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_21001.vcf.gz --id UKB-b:19953 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_21001.txt.gz --cohort_controls 461460 --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": "2019-09-13T03:55:46.615098",
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"bcftools_viewCommand": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-19953/ukb-b-19953.vcf.gz; Date=Sun May 10 12:42:39 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-19953/UKB-b-19953_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-19953/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:41:58 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-19953/UKB-b-19953_data.vcf.gz ...
Read summary statistics for 9851866 SNPs.
Dropped 14738 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, 1289166 SNPs remain.
After merging with regression SNP LD, 1289166 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.2332 (0.0073)
Lambda GC: 2.4891
Mean Chi^2: 3.3813
Intercept: 1.1909 (0.0168)
Ratio: 0.0802 (0.0071)
Analysis finished at Thu Oct 17 14:43:43 2019
Total time elapsed: 1.0m:44.5sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9499,
"inflation_factor": 1.92,
"mean_EFFECT": 0.0001,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 458,
"n_p_sig": 69096,
"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": 184849,
"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": 1289166,
"ldsc_nsnp_merge_regression_ld": 1289166,
"ldsc_observed_scale_h2_beta": 0.2332,
"ldsc_observed_scale_h2_se": 0.0073,
"ldsc_intercept_beta": 1.1909,
"ldsc_intercept_se": 0.0168,
"ldsc_lambda_gc": 2.4891,
"ldsc_mean_chisq": 3.3813,
"ldsc_ratio": 0.0802
}
Flags
| name | value |
|---|---|
| af_correlation | FALSE |
| inflation_factor | TRUE |
| n | TRUE |
| is_snpid_non_unique | FALSE |
| mean_EFFECT_nonfinite | FALSE |
| mean_EFFECT_05 | FALSE |
| mean_EFFECT_01 | FALSE |
| mean_chisq | TRUE |
| n_p_sig | TRUE |
| 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 | 3 | 58 | 0 | 9837196 | 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 | 9851866 | 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 | 8.622825e+00 | 5.748290e+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 | 7.886027e+07 | 5.628334e+07 | 828.0000000 | 3.259061e+07 | 6.948835e+07 | 1.145912e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 5.300000e-05 | 1.083130e-02 | -0.2492440 | -4.165100e-03 | 7.700000e-06 | 4.207500e-03 | 1.809370e-01 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 6.812200e-03 | 6.447500e-03 | 0.0019027 | 2.333800e-03 | 3.913400e-03 | 9.029200e-03 | 1.001310e-01 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.946199e-01 | 3.116705e-01 | 0.0000000 | 9.900110e-02 | 3.500000e-01 | 6.600001e-01 | 1.000000e+00 | ▇▅▃▃▃ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.946221e-01 | 3.116499e-01 | 0.0000000 | 9.906580e-02 | 3.488897e-01 | 6.615598e-01 | 1.000000e+00 | ▇▃▃▃▃ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.035077e-01 | 2.568614e-01 | 0.0009930 | 1.317000e-02 | 7.791400e-02 | 3.164560e-01 | 9.990080e-01 | ▇▂▁▁▁ |
| numeric | AF_reference | 184849 | 0.9812372 | NA | NA | NA | NA | NA | NA | NA | 2.068392e-01 | 2.482924e-01 | 0.0000000 | 1.198080e-02 | 9.984030e-02 | 3.202880e-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 | 0.0010389 | 0.0034984 | 0.7700005 | 0.7664894 | 0.623764 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0021460 | 0.0034658 | 0.5400003 | 0.5357888 | 0.400407 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | 0.0069251 | 0.0055411 | 0.2099999 | 0.2113806 | 0.103550 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0041051 | 0.0034125 | 0.2300001 | 0.2289874 | 0.456845 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | -0.0120947 | 0.0067288 | 0.0719996 | 0.0722629 | 0.074497 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | 0.0003343 | 0.0038979 | 0.9299999 | 0.9316496 | 0.240940 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | -0.0005041 | 0.0048622 | 0.9199999 | 0.9174300 | 0.913458 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | -0.0004714 | 0.0032674 | 0.8900000 | 0.8852777 | 0.116281 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | 0.0011474 | 0.0047867 | 0.8100000 | 0.8105534 | 0.067302 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0003037 | 0.0024198 | 0.9000000 | 0.9001199 | 0.515705 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51219704 | rs147475742 | G | A | 0.0021638 | 0.0051104 | 0.6700003 | 0.6719921 | 0.041948 | 0.0473243 | NA |
| 22 | 51219766 | rs182321900 | C | T | -0.0025239 | 0.0238146 | 0.9199999 | 0.9155969 | 0.001936 | NA | NA |
| 22 | 51220146 | rs868950473 | C | T | -0.0053662 | 0.0235893 | 0.8200001 | 0.8200470 | 0.001985 | NA | NA |
| 22 | 51221190 | rs369304721 | G | A | 0.0031475 | 0.0051023 | 0.5400003 | 0.5373165 | 0.049729 | NA | NA |
| 22 | 51221731 | rs115055839 | T | C | 0.0045989 | 0.0038159 | 0.2300001 | 0.2281262 | 0.073237 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | -0.0004741 | 0.0044918 | 0.9199999 | 0.9159352 | 0.054474 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | -0.0003516 | 0.0045135 | 0.9400001 | 0.9379070 | 0.054102 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | 0.0044404 | 0.0038297 | 0.2500000 | 0.2462752 | 0.073071 | 0.0730831 | NA |
| 22 | 51232488 | rs376461333 | A | G | 0.0032650 | 0.0090184 | 0.7199992 | 0.7173211 | 0.020052 | NA | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0051138 | 0.0023424 | 0.0290001 | 0.0290227 | 0.297905 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623764 ES:SE:LP:AF:ID 0.00103892:0.0034984:0.113509:0.623764:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400407 ES:SE:LP:AF:ID 0.00214602:0.00346583:0.267606:0.400407:rs2462492
1 86028 rs114608975 T C . PASS AF=0.10355 ES:SE:LP:AF:ID 0.00692512:0.00554108:0.677781:0.10355:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456845 ES:SE:LP:AF:ID 0.00410514:0.0034125:0.638272:0.456845:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074497 ES:SE:LP:AF:ID -0.0120947:0.00672878:1.14267:0.074497:rs8179466
1 534192 rs6680723 C T . PASS AF=0.24094 ES:SE:LP:AF:ID 0.000334321:0.0038979:0.0315171:0.24094:rs6680723
1 546697 rs12025928 A G . PASS AF=0.913458 ES:SE:LP:AF:ID -0.000504077:0.00486225:0.0362122:0.913458:rs12025928
1 693731 rs12238997 A G . PASS AF=0.116281 ES:SE:LP:AF:ID -0.00047142:0.00326735:0.05061:0.116281:rs12238997
1 705882 rs72631875 G A . PASS AF=0.067302 ES:SE:LP:AF:ID 0.00114744:0.00478674:0.091515:0.067302:rs72631875
1 706368 rs12029736 A G . PASS AF=0.515705 ES:SE:LP:AF:ID -0.00030371:0.00241981:0.0457575:0.515705:rs12029736
1 714596 rs149887893 T C . PASS AF=0.033007 ES:SE:LP:AF:ID 0.00185715:0.00610021:0.119186:0.033007:rs149887893
1 715265 rs12184267 C T . PASS AF=0.036622 ES:SE:LP:AF:ID 0.00115093:0.00554111:0.0757207:0.036622:rs12184267
1 715367 rs12184277 A G . PASS AF=0.03674 ES:SE:LP:AF:ID 0.00141515:0.00551996:0.09691:0.03674:rs12184277
1 717485 rs12184279 C A . PASS AF=0.036439 ES:SE:LP:AF:ID 9.67154e-05:0.00555979:0.00436481:0.036439:rs12184279
1 717587 rs144155419 G A . PASS AF=0.016397 ES:SE:LP:AF:ID -0.00305442:0.00856376:0.142668:0.016397:rs144155419
1 720381 rs116801199 G T . PASS AF=0.036978 ES:SE:LP:AF:ID 0.000709829:0.00549827:0.0457575:0.036978:rs116801199
1 721290 rs12565286 G C . PASS AF=0.037075 ES:SE:LP:AF:ID 0.000512106:0.0054794:0.0315171:0.037075:rs12565286
1 722670 rs116030099 T C . PASS AF=0.101205 ES:SE:LP:AF:ID 0.00167835:0.00399248:0.173925:0.101205:rs116030099
1 723891 rs2977670 G C . PASS AF=0.959089 ES:SE:LP:AF:ID 0.000396146:0.00528462:0.0268721:0.959089:rs2977670
1 724849 rs12126395 C A . PASS AF=0.031449 ES:SE:LP:AF:ID 0.00601786:0.0095932:0.275724:0.031449:rs12126395
1 725060 rs865924913 A T . PASS AF=0.053249 ES:SE:LP:AF:ID -0.00139956:0.00763279:0.0705811:0.053249:rs865924913
1 726794 rs28454925 C G . PASS AF=0.036596 ES:SE:LP:AF:ID -5.81846e-05:0.00551461:0.00436481:0.036596:rs28454925
1 729632 rs116720794 C T . PASS AF=0.036913 ES:SE:LP:AF:ID -0.0012656:0.00546443:0.0861861:0.036913:rs116720794
1 729679 rs4951859 C G . PASS AF=0.843254 ES:SE:LP:AF:ID 0.000984938:0.00283131:0.136677:0.843254:rs4951859
1 730087 rs148120343 T C . PASS AF=0.055886 ES:SE:LP:AF:ID -0.00969616:0.00458477:1.46852:0.055886:rs148120343
1 731718 rs58276399 T C . PASS AF=0.122255 ES:SE:LP:AF:ID -0.000680229:0.00309938:0.0809219:0.122255:rs58276399
1 732989 rs369030935 C T . PASS AF=0.025719 ES:SE:LP:AF:ID -0.00847226:0.00762133:0.568636:0.025719:rs369030935
1 734349 rs141242758 T C . PASS AF=0.121497 ES:SE:LP:AF:ID -0.000379714:0.00310068:0.0457575:0.121497:rs141242758
1 736289 rs79010578 T A . PASS AF=0.132297 ES:SE:LP:AF:ID -0.000914003:0.00305582:0.119186:0.132297:rs79010578
1 736689 rs181876450 T C . PASS AF=0.011132 ES:SE:LP:AF:ID -0.00178878:0.0111102:0.0604807:0.011132:rs181876450
1 740284 rs61770167 C T . PASS AF=0.005695 ES:SE:LP:AF:ID 0.0218699:0.0143458:0.886057:0.005695:rs61770167
1 742813 rs112573343 C T . PASS AF=0.002275 ES:SE:LP:AF:ID 0.011722:0.0240777:0.200659:0.002275:rs112573343
1 746189 rs139221807 A G . PASS AF=0.001031 ES:SE:LP:AF:ID -0.00367847:0.0394423:0.0315171:0.001031:rs139221807
1 752478 rs146277091 G A . PASS AF=0.036829 ES:SE:LP:AF:ID 0.000182607:0.0054091:0.0132283:0.036829:rs146277091
1 752566 rs3094315 G A . PASS AF=0.838993 ES:SE:LP:AF:ID 0.000915797:0.00274194:0.130768:0.838993:rs3094315
1 752721 rs3131972 A G . PASS AF=0.838624 ES:SE:LP:AF:ID 0.00133006:0.00273898:0.200659:0.838624:rs3131972
1 753405 rs3115860 C A . PASS AF=0.86983 ES:SE:LP:AF:ID 0.000410906:0.00293925:0.05061:0.86983:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129815 ES:SE:LP:AF:ID -0.00134096:0.00294532:0.187087:0.129815:rs2073813
1 754063 rs12184312 G T . PASS AF=0.03734 ES:SE:LP:AF:ID -0.00128345:0.00531746:0.091515:0.03734:rs12184312
1 754105 rs12184325 C T . PASS AF=0.037582 ES:SE:LP:AF:ID -0.00108394:0.00528399:0.0757207:0.037582:rs12184325
1 754182 rs3131969 A G . PASS AF=0.869176 ES:SE:LP:AF:ID 0.00082542:0.00293351:0.107905:0.869176:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869273 ES:SE:LP:AF:ID 0.000768575:0.00293467:0.102373:0.869273:rs3131968
1 754211 rs12184313 G A . PASS AF=0.037539 ES:SE:LP:AF:ID -0.00126684:0.00530693:0.091515:0.037539:rs12184313
1 754334 rs3131967 T C . PASS AF=0.869178 ES:SE:LP:AF:ID 0.000854505:0.00293345:0.113509:0.869178:rs3131967
1 754433 rs150578204 G A . PASS AF=0.00512 ES:SE:LP:AF:ID -0.00873723:0.0150615:0.251812:0.00512:rs150578204
1 754458 rs142682604 G T . PASS AF=0.005087 ES:SE:LP:AF:ID -0.00890754:0.0151004:0.251812:0.005087:rs142682604
1 754503 rs3115859 G A . PASS AF=0.838078 ES:SE:LP:AF:ID 0.00138116:0.00273142:0.21467:0.838078:rs3115859
1 754629 rs10454459 A G . PASS AF=0.037551 ES:SE:LP:AF:ID -0.00161633:0.00531444:0.119186:0.037551:rs10454459
1 754964 rs3131966 C T . PASS AF=0.838711 ES:SE:LP:AF:ID 0.00134632:0.00273912:0.207608:0.838711:rs3131966
1 755240 rs181660517 T G . PASS AF=0.013772 ES:SE:LP:AF:ID 0.00397392:0.00956042:0.167491:0.013772:rs181660517