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_2463.vcf.gz --id UKB-b:13346 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_2463.txt.gz --cohort_cases 44502 --cohort_controls 415887 --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": "2019-09-12T19:32:00.214709",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-13346/ukb-b-13346.vcf.gz; Date=Sun May 10 04:41:43 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-13346/UKB-b-13346_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-13346/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:42:19 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-13346/UKB-b-13346_data.vcf.gz ...
Read summary statistics for 8074242 SNPs.
Dropped 6260 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, 1282991 SNPs remain.
After merging with regression SNP LD, 1282991 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0187 (0.0017)
Lambda GC: 1.1493
Mean Chi^2: 1.1823
Intercept: 1.0156 (0.0077)
Ratio: 0.0858 (0.0422)
Analysis finished at Thu Oct 17 14:43:52 2019
Total time elapsed: 1.0m:32.73sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9434,
"inflation_factor": 1.1474,
"mean_EFFECT": -4.928e-07,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 18,
"n_p_sig": 794,
"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": 75443,
"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": 1282991,
"ldsc_nsnp_merge_regression_ld": 1282991,
"ldsc_observed_scale_h2_beta": 0.0187,
"ldsc_observed_scale_h2_se": 0.0017,
"ldsc_intercept_beta": 1.0156,
"ldsc_intercept_se": 0.0077,
"ldsc_lambda_gc": 1.1493,
"ldsc_mean_chisq": 1.1823,
"ldsc_ratio": 0.0856
}
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 | 8068010 | 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 | 8074242 | 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 | 8.659894e+00 | 5.763470e+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 | 7.871120e+07 | 5.640493e+07 | 828.0000000 | 3.228190e+07 | 6.918740e+07 | 1.145381e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -5.000000e-07 | 1.607200e-03 | -0.0164336 | -7.543000e-04 | -2.700000e-06 | 7.532000e-04 | 1.620840e-02 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.327400e-03 | 8.118000e-04 | 0.0005941 | 6.895000e-04 | 9.666000e-04 | 1.751800e-03 | 7.317400e-03 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.813635e-01 | 2.938879e-01 | 0.0000000 | 2.200002e-01 | 4.700002e-01 | 7.400005e-01 | 1.000000e+00 | ▇▇▇▆▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.813637e-01 | 2.938618e-01 | 0.0000000 | 2.220676e-01 | 4.749727e-01 | 7.360042e-01 | 9.999999e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.434927e-01 | 2.606437e-01 | 0.0078650 | 3.403000e-02 | 1.336350e-01 | 3.858428e-01 | 9.921350e-01 | ▇▂▂▁▁ |
| numeric | AF_reference | 75443 | 0.9906563 | NA | NA | NA | NA | NA | NA | NA | 2.428192e-01 | 2.524914e-01 | 0.0000000 | 3.534350e-02 | 1.491610e-01 | 3.811900e-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 | 0.0009562 | 0.0010931 | 0.3800004 | 0.3817152 | 0.623786 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0004141 | 0.0010829 | 0.6999999 | 0.7021539 | 0.400412 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | -0.0002936 | 0.0017314 | 0.8700001 | 0.8653647 | 0.103546 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0003161 | 0.0010662 | 0.7700005 | 0.7668929 | 0.456858 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | 0.0007558 | 0.0021025 | 0.7199992 | 0.7192295 | 0.074506 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | 0.0008865 | 0.0012181 | 0.4700002 | 0.4667426 | 0.240957 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | -0.0003244 | 0.0015196 | 0.8300000 | 0.8309684 | 0.913488 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | 0.0001471 | 0.0010206 | 0.8900000 | 0.8853791 | 0.116345 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | -0.0008291 | 0.0014959 | 0.5800000 | 0.5794087 | 0.067264 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0000018 | 0.0007561 | 1.0000000 | 0.9980656 | 0.515587 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51219006 | rs28729663 | G | A | 0.0008014 | 0.0009122 | 0.3800004 | 0.3796784 | 0.137944 | 0.2052720 | NA |
| 22 | 51219387 | rs9616832 | T | C | 0.0006408 | 0.0011841 | 0.5900000 | 0.5883664 | 0.073747 | 0.0654952 | NA |
| 22 | 51219704 | rs147475742 | G | A | -0.0001518 | 0.0015867 | 0.9199999 | 0.9238045 | 0.041966 | 0.0473243 | NA |
| 22 | 51221190 | rs369304721 | G | A | 0.0005364 | 0.0015842 | 0.7300002 | 0.7349291 | 0.049733 | NA | NA |
| 22 | 51221731 | rs115055839 | T | C | 0.0005618 | 0.0011849 | 0.6400000 | 0.6353745 | 0.073237 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | 0.0004668 | 0.0013951 | 0.7400005 | 0.7379258 | 0.054451 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | 0.0004854 | 0.0014018 | 0.7300002 | 0.7291370 | 0.054079 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | 0.0005563 | 0.0011892 | 0.6400000 | 0.6399020 | 0.073071 | 0.0730831 | NA |
| 22 | 51232488 | rs376461333 | A | G | -0.0022296 | 0.0028019 | 0.4299995 | 0.4261795 | 0.020038 | NA | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0014460 | 0.0007273 | 0.0470002 | 0.0467717 | 0.298007 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623786 ES:SE:LP:AF:ID 0.00095617:0.00109309:0.420216:0.623786:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400412 ES:SE:LP:AF:ID -0.000414109:0.00108288:0.154902:0.400412:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103546 ES:SE:LP:AF:ID -0.000293566:0.00173145:0.0604807:0.103546:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456858 ES:SE:LP:AF:ID 0.000316063:0.00106619:0.113509:0.456858:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074506 ES:SE:LP:AF:ID 0.000755825:0.0021025:0.142668:0.074506:rs8179466
1 534192 rs6680723 C T . PASS AF=0.240957 ES:SE:LP:AF:ID 0.000886491:0.00121806:0.327902:0.240957:rs6680723
1 546697 rs12025928 A G . PASS AF=0.913488 ES:SE:LP:AF:ID -0.000324382:0.00151964:0.0809219:0.913488:rs12025928
1 693731 rs12238997 A G . PASS AF=0.116345 ES:SE:LP:AF:ID 0.000147129:0.00102064:0.05061:0.116345:rs12238997
1 705882 rs72631875 G A . PASS AF=0.067264 ES:SE:LP:AF:ID -0.000829095:0.00149589:0.236572:0.067264:rs72631875
1 706368 rs12029736 A G . PASS AF=0.515587 ES:SE:LP:AF:ID -1.83294e-06:0.000756053:-0:0.515587:rs12029736
1 714596 rs149887893 T C . PASS AF=0.033022 ES:SE:LP:AF:ID 0.000889526:0.0019058:0.19382:0.033022:rs149887893
1 715265 rs12184267 C T . PASS AF=0.03664 ES:SE:LP:AF:ID 0.000123479:0.00173105:0.0268721:0.03664:rs12184267
1 715367 rs12184277 A G . PASS AF=0.036756 ES:SE:LP:AF:ID 4.82045e-05:0.00172455:0.00877392:0.036756:rs12184277
1 717485 rs12184279 C A . PASS AF=0.036456 ES:SE:LP:AF:ID 0.00015786:0.00173693:0.0315171:0.036456:rs12184279
1 717587 rs144155419 G A . PASS AF=0.016413 ES:SE:LP:AF:ID 0.00178352:0.00267446:0.30103:0.016413:rs144155419
1 720381 rs116801199 G T . PASS AF=0.036995 ES:SE:LP:AF:ID 0.000335267:0.00171772:0.0705811:0.036995:rs116801199
1 721290 rs12565286 G C . PASS AF=0.037092 ES:SE:LP:AF:ID 0.000338599:0.00171179:0.0757207:0.037092:rs12565286
1 722670 rs116030099 T C . PASS AF=0.101204 ES:SE:LP:AF:ID 0.000788719:0.00124746:0.275724:0.101204:rs116030099
1 723891 rs2977670 G C . PASS AF=0.959075 ES:SE:LP:AF:ID 0.000153213:0.00165101:0.0315171:0.959075:rs2977670
1 724849 rs12126395 C A . PASS AF=0.031455 ES:SE:LP:AF:ID -0.000605243:0.00299717:0.0757207:0.031455:rs12126395
1 725060 rs865924913 A T . PASS AF=0.053262 ES:SE:LP:AF:ID -0.000168916:0.0023841:0.0268721:0.053262:rs865924913
1 726794 rs28454925 C G . PASS AF=0.036611 ES:SE:LP:AF:ID 0.000235638:0.00172287:0.05061:0.036611:rs28454925
1 729632 rs116720794 C T . PASS AF=0.036927 ES:SE:LP:AF:ID 0.000170169:0.00170723:0.0362122:0.036927:rs116720794
1 729679 rs4951859 C G . PASS AF=0.843178 ES:SE:LP:AF:ID 7.30028e-05:0.000884562:0.0315171:0.843178:rs4951859
1 730087 rs148120343 T C . PASS AF=0.05594 ES:SE:LP:AF:ID -0.00200365:0.00143193:0.79588:0.05594:rs148120343
1 731718 rs58276399 T C . PASS AF=0.122326 ES:SE:LP:AF:ID -3.38615e-05:0.000968197:0.0132283:0.122326:rs58276399
1 732989 rs369030935 C T . PASS AF=0.025702 ES:SE:LP:AF:ID -8.05693e-05:0.0023822:0.0132283:0.025702:rs369030935
1 734349 rs141242758 T C . PASS AF=0.121569 ES:SE:LP:AF:ID -4.82609e-05:0.000968587:0.0177288:0.121569:rs141242758
1 736289 rs79010578 T A . PASS AF=0.13237 ES:SE:LP:AF:ID -0.000163877:0.000954649:0.0655015:0.13237:rs79010578
1 736689 rs181876450 T C . PASS AF=0.011135 ES:SE:LP:AF:ID -0.000495242:0.00347153:0.05061:0.011135:rs181876450
1 752478 rs146277091 G A . PASS AF=0.036842 ES:SE:LP:AF:ID 0.000130234:0.00168993:0.0268721:0.036842:rs146277091
1 752566 rs3094315 G A . PASS AF=0.838924 ES:SE:LP:AF:ID 0.000421674:0.000856644:0.207608:0.838924:rs3094315
1 752721 rs3131972 A G . PASS AF=0.838551 ES:SE:LP:AF:ID 0.000413994:0.000855722:0.200659:0.838551:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869771 ES:SE:LP:AF:ID 0.000390634:0.000918219:0.173925:0.869771:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129878 ES:SE:LP:AF:ID -0.000329345:0.000920094:0.142668:0.129878:rs2073813
1 754063 rs12184312 G T . PASS AF=0.03735 ES:SE:LP:AF:ID 5.10185e-05:0.00166137:0.00877392:0.03735:rs12184312
1 754105 rs12184325 C T . PASS AF=0.037594 ES:SE:LP:AF:ID 9.19199e-05:0.00165088:0.0177288:0.037594:rs12184325
1 754182 rs3131969 A G . PASS AF=0.869113 ES:SE:LP:AF:ID 0.0004258:0.000916425:0.19382:0.869113:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869211 ES:SE:LP:AF:ID 0.00043015:0.000916789:0.19382:0.869211:rs3131968
1 754211 rs12184313 G A . PASS AF=0.037553 ES:SE:LP:AF:ID 4.56139e-05:0.00165801:0.00877392:0.037553:rs12184313
1 754334 rs3131967 T C . PASS AF=0.869116 ES:SE:LP:AF:ID 0.000406406:0.000916407:0.180456:0.869116:rs3131967
1 754503 rs3115859 G A . PASS AF=0.838005 ES:SE:LP:AF:ID 0.000462919:0.000853356:0.229148:0.838005:rs3115859
1 754629 rs10454459 A G . PASS AF=0.037566 ES:SE:LP:AF:ID 4.00296e-06:0.00166033:-0:0.037566:rs10454459
1 754964 rs3131966 C T . PASS AF=0.838638 ES:SE:LP:AF:ID 0.000490942:0.000855763:0.244125:0.838638:rs3131966
1 755240 rs181660517 T G . PASS AF=0.013773 ES:SE:LP:AF:ID -0.0045436:0.00298722:0.886057:0.013773:rs181660517
1 755775 rs3131965 A G . PASS AF=0.839752 ES:SE:LP:AF:ID 0.000556242:0.000867317:0.283997:0.839752:rs3131965
1 755890 rs3115858 A T . PASS AF=0.869396 ES:SE:LP:AF:ID 0.00045447:0.000915343:0.207608:0.869396:rs3115858
1 756604 rs3131962 A G . PASS AF=0.868943 ES:SE:LP:AF:ID 0.000418675:0.000913039:0.187087:0.868943:rs3131962
1 757640 rs3115853 G A . PASS AF=0.867894 ES:SE:LP:AF:ID 0.000503456:0.000911291:0.236572:0.867894:rs3115853
1 757734 rs4951929 C T . PASS AF=0.869085 ES:SE:LP:AF:ID 0.000467314:0.000913786:0.21467:0.869085:rs4951929