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_2217.vcf.gz --id UKB-b:5801 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_2217.txt.gz --cohort_controls 397498 --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": "2019-09-13T03:58:42.696298",
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"bcftools_viewCommand": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-5801/ukb-b-5801.vcf.gz; Date=Sun May 10 08:13:32 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-5801/UKB-b-5801_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-5801/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:45:34 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-5801/UKB-b-5801_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: 0.0753 (0.0032)
Lambda GC: 1.4493
Mean Chi^2: 1.6233
Intercept: 1.0383 (0.0103)
Ratio: 0.0614 (0.0165)
Analysis finished at Thu Oct 17 14:47:04 2019
Total time elapsed: 1.0m:30.31sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9499,
"inflation_factor": 1.3107,
"mean_EFFECT": 3.161e-06,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 84,
"n_p_sig": 7302,
"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": 0.0753,
"ldsc_observed_scale_h2_se": 0.0032,
"ldsc_intercept_beta": 1.0383,
"ldsc_intercept_se": 0.0103,
"ldsc_lambda_gc": 1.4493,
"ldsc_mean_chisq": 1.6233,
"ldsc_ratio": 0.0614
}
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 | 1.0000000 | 4.000000e+00 | 8.000000e+00 | 1.300000e+01 | 2.200000e+01 | ▇▅▃▂▂ |
| numeric | POS | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 7.886027e+07 | 5.628334e+07 | 828.0000000 | 3.259061e+07 | 6.948835e+07 | 1.145912e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.200000e-06 | 1.107940e-02 | -0.1747150 | -3.707100e-03 | 1.140000e-05 | 3.723300e-03 | 1.717730e-01 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 7.724000e-03 | 7.323700e-03 | 0.0021597 | 2.643800e-03 | 4.433500e-03 | 1.023090e-02 | 1.137990e-01 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.570733e-01 | 3.003873e-01 | 0.0000000 | 1.900002e-01 | 4.400003e-01 | 7.199992e-01 | 1.000000e+00 | ▇▆▆▅▆ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.570749e-01 | 3.003622e-01 | 0.0000000 | 1.861837e-01 | 4.424712e-01 | 7.168356e-01 | 1.000000e+00 | ▇▆▆▆▅ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.035057e-01 | 2.568701e-01 | 0.0009300 | 1.316600e-02 | 7.790800e-02 | 3.164580e-01 | 9.990700e-01 | ▇▂▁▁▁ |
| numeric | AF_reference | 184849 | 0.9812372 | NA | NA | NA | NA | NA | NA | NA | 2.068392e-01 | 2.482924e-01 | 0.0000000 | 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 | -0.0044226 | 0.0039716 | 0.2700001 | 0.2654692 | 0.623759 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0043646 | 0.0039362 | 0.2700001 | 0.2674977 | 0.400476 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | 0.0024298 | 0.0062985 | 0.6999999 | 0.6996667 | 0.103522 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0048985 | 0.0038763 | 0.2099999 | 0.2063422 | 0.456969 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | -0.0051115 | 0.0076495 | 0.5000000 | 0.5039970 | 0.074475 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | 0.0011684 | 0.0044284 | 0.7899998 | 0.7918961 | 0.241026 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | -0.0098071 | 0.0055161 | 0.0749998 | 0.0754193 | 0.913350 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | -0.0047053 | 0.0037088 | 0.2000000 | 0.2045502 | 0.116456 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | 0.0103846 | 0.0054287 | 0.0560003 | 0.0557604 | 0.067409 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0007938 | 0.0027481 | 0.7700005 | 0.7726821 | 0.515644 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51219704 | rs147475742 | G | A | 0.0000129 | 0.0057780 | 1.0000000 | 0.9982226 | 0.041853 | 0.0473243 | NA |
| 22 | 51219766 | rs182321900 | C | T | 0.0599744 | 0.0270980 | 0.0269998 | 0.0268810 | 0.001917 | NA | NA |
| 22 | 51220146 | rs868950473 | C | T | 0.0473947 | 0.0268511 | 0.0779992 | 0.0775481 | 0.001963 | NA | NA |
| 22 | 51221190 | rs369304721 | G | A | 0.0039269 | 0.0057607 | 0.5000000 | 0.4954469 | 0.049729 | NA | NA |
| 22 | 51221731 | rs115055839 | T | C | -0.0011539 | 0.0043094 | 0.7899998 | 0.7888886 | 0.073209 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | 0.0053629 | 0.0050694 | 0.2900000 | 0.2901026 | 0.054517 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | 0.0055392 | 0.0050943 | 0.2800000 | 0.2768932 | 0.054141 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | -0.0006815 | 0.0043248 | 0.8700001 | 0.8747934 | 0.073053 | 0.0730831 | NA |
| 22 | 51232488 | rs376461333 | A | G | -0.0027288 | 0.0101914 | 0.7899998 | 0.7888855 | 0.020034 | NA | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0031933 | 0.0026444 | 0.2300001 | 0.2272153 | 0.297987 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623759 ES:SE:LP:AF:ID -0.00442257:0.00397157:0.568636:0.623759:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400476 ES:SE:LP:AF:ID 0.00436463:0.0039362:0.568636:0.400476:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103522 ES:SE:LP:AF:ID 0.00242976:0.00629846:0.154902:0.103522:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456969 ES:SE:LP:AF:ID -0.00489847:0.00387633:0.677781:0.456969:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074475 ES:SE:LP:AF:ID -0.00511151:0.00764951:0.30103:0.074475:rs8179466
1 534192 rs6680723 C T . PASS AF=0.241026 ES:SE:LP:AF:ID 0.00116843:0.0044284:0.102373:0.241026:rs6680723
1 546697 rs12025928 A G . PASS AF=0.91335 ES:SE:LP:AF:ID -0.00980709:0.00551609:1.12494:0.91335:rs12025928
1 693731 rs12238997 A G . PASS AF=0.116456 ES:SE:LP:AF:ID -0.00470532:0.00370879:0.69897:0.116456:rs12238997
1 705882 rs72631875 G A . PASS AF=0.067409 ES:SE:LP:AF:ID 0.0103846:0.00542871:1.25181:0.067409:rs72631875
1 706368 rs12029736 A G . PASS AF=0.515644 ES:SE:LP:AF:ID 0.000793842:0.00274811:0.113509:0.515644:rs12029736
1 714596 rs149887893 T C . PASS AF=0.032879 ES:SE:LP:AF:ID -0.00230646:0.00693909:0.130768:0.032879:rs149887893
1 715265 rs12184267 C T . PASS AF=0.036484 ES:SE:LP:AF:ID -0.000357199:0.00630267:0.0222764:0.036484:rs12184267
1 715367 rs12184277 A G . PASS AF=0.036595 ES:SE:LP:AF:ID -0.000769108:0.00627905:0.0457575:0.036595:rs12184277
1 717485 rs12184279 C A . PASS AF=0.036303 ES:SE:LP:AF:ID -0.000306636:0.00632365:0.0177288:0.036303:rs12184279
1 717587 rs144155419 G A . PASS AF=0.016466 ES:SE:LP:AF:ID 0.00599812:0.00970433:0.267606:0.016466:rs144155419
1 720381 rs116801199 G T . PASS AF=0.036847 ES:SE:LP:AF:ID -0.00060324:0.00625268:0.0362122:0.036847:rs116801199
1 721290 rs12565286 G C . PASS AF=0.036939 ES:SE:LP:AF:ID -0.00103753:0.00623165:0.0604807:0.036939:rs12565286
1 722670 rs116030099 T C . PASS AF=0.101246 ES:SE:LP:AF:ID 0.00626136:0.00453404:0.769551:0.101246:rs116030099
1 723891 rs2977670 G C . PASS AF=0.959282 ES:SE:LP:AF:ID 0.000824604:0.00601419:0.05061:0.959282:rs2977670
1 724849 rs12126395 C A . PASS AF=0.031432 ES:SE:LP:AF:ID -0.00557627:0.0108989:0.21467:0.031432:rs12126395
1 725060 rs865924913 A T . PASS AF=0.053258 ES:SE:LP:AF:ID -0.00663871:0.00866607:0.356547:0.053258:rs865924913
1 726794 rs28454925 C G . PASS AF=0.036449 ES:SE:LP:AF:ID -0.000689111:0.00627278:0.0409586:0.036449:rs28454925
1 729632 rs116720794 C T . PASS AF=0.036767 ES:SE:LP:AF:ID -0.000777453:0.00621484:0.0457575:0.036767:rs116720794
1 729679 rs4951859 C G . PASS AF=0.843187 ES:SE:LP:AF:ID 0.00345934:0.00321494:0.552842:0.843187:rs4951859
1 730087 rs148120343 T C . PASS AF=0.055928 ES:SE:LP:AF:ID -0.00480558:0.00520506:0.443698:0.055928:rs148120343
1 731718 rs58276399 T C . PASS AF=0.122509 ES:SE:LP:AF:ID -0.00334135:0.00351683:0.468521:0.122509:rs58276399
1 732989 rs369030935 C T . PASS AF=0.025724 ES:SE:LP:AF:ID 0.00791559:0.00865024:0.443698:0.025724:rs369030935
1 734349 rs141242758 T C . PASS AF=0.121761 ES:SE:LP:AF:ID -0.00341035:0.00351825:0.481486:0.121761:rs141242758
1 736289 rs79010578 T A . PASS AF=0.132342 ES:SE:LP:AF:ID -0.0042443:0.00346986:0.657577:0.132342:rs79010578
1 736689 rs181876450 T C . PASS AF=0.011188 ES:SE:LP:AF:ID 0.00561671:0.0125686:0.187087:0.011188:rs181876450
1 740284 rs61770167 C T . PASS AF=0.00572 ES:SE:LP:AF:ID 0.0140454:0.0162498:0.408935:0.00572:rs61770167
1 742813 rs112573343 C T . PASS AF=0.002224 ES:SE:LP:AF:ID -0.00832358:0.0277548:0.119186:0.002224:rs112573343
1 746189 rs139221807 A G . PASS AF=0.00101 ES:SE:LP:AF:ID -0.0121337:0.0454017:0.102373:0.00101:rs139221807
1 752478 rs146277091 G A . PASS AF=0.036672 ES:SE:LP:AF:ID 0.00081064:0.00615274:0.0457575:0.036672:rs146277091
1 752566 rs3094315 G A . PASS AF=0.838999 ES:SE:LP:AF:ID 0.00274986:0.00311385:0.420216:0.838999:rs3094315
1 752721 rs3131972 A G . PASS AF=0.838621 ES:SE:LP:AF:ID 0.00260675:0.00311033:0.39794:0.838621:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869714 ES:SE:LP:AF:ID 0.00326046:0.00333689:0.481486:0.869714:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129954 ES:SE:LP:AF:ID -0.00320591:0.00334344:0.468521:0.129954:rs2073813
1 754063 rs12184312 G T . PASS AF=0.037191 ES:SE:LP:AF:ID -0.00044222:0.0060483:0.0268721:0.037191:rs12184312
1 754105 rs12184325 C T . PASS AF=0.037437 ES:SE:LP:AF:ID -0.000369474:0.00600985:0.0222764:0.037437:rs12184325
1 754182 rs3131969 A G . PASS AF=0.869056 ES:SE:LP:AF:ID 0.00309157:0.00333022:0.455932:0.869056:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869155 ES:SE:LP:AF:ID 0.00304082:0.00333152:0.443698:0.869155:rs3131968
1 754211 rs12184313 G A . PASS AF=0.037385 ES:SE:LP:AF:ID -0.000674701:0.00603674:0.0409586:0.037385:rs12184313
1 754334 rs3131967 T C . PASS AF=0.869057 ES:SE:LP:AF:ID 0.0029997:0.00333016:0.431798:0.869057:rs3131967
1 754433 rs150578204 G A . PASS AF=0.005062 ES:SE:LP:AF:ID -0.032218:0.0172014:1.21467:0.005062:rs150578204
1 754458 rs142682604 G T . PASS AF=0.005028 ES:SE:LP:AF:ID -0.0320824:0.0172446:1.20066:0.005028:rs142682604
1 754503 rs3115859 G A . PASS AF=0.83808 ES:SE:LP:AF:ID 0.00281822:0.00310201:0.443698:0.83808:rs3115859
1 754629 rs10454459 A G . PASS AF=0.037398 ES:SE:LP:AF:ID -0.000646661:0.0060451:0.0409586:0.037398:rs10454459
1 754964 rs3131966 C T . PASS AF=0.838718 ES:SE:LP:AF:ID 0.00299837:0.00311087:0.468521:0.838718:rs3131966
1 755240 rs181660517 T G . PASS AF=0.013792 ES:SE:LP:AF:ID 0.00439187:0.0108485:0.161151:0.013792:rs181660517