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_41210_Q181.vcf.gz --id UKB-b:17446 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_41210_Q181.txt.gz --cohort_cases 1490 --cohort_controls 461520 --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": "2019-09-13T13:06:24.197603",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-17446/ukb-b-17446.vcf.gz; Date=Sun May 10 13:26:12 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-17446/UKB-b-17446_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-17446/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:40:19 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-17446/UKB-b-17446_data.vcf.gz ...
Read summary statistics for 2482344 SNPs.
Dropped 290 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, 628544 SNPs remain.
After merging with regression SNP LD, 628544 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: -0.0011 (0.0012)
Lambda GC: 0.9968
Mean Chi^2: 1.0005
Intercept: 1.0122 (0.0105)
Ratio: 27.0259 (23.0737)
Analysis finished at Thu Oct 17 14:40:52 2019
Total time elapsed: 32.99sQC metrics
Metrics
Metrics
{
"af_correlation": 0.7663,
"inflation_factor": 1,
"mean_EFFECT": 5.2987e-08,
"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": 19676,
"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": 628544,
"ldsc_nsnp_merge_regression_ld": 628544,
"ldsc_observed_scale_h2_beta": "NA",
"ldsc_observed_scale_h2_se": "NA",
"ldsc_intercept_beta": 1.0122,
"ldsc_intercept_se": 0.0105,
"ldsc_lambda_gc": 0.9968,
"ldsc_mean_chisq": 1.0005,
"ldsc_ratio": 24.4
}
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 | 2482057 | 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 | 2482344 | 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 | 8.652456e+00 | 5.765658e+00 | 1.000e+00 | 4.000000e+00 | 8.000000e+00 | 1.300000e+01 | 2.200000e+01 | ▇▅▃▂▂ |
| numeric | POS | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 7.855697e+07 | 5.662109e+07 | 5.687e+03 | 3.168440e+07 | 6.898712e+07 | 1.147466e+08 | 2.491917e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.000000e-07 | 1.250000e-04 | -6.027e-04 | -8.330000e-05 | 0.000000e+00 | 8.440000e-05 | 6.299000e-04 | ▁▂▇▂▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.249000e-04 | 6.700000e-06 | 1.134e-04 | 1.192000e-04 | 1.229000e-04 | 1.295000e-04 | 2.456000e-04 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 5.006702e-01 | 2.889627e-01 | 4.400e-06 | 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 | 5.006703e-01 | 2.889345e-01 | 4.400e-06 | 2.512953e-01 | 5.008016e-01 | 7.507911e-01 | 9.999998e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.540067e-01 | 1.507648e-01 | 2.349e-01 | 3.215380e-01 | 4.318870e-01 | 5.750620e-01 | 7.651000e-01 | ▇▆▅▅▃ |
| numeric | AF_reference | 19676 | 0.9920736 | NA | NA | NA | NA | NA | NA | NA | 4.355476e-01 | 1.758775e-01 | 1.997e-04 | 2.967250e-01 | 4.201280e-01 | 5.640970e-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 | -0.0002892 | 0.0002087 | 0.1700000 | 0.1657753 | 0.623765 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0002732 | 0.0002068 | 0.1900002 | 0.1863971 | 0.400401 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0001248 | 0.0002036 | 0.5400003 | 0.5398941 | 0.456846 | 0.4207270 | NA |
| 1 | 534192 | rs6680723 | C | T | 0.0002535 | 0.0002325 | 0.2800000 | 0.2756797 | 0.240959 | NA | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0003114 | 0.0001444 | 0.0309999 | 0.0309930 | 0.515645 | 0.2751600 | NA |
| 1 | 763394 | rs369924889 | G | A | -0.0002349 | 0.0001692 | 0.1700000 | 0.1651135 | 0.706753 | 0.6176120 | NA |
| 1 | 768253 | rs2977608 | A | C | -0.0001922 | 0.0001381 | 0.1600000 | 0.1641008 | 0.761297 | 0.4894170 | NA |
| 1 | 776546 | rs12124819 | A | G | -0.0002493 | 0.0001543 | 0.1100001 | 0.1061195 | 0.265385 | 0.0756789 | NA |
| 1 | 814495 | rs74461805 | C | A | 0.0000373 | 0.0001979 | 0.8499999 | 0.8503586 | 0.340396 | NA | NA |
| 1 | 830181 | rs28444699 | A | G | -0.0001301 | 0.0001324 | 0.3300000 | 0.3258517 | 0.697255 | 0.6912940 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51180501 | rs5770999 | T | C | -0.0000596 | 0.0001359 | 0.6600001 | 0.6607989 | 0.713656 | 0.6369810 | NA |
| 22 | 51181919 | rs9616825 | G | C | 0.0000666 | 0.0001352 | 0.6200004 | 0.6222183 | 0.695470 | 0.6194090 | NA |
| 22 | 51182485 | rs6009961 | A | G | -0.0000666 | 0.0001363 | 0.6300007 | 0.6251564 | 0.715502 | 0.6383790 | NA |
| 22 | 51186143 | rs2879914 | T | C | 0.0000571 | 0.0001264 | 0.6499995 | 0.6515954 | 0.381825 | 0.2733630 | NA |
| 22 | 51186228 | rs3865766 | C | T | 0.0001004 | 0.0001232 | 0.4100001 | 0.4147999 | 0.451061 | 0.4532750 | NA |
| 22 | 51197266 | rs61290853 | A | G | 0.0001635 | 0.0001272 | 0.2000000 | 0.1984651 | 0.386333 | 0.4229230 | NA |
| 22 | 51198027 | rs34939255 | A | G | -0.0002434 | 0.0001440 | 0.0909997 | 0.0909142 | 0.254562 | 0.0984425 | NA |
| 22 | 51211106 | rs9628250 | T | C | -0.0002785 | 0.0001427 | 0.0510000 | 0.0510558 | 0.271547 | 0.1671330 | NA |
| 22 | 51212875 | rs2238837 | A | C | 0.0001052 | 0.0001356 | 0.4400003 | 0.4380669 | 0.331457 | 0.3724040 | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0002409 | 0.0001388 | 0.0830004 | 0.0827042 | 0.297974 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623765 ES:SE:LP:AF:ID -0.000289236:0.000208698:0.769551:0.623765:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400401 ES:SE:LP:AF:ID -0.000273188:0.000206755:0.721246:0.400401:rs2462492
1 91536 rs6702460 G T . PASS AF=0.456846 ES:SE:LP:AF:ID -0.000124786:0.000203575:0.267606:0.456846:rs6702460
1 534192 rs6680723 C T . PASS AF=0.240959 ES:SE:LP:AF:ID 0.000253483:0.000232537:0.552842:0.240959:rs6680723
1 706368 rs12029736 A G . PASS AF=0.515645 ES:SE:LP:AF:ID -0.000311395:0.000144354:1.50864:0.515645:rs12029736
1 763394 rs3115847 G A . PASS AF=0.706753 ES:SE:LP:AF:ID -0.000234921:0.000169242:0.769551:0.706753:rs3115847
1 768253 rs2977608 A C . PASS AF=0.761297 ES:SE:LP:AF:ID -0.000192197:0.000138131:0.79588:0.761297:rs2977608
1 776546 rs12124819 A G . PASS AF=0.265385 ES:SE:LP:AF:ID -0.000249326:0.000154297:0.958607:0.265385:rs12124819
1 814495 rs74461805 C A . PASS AF=0.340396 ES:SE:LP:AF:ID 3.73426e-05:0.000197935:0.0705811:0.340396:rs74461805
1 830181 rs28444699 A G . PASS AF=0.697255 ES:SE:LP:AF:ID -0.000130112:0.000132429:0.481486:0.697255:rs28444699
1 831489 rs4970385 C T . PASS AF=0.705397 ES:SE:LP:AF:ID -0.000162281:0.000130032:0.677781:0.705397:rs4970385
1 831909 rs9697642 C T . PASS AF=0.705442 ES:SE:LP:AF:ID -0.000166238:0.000130028:0.69897:0.705442:rs9697642
1 832066 rs9697380 G C . PASS AF=0.705627 ES:SE:LP:AF:ID -0.000168057:0.000130034:0.69897:0.705627:rs9697380
1 832318 rs4500250 C A . PASS AF=0.705655 ES:SE:LP:AF:ID -0.000165643:0.000130047:0.69897:0.705655:rs4500250
1 832398 rs4553118 T C . PASS AF=0.730129 ES:SE:LP:AF:ID -0.000156849:0.000133591:0.619789:0.730129:rs4553118
1 832918 rs28765502 T C . PASS AF=0.294377 ES:SE:LP:AF:ID 0.000165271:0.000130041:0.69897:0.294377:rs28765502
1 833223 rs13303211 C T . PASS AF=0.236703 ES:SE:LP:AF:ID 0.000120434:0.000138449:0.420216:0.236703:rs13303211
1 833302 rs28752186 C T . PASS AF=0.236691 ES:SE:LP:AF:ID 0.000120692:0.00013845:0.420216:0.236691:rs28752186
1 833641 rs28594623 T C . PASS AF=0.23975 ES:SE:LP:AF:ID 8.52331e-05:0.000138007:0.267606:0.23975:rs28594623
1 833824 rs28484835 T C . PASS AF=0.236693 ES:SE:LP:AF:ID 0.000120556:0.000138449:0.420216:0.236693:rs28484835
1 834832 rs4411087 G C . PASS AF=0.237178 ES:SE:LP:AF:ID 0.000121534:0.000138343:0.420216:0.237178:rs4411087
1 835499 rs4422948 A G . PASS AF=0.241162 ES:SE:LP:AF:ID 0.000113023:0.000137378:0.387216:0.241162:rs4422948
1 836896 rs28705752 T C . PASS AF=0.269511 ES:SE:LP:AF:ID 0.00014868:0.000132559:0.585027:0.269511:rs28705752
1 838555 rs4970383 C A . PASS AF=0.246204 ES:SE:LP:AF:ID 0.000133052:0.000136433:0.481486:0.246204:rs4970383
1 839103 rs28562941 A G . PASS AF=0.270019 ES:SE:LP:AF:ID 0.000131502:0.000132652:0.49485:0.270019:rs28562941
1 840753 rs4970382 T C . PASS AF=0.400124 ES:SE:LP:AF:ID 7.95046e-05:0.000119943:0.29243:0.400124:rs4970382
1 841085 rs1574243 C G . PASS AF=0.237102 ES:SE:LP:AF:ID 0.000153351:0.000139319:0.568636:0.237102:rs1574243
1 842362 rs28540380 C T . PASS AF=0.235323 ES:SE:LP:AF:ID 9.78869e-05:0.000141405:0.309804:0.235323:rs28540380
1 843405 rs11516185 A G . PASS AF=0.362606 ES:SE:LP:AF:ID -0.000132854:0.000148893:0.431798:0.362606:rs11516185
1 850218 rs6664536 T A . PASS AF=0.590331 ES:SE:LP:AF:ID -0.000128851:0.000119594:0.552842:0.590331:rs6664536
1 850371 rs6679046 G T . PASS AF=0.603723 ES:SE:LP:AF:ID -0.000132378:0.000120265:0.568636:0.603723:rs6679046
1 850780 rs6657440 C T . PASS AF=0.603942 ES:SE:LP:AF:ID -0.000124828:0.000120248:0.522879:0.603942:rs6657440
1 852037 rs4970463 G A . PASS AF=0.589686 ES:SE:LP:AF:ID -0.000131014:0.000119788:0.568636:0.589686:rs4970463
1 852063 rs28436996 G A . PASS AF=0.589665 ES:SE:LP:AF:ID -0.000134054:0.000119734:0.585027:0.589665:rs28436996
1 852875 rs13303369 C T . PASS AF=0.607671 ES:SE:LP:AF:ID -0.000162458:0.000120516:0.744727:0.607671:rs13303369
1 853954 rs1806509 C A . PASS AF=0.607829 ES:SE:LP:AF:ID -0.000164816:0.000120533:0.769551:0.607829:rs1806509
1 854777 rs13303019 A G . PASS AF=0.610316 ES:SE:LP:AF:ID -0.000160103:0.000120651:0.744727:0.610316:rs13303019
1 854978 rs13303057 A C . PASS AF=0.603283 ES:SE:LP:AF:ID -0.000132259:0.000120294:0.568636:0.603283:rs13303057
1 855075 rs6673914 C G . PASS AF=0.610337 ES:SE:LP:AF:ID -0.000160398:0.000120654:0.744727:0.610337:rs6673914
1 856099 rs28534711 T G . PASS AF=0.389936 ES:SE:LP:AF:ID 0.000159967:0.000120677:0.744727:0.389936:rs28534711
1 856108 rs28742275 A G . PASS AF=0.38992 ES:SE:LP:AF:ID 0.000160037:0.000120683:0.744727:0.38992:rs28742275
1 856476 rs4040605 A G . PASS AF=0.350356 ES:SE:LP:AF:ID 6.88111e-05:0.000123975:0.236572:0.350356:rs4040605
1 862866 rs3892970 C T . PASS AF=0.763122 ES:SE:LP:AF:ID 0.000131977:0.000139046:0.468521:0.763122:rs3892970
1 864938 rs2340587 G A . PASS AF=0.760006 ES:SE:LP:AF:ID 8.58556e-05:0.000138016:0.275724:0.760006:rs2340587
1 866893 rs2880024 T C . PASS AF=0.610552 ES:SE:LP:AF:ID -1.94653e-05:0.000121331:0.0604807:0.610552:rs2880024
1 868418 rs28546443 C T . PASS AF=0.297867 ES:SE:LP:AF:ID -5.41222e-05:0.000133307:0.167491:0.297867:rs28546443
1 870645 rs28576697 T C . PASS AF=0.291285 ES:SE:LP:AF:ID 1.86583e-05:0.000132246:0.05061:0.291285:rs28576697
1 871334 rs4072383 G T . PASS AF=0.72062 ES:SE:LP:AF:ID 4.70516e-05:0.000131427:0.142668:0.72062:rs4072383
1 872352 rs1806780 G C . PASS AF=0.267527 ES:SE:LP:AF:ID -6.62229e-05:0.000133208:0.207608:0.267527:rs1806780
1 873558 rs1110052 G T . PASS AF=0.715251 ES:SE:LP:AF:ID 1.17609e-05:0.000130382:0.0315171:0.715251:rs1110052