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_3761.vcf.gz --id UKB-b:7556 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_3761.txt.gz --cohort_controls 91982 --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": "2019-09-13T03:45:50.579631",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-7556/ukb-b-7556.vcf.gz; Date=Sat May 9 18:26:09 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-7556/UKB-b-7556_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-7556/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:40:18 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-7556/UKB-b-7556_data.vcf.gz ...
Read summary statistics for 8953808 SNPs.
Dropped 8517 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, 1286997 SNPs remain.
After merging with regression SNP LD, 1286997 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.091 (0.009)
Lambda GC: 1.1374
Mean Chi^2: 1.1911
Intercept: 1.0251 (0.0077)
Ratio: 0.1315 (0.0403)
Analysis finished at Thu Oct 17 14:42:52 2019
Total time elapsed: 2.0m:34.34sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9475,
"inflation_factor": 1.0966,
"mean_EFFECT": 0,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 37,
"n_p_sig": 1836,
"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": 91563,
"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": 1286997,
"ldsc_nsnp_merge_regression_ld": 1286997,
"ldsc_observed_scale_h2_beta": 0.091,
"ldsc_observed_scale_h2_se": 0.009,
"ldsc_intercept_beta": 1.0251,
"ldsc_intercept_se": 0.0077,
"ldsc_lambda_gc": 1.1374,
"ldsc_mean_chisq": 1.1911,
"ldsc_ratio": 0.1313
}
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 | 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 | 8945331 | 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 | 8953808 | 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 | 8.644914e+00 | 5.758780e+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 | 7.879097e+07 | 5.634180e+07 | 828.0000000 | 3.242949e+07 | 6.935564e+07 | 1.145514e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.150000e-05 | 1.556990e-02 | -0.2879450 | -6.233000e-03 | 1.710000e-05 | 6.269300e-03 | 1.596730e-01 | ▁▁▁▇▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.224430e-02 | 8.877100e-03 | 0.0044969 | 5.363000e-03 | 8.170100e-03 | 1.677530e-02 | 1.094940e-01 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.838030e-01 | 2.940704e-01 | 0.0000000 | 2.200002e-01 | 4.799997e-01 | 7.400005e-01 | 1.000000e+00 | ▇▇▇▆▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.838013e-01 | 2.940440e-01 | 0.0000000 | 2.249887e-01 | 4.787094e-01 | 7.387047e-01 | 9.999998e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.213677e-01 | 2.586645e-01 | 0.0038060 | 2.108800e-02 | 1.031610e-01 | 3.494150e-01 | 9.961940e-01 | ▇▂▁▁▁ |
| numeric | AF_reference | 91563 | 0.9897738 | NA | NA | NA | NA | NA | NA | NA | 2.215722e-01 | 2.505877e-01 | 0.0000000 | 1.837060e-02 | 1.204070e-01 | 3.474440e-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 | -0.0028368 | 0.0082876 | 0.7300002 | 0.7321262 | 0.623476 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0037514 | 0.0082248 | 0.6499995 | 0.6483061 | 0.400312 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | -0.0016810 | 0.0131540 | 0.9000000 | 0.8983115 | 0.103364 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0006862 | 0.0080923 | 0.9299999 | 0.9324236 | 0.456474 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | -0.0148687 | 0.0158606 | 0.3500000 | 0.3485213 | 0.074839 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0068087 | 0.0092636 | 0.4600002 | 0.4623414 | 0.240779 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | -0.0080379 | 0.0115781 | 0.4899999 | 0.4875366 | 0.913677 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | -0.0101489 | 0.0077384 | 0.1900002 | 0.1896894 | 0.116273 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | -0.0017549 | 0.0113836 | 0.8800001 | 0.8774819 | 0.067284 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0041845 | 0.0057316 | 0.4700002 | 0.4653465 | 0.515762 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51219006 | rs28729663 | G | A | 0.0039389 | 0.0069330 | 0.5700002 | 0.5699444 | 0.137866 | 0.2052720 | NA |
| 22 | 51219387 | rs9616832 | T | C | 0.0068217 | 0.0090051 | 0.4500005 | 0.4487285 | 0.073655 | 0.0654952 | NA |
| 22 | 51219704 | rs147475742 | G | A | -0.0056674 | 0.0120092 | 0.6400000 | 0.6369804 | 0.042267 | 0.0473243 | NA |
| 22 | 51221190 | rs369304721 | G | A | 0.0082946 | 0.0120401 | 0.4899999 | 0.4908791 | 0.049766 | NA | NA |
| 22 | 51221731 | rs115055839 | T | C | 0.0068233 | 0.0090128 | 0.4500005 | 0.4490106 | 0.073093 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | -0.0068166 | 0.0106400 | 0.5199996 | 0.5217425 | 0.054198 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | -0.0062279 | 0.0106916 | 0.5600000 | 0.5602266 | 0.053821 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | 0.0064876 | 0.0090449 | 0.4700002 | 0.4732074 | 0.072963 | 0.0730831 | NA |
| 22 | 51232488 | rs376461333 | A | G | -0.0239238 | 0.0212699 | 0.2599998 | 0.2606854 | 0.019953 | NA | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0037299 | 0.0055280 | 0.5000000 | 0.4998482 | 0.295849 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623476 ES:SE:LP:AF:ID -0.00283682:0.00828756:0.136677:0.623476:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400312 ES:SE:LP:AF:ID 0.00375145:0.00822476:0.187087:0.400312:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103364 ES:SE:LP:AF:ID -0.00168101:0.013154:0.0457575:0.103364:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456474 ES:SE:LP:AF:ID 0.000686195:0.00809231:0.0315171:0.456474:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074839 ES:SE:LP:AF:ID -0.0148687:0.0158606:0.455932:0.074839:rs8179466
1 534192 rs6680723 C T . PASS AF=0.240779 ES:SE:LP:AF:ID -0.00680871:0.00926359:0.337242:0.240779:rs6680723
1 546697 rs12025928 A G . PASS AF=0.913677 ES:SE:LP:AF:ID -0.00803789:0.0115781:0.309804:0.913677:rs12025928
1 693731 rs12238997 A G . PASS AF=0.116273 ES:SE:LP:AF:ID -0.0101489:0.0077384:0.721246:0.116273:rs12238997
1 705882 rs72631875 G A . PASS AF=0.067284 ES:SE:LP:AF:ID -0.00175492:0.0113836:0.0555173:0.067284:rs72631875
1 706368 rs12029736 A G . PASS AF=0.515762 ES:SE:LP:AF:ID -0.00418447:0.00573159:0.327902:0.515762:rs12029736
1 714596 rs149887893 T C . PASS AF=0.033675 ES:SE:LP:AF:ID -0.0126329:0.0143073:0.420216:0.033675:rs149887893
1 715265 rs12184267 C T . PASS AF=0.037314 ES:SE:LP:AF:ID -0.014365:0.0130182:0.568636:0.037314:rs12184267
1 715367 rs12184277 A G . PASS AF=0.037447 ES:SE:LP:AF:ID -0.0135856:0.0129683:0.537602:0.037447:rs12184277
1 717485 rs12184279 C A . PASS AF=0.037113 ES:SE:LP:AF:ID -0.0145743:0.013066:0.585027:0.037113:rs12184279
1 717587 rs144155419 G A . PASS AF=0.016616 ES:SE:LP:AF:ID -0.0183656:0.0201162:0.443698:0.016616:rs144155419
1 720381 rs116801199 G T . PASS AF=0.037688 ES:SE:LP:AF:ID -0.0135161:0.012917:0.522879:0.037688:rs116801199
1 721290 rs12565286 G C . PASS AF=0.037769 ES:SE:LP:AF:ID -0.0133869:0.0128756:0.522879:0.037769:rs12565286
1 722670 rs116030099 T C . PASS AF=0.101373 ES:SE:LP:AF:ID 5.83916e-05:0.00944212:-0:0.101373:rs116030099
1 723891 rs2977670 G C . PASS AF=0.958187 ES:SE:LP:AF:ID 0.0106201:0.0124086:0.408935:0.958187:rs2977670
1 724849 rs12126395 C A . PASS AF=0.031249 ES:SE:LP:AF:ID -0.0119551:0.023013:0.221849:0.031249:rs12126395
1 725060 rs865924913 A T . PASS AF=0.053137 ES:SE:LP:AF:ID -0.00816589:0.0181067:0.187087:0.053137:rs865924913
1 726794 rs28454925 C G . PASS AF=0.037194 ES:SE:LP:AF:ID -0.0139829:0.0129774:0.552842:0.037194:rs28454925
1 729632 rs116720794 C T . PASS AF=0.037524 ES:SE:LP:AF:ID -0.0146135:0.0128575:0.585027:0.037524:rs116720794
1 729679 rs4951859 C G . PASS AF=0.842761 ES:SE:LP:AF:ID 0.0105913:0.00670647:0.958607:0.842761:rs4951859
1 730087 rs148120343 T C . PASS AF=0.055098 ES:SE:LP:AF:ID -0.0138651:0.0109547:0.677781:0.055098:rs148120343
1 731718 rs58276399 T C . PASS AF=0.122128 ES:SE:LP:AF:ID -0.0112275:0.00734471:0.886057:0.122128:rs58276399
1 732989 rs369030935 C T . PASS AF=0.025433 ES:SE:LP:AF:ID 0.00303119:0.0182104:0.0604807:0.025433:rs369030935
1 734349 rs141242758 T C . PASS AF=0.12134 ES:SE:LP:AF:ID -0.0109021:0.00734852:0.853872:0.12134:rs141242758
1 736289 rs79010578 T A . PASS AF=0.132332 ES:SE:LP:AF:ID -0.00956048:0.00724055:0.721246:0.132332:rs79010578
1 736689 rs181876450 T C . PASS AF=0.011294 ES:SE:LP:AF:ID 0.0127927:0.0261098:0.207608:0.011294:rs181876450
1 740284 rs61770167 C T . PASS AF=0.005664 ES:SE:LP:AF:ID 0.0114336:0.034093:0.130768:0.005664:rs61770167
1 752478 rs146277091 G A . PASS AF=0.037406 ES:SE:LP:AF:ID -0.0127625:0.0127301:0.49485:0.037406:rs146277091
1 752566 rs3094315 G A . PASS AF=0.838344 ES:SE:LP:AF:ID 0.0116216:0.0064859:1.13668:0.838344:rs3094315
1 752721 rs3131972 A G . PASS AF=0.837925 ES:SE:LP:AF:ID 0.0115377:0.00647735:1.12494:0.837925:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869634 ES:SE:LP:AF:ID 0.0105198:0.00695309:0.886057:0.869634:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129881 ES:SE:LP:AF:ID -0.0113185:0.00697236:1:0.129881:rs2073813
1 754063 rs12184312 G T . PASS AF=0.037958 ES:SE:LP:AF:ID -0.0127513:0.0125049:0.508638:0.037958:rs12184312
1 754105 rs12184325 C T . PASS AF=0.038204 ES:SE:LP:AF:ID -0.0132248:0.0124269:0.537602:0.038204:rs12184325
1 754182 rs3131969 A G . PASS AF=0.86897 ES:SE:LP:AF:ID 0.0104413:0.00693856:0.886057:0.86897:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869031 ES:SE:LP:AF:ID 0.0103853:0.00693989:0.886057:0.869031:rs3131968
1 754211 rs12184313 G A . PASS AF=0.038173 ES:SE:LP:AF:ID -0.0116887:0.0124795:0.455932:0.038173:rs12184313
1 754334 rs3131967 T C . PASS AF=0.868969 ES:SE:LP:AF:ID 0.0105192:0.00693829:0.886057:0.868969:rs3131967
1 754433 rs150578204 G A . PASS AF=0.005202 ES:SE:LP:AF:ID 0.00505831:0.0353826:0.05061:0.005202:rs150578204
1 754458 rs142682604 G T . PASS AF=0.005168 ES:SE:LP:AF:ID 0.00259989:0.0354678:0.0268721:0.005168:rs142682604
1 754503 rs3115859 G A . PASS AF=0.837388 ES:SE:LP:AF:ID 0.01188:0.0064606:1.18046:0.837388:rs3115859
1 754629 rs10454459 A G . PASS AF=0.038193 ES:SE:LP:AF:ID -0.0110633:0.0124962:0.420216:0.038193:rs10454459
1 754964 rs3131966 C T . PASS AF=0.838007 ES:SE:LP:AF:ID 0.0118982:0.00647819:1.18046:0.838007:rs3131966
1 755240 rs181660517 T G . PASS AF=0.013914 ES:SE:LP:AF:ID -0.000784747:0.0225521:0.0132283:0.013914:rs181660517
1 755435 rs184270342 T G . PASS AF=0.005565 ES:SE:LP:AF:ID 0.012664:0.034957:0.142668:0.005565:rs184270342
1 755775 rs3131965 A G . PASS AF=0.839275 ES:SE:LP:AF:ID 0.0115892:0.00656932:1.10791:0.839275:rs3131965