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_41246_1460.vcf.gz --id UKB-b:3444 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_41246_1460.txt.gz --cohort_cases 1558 --cohort_controls 442497 --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": "2019-09-13T13:18:08.255570",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-3444/ukb-b-3444.vcf.gz; Date=Sat May 9 19:52:55 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-3444/UKB-b-3444_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-3444/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:43:41 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-3444/UKB-b-3444_data.vcf.gz ...
Read summary statistics for 2596562 SNPs.
Dropped 305 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, 655093 SNPs remain.
After merging with regression SNP LD, 655093 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: -0.0009 (0.0013)
Lambda GC: 1.0223
Mean Chi^2: 1.0181
Intercept: 1.0273 (0.0103)
Ratio: 1.5109 (0.5696)
Analysis finished at Thu Oct 17 14:44:08 2019
Total time elapsed: 26.98sQC metrics
Metrics
Metrics
{
"af_correlation": 0.7795,
"inflation_factor": 1,
"mean_EFFECT": -2.6011e-07,
"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": 20579,
"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": 655093,
"ldsc_nsnp_merge_regression_ld": 655093,
"ldsc_observed_scale_h2_beta": "NA",
"ldsc_observed_scale_h2_se": "NA",
"ldsc_intercept_beta": 1.0273,
"ldsc_intercept_se": 0.0103,
"ldsc_lambda_gc": 1.0223,
"ldsc_mean_chisq": 1.0181,
"ldsc_ratio": 1.5083
}
Flags
| name | value |
|---|---|
| af_correlation | FALSE |
| 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 | 2596260 | 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 | 2596562 | 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 | 8.663194e+00 | 5.768918e+00 | 1.0000000 | 4.000000e+00 | 8.000000e+00 | 1.300000e+01 | 2.200000e+01 | ▇▅▃▂▂ |
| numeric | POS | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 7.853810e+07 | 5.661712e+07 | 5687.0000000 | 3.172572e+07 | 6.891138e+07 | 1.147085e+08 | 2.491917e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -3.000000e-07 | 1.353000e-04 | -0.0006411 | -9.090000e-05 | -2.000000e-07 | 9.100000e-05 | 6.908000e-04 | ▁▂▇▂▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.340000e-04 | 7.800000e-06 | 0.0001211 | 1.274000e-04 | 1.318000e-04 | 1.395000e-04 | 2.626000e-04 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.975985e-01 | 2.897675e-01 | 0.0000048 | 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 | 4.975998e-01 | 2.897425e-01 | 0.0000048 | 2.468406e-01 | 4.964758e-01 | 7.481253e-01 | 9.999991e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.497418e-01 | 1.563404e-01 | 0.2246470 | 3.122300e-01 | 4.258890e-01 | 5.746140e-01 | 7.753530e-01 | ▇▆▅▃▃ |
| numeric | AF_reference | 20579 | 0.9920745 | NA | NA | NA | NA | NA | NA | NA | 4.317495e-01 | 1.794828e-01 | 0.0001997 | 2.897360e-01 | 4.147360e-01 | 5.632990e-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 | 0.0000752 | 0.0002228 | 0.7400005 | 0.7358406 | 0.623754 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0002108 | 0.0002207 | 0.3400001 | 0.3394788 | 0.400423 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0001525 | 0.0002173 | 0.4799997 | 0.4828884 | 0.456858 | 0.4207270 | NA |
| 1 | 534192 | rs6680723 | C | T | 0.0004813 | 0.0002483 | 0.0530005 | 0.0525468 | 0.240915 | NA | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0000216 | 0.0001541 | 0.8900000 | 0.8885924 | 0.515717 | 0.2751600 | NA |
| 1 | 763394 | rs369924889 | G | A | -0.0003310 | 0.0001807 | 0.0669993 | 0.0669325 | 0.706659 | 0.6176120 | NA |
| 1 | 768253 | rs2977608 | A | C | 0.0000544 | 0.0001475 | 0.7099994 | 0.7122601 | 0.761332 | 0.4894170 | NA |
| 1 | 776546 | rs12124819 | A | G | 0.0002822 | 0.0001648 | 0.0870001 | 0.0868057 | 0.265297 | 0.0756789 | NA |
| 1 | 808631 | rs11240779 | G | A | 0.0002280 | 0.0001498 | 0.1299999 | 0.1279534 | 0.772641 | 0.4534740 | NA |
| 1 | 808928 | rs11240780 | C | T | 0.0002177 | 0.0001500 | 0.1499999 | 0.1467629 | 0.772871 | 0.4522760 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51180501 | rs5770999 | T | C | -1.50e-06 | 0.0001451 | 0.9900000 | 0.9917045 | 0.713706 | 0.6369810 | NA |
| 22 | 51181919 | rs9616825 | G | C | 4.00e-06 | 0.0001443 | 0.9800000 | 0.9779598 | 0.695493 | 0.6194090 | NA |
| 22 | 51182485 | rs6009961 | A | G | -3.10e-06 | 0.0001455 | 0.9800000 | 0.9827657 | 0.715541 | 0.6383790 | NA |
| 22 | 51186143 | rs2879914 | T | C | 9.15e-05 | 0.0001349 | 0.5000000 | 0.4978427 | 0.382029 | 0.2733630 | NA |
| 22 | 51186228 | rs3865766 | C | T | -1.00e-05 | 0.0001315 | 0.9400001 | 0.9392958 | 0.451296 | 0.4532750 | NA |
| 22 | 51197266 | rs61290853 | A | G | -6.95e-05 | 0.0001358 | 0.6100002 | 0.6087984 | 0.386528 | 0.4229230 | NA |
| 22 | 51198027 | rs34939255 | A | G | 1.80e-06 | 0.0001537 | 0.9900000 | 0.9906171 | 0.254378 | 0.0984425 | NA |
| 22 | 51211106 | rs9628250 | T | C | 5.88e-05 | 0.0001524 | 0.6999999 | 0.6997885 | 0.271369 | 0.1671330 | NA |
| 22 | 51212875 | rs2238837 | A | C | 1.44e-05 | 0.0001448 | 0.9199999 | 0.9209470 | 0.331620 | 0.3724040 | NA |
| 22 | 51237063 | rs3896457 | T | C | 5.26e-05 | 0.0001482 | 0.7199992 | 0.7225550 | 0.298162 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623754 ES:SE:LP:AF:ID 7.51619e-05:0.00022279:0.130768:0.623754:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400423 ES:SE:LP:AF:ID 0.000210847:0.000220737:0.468521:0.400423:rs2462492
1 91536 rs6702460 G T . PASS AF=0.456858 ES:SE:LP:AF:ID -0.000152471:0.000217299:0.318759:0.456858:rs6702460
1 534192 rs6680723 C T . PASS AF=0.240915 ES:SE:LP:AF:ID 0.000481301:0.000248269:1.27572:0.240915:rs6680723
1 706368 rs12029736 A G . PASS AF=0.515717 ES:SE:LP:AF:ID -2.15918e-05:0.000154133:0.05061:0.515717:rs12029736
1 763394 rs3115847 G A . PASS AF=0.706659 ES:SE:LP:AF:ID -0.000331047:0.00018069:1.17393:0.706659:rs3115847
1 768253 rs2977608 A C . PASS AF=0.761332 ES:SE:LP:AF:ID 5.44168e-05:0.000147542:0.148742:0.761332:rs2977608
1 776546 rs12124819 A G . PASS AF=0.265297 ES:SE:LP:AF:ID 0.000282171:0.000164772:1.06048:0.265297:rs12124819
1 808631 rs11240779 G A . PASS AF=0.772641 ES:SE:LP:AF:ID 0.000228:0.000149781:0.886057:0.772641:rs11240779
1 808928 rs11240780 C T . PASS AF=0.772871 ES:SE:LP:AF:ID 0.000217707:0.000150033:0.823909:0.772871:rs11240780
1 814495 rs74461805 C A . PASS AF=0.340404 ES:SE:LP:AF:ID 4.66232e-05:0.000211369:0.0809219:0.340404:rs74461805
1 830181 rs28444699 A G . PASS AF=0.69724 ES:SE:LP:AF:ID 4.32461e-06:0.00014137:0.00877392:0.69724:rs28444699
1 831489 rs4970385 C T . PASS AF=0.705407 ES:SE:LP:AF:ID 1.67793e-05:0.000138809:0.0457575:0.705407:rs4970385
1 831909 rs9697642 C T . PASS AF=0.705451 ES:SE:LP:AF:ID 1.73647e-05:0.000138804:0.0457575:0.705451:rs9697642
1 832066 rs9697380 G C . PASS AF=0.705637 ES:SE:LP:AF:ID 1.49314e-05:0.000138809:0.0409586:0.705637:rs9697380
1 832318 rs4500250 C A . PASS AF=0.705664 ES:SE:LP:AF:ID 1.4247e-05:0.000138824:0.0362122:0.705664:rs4500250
1 832398 rs4553118 T C . PASS AF=0.73011 ES:SE:LP:AF:ID -2.05246e-05:0.000142614:0.05061:0.73011:rs4553118
1 832918 rs28765502 T C . PASS AF=0.294369 ES:SE:LP:AF:ID -1.57369e-05:0.000138817:0.0409586:0.294369:rs28765502
1 833223 rs13303211 C T . PASS AF=0.236694 ES:SE:LP:AF:ID -6.72943e-06:0.000147829:0.0177288:0.236694:rs13303211
1 833302 rs28752186 C T . PASS AF=0.236683 ES:SE:LP:AF:ID -6.62139e-06:0.000147828:0.0177288:0.236683:rs28752186
1 833641 rs28594623 T C . PASS AF=0.239752 ES:SE:LP:AF:ID -3.54725e-05:0.000147353:0.091515:0.239752:rs28594623
1 833824 rs28484835 T C . PASS AF=0.236686 ES:SE:LP:AF:ID -8.96771e-06:0.000147827:0.0222764:0.236686:rs28484835
1 834832 rs4411087 G C . PASS AF=0.237171 ES:SE:LP:AF:ID -8.01367e-06:0.000147715:0.0177288:0.237171:rs4411087
1 835499 rs4422948 A G . PASS AF=0.24115 ES:SE:LP:AF:ID 7.08536e-05:0.000146677:0.200659:0.24115:rs4422948
1 836896 rs28705752 T C . PASS AF=0.269506 ES:SE:LP:AF:ID 1.83282e-05:0.000141532:0.0457575:0.269506:rs28705752
1 838555 rs4970383 C A . PASS AF=0.24624 ES:SE:LP:AF:ID 7.34112e-05:0.000145674:0.21467:0.24624:rs4970383
1 839103 rs28562941 A G . PASS AF=0.270009 ES:SE:LP:AF:ID 7.33394e-06:0.000141629:0.0177288:0.270009:rs28562941
1 840753 rs4970382 T C . PASS AF=0.40027 ES:SE:LP:AF:ID -1.79294e-05:0.000128054:0.05061:0.40027:rs4970382
1 841085 rs1574243 C G . PASS AF=0.237175 ES:SE:LP:AF:ID 4.95712e-05:0.000148731:0.130768:0.237175:rs1574243
1 842362 rs28540380 C T . PASS AF=0.235344 ES:SE:LP:AF:ID 0.000103806:0.000151007:0.309804:0.235344:rs28540380
1 843405 rs11516185 A G . PASS AF=0.362626 ES:SE:LP:AF:ID -4.03076e-05:0.000158964:0.09691:0.362626:rs11516185
1 850218 rs6664536 T A . PASS AF=0.590345 ES:SE:LP:AF:ID 1.68219e-05:0.000127693:0.0457575:0.590345:rs6664536
1 850371 rs6679046 G T . PASS AF=0.60371 ES:SE:LP:AF:ID 5.21895e-05:0.000128415:0.167491:0.60371:rs6679046
1 850780 rs6657440 C T . PASS AF=0.603932 ES:SE:LP:AF:ID 5.62255e-05:0.000128397:0.180456:0.603932:rs6657440
1 852037 rs4970463 G A . PASS AF=0.589707 ES:SE:LP:AF:ID 1.57921e-05:0.0001279:0.0457575:0.589707:rs4970463
1 852063 rs28436996 G A . PASS AF=0.589692 ES:SE:LP:AF:ID 1.25593e-05:0.000127844:0.0362122:0.589692:rs28436996
1 852875 rs13303369 C T . PASS AF=0.607682 ES:SE:LP:AF:ID 3.16494e-05:0.000128689:0.091515:0.607682:rs13303369
1 853954 rs1806509 C A . PASS AF=0.607841 ES:SE:LP:AF:ID 2.9747e-05:0.000128706:0.0861861:0.607841:rs1806509
1 854777 rs13303019 A G . PASS AF=0.610325 ES:SE:LP:AF:ID 4.4961e-05:0.000128833:0.136677:0.610325:rs13303019
1 854978 rs13303057 A C . PASS AF=0.603278 ES:SE:LP:AF:ID 4.77209e-05:0.000128446:0.148742:0.603278:rs13303057
1 855075 rs6673914 C G . PASS AF=0.610343 ES:SE:LP:AF:ID 4.70893e-05:0.000128836:0.148742:0.610343:rs6673914
1 856099 rs28534711 T G . PASS AF=0.389931 ES:SE:LP:AF:ID -4.26314e-05:0.000128861:0.130768:0.389931:rs28534711
1 856108 rs28742275 A G . PASS AF=0.389915 ES:SE:LP:AF:ID -4.28256e-05:0.000128868:0.130768:0.389915:rs28742275
1 856476 rs4040605 A G . PASS AF=0.35033 ES:SE:LP:AF:ID -1.12092e-05:0.000132367:0.0315171:0.35033:rs4040605
1 858801 rs7418179 A G . PASS AF=0.765924 ES:SE:LP:AF:ID -0.000152891:0.000148627:0.522879:0.765924:rs7418179
1 860416 rs61464428 G A . PASS AF=0.766738 ES:SE:LP:AF:ID -0.000131964:0.000148702:0.431798:0.766738:rs61464428
1 860688 rs60837925 G A . PASS AF=0.766195 ES:SE:LP:AF:ID -0.00013626:0.000148599:0.443698:0.766195:rs60837925
1 861630 rs2879816 G A . PASS AF=0.766348 ES:SE:LP:AF:ID -0.000143212:0.000148635:0.468521:0.766348:rs2879816
1 862866 rs3892970 C T . PASS AF=0.763216 ES:SE:LP:AF:ID -0.000129667:0.000148499:0.420216:0.763216:rs3892970
1 864938 rs2340587 G A . PASS AF=0.760106 ES:SE:LP:AF:ID -9.95515e-05:0.000147399:0.30103:0.760106:rs2340587