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_40006_C541.vcf.gz --id UKB-b:14956 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_40006_C541.txt.gz --cohort_cases 1494 --cohort_controls 461516 --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": "2019-09-13T04:50:20.492548",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-14956/ukb-b-14956.vcf.gz; Date=Sun May 10 03:57:44 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-14956/UKB-b-14956_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-14956/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:43:49 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-14956/UKB-b-14956_data.vcf.gz ...
Read summary statistics for 2489138 SNPs.
Dropped 291 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, 630116 SNPs remain.
After merging with regression SNP LD, 630116 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0028 (0.0012)
Lambda GC: 1.0288
Mean Chi^2: 1.0261
Intercept: 0.9973 (0.0096)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Thu Oct 17 14:44:27 2019
Total time elapsed: 37.97sQC metrics
Metrics
Metrics
{
"af_correlation": 0.7672,
"inflation_factor": 1.0475,
"mean_EFFECT": -1.3459e-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": 19742,
"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": 630116,
"ldsc_nsnp_merge_regression_ld": 630116,
"ldsc_observed_scale_h2_beta": 0.0028,
"ldsc_observed_scale_h2_se": 0.0012,
"ldsc_intercept_beta": 0.9973,
"ldsc_intercept_se": 0.0096,
"ldsc_lambda_gc": 1.0288,
"ldsc_mean_chisq": 1.0261,
"ldsc_ratio": -0.1034
}
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 | 2488850 | 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 | 2489138 | 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 | 5.766005e+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 | 7.855680e+07 | 5.662456e+07 | 5687.0000000 | 3.168010e+07 | 6.898639e+07 | 1.147562e+08 | 2.491917e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -1.300000e-06 | 1.273000e-04 | -0.0006576 | -8.670000e-05 | -8.000000e-07 | 8.420000e-05 | 6.624000e-04 | ▁▂▇▂▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.251000e-04 | 6.700000e-06 | 0.0001136 | 1.194000e-04 | 1.232000e-04 | 1.298000e-04 | 2.460000e-04 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.957702e-01 | 2.900783e-01 | 0.0000004 | 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 | 4.957708e-01 | 2.900532e-01 | 0.0000004 | 2.433120e-01 | 4.941606e-01 | 7.474720e-01 | 9.999996e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.537703e-01 | 1.511048e-01 | 0.2342710 | 3.209720e-01 | 4.315500e-01 | 5.750628e-01 | 7.657290e-01 | ▇▆▅▅▃ |
| numeric | AF_reference | 19742 | 0.9920687 | NA | NA | NA | NA | NA | NA | NA | 4.353415e-01 | 1.761073e-01 | 0.0001997 | 2.963260e-01 | 4.199280e-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 | 0.0000834 | 0.0002090 | 0.6899999 | 0.6898723 | 0.623765 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0000967 | 0.0002071 | 0.6400000 | 0.6405528 | 0.400401 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0001755 | 0.0002039 | 0.3900004 | 0.3895204 | 0.456846 | 0.4207270 | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0000247 | 0.0002329 | 0.9199999 | 0.9156768 | 0.240959 | NA | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0000252 | 0.0001446 | 0.8600001 | 0.8616149 | 0.515645 | 0.2751600 | NA |
| 1 | 763394 | rs369924889 | G | A | 0.0000871 | 0.0001695 | 0.6100002 | 0.6074581 | 0.706753 | 0.6176120 | NA |
| 1 | 768253 | rs2977608 | A | C | -0.0000629 | 0.0001384 | 0.6499995 | 0.6491856 | 0.761297 | 0.4894170 | NA |
| 1 | 776546 | rs12124819 | A | G | 0.0002983 | 0.0001546 | 0.0539995 | 0.0535775 | 0.265385 | 0.0756789 | NA |
| 1 | 814495 | rs74461805 | C | A | -0.0000771 | 0.0001983 | 0.6999999 | 0.6972684 | 0.340396 | NA | NA |
| 1 | 830181 | rs28444699 | A | G | 0.0001571 | 0.0001326 | 0.2399999 | 0.2363267 | 0.697255 | 0.6912940 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51180501 | rs5770999 | T | C | -0.0001448 | 0.0001361 | 0.2900000 | 0.2873295 | 0.713656 | 0.6369810 | NA |
| 22 | 51181919 | rs9616825 | G | C | -0.0001859 | 0.0001354 | 0.1700000 | 0.1696361 | 0.695470 | 0.6194090 | NA |
| 22 | 51182485 | rs6009961 | A | G | -0.0001516 | 0.0001365 | 0.2700001 | 0.2667311 | 0.715502 | 0.6383790 | NA |
| 22 | 51186143 | rs2879914 | T | C | 0.0000443 | 0.0001266 | 0.7300002 | 0.7261588 | 0.381825 | 0.2733630 | NA |
| 22 | 51186228 | rs3865766 | C | T | 0.0000251 | 0.0001234 | 0.8400000 | 0.8388717 | 0.451061 | 0.4532750 | NA |
| 22 | 51197266 | rs61290853 | A | G | -0.0000353 | 0.0001274 | 0.7800007 | 0.7818109 | 0.386333 | 0.4229230 | NA |
| 22 | 51198027 | rs34939255 | A | G | -0.0001596 | 0.0001442 | 0.2700001 | 0.2684945 | 0.254562 | 0.0984425 | NA |
| 22 | 51211106 | rs9628250 | T | C | -0.0001523 | 0.0001430 | 0.2900000 | 0.2867413 | 0.271547 | 0.1671330 | NA |
| 22 | 51212875 | rs2238837 | A | C | 0.0000363 | 0.0001358 | 0.7899998 | 0.7890308 | 0.331457 | 0.3724040 | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0000802 | 0.0001391 | 0.5600000 | 0.5640635 | 0.297974 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623765 ES:SE:LP:AF:ID 8.34133e-05:0.000209041:0.161151:0.623765:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400401 ES:SE:LP:AF:ID 9.66985e-05:0.000207096:0.19382:0.400401:rs2462492
1 91536 rs6702460 G T . PASS AF=0.456846 ES:SE:LP:AF:ID -0.000175462:0.00020391:0.408935:0.456846:rs6702460
1 534192 rs6680723 C T . PASS AF=0.240959 ES:SE:LP:AF:ID -2.46618e-05:0.00023292:0.0362122:0.240959:rs6680723
1 706368 rs12029736 A G . PASS AF=0.515645 ES:SE:LP:AF:ID 2.52051e-05:0.000144592:0.0655015:0.515645:rs12029736
1 763394 rs3115847 G A . PASS AF=0.706753 ES:SE:LP:AF:ID 8.70833e-05:0.00016952:0.21467:0.706753:rs3115847
1 768253 rs2977608 A C . PASS AF=0.761297 ES:SE:LP:AF:ID -6.29382e-05:0.000138358:0.187087:0.761297:rs2977608
1 776546 rs12124819 A G . PASS AF=0.265385 ES:SE:LP:AF:ID 0.00029832:0.000154551:1.26761:0.265385:rs12124819
1 814495 rs74461805 C A . PASS AF=0.340396 ES:SE:LP:AF:ID -7.71256e-05:0.000198261:0.154902:0.340396:rs74461805
1 830181 rs28444699 A G . PASS AF=0.697255 ES:SE:LP:AF:ID 0.000157083:0.000132647:0.619789:0.697255:rs28444699
1 831489 rs4970385 C T . PASS AF=0.705397 ES:SE:LP:AF:ID 0.000113113:0.000130246:0.408935:0.705397:rs4970385
1 831909 rs9697642 C T . PASS AF=0.705442 ES:SE:LP:AF:ID 0.00011216:0.000130242:0.408935:0.705442:rs9697642
1 832066 rs9697380 G C . PASS AF=0.705627 ES:SE:LP:AF:ID 0.000109952:0.000130248:0.39794:0.705627:rs9697380
1 832318 rs4500250 C A . PASS AF=0.705655 ES:SE:LP:AF:ID 0.000109412:0.000130261:0.39794:0.705655:rs4500250
1 832398 rs4553118 T C . PASS AF=0.730129 ES:SE:LP:AF:ID 0.000139991:0.000133811:0.522879:0.730129:rs4553118
1 832918 rs28765502 T C . PASS AF=0.294377 ES:SE:LP:AF:ID -0.000110024:0.000130255:0.39794:0.294377:rs28765502
1 833223 rs13303211 C T . PASS AF=0.236703 ES:SE:LP:AF:ID -0.000142334:0.000138677:0.522879:0.236703:rs13303211
1 833302 rs28752186 C T . PASS AF=0.236691 ES:SE:LP:AF:ID -0.000142113:0.000138678:0.508638:0.236691:rs28752186
1 833641 rs28594623 T C . PASS AF=0.23975 ES:SE:LP:AF:ID -0.000138875:0.000138234:0.49485:0.23975:rs28594623
1 833824 rs28484835 T C . PASS AF=0.236693 ES:SE:LP:AF:ID -0.000142224:0.000138677:0.508638:0.236693:rs28484835
1 834832 rs4411087 G C . PASS AF=0.237178 ES:SE:LP:AF:ID -0.000140902:0.000138571:0.508638:0.237178:rs4411087
1 835499 rs4422948 A G . PASS AF=0.241162 ES:SE:LP:AF:ID -0.000153094:0.000137604:0.568636:0.241162:rs4422948
1 836896 rs28705752 T C . PASS AF=0.269511 ES:SE:LP:AF:ID -0.000118242:0.000132777:0.431798:0.269511:rs28705752
1 838555 rs4970383 C A . PASS AF=0.246204 ES:SE:LP:AF:ID -0.00013431:0.000136658:0.481486:0.246204:rs4970383
1 839103 rs28562941 A G . PASS AF=0.270019 ES:SE:LP:AF:ID -0.000126992:0.000132871:0.468521:0.270019:rs28562941
1 840753 rs4970382 T C . PASS AF=0.400124 ES:SE:LP:AF:ID -9.75345e-05:0.000120141:0.376751:0.400124:rs4970382
1 841085 rs1574243 C G . PASS AF=0.237102 ES:SE:LP:AF:ID -0.000144122:0.000139548:0.522879:0.237102:rs1574243
1 842362 rs28540380 C T . PASS AF=0.235323 ES:SE:LP:AF:ID -0.000186872:0.000141638:0.721246:0.235323:rs28540380
1 843405 rs11516185 A G . PASS AF=0.362606 ES:SE:LP:AF:ID 0.000128391:0.000149138:0.408935:0.362606:rs11516185
1 850218 rs6664536 T A . PASS AF=0.590331 ES:SE:LP:AF:ID 1.23335e-05:0.000119791:0.0362122:0.590331:rs6664536
1 850371 rs6679046 G T . PASS AF=0.603723 ES:SE:LP:AF:ID 4.77165e-06:0.000120463:0.0132283:0.603723:rs6679046
1 850780 rs6657440 C T . PASS AF=0.603942 ES:SE:LP:AF:ID 7.23787e-06:0.000120446:0.0222764:0.603942:rs6657440
1 852037 rs4970463 G A . PASS AF=0.589686 ES:SE:LP:AF:ID -1.74283e-06:0.000119985:0.00436481:0.589686:rs4970463
1 852063 rs28436996 G A . PASS AF=0.589665 ES:SE:LP:AF:ID 1.18193e-06:0.000119931:0.00436481:0.589665:rs28436996
1 852875 rs13303369 C T . PASS AF=0.607671 ES:SE:LP:AF:ID 1.11121e-06:0.000120715:0.00436481:0.607671:rs13303369
1 853954 rs1806509 C A . PASS AF=0.607829 ES:SE:LP:AF:ID -2.4657e-07:0.000120731:-0:0.607829:rs1806509
1 854777 rs13303019 A G . PASS AF=0.610316 ES:SE:LP:AF:ID -1.86635e-05:0.00012085:0.0555173:0.610316:rs13303019
1 854978 rs13303057 A C . PASS AF=0.603283 ES:SE:LP:AF:ID -2.02007e-06:0.000120492:0.00436481:0.603283:rs13303057
1 855075 rs6673914 C G . PASS AF=0.610337 ES:SE:LP:AF:ID -1.67679e-05:0.000120852:0.05061:0.610337:rs6673914
1 856099 rs28534711 T G . PASS AF=0.389936 ES:SE:LP:AF:ID 2.27817e-05:0.000120875:0.0705811:0.389936:rs28534711
1 856108 rs28742275 A G . PASS AF=0.38992 ES:SE:LP:AF:ID 2.28452e-05:0.000120882:0.0705811:0.38992:rs28742275
1 856476 rs4040605 A G . PASS AF=0.350356 ES:SE:LP:AF:ID 1.64696e-05:0.000124179:0.05061:0.350356:rs4040605
1 862866 rs3892970 C T . PASS AF=0.763122 ES:SE:LP:AF:ID 1.57955e-06:0.000139275:0.00436481:0.763122:rs3892970
1 864938 rs2340587 G A . PASS AF=0.760006 ES:SE:LP:AF:ID -3.73486e-05:0.000138243:0.102373:0.760006:rs2340587
1 866893 rs2880024 T C . PASS AF=0.610552 ES:SE:LP:AF:ID 7.05538e-06:0.00012153:0.0222764:0.610552:rs2880024
1 868418 rs28546443 C T . PASS AF=0.297867 ES:SE:LP:AF:ID 8.53061e-05:0.000133527:0.283997:0.297867:rs28546443
1 870645 rs28576697 T C . PASS AF=0.291285 ES:SE:LP:AF:ID 0.000160653:0.000132464:0.638272:0.291285:rs28576697
1 871334 rs4072383 G T . PASS AF=0.72062 ES:SE:LP:AF:ID -5.21936e-05:0.000131643:0.161151:0.72062:rs4072383
1 872352 rs1806780 G C . PASS AF=0.267527 ES:SE:LP:AF:ID 6.65524e-05:0.000133428:0.207608:0.267527:rs1806780
1 873558 rs1110052 G T . PASS AF=0.715251 ES:SE:LP:AF:ID -5.44386e-05:0.000130597:0.167491:0.715251:rs1110052