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_1730.vcf.gz --id UKB-b:2823 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_41246_1730.txt.gz --cohort_cases 1417 --cohort_controls 442591 --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": "2019-09-13T13:19:48.548091",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-2823/ukb-b-2823.vcf.gz; Date=Sun May 10 10:34:33 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-2823/UKB-b-2823_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-2823/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:43:00 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-2823/UKB-b-2823_data.vcf.gz ...
Read summary statistics for 2350669 SNPs.
Dropped 259 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, 597365 SNPs remain.
After merging with regression SNP LD, 597365 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0005 (0.0013)
Lambda GC: 1.0383
Mean Chi^2: 1.0229
Intercept: 1.0173 (0.0096)
Ratio: 0.758 (0.4208)
Analysis finished at Thu Oct 17 14:43:34 2019
Total time elapsed: 34.86sQC metrics
Metrics
Metrics
{
"af_correlation": 0.7495,
"inflation_factor": 1.0475,
"mean_EFFECT": -5.5563e-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": 18610,
"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": 597365,
"ldsc_nsnp_merge_regression_ld": 597365,
"ldsc_observed_scale_h2_beta": 0.0005,
"ldsc_observed_scale_h2_se": 0.0013,
"ldsc_intercept_beta": 1.0173,
"ldsc_intercept_se": 0.0096,
"ldsc_lambda_gc": 1.0383,
"ldsc_mean_chisq": 1.0229,
"ldsc_ratio": 0.7555
}
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 | 2350412 | 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 | 2350669 | 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 | 8.650644e+00 | 5.768503e+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 | 7.861989e+07 | 5.665879e+07 | 5687.0000000 | 3.174286e+07 | 6.904662e+07 | 1.147892e+08 | 2.491722e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -6.000000e-07 | 1.280000e-04 | -0.0006496 | -8.810000e-05 | -8.000000e-07 | 8.600000e-05 | 6.341000e-04 | ▁▂▇▂▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.264000e-04 | 6.200000e-06 | 0.0001155 | 1.213000e-04 | 1.247000e-04 | 1.306000e-04 | 2.500000e-04 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.938240e-01 | 2.890949e-01 | 0.0000008 | 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 | 4.938239e-01 | 2.890642e-01 | 0.0000008 | 2.418769e-01 | 4.904245e-01 | 7.438975e-01 | 9.999998e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.584689e-01 | 1.440644e-01 | 0.2470010 | 3.316000e-01 | 4.385160e-01 | 5.747940e-01 | 7.529990e-01 | ▇▆▅▅▃ |
| numeric | AF_reference | 18610 | 0.9920831 | NA | NA | NA | NA | NA | NA | NA | 4.395253e-01 | 1.717267e-01 | 0.0001997 | 3.051120e-01 | 4.259190e-01 | 5.648960e-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 | -0.0002979 | 0.0002126 | 0.1600000 | 0.1610542 | 0.623743 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0002285 | 0.0002106 | 0.2800000 | 0.2779714 | 0.400425 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0002612 | 0.0002073 | 0.2099999 | 0.2077458 | 0.456849 | 0.4207270 | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0002725 | 0.0001471 | 0.0640000 | 0.0638372 | 0.515722 | 0.2751600 | NA |
| 1 | 763394 | rs369924889 | G | A | -0.0002123 | 0.0001724 | 0.2200002 | 0.2180250 | 0.706655 | 0.6176120 | NA |
| 1 | 776546 | rs12124819 | A | G | -0.0001622 | 0.0001572 | 0.2999998 | 0.3023004 | 0.265288 | 0.0756789 | NA |
| 1 | 814495 | rs74461805 | C | A | -0.0000030 | 0.0002017 | 0.9900000 | 0.9881777 | 0.340408 | NA | NA |
| 1 | 830181 | rs28444699 | A | G | 0.0001619 | 0.0001349 | 0.2300001 | 0.2299845 | 0.697238 | 0.6912940 | NA |
| 1 | 831489 | rs4970385 | C | T | 0.0001103 | 0.0001324 | 0.4000000 | 0.4046986 | 0.705402 | 0.6491610 | NA |
| 1 | 831909 | rs9697642 | C | T | 0.0001072 | 0.0001324 | 0.4199997 | 0.4183041 | 0.705446 | 0.6485620 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51180501 | rs5770999 | T | C | -2.42e-05 | 0.0001384 | 0.8600001 | 0.8610309 | 0.713713 | 0.6369810 | NA |
| 22 | 51181919 | rs9616825 | G | C | -5.32e-05 | 0.0001377 | 0.6999999 | 0.6991264 | 0.695499 | 0.6194090 | NA |
| 22 | 51182485 | rs6009961 | A | G | -5.62e-05 | 0.0001388 | 0.6899999 | 0.6856048 | 0.715547 | 0.6383790 | NA |
| 22 | 51186143 | rs2879914 | T | C | 5.47e-05 | 0.0001287 | 0.6700003 | 0.6708573 | 0.382038 | 0.2733630 | NA |
| 22 | 51186228 | rs3865766 | C | T | 1.82e-05 | 0.0001255 | 0.8800001 | 0.8846505 | 0.451310 | 0.4532750 | NA |
| 22 | 51197266 | rs61290853 | A | G | -3.00e-06 | 0.0001295 | 0.9800000 | 0.9813016 | 0.386535 | 0.4229230 | NA |
| 22 | 51198027 | rs34939255 | A | G | -1.72e-04 | 0.0001467 | 0.2399999 | 0.2410005 | 0.254372 | 0.0984425 | NA |
| 22 | 51211106 | rs9628250 | T | C | -6.92e-05 | 0.0001454 | 0.6300007 | 0.6340669 | 0.271361 | 0.1671330 | NA |
| 22 | 51212875 | rs2238837 | A | C | 4.99e-05 | 0.0001381 | 0.7199992 | 0.7181619 | 0.331626 | 0.3724040 | NA |
| 22 | 51237063 | rs3896457 | T | C | 4.14e-05 | 0.0001414 | 0.7700005 | 0.7698679 | 0.298163 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623743 ES:SE:LP:AF:ID -0.000297899:0.000212552:0.79588:0.623743:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400425 ES:SE:LP:AF:ID 0.000228473:0.000210596:0.552842:0.400425:rs2462492
1 91536 rs6702460 G T . PASS AF=0.456849 ES:SE:LP:AF:ID -0.000261178:0.000207319:0.677781:0.456849:rs6702460
1 706368 rs12029736 A G . PASS AF=0.515722 ES:SE:LP:AF:ID 0.000272543:0.000147057:1.19382:0.515722:rs12029736
1 763394 rs3115847 G A . PASS AF=0.706655 ES:SE:LP:AF:ID -0.000212347:0.000172388:0.657577:0.706655:rs3115847
1 776546 rs12124819 A G . PASS AF=0.265288 ES:SE:LP:AF:ID -0.00016216:0.000157206:0.522879:0.265288:rs12124819
1 814495 rs74461805 C A . PASS AF=0.340408 ES:SE:LP:AF:ID -2.98812e-06:0.00020166:0.00436481:0.340408:rs74461805
1 830181 rs28444699 A G . PASS AF=0.697238 ES:SE:LP:AF:ID 0.000161899:0.000134871:0.638272:0.697238:rs28444699
1 831489 rs4970385 C T . PASS AF=0.705402 ES:SE:LP:AF:ID 0.000110346:0.000132427:0.39794:0.705402:rs4970385
1 831909 rs9697642 C T . PASS AF=0.705446 ES:SE:LP:AF:ID 0.000107178:0.000132422:0.376751:0.705446:rs9697642
1 832066 rs9697380 G C . PASS AF=0.705633 ES:SE:LP:AF:ID 0.000111478:0.000132427:0.39794:0.705633:rs9697380
1 832318 rs4500250 C A . PASS AF=0.70566 ES:SE:LP:AF:ID 0.000111296:0.000132441:0.39794:0.70566:rs4500250
1 832398 rs4553118 T C . PASS AF=0.730103 ES:SE:LP:AF:ID 8.25427e-05:0.000136057:0.267606:0.730103:rs4553118
1 832918 rs28765502 T C . PASS AF=0.294374 ES:SE:LP:AF:ID -0.000111566:0.000132435:0.39794:0.294374:rs28765502
1 836896 rs28705752 T C . PASS AF=0.269512 ES:SE:LP:AF:ID -6.21883e-05:0.000135027:0.187087:0.269512:rs28705752
1 839103 rs28562941 A G . PASS AF=0.270014 ES:SE:LP:AF:ID -5.79721e-05:0.000135119:0.173925:0.270014:rs28562941
1 840753 rs4970382 T C . PASS AF=0.400281 ES:SE:LP:AF:ID -3.64622e-05:0.000122171:0.113509:0.400281:rs4970382
1 843405 rs11516185 A G . PASS AF=0.362606 ES:SE:LP:AF:ID -0.000273174:0.000151663:1.14267:0.362606:rs11516185
1 850218 rs6664536 T A . PASS AF=0.59034 ES:SE:LP:AF:ID 5.52388e-05:0.000121831:0.187087:0.59034:rs6664536
1 850371 rs6679046 G T . PASS AF=0.603703 ES:SE:LP:AF:ID 6.99199e-05:0.000122518:0.244125:0.603703:rs6679046
1 850780 rs6657440 C T . PASS AF=0.603925 ES:SE:LP:AF:ID 6.8621e-05:0.000122501:0.236572:0.603925:rs6657440
1 852037 rs4970463 G A . PASS AF=0.589702 ES:SE:LP:AF:ID 5.17582e-05:0.000122028:0.173925:0.589702:rs4970463
1 852063 rs28436996 G A . PASS AF=0.589687 ES:SE:LP:AF:ID 5.10365e-05:0.000121974:0.167491:0.589687:rs28436996
1 852875 rs13303369 C T . PASS AF=0.607675 ES:SE:LP:AF:ID 6.74032e-05:0.00012278:0.236572:0.607675:rs13303369
1 853954 rs1806509 C A . PASS AF=0.607834 ES:SE:LP:AF:ID 6.55486e-05:0.000122796:0.229148:0.607834:rs1806509
1 854777 rs13303019 A G . PASS AF=0.610318 ES:SE:LP:AF:ID 7.45022e-05:0.000122917:0.267606:0.610318:rs13303019
1 854978 rs13303057 A C . PASS AF=0.603271 ES:SE:LP:AF:ID 6.51102e-05:0.000122548:0.221849:0.603271:rs13303057
1 855075 rs6673914 C G . PASS AF=0.610337 ES:SE:LP:AF:ID 7.39063e-05:0.00012292:0.259637:0.610337:rs6673914
1 856099 rs28534711 T G . PASS AF=0.389938 ES:SE:LP:AF:ID -8.36336e-05:0.000122944:0.30103:0.389938:rs28534711
1 856108 rs28742275 A G . PASS AF=0.389921 ES:SE:LP:AF:ID -8.36874e-05:0.000122951:0.30103:0.389921:rs28742275
1 856476 rs4040605 A G . PASS AF=0.350331 ES:SE:LP:AF:ID -5.78506e-05:0.000126289:0.187087:0.350331:rs4040605
1 866893 rs2880024 T C . PASS AF=0.610579 ES:SE:LP:AF:ID 3.7916e-05:0.000123577:0.119186:0.610579:rs2880024
1 868418 rs28546443 C T . PASS AF=0.297795 ES:SE:LP:AF:ID 3.93132e-05:0.000135827:0.113509:0.297795:rs28546443
1 870645 rs28576697 T C . PASS AF=0.291174 ES:SE:LP:AF:ID -7.0411e-05:0.000134701:0.221849:0.291174:rs28576697
1 871334 rs4072383 G T . PASS AF=0.720698 ES:SE:LP:AF:ID -1.37635e-05:0.000133871:0.0362122:0.720698:rs4072383
1 872352 rs1806780 G C . PASS AF=0.267467 ES:SE:LP:AF:ID -2.44992e-05:0.000135678:0.0655015:0.267467:rs1806780
1 873558 rs1110052 G T . PASS AF=0.715329 ES:SE:LP:AF:ID -4.21812e-05:0.000132817:0.124939:0.715329:rs1110052
1 875770 rs4970379 A G . PASS AF=0.599983 ES:SE:LP:AF:ID 4.35133e-05:0.000124601:0.136677:0.599983:rs4970379
1 881627 rs2272757 G A . PASS AF=0.652317 ES:SE:LP:AF:ID 4.16547e-05:0.000125869:0.130768:0.652317:rs2272757
1 891059 rs13303065 C T . PASS AF=0.652369 ES:SE:LP:AF:ID 4.11284e-05:0.000125852:0.130768:0.652369:rs13303065
1 891945 rs13303106 A G . PASS AF=0.652428 ES:SE:LP:AF:ID 4.48682e-05:0.000125997:0.142668:0.652428:rs13303106
1 900505 rs28705211 G C . PASS AF=0.27177 ES:SE:LP:AF:ID -9.74018e-05:0.000136221:0.327902:0.27177:rs28705211
1 903245 rs28690976 A G . PASS AF=0.566917 ES:SE:LP:AF:ID 5.71982e-05:0.000125134:0.187087:0.566917:rs28690976
1 909073 rs3892467 C T . PASS AF=0.38674 ES:SE:LP:AF:ID -0.000109445:0.000124813:0.420216:0.38674:rs3892467
1 909238 rs3829740 G C . PASS AF=0.571395 ES:SE:LP:AF:ID 9.21085e-05:0.000120883:0.346787:0.571395:rs3829740
1 910394 rs28477686 C T . PASS AF=0.324413 ES:SE:LP:AF:ID 8.6844e-05:0.000131066:0.29243:0.324413:rs28477686
1 912049 rs7367995 T C . PASS AF=0.585286 ES:SE:LP:AF:ID -2.05527e-07:0.000122114:-0:0.585286:rs7367995
1 913889 rs2340596 G A . PASS AF=0.599249 ES:SE:LP:AF:ID -2.20229e-05:0.000122314:0.0655015:0.599249:rs2340596
1 914333 rs13302979 C G . PASS AF=0.602552 ES:SE:LP:AF:ID -1.93669e-05:0.000122681:0.0604807:0.602552:rs13302979
1 914852 rs13303368 G C . PASS AF=0.600097 ES:SE:LP:AF:ID -1.64449e-05:0.000122448:0.05061:0.600097:rs13303368