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_1220.vcf.gz --id UKB-b:12948 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20002_1220.txt.gz --cohort_cases 18228 --cohort_controls 444705 --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": "2019-09-13T08:02:36.769495",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-12948/ukb-b-12948.vcf.gz; Date=Sun May 10 07:04:04 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-12948/UKB-b-12948_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-12948/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:42:12 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-12948/UKB-b-12948_data.vcf.gz ...
Read summary statistics for 6800595 SNPs.
Dropped 3999 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, 1256603 SNPs remain.
After merging with regression SNP LD, 1256603 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0342 (0.0021)
Lambda GC: 1.2704
Mean Chi^2: 1.3698
Intercept: 1.0583 (0.0095)
Ratio: 0.1576 (0.0256)
Analysis finished at Thu Oct 17 14:43:34 2019
Total time elapsed: 1.0m:22.14sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9339,
"inflation_factor": 1.2544,
"mean_EFFECT": 1.4736e-06,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 63,
"n_p_sig": 5014,
"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": 62471,
"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": 1256603,
"ldsc_nsnp_merge_regression_ld": 1256603,
"ldsc_observed_scale_h2_beta": 0.0342,
"ldsc_observed_scale_h2_se": 0.0021,
"ldsc_intercept_beta": 1.0583,
"ldsc_intercept_se": 0.0095,
"ldsc_lambda_gc": 1.2704,
"ldsc_mean_chisq": 1.3698,
"ldsc_ratio": 0.1577
}
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 | 1.0000000 | 3 | 58 | 0 | 6796618 | 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 | 6800595 | 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 | 8.663648e+00 | 5.764698e+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 | 7.861041e+07 | 5.647166e+07 | 828.0000000 | 3.209676e+07 | 6.905336e+07 | 1.145046e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.500000e-06 | 8.196000e-04 | -0.0155439 | -4.556000e-04 | -3.600000e-06 | 4.495000e-04 | 1.518810e-02 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 6.758000e-04 | 2.968000e-04 | 0.0003884 | 4.365000e-04 | 5.529000e-04 | 8.408000e-04 | 3.394800e-03 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.640364e-01 | 2.987547e-01 | 0.0000000 | 2.000000e-01 | 4.500005e-01 | 7.199992e-01 | 1.000000e+00 | ▇▆▆▆▆ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.640374e-01 | 2.987279e-01 | 0.0000000 | 1.962183e-01 | 4.517237e-01 | 7.226137e-01 | 9.999997e-01 | ▇▆▆▆▆ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.825304e-01 | 2.589456e-01 | 0.0192020 | 6.602800e-02 | 1.889210e-01 | 4.415650e-01 | 9.807980e-01 | ▇▃▂▂▁ |
| numeric | AF_reference | 62471 | 0.9908139 | NA | NA | NA | NA | NA | NA | NA | 2.805246e-01 | 2.510872e-01 | 0.0000000 | 7.468050e-02 | 2.000800e-01 | 4.339060e-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 | -0.0003687 | 0.0007145 | 0.6100002 | 0.6057999 | 0.623763 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0006902 | 0.0007078 | 0.3300000 | 0.3295401 | 0.400401 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | 0.0002644 | 0.0011317 | 0.8200001 | 0.8152828 | 0.103556 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0007180 | 0.0006970 | 0.2999998 | 0.3028919 | 0.456851 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | -0.0014969 | 0.0013742 | 0.2800000 | 0.2760139 | 0.074508 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0002730 | 0.0007961 | 0.7300002 | 0.7316283 | 0.240960 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | 0.0008120 | 0.0009932 | 0.4100001 | 0.4135834 | 0.913473 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | -0.0003268 | 0.0006672 | 0.6200004 | 0.6242949 | 0.116325 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | -0.0006782 | 0.0009777 | 0.4899999 | 0.4878576 | 0.067285 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0005798 | 0.0004942 | 0.2399999 | 0.2407348 | 0.515650 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51219006 | rs28729663 | G | A | 0.0004666 | 0.0005964 | 0.4299995 | 0.4340523 | 0.137953 | 0.2052720 | NA |
| 22 | 51219387 | rs9616832 | T | C | 0.0002575 | 0.0007742 | 0.7400005 | 0.7394544 | 0.073747 | 0.0654952 | NA |
| 22 | 51219704 | rs147475742 | G | A | 0.0000719 | 0.0010374 | 0.9400001 | 0.9447766 | 0.041955 | 0.0473243 | NA |
| 22 | 51221190 | rs369304721 | G | A | 0.0004271 | 0.0010357 | 0.6800001 | 0.6801065 | 0.049731 | NA | NA |
| 22 | 51221731 | rs115055839 | T | C | 0.0002392 | 0.0007746 | 0.7600007 | 0.7575233 | 0.073238 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | 0.0009453 | 0.0009120 | 0.2999998 | 0.2999798 | 0.054459 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | 0.0009689 | 0.0009164 | 0.2900000 | 0.2904071 | 0.054088 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | 0.0001229 | 0.0007774 | 0.8700001 | 0.8744096 | 0.073073 | 0.0730831 | NA |
| 22 | 51232488 | rs376461333 | A | G | 0.0020219 | 0.0018315 | 0.2700001 | 0.2696014 | 0.020043 | NA | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0000811 | 0.0004755 | 0.8600001 | 0.8645032 | 0.297971 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623763 ES:SE:LP:AF:ID -0.00036873:0.000714485:0.21467:0.623763:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400401 ES:SE:LP:AF:ID 0.000690161:0.000707828:0.481486:0.400401:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103556 ES:SE:LP:AF:ID 0.000264379:0.00113168:0.0861861:0.103556:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456851 ES:SE:LP:AF:ID 0.000718039:0.000696955:0.522879:0.456851:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074508 ES:SE:LP:AF:ID -0.00149694:0.0013742:0.552842:0.074508:rs8179466
1 534192 rs6680723 C T . PASS AF=0.24096 ES:SE:LP:AF:ID -0.000273029:0.000796095:0.136677:0.24096:rs6680723
1 546697 rs12025928 A G . PASS AF=0.913473 ES:SE:LP:AF:ID 0.000812023:0.000993174:0.387216:0.913473:rs12025928
1 693731 rs12238997 A G . PASS AF=0.116325 ES:SE:LP:AF:ID -0.000326761:0.000667169:0.207608:0.116325:rs12238997
1 705882 rs72631875 G A . PASS AF=0.067285 ES:SE:LP:AF:ID -0.000678222:0.000977659:0.309804:0.067285:rs72631875
1 706368 rs12029736 A G . PASS AF=0.51565 ES:SE:LP:AF:ID -0.00057978:0.000494207:0.619789:0.51565:rs12029736
1 714596 rs149887893 T C . PASS AF=0.033004 ES:SE:LP:AF:ID -1.20962e-06:0.00124593:-0:0.033004:rs149887893
1 715265 rs12184267 C T . PASS AF=0.036621 ES:SE:LP:AF:ID -0.000272127:0.0011317:0.091515:0.036621:rs12184267
1 715367 rs12184277 A G . PASS AF=0.036737 ES:SE:LP:AF:ID -0.00035734:0.00112742:0.124939:0.036737:rs12184277
1 717485 rs12184279 C A . PASS AF=0.036437 ES:SE:LP:AF:ID -0.000380741:0.00113555:0.130768:0.036437:rs12184279
1 720381 rs116801199 G T . PASS AF=0.036976 ES:SE:LP:AF:ID -0.000372195:0.00112297:0.130768:0.036976:rs116801199
1 721290 rs12565286 G C . PASS AF=0.037072 ES:SE:LP:AF:ID -0.000314934:0.00111912:0.107905:0.037072:rs12565286
1 722670 rs116030099 T C . PASS AF=0.101199 ES:SE:LP:AF:ID 0.00100579:0.000815409:0.657577:0.101199:rs116030099
1 723891 rs2977670 G C . PASS AF=0.959096 ES:SE:LP:AF:ID 9.31819e-05:0.00107937:0.0315171:0.959096:rs2977670
1 724849 rs12126395 C A . PASS AF=0.031451 ES:SE:LP:AF:ID -0.000148581:0.00195937:0.0268721:0.031451:rs12126395
1 725060 rs865924913 A T . PASS AF=0.053254 ES:SE:LP:AF:ID 0.00107431:0.00155877:0.309804:0.053254:rs865924913
1 726794 rs28454925 C G . PASS AF=0.03659 ES:SE:LP:AF:ID -0.000452395:0.00112637:0.161151:0.03659:rs28454925
1 729632 rs116720794 C T . PASS AF=0.036907 ES:SE:LP:AF:ID -0.000468302:0.00111611:0.173925:0.036907:rs116720794
1 729679 rs4951859 C G . PASS AF=0.843212 ES:SE:LP:AF:ID 0.000196164:0.000578187:0.136677:0.843212:rs4951859
1 730087 rs148120343 T C . PASS AF=0.055911 ES:SE:LP:AF:ID 1.55566e-05:0.000936175:0.00436481:0.055911:rs148120343
1 731718 rs58276399 T C . PASS AF=0.122307 ES:SE:LP:AF:ID -0.000212398:0.000632876:0.130768:0.122307:rs58276399
1 732989 rs369030935 C T . PASS AF=0.025713 ES:SE:LP:AF:ID -0.000591981:0.00155674:0.154902:0.025713:rs369030935
1 734349 rs141242758 T C . PASS AF=0.121549 ES:SE:LP:AF:ID -0.00017595:0.000633142:0.107905:0.121549:rs141242758
1 736289 rs79010578 T A . PASS AF=0.13233 ES:SE:LP:AF:ID -0.000238765:0.000624024:0.154902:0.13233:rs79010578
1 752478 rs146277091 G A . PASS AF=0.036821 ES:SE:LP:AF:ID -0.000289563:0.00110482:0.102373:0.036821:rs146277091
1 752566 rs3094315 G A . PASS AF=0.838951 ES:SE:LP:AF:ID 0.000141559:0.000559933:0.09691:0.838951:rs3094315
1 752721 rs3131972 A G . PASS AF=0.83858 ES:SE:LP:AF:ID 7.27495e-05:0.000559331:0.0457575:0.83858:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869781 ES:SE:LP:AF:ID 0.000126346:0.000600184:0.0809219:0.869781:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129871 ES:SE:LP:AF:ID -0.000174993:0.000601412:0.113509:0.129871:rs2073813
1 754063 rs12184312 G T . PASS AF=0.037332 ES:SE:LP:AF:ID -0.000304027:0.00108609:0.107905:0.037332:rs12184312
1 754105 rs12184325 C T . PASS AF=0.037576 ES:SE:LP:AF:ID -0.000280731:0.00107923:0.102373:0.037576:rs12184325
1 754182 rs3131969 A G . PASS AF=0.869123 ES:SE:LP:AF:ID 8.6787e-05:0.000599009:0.0555173:0.869123:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869221 ES:SE:LP:AF:ID 0.000111562:0.000599246:0.0705811:0.869221:rs3131968
1 754211 rs12184313 G A . PASS AF=0.037534 ES:SE:LP:AF:ID -0.000365413:0.0010839:0.130768:0.037534:rs12184313
1 754334 rs3131967 T C . PASS AF=0.869126 ES:SE:LP:AF:ID 9.51927e-05:0.000598996:0.0604807:0.869126:rs3131967
1 754503 rs3115859 G A . PASS AF=0.838033 ES:SE:LP:AF:ID 0.000101993:0.000557778:0.0705811:0.838033:rs3115859
1 754629 rs10454459 A G . PASS AF=0.037547 ES:SE:LP:AF:ID -0.000389259:0.00108543:0.142668:0.037547:rs10454459
1 754964 rs3131966 C T . PASS AF=0.838664 ES:SE:LP:AF:ID 0.000103031:0.000559345:0.0705811:0.838664:rs3131966
1 755775 rs3131965 A G . PASS AF=0.839777 ES:SE:LP:AF:ID 0.000138306:0.000566911:0.091515:0.839777:rs3131965
1 755890 rs3115858 A T . PASS AF=0.869405 ES:SE:LP:AF:ID 0.000143899:0.000598304:0.091515:0.869405:rs3115858
1 756604 rs3131962 A G . PASS AF=0.868952 ES:SE:LP:AF:ID 0.000106859:0.000596799:0.0655015:0.868952:rs3131962
1 757640 rs3115853 G A . PASS AF=0.867905 ES:SE:LP:AF:ID 2.9438e-05:0.000595655:0.0177288:0.867905:rs3115853
1 757734 rs4951929 C T . PASS AF=0.869095 ES:SE:LP:AF:ID 0.00012848:0.000597287:0.0809219:0.869095:rs4951929
1 757936 rs4951862 C A . PASS AF=0.869104 ES:SE:LP:AF:ID 0.000127114:0.000597333:0.0809219:0.869104:rs4951862
1 758144 rs3131956 A G . PASS AF=0.869112 ES:SE:LP:AF:ID 0.000128559:0.000597347:0.0809219:0.869112:rs3131956
1 758626 rs3131954 C T . PASS AF=0.869589 ES:SE:LP:AF:ID 0.000142378:0.000598986:0.091515:0.869589:rs3131954