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_3079.vcf.gz --id UKB-b:2317 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_3079.txt.gz --cohort_cases 1446 --cohort_controls 461106 --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": "2019-09-12T19:39:21.969597",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-2317/ukb-b-2317.vcf.gz; Date=Sun May 10 12:21:14 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-2317/UKB-b-2317_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-2317/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:42:28 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-2317/UKB-b-2317_data.vcf.gz ...
Read summary statistics for 2405349 SNPs.
Dropped 272 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, 610356 SNPs remain.
After merging with regression SNP LD, 610356 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.001 (0.0012)
Lambda GC: 1.0189
Mean Chi^2: 1.0222
Intercept: 1.0113 (0.0094)
Ratio: 0.5078 (0.4234)
Analysis finished at Thu Oct 17 14:43:00 2019
Total time elapsed: 32.27sQC metrics
Metrics
Metrics
{
"af_correlation": 0.7566,
"inflation_factor": 1,
"mean_EFFECT": 5.1593e-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": 19022,
"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": 610356,
"ldsc_nsnp_merge_regression_ld": 610356,
"ldsc_observed_scale_h2_beta": 0.001,
"ldsc_observed_scale_h2_se": 0.0012,
"ldsc_intercept_beta": 1.0113,
"ldsc_intercept_se": 0.0094,
"ldsc_lambda_gc": 1.0189,
"ldsc_mean_chisq": 1.0222,
"ldsc_ratio": 0.509
}
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 | 2405080 | 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 | 2405349 | 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 | 8.651136e+00 | 5.766566e+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 | 7.858632e+07 | 5.662924e+07 | 5687.0000000 | 3.172276e+07 | 6.902172e+07 | 1.147694e+08 | 2.491722e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 5.000000e-07 | 1.241000e-04 | -0.0005978 | -8.290000e-05 | 9.000000e-07 | 8.380000e-05 | 5.850000e-04 | ▁▂▇▂▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.229000e-04 | 6.200000e-06 | 0.0001120 | 1.176000e-04 | 1.211000e-04 | 1.271000e-04 | 2.425000e-04 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.977467e-01 | 2.893169e-01 | 0.0000018 | 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 | 4.977463e-01 | 2.892891e-01 | 0.0000018 | 2.471979e-01 | 4.968523e-01 | 7.485450e-01 | 9.999999e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.565966e-01 | 1.468618e-01 | 0.2420480 | 3.273900e-01 | 4.358400e-01 | 5.749090e-01 | 7.579520e-01 | ▇▆▅▅▃ |
| numeric | AF_reference | 19022 | 0.9920918 | NA | NA | NA | NA | NA | NA | NA | 4.378482e-01 | 1.734093e-01 | 0.0001997 | 3.015180e-01 | 4.235220e-01 | 5.644970e-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 | 0.0001324 | 0.0002061 | 0.5199996 | 0.5205427 | 0.623761 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0001085 | 0.0002042 | 0.5999997 | 0.5952455 | 0.400408 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0000558 | 0.0002010 | 0.7800007 | 0.7812511 | 0.456849 | 0.4207270 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0002416 | 0.0001425 | 0.0899995 | 0.0901056 | 0.515661 | 0.2751600 | NA |
| 1 | 763394 | rs369924889 | G | A | -0.0000348 | 0.0001671 | 0.8300000 | 0.8348821 | 0.706785 | 0.6176120 | NA |
| 1 | 776546 | rs12124819 | A | G | -0.0000163 | 0.0001524 | 0.9100000 | 0.9147295 | 0.265391 | 0.0756789 | NA |
| 1 | 814495 | rs74461805 | C | A | -0.0002702 | 0.0001955 | 0.1700000 | 0.1668680 | 0.340401 | NA | NA |
| 1 | 830181 | rs28444699 | A | G | -0.0001145 | 0.0001308 | 0.3800004 | 0.3814335 | 0.697269 | 0.6912940 | NA |
| 1 | 831489 | rs4970385 | C | T | -0.0001233 | 0.0001284 | 0.3400001 | 0.3368668 | 0.705416 | 0.6491610 | NA |
| 1 | 831909 | rs9697642 | C | T | -0.0001221 | 0.0001284 | 0.3400001 | 0.3415591 | 0.705461 | 0.6485620 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51180501 | rs5770999 | T | C | 0.0000604 | 0.0001341 | 0.6499995 | 0.6523815 | 0.713629 | 0.6369810 | NA |
| 22 | 51181919 | rs9616825 | G | C | -0.0000406 | 0.0001335 | 0.7600007 | 0.7607159 | 0.695448 | 0.6194090 | NA |
| 22 | 51182485 | rs6009961 | A | G | 0.0000460 | 0.0001346 | 0.7300002 | 0.7322524 | 0.715477 | 0.6383790 | NA |
| 22 | 51186143 | rs2879914 | T | C | -0.0000368 | 0.0001248 | 0.7700005 | 0.7682156 | 0.381806 | 0.2733630 | NA |
| 22 | 51186228 | rs3865766 | C | T | -0.0001010 | 0.0001216 | 0.4100001 | 0.4061789 | 0.451045 | 0.4532750 | NA |
| 22 | 51197266 | rs61290853 | A | G | -0.0000233 | 0.0001256 | 0.8499999 | 0.8525056 | 0.386305 | 0.4229230 | NA |
| 22 | 51198027 | rs34939255 | A | G | 0.0001983 | 0.0001422 | 0.1600000 | 0.1630256 | 0.254554 | 0.0984425 | NA |
| 22 | 51211106 | rs9628250 | T | C | 0.0001608 | 0.0001409 | 0.2500000 | 0.2539635 | 0.271549 | 0.1671330 | NA |
| 22 | 51212875 | rs2238837 | A | C | -0.0000111 | 0.0001339 | 0.9299999 | 0.9338056 | 0.331421 | 0.3724040 | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0000921 | 0.0001371 | 0.5000000 | 0.5017827 | 0.297934 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623761 ES:SE:LP:AF:ID 0.000132407:0.000206078:0.283997:0.623761:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400408 ES:SE:LP:AF:ID -0.00010846:0.00020416:0.221849:0.400408:rs2462492
1 91536 rs6702460 G T . PASS AF=0.456849 ES:SE:LP:AF:ID -5.58216e-05:0.000201022:0.107905:0.456849:rs6702460
1 706368 rs12029736 A G . PASS AF=0.515661 ES:SE:LP:AF:ID -0.000241586:0.000142542:1.04576:0.515661:rs12029736
1 763394 rs3115847 G A . PASS AF=0.706785 ES:SE:LP:AF:ID -3.48375e-05:0.000167131:0.0809219:0.706785:rs3115847
1 776546 rs12124819 A G . PASS AF=0.265391 ES:SE:LP:AF:ID -1.63134e-05:0.000152355:0.0409586:0.265391:rs12124819
1 814495 rs74461805 C A . PASS AF=0.340401 ES:SE:LP:AF:ID -0.000270189:0.000195458:0.769551:0.340401:rs74461805
1 830181 rs28444699 A G . PASS AF=0.697269 ES:SE:LP:AF:ID -0.000114454:0.000130766:0.420216:0.697269:rs28444699
1 831489 rs4970385 C T . PASS AF=0.705416 ES:SE:LP:AF:ID -0.000123314:0.000128402:0.468521:0.705416:rs4970385
1 831909 rs9697642 C T . PASS AF=0.705461 ES:SE:LP:AF:ID -0.000122117:0.000128397:0.468521:0.705461:rs9697642
1 832066 rs9697380 G C . PASS AF=0.705646 ES:SE:LP:AF:ID -0.000124258:0.000128403:0.481486:0.705646:rs9697380
1 832318 rs4500250 C A . PASS AF=0.705674 ES:SE:LP:AF:ID -0.000125281:0.000128416:0.481486:0.705674:rs4500250
1 832398 rs4553118 T C . PASS AF=0.73015 ES:SE:LP:AF:ID -0.000151649:0.000131916:0.60206:0.73015:rs4553118
1 832918 rs28765502 T C . PASS AF=0.294358 ES:SE:LP:AF:ID 0.0001244:0.000128411:0.481486:0.294358:rs28765502
1 836896 rs28705752 T C . PASS AF=0.269496 ES:SE:LP:AF:ID 0.000118744:0.0001309:0.443698:0.269496:rs28705752
1 838555 rs4970383 C A . PASS AF=0.246188 ES:SE:LP:AF:ID 0.000121394:0.000134724:0.431798:0.246188:rs4970383
1 839103 rs28562941 A G . PASS AF=0.270006 ES:SE:LP:AF:ID 0.000107899:0.000130992:0.387216:0.270006:rs28562941
1 840753 rs4970382 T C . PASS AF=0.400096 ES:SE:LP:AF:ID 3.55899e-05:0.00011844:0.119186:0.400096:rs4970382
1 843405 rs11516185 A G . PASS AF=0.362612 ES:SE:LP:AF:ID 0.000321242:0.00014702:1.5376:0.362612:rs11516185
1 850218 rs6664536 T A . PASS AF=0.590346 ES:SE:LP:AF:ID 6.04117e-06:0.000118091:0.0177288:0.590346:rs6664536
1 850371 rs6679046 G T . PASS AF=0.603738 ES:SE:LP:AF:ID -1.9121e-05:0.000118754:0.0604807:0.603738:rs6679046
1 850780 rs6657440 C T . PASS AF=0.603952 ES:SE:LP:AF:ID -2.97132e-05:0.000118736:0.09691:0.603952:rs6657440
1 852037 rs4970463 G A . PASS AF=0.589702 ES:SE:LP:AF:ID 1.33654e-05:0.000118283:0.0409586:0.589702:rs4970463
1 852063 rs28436996 G A . PASS AF=0.589681 ES:SE:LP:AF:ID 9.66959e-06:0.00011823:0.0315171:0.589681:rs28436996
1 852875 rs13303369 C T . PASS AF=0.607683 ES:SE:LP:AF:ID 7.59629e-06:0.000119002:0.0222764:0.607683:rs13303369
1 853954 rs1806509 C A . PASS AF=0.607842 ES:SE:LP:AF:ID 1.0822e-05:0.000119019:0.0315171:0.607842:rs1806509
1 854777 rs13303019 A G . PASS AF=0.610323 ES:SE:LP:AF:ID -2.42802e-06:0.000119135:0.00877392:0.610323:rs13303019
1 854978 rs13303057 A C . PASS AF=0.603299 ES:SE:LP:AF:ID -1.55178e-05:0.000118783:0.0457575:0.603299:rs13303057
1 855075 rs6673914 C G . PASS AF=0.610344 ES:SE:LP:AF:ID -3.53505e-06:0.000119138:0.00877392:0.610344:rs6673914
1 856099 rs28534711 T G . PASS AF=0.389928 ES:SE:LP:AF:ID -9.70143e-07:0.00011916:0.00436481:0.389928:rs28534711
1 856108 rs28742275 A G . PASS AF=0.389912 ES:SE:LP:AF:ID -7.34073e-07:0.000119166:-0:0.389912:rs28742275
1 856476 rs4040605 A G . PASS AF=0.350338 ES:SE:LP:AF:ID 6.22234e-05:0.000122419:0.21467:0.350338:rs4040605
1 866893 rs2880024 T C . PASS AF=0.610569 ES:SE:LP:AF:ID -4.38408e-05:0.000119807:0.148742:0.610569:rs2880024
1 868418 rs28546443 C T . PASS AF=0.297844 ES:SE:LP:AF:ID 0.000170302:0.000131638:0.69897:0.297844:rs28546443
1 870645 rs28576697 T C . PASS AF=0.291264 ES:SE:LP:AF:ID 0.000167069:0.000130587:0.69897:0.291264:rs28576697
1 871334 rs4072383 G T . PASS AF=0.720647 ES:SE:LP:AF:ID -0.000143146:0.00012978:0.568636:0.720647:rs4072383
1 872352 rs1806780 G C . PASS AF=0.267501 ES:SE:LP:AF:ID 0.000142964:0.00013154:0.552842:0.267501:rs1806780
1 873558 rs1110052 G T . PASS AF=0.71528 ES:SE:LP:AF:ID -9.76765e-05:0.000128749:0.346787:0.71528:rs1110052
1 875770 rs4970379 A G . PASS AF=0.600089 ES:SE:LP:AF:ID -8.0686e-05:0.000120805:0.30103:0.600089:rs4970379
1 877147 rs114982608 G A . PASS AF=0.242942 ES:SE:LP:AF:ID 0.000109459:0.000136825:0.376751:0.242942:rs114982608
1 881627 rs2272757 G A . PASS AF=0.652412 ES:SE:LP:AF:ID -3.28012e-05:0.000122031:0.102373:0.652412:rs2272757
1 882033 rs2272756 G A . PASS AF=0.243893 ES:SE:LP:AF:ID 0.00017162:0.000135919:0.677781:0.243893:rs2272756
1 890104 rs28631199 G A . PASS AF=0.246748 ES:SE:LP:AF:ID 0.000152057:0.000135382:0.585027:0.246748:rs28631199
1 891059 rs13303065 C T . PASS AF=0.652451 ES:SE:LP:AF:ID -2.39859e-05:0.000122012:0.0757207:0.652451:rs13303065
1 891945 rs13303106 A G . PASS AF=0.652513 ES:SE:LP:AF:ID -2.24376e-05:0.000122154:0.0705811:0.652513:rs13303106
1 900505 rs28705211 G C . PASS AF=0.27174 ES:SE:LP:AF:ID 0.000178402:0.000132061:0.744727:0.27174:rs28705211
1 903245 rs28690976 A G . PASS AF=0.566941 ES:SE:LP:AF:ID -0.000121552:0.000121328:0.49485:0.566941:rs28690976
1 909073 rs3892467 C T . PASS AF=0.386681 ES:SE:LP:AF:ID -0.000111775:0.000120995:0.443698:0.386681:rs3892467
1 909238 rs3829740 G C . PASS AF=0.57139 ES:SE:LP:AF:ID -0.000202276:0.000117181:1.07572:0.57139:rs3829740
1 910394 rs28477686 C T . PASS AF=0.324452 ES:SE:LP:AF:ID 8.29932e-05:0.00012701:0.29243:0.324452:rs28477686