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_3466.vcf.gz --id UKB-b:2732 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_3466.txt.gz --cohort_controls 33304 --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": "2019-09-13T03:43:09.528364",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-2732/ukb-b-2732.vcf.gz; Date=Sun May 10 09:55:14 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-2732/UKB-b-2732_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-2732/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:42:53 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-2732/UKB-b-2732_data.vcf.gz ...
Read summary statistics for 7664264 SNPs.
Dropped 5476 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, 1278839 SNPs remain.
After merging with regression SNP LD, 1278839 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0913 (0.0175)
Lambda GC: 1.0836
Mean Chi^2: 1.0895
Intercept: 1.0294 (0.0075)
Ratio: 0.3285 (0.0843)
Analysis finished at Thu Oct 17 14:44:19 2019
Total time elapsed: 1.0m:26.03sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9411,
"inflation_factor": 1.0966,
"mean_EFFECT": -0,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 3,
"n_p_sig": 283,
"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": 71290,
"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": 1278839,
"ldsc_nsnp_merge_regression_ld": 1278839,
"ldsc_observed_scale_h2_beta": 0.0913,
"ldsc_observed_scale_h2_se": 0.0175,
"ldsc_intercept_beta": 1.0294,
"ldsc_intercept_se": 0.0075,
"ldsc_lambda_gc": 1.0836,
"ldsc_mean_chisq": 1.0895,
"ldsc_ratio": 0.3285
}
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 | 3 | 58 | 0 | 7658812 | 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 | 7664264 | 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 | 8.662120e+00 | 5.764524e+00 | 1.000000 | 4.000000e+00 | 8.000000e+00 | 1.300000e+01 | 2.200000e+01 | ▇▅▅▂▂ |
| numeric | POS | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 7.867389e+07 | 5.644022e+07 | 828.000000 | 3.219725e+07 | 6.911895e+07 | 1.145649e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -3.290000e-05 | 2.078070e-02 | -0.233318 | -1.026780e-02 | 5.800000e-06 | 1.018660e-02 | 2.205470e-01 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.769630e-02 | 9.851500e-03 | 0.008581 | 9.903700e-03 | 1.341300e-02 | 2.294760e-02 | 1.058610e-01 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.880889e-01 | 2.919099e-01 | 0.000000 | 2.300001e-01 | 4.799997e-01 | 7.400005e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.880901e-01 | 2.918835e-01 | 0.000000 | 2.321951e-01 | 4.831848e-01 | 7.412444e-01 | 9.999999e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.551655e-01 | 2.608233e-01 | 0.010510 | 4.238600e-02 | 1.502860e-01 | 4.031890e-01 | 9.894900e-01 | ▇▂▂▁▁ |
| numeric | AF_reference | 71290 | 0.9906984 | NA | NA | NA | NA | NA | NA | NA | 2.541822e-01 | 2.526881e-01 | 0.000000 | 4.652560e-02 | 1.645370e-01 | 3.977640e-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 | -0.0184449 | 0.0158636 | 0.2399999 | 0.2449438 | 0.622050 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0049418 | 0.0157111 | 0.7499995 | 0.7531104 | 0.399531 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | 0.0554800 | 0.0252223 | 0.0280001 | 0.0278324 | 0.103531 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0386845 | 0.0155301 | 0.0129999 | 0.0127407 | 0.455940 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | 0.0015765 | 0.0307521 | 0.9599999 | 0.9591150 | 0.073908 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0001992 | 0.0175235 | 0.9900000 | 0.9909320 | 0.243714 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | 0.0247147 | 0.0224007 | 0.2700001 | 0.2698968 | 0.914866 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | -0.0026278 | 0.0147739 | 0.8600001 | 0.8588247 | 0.116508 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | -0.0063696 | 0.0219509 | 0.7700005 | 0.7716814 | 0.066257 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0076828 | 0.0109824 | 0.4799997 | 0.4842043 | 0.512713 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51219006 | rs28729663 | G | A | 0.0076149 | 0.0133919 | 0.5700002 | 0.5696126 | 0.135399 | 0.2052720 | NA |
| 22 | 51219387 | rs9616832 | T | C | -0.0012865 | 0.0173690 | 0.9400001 | 0.9409575 | 0.071902 | 0.0654952 | NA |
| 22 | 51219704 | rs147475742 | G | A | -0.0057008 | 0.0232818 | 0.8100000 | 0.8065659 | 0.040848 | 0.0473243 | NA |
| 22 | 51221190 | rs369304721 | G | A | -0.0005945 | 0.0232763 | 0.9800000 | 0.9796233 | 0.048309 | NA | NA |
| 22 | 51221731 | rs115055839 | T | C | 0.0011725 | 0.0173788 | 0.9500000 | 0.9462102 | 0.071484 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | 0.0102120 | 0.0204634 | 0.6200004 | 0.6177531 | 0.053810 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | 0.0103466 | 0.0205563 | 0.6100002 | 0.6147324 | 0.053426 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | 0.0033240 | 0.0174214 | 0.8499999 | 0.8486829 | 0.071385 | 0.0730831 | NA |
| 22 | 51232488 | rs376461333 | A | G | 0.0614527 | 0.0418005 | 0.1400000 | 0.1415231 | 0.019308 | NA | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0025211 | 0.0105537 | 0.8100000 | 0.8111955 | 0.297391 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.62205 ES:SE:LP:AF:ID -0.0184449:0.0158636:0.619789:0.62205:rs10399793
1 54676 rs2462492 C T . PASS AF=0.399531 ES:SE:LP:AF:ID -0.00494178:0.0157111:0.124939:0.399531:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103531 ES:SE:LP:AF:ID 0.05548:0.0252223:1.55284:0.103531:rs114608975
1 91536 rs6702460 G T . PASS AF=0.45594 ES:SE:LP:AF:ID -0.0386845:0.0155301:1.88606:0.45594:rs6702460
1 234313 rs8179466 C T . PASS AF=0.073908 ES:SE:LP:AF:ID 0.00157648:0.0307521:0.0177288:0.073908:rs8179466
1 534192 rs6680723 C T . PASS AF=0.243714 ES:SE:LP:AF:ID -0.00019916:0.0175235:0.00436481:0.243714:rs6680723
1 546697 rs12025928 A G . PASS AF=0.914866 ES:SE:LP:AF:ID 0.0247147:0.0224007:0.568636:0.914866:rs12025928
1 693731 rs12238997 A G . PASS AF=0.116508 ES:SE:LP:AF:ID -0.00262784:0.0147739:0.0655015:0.116508:rs12238997
1 705882 rs72631875 G A . PASS AF=0.066257 ES:SE:LP:AF:ID -0.00636963:0.0219509:0.113509:0.066257:rs72631875
1 706368 rs12029736 A G . PASS AF=0.512713 ES:SE:LP:AF:ID -0.00768281:0.0109824:0.318759:0.512713:rs12029736
1 714596 rs149887893 T C . PASS AF=0.033185 ES:SE:LP:AF:ID 0.0324816:0.0276034:0.619789:0.033185:rs149887893
1 715265 rs12184267 C T . PASS AF=0.036826 ES:SE:LP:AF:ID 0.027508:0.0250526:0.568636:0.036826:rs12184267
1 715367 rs12184277 A G . PASS AF=0.036896 ES:SE:LP:AF:ID 0.030734:0.0249798:0.657577:0.036896:rs12184277
1 717485 rs12184279 C A . PASS AF=0.036622 ES:SE:LP:AF:ID 0.0264861:0.0251408:0.537602:0.036622:rs12184279
1 717587 rs144155419 G A . PASS AF=0.016758 ES:SE:LP:AF:ID 0.0447972:0.0382811:0.619789:0.016758:rs144155419
1 720381 rs116801199 G T . PASS AF=0.037181 ES:SE:LP:AF:ID 0.0290245:0.0248597:0.619789:0.037181:rs116801199
1 721290 rs12565286 G C . PASS AF=0.037302 ES:SE:LP:AF:ID 0.0285025:0.0247533:0.60206:0.037302:rs12565286
1 722670 rs116030099 T C . PASS AF=0.099884 ES:SE:LP:AF:ID 0.00812576:0.0182129:0.180456:0.099884:rs116030099
1 723891 rs2977670 G C . PASS AF=0.958672 ES:SE:LP:AF:ID -0.030139:0.0237708:0.69897:0.958672:rs2977670
1 724849 rs12126395 C A . PASS AF=0.030999 ES:SE:LP:AF:ID -0.00653339:0.0442129:0.0555173:0.030999:rs12126395
1 725060 rs865924913 A T . PASS AF=0.053365 ES:SE:LP:AF:ID -0.0179296:0.034692:0.21467:0.053365:rs865924913
1 726794 rs28454925 C G . PASS AF=0.036904 ES:SE:LP:AF:ID 0.0287123:0.0249037:0.60206:0.036904:rs28454925
1 729632 rs116720794 C T . PASS AF=0.037112 ES:SE:LP:AF:ID 0.0295972:0.0247155:0.638272:0.037112:rs116720794
1 729679 rs4951859 C G . PASS AF=0.842302 ES:SE:LP:AF:ID -0.00837304:0.0127517:0.29243:0.842302:rs4951859
1 730087 rs148120343 T C . PASS AF=0.057061 ES:SE:LP:AF:ID -0.0472024:0.0206263:1.65758:0.057061:rs148120343
1 731718 rs58276399 T C . PASS AF=0.123171 ES:SE:LP:AF:ID -0.00553781:0.0139711:0.161151:0.123171:rs58276399
1 732989 rs369030935 C T . PASS AF=0.02566 ES:SE:LP:AF:ID -0.00618338:0.0348969:0.0655015:0.02566:rs369030935
1 734349 rs141242758 T C . PASS AF=0.122354 ES:SE:LP:AF:ID -0.00698718:0.01398:0.207608:0.122354:rs141242758
1 736289 rs79010578 T A . PASS AF=0.132504 ES:SE:LP:AF:ID 0.00143299:0.0138278:0.0362122:0.132504:rs79010578
1 736689 rs181876450 T C . PASS AF=0.011667 ES:SE:LP:AF:ID 0.00891866:0.0494426:0.0655015:0.011667:rs181876450
1 752478 rs146277091 G A . PASS AF=0.037142 ES:SE:LP:AF:ID 0.0279423:0.0244336:0.60206:0.037142:rs146277091
1 752566 rs3094315 G A . PASS AF=0.838493 ES:SE:LP:AF:ID -0.00677792:0.0123863:0.236572:0.838493:rs3094315
1 752721 rs3131972 A G . PASS AF=0.838073 ES:SE:LP:AF:ID -0.00809338:0.0123668:0.29243:0.838073:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869233 ES:SE:LP:AF:ID 0.00504774:0.0132932:0.154902:0.869233:rs3115860
1 753541 rs2073813 G A . PASS AF=0.130684 ES:SE:LP:AF:ID -0.00239918:0.0133043:0.0655015:0.130684:rs2073813
1 754063 rs12184312 G T . PASS AF=0.037583 ES:SE:LP:AF:ID 0.0274629:0.0240416:0.60206:0.037583:rs12184312
1 754105 rs12184325 C T . PASS AF=0.037851 ES:SE:LP:AF:ID 0.0260632:0.023891:0.552842:0.037851:rs12184325
1 754182 rs3131969 A G . PASS AF=0.868599 ES:SE:LP:AF:ID 0.00350642:0.0132642:0.102373:0.868599:rs3131969
1 754192 rs3131968 A G . PASS AF=0.868663 ES:SE:LP:AF:ID 0.00330041:0.0132669:0.09691:0.868663:rs3131968
1 754211 rs12184313 G A . PASS AF=0.037804 ES:SE:LP:AF:ID 0.0273099:0.0239803:0.60206:0.037804:rs12184313
1 754334 rs3131967 T C . PASS AF=0.868614 ES:SE:LP:AF:ID 0.00378482:0.0132637:0.107905:0.868614:rs3131967
1 754503 rs3115859 G A . PASS AF=0.837574 ES:SE:LP:AF:ID -0.00831644:0.0123401:0.30103:0.837574:rs3115859
1 754629 rs10454459 A G . PASS AF=0.037827 ES:SE:LP:AF:ID 0.0262126:0.0240129:0.552842:0.037827:rs10454459
1 754964 rs3131966 C T . PASS AF=0.838234 ES:SE:LP:AF:ID -0.00800998:0.0123765:0.283997:0.838234:rs3131966
1 755240 rs181660517 T G . PASS AF=0.013804 ES:SE:LP:AF:ID -0.0264432:0.0434937:0.267606:0.013804:rs181660517
1 755775 rs3131965 A G . PASS AF=0.839406 ES:SE:LP:AF:ID -0.0048604:0.0125453:0.154902:0.839406:rs3131965
1 755890 rs3115858 A T . PASS AF=0.868865 ES:SE:LP:AF:ID 0.00684936:0.0132499:0.21467:0.868865:rs3115858
1 756604 rs3131962 A G . PASS AF=0.868386 ES:SE:LP:AF:ID 0.0082677:0.0132182:0.275724:0.868386:rs3131962
1 757640 rs3115853 G A . PASS AF=0.867306 ES:SE:LP:AF:ID 0.00454053:0.013193:0.136677:0.867306:rs3115853
1 757734 rs4951929 C T . PASS AF=0.868607 ES:SE:LP:AF:ID 0.00710371:0.0132315:0.229148:0.868607:rs4951929