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_21002.vcf.gz --id UKB-b:11842 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_21002.txt.gz --cohort_controls 461632 --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": "2019-09-13T03:58:48.606103",
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"bcftools_viewCommand": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-11842/ukb-b-11842.vcf.gz; Date=Sun May 10 04:28:12 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-11842/UKB-b-11842_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-11842/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:41:09 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-11842/UKB-b-11842_data.vcf.gz ...
Read summary statistics for 9851866 SNPs.
Dropped 14738 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, 1289166 SNPs remain.
After merging with regression SNP LD, 1289166 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.2513 (0.0077)
Lambda GC: 2.5617
Mean Chi^2: 3.5945
Intercept: 1.2464 (0.0205)
Ratio: 0.0949 (0.0079)
Analysis finished at Thu Oct 17 14:42:55 2019
Total time elapsed: 1.0m:46.04sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9499,
"inflation_factor": 2.0012,
"mean_EFFECT": 0,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 498,
"n_p_sig": 84931,
"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": 184849,
"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": 1289166,
"ldsc_nsnp_merge_regression_ld": 1289166,
"ldsc_observed_scale_h2_beta": 0.2513,
"ldsc_observed_scale_h2_se": 0.0077,
"ldsc_intercept_beta": 1.2464,
"ldsc_intercept_se": 0.0205,
"ldsc_lambda_gc": 2.5617,
"ldsc_mean_chisq": 3.5945,
"ldsc_ratio": 0.095
}
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 | 9837196 | 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 | 9851866 | 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 | 8.622825e+00 | 5.748290e+00 | 1.000000 | 4.000000e+00 | 8.000000e+00 | 1.300000e+01 | 2.200000e+01 | ▇▅▃▂▂ |
| numeric | POS | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 7.886027e+07 | 5.628334e+07 | 828.000000 | 3.259061e+07 | 6.948835e+07 | 1.145912e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.040000e-05 | 9.594400e-03 | -0.260137 | -3.726800e-03 | -8.200000e-06 | 3.723300e-03 | 1.673870e-01 | ▁▁▃▇▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 5.977300e-03 | 5.657300e-03 | 0.001670 | 2.047800e-03 | 3.433800e-03 | 7.922600e-03 | 8.776190e-02 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.916297e-01 | 3.122625e-01 | 0.000000 | 9.499920e-02 | 3.400001e-01 | 6.600001e-01 | 1.000000e+00 | ▇▃▃▃▃ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.916307e-01 | 3.122406e-01 | 0.000000 | 9.455190e-02 | 3.441146e-01 | 6.588174e-01 | 1.000000e+00 | ▇▃▃▃▃ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.035077e-01 | 2.568613e-01 | 0.000994 | 1.316900e-02 | 7.791300e-02 | 3.164560e-01 | 9.990080e-01 | ▇▂▁▁▁ |
| numeric | AF_reference | 184849 | 0.9812372 | NA | NA | NA | NA | NA | NA | NA | 2.068392e-01 | 2.482924e-01 | 0.000000 | 1.198080e-02 | 9.984030e-02 | 3.202880e-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 | 0.0010369 | 0.0030687 | 0.7400005 | 0.7354428 | 0.623762 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0024934 | 0.0030403 | 0.4100001 | 0.4121342 | 0.400410 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | 0.0051204 | 0.0048604 | 0.2900000 | 0.2921150 | 0.103555 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0048396 | 0.0029935 | 0.1100001 | 0.1059399 | 0.456845 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | -0.0115824 | 0.0059020 | 0.0500000 | 0.0497109 | 0.074504 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0018055 | 0.0034193 | 0.5999997 | 0.5974783 | 0.240939 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | 0.0028673 | 0.0042653 | 0.5000000 | 0.5014262 | 0.913463 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | -0.0018703 | 0.0028662 | 0.5099998 | 0.5140543 | 0.116272 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | -0.0013154 | 0.0041988 | 0.7499995 | 0.7540697 | 0.067302 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0004800 | 0.0021226 | 0.8200001 | 0.8210978 | 0.515699 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51219704 | rs147475742 | G | A | -0.0030052 | 0.0044873 | 0.5000000 | 0.5030365 | 0.041944 | 0.0473243 | NA |
| 22 | 51219766 | rs182321900 | C | T | -0.0189898 | 0.0209139 | 0.3599996 | 0.3638788 | 0.001936 | NA | NA |
| 22 | 51220146 | rs868950473 | C | T | -0.0216325 | 0.0207160 | 0.2999998 | 0.2963738 | 0.001984 | NA | NA |
| 22 | 51221190 | rs369304721 | G | A | 0.0017017 | 0.0044800 | 0.6999999 | 0.7040610 | 0.049728 | NA | NA |
| 22 | 51221731 | rs115055839 | T | C | 0.0029820 | 0.0033505 | 0.3700002 | 0.3734590 | 0.073237 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | 0.0016227 | 0.0039440 | 0.6800001 | 0.6807501 | 0.054474 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | 0.0017203 | 0.0039631 | 0.6600001 | 0.6642258 | 0.054103 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | 0.0029015 | 0.0033626 | 0.3900004 | 0.3882108 | 0.073071 | 0.0730831 | NA |
| 22 | 51232488 | rs376461333 | A | G | 0.0037843 | 0.0079186 | 0.6300007 | 0.6327173 | 0.020052 | NA | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0023633 | 0.0020567 | 0.2500000 | 0.2505290 | 0.297898 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623762 ES:SE:LP:AF:ID 0.00103691:0.00306874:0.130768:0.623762:rs10399793
1 54676 rs2462492 C T . PASS AF=0.40041 ES:SE:LP:AF:ID 0.00249345:0.00304026:0.387216:0.40041:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103555 ES:SE:LP:AF:ID 0.00512043:0.00486043:0.537602:0.103555:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456845 ES:SE:LP:AF:ID 0.00483957:0.00299346:0.958607:0.456845:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074504 ES:SE:LP:AF:ID -0.0115824:0.00590203:1.30103:0.074504:rs8179466
1 534192 rs6680723 C T . PASS AF=0.240939 ES:SE:LP:AF:ID -0.00180551:0.00341933:0.221849:0.240939:rs6680723
1 546697 rs12025928 A G . PASS AF=0.913463 ES:SE:LP:AF:ID 0.00286733:0.00426529:0.30103:0.913463:rs12025928
1 693731 rs12238997 A G . PASS AF=0.116272 ES:SE:LP:AF:ID -0.00187033:0.00286624:0.29243:0.116272:rs12238997
1 705882 rs72631875 G A . PASS AF=0.067302 ES:SE:LP:AF:ID -0.0013154:0.00419883:0.124939:0.067302:rs72631875
1 706368 rs12029736 A G . PASS AF=0.515699 ES:SE:LP:AF:ID -0.000479995:0.00212262:0.0861861:0.515699:rs12029736
1 714596 rs149887893 T C . PASS AF=0.033008 ES:SE:LP:AF:ID -0.00205807:0.00535095:0.154902:0.033008:rs149887893
1 715265 rs12184267 C T . PASS AF=0.036624 ES:SE:LP:AF:ID -0.00208124:0.00486055:0.173925:0.036624:rs12184267
1 715367 rs12184277 A G . PASS AF=0.036742 ES:SE:LP:AF:ID -0.00198071:0.00484201:0.167491:0.036742:rs12184277
1 717485 rs12184279 C A . PASS AF=0.036439 ES:SE:LP:AF:ID -0.00313765:0.00487704:0.283997:0.036439:rs12184279
1 717587 rs144155419 G A . PASS AF=0.016396 ES:SE:LP:AF:ID -0.0027306:0.0075126:0.142668:0.016396:rs144155419
1 720381 rs116801199 G T . PASS AF=0.036979 ES:SE:LP:AF:ID -0.0025959:0.00482299:0.229148:0.036979:rs116801199
1 721290 rs12565286 G C . PASS AF=0.037076 ES:SE:LP:AF:ID -0.00268015:0.00480645:0.236572:0.037076:rs12565286
1 722670 rs116030099 T C . PASS AF=0.101204 ES:SE:LP:AF:ID 0.00189174:0.0035022:0.229148:0.101204:rs116030099
1 723891 rs2977670 G C . PASS AF=0.95909 ES:SE:LP:AF:ID 0.00365446:0.00463571:0.366532:0.95909:rs2977670
1 724849 rs12126395 C A . PASS AF=0.031449 ES:SE:LP:AF:ID 0.00405975:0.00841543:0.200659:0.031449:rs12126395
1 725060 rs865924913 A T . PASS AF=0.053249 ES:SE:LP:AF:ID 0.00154442:0.00669543:0.0861861:0.053249:rs865924913
1 726794 rs28454925 C G . PASS AF=0.036595 ES:SE:LP:AF:ID -0.0034016:0.00483743:0.318759:0.036595:rs28454925
1 729632 rs116720794 C T . PASS AF=0.036912 ES:SE:LP:AF:ID -0.00423305:0.0047934:0.420216:0.036912:rs116720794
1 729679 rs4951859 C G . PASS AF=0.843262 ES:SE:LP:AF:ID 0.00291444:0.0024837:0.619789:0.843262:rs4951859
1 730087 rs148120343 T C . PASS AF=0.055881 ES:SE:LP:AF:ID -0.00655109:0.00402196:1:0.055881:rs148120343
1 731718 rs58276399 T C . PASS AF=0.122247 ES:SE:LP:AF:ID -0.00228461:0.00271888:0.39794:0.122247:rs58276399
1 732989 rs369030935 C T . PASS AF=0.025715 ES:SE:LP:AF:ID -0.00611727:0.00668585:0.443698:0.025715:rs369030935
1 734349 rs141242758 T C . PASS AF=0.12149 ES:SE:LP:AF:ID -0.00206051:0.00272002:0.346787:0.12149:rs141242758
1 736289 rs79010578 T A . PASS AF=0.132291 ES:SE:LP:AF:ID -0.00302724:0.00268063:0.585027:0.132291:rs79010578
1 736689 rs181876450 T C . PASS AF=0.011133 ES:SE:LP:AF:ID 0.004556:0.00974559:0.19382:0.011133:rs181876450
1 740284 rs61770167 C T . PASS AF=0.005694 ES:SE:LP:AF:ID 0.01562:0.0125849:0.677781:0.005694:rs61770167
1 742813 rs112573343 C T . PASS AF=0.002274 ES:SE:LP:AF:ID 0.00656841:0.0211247:0.119186:0.002274:rs112573343
1 746189 rs139221807 A G . PASS AF=0.001031 ES:SE:LP:AF:ID 0.00586446:0.0346049:0.0604807:0.001031:rs139221807
1 752478 rs146277091 G A . PASS AF=0.036828 ES:SE:LP:AF:ID -0.00326505:0.00474487:0.309804:0.036828:rs146277091
1 752566 rs3094315 G A . PASS AF=0.839004 ES:SE:LP:AF:ID 0.0026092:0.00240531:0.552842:0.839004:rs3094315
1 752721 rs3131972 A G . PASS AF=0.838634 ES:SE:LP:AF:ID 0.0029732:0.00240271:0.657577:0.838634:rs3131972
1 753405 rs3115860 C A . PASS AF=0.86984 ES:SE:LP:AF:ID 0.00190082:0.00257841:0.337242:0.86984:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129805 ES:SE:LP:AF:ID -0.00256577:0.00258374:0.49485:0.129805:rs2073813
1 754063 rs12184312 G T . PASS AF=0.037339 ES:SE:LP:AF:ID -0.00501756:0.00466449:0.552842:0.037339:rs12184312
1 754105 rs12184325 C T . PASS AF=0.037581 ES:SE:LP:AF:ID -0.00489991:0.00463513:0.537602:0.037581:rs12184325
1 754182 rs3131969 A G . PASS AF=0.869186 ES:SE:LP:AF:ID 0.00220013:0.00257337:0.408935:0.869186:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869283 ES:SE:LP:AF:ID 0.0021705:0.00257439:0.39794:0.869283:rs3131968
1 754211 rs12184313 G A . PASS AF=0.037538 ES:SE:LP:AF:ID -0.00499352:0.00465525:0.552842:0.037538:rs12184313
1 754334 rs3131967 T C . PASS AF=0.869188 ES:SE:LP:AF:ID 0.00224442:0.00257332:0.420216:0.869188:rs3131967
1 754433 rs150578204 G A . PASS AF=0.005121 ES:SE:LP:AF:ID -0.00685918:0.0132113:0.221849:0.005121:rs150578204
1 754458 rs142682604 G T . PASS AF=0.005087 ES:SE:LP:AF:ID -0.00718:0.0132454:0.229148:0.005087:rs142682604
1 754503 rs3115859 G A . PASS AF=0.838088 ES:SE:LP:AF:ID 0.00305342:0.00239607:0.69897:0.838088:rs3115859
1 754629 rs10454459 A G . PASS AF=0.03755 ES:SE:LP:AF:ID -0.00522527:0.00466184:0.585027:0.03755:rs10454459
1 754964 rs3131966 C T . PASS AF=0.838721 ES:SE:LP:AF:ID 0.00305911:0.00240283:0.69897:0.838721:rs3131966
1 755240 rs181660517 T G . PASS AF=0.013769 ES:SE:LP:AF:ID 0.0165886:0.00838727:1.31876:0.013769:rs181660517