 6.42117e-06:0.000722566:0.00436481:0.565133:rs28690976
1 909073 rs3892467 C T . PASS AF=0.386217 ES:SE:LP:AF:ID 0.00112552:0.000721055:0.920819:0.386217:rs3892467
1 909238 rs3829740 G C . PASS AF=0.572164 ES:SE:LP:AF:ID 0.000316955:0.000697582:0.187087:0.572164:rs3829740
1 912049 rs7367995 T C . PASS AF=0.585242 ES:SE:LP:AF:ID -8.5644e-05:0.000704346:0.0457575:0.585242:rs7367995
1 913889 rs2340596 G A . PASS AF=0.600025 ES:SE:LP:AF:ID 6.25444e-05:0.000705746:0.0315171:0.600025:rs2340596
1 914333 rs13302979 C G . PASS AF=0.60338 ES:SE:LP:AF:ID -5.31785e-05:0.00070814:0.0268721:0.60338:rs13302979
1 914852 rs13303368 G C . PASS AF=0.60101 ES:SE:LP:AF:ID -1.71799e-05:0.000706837:0.00877392:0.60101:rs13303368
1 914940 rs13303033 T C . PASS AF=0.585327 ES:SE:LP:AF:ID 0.000211323:0.00070332:0.119186:0.585327:rs13303033
1 916834 rs6694632 G A . PASS AF=0.590116 ES:SE:LP:AF:ID 8.28207e-05:0.000704287:0.0409586:0.590116:rs6694632
1 918384 rs13303118 G T . PASS AF=0.585329 ES:SE:LP:AF:ID 0.000217905:0.000703057:0.119186:0.585329:rs13303118
1 918573 rs2341354 A G . PASS AF=0.590348 ES:SE:LP:AF:ID 0.000151443:0.000703792:0.0809219:0.590348:rs2341354
1 919501 rs4970414 G T . PASS AF=0.583818 ES:SE:LP:AF:ID 0.000309112:0.000729788:0.173925:0.583818:rs4970414
1 921716 rs13303278 C A . PASS AF=0.569359 ES:SE:LP:AF:ID -8.6711e-06:0.000704435:0.00436481:0.569359:rs13303278
1 924528 rs34712273 C A . PASS AF=0.579293 ES:SE:LP:AF:ID -0.000419577:0.000707161:0.259637:0.579293:rs34712273
1 930533 rs3128110 C G . PASS AF=0.384463 ES:SE:LP:AF:ID 0.000410115:0.000716669:0.244125:0.384463:rs3128110
1 930567 rs3121574 A G . PASS AF=0.384502 ES:SE:LP:AF:ID 0.000405801:0.000716696:0.244125:0.384502:rs3121574
1 930751 rs3128111 C G . PASS AF=0.383433 ES:SE:LP:AF:ID 0.000476315:0.000717338:0.29243:0.383433:rs3128111
1 931166 rs2710880 A G . PASS AF=0.385119 ES:SE:LP:AF:ID 0.000523991:0.000716876:0.337242:0.385119:rs2710880
1 931362 rs2799060 G A . PASS AF=0.383925 ES:SE:LP:AF:ID 0.00053625:0.000717382:0.346787:0.383925:rs2799060
1 933790 rs9442392 G A . PASS AF=0.579341 ES:SE:LP:AF:ID -0.000420837:0.000706511:0.259637:0.579341:rs9442392
1 936111 rs1936360 C T . PASS AF=0.574447 ES:SE:LP:AF:ID -0.000420703:0.000706558:0.259637:0.574447:rs1936360
1 940005 rs2799056 A G . PASS AF=0.396767 ES:SE:LP:AF:ID 0.000321635:0.000714694:0.187087:0.396767:rs2799056
1 940096 rs4503294 C T . PASS AF=0.56692 ES:SE:LP:AF:ID -0.00030721:0.000704424:0.180456:0.56692:rs4503294
1 941284 rs3128116 C T . PASS AF=0.39488 ES:SE:LP:AF:ID 0.000224235:0.000714655:0.124939:0.39488:rs3128116
1 941334 rs57683598 G A . PASS AF=0.394898 ES:SE:LP:AF:ID 0.000224377:0.000714653:0.124939:0.394898:rs57683598
1 941539 rs9778087 C T . PASS AF=0.397189 ES:SE:LP:AF:ID 0.000339683:0.000714763:0.200659:0.397189:rs9778087