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_2385.vcf.gz --id UKB-b:5262 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_2385.txt.gz --cohort_controls 195644 --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": "2019-09-12T19:36:08.269907",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-5262/ukb-b-5262.vcf.gz; Date=Sat May 9 16:27:34 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-5262/UKB-b-5262_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-5262/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:45:09 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-5262/UKB-b-5262_data.vcf.gz ...
Read summary statistics for 9485502 SNPs.
Dropped 11516 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, 1288320 SNPs remain.
After merging with regression SNP LD, 1288320 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.067 (0.0042)
Lambda GC: 1.2061
Mean Chi^2: 1.2794
Intercept: 1.024 (0.008)
Ratio: 0.0859 (0.0288)
Analysis finished at Thu Oct 17 14:46:41 2019
Total time elapsed: 1.0m:31.93sQC metrics
Metrics
Metrics
{
"af_correlation": 0.949,
"inflation_factor": 1.1474,
"mean_EFFECT": -9.7653e-06,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 37,
"n_p_sig": 4037,
"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": 131742,
"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": 1288320,
"ldsc_nsnp_merge_regression_ld": 1288320,
"ldsc_observed_scale_h2_beta": 0.067,
"ldsc_observed_scale_h2_se": 0.0042,
"ldsc_intercept_beta": 1.024,
"ldsc_intercept_se": 0.008,
"ldsc_lambda_gc": 1.2061,
"ldsc_mean_chisq": 1.2794,
"ldsc_ratio": 0.0859
}
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 | 9474046 | 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 | 9485502 | 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 | 8.629286e+00 | 5.752027e+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 | 7.883641e+07 | 5.630897e+07 | 828.0000000 | 3.253720e+07 | 6.942186e+07 | 1.145710e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -9.800000e-06 | 4.155400e-03 | -0.0580537 | -1.486300e-03 | -2.600000e-06 | 1.484100e-03 | 6.311650e-02 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.102800e-03 | 2.588200e-03 | 0.0009853 | 1.192100e-03 | 1.919900e-03 | 4.224300e-03 | 3.340210e-02 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.786615e-01 | 2.949814e-01 | 0.0000000 | 2.200002e-01 | 4.700002e-01 | 7.300002e-01 | 1.000000e+00 | ▇▇▇▆▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.786614e-01 | 2.949558e-01 | 0.0000000 | 2.179821e-01 | 4.720445e-01 | 7.340285e-01 | 9.999999e-01 | ▇▇▆▆▆ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.100946e-01 | 2.573475e-01 | 0.0017890 | 1.589700e-02 | 8.728700e-02 | 3.289190e-01 | 9.982110e-01 | ▇▂▁▁▁ |
| numeric | AF_reference | 131742 | 0.9861112 | NA | NA | NA | NA | NA | NA | NA | 2.117131e-01 | 2.490827e-01 | 0.0000000 | 1.357830e-02 | 1.068290e-01 | 3.300720e-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 | -0.0002548 | 0.0018108 | 0.8900000 | 0.8880779 | 0.624241 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0013510 | 0.0017985 | 0.4500005 | 0.4525468 | 0.399700 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | 0.0007724 | 0.0028614 | 0.7899998 | 0.7872237 | 0.103706 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0013106 | 0.0017704 | 0.4600002 | 0.4591359 | 0.456549 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | 0.0023327 | 0.0034829 | 0.5000000 | 0.5030182 | 0.074583 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0004869 | 0.0020201 | 0.8100000 | 0.8095476 | 0.240593 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | 0.0004120 | 0.0025183 | 0.8700001 | 0.8700488 | 0.913284 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | -0.0009618 | 0.0016960 | 0.5700002 | 0.5706601 | 0.115757 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | -0.0020669 | 0.0024764 | 0.4000000 | 0.4039350 | 0.067661 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0005439 | 0.0012549 | 0.6600001 | 0.6647068 | 0.516544 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51219704 | rs147475742 | G | A | -0.0029995 | 0.0026316 | 0.2500000 | 0.2543781 | 0.042033 | 0.0473243 | NA |
| 22 | 51219766 | rs182321900 | C | T | -0.0134656 | 0.0121177 | 0.2700001 | 0.2664676 | 0.001981 | NA | NA |
| 22 | 51220146 | rs868950473 | C | T | -0.0101274 | 0.0120275 | 0.4000000 | 0.3997766 | 0.002031 | NA | NA |
| 22 | 51221190 | rs369304721 | G | A | -0.0042521 | 0.0026240 | 0.1100001 | 0.1051356 | 0.049878 | NA | NA |
| 22 | 51221731 | rs115055839 | T | C | -0.0027458 | 0.0019645 | 0.1600000 | 0.1622040 | 0.073315 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | -0.0053837 | 0.0023182 | 0.0200000 | 0.0202157 | 0.054160 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | -0.0056398 | 0.0023297 | 0.0150000 | 0.0154839 | 0.053781 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | -0.0027173 | 0.0019716 | 0.1700000 | 0.1681397 | 0.073115 | 0.0730831 | NA |
| 22 | 51232488 | rs376461333 | A | G | -0.0085876 | 0.0046527 | 0.0649995 | 0.0649324 | 0.019979 | NA | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0010320 | 0.0012070 | 0.3900004 | 0.3925514 | 0.298196 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.624241 ES:SE:LP:AF:ID -0.000254849:0.00181082:0.05061:0.624241:rs10399793
1 54676 rs2462492 C T . PASS AF=0.3997 ES:SE:LP:AF:ID 0.00135102:0.00179854:0.346787:0.3997:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103706 ES:SE:LP:AF:ID 0.000772353:0.00286144:0.102373:0.103706:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456549 ES:SE:LP:AF:ID -0.00131056:0.00177038:0.337242:0.456549:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074583 ES:SE:LP:AF:ID 0.00233266:0.00348289:0.30103:0.074583:rs8179466
1 534192 rs6680723 C T . PASS AF=0.240593 ES:SE:LP:AF:ID -0.000486854:0.00202006:0.091515:0.240593:rs6680723
1 546697 rs12025928 A G . PASS AF=0.913284 ES:SE:LP:AF:ID 0.00041198:0.00251827:0.0604807:0.913284:rs12025928
1 693731 rs12238997 A G . PASS AF=0.115757 ES:SE:LP:AF:ID -0.00096175:0.00169597:0.244125:0.115757:rs12238997
1 705882 rs72631875 G A . PASS AF=0.067661 ES:SE:LP:AF:ID -0.00206688:0.00247645:0.39794:0.067661:rs72631875
1 706368 rs12029736 A G . PASS AF=0.516544 ES:SE:LP:AF:ID -0.000543895:0.00125488:0.180456:0.516544:rs12029736
1 714596 rs149887893 T C . PASS AF=0.033133 ES:SE:LP:AF:ID 0.000856169:0.00315622:0.102373:0.033133:rs149887893
1 715265 rs12184267 C T . PASS AF=0.036795 ES:SE:LP:AF:ID 0.000431233:0.00286414:0.0555173:0.036795:rs12184267
1 715367 rs12184277 A G . PASS AF=0.036923 ES:SE:LP:AF:ID 0.000671926:0.00285274:0.091515:0.036923:rs12184277
1 717485 rs12184279 C A . PASS AF=0.03664 ES:SE:LP:AF:ID 0.000549662:0.00287236:0.0705811:0.03664:rs12184279
1 717587 rs144155419 G A . PASS AF=0.016417 ES:SE:LP:AF:ID -0.00562724:0.00443835:0.69897:0.016417:rs144155419
1 720381 rs116801199 G T . PASS AF=0.037189 ES:SE:LP:AF:ID 0.000542813:0.00284036:0.0705811:0.037189:rs116801199
1 721290 rs12565286 G C . PASS AF=0.037286 ES:SE:LP:AF:ID 0.000562182:0.00283061:0.0757207:0.037286:rs12565286
1 722670 rs116030099 T C . PASS AF=0.101578 ES:SE:LP:AF:ID 0.00115364:0.00206647:0.236572:0.101578:rs116030099
1 723891 rs2977670 G C . PASS AF=0.958872 ES:SE:LP:AF:ID -0.000363676:0.00273078:0.05061:0.958872:rs2977670
1 724849 rs12126395 C A . PASS AF=0.031376 ES:SE:LP:AF:ID -0.0073147:0.00501109:0.853872:0.031376:rs12126395
1 725060 rs865924913 A T . PASS AF=0.053192 ES:SE:LP:AF:ID -0.000588305:0.00395906:0.0555173:0.053192:rs865924913
1 726794 rs28454925 C G . PASS AF=0.036783 ES:SE:LP:AF:ID -7.68208e-05:0.00285043:0.00877392:0.036783:rs28454925
1 729632 rs116720794 C T . PASS AF=0.03708 ES:SE:LP:AF:ID 0.000650143:0.00282465:0.0861861:0.03708:rs116720794
1 729679 rs4951859 C G . PASS AF=0.843607 ES:SE:LP:AF:ID 0.00050012:0.00146753:0.136677:0.843607:rs4951859
1 730087 rs148120343 T C . PASS AF=0.055738 ES:SE:LP:AF:ID -0.0017088:0.00237865:0.327902:0.055738:rs148120343
1 731718 rs58276399 T C . PASS AF=0.121658 ES:SE:LP:AF:ID -0.000911784:0.00160972:0.244125:0.121658:rs58276399
1 732989 rs369030935 C T . PASS AF=0.025828 ES:SE:LP:AF:ID 0.00159347:0.00393982:0.161151:0.025828:rs369030935
1 734349 rs141242758 T C . PASS AF=0.120929 ES:SE:LP:AF:ID -0.000950789:0.00161032:0.259637:0.120929:rs141242758
1 736289 rs79010578 T A . PASS AF=0.131908 ES:SE:LP:AF:ID 0.000248726:0.00158501:0.0555173:0.131908:rs79010578
1 736689 rs181876450 T C . PASS AF=0.011186 ES:SE:LP:AF:ID -0.000112539:0.00573888:0.00877392:0.011186:rs181876450
1 740284 rs61770167 C T . PASS AF=0.005654 ES:SE:LP:AF:ID -0.00361768:0.00748722:0.200659:0.005654:rs61770167
1 742813 rs112573343 C T . PASS AF=0.002272 ES:SE:LP:AF:ID 0.00136079:0.0125068:0.0409586:0.002272:rs112573343
1 752478 rs146277091 G A . PASS AF=0.036976 ES:SE:LP:AF:ID 0.000419763:0.0027969:0.0555173:0.036976:rs146277091
1 752566 rs3094315 G A . PASS AF=0.839447 ES:SE:LP:AF:ID 0.000206173:0.00142212:0.0555173:0.839447:rs3094315
1 752721 rs3131972 A G . PASS AF=0.839083 ES:SE:LP:AF:ID 0.000207147:0.00142044:0.0555173:0.839083:rs3131972
1 753405 rs3115860 C A . PASS AF=0.870444 ES:SE:LP:AF:ID 0.000759857:0.0015263:0.207608:0.870444:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129208 ES:SE:LP:AF:ID -0.000784581:0.00152896:0.21467:0.129208:rs2073813
1 754063 rs12184312 G T . PASS AF=0.037538 ES:SE:LP:AF:ID 0.000586279:0.00274795:0.0809219:0.037538:rs12184312
1 754105 rs12184325 C T . PASS AF=0.037758 ES:SE:LP:AF:ID 0.000628089:0.00273168:0.0861861:0.037758:rs12184325
1 754182 rs3131969 A G . PASS AF=0.869796 ES:SE:LP:AF:ID 0.000769536:0.00152303:0.21467:0.869796:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869925 ES:SE:LP:AF:ID 0.000690126:0.00152371:0.187087:0.869925:rs3131968
1 754211 rs12184313 G A . PASS AF=0.03773 ES:SE:LP:AF:ID 0.000616953:0.00274298:0.0861861:0.03773:rs12184313
1 754334 rs3131967 T C . PASS AF=0.8698 ES:SE:LP:AF:ID 0.000778134:0.00152302:0.21467:0.8698:rs3131967
1 754433 rs150578204 G A . PASS AF=0.00519 ES:SE:LP:AF:ID -0.00295142:0.00773843:0.154902:0.00519:rs150578204
1 754458 rs142682604 G T . PASS AF=0.005155 ES:SE:LP:AF:ID -0.00284769:0.0077593:0.148742:0.005155:rs142682604
1 754503 rs3115859 G A . PASS AF=0.838536 ES:SE:LP:AF:ID 0.000368313:0.00141651:0.102373:0.838536:rs3115859
1 754629 rs10454459 A G . PASS AF=0.037744 ES:SE:LP:AF:ID 0.000582249:0.0027465:0.0809219:0.037744:rs10454459
1 754964 rs3131966 C T . PASS AF=0.839162 ES:SE:LP:AF:ID 0.000347308:0.00142045:0.091515:0.839162:rs3131966
1 755240 rs181660517 T G . PASS AF=0.013677 ES:SE:LP:AF:ID -0.000777908:0.00498011:0.0555173:0.013677:rs181660517
1 755435 rs184270342 T G . PASS AF=0.005481 ES:SE:LP:AF:ID -0.000687741:0.00769364:0.0315171:0.005481:rs184270342