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_20002_1473.vcf.gz --id UKB-b:10912 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20002_1473.txt.gz --cohort_cases 56753 --cohort_controls 406180 --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": "2019-09-12T22:39:34.122333",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-10912/ukb-b-10912.vcf.gz; Date=Sun May 10 13:59:17 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-10912/UKB-b-10912_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-10912/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-10912/UKB-b-10912_data.vcf.gz ...
Read summary statistics for 8423993 SNPs.
Dropped 6963 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, 1284984 SNPs remain.
After merging with regression SNP LD, 1284984 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0427 (0.0035)
Lambda GC: 1.2808
Mean Chi^2: 1.442
Intercept: 1.0578 (0.0104)
Ratio: 0.1307 (0.0234)
Analysis finished at Thu Oct 17 14:41:58 2019
Total time elapsed: 1.0m:39.84sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9452,
"inflation_factor": 1.1999,
"mean_EFFECT": 9.7734e-06,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 81,
"n_p_sig": 7411,
"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": 79773,
"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": 1284984,
"ldsc_nsnp_merge_regression_ld": 1284984,
"ldsc_observed_scale_h2_beta": 0.0427,
"ldsc_observed_scale_h2_se": 0.0035,
"ldsc_intercept_beta": 1.0578,
"ldsc_intercept_se": 0.0104,
"ldsc_lambda_gc": 1.2808,
"ldsc_mean_chisq": 1.442,
"ldsc_ratio": 0.1308
}
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 | 1.0000000 | 3 | 58 | 0 | 8417062 | 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 | 8423993 | 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 | 8.653911e+00 | 5.761731e+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 | 7.874211e+07 | 5.637548e+07 | 828.0000000 | 3.234846e+07 | 6.924860e+07 | 1.145444e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 9.800000e-06 | 2.021800e-03 | -0.0470181 | -8.955000e-04 | 5.900000e-06 | 9.087000e-04 | 4.004830e-02 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.575400e-03 | 1.035500e-03 | 0.0006538 | 7.660000e-04 | 1.107900e-03 | 2.113200e-03 | 1.021980e-02 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.680747e-01 | 2.974899e-01 | 0.0000000 | 2.000000e-01 | 4.600002e-01 | 7.300002e-01 | 1.000000e+00 | ▇▆▆▆▆ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.680749e-01 | 2.974640e-01 | 0.0000000 | 2.028689e-01 | 4.569491e-01 | 7.257796e-01 | 1.000000e+00 | ▇▆▆▆▆ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.341729e-01 | 2.599903e-01 | 0.0061680 | 2.818000e-02 | 1.208740e-01 | 3.707900e-01 | 9.938310e-01 | ▇▂▂▁▁ |
| numeric | AF_reference | 79773 | 0.9905303 | NA | NA | NA | NA | NA | NA | NA | 2.337574e-01 | 2.519073e-01 | 0.0000000 | 2.715650e-02 | 1.369810e-01 | 3.674120e-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 | 0.0002889 | 0.0012024 | 0.8100000 | 0.8101064 | 0.623763 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0015583 | 0.0011912 | 0.1900002 | 0.1908099 | 0.400401 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | -0.0002178 | 0.0019044 | 0.9100000 | 0.9089598 | 0.103556 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0004818 | 0.0011729 | 0.6800001 | 0.6812486 | 0.456851 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | 0.0012363 | 0.0023126 | 0.5900000 | 0.5929205 | 0.074508 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0026058 | 0.0013397 | 0.0519996 | 0.0517648 | 0.240960 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | -0.0016814 | 0.0016714 | 0.3100002 | 0.3144122 | 0.913473 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | 0.0017857 | 0.0011227 | 0.1100001 | 0.1117318 | 0.116325 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | 0.0014139 | 0.0016452 | 0.3900004 | 0.3901326 | 0.067285 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0000534 | 0.0008317 | 0.9500000 | 0.9487800 | 0.515650 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51219006 | rs28729663 | G | A | -0.0013739 | 0.0010040 | 0.1700000 | 0.1711724 | 0.137953 | 0.2052720 | NA |
| 22 | 51219387 | rs9616832 | T | C | -0.0019387 | 0.0013032 | 0.1400000 | 0.1368423 | 0.073747 | 0.0654952 | NA |
| 22 | 51219704 | rs147475742 | G | A | -0.0005850 | 0.0017464 | 0.7400005 | 0.7376228 | 0.041955 | 0.0473243 | NA |
| 22 | 51221190 | rs369304721 | G | A | -0.0017340 | 0.0017436 | 0.3200000 | 0.3199824 | 0.049731 | NA | NA |
| 22 | 51221731 | rs115055839 | T | C | -0.0019606 | 0.0013040 | 0.1299999 | 0.1327110 | 0.073238 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | -0.0005268 | 0.0015353 | 0.7300002 | 0.7315102 | 0.054459 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | -0.0004514 | 0.0015427 | 0.7700005 | 0.7698397 | 0.054088 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | -0.0019529 | 0.0013088 | 0.1400000 | 0.1356500 | 0.073073 | 0.0730831 | NA |
| 22 | 51232488 | rs376461333 | A | G | 0.0033628 | 0.0030832 | 0.2800000 | 0.2754039 | 0.020043 | NA | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0020517 | 0.0008005 | 0.0100000 | 0.0103725 | 0.297971 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623763 ES:SE:LP:AF:ID 0.000288916:0.00120237:0.091515:0.623763:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400401 ES:SE:LP:AF:ID -0.00155826:0.00119116:0.721246:0.400401:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103556 ES:SE:LP:AF:ID -0.000217774:0.00190444:0.0409586:0.103556:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456851 ES:SE:LP:AF:ID -0.000481768:0.00117287:0.167491:0.456851:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074508 ES:SE:LP:AF:ID 0.00123632:0.00231257:0.229148:0.074508:rs8179466
1 534192 rs6680723 C T . PASS AF=0.24096 ES:SE:LP:AF:ID -0.00260583:0.0013397:1.284:0.24096:rs6680723
1 546697 rs12025928 A G . PASS AF=0.913473 ES:SE:LP:AF:ID -0.0016814:0.00167136:0.508638:0.913473:rs12025928
1 693731 rs12238997 A G . PASS AF=0.116325 ES:SE:LP:AF:ID 0.00178567:0.00112274:0.958607:0.116325:rs12238997
1 705882 rs72631875 G A . PASS AF=0.067285 ES:SE:LP:AF:ID 0.00141389:0.00164525:0.408935:0.067285:rs72631875
1 706368 rs12029736 A G . PASS AF=0.51565 ES:SE:LP:AF:ID 5.34257e-05:0.000831672:0.0222764:0.51565:rs12029736
1 714596 rs149887893 T C . PASS AF=0.033004 ES:SE:LP:AF:ID -0.00201921:0.00209671:0.468521:0.033004:rs149887893
1 715265 rs12184267 C T . PASS AF=0.036621 ES:SE:LP:AF:ID -0.00194798:0.00190448:0.508638:0.036621:rs12184267
1 715367 rs12184277 A G . PASS AF=0.036737 ES:SE:LP:AF:ID -0.00198817:0.00189727:0.537602:0.036737:rs12184277
1 717485 rs12184279 C A . PASS AF=0.036437 ES:SE:LP:AF:ID -0.00224297:0.00191095:0.619789:0.036437:rs12184279
1 717587 rs144155419 G A . PASS AF=0.016407 ES:SE:LP:AF:ID 0.00369333:0.00294233:0.677781:0.016407:rs144155419
1 720381 rs116801199 G T . PASS AF=0.036976 ES:SE:LP:AF:ID -0.00190491:0.00188978:0.508638:0.036976:rs116801199
1 721290 rs12565286 G C . PASS AF=0.037072 ES:SE:LP:AF:ID -0.00196627:0.00188331:0.522879:0.037072:rs12565286
1 722670 rs116030099 T C . PASS AF=0.101199 ES:SE:LP:AF:ID 0.000318373:0.00137221:0.0861861:0.101199:rs116030099
1 723891 rs2977670 G C . PASS AF=0.959096 ES:SE:LP:AF:ID 0.00154725:0.00181641:0.408935:0.959096:rs2977670
1 724849 rs12126395 C A . PASS AF=0.031451 ES:SE:LP:AF:ID 0.00176278:0.00329731:0.229148:0.031451:rs12126395
1 725060 rs865924913 A T . PASS AF=0.053254 ES:SE:LP:AF:ID 0.000753692:0.00262316:0.113509:0.053254:rs865924913
1 726794 rs28454925 C G . PASS AF=0.03659 ES:SE:LP:AF:ID -0.00215809:0.0018955:0.60206:0.03659:rs28454925
1 729632 rs116720794 C T . PASS AF=0.036907 ES:SE:LP:AF:ID -0.00196982:0.00187824:0.537602:0.036907:rs116720794
1 729679 rs4951859 C G . PASS AF=0.843212 ES:SE:LP:AF:ID -0.00112542:0.000972998:0.60206:0.843212:rs4951859
1 730087 rs148120343 T C . PASS AF=0.055911 ES:SE:LP:AF:ID -0.000730625:0.00157544:0.19382:0.055911:rs148120343
1 731718 rs58276399 T C . PASS AF=0.122307 ES:SE:LP:AF:ID 0.00223555:0.00106503:1.4437:0.122307:rs58276399
1 732989 rs369030935 C T . PASS AF=0.025713 ES:SE:LP:AF:ID 0.00289022:0.00261975:0.568636:0.025713:rs369030935
1 734349 rs141242758 T C . PASS AF=0.121549 ES:SE:LP:AF:ID 0.00219823:0.00106548:1.40894:0.121549:rs141242758
1 736289 rs79010578 T A . PASS AF=0.13233 ES:SE:LP:AF:ID 0.000802286:0.00105013:0.356547:0.13233:rs79010578
1 736689 rs181876450 T C . PASS AF=0.011134 ES:SE:LP:AF:ID 0.00118267:0.00381823:0.119186:0.011134:rs181876450
1 752478 rs146277091 G A . PASS AF=0.036821 ES:SE:LP:AF:ID -0.00200058:0.00185925:0.552842:0.036821:rs146277091
1 752566 rs3094315 G A . PASS AF=0.838951 ES:SE:LP:AF:ID -0.00145585:0.00094228:0.920819:0.838951:rs3094315
1 752721 rs3131972 A G . PASS AF=0.83858 ES:SE:LP:AF:ID -0.00140292:0.000941266:0.853872:0.83858:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869781 ES:SE:LP:AF:ID -0.00257186:0.00101002:1.95861:0.869781:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129871 ES:SE:LP:AF:ID 0.00241767:0.00101208:1.76955:0.129871:rs2073813
1 754063 rs12184312 G T . PASS AF=0.037332 ES:SE:LP:AF:ID -0.00176106:0.00182772:0.468521:0.037332:rs12184312
1 754105 rs12184325 C T . PASS AF=0.037576 ES:SE:LP:AF:ID -0.00157254:0.00181617:0.408935:0.037576:rs12184325
1 754182 rs3131969 A G . PASS AF=0.869123 ES:SE:LP:AF:ID -0.00251845:0.00100804:1.92082:0.869123:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869221 ES:SE:LP:AF:ID -0.00251568:0.00100844:1.88606:0.869221:rs3131968
1 754211 rs12184313 G A . PASS AF=0.037534 ES:SE:LP:AF:ID -0.00167153:0.00182403:0.443698:0.037534:rs12184313
1 754334 rs3131967 T C . PASS AF=0.869126 ES:SE:LP:AF:ID -0.00250893:0.00100802:1.88606:0.869126:rs3131967
1 754503 rs3115859 G A . PASS AF=0.838033 ES:SE:LP:AF:ID -0.00142875:0.000938653:0.886057:0.838033:rs3115859
1 754629 rs10454459 A G . PASS AF=0.037547 ES:SE:LP:AF:ID -0.00174035:0.00182661:0.468521:0.037547:rs10454459
1 754964 rs3131966 C T . PASS AF=0.838664 ES:SE:LP:AF:ID -0.0014806:0.000941291:0.920819:0.838664:rs3131966
1 755240 rs181660517 T G . PASS AF=0.013773 ES:SE:LP:AF:ID 0.000271326:0.0032857:0.0315171:0.013773:rs181660517
1 755775 rs3131965 A G . PASS AF=0.839777 ES:SE:LP:AF:ID -0.00156446:0.000954022:1:0.839777:rs3131965
1 755890 rs3115858 A T . PASS AF=0.869405 ES:SE:LP:AF:ID -0.00253462:0.00100685:1.92082:0.869405:rs3115858
1 756604 rs3131962 A G . PASS AF=0.868952 ES:SE:LP:AF:ID -0.00262925:0.00100432:2.05552:0.868952:rs3131962
1 757640 rs3115853 G A . PASS AF=0.867905 ES:SE:LP:AF:ID -0.00259317:0.00100239:2.01323:0.867905:rs3115853
1 757734 rs4951929 C T . PASS AF=0.869095 ES:SE:LP:AF:ID -0.00259517:0.00100514:2.00877:0.869095:rs4951929