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_41229_5LD.vcf.gz --id UKB-b:12383 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_41229_5LD.txt.gz --cohort_cases 1047 --cohort_controls 461963 --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": "2019-09-13T07:02:23.019178",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-12383/ukb-b-12383.vcf.gz; Date=Sat May 9 17:21:07 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-12383/UKB-b-12383_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-12383/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:41:54 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-12383/UKB-b-12383_data.vcf.gz ...
Read summary statistics for 1479631 SNPs.
Dropped 127 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, 382693 SNPs remain.
After merging with regression SNP LD, 382693 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0023 (0.0016)
Lambda GC: 1.051
Mean Chi^2: 1.0591
Intercept: 1.0336 (0.0132)
Ratio: 0.5686 (0.224)
Analysis finished at Thu Oct 17 14:42:17 2019
Total time elapsed: 23.06sQC metrics
Metrics
Metrics
{
"af_correlation": 0.5865,
"inflation_factor": 1.0475,
"mean_EFFECT": -2.9503e-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": 11810,
"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": 382693,
"ldsc_nsnp_merge_regression_ld": 382693,
"ldsc_observed_scale_h2_beta": 0.0023,
"ldsc_observed_scale_h2_se": 0.0016,
"ldsc_intercept_beta": 1.0336,
"ldsc_intercept_se": 0.0132,
"ldsc_lambda_gc": 1.051,
"ldsc_mean_chisq": 1.0591,
"ldsc_ratio": 0.5685
}
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 | 1479506 | 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 | 1479631 | 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 | 8.675333e+00 | 5.768775e+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 | 7.854005e+07 | 5.656346e+07 | 1.23330e+04 | 3.163416e+07 | 6.903384e+07 | 1.145088e+08 | 2.491722e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -3.000000e-06 | 1.042000e-04 | -5.47300e-04 | -7.280000e-05 | -3.000000e-06 | 6.700000e-05 | 5.002000e-04 | ▁▂▇▂▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.011000e-04 | 2.700000e-06 | 9.53000e-05 | 9.940000e-05 | 1.006000e-04 | 1.024000e-04 | 1.883000e-04 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.915880e-01 | 2.919808e-01 | 2.00000e-07 | 2.399999e-01 | 4.899999e-01 | 7.400005e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.915855e-01 | 2.919581e-01 | 2.00000e-07 | 2.362630e-01 | 4.893535e-01 | 7.448628e-01 | 9.999998e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.824294e-01 | 9.509950e-02 | 3.34289e-01 | 3.987610e-01 | 4.737230e-01 | 5.612785e-01 | 6.657110e-01 | ▇▇▆▅▅ |
| numeric | AF_reference | 11810 | 0.9920183 | NA | NA | NA | NA | NA | NA | NA | 4.607153e-01 | 1.447735e-01 | 1.99700e-04 | 3.526360e-01 | 4.548720e-01 | 5.640970e-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 | -0.0001886 | 0.0001753 | 0.2800000 | 0.2819687 | 0.623765 | 0.782149 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0000125 | 0.0001737 | 0.9400001 | 0.9424522 | 0.400401 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0001399 | 0.0001710 | 0.4100001 | 0.4131550 | 0.456846 | 0.420727 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0000387 | 0.0001213 | 0.7499995 | 0.7497958 | 0.515645 | 0.275160 | NA |
| 1 | 814495 | rs74461805 | C | A | 0.0003524 | 0.0001663 | 0.0340001 | 0.0340384 | 0.340396 | NA | NA |
| 1 | 840753 | rs4970382 | T | C | -0.0000332 | 0.0001008 | 0.7400005 | 0.7419022 | 0.400124 | 0.468850 | NA |
| 1 | 843405 | rs11516185 | A | G | 0.0002383 | 0.0001251 | 0.0569994 | 0.0566881 | 0.362606 | 0.375399 | NA |
| 1 | 850218 | rs6664536 | T | A | -0.0001482 | 0.0001005 | 0.1400000 | 0.1401873 | 0.590331 | 0.345248 | NA |
| 1 | 850371 | rs6679046 | G | T | -0.0001663 | 0.0001010 | 0.1000000 | 0.0996684 | 0.603723 | 0.508786 | NA |
| 1 | 850780 | rs6657440 | C | T | -0.0001687 | 0.0001010 | 0.0949992 | 0.0948339 | 0.603942 | 0.560304 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51158017 | rs6010065 | G | C | 0.0000353 | 0.0000990 | 0.7199992 | 0.7214867 | 0.461411 | 0.547524 | NA |
| 22 | 51158499 | rs8136930 | T | G | 0.0000326 | 0.0000991 | 0.7400005 | 0.7420958 | 0.461636 | 0.544728 | NA |
| 22 | 51161019 | rs5770994 | C | T | -0.0000805 | 0.0000988 | 0.4199997 | 0.4153261 | 0.482204 | 0.425719 | NA |
| 22 | 51163039 | rs715584 | G | T | -0.0000398 | 0.0001004 | 0.6899999 | 0.6913957 | 0.426975 | 0.473642 | NA |
| 22 | 51164109 | rs5770995 | G | C | -0.0000146 | 0.0000997 | 0.8800001 | 0.8837946 | 0.452705 | 0.510982 | NA |
| 22 | 51164115 | rs5770996 | C | T | 0.0000242 | 0.0000997 | 0.8100000 | 0.8083502 | 0.456917 | 0.514776 | NA |
| 22 | 51174048 | rs9628245 | G | C | 0.0001404 | 0.0001126 | 0.2099999 | 0.2126951 | 0.380135 | 0.433107 | NA |
| 22 | 51186143 | rs2879914 | T | C | -0.0000920 | 0.0001062 | 0.3900004 | 0.3861508 | 0.381825 | 0.273363 | NA |
| 22 | 51186228 | rs3865766 | C | T | -0.0001115 | 0.0001035 | 0.2800000 | 0.2811600 | 0.451061 | 0.453275 | NA |
| 22 | 51197266 | rs61290853 | A | G | -0.0001663 | 0.0001068 | 0.1199999 | 0.1195413 | 0.386333 | 0.422923 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623765 ES:SE:LP:AF:ID -0.000188613:0.000175306:0.552842:0.623765:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400401 ES:SE:LP:AF:ID -1.25372e-05:0.000173674:0.0268721:0.400401:rs2462492
1 91536 rs6702460 G T . PASS AF=0.456846 ES:SE:LP:AF:ID 0.000139941:0.000171003:0.387216:0.456846:rs6702460
1 706368 rs12029736 A G . PASS AF=0.515645 ES:SE:LP:AF:ID -3.86699e-05:0.000121257:0.124939:0.515645:rs12029736
1 814495 rs74461805 C A . PASS AF=0.340396 ES:SE:LP:AF:ID 0.000352418:0.000166265:1.46852:0.340396:rs74461805
1 840753 rs4970382 T C . PASS AF=0.400124 ES:SE:LP:AF:ID -3.31812e-05:0.000100752:0.130768:0.400124:rs4970382
1 843405 rs11516185 A G . PASS AF=0.362606 ES:SE:LP:AF:ID 0.000238345:0.000125069:1.24413:0.362606:rs11516185
1 850218 rs6664536 T A . PASS AF=0.590331 ES:SE:LP:AF:ID -0.000148185:0.000100458:0.853872:0.590331:rs6664536
1 850371 rs6679046 G T . PASS AF=0.603723 ES:SE:LP:AF:ID -0.000166329:0.000101022:1:0.603723:rs6679046
1 850780 rs6657440 C T . PASS AF=0.603942 ES:SE:LP:AF:ID -0.000168727:0.000101008:1.02228:0.603942:rs6657440
1 852037 rs4970463 G A . PASS AF=0.589686 ES:SE:LP:AF:ID -0.000150597:0.000100622:0.886057:0.589686:rs4970463
1 852063 rs28436996 G A . PASS AF=0.589665 ES:SE:LP:AF:ID -0.000150065:0.000100576:0.853872:0.589665:rs28436996
1 852875 rs13303369 C T . PASS AF=0.607671 ES:SE:LP:AF:ID -0.000164237:0.000101234:1:0.607671:rs13303369
1 853954 rs1806509 C A . PASS AF=0.607829 ES:SE:LP:AF:ID -0.000163742:0.000101247:0.958607:0.607829:rs1806509
1 854777 rs13303019 A G . PASS AF=0.610316 ES:SE:LP:AF:ID -0.000148101:0.000101347:0.853872:0.610316:rs13303019
1 854978 rs13303057 A C . PASS AF=0.603283 ES:SE:LP:AF:ID -0.000161274:0.000101047:0.958607:0.603283:rs13303057
1 855075 rs6673914 C G . PASS AF=0.610337 ES:SE:LP:AF:ID -0.000147319:0.000101349:0.823909:0.610337:rs6673914
1 856099 rs28534711 T G . PASS AF=0.389936 ES:SE:LP:AF:ID 0.000147276:0.000101368:0.823909:0.389936:rs28534711
1 856108 rs28742275 A G . PASS AF=0.38992 ES:SE:LP:AF:ID 0.000147388:0.000101373:0.823909:0.38992:rs28742275
1 856476 rs4040605 A G . PASS AF=0.350356 ES:SE:LP:AF:ID 0.000189823:0.000104139:1.16749:0.350356:rs4040605
1 866893 rs2880024 T C . PASS AF=0.610552 ES:SE:LP:AF:ID -0.000113747:0.000101917:0.585027:0.610552:rs2880024
1 875770 rs4970379 A G . PASS AF=0.600085 ES:SE:LP:AF:ID -0.000186092:0.000102765:1.1549:0.600085:rs4970379
1 881627 rs2272757 G A . PASS AF=0.652393 ES:SE:LP:AF:ID -0.000219338:0.00010381:1.45593:0.652393:rs2272757
1 891059 rs13303065 C T . PASS AF=0.652432 ES:SE:LP:AF:ID -0.000219588:0.000103794:1.46852:0.652432:rs13303065
1 891945 rs13303106 A G . PASS AF=0.652494 ES:SE:LP:AF:ID -0.000220636:0.000103915:1.46852:0.652494:rs13303106
1 903245 rs28690976 A G . PASS AF=0.566938 ES:SE:LP:AF:ID -0.000156318:0.00010321:0.886057:0.566938:rs28690976
1 909073 rs3892467 C T . PASS AF=0.386681 ES:SE:LP:AF:ID -0.000157726:0.00010293:0.886057:0.386681:rs3892467
1 909238 rs3829740 G C . PASS AF=0.571408 ES:SE:LP:AF:ID -0.000143613:9.96845e-05:0.823909:0.571408:rs3829740
1 912049 rs7367995 T C . PASS AF=0.585249 ES:SE:LP:AF:ID -0.000206959:0.000100695:1.39794:0.585249:rs7367995
1 913889 rs2340596 G A . PASS AF=0.59921 ES:SE:LP:AF:ID -0.000192696:0.000100859:1.25181:0.59921:rs2340596
1 914333 rs13302979 C G . PASS AF=0.602516 ES:SE:LP:AF:ID -0.000172587:0.000101164:1.05552:0.602516:rs13302979
1 914852 rs13303368 G C . PASS AF=0.600074 ES:SE:LP:AF:ID -0.000193032:0.00010097:1.25181:0.600074:rs13303368
1 914940 rs13303033 T C . PASS AF=0.584289 ES:SE:LP:AF:ID -0.000192374:0.000100404:1.25964:0.584289:rs13303033
1 916834 rs6694632 G A . PASS AF=0.589102 ES:SE:LP:AF:ID -0.0001672:0.000100557:1.01773:0.589102:rs6694632
1 918384 rs13303118 G T . PASS AF=0.584202 ES:SE:LP:AF:ID -0.000188068:0.000100357:1.21467:0.584202:rs13303118
1 918573 rs2341354 A G . PASS AF=0.589327 ES:SE:LP:AF:ID -0.000170506:0.00010048:1.04576:0.589327:rs2341354
1 919501 rs4970414 G T . PASS AF=0.583926 ES:SE:LP:AF:ID -0.000201309:0.000103913:1.27572:0.583926:rs4970414
1 921716 rs13303278 C A . PASS AF=0.567888 ES:SE:LP:AF:ID -8.41766e-05:0.000100273:0.39794:0.567888:rs13303278
1 924528 rs34712273 C A . PASS AF=0.578491 ES:SE:LP:AF:ID -0.000103538:0.00010056:0.522879:0.578491:rs34712273
1 930533 rs3128110 C G . PASS AF=0.386082 ES:SE:LP:AF:ID 8.054e-05:0.000101845:0.366532:0.386082:rs3128110
1 930567 rs3121574 A G . PASS AF=0.386124 ES:SE:LP:AF:ID 8.4316e-05:0.000101847:0.387216:0.386124:rs3121574
1 930751 rs3128111 C G . PASS AF=0.385079 ES:SE:LP:AF:ID 7.38337e-05:0.000101909:0.327902:0.385079:rs3128111
1 931166 rs2710880 A G . PASS AF=0.386765 ES:SE:LP:AF:ID 9.33093e-05:0.000101864:0.443698:0.386765:rs2710880
1 931362 rs2799060 G A . PASS AF=0.385571 ES:SE:LP:AF:ID 8.23585e-05:0.000101913:0.376751:0.385571:rs2799060
1 933790 rs9442392 G A . PASS AF=0.578604 ES:SE:LP:AF:ID -7.73816e-05:0.000100526:0.356547:0.578604:rs9442392
1 936111 rs1936360 C T . PASS AF=0.573545 ES:SE:LP:AF:ID -8.75813e-05:0.000100502:0.420216:0.573545:rs1936360
1 940005 rs2799056 A G . PASS AF=0.399264 ES:SE:LP:AF:ID 0.000114803:0.000101492:0.585027:0.399264:rs2799056
1 940096 rs4503294 C T . PASS AF=0.565308 ES:SE:LP:AF:ID -0.000109986:0.000100215:0.568636:0.565308:rs4503294
1 941284 rs3128116 C T . PASS AF=0.397398 ES:SE:LP:AF:ID 9.46813e-05:0.000101514:0.455932:0.397398:rs3128116
1 941334 rs57683598 G A . PASS AF=0.397402 ES:SE:LP:AF:ID 9.60265e-05:0.00010152:0.468521:0.397402:rs57683598