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_20084_476.vcf.gz --id UKB-b:129 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20084_476.txt.gz --cohort_cases 1478 --cohort_controls 63471 --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": "2019-09-13T13:38:50.422973",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-129/ukb-b-129.vcf.gz; Date=Fri May 8 05:21:29 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-129/UKB-b-129_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-129/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:42:10 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-129/UKB-b-129_data.vcf.gz ...
Read summary statistics for 2462654 SNPs.
Dropped 286 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, 623869 SNPs remain.
After merging with regression SNP LD, 623869 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0163 (0.0089)
Lambda GC: 1.0204
Mean Chi^2: 1.0164
Intercept: 0.9913 (0.01)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Thu Oct 17 14:42:47 2019
Total time elapsed: 37.01sQC metrics
Metrics
Metrics
{
"af_correlation": 0.7643,
"inflation_factor": 1.0475,
"mean_EFFECT": -3.4229e-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": 19512,
"n_est": "NA",
"ratio_se_n": "NA",
"mean_diff": "NaN",
"ratio_diff": "NaN",
"sd_y_est1": "NA",
"sd_y_est2": "NA",
"r2_sum1": 0,
"r2_sum2": 0,
"r2_sum3": 0,
"r2_sum4": 0,
"ldsc_nsnp_merge_refpanel_ld": 623869,
"ldsc_nsnp_merge_regression_ld": 623869,
"ldsc_observed_scale_h2_beta": 0.0163,
"ldsc_observed_scale_h2_se": 0.0089,
"ldsc_intercept_beta": 0.9913,
"ldsc_intercept_se": 0.01,
"ldsc_lambda_gc": 1.0204,
"ldsc_mean_chisq": 1.0164,
"ldsc_ratio": -0.5305
}
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 | 2462371 | 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 | 2462654 | 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 | 8.651948e+00 | 5.766156e+00 | 1.0000000 | 4.000000e+00 | 8.000000e+00 | 1.300000e+01 | 2.200000e+01 | <U+2587><U+2585><U+2583><U+2582><U+2582> |
| numeric | POS | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 7.856018e+07 | 5.662174e+07 | 5687.0000000 | 3.168530e+07 | 6.898754e+07 | 1.147591e+08 | 2.491722e+08 | <U+2587><U+2586><U+2585><U+2582><U+2581> |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -3.400000e-06 | 8.875000e-04 | -0.0041101 | -5.994000e-04 | 2.000000e-07 | 5.950000e-04 | 4.331000e-03 | <U+2581><U+2582><U+2587><U+2582><U+2581> |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 8.766000e-04 | 4.630000e-05 | 0.0007975 | 8.374000e-04 | 8.634000e-04 | 9.085000e-04 | 1.728600e-03 | <U+2587><U+2581><U+2581><U+2581><U+2581> |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.963438e-01 | 2.899236e-01 | 0.0000024 | 2.399999e-01 | 4.899999e-01 | 7.499995e-01 | 1.000000e+00 | <U+2587><U+2587><U+2587><U+2587><U+2587> |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.963460e-01 | 2.898952e-01 | 0.0000024 | 2.437257e-01 | 4.943827e-01 | 7.477717e-01 | 9.999995e-01 | <U+2587><U+2587><U+2587><U+2587><U+2587> |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.547232e-01 | 1.497802e-01 | 0.2368070 | 3.230160e-01 | 4.329335e-01 | 5.751848e-01 | 7.631930e-01 | <U+2587><U+2586><U+2585><U+2585><U+2583> |
| numeric | AF_reference | 19512 | 0.9920768 | NA | NA | NA | NA | NA | NA | NA | 4.361929e-01 | 1.752416e-01 | 0.0001997 | 2.979230e-01 | 4.211260e-01 | 5.642970e-01 | 1.000000e+00 | <U+2582><U+2587><U+2587><U+2583><U+2581> |
Head and tail
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 49298 | rs200943160 | T | C | -0.0007573 | 0.0014697 | 0.6100002 | 0.6063890 | 0.623812 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0013942 | 0.0014654 | 0.3400001 | 0.3414080 | 0.399144 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0006546 | 0.0014414 | 0.6499995 | 0.6497172 | 0.455916 | 0.4207270 | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0004222 | 0.0016416 | 0.8000000 | 0.7970377 | 0.242057 | NA | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0009641 | 0.0010161 | 0.3400001 | 0.3426731 | 0.513304 | 0.2751600 | NA |
| 1 | 763394 | rs369924889 | G | A | 0.0034555 | 0.0011904 | 0.0037000 | 0.0036978 | 0.705804 | 0.6176120 | NA |
| 1 | 768253 | rs2977608 | A | C | 0.0019346 | 0.0009648 | 0.0449997 | 0.0449431 | 0.758252 | 0.4894170 | NA |
| 1 | 776546 | rs12124819 | A | G | 0.0017326 | 0.0010902 | 0.1100001 | 0.1119925 | 0.263729 | 0.0756789 | NA |
| 1 | 814495 | rs74461805 | C | A | 0.0005973 | 0.0013914 | 0.6700003 | 0.6677027 | 0.340108 | NA | NA |
| 1 | 830181 | rs28444699 | A | G | 0.0015583 | 0.0009309 | 0.0940005 | 0.0941277 | 0.696612 | 0.6912940 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51180501 | rs5770999 | T | C | 0.0001525 | 0.0009521 | 0.8700001 | 0.8727854 | 0.712571 | 0.6369810 | NA |
| 22 | 51181919 | rs9616825 | G | C | -0.0003720 | 0.0009482 | 0.6899999 | 0.6948077 | 0.695031 | 0.6194090 | NA |
| 22 | 51182485 | rs6009961 | A | G | 0.0000022 | 0.0009553 | 1.0000000 | 0.9981831 | 0.714237 | 0.6383790 | NA |
| 22 | 51186143 | rs2879914 | T | C | -0.0004508 | 0.0008897 | 0.6100002 | 0.6123386 | 0.380077 | 0.2733630 | NA |
| 22 | 51186228 | rs3865766 | C | T | 0.0001945 | 0.0008666 | 0.8200001 | 0.8223664 | 0.449547 | 0.4532750 | NA |
| 22 | 51197266 | rs61290853 | A | G | -0.0000850 | 0.0008930 | 0.9199999 | 0.9241497 | 0.386693 | 0.4229230 | NA |
| 22 | 51198027 | rs34939255 | A | G | 0.0002837 | 0.0010128 | 0.7800007 | 0.7793898 | 0.254586 | 0.0984425 | NA |
| 22 | 51211106 | rs9628250 | T | C | -0.0005742 | 0.0010047 | 0.5700002 | 0.5676622 | 0.271468 | 0.1671330 | NA |
| 22 | 51212875 | rs2238837 | A | C | -0.0000787 | 0.0009550 | 0.9299999 | 0.9343561 | 0.331351 | 0.3724040 | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0009490 | 0.0009756 | 0.3300000 | 0.3307258 | 0.298393 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623812 ES:SE:LP:AF:ID -0.000757256:0.00146973:0.21467:0.623812:rs10399793
1 54676 rs2462492 C T . PASS AF=0.399144 ES:SE:LP:AF:ID 0.00139421:0.00146545:0.468521:0.399144:rs2462492
1 91536 rs6702460 G T . PASS AF=0.455916 ES:SE:LP:AF:ID 0.00065461:0.00144138:0.187087:0.455916:rs6702460
1 534192 rs6680723 C T . PASS AF=0.242057 ES:SE:LP:AF:ID -0.000422204:0.00164165:0.09691:0.242057:rs6680723
1 706368 rs12029736 A G . PASS AF=0.513304 ES:SE:LP:AF:ID 0.000964136:0.00101606:0.468521:0.513304:rs12029736
1 763394 rs3115847 G A . PASS AF=0.705804 ES:SE:LP:AF:ID 0.00345547:0.00119037:2.4318:0.705804:rs3115847
1 768253 rs2977608 A C . PASS AF=0.758252 ES:SE:LP:AF:ID 0.00193461:0.000964803:1.34679:0.758252:rs2977608
1 776546 rs12124819 A G . PASS AF=0.263729 ES:SE:LP:AF:ID 0.00173264:0.00109019:0.958607:0.263729:rs12124819
1 814495 rs74461805 C A . PASS AF=0.340108 ES:SE:LP:AF:ID 0.000597328:0.00139139:0.173925:0.340108:rs74461805
1 830181 rs28444699 A G . PASS AF=0.696612 ES:SE:LP:AF:ID 0.0015583:0.000930876:1.02687:0.696612:rs28444699
1 831489 rs4970385 C T . PASS AF=0.705031 ES:SE:LP:AF:ID 0.000992988:0.000914013:0.552842:0.705031:rs4970385
1 831909 rs9697642 C T . PASS AF=0.705083 ES:SE:LP:AF:ID 0.000994712:0.000914012:0.552842:0.705083:rs9697642
1 832066 rs9697380 G C . PASS AF=0.705261 ES:SE:LP:AF:ID 0.000975663:0.00091397:0.537602:0.705261:rs9697380
1 832318 rs4500250 C A . PASS AF=0.705286 ES:SE:LP:AF:ID 0.000973552:0.000914097:0.537602:0.705286:rs4500250
1 832398 rs4553118 T C . PASS AF=0.730154 ES:SE:LP:AF:ID 0.000921687:0.000939314:0.481486:0.730154:rs4553118
1 832918 rs28765502 T C . PASS AF=0.294729 ES:SE:LP:AF:ID -0.000976688:0.000914069:0.537602:0.294729:rs28765502
1 833223 rs13303211 C T . PASS AF=0.237157 ES:SE:LP:AF:ID -0.000916296:0.000973136:0.455932:0.237157:rs13303211
1 833302 rs28752186 C T . PASS AF=0.237168 ES:SE:LP:AF:ID -0.000935722:0.00097315:0.468521:0.237168:rs28752186
1 833641 rs28594623 T C . PASS AF=0.240204 ES:SE:LP:AF:ID -0.00088009:0.000970259:0.443698:0.240204:rs28594623
1 833824 rs28484835 T C . PASS AF=0.23716 ES:SE:LP:AF:ID -0.000933647:0.000973191:0.468521:0.23716:rs28484835
1 834832 rs4411087 G C . PASS AF=0.237633 ES:SE:LP:AF:ID -0.000925873:0.000972462:0.468521:0.237633:rs4411087
1 835499 rs4422948 A G . PASS AF=0.241744 ES:SE:LP:AF:ID -0.000948863:0.00096519:0.481486:0.241744:rs4422948
1 836896 rs28705752 T C . PASS AF=0.269687 ES:SE:LP:AF:ID -0.000206092:0.00093263:0.0809219:0.269687:rs28705752
1 838555 rs4970383 C A . PASS AF=0.245985 ES:SE:LP:AF:ID -0.00018268:0.000960147:0.0705811:0.245985:rs4970383
1 839103 rs28562941 A G . PASS AF=0.270067 ES:SE:LP:AF:ID -0.00020712:0.000933395:0.0861861:0.270067:rs28562941
1 840753 rs4970382 T C . PASS AF=0.400406 ES:SE:LP:AF:ID -0.0011451:0.000843776:0.769551:0.400406:rs4970382
1 841085 rs1574243 C G . PASS AF=0.236925 ES:SE:LP:AF:ID -0.000291075:0.000980925:0.113509:0.236925:rs1574243
1 843405 rs11516185 A G . PASS AF=0.362367 ES:SE:LP:AF:ID -0.000692716:0.0010498:0.29243:0.362367:rs11516185
1 850218 rs6664536 T A . PASS AF=0.589315 ES:SE:LP:AF:ID -0.000115626:0.000839202:0.05061:0.589315:rs6664536
1 850371 rs6679046 G T . PASS AF=0.603035 ES:SE:LP:AF:ID 3.77381e-05:0.000843216:0.0177288:0.603035:rs6679046
1 850780 rs6657440 C T . PASS AF=0.603381 ES:SE:LP:AF:ID 3.72966e-05:0.000843231:0.0177288:0.603381:rs6657440
1 852037 rs4970463 G A . PASS AF=0.588673 ES:SE:LP:AF:ID -0.000113271:0.000840399:0.05061:0.588673:rs4970463
1 852063 rs28436996 G A . PASS AF=0.588649 ES:SE:LP:AF:ID -7.54792e-05:0.00084002:0.0315171:0.588649:rs28436996
1 852875 rs13303369 C T . PASS AF=0.606715 ES:SE:LP:AF:ID 0.000150432:0.000844618:0.0655015:0.606715:rs13303369
1 853954 rs1806509 C A . PASS AF=0.60688 ES:SE:LP:AF:ID 0.000139025:0.000844706:0.0604807:0.60688:rs1806509
1 854777 rs13303019 A G . PASS AF=0.609504 ES:SE:LP:AF:ID 8.94074e-05:0.000845551:0.0362122:0.609504:rs13303019
1 854978 rs13303057 A C . PASS AF=0.602505 ES:SE:LP:AF:ID 4.14387e-05:0.000843363:0.0177288:0.602505:rs13303057
1 855075 rs6673914 C G . PASS AF=0.609473 ES:SE:LP:AF:ID 0.000100412:0.000845439:0.0409586:0.609473:rs6673914
1 856099 rs28534711 T G . PASS AF=0.390835 ES:SE:LP:AF:ID -0.000119927:0.000845782:0.05061:0.390835:rs28534711
1 856108 rs28742275 A G . PASS AF=0.390777 ES:SE:LP:AF:ID -0.000120175:0.000845869:0.05061:0.390777:rs28742275
1 856476 rs4040605 A G . PASS AF=0.352504 ES:SE:LP:AF:ID -0.00028004:0.00086826:0.124939:0.352504:rs4040605
1 862866 rs3892970 C T . PASS AF=0.761109 ES:SE:LP:AF:ID -0.000650409:0.000974634:0.30103:0.761109:rs3892970
1 864938 rs2340587 G A . PASS AF=0.758149 ES:SE:LP:AF:ID -0.000742744:0.000967272:0.356547:0.758149:rs2340587
1 866893 rs2880024 T C . PASS AF=0.608083 ES:SE:LP:AF:ID 0.000402044:0.000853263:0.19382:0.608083:rs2880024
1 868418 rs28546443 C T . PASS AF=0.301063 ES:SE:LP:AF:ID -0.000214839:0.000934527:0.0861861:0.301063:rs28546443
1 870645 rs28576697 T C . PASS AF=0.293389 ES:SE:LP:AF:ID -0.000291795:0.000924312:0.124939:0.293389:rs28576697
1 871334 rs4072383 G T . PASS AF=0.717959 ES:SE:LP:AF:ID 0.000121988:0.000919514:0.05061:0.717959:rs4072383
1 872352 rs1806780 G C . PASS AF=0.269716 ES:SE:LP:AF:ID -0.000389532:0.00093093:0.167491:0.269716:rs1806780
1 873558 rs1110052 G T . PASS AF=0.71249 ES:SE:LP:AF:ID -2.48653e-05:0.000912631:0.00877392:0.71249:rs1110052
1 875770 rs4970379 A G . PASS AF=0.598464 ES:SE:LP:AF:ID 6.23744e-05:0.000859644:0.0268721:0.598464:rs4970379