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_2600.vcf.gz --id UKB-b:3255 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_41246_2600.txt.gz --cohort_cases 1508 --cohort_controls 442481 --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": "2019-09-13T13:22:25.611483",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-3255/ukb-b-3255.vcf.gz; Date=Sun May 10 06:31:15 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-3255/UKB-b-3255_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-3255/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:43:23 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-3255/UKB-b-3255_data.vcf.gz ...
Read summary statistics for 2511781 SNPs.
Dropped 296 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, 635593 SNPs remain.
After merging with regression SNP LD, 635593 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: -0.0005 (0.0015)
Lambda GC: 1.0193
Mean Chi^2: 1.0129
Intercept: 1.018 (0.0105)
Ratio: 1.3976 (0.8132)
Analysis finished at Thu Oct 17 14:44:00 2019
Total time elapsed: 36.6sQC metrics
Metrics
Metrics
{
"af_correlation": 0.7699,
"inflation_factor": 1,
"mean_EFFECT": 4.0299e-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": 19930,
"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": 635593,
"ldsc_nsnp_merge_regression_ld": 635593,
"ldsc_observed_scale_h2_beta": "NA",
"ldsc_observed_scale_h2_se": "NA",
"ldsc_intercept_beta": 1.018,
"ldsc_intercept_se": 0.0105,
"ldsc_lambda_gc": 1.0193,
"ldsc_mean_chisq": 1.0129,
"ldsc_ratio": 1.3953
}
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 | 2511488 | 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 | 2511781 | 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 | 8.654308e+00 | 5.765640e+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 | 7.855329e+07 | 5.661838e+07 | 5687.0000000 | 3.168783e+07 | 6.897984e+07 | 1.147579e+08 | 2.491917e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.000000e-07 | 1.325000e-04 | -0.0006556 | -8.890000e-05 | -1.000000e-07 | 8.950000e-05 | 6.994000e-04 | ▁▂▇▂▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.314000e-04 | 7.200000e-06 | 0.0001191 | 1.253000e-04 | 1.293000e-04 | 1.364000e-04 | 2.579000e-04 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.980128e-01 | 2.891546e-01 | 0.0000025 | 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 | 4.980144e-01 | 2.891284e-01 | 0.0000025 | 2.469583e-01 | 4.968669e-01 | 7.488151e-01 | 9.999988e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.529373e-01 | 1.522214e-01 | 0.2320960 | 3.190370e-01 | 4.303630e-01 | 5.749940e-01 | 7.679040e-01 | ▇▆▅▅▃ |
| numeric | AF_reference | 19930 | 0.9920654 | NA | NA | NA | NA | NA | NA | NA | 4.345876e-01 | 1.768264e-01 | 0.0001997 | 2.949280e-01 | 4.187300e-01 | 5.638980e-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 | -0.0000441 | 0.0002192 | 0.8400000 | 0.8407411 | 0.623741 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0000621 | 0.0002172 | 0.7700005 | 0.7748924 | 0.400425 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0001840 | 0.0002138 | 0.3900004 | 0.3894535 | 0.456855 | 0.4207270 | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0001337 | 0.0002443 | 0.5800000 | 0.5841797 | 0.240916 | NA | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0000863 | 0.0001517 | 0.5700002 | 0.5692873 | 0.515715 | 0.2751600 | NA |
| 1 | 763394 | rs369924889 | G | A | -0.0003047 | 0.0001778 | 0.0870001 | 0.0865906 | 0.706658 | 0.6176120 | NA |
| 1 | 768253 | rs2977608 | A | C | -0.0000925 | 0.0001452 | 0.5199996 | 0.5238853 | 0.761327 | 0.4894170 | NA |
| 1 | 776546 | rs12124819 | A | G | 0.0000821 | 0.0001621 | 0.6100002 | 0.6127731 | 0.265285 | 0.0756789 | NA |
| 1 | 814495 | rs74461805 | C | A | -0.0000949 | 0.0002080 | 0.6499995 | 0.6480407 | 0.340407 | NA | NA |
| 1 | 830181 | rs28444699 | A | G | 0.0000497 | 0.0001391 | 0.7199992 | 0.7211291 | 0.697226 | 0.6912940 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51180501 | rs5770999 | T | C | 0.0000919 | 0.0001427 | 0.5199996 | 0.5198048 | 0.713710 | 0.6369810 | NA |
| 22 | 51181919 | rs9616825 | G | C | 0.0001110 | 0.0001420 | 0.4299995 | 0.4345280 | 0.695496 | 0.6194090 | NA |
| 22 | 51182485 | rs6009961 | A | G | 0.0000739 | 0.0001432 | 0.6100002 | 0.6059448 | 0.715544 | 0.6383790 | NA |
| 22 | 51186143 | rs2879914 | T | C | 0.0000273 | 0.0001328 | 0.8400000 | 0.8370157 | 0.382034 | 0.2733630 | NA |
| 22 | 51186228 | rs3865766 | C | T | 0.0001285 | 0.0001294 | 0.3200000 | 0.3206687 | 0.451303 | 0.4532750 | NA |
| 22 | 51197266 | rs61290853 | A | G | 0.0000393 | 0.0001336 | 0.7700005 | 0.7684422 | 0.386531 | 0.4229230 | NA |
| 22 | 51198027 | rs34939255 | A | G | -0.0001378 | 0.0001513 | 0.3599996 | 0.3624251 | 0.254376 | 0.0984425 | NA |
| 22 | 51211106 | rs9628250 | T | C | -0.0000580 | 0.0001500 | 0.6999999 | 0.6987749 | 0.271366 | 0.1671330 | NA |
| 22 | 51212875 | rs2238837 | A | C | -0.0000330 | 0.0001425 | 0.8200001 | 0.8167962 | 0.331624 | 0.3724040 | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0000792 | 0.0001458 | 0.5900000 | 0.5868895 | 0.298159 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623741 ES:SE:LP:AF:ID -4.40539e-05:0.000219233:0.0757207:0.623741:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400425 ES:SE:LP:AF:ID 6.21183e-05:0.000217211:0.113509:0.400425:rs2462492
1 91536 rs6702460 G T . PASS AF=0.456855 ES:SE:LP:AF:ID -0.000184024:0.00021383:0.408935:0.456855:rs6702460
1 534192 rs6680723 C T . PASS AF=0.240916 ES:SE:LP:AF:ID -0.00013371:0.000244313:0.236572:0.240916:rs6680723
1 706368 rs12029736 A G . PASS AF=0.515715 ES:SE:LP:AF:ID -8.63196e-05:0.000151677:0.244125:0.515715:rs12029736
1 763394 rs3115847 G A . PASS AF=0.706658 ES:SE:LP:AF:ID -0.000304703:0.000177808:1.06048:0.706658:rs3115847
1 768253 rs2977608 A C . PASS AF=0.761327 ES:SE:LP:AF:ID -9.25407e-05:0.000145192:0.283997:0.761327:rs2977608
1 776546 rs12124819 A G . PASS AF=0.265285 ES:SE:LP:AF:ID 8.20667e-05:0.000162149:0.21467:0.265285:rs12124819
1 814495 rs74461805 C A . PASS AF=0.340407 ES:SE:LP:AF:ID -9.49495e-05:0.000208001:0.187087:0.340407:rs74461805
1 830181 rs28444699 A G . PASS AF=0.697226 ES:SE:LP:AF:ID 4.96557e-05:0.000139111:0.142668:0.697226:rs28444699
1 831489 rs4970385 C T . PASS AF=0.705387 ES:SE:LP:AF:ID -2.86505e-05:0.000136591:0.0809219:0.705387:rs4970385
1 831909 rs9697642 C T . PASS AF=0.705431 ES:SE:LP:AF:ID -3.02579e-05:0.000136586:0.0861861:0.705431:rs9697642
1 832066 rs9697380 G C . PASS AF=0.705618 ES:SE:LP:AF:ID -2.79214e-05:0.000136591:0.0757207:0.705618:rs9697380
1 832318 rs4500250 C A . PASS AF=0.705645 ES:SE:LP:AF:ID -2.7865e-05:0.000136605:0.0757207:0.705645:rs4500250
1 832398 rs4553118 T C . PASS AF=0.73009 ES:SE:LP:AF:ID -1.20568e-05:0.000140334:0.0315171:0.73009:rs4553118
1 832918 rs28765502 T C . PASS AF=0.294389 ES:SE:LP:AF:ID 2.72471e-05:0.000136599:0.0757207:0.294389:rs28765502
1 833223 rs13303211 C T . PASS AF=0.23671 ES:SE:LP:AF:ID -5.9466e-05:0.000145466:0.167491:0.23671:rs13303211
1 833302 rs28752186 C T . PASS AF=0.2367 ES:SE:LP:AF:ID -6.54866e-05:0.000145465:0.187087:0.2367:rs28752186
1 833641 rs28594623 T C . PASS AF=0.239769 ES:SE:LP:AF:ID -7.00223e-05:0.000144998:0.200659:0.239769:rs28594623
1 833824 rs28484835 T C . PASS AF=0.236702 ES:SE:LP:AF:ID -6.54974e-05:0.000145464:0.187087:0.236702:rs28484835
1 834832 rs4411087 G C . PASS AF=0.237188 ES:SE:LP:AF:ID -5.9219e-05:0.000145353:0.167491:0.237188:rs4411087
1 835499 rs4422948 A G . PASS AF=0.241162 ES:SE:LP:AF:ID -3.81222e-05:0.000144334:0.102373:0.241162:rs4422948
1 836896 rs28705752 T C . PASS AF=0.269518 ES:SE:LP:AF:ID -2.78294e-08:0.000139272:-0:0.269518:rs28705752
1 838555 rs4970383 C A . PASS AF=0.246251 ES:SE:LP:AF:ID -2.36553e-05:0.000143348:0.0604807:0.246251:rs4970383
1 839103 rs28562941 A G . PASS AF=0.27002 ES:SE:LP:AF:ID -1.37754e-05:0.000139367:0.0362122:0.27002:rs28562941
1 840753 rs4970382 T C . PASS AF=0.400286 ES:SE:LP:AF:ID -6.12323e-05:0.000126012:0.200659:0.400286:rs4970382
1 841085 rs1574243 C G . PASS AF=0.237188 ES:SE:LP:AF:ID 2.20984e-05:0.000146356:0.0555173:0.237188:rs1574243
1 842362 rs28540380 C T . PASS AF=0.235354 ES:SE:LP:AF:ID -4.40546e-05:0.000148597:0.113509:0.235354:rs28540380
1 843405 rs11516185 A G . PASS AF=0.362617 ES:SE:LP:AF:ID 4.21059e-06:0.000156429:0.00877392:0.362617:rs11516185
1 850218 rs6664536 T A . PASS AF=0.590337 ES:SE:LP:AF:ID 5.35106e-06:0.00012566:0.0132283:0.590337:rs6664536
1 850371 rs6679046 G T . PASS AF=0.603701 ES:SE:LP:AF:ID 4.69186e-05:0.000126371:0.148742:0.603701:rs6679046
1 850780 rs6657440 C T . PASS AF=0.603922 ES:SE:LP:AF:ID 3.48881e-05:0.000126353:0.107905:0.603922:rs6657440
1 852037 rs4970463 G A . PASS AF=0.589699 ES:SE:LP:AF:ID -1.26077e-05:0.000125863:0.0362122:0.589699:rs4970463
1 852063 rs28436996 G A . PASS AF=0.589684 ES:SE:LP:AF:ID -1.28848e-05:0.000125808:0.0362122:0.589684:rs28436996
1 852875 rs13303369 C T . PASS AF=0.607672 ES:SE:LP:AF:ID 2.80811e-05:0.000126641:0.0861861:0.607672:rs13303369
1 853954 rs1806509 C A . PASS AF=0.607833 ES:SE:LP:AF:ID 4.03876e-05:0.000126657:0.124939:0.607833:rs1806509
1 854777 rs13303019 A G . PASS AF=0.610317 ES:SE:LP:AF:ID 5.34905e-05:0.000126782:0.173925:0.610317:rs13303019
1 854978 rs13303057 A C . PASS AF=0.603269 ES:SE:LP:AF:ID 4.81057e-05:0.000126401:0.154902:0.603269:rs13303057
1 855075 rs6673914 C G . PASS AF=0.610335 ES:SE:LP:AF:ID 4.85263e-05:0.000126784:0.154902:0.610335:rs6673914
1 856099 rs28534711 T G . PASS AF=0.389939 ES:SE:LP:AF:ID -4.80622e-05:0.00012681:0.154902:0.389939:rs28534711
1 856108 rs28742275 A G . PASS AF=0.389923 ES:SE:LP:AF:ID -4.79212e-05:0.000126816:0.148742:0.389923:rs28742275
1 856476 rs4040605 A G . PASS AF=0.350336 ES:SE:LP:AF:ID 3.032e-05:0.00013026:0.0861861:0.350336:rs4040605
1 858801 rs7418179 A G . PASS AF=0.765915 ES:SE:LP:AF:ID 5.67135e-05:0.000146256:0.154902:0.765915:rs7418179
1 860416 rs61464428 G A . PASS AF=0.766728 ES:SE:LP:AF:ID 3.74819e-05:0.00014633:0.09691:0.766728:rs61464428
1 860688 rs60837925 G A . PASS AF=0.766187 ES:SE:LP:AF:ID 4.41838e-05:0.000146229:0.119186:0.766187:rs60837925
1 861630 rs2879816 G A . PASS AF=0.766339 ES:SE:LP:AF:ID 4.096e-05:0.000146264:0.107905:0.766339:rs2879816
1 862866 rs3892970 C T . PASS AF=0.763206 ES:SE:LP:AF:ID 3.40619e-05:0.00014613:0.0861861:0.763206:rs3892970
1 864938 rs2340587 G A . PASS AF=0.760093 ES:SE:LP:AF:ID 9.37877e-05:0.000145047:0.283997:0.760093:rs2340587
1 866893 rs2880024 T C . PASS AF=0.610571 ES:SE:LP:AF:ID -6.45105e-05:0.000127462:0.21467:0.610571:rs2880024
1 868418 rs28546443 C T . PASS AF=0.297789 ES:SE:LP:AF:ID -0.000173476:0.000140102:0.657577:0.297789:rs28546443