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_2443.vcf.gz --id UKB-b:10753 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_2443.txt.gz --cohort_cases 22340 --cohort_controls 439238 --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": "2019-09-12T19:32:51.151302",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-10753/ukb-b-10753.vcf.gz; Date=Sun May 10 11:40:21 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-10753/UKB-b-10753_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-10753/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-10753/UKB-b-10753_data.vcf.gz ...
Read summary statistics for 7090283 SNPs.
Dropped 4432 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, 1266362 SNPs remain.
After merging with regression SNP LD, 1266362 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0418 (0.0025)
Lambda GC: 1.3158
Mean Chi^2: 1.4398
Intercept: 1.0622 (0.0095)
Ratio: 0.1413 (0.0216)
Analysis finished at Thu Oct 17 14:41:44 2019
Total time elapsed: 1.0m:25.41sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9366,
"inflation_factor": 1.3107,
"mean_EFFECT": 3.2182e-06,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 71,
"n_p_sig": 7252,
"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": 65287,
"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": 1266362,
"ldsc_nsnp_merge_regression_ld": 1266362,
"ldsc_observed_scale_h2_beta": 0.0418,
"ldsc_observed_scale_h2_se": 0.0025,
"ldsc_intercept_beta": 1.0622,
"ldsc_intercept_se": 0.0095,
"ldsc_lambda_gc": 1.3158,
"ldsc_mean_chisq": 1.4398,
"ldsc_ratio": 0.1414
}
Flags
| name | value |
|---|---|
| af_correlation | FALSE |
| inflation_factor | TRUE |
| 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 | 7085873 | 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 | 7090283 | 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 | 8.663757e+00 | 5.764153e+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 | 7.864069e+07 | 5.646319e+07 | 828.0000000 | 3.214148e+07 | 6.906834e+07 | 1.145310e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 3.200000e-06 | 9.781000e-04 | -0.0205539 | -5.274000e-04 | -1.500000e-06 | 5.232000e-04 | 1.907900e-02 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 7.867000e-04 | 3.763000e-04 | 0.0004288 | 4.853000e-04 | 6.281000e-04 | 9.923000e-04 | 4.654600e-03 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 4.589976e-01 | 2.999277e-01 | 0.0000000 | 1.900002e-01 | 4.400003e-01 | 7.199992e-01 | 1.000000e+00 | ▇▆▆▅▆ |
| numeric | PVAL_ztest | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 4.589998e-01 | 2.999040e-01 | 0.0000000 | 1.889704e-01 | 4.448444e-01 | 7.185612e-01 | 9.999999e-01 | ▇▆▆▆▆ |
| numeric | AF | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 2.729298e-01 | 2.600435e-01 | 0.0156670 | 5.717200e-02 | 1.753390e-01 | 4.287120e-01 | 9.843330e-01 | ▇▃▂▁▁ |
| numeric | AF_reference | 65287 | 0.990792 | NA | NA | NA | NA | NA | NA | NA | 2.713236e-01 | 2.520104e-01 | 0.0000000 | 6.449680e-02 | 1.877000e-01 | 4.215260e-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 | -0.0001638 | 0.0007889 | 0.8400000 | 0.8355474 | 0.623758 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0009415 | 0.0007816 | 0.2300001 | 0.2283698 | 0.400404 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | -0.0008915 | 0.0012497 | 0.4799997 | 0.4756414 | 0.103552 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0010755 | 0.0007696 | 0.1600000 | 0.1622484 | 0.456852 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | -0.0007441 | 0.0015173 | 0.6200004 | 0.6238518 | 0.074509 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | 0.0001466 | 0.0008791 | 0.8700001 | 0.8675579 | 0.240944 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | 0.0008064 | 0.0010966 | 0.4600002 | 0.4621604 | 0.913480 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | -0.0001391 | 0.0007366 | 0.8499999 | 0.8502451 | 0.116340 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | -0.0006119 | 0.0010796 | 0.5700002 | 0.5708431 | 0.067271 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0006378 | 0.0005457 | 0.2399999 | 0.2425232 | 0.515590 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51219006 | rs28729663 | G | A | 0.0003617 | 0.0006586 | 0.5800000 | 0.5828958 | 0.137974 | 0.2052720 | NA |
| 22 | 51219387 | rs9616832 | T | C | 0.0004654 | 0.0008549 | 0.5900000 | 0.5861575 | 0.073740 | 0.0654952 | NA |
| 22 | 51219704 | rs147475742 | G | A | 0.0005735 | 0.0011456 | 0.6200004 | 0.6166449 | 0.041955 | 0.0473243 | NA |
| 22 | 51221190 | rs369304721 | G | A | 0.0009985 | 0.0011438 | 0.3800004 | 0.3827208 | 0.049724 | NA | NA |
| 22 | 51221731 | rs115055839 | T | C | 0.0004863 | 0.0008555 | 0.5700002 | 0.5697629 | 0.073231 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | 0.0005610 | 0.0010069 | 0.5800000 | 0.5774315 | 0.054488 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | 0.0005516 | 0.0010118 | 0.5900000 | 0.5856291 | 0.054118 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | 0.0003868 | 0.0008586 | 0.6499995 | 0.6523307 | 0.073066 | 0.0730831 | NA |
| 22 | 51232488 | rs376461333 | A | G | 0.0011861 | 0.0020231 | 0.5600000 | 0.5576898 | 0.020043 | NA | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0000785 | 0.0005251 | 0.8800001 | 0.8811220 | 0.297942 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623758 ES:SE:LP:AF:ID -0.000163777:0.000788936:0.0757207:0.623758:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400404 ES:SE:LP:AF:ID 0.000941479:0.00078159:0.638272:0.400404:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103552 ES:SE:LP:AF:ID -0.000891456:0.00124971:0.318759:0.103552:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456852 ES:SE:LP:AF:ID 0.00107554:0.00076959:0.79588:0.456852:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074509 ES:SE:LP:AF:ID -0.000744077:0.00151729:0.207608:0.074509:rs8179466
1 534192 rs6680723 C T . PASS AF=0.240944 ES:SE:LP:AF:ID 0.000146602:0.000879113:0.0604807:0.240944:rs6680723
1 546697 rs12025928 A G . PASS AF=0.91348 ES:SE:LP:AF:ID 0.000806353:0.00109664:0.337242:0.91348:rs12025928
1 693731 rs12238997 A G . PASS AF=0.11634 ES:SE:LP:AF:ID -0.000139076:0.000736609:0.0705811:0.11634:rs12238997
1 705882 rs72631875 G A . PASS AF=0.067271 ES:SE:LP:AF:ID -0.000611934:0.00107961:0.244125:0.067271:rs72631875
1 706368 rs12029736 A G . PASS AF=0.51559 ES:SE:LP:AF:ID -0.000637793:0.000545727:0.619789:0.51559:rs12029736
1 714596 rs149887893 T C . PASS AF=0.033001 ES:SE:LP:AF:ID -0.00076986:0.00137582:0.236572:0.033001:rs149887893
1 715265 rs12184267 C T . PASS AF=0.036618 ES:SE:LP:AF:ID -0.00101819:0.0012497:0.376751:0.036618:rs12184267
1 715367 rs12184277 A G . PASS AF=0.036734 ES:SE:LP:AF:ID -0.00107776:0.00124499:0.408935:0.036734:rs12184277
1 717485 rs12184279 C A . PASS AF=0.036434 ES:SE:LP:AF:ID -0.00121871:0.00125395:0.481486:0.036434:rs12184279
1 717587 rs144155419 G A . PASS AF=0.016397 ES:SE:LP:AF:ID 0.00145178:0.00193125:0.346787:0.016397:rs144155419
1 720381 rs116801199 G T . PASS AF=0.036973 ES:SE:LP:AF:ID -0.00114664:0.00124004:0.443698:0.036973:rs116801199
1 721290 rs12565286 G C . PASS AF=0.037071 ES:SE:LP:AF:ID -0.00112227:0.00123578:0.443698:0.037071:rs12565286
1 722670 rs116030099 T C . PASS AF=0.101239 ES:SE:LP:AF:ID 0.000800815:0.000900207:0.431798:0.101239:rs116030099
1 723891 rs2977670 G C . PASS AF=0.959104 ES:SE:LP:AF:ID 0.000987017:0.00119201:0.387216:0.959104:rs2977670
1 724849 rs12126395 C A . PASS AF=0.031449 ES:SE:LP:AF:ID 0.00170843:0.00216342:0.366532:0.031449:rs12126395
1 725060 rs865924913 A T . PASS AF=0.053264 ES:SE:LP:AF:ID 0.00146718:0.00172071:0.408935:0.053264:rs865924913
1 726794 rs28454925 C G . PASS AF=0.03659 ES:SE:LP:AF:ID -0.00125416:0.00124377:0.508638:0.03659:rs28454925
1 729632 rs116720794 C T . PASS AF=0.036907 ES:SE:LP:AF:ID -0.00126336:0.00123243:0.508638:0.036907:rs116720794
1 729679 rs4951859 C G . PASS AF=0.843217 ES:SE:LP:AF:ID 0.000247883:0.000638423:0.154902:0.843217:rs4951859
1 730087 rs148120343 T C . PASS AF=0.055937 ES:SE:LP:AF:ID 0.000198343:0.00103353:0.0705811:0.055937:rs148120343
1 731718 rs58276399 T C . PASS AF=0.122317 ES:SE:LP:AF:ID -6.30538e-05:0.000698774:0.0315171:0.122317:rs58276399
1 732989 rs369030935 C T . PASS AF=0.025712 ES:SE:LP:AF:ID -0.000811726:0.00171898:0.19382:0.025712:rs369030935
1 734349 rs141242758 T C . PASS AF=0.121559 ES:SE:LP:AF:ID -1.79284e-05:0.00069907:0.00877392:0.121559:rs141242758
1 736289 rs79010578 T A . PASS AF=0.132344 ES:SE:LP:AF:ID -0.000385086:0.000688992:0.236572:0.132344:rs79010578
1 752478 rs146277091 G A . PASS AF=0.03682 ES:SE:LP:AF:ID -0.000944015:0.00122:0.356547:0.03682:rs146277091
1 752566 rs3094315 G A . PASS AF=0.838951 ES:SE:LP:AF:ID 0.000286226:0.000618258:0.19382:0.838951:rs3094315
1 752721 rs3131972 A G . PASS AF=0.838578 ES:SE:LP:AF:ID 0.000226273:0.00061759:0.148742:0.838578:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869769 ES:SE:LP:AF:ID 0.000198458:0.00066269:0.119186:0.869769:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129882 ES:SE:LP:AF:ID -0.000241584:0.000664041:0.142668:0.129882:rs2073813
1 754063 rs12184312 G T . PASS AF=0.037331 ES:SE:LP:AF:ID -0.00101953:0.0011993:0.39794:0.037331:rs12184312
1 754105 rs12184325 C T . PASS AF=0.037575 ES:SE:LP:AF:ID -0.00102687:0.00119172:0.408935:0.037575:rs12184325
1 754182 rs3131969 A G . PASS AF=0.86911 ES:SE:LP:AF:ID 0.000164733:0.000661389:0.09691:0.86911:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869208 ES:SE:LP:AF:ID 0.000200895:0.000661653:0.119186:0.869208:rs3131968
1 754211 rs12184313 G A . PASS AF=0.037533 ES:SE:LP:AF:ID -0.00110686:0.00119688:0.443698:0.037533:rs12184313
1 754334 rs3131967 T C . PASS AF=0.869112 ES:SE:LP:AF:ID 0.000170793:0.000661376:0.09691:0.869112:rs3131967
1 754503 rs3115859 G A . PASS AF=0.838031 ES:SE:LP:AF:ID 0.000254527:0.000615874:0.167491:0.838031:rs3115859
1 754629 rs10454459 A G . PASS AF=0.037546 ES:SE:LP:AF:ID -0.00113027:0.00119857:0.455932:0.037546:rs10454459
1 754964 rs3131966 C T . PASS AF=0.838662 ES:SE:LP:AF:ID 0.000259956:0.000617605:0.173925:0.838662:rs3131966
1 755775 rs3131965 A G . PASS AF=0.839773 ES:SE:LP:AF:ID 0.000230565:0.000625952:0.148742:0.839773:rs3131965
1 755890 rs3115858 A T . PASS AF=0.869393 ES:SE:LP:AF:ID 0.000211863:0.000660612:0.124939:0.869393:rs3115858
1 756604 rs3131962 A G . PASS AF=0.868941 ES:SE:LP:AF:ID 0.000188203:0.000658956:0.107905:0.868941:rs3131962
1 757640 rs3115853 G A . PASS AF=0.867892 ES:SE:LP:AF:ID 0.000102665:0.000657692:0.0555173:0.867892:rs3115853
1 757734 rs4951929 C T . PASS AF=0.869083 ES:SE:LP:AF:ID 0.000202902:0.000659488:0.119186:0.869083:rs4951929
1 757936 rs4951862 C A . PASS AF=0.869091 ES:SE:LP:AF:ID 0.000201333:0.000659538:0.119186:0.869091:rs4951862
1 758144 rs3131956 A G . PASS AF=0.869099 ES:SE:LP:AF:ID 0.000202734:0.000659554:0.119186:0.869099:rs3131956