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_6177_1.vcf.gz --id UKB-b:11740 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_6177_1.txt.gz --cohort_cases 48450 --cohort_controls 161188 --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": "2019-09-13T01:57:56.540462",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-11740/ukb-b-11740.vcf.gz; Date=Sat May 9 15:30:36 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-11740/UKB-b-11740_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-11740/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:41:06 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-11740/UKB-b-11740_data.vcf.gz ...
Read summary statistics for 8195067 SNPs.
Dropped 6486 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, 1283803 SNPs remain.
After merging with regression SNP LD, 1283803 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0701 (0.0052)
Lambda GC: 1.2269
Mean Chi^2: 1.3207
Intercept: 1.0356 (0.0091)
Ratio: 0.1109 (0.0285)
Analysis finished at Thu Oct 17 14:42:35 2019
Total time elapsed: 1.0m:29.32sQC metrics
Metrics
Metrics
{
"af_correlation": 0.944,
"inflation_factor": 1.1999,
"mean_EFFECT": 5.2743e-06,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 50,
"n_p_sig": 3737,
"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": 76837,
"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": 1283803,
"ldsc_nsnp_merge_regression_ld": 1283803,
"ldsc_observed_scale_h2_beta": 0.0701,
"ldsc_observed_scale_h2_se": 0.0052,
"ldsc_intercept_beta": 1.0356,
"ldsc_intercept_se": 0.0091,
"ldsc_lambda_gc": 1.2269,
"ldsc_mean_chisq": 1.3207,
"ldsc_ratio": 0.111
}
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 | 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 | 1.000000 | 3 | 58 | 0 | 8188610 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| character | REF | 0 | 1.000000 | 1 | 1 | 0 | 4 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| character | ALT | 0 | 1.000000 | 1 | 1 | 0 | 4 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| logical | N | 8195067 | 0.000000 | NA | NA | NA | NA | NA | NaN | : | NA | NA | NA | NA | NA | NA | NA | NA |
| numeric | CHROM | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 8.657137e+00 | 5.763124e+00 | 1.0000000 | 4.000000e+00 | 8.000000e+00 | 1.300000e+01 | 2.200000e+01 | ▇▅▅▂▂ |
| numeric | POS | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 7.872414e+07 | 5.639110e+07 | 828.0000000 | 3.231000e+07 | 6.920229e+07 | 1.145414e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 5.300000e-06 | 3.573600e-03 | -0.0729240 | -1.654200e-03 | -2.000000e-07 | 1.659400e-03 | 5.868920e-02 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 2.870200e-03 | 1.801200e-03 | 0.0012516 | 1.457500e-03 | 2.065000e-03 | 3.807800e-03 | 1.585620e-02 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 4.728249e-01 | 2.962783e-01 | 0.0000000 | 2.099999e-01 | 4.600002e-01 | 7.300002e-01 | 1.000000e+00 | ▇▇▇▆▆ |
| numeric | PVAL_ztest | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 4.728261e-01 | 2.962540e-01 | 0.0000000 | 2.097140e-01 | 4.633361e-01 | 7.296307e-01 | 9.999998e-01 | ▇▆▆▆▆ |
| numeric | AF | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 2.401855e-01 | 2.604392e-01 | 0.0072240 | 3.189600e-02 | 1.291260e-01 | 3.807040e-01 | 9.927760e-01 | ▇▂▂▁▁ |
| numeric | AF_reference | 76837 | 0.990624 | NA | NA | NA | NA | NA | NA | NA | 2.396009e-01 | 2.523119e-01 | 0.0000000 | 3.234820e-02 | 1.447680e-01 | 3.761980e-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 | -0.0003465 | 0.0023009 | 0.8800001 | 0.8802837 | 0.624267 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0009628 | 0.0022847 | 0.6700003 | 0.6734445 | 0.399695 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | -0.0004635 | 0.0036368 | 0.9000000 | 0.8985922 | 0.103686 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0024627 | 0.0022478 | 0.2700001 | 0.2732547 | 0.456603 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | 0.0001486 | 0.0044204 | 0.9699999 | 0.9731887 | 0.074622 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0017387 | 0.0025663 | 0.5000000 | 0.4980735 | 0.240711 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | 0.0005715 | 0.0032019 | 0.8600001 | 0.8583295 | 0.913394 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | 0.0021977 | 0.0021531 | 0.3100002 | 0.3073920 | 0.115933 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | -0.0011335 | 0.0031489 | 0.7199992 | 0.7188664 | 0.067538 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0003230 | 0.0015936 | 0.8400000 | 0.8393719 | 0.516309 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51219006 | rs28729663 | G | A | -0.0008233 | 0.0019202 | 0.6700003 | 0.6681025 | 0.137965 | 0.2052720 | NA |
| 22 | 51219387 | rs9616832 | T | C | -0.0022670 | 0.0024945 | 0.3599996 | 0.3634437 | 0.073841 | 0.0654952 | NA |
| 22 | 51219704 | rs147475742 | G | A | 0.0013802 | 0.0033423 | 0.6800001 | 0.6796442 | 0.042049 | 0.0473243 | NA |
| 22 | 51221190 | rs369304721 | G | A | -0.0020981 | 0.0033344 | 0.5300002 | 0.5291964 | 0.049860 | NA | NA |
| 22 | 51221731 | rs115055839 | T | C | -0.0024025 | 0.0024968 | 0.3400001 | 0.3359293 | 0.073283 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | 0.0010873 | 0.0029428 | 0.7099994 | 0.7117702 | 0.054302 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | 0.0009644 | 0.0029572 | 0.7400005 | 0.7443253 | 0.053924 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | -0.0023342 | 0.0025060 | 0.3500000 | 0.3516275 | 0.073082 | 0.0730831 | NA |
| 22 | 51232488 | rs376461333 | A | G | 0.0002814 | 0.0059024 | 0.9599999 | 0.9619732 | 0.020066 | NA | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0037726 | 0.0015346 | 0.0140001 | 0.0139555 | 0.298014 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.624267 ES:SE:LP:AF:ID -0.000346533:0.00230087:0.0555173:0.624267:rs10399793
1 54676 rs2462492 C T . PASS AF=0.399695 ES:SE:LP:AF:ID 0.000962839:0.00228472:0.173925:0.399695:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103686 ES:SE:LP:AF:ID -0.000463476:0.00363682:0.0457575:0.103686:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456603 ES:SE:LP:AF:ID 0.00246274:0.00224785:0.568636:0.456603:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074622 ES:SE:LP:AF:ID 0.000148565:0.00442036:0.0132283:0.074622:rs8179466
1 534192 rs6680723 C T . PASS AF=0.240711 ES:SE:LP:AF:ID -0.00173873:0.0025663:0.30103:0.240711:rs6680723
1 546697 rs12025928 A G . PASS AF=0.913394 ES:SE:LP:AF:ID 0.000571537:0.00320187:0.0655015:0.913394:rs12025928
1 693731 rs12238997 A G . PASS AF=0.115933 ES:SE:LP:AF:ID 0.00219769:0.0021531:0.508638:0.115933:rs12238997
1 705882 rs72631875 G A . PASS AF=0.067538 ES:SE:LP:AF:ID -0.00113353:0.00314892:0.142668:0.067538:rs72631875
1 706368 rs12029736 A G . PASS AF=0.516309 ES:SE:LP:AF:ID 0.000323022:0.00159362:0.0757207:0.516309:rs12029736
1 714596 rs149887893 T C . PASS AF=0.033135 ES:SE:LP:AF:ID -0.00153109:0.00400934:0.154902:0.033135:rs149887893
1 715265 rs12184267 C T . PASS AF=0.0368 ES:SE:LP:AF:ID -0.00045195:0.00363807:0.0457575:0.0368:rs12184267
1 715367 rs12184277 A G . PASS AF=0.036924 ES:SE:LP:AF:ID -0.00051511:0.00362362:0.05061:0.036924:rs12184277
1 717485 rs12184279 C A . PASS AF=0.036638 ES:SE:LP:AF:ID -0.00107018:0.00364867:0.113509:0.036638:rs12184279
1 717587 rs144155419 G A . PASS AF=0.016464 ES:SE:LP:AF:ID 0.00758596:0.00562923:0.744727:0.016464:rs144155419
1 720381 rs116801199 G T . PASS AF=0.037184 ES:SE:LP:AF:ID -0.000822214:0.00360809:0.0861861:0.037184:rs116801199
1 721290 rs12565286 G C . PASS AF=0.037284 ES:SE:LP:AF:ID -0.000859396:0.00359551:0.091515:0.037284:rs12565286
1 722670 rs116030099 T C . PASS AF=0.101305 ES:SE:LP:AF:ID 0.00134157:0.00262771:0.21467:0.101305:rs116030099
1 723891 rs2977670 G C . PASS AF=0.958869 ES:SE:LP:AF:ID -0.00120769:0.00346862:0.136677:0.958869:rs2977670
1 724849 rs12126395 C A . PASS AF=0.031492 ES:SE:LP:AF:ID 0.00662312:0.00633547:0.522879:0.031492:rs12126395
1 725060 rs865924913 A T . PASS AF=0.053094 ES:SE:LP:AF:ID 0.00574751:0.00503728:0.60206:0.053094:rs865924913
1 726794 rs28454925 C G . PASS AF=0.036774 ES:SE:LP:AF:ID -0.00126448:0.00362131:0.136677:0.036774:rs28454925
1 729632 rs116720794 C T . PASS AF=0.037076 ES:SE:LP:AF:ID -0.001189:0.00358816:0.130768:0.037076:rs116720794
1 729679 rs4951859 C G . PASS AF=0.843414 ES:SE:LP:AF:ID -0.00243337:0.00186332:0.721246:0.843414:rs4951859
1 730087 rs148120343 T C . PASS AF=0.055776 ES:SE:LP:AF:ID 0.00145945:0.00301954:0.200659:0.055776:rs148120343
1 731718 rs58276399 T C . PASS AF=0.121895 ES:SE:LP:AF:ID 0.00253814:0.00204285:0.677781:0.121895:rs58276399
1 732989 rs369030935 C T . PASS AF=0.025878 ES:SE:LP:AF:ID 0.0071277:0.00500206:0.823909:0.025878:rs369030935
1 734349 rs141242758 T C . PASS AF=0.12116 ES:SE:LP:AF:ID 0.00245721:0.00204367:0.638272:0.12116:rs141242758
1 736289 rs79010578 T A . PASS AF=0.132047 ES:SE:LP:AF:ID 0.00129869:0.00201298:0.283997:0.132047:rs79010578
1 736689 rs181876450 T C . PASS AF=0.011131 ES:SE:LP:AF:ID 0.00783604:0.00731337:0.552842:0.011131:rs181876450
1 752478 rs146277091 G A . PASS AF=0.036993 ES:SE:LP:AF:ID -0.00112735:0.00355166:0.124939:0.036993:rs146277091
1 752566 rs3094315 G A . PASS AF=0.839209 ES:SE:LP:AF:ID -0.00215977:0.00180569:0.638272:0.839209:rs3094315
1 752721 rs3131972 A G . PASS AF=0.838837 ES:SE:LP:AF:ID -0.00248321:0.00180352:0.769551:0.838837:rs3131972
1 753405 rs3115860 C A . PASS AF=0.870174 ES:SE:LP:AF:ID -0.0028301:0.00193733:0.853872:0.870174:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129469 ES:SE:LP:AF:ID 0.00304926:0.00194085:0.920819:0.129469:rs2073813
1 754063 rs12184312 G T . PASS AF=0.037556 ES:SE:LP:AF:ID -0.000893952:0.00348952:0.09691:0.037556:rs12184312
1 754105 rs12184325 C T . PASS AF=0.037778 ES:SE:LP:AF:ID -0.000566074:0.00346884:0.0604807:0.037778:rs12184325
1 754182 rs3131969 A G . PASS AF=0.869527 ES:SE:LP:AF:ID -0.00318183:0.00193324:1:0.869527:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869655 ES:SE:LP:AF:ID -0.00314991:0.00193411:1:0.869655:rs3131968
1 754211 rs12184313 G A . PASS AF=0.037747 ES:SE:LP:AF:ID -0.000839201:0.00348338:0.091515:0.037747:rs12184313
1 754334 rs3131967 T C . PASS AF=0.869532 ES:SE:LP:AF:ID -0.0031739:0.00193323:1:0.869532:rs3131967
1 754503 rs3115859 G A . PASS AF=0.838292 ES:SE:LP:AF:ID -0.00256809:0.00179856:0.823909:0.838292:rs3115859
1 754629 rs10454459 A G . PASS AF=0.037758 ES:SE:LP:AF:ID -0.000784151:0.00348807:0.0861861:0.037758:rs10454459
1 754964 rs3131966 C T . PASS AF=0.838917 ES:SE:LP:AF:ID -0.00260943:0.00180361:0.823909:0.838917:rs3131966
1 755240 rs181660517 T G . PASS AF=0.013685 ES:SE:LP:AF:ID 0.00554712:0.00631471:0.420216:0.013685:rs181660517
1 755775 rs3131965 A G . PASS AF=0.839989 ES:SE:LP:AF:ID -0.00231136:0.00182827:0.677781:0.839989:rs3131965
1 755890 rs3115858 A T . PASS AF=0.869803 ES:SE:LP:AF:ID -0.00273658:0.00193132:0.79588:0.869803:rs3115858
1 756604 rs3131962 A G . PASS AF=0.869356 ES:SE:LP:AF:ID -0.00290628:0.00192644:0.886057:0.869356:rs3131962
1 757640 rs3115853 G A . PASS AF=0.868288 ES:SE:LP:AF:ID -0.00330809:0.00192213:1.07058:0.868288:rs3115853
1 757734 rs4951929 C T . PASS AF=0.869497 ES:SE:LP:AF:ID -0.00281522:0.00192798:0.853872:0.869497:rs4951929