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_20003_1141182800.vcf.gz --id UKB-b:16117 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20003_1141182800.txt.gz --cohort_cases 1143 --cohort_controls 461790 --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": "2019-09-13T02:17:38.683832",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-16117/ukb-b-16117.vcf.gz; Date=Sat May 9 19:34:39 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-16117/UKB-b-16117_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-16117/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:44:47 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-16117/UKB-b-16117_data.vcf.gz ...
Read summary statistics for 1751699 SNPs.
Dropped 167 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, 451294 SNPs remain.
After merging with regression SNP LD, 451294 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: -0.0006 (0.0012)
Lambda GC: 1.0123
Mean Chi^2: 1.0107
Intercept: 1.017 (0.0098)
Ratio: 1.5905 (0.9218)
Analysis finished at Thu Oct 17 14:45:15 2019
Total time elapsed: 27.78sQC metrics
Metrics
Metrics
{
"af_correlation": 0.6483,
"inflation_factor": 1,
"mean_EFFECT": -1.0018e-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": 13849,
"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": 451294,
"ldsc_nsnp_merge_regression_ld": 451294,
"ldsc_observed_scale_h2_beta": "NA",
"ldsc_observed_scale_h2_se": "NA",
"ldsc_intercept_beta": 1.017,
"ldsc_intercept_se": 0.0098,
"ldsc_lambda_gc": 1.0123,
"ldsc_mean_chisq": 1.0107,
"ldsc_ratio": 1.5888
}
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 | 1.000000 | 4 | 58 | 0 | 1751534 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| character | REF | 0 | 1.000000 | 1 | 1 | 0 | 4 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| character | ALT | 0 | 1.000000 | 1 | 1 | 0 | 4 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| logical | N | 1751699 | 0.000000 | NA | NA | NA | NA | NA | NaN | : | NA | NA | NA | NA | NA | NA | NA | NA |
| numeric | CHROM | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 8.657450e+00 | 5.766095e+00 | 1.00000e+00 | 4.000000e+00 | 8.000000e+00 | 1.300000e+01 | 2.200000e+01 | ▇▅▅▂▂ |
| numeric | POS | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 7.868006e+07 | 5.662554e+07 | 1.23330e+04 | 3.177972e+07 | 6.929472e+07 | 1.147868e+08 | 2.491722e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | -1.000000e-06 | 1.068000e-04 | -5.67400e-04 | -7.260000e-05 | -1.200000e-06 | 7.120000e-05 | 5.504000e-04 | ▁▂▇▂▁ |
| numeric | SE | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 1.064000e-04 | 3.400000e-06 | 9.94000e-05 | 1.039000e-04 | 1.056000e-04 | 1.083000e-04 | 2.020000e-04 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 4.990542e-01 | 2.889863e-01 | 7.10000e-06 | 2.500000e-01 | 5.000000e-01 | 7.499995e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 4.990526e-01 | 2.889581e-01 | 7.10000e-06 | 2.483662e-01 | 4.991525e-01 | 7.492747e-01 | 9.999990e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 4.762714e-01 | 1.112254e-01 | 3.06212e-01 | 3.785750e-01 | 4.642000e-01 | 5.679890e-01 | 6.937870e-01 | ▇▇▆▅▅ |
| numeric | AF_reference | 13849 | 0.992094 | NA | NA | NA | NA | NA | NA | NA | 4.552105e-01 | 1.527358e-01 | 1.99700e-04 | 3.392570e-01 | 4.476840e-01 | 5.652960e-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 | -0.0000756 | 0.0001830 | 0.6800001 | 0.6793209 | 0.623763 | 0.782149 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0001898 | 0.0001813 | 0.2999998 | 0.2951572 | 0.400401 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0000536 | 0.0001785 | 0.7600007 | 0.7639674 | 0.456851 | 0.420727 | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0000703 | 0.0001266 | 0.5800000 | 0.5785939 | 0.515650 | 0.275160 | NA |
| 1 | 814495 | rs74461805 | C | A | 0.0001822 | 0.0001736 | 0.2900000 | 0.2939382 | 0.340397 | NA | NA |
| 1 | 840753 | rs4970382 | T | C | -0.0000165 | 0.0001052 | 0.8800001 | 0.8754430 | 0.400106 | 0.468850 | NA |
| 1 | 843405 | rs11516185 | A | G | -0.0001158 | 0.0001306 | 0.3800004 | 0.3751488 | 0.362599 | 0.375399 | NA |
| 1 | 850218 | rs6664536 | T | A | -0.0000008 | 0.0001049 | 0.9900000 | 0.9939678 | 0.590333 | 0.345248 | NA |
| 1 | 850371 | rs6679046 | G | T | 0.0000345 | 0.0001055 | 0.7400005 | 0.7438989 | 0.603726 | 0.508786 | NA |
| 1 | 850780 | rs6657440 | C | T | 0.0000190 | 0.0001054 | 0.8600001 | 0.8566475 | 0.603944 | 0.560304 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51161019 | rs5770994 | C | T | -0.0002913 | 0.0001031 | 0.0047000 | 0.0047425 | 0.482193 | 0.425719 | NA |
| 22 | 51163039 | rs715584 | G | T | 0.0002586 | 0.0001048 | 0.0140001 | 0.0135607 | 0.426984 | 0.473642 | NA |
| 22 | 51164109 | rs5770995 | G | C | 0.0002404 | 0.0001041 | 0.0210000 | 0.0209561 | 0.452716 | 0.510982 | NA |
| 22 | 51164115 | rs5770996 | C | T | 0.0002445 | 0.0001041 | 0.0189998 | 0.0187803 | 0.456928 | 0.514776 | NA |
| 22 | 51164287 | rs6009957 | T | C | 0.0001541 | 0.0001119 | 0.1700000 | 0.1684652 | 0.306554 | 0.415535 | NA |
| 22 | 51174048 | rs9628245 | G | C | 0.0002639 | 0.0001176 | 0.0250000 | 0.0248056 | 0.380130 | 0.433107 | NA |
| 22 | 51186143 | rs2879914 | T | C | 0.0002561 | 0.0001108 | 0.0210000 | 0.0208155 | 0.381826 | 0.273363 | NA |
| 22 | 51186228 | rs3865766 | C | T | 0.0003081 | 0.0001080 | 0.0043000 | 0.0043375 | 0.451063 | 0.453275 | NA |
| 22 | 51197266 | rs61290853 | A | G | 0.0002688 | 0.0001115 | 0.0160000 | 0.0159262 | 0.386333 | 0.422923 | NA |
| 22 | 51212875 | rs2238837 | A | C | 0.0002220 | 0.0001189 | 0.0619998 | 0.0618984 | 0.331455 | 0.372404 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623763 ES:SE:LP:AF:ID -7.56479e-05:0.000182994:0.167491:0.623763:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400401 ES:SE:LP:AF:ID 0.000189788:0.00018129:0.522879:0.400401:rs2462492
1 91536 rs6702460 G T . PASS AF=0.456851 ES:SE:LP:AF:ID -5.36006e-05:0.000178505:0.119186:0.456851:rs6702460
1 706368 rs12029736 A G . PASS AF=0.51565 ES:SE:LP:AF:ID 7.03059e-05:0.000126577:0.236572:0.51565:rs12029736
1 814495 rs74461805 C A . PASS AF=0.340397 ES:SE:LP:AF:ID 0.000182156:0.000173561:0.537602:0.340397:rs74461805
1 840753 rs4970382 T C . PASS AF=0.400106 ES:SE:LP:AF:ID -1.64854e-05:0.000105171:0.0555173:0.400106:rs4970382
1 843405 rs11516185 A G . PASS AF=0.362599 ES:SE:LP:AF:ID -0.000115788:0.000130558:0.420216:0.362599:rs11516185
1 850218 rs6664536 T A . PASS AF=0.590333 ES:SE:LP:AF:ID -7.92792e-07:0.000104863:0.00436481:0.590333:rs6664536
1 850371 rs6679046 G T . PASS AF=0.603726 ES:SE:LP:AF:ID 3.44506e-05:0.000105452:0.130768:0.603726:rs6679046
1 850780 rs6657440 C T . PASS AF=0.603944 ES:SE:LP:AF:ID 1.90465e-05:0.000105437:0.0655015:0.603944:rs6657440
1 852037 rs4970463 G A . PASS AF=0.589688 ES:SE:LP:AF:ID 2.68762e-06:0.000105033:0.00877392:0.589688:rs4970463
1 852063 rs28436996 G A . PASS AF=0.589667 ES:SE:LP:AF:ID -1.47298e-06:0.000104986:0.00436481:0.589667:rs28436996
1 852875 rs13303369 C T . PASS AF=0.607674 ES:SE:LP:AF:ID 3.35277e-05:0.000105672:0.124939:0.607674:rs13303369
1 853954 rs1806509 C A . PASS AF=0.607833 ES:SE:LP:AF:ID 3.23229e-05:0.000105687:0.119186:0.607833:rs1806509
1 854777 rs13303019 A G . PASS AF=0.610318 ES:SE:LP:AF:ID 2.08294e-05:0.000105791:0.0757207:0.610318:rs13303019
1 854978 rs13303057 A C . PASS AF=0.603286 ES:SE:LP:AF:ID 3.27673e-05:0.000105477:0.119186:0.603286:rs13303057
1 855075 rs6673914 C G . PASS AF=0.610338 ES:SE:LP:AF:ID 2.16958e-05:0.000105793:0.0757207:0.610338:rs6673914
1 856099 rs28534711 T G . PASS AF=0.389935 ES:SE:LP:AF:ID -2.23359e-05:0.000105813:0.0809219:0.389935:rs28534711
1 856108 rs28742275 A G . PASS AF=0.389918 ES:SE:LP:AF:ID -2.21699e-05:0.000105818:0.0809219:0.389918:rs28742275
1 856476 rs4040605 A G . PASS AF=0.350351 ES:SE:LP:AF:ID -6.60213e-05:0.000108705:0.267606:0.350351:rs4040605
1 866893 rs2880024 T C . PASS AF=0.610554 ES:SE:LP:AF:ID -3.25035e-05:0.000106387:0.119186:0.610554:rs2880024
1 875770 rs4970379 A G . PASS AF=0.600084 ES:SE:LP:AF:ID -5.18322e-05:0.000107271:0.200659:0.600084:rs4970379
1 881627 rs2272757 G A . PASS AF=0.65239 ES:SE:LP:AF:ID -4.071e-05:0.000108362:0.148742:0.65239:rs2272757
1 891059 rs13303065 C T . PASS AF=0.652428 ES:SE:LP:AF:ID -4.56179e-05:0.000108345:0.173925:0.652428:rs13303065
1 891945 rs13303106 A G . PASS AF=0.65249 ES:SE:LP:AF:ID -4.58341e-05:0.000108471:0.173925:0.65249:rs13303106
1 903245 rs28690976 A G . PASS AF=0.566937 ES:SE:LP:AF:ID -5.45333e-05:0.000107736:0.21467:0.566937:rs28690976
1 909073 rs3892467 C T . PASS AF=0.386683 ES:SE:LP:AF:ID 1.68696e-06:0.000107443:0.00436481:0.386683:rs3892467
1 909238 rs3829740 G C . PASS AF=0.571412 ES:SE:LP:AF:ID -4.33628e-05:0.000104055:0.167491:0.571412:rs3829740
1 910394 rs28477686 C T . PASS AF=0.324456 ES:SE:LP:AF:ID 5.29125e-06:0.000112785:0.0177288:0.324456:rs28477686
1 912049 rs7367995 T C . PASS AF=0.58525 ES:SE:LP:AF:ID -2.97354e-05:0.00010511:0.107905:0.58525:rs7367995
1 913889 rs2340596 G A . PASS AF=0.599213 ES:SE:LP:AF:ID -8.18723e-05:0.000105281:0.356547:0.599213:rs2340596
1 914333 rs13302979 C G . PASS AF=0.602521 ES:SE:LP:AF:ID -9.16329e-05:0.000105601:0.408935:0.602521:rs13302979
1 914852 rs13303368 G C . PASS AF=0.600078 ES:SE:LP:AF:ID -7.94704e-05:0.000105398:0.346787:0.600078:rs13303368
1 914940 rs13303033 T C . PASS AF=0.584292 ES:SE:LP:AF:ID -5.61992e-05:0.000104806:0.229148:0.584292:rs13303033
1 916834 rs6694632 G A . PASS AF=0.589106 ES:SE:LP:AF:ID -6.08008e-05:0.000104966:0.251812:0.589106:rs6694632
1 918384 rs13303118 G T . PASS AF=0.584205 ES:SE:LP:AF:ID -5.49829e-05:0.000104757:0.221849:0.584205:rs13303118
1 918573 rs2341354 A G . PASS AF=0.589331 ES:SE:LP:AF:ID -5.29307e-05:0.000104886:0.21467:0.589331:rs2341354
1 919501 rs4970414 G T . PASS AF=0.583932 ES:SE:LP:AF:ID -6.64369e-06:0.00010847:0.0222764:0.583932:rs4970414
1 921716 rs13303278 C A . PASS AF=0.567888 ES:SE:LP:AF:ID -7.53759e-05:0.00010467:0.327902:0.567888:rs13303278
1 924528 rs34712273 C A . PASS AF=0.578492 ES:SE:LP:AF:ID -7.53095e-05:0.000104971:0.327902:0.578492:rs34712273
1 930533 rs3128110 C G . PASS AF=0.386078 ES:SE:LP:AF:ID 3.61858e-05:0.000106312:0.136677:0.386078:rs3128110
1 930567 rs3121574 A G . PASS AF=0.38612 ES:SE:LP:AF:ID 3.55822e-05:0.000106315:0.130768:0.38612:rs3121574
1 930751 rs3128111 C G . PASS AF=0.385075 ES:SE:LP:AF:ID 3.72082e-05:0.000106379:0.136677:0.385075:rs3128111
1 931166 rs2710880 A G . PASS AF=0.386762 ES:SE:LP:AF:ID 3.74168e-05:0.000106332:0.142668:0.386762:rs2710880
1 931362 rs2799060 G A . PASS AF=0.385568 ES:SE:LP:AF:ID 3.72905e-05:0.000106384:0.136677:0.385568:rs2799060
1 933790 rs9442392 G A . PASS AF=0.578607 ES:SE:LP:AF:ID -7.47474e-05:0.000104935:0.318759:0.578607:rs9442392
1 936111 rs1936360 C T . PASS AF=0.573546 ES:SE:LP:AF:ID -8.34676e-05:0.00010491:0.366532:0.573546:rs1936360
1 940005 rs2799056 A G . PASS AF=0.399259 ES:SE:LP:AF:ID 8.94103e-05:0.000105944:0.39794:0.399259:rs2799056
1 940096 rs4503294 C T . PASS AF=0.565311 ES:SE:LP:AF:ID -0.000126806:0.00010461:0.638272:0.565311:rs4503294
1 941284 rs3128116 C T . PASS AF=0.397393 ES:SE:LP:AF:ID 7.73977e-05:0.000105966:0.327902:0.397393:rs3128116