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_20003_1140861958.vcf.gz --id UKB-b:11268 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20003_1140861958.txt.gz --cohort_cases 52427 --cohort_controls 410506 --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": "2019-09-13T13:41:29.433375",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-11268/ukb-b-11268.vcf.gz; Date=Sun May 10 12:29:59 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-11268/UKB-b-11268_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-11268/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:40:19 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-11268/UKB-b-11268_data.vcf.gz ...
Read summary statistics for 8308947 SNPs.
Dropped 6732 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, 1284446 SNPs remain.
After merging with regression SNP LD, 1284446 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0272 (0.0018)
Lambda GC: 1.218
Mean Chi^2: 1.2784
Intercept: 1.0351 (0.0083)
Ratio: 0.1261 (0.0298)
Analysis finished at Thu Oct 17 14:41:59 2019
Total time elapsed: 1.0m:40.12sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9446,
"inflation_factor": 1.1474,
"mean_EFFECT": 7.077e-08,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 41,
"n_p_sig": 2408,
"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": 78191,
"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": 1284446,
"ldsc_nsnp_merge_regression_ld": 1284446,
"ldsc_observed_scale_h2_beta": 0.0272,
"ldsc_observed_scale_h2_se": 0.0018,
"ldsc_intercept_beta": 1.0351,
"ldsc_intercept_se": 0.0083,
"ldsc_lambda_gc": 1.218,
"ldsc_mean_chisq": 1.2784,
"ldsc_ratio": 0.1261
}
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 | 8302245 | 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 | 8308947 | 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 | 8.654893e+00 | 5.762310e+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 | 7.873985e+07 | 5.638491e+07 | 828.0000000 | 3.233316e+07 | 6.923549e+07 | 1.145476e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.000000e-07 | 1.851000e-03 | -0.0297996 | -8.409000e-04 | -3.200000e-06 | 8.409000e-04 | 2.120120e-02 | ▁▁▇▃▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.483300e-03 | 9.529000e-04 | 0.0006311 | 7.372000e-04 | 1.055400e-03 | 1.977600e-03 | 9.867500e-03 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.743987e-01 | 2.959874e-01 | 0.0000000 | 2.099999e-01 | 4.700002e-01 | 7.300002e-01 | 1.000000e+00 | ▇▇▇▆▆ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.743984e-01 | 2.959632e-01 | 0.0000000 | 2.114564e-01 | 4.654643e-01 | 7.313054e-01 | 1.000000e+00 | ▇▆▆▆▆ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.371624e-01 | 2.602311e-01 | 0.0066760 | 3.000400e-02 | 1.251000e-01 | 3.756040e-01 | 9.933240e-01 | ▇▂▂▁▁ |
| numeric | AF_reference | 78191 | 0.9905895 | NA | NA | NA | NA | NA | NA | NA | 2.366622e-01 | 2.521229e-01 | 0.0000000 | 2.975240e-02 | 1.409740e-01 | 3.718050e-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 | 0.0010714 | 0.0011612 | 0.3599996 | 0.3561722 | 0.623763 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0000302 | 0.0011504 | 0.9800000 | 0.9790345 | 0.400401 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | -0.0010301 | 0.0018392 | 0.5800000 | 0.5754403 | 0.103556 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0014516 | 0.0011327 | 0.2000000 | 0.2000205 | 0.456851 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | -0.0009739 | 0.0022334 | 0.6600001 | 0.6627764 | 0.074508 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0022632 | 0.0012938 | 0.0800000 | 0.0802531 | 0.240960 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | -0.0002865 | 0.0016141 | 0.8600001 | 0.8591020 | 0.913473 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | 0.0009804 | 0.0010843 | 0.3700002 | 0.3659050 | 0.116325 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | 0.0003359 | 0.0015889 | 0.8300000 | 0.8325514 | 0.067285 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0001241 | 0.0008032 | 0.8800001 | 0.8772455 | 0.515650 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51219006 | rs28729663 | G | A | -0.0001990 | 0.0009692 | 0.8400000 | 0.8373456 | 0.137953 | 0.2052720 | NA |
| 22 | 51219387 | rs9616832 | T | C | -0.0011490 | 0.0012580 | 0.3599996 | 0.3610585 | 0.073747 | 0.0654952 | NA |
| 22 | 51219704 | rs147475742 | G | A | -0.0003510 | 0.0016859 | 0.8400000 | 0.8350580 | 0.041955 | 0.0473243 | NA |
| 22 | 51221190 | rs369304721 | G | A | -0.0009518 | 0.0016832 | 0.5700002 | 0.5717528 | 0.049731 | NA | NA |
| 22 | 51221731 | rs115055839 | T | C | -0.0011342 | 0.0012588 | 0.3700002 | 0.3676111 | 0.073238 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | 0.0010474 | 0.0014821 | 0.4799997 | 0.4797344 | 0.054459 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | 0.0011190 | 0.0014892 | 0.4500005 | 0.4524210 | 0.054088 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | -0.0012195 | 0.0012634 | 0.3300000 | 0.3344143 | 0.073073 | 0.0730831 | NA |
| 22 | 51232488 | rs376461333 | A | G | 0.0042435 | 0.0029763 | 0.1499999 | 0.1539405 | 0.020043 | NA | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0008885 | 0.0007727 | 0.2500000 | 0.2502285 | 0.297971 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623763 ES:SE:LP:AF:ID 0.00107142:0.0011612:0.443698:0.623763:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400401 ES:SE:LP:AF:ID 3.02315e-05:0.00115039:0.00877392:0.400401:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103556 ES:SE:LP:AF:ID -0.00103008:0.00183924:0.236572:0.103556:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456851 ES:SE:LP:AF:ID 0.00145156:0.00113271:0.69897:0.456851:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074508 ES:SE:LP:AF:ID -0.000973948:0.0022334:0.180456:0.074508:rs8179466
1 534192 rs6680723 C T . PASS AF=0.24096 ES:SE:LP:AF:ID -0.00226321:0.00129384:1.09691:0.24096:rs6680723
1 546697 rs12025928 A G . PASS AF=0.913473 ES:SE:LP:AF:ID -0.000286538:0.00161414:0.0655015:0.913473:rs12025928
1 693731 rs12238997 A G . PASS AF=0.116325 ES:SE:LP:AF:ID 0.000980392:0.0010843:0.431798:0.116325:rs12238997
1 705882 rs72631875 G A . PASS AF=0.067285 ES:SE:LP:AF:ID 0.000335946:0.00158892:0.0809219:0.067285:rs72631875
1 706368 rs12029736 A G . PASS AF=0.51565 ES:SE:LP:AF:ID 0.000124064:0.000803201:0.0555173:0.51565:rs12029736
1 714596 rs149887893 T C . PASS AF=0.033004 ES:SE:LP:AF:ID -0.000658688:0.00202493:0.130768:0.033004:rs149887893
1 715265 rs12184267 C T . PASS AF=0.036621 ES:SE:LP:AF:ID -0.000177301:0.00183928:0.0362122:0.036621:rs12184267
1 715367 rs12184277 A G . PASS AF=0.036737 ES:SE:LP:AF:ID -0.000201638:0.00183232:0.0409586:0.036737:rs12184277
1 717485 rs12184279 C A . PASS AF=0.036437 ES:SE:LP:AF:ID -0.000449953:0.00184553:0.091515:0.036437:rs12184279
1 717587 rs144155419 G A . PASS AF=0.016407 ES:SE:LP:AF:ID 0.00201754:0.0028416:0.318759:0.016407:rs144155419
1 720381 rs116801199 G T . PASS AF=0.036976 ES:SE:LP:AF:ID -0.00030763:0.00182509:0.0604807:0.036976:rs116801199
1 721290 rs12565286 G C . PASS AF=0.037072 ES:SE:LP:AF:ID -0.000346519:0.00181883:0.0705811:0.037072:rs12565286
1 722670 rs116030099 T C . PASS AF=0.101199 ES:SE:LP:AF:ID 0.00065281:0.00132523:0.207608:0.101199:rs116030099
1 723891 rs2977670 G C . PASS AF=0.959096 ES:SE:LP:AF:ID -2.48793e-05:0.00175423:0.00436481:0.959096:rs2977670
1 724849 rs12126395 C A . PASS AF=0.031451 ES:SE:LP:AF:ID -0.00106652:0.00318443:0.130768:0.031451:rs12126395
1 725060 rs865924913 A T . PASS AF=0.053254 ES:SE:LP:AF:ID 0.0055755:0.00253336:1.55284:0.053254:rs865924913
1 726794 rs28454925 C G . PASS AF=0.03659 ES:SE:LP:AF:ID -0.000465142:0.00183061:0.09691:0.03659:rs28454925
1 729632 rs116720794 C T . PASS AF=0.036907 ES:SE:LP:AF:ID -0.000579549:0.00181394:0.124939:0.036907:rs116720794
1 729679 rs4951859 C G . PASS AF=0.843212 ES:SE:LP:AF:ID -0.000794558:0.000939688:0.39794:0.843212:rs4951859
1 730087 rs148120343 T C . PASS AF=0.055911 ES:SE:LP:AF:ID 0.00136213:0.0015215:0.431798:0.055911:rs148120343
1 731718 rs58276399 T C . PASS AF=0.122307 ES:SE:LP:AF:ID 0.00125651:0.00102857:0.657577:0.122307:rs58276399
1 732989 rs369030935 C T . PASS AF=0.025713 ES:SE:LP:AF:ID -0.000254895:0.00253007:0.0362122:0.025713:rs369030935
1 734349 rs141242758 T C . PASS AF=0.121549 ES:SE:LP:AF:ID 0.00122257:0.001029:0.638272:0.121549:rs141242758
1 736289 rs79010578 T A . PASS AF=0.13233 ES:SE:LP:AF:ID 0.000531585:0.00101418:0.221849:0.13233:rs79010578
1 736689 rs181876450 T C . PASS AF=0.011134 ES:SE:LP:AF:ID 0.00454518:0.00368751:0.657577:0.011134:rs181876450
1 752478 rs146277091 G A . PASS AF=0.036821 ES:SE:LP:AF:ID -0.000174073:0.0017956:0.0362122:0.036821:rs146277091
1 752566 rs3094315 G A . PASS AF=0.838951 ES:SE:LP:AF:ID -0.000739959:0.000910022:0.376751:0.838951:rs3094315
1 752721 rs3131972 A G . PASS AF=0.83858 ES:SE:LP:AF:ID -0.000759404:0.000909043:0.39794:0.83858:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869781 ES:SE:LP:AF:ID -0.00121997:0.000975438:0.677781:0.869781:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129871 ES:SE:LP:AF:ID 0.00126854:0.000977435:0.721246:0.129871:rs2073813
1 754063 rs12184312 G T . PASS AF=0.037332 ES:SE:LP:AF:ID -0.000217426:0.00176515:0.0457575:0.037332:rs12184312
1 754105 rs12184325 C T . PASS AF=0.037576 ES:SE:LP:AF:ID 1.16596e-05:0.001754:0.00436481:0.037576:rs12184325
1 754182 rs3131969 A G . PASS AF=0.869123 ES:SE:LP:AF:ID -0.001308:0.000973529:0.744727:0.869123:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869221 ES:SE:LP:AF:ID -0.00129064:0.000973915:0.721246:0.869221:rs3131968
1 754211 rs12184313 G A . PASS AF=0.037534 ES:SE:LP:AF:ID -0.000168733:0.00176159:0.0362122:0.037534:rs12184313
1 754334 rs3131967 T C . PASS AF=0.869126 ES:SE:LP:AF:ID -0.00130348:0.000973509:0.744727:0.869126:rs3131967
1 754503 rs3115859 G A . PASS AF=0.838033 ES:SE:LP:AF:ID -0.00083908:0.000906519:0.455932:0.838033:rs3115859
1 754629 rs10454459 A G . PASS AF=0.037547 ES:SE:LP:AF:ID -0.000148296:0.00176407:0.0315171:0.037547:rs10454459
1 754964 rs3131966 C T . PASS AF=0.838664 ES:SE:LP:AF:ID -0.000879779:0.000909067:0.481486:0.838664:rs3131966
1 755240 rs181660517 T G . PASS AF=0.013773 ES:SE:LP:AF:ID 0.00316204:0.00317322:0.49485:0.013773:rs181660517
1 755775 rs3131965 A G . PASS AF=0.839777 ES:SE:LP:AF:ID -0.00090745:0.000921362:0.49485:0.839777:rs3131965
1 755890 rs3115858 A T . PASS AF=0.869405 ES:SE:LP:AF:ID -0.0012266:0.000972383:0.677781:0.869405:rs3115858
1 756604 rs3131962 A G . PASS AF=0.868952 ES:SE:LP:AF:ID -0.00132614:0.000969937:0.769551:0.868952:rs3131962
1 757640 rs3115853 G A . PASS AF=0.867905 ES:SE:LP:AF:ID -0.00147927:0.000968078:0.886057:0.867905:rs3115853
1 757734 rs4951929 C T . PASS AF=0.869095 ES:SE:LP:AF:ID -0.00131342:0.000970731:0.744727:0.869095:rs4951929