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_41245_1780.vcf.gz --id UKB-b:3389 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_41245_1780.txt.gz --cohort_cases 1343 --cohort_controls 459767 --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": "2019-09-13T13:15:43.669334",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-3389/ukb-b-3389.vcf.gz; Date=Sat May 9 14:53:53 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-3389/UKB-b-3389_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-3389/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:43:27 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-3389/UKB-b-3389_data.vcf.gz ...
Read summary statistics for 2209735 SNPs.
Dropped 234 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, 563221 SNPs remain.
After merging with regression SNP LD, 563221 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0026 (0.0012)
Lambda GC: 1.0431
Mean Chi^2: 1.037
Intercept: 1.0096 (0.0102)
Ratio: 0.2581 (0.2749)
Analysis finished at Thu Oct 17 14:43:56 2019
Total time elapsed: 29.1sQC metrics
Metrics
Metrics
{
"af_correlation": 0.73,
"inflation_factor": 1.0475,
"mean_EFFECT": 1.072e-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": 17498,
"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": 563221,
"ldsc_nsnp_merge_regression_ld": 563221,
"ldsc_observed_scale_h2_beta": 0.0026,
"ldsc_observed_scale_h2_se": 0.0012,
"ldsc_intercept_beta": 1.0096,
"ldsc_intercept_se": 0.0102,
"ldsc_lambda_gc": 1.0431,
"ldsc_mean_chisq": 1.037,
"ldsc_ratio": 0.2595
}
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 | FALSE |
| 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 | 2209503 | 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 | 2209735 | 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 | 8.647441e+00 | 5.765231e+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 | 7.867576e+07 | 5.665900e+07 | 5687.0000000 | 3.177796e+07 | 6.918526e+07 | 1.147898e+08 | 2.491722e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.000000e-07 | 1.198000e-04 | -0.0005526 | -8.090000e-05 | 0.000000e+00 | 8.140000e-05 | 5.912000e-04 | ▁▂▇▂▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.178000e-04 | 5.200000e-06 | 0.0001083 | 1.136000e-04 | 1.164000e-04 | 1.213000e-04 | 2.282000e-04 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.938395e-01 | 2.902562e-01 | 0.0000003 | 2.399999e-01 | 4.899999e-01 | 7.499995e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.938417e-01 | 2.902318e-01 | 0.0000003 | 2.401871e-01 | 4.905064e-01 | 7.456765e-01 | 9.999999e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.628342e-01 | 1.366167e-01 | 0.2606110 | 3.428730e-01 | 4.447540e-01 | 5.738220e-01 | 7.393890e-01 | ▇▆▅▅▃ |
| numeric | AF_reference | 17498 | 0.9920814 | NA | NA | NA | NA | NA | NA | NA | 4.432839e-01 | 1.672935e-01 | 0.0001997 | 3.130990e-01 | 4.315100e-01 | 5.650960e-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 | -0.0004880 | 0.0001993 | 0.0140001 | 0.0143427 | 0.623779 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0003007 | 0.0001975 | 0.1299999 | 0.1278401 | 0.400401 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0001037 | 0.0001944 | 0.5900000 | 0.5938620 | 0.456862 | 0.4207270 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0001005 | 0.0001379 | 0.4700002 | 0.4659422 | 0.515668 | 0.2751600 | NA |
| 1 | 763394 | rs369924889 | G | A | -0.0002780 | 0.0001616 | 0.0850002 | 0.0854833 | 0.706756 | 0.6176120 | NA |
| 1 | 776546 | rs12124819 | A | G | 0.0000734 | 0.0001474 | 0.6200004 | 0.6183954 | 0.265365 | 0.0756789 | NA |
| 1 | 814495 | rs74461805 | C | A | -0.0002545 | 0.0001890 | 0.1800002 | 0.1782887 | 0.340385 | NA | NA |
| 1 | 830181 | rs28444699 | A | G | -0.0000447 | 0.0001265 | 0.7199992 | 0.7237975 | 0.697214 | 0.6912940 | NA |
| 1 | 831489 | rs4970385 | C | T | -0.0000919 | 0.0001242 | 0.4600002 | 0.4590597 | 0.705350 | 0.6491610 | NA |
| 1 | 831909 | rs9697642 | C | T | -0.0000906 | 0.0001242 | 0.4700002 | 0.4655919 | 0.705395 | 0.6485620 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51174662 | rs1858748 | T | C | 0.0000221 | 0.0001333 | 0.8700001 | 0.8685524 | 0.737440 | 0.732827 | NA |
| 22 | 51180501 | rs5770999 | T | C | 0.0001194 | 0.0001297 | 0.3599996 | 0.3573801 | 0.713660 | 0.636981 | NA |
| 22 | 51181919 | rs9616825 | G | C | 0.0000601 | 0.0001291 | 0.6400000 | 0.6413011 | 0.695490 | 0.619409 | NA |
| 22 | 51182485 | rs6009961 | A | G | 0.0001276 | 0.0001302 | 0.3300000 | 0.3267976 | 0.715505 | 0.638379 | NA |
| 22 | 51186143 | rs2879914 | T | C | -0.0001053 | 0.0001207 | 0.3800004 | 0.3828731 | 0.381816 | 0.273363 | NA |
| 22 | 51186228 | rs3865766 | C | T | -0.0000439 | 0.0001176 | 0.7099994 | 0.7086970 | 0.451059 | 0.453275 | NA |
| 22 | 51197266 | rs61290853 | A | G | -0.0000527 | 0.0001214 | 0.6600001 | 0.6642091 | 0.386346 | 0.422923 | NA |
| 22 | 51211106 | rs9628250 | T | C | 0.0001224 | 0.0001363 | 0.3700002 | 0.3691925 | 0.271567 | 0.167133 | NA |
| 22 | 51212875 | rs2238837 | A | C | -0.0000601 | 0.0001295 | 0.6400000 | 0.6423638 | 0.331445 | 0.372404 | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0001097 | 0.0001326 | 0.4100001 | 0.4082031 | 0.297972 | 0.205072 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623779 ES:SE:LP:AF:ID -0.000487984:0.000199294:1.85387:0.623779:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400401 ES:SE:LP:AF:ID 0.00030071:0.000197488:0.886057:0.400401:rs2462492
1 91536 rs6702460 G T . PASS AF=0.456862 ES:SE:LP:AF:ID 0.000103673:0.000194418:0.229148:0.456862:rs6702460
1 706368 rs12029736 A G . PASS AF=0.515668 ES:SE:LP:AF:ID -0.000100525:0.000137876:0.327902:0.515668:rs12029736
1 763394 rs3115847 G A . PASS AF=0.706756 ES:SE:LP:AF:ID -0.000277979:0.000161642:1.07058:0.706756:rs3115847
1 776546 rs12124819 A G . PASS AF=0.265365 ES:SE:LP:AF:ID 7.34063e-05:0.000147365:0.207608:0.265365:rs12124819
1 814495 rs74461805 C A . PASS AF=0.340385 ES:SE:LP:AF:ID -0.00025446:0.000189043:0.744727:0.340385:rs74461805
1 830181 rs28444699 A G . PASS AF=0.697214 ES:SE:LP:AF:ID -4.46937e-05:0.000126472:0.142668:0.697214:rs28444699
1 831489 rs4970385 C T . PASS AF=0.70535 ES:SE:LP:AF:ID -9.19446e-05:0.000124183:0.337242:0.70535:rs4970385
1 831909 rs9697642 C T . PASS AF=0.705395 ES:SE:LP:AF:ID -9.06097e-05:0.000124179:0.327902:0.705395:rs9697642
1 832066 rs9697380 G C . PASS AF=0.70558 ES:SE:LP:AF:ID -9.1634e-05:0.000124185:0.337242:0.70558:rs9697380
1 832318 rs4500250 C A . PASS AF=0.705609 ES:SE:LP:AF:ID -9.19197e-05:0.000124198:0.337242:0.705609:rs4500250
1 832398 rs4553118 T C . PASS AF=0.730079 ES:SE:LP:AF:ID -4.53226e-05:0.000127583:0.142668:0.730079:rs4553118
1 832918 rs28765502 T C . PASS AF=0.294424 ES:SE:LP:AF:ID 9.13795e-05:0.000124192:0.337242:0.294424:rs28765502
1 836896 rs28705752 T C . PASS AF=0.269537 ES:SE:LP:AF:ID 7.07391e-05:0.000126593:0.236572:0.269537:rs28705752
1 839103 rs28562941 A G . PASS AF=0.270039 ES:SE:LP:AF:ID 9.07236e-05:0.000126682:0.327902:0.270039:rs28562941
1 840753 rs4970382 T C . PASS AF=0.400164 ES:SE:LP:AF:ID 9.24539e-05:0.000114551:0.376751:0.400164:rs4970382
1 843405 rs11516185 A G . PASS AF=0.362585 ES:SE:LP:AF:ID 8.18579e-05:0.000142192:0.251812:0.362585:rs11516185
1 850218 rs6664536 T A . PASS AF=0.590329 ES:SE:LP:AF:ID -3.50501e-05:0.000114214:0.119186:0.590329:rs6664536
1 850371 rs6679046 G T . PASS AF=0.603723 ES:SE:LP:AF:ID -1.15698e-05:0.000114856:0.0362122:0.603723:rs6679046
1 850780 rs6657440 C T . PASS AF=0.603947 ES:SE:LP:AF:ID -3.53132e-06:0.00011484:0.00877392:0.603947:rs6657440
1 852037 rs4970463 G A . PASS AF=0.589685 ES:SE:LP:AF:ID -3.77257e-05:0.000114399:0.130768:0.589685:rs4970463
1 852063 rs28436996 G A . PASS AF=0.589666 ES:SE:LP:AF:ID -3.76189e-05:0.000114348:0.130768:0.589666:rs28436996
1 852875 rs13303369 C T . PASS AF=0.607671 ES:SE:LP:AF:ID -2.11913e-05:0.000115095:0.0705811:0.607671:rs13303369
1 853954 rs1806509 C A . PASS AF=0.607829 ES:SE:LP:AF:ID -2.18559e-05:0.00011511:0.0705811:0.607829:rs1806509
1 854777 rs13303019 A G . PASS AF=0.610322 ES:SE:LP:AF:ID -1.0531e-05:0.000115223:0.0315171:0.610322:rs13303019
1 854978 rs13303057 A C . PASS AF=0.603285 ES:SE:LP:AF:ID -8.03894e-06:0.000114884:0.0268721:0.603285:rs13303057
1 855075 rs6673914 C G . PASS AF=0.610343 ES:SE:LP:AF:ID -1.07263e-05:0.000115225:0.0315171:0.610343:rs6673914
1 856099 rs28534711 T G . PASS AF=0.389932 ES:SE:LP:AF:ID 1.14541e-05:0.000115247:0.0362122:0.389932:rs28534711
1 856108 rs28742275 A G . PASS AF=0.389916 ES:SE:LP:AF:ID 1.17426e-05:0.000115253:0.0362122:0.389916:rs28742275
1 856476 rs4040605 A G . PASS AF=0.350359 ES:SE:LP:AF:ID -3.60704e-05:0.000118404:0.119186:0.350359:rs4040605
1 866893 rs2880024 T C . PASS AF=0.61053 ES:SE:LP:AF:ID -1.4249e-05:0.000115874:0.0457575:0.61053:rs2880024
1 868418 rs28546443 C T . PASS AF=0.297908 ES:SE:LP:AF:ID -1.22173e-05:0.00012731:0.0362122:0.297908:rs28546443
1 870645 rs28576697 T C . PASS AF=0.291301 ES:SE:LP:AF:ID 3.55971e-06:0.000126293:0.00877392:0.291301:rs28576697
1 871334 rs4072383 G T . PASS AF=0.720599 ES:SE:LP:AF:ID 2.6506e-05:0.000125511:0.0809219:0.720599:rs4072383
1 872352 rs1806780 G C . PASS AF=0.267549 ES:SE:LP:AF:ID -6.71508e-05:0.000127213:0.221849:0.267549:rs1806780
1 873558 rs1110052 G T . PASS AF=0.715235 ES:SE:LP:AF:ID 2.59204e-05:0.000124517:0.0757207:0.715235:rs1110052
1 875770 rs4970379 A G . PASS AF=0.600067 ES:SE:LP:AF:ID 0.000113217:0.000116843:0.481486:0.600067:rs4970379
1 881627 rs2272757 G A . PASS AF=0.652403 ES:SE:LP:AF:ID 0.000199222:0.000118033:1.04096:0.652403:rs2272757
1 891059 rs13303065 C T . PASS AF=0.652441 ES:SE:LP:AF:ID 0.000193979:0.000118014:1:0.652441:rs13303065
1 891945 rs13303106 A G . PASS AF=0.652502 ES:SE:LP:AF:ID 0.000196487:0.000118151:1.01773:0.652502:rs13303106
1 900505 rs28705211 G C . PASS AF=0.271781 ES:SE:LP:AF:ID -0.000194693:0.000127725:0.886057:0.271781:rs28705211
1 903245 rs28690976 A G . PASS AF=0.56696 ES:SE:LP:AF:ID 0.000142551:0.00011736:0.657577:0.56696:rs28690976
1 909073 rs3892467 C T . PASS AF=0.386684 ES:SE:LP:AF:ID 1.88375e-05:0.000117018:0.0604807:0.386684:rs3892467
1 909238 rs3829740 G C . PASS AF=0.571429 ES:SE:LP:AF:ID -1.93015e-05:0.00011333:0.0655015:0.571429:rs3829740
1 910394 rs28477686 C T . PASS AF=0.324455 ES:SE:LP:AF:ID -0.00014484:0.000122843:0.619789:0.324455:rs28477686
1 912049 rs7367995 T C . PASS AF=0.585258 ES:SE:LP:AF:ID 0.000132422:0.000114488:0.60206:0.585258:rs7367995
1 913889 rs2340596 G A . PASS AF=0.599234 ES:SE:LP:AF:ID 0.000133667:0.000114677:0.619789:0.599234:rs2340596
1 914333 rs13302979 C G . PASS AF=0.602534 ES:SE:LP:AF:ID 0.000103415:0.000115022:0.431798:0.602534:rs13302979
1 914852 rs13303368 G C . PASS AF=0.600092 ES:SE:LP:AF:ID 0.00013027:0.000114803:0.585027:0.600092:rs13303368