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_2470.vcf.gz --id UKB-b:6652 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_41246_2470.txt.gz --cohort_cases 1471 --cohort_controls 442454 --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": "2019-09-13T13:21:58.475862",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-6652/ukb-b-6652.vcf.gz; Date=Sat May 9 12:57:06 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-6652/UKB-b-6652_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-6652/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-6652/UKB-b-6652_data.vcf.gz ...
Read summary statistics for 2450764 SNPs.
Dropped 283 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, 621069 SNPs remain.
After merging with regression SNP LD, 621069 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: -0.0017 (0.0014)
Lambda GC: 1.1334
Mean Chi^2: 1.1244
Intercept: 1.1415 (0.0108)
Ratio: 1.1375 (0.0864)
Analysis finished at Thu Oct 17 14:40:51 2019
Total time elapsed: 32.97sQC metrics
Metrics
Metrics
{
"af_correlation": 0.7624,
"inflation_factor": 1.1474,
"mean_EFFECT": 1.3906e-06,
"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": 19404,
"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": 621069,
"ldsc_nsnp_merge_regression_ld": 621069,
"ldsc_observed_scale_h2_beta": "NA",
"ldsc_observed_scale_h2_se": "NA",
"ldsc_intercept_beta": 1.1415,
"ldsc_intercept_se": 0.0108,
"ldsc_lambda_gc": 1.1334,
"ldsc_mean_chisq": 1.1244,
"ldsc_ratio": 1.1375
}
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 | 2450484 | 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 | 2450764 | 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 | 8.652162e+00 | 5.766321e+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 | 7.856260e+07 | 5.661981e+07 | 5687.0000000 | 3.169393e+07 | 6.898754e+07 | 1.147678e+08 | 2.491722e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.400000e-06 | 1.367000e-04 | -0.0007560 | -9.080000e-05 | 8.000000e-07 | 9.340000e-05 | 6.748000e-04 | ▁▁▇▂▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.291000e-04 | 6.800000e-06 | 0.0001174 | 1.234000e-04 | 1.272000e-04 | 1.337000e-04 | 2.541000e-04 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.817019e-01 | 2.927759e-01 | 0.0000009 | 2.200002e-01 | 4.700002e-01 | 7.300002e-01 | 1.000000e+00 | ▇▇▇▆▆ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.817019e-01 | 2.927485e-01 | 0.0000009 | 2.238978e-01 | 4.745490e-01 | 7.348191e-01 | 9.999995e-01 | ▇▇▇▇▆ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.551410e-01 | 1.491910e-01 | 0.2379340 | 3.239620e-01 | 4.335930e-01 | 5.750772e-01 | 7.620660e-01 | ▇▆▅▅▃ |
| numeric | AF_reference | 19404 | 0.9920825 | NA | NA | NA | NA | NA | NA | NA | 4.365522e-01 | 1.748760e-01 | 0.0001997 | 2.987220e-01 | 4.217250e-01 | 5.642970e-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 | -0.0002620 | 0.0002160 | 0.2300001 | 0.2251040 | 0.623749 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0001061 | 0.0002140 | 0.6200004 | 0.6202207 | 0.400420 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0000485 | 0.0002107 | 0.8200001 | 0.8180844 | 0.456859 | 0.4207270 | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0002582 | 0.0002407 | 0.2800000 | 0.2833847 | 0.240912 | NA | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0002542 | 0.0001494 | 0.0890000 | 0.0889624 | 0.515716 | 0.2751600 | NA |
| 1 | 763394 | rs369924889 | G | A | -0.0000217 | 0.0001752 | 0.9000000 | 0.9012697 | 0.706659 | 0.6176120 | NA |
| 1 | 768253 | rs2977608 | A | C | -0.0001132 | 0.0001430 | 0.4299995 | 0.4287073 | 0.761326 | 0.4894170 | NA |
| 1 | 776546 | rs12124819 | A | G | -0.0001016 | 0.0001598 | 0.5199996 | 0.5246384 | 0.265287 | 0.0756789 | NA |
| 1 | 814495 | rs74461805 | C | A | 0.0001904 | 0.0002049 | 0.3500000 | 0.3527929 | 0.340409 | NA | NA |
| 1 | 830181 | rs28444699 | A | G | -0.0000014 | 0.0001371 | 0.9900000 | 0.9917926 | 0.697228 | 0.6912940 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51180501 | rs5770999 | T | C | 0.0001870 | 0.0001407 | 0.1800002 | 0.1837192 | 0.713710 | 0.6369810 | NA |
| 22 | 51181919 | rs9616825 | G | C | 0.0001991 | 0.0001400 | 0.1499999 | 0.1548210 | 0.695497 | 0.6194090 | NA |
| 22 | 51182485 | rs6009961 | A | G | 0.0001753 | 0.0001411 | 0.2099999 | 0.2140376 | 0.715545 | 0.6383790 | NA |
| 22 | 51186143 | rs2879914 | T | C | 0.0001455 | 0.0001309 | 0.2700001 | 0.2661250 | 0.382031 | 0.2733630 | NA |
| 22 | 51186228 | rs3865766 | C | T | 0.0001185 | 0.0001275 | 0.3500000 | 0.3526466 | 0.451301 | 0.4532750 | NA |
| 22 | 51197266 | rs61290853 | A | G | 0.0001566 | 0.0001317 | 0.2300001 | 0.2342398 | 0.386527 | 0.4229230 | NA |
| 22 | 51198027 | rs34939255 | A | G | 0.0000549 | 0.0001491 | 0.7099994 | 0.7125388 | 0.254380 | 0.0984425 | NA |
| 22 | 51211106 | rs9628250 | T | C | 0.0000646 | 0.0001478 | 0.6600001 | 0.6619349 | 0.271368 | 0.1671330 | NA |
| 22 | 51212875 | rs2238837 | A | C | 0.0001781 | 0.0001404 | 0.2000000 | 0.2047068 | 0.331620 | 0.3724040 | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0001426 | 0.0001437 | 0.3200000 | 0.3209743 | 0.298157 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623749 ES:SE:LP:AF:ID -0.000262025:0.000216002:0.638272:0.623749:rs10399793
1 54676 rs2462492 C T . PASS AF=0.40042 ES:SE:LP:AF:ID 0.000106051:0.000214012:0.207608:0.40042:rs2462492
1 91536 rs6702460 G T . PASS AF=0.456859 ES:SE:LP:AF:ID 4.84584e-05:0.00021068:0.0861861:0.456859:rs6702460
1 534192 rs6680723 C T . PASS AF=0.240912 ES:SE:LP:AF:ID -0.000258222:0.000240711:0.552842:0.240912:rs6680723
1 706368 rs12029736 A G . PASS AF=0.515716 ES:SE:LP:AF:ID -0.000254182:0.00014944:1.05061:0.515716:rs12029736
1 763394 rs3115847 G A . PASS AF=0.706659 ES:SE:LP:AF:ID -2.17329e-05:0.000175184:0.0457575:0.706659:rs3115847
1 768253 rs2977608 A C . PASS AF=0.761326 ES:SE:LP:AF:ID -0.000113209:0.000143048:0.366532:0.761326:rs2977608
1 776546 rs12124819 A G . PASS AF=0.265287 ES:SE:LP:AF:ID -0.000101638:0.000159755:0.283997:0.265287:rs12124819
1 814495 rs74461805 C A . PASS AF=0.340409 ES:SE:LP:AF:ID 0.000190418:0.00020493:0.455932:0.340409:rs74461805
1 830181 rs28444699 A G . PASS AF=0.697228 ES:SE:LP:AF:ID -1.40987e-06:0.000137059:0.00436481:0.697228:rs28444699
1 831489 rs4970385 C T . PASS AF=0.705392 ES:SE:LP:AF:ID -7.35486e-05:0.000134575:0.236572:0.705392:rs4970385
1 831909 rs9697642 C T . PASS AF=0.705436 ES:SE:LP:AF:ID -7.44975e-05:0.000134571:0.236572:0.705436:rs9697642
1 832066 rs9697380 G C . PASS AF=0.705622 ES:SE:LP:AF:ID -7.63149e-05:0.000134576:0.244125:0.705622:rs9697380
1 832318 rs4500250 C A . PASS AF=0.70565 ES:SE:LP:AF:ID -7.57947e-05:0.00013459:0.244125:0.70565:rs4500250
1 832398 rs4553118 T C . PASS AF=0.730094 ES:SE:LP:AF:ID 8.86606e-06:0.000138264:0.0222764:0.730094:rs4553118
1 832918 rs28765502 T C . PASS AF=0.294384 ES:SE:LP:AF:ID 7.52384e-05:0.000134583:0.236572:0.294384:rs28765502
1 833641 rs28594623 T C . PASS AF=0.239768 ES:SE:LP:AF:ID 0.000223172:0.000142858:0.920819:0.239768:rs28594623
1 835499 rs4422948 A G . PASS AF=0.241163 ES:SE:LP:AF:ID 0.000199651:0.000142204:0.79588:0.241163:rs4422948
1 836896 rs28705752 T C . PASS AF=0.269519 ES:SE:LP:AF:ID 0.000281591:0.000137216:1.39794:0.269519:rs28705752
1 838555 rs4970383 C A . PASS AF=0.246253 ES:SE:LP:AF:ID 0.000189589:0.000141232:0.744727:0.246253:rs4970383
1 839103 rs28562941 A G . PASS AF=0.270021 ES:SE:LP:AF:ID 0.000274879:0.000137309:1.34679:0.270021:rs28562941
1 840753 rs4970382 T C . PASS AF=0.40029 ES:SE:LP:AF:ID 1.69199e-05:0.000124152:0.05061:0.40029:rs4970382
1 843405 rs11516185 A G . PASS AF=0.362612 ES:SE:LP:AF:ID 0.000141919:0.000154121:0.443698:0.362612:rs11516185
1 850218 rs6664536 T A . PASS AF=0.590335 ES:SE:LP:AF:ID 6.50527e-06:0.000123805:0.0177288:0.590335:rs6664536
1 850371 rs6679046 G T . PASS AF=0.603697 ES:SE:LP:AF:ID 1.12703e-06:0.000124505:0.00436481:0.603697:rs6679046
1 850780 rs6657440 C T . PASS AF=0.603919 ES:SE:LP:AF:ID -6.11785e-06:0.000124488:0.0177288:0.603919:rs6657440
1 852037 rs4970463 G A . PASS AF=0.589697 ES:SE:LP:AF:ID 8.94217e-06:0.000124006:0.0268721:0.589697:rs4970463
1 852063 rs28436996 G A . PASS AF=0.589682 ES:SE:LP:AF:ID 1.11488e-05:0.000123951:0.0315171:0.589682:rs28436996
1 852875 rs13303369 C T . PASS AF=0.607669 ES:SE:LP:AF:ID -5.54889e-06:0.000124771:0.0177288:0.607669:rs13303369
1 853954 rs1806509 C A . PASS AF=0.607829 ES:SE:LP:AF:ID -2.93146e-06:0.000124787:0.00877392:0.607829:rs1806509
1 854777 rs13303019 A G . PASS AF=0.610313 ES:SE:LP:AF:ID -7.65064e-06:0.00012491:0.0222764:0.610313:rs13303019
1 854978 rs13303057 A C . PASS AF=0.603264 ES:SE:LP:AF:ID 1.08307e-05:0.000124535:0.0315171:0.603264:rs13303057
1 855075 rs6673914 C G . PASS AF=0.610331 ES:SE:LP:AF:ID -7.80752e-06:0.000124913:0.0222764:0.610331:rs6673914
1 856099 rs28534711 T G . PASS AF=0.389943 ES:SE:LP:AF:ID 4.17354e-06:0.000124938:0.0132283:0.389943:rs28534711
1 856108 rs28742275 A G . PASS AF=0.389927 ES:SE:LP:AF:ID 4.42682e-06:0.000124944:0.0132283:0.389927:rs28742275
1 856476 rs4040605 A G . PASS AF=0.35034 ES:SE:LP:AF:ID 4.6644e-05:0.000128337:0.142668:0.35034:rs4040605
1 864938 rs2340587 G A . PASS AF=0.760092 ES:SE:LP:AF:ID 1.53945e-05:0.000142906:0.0409586:0.760092:rs2340587
1 866893 rs2880024 T C . PASS AF=0.610568 ES:SE:LP:AF:ID -6.9422e-05:0.00012558:0.236572:0.610568:rs2880024
1 868418 rs28546443 C T . PASS AF=0.297795 ES:SE:LP:AF:ID 0.000131517:0.000138034:0.468521:0.297795:rs28546443
1 870645 rs28576697 T C . PASS AF=0.291185 ES:SE:LP:AF:ID 0.000114262:0.000136886:0.39794:0.291185:rs28576697
1 871334 rs4072383 G T . PASS AF=0.72069 ES:SE:LP:AF:ID -3.52628e-05:0.000136041:0.09691:0.72069:rs4072383
1 872352 rs1806780 G C . PASS AF=0.267478 ES:SE:LP:AF:ID 6.5814e-05:0.000137879:0.200659:0.267478:rs1806780
1 873558 rs1110052 G T . PASS AF=0.715324 ES:SE:LP:AF:ID -4.9135e-05:0.000134971:0.142668:0.715324:rs1110052
1 875770 rs4970379 A G . PASS AF=0.599966 ES:SE:LP:AF:ID -0.000149961:0.000126623:0.619789:0.599966:rs4970379
1 877147 rs114982608 G A . PASS AF=0.242972 ES:SE:LP:AF:ID 8.99546e-05:0.000143411:0.275724:0.242972:rs114982608
1 881627 rs2272757 G A . PASS AF=0.652302 ES:SE:LP:AF:ID -0.000164628:0.00012791:0.69897:0.652302:rs2272757
1 882033 rs2272756 G A . PASS AF=0.24393 ES:SE:LP:AF:ID 0.000117035:0.000142468:0.387216:0.24393:rs2272756
1 890104 rs28631199 G A . PASS AF=0.246807 ES:SE:LP:AF:ID 0.000134122:0.0001419:0.468521:0.246807:rs28631199
1 891059 rs13303065 C T . PASS AF=0.652355 ES:SE:LP:AF:ID -0.00015209:0.000127892:0.638272:0.652355:rs13303065
1 891945 rs13303106 A G . PASS AF=0.652412 ES:SE:LP:AF:ID -0.000156088:0.00012804:0.657577:0.652412:rs13303106