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_4120.vcf.gz --id UKB-b:6027 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_4120.txt.gz --cohort_controls 146171 --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": "2019-09-12T21:07:55.680659",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-6027/ukb-b-6027.vcf.gz; Date=Thu May 7 21:45:18 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-6027/UKB-b-6027_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-6027/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:45:41 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-6027/UKB-b-6027_data.vcf.gz ...
Read summary statistics for 9301580 SNPs.
Dropped 10287 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, 1287944 SNPs remain.
After merging with regression SNP LD, 1287944 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.2576 (0.0232)
Lambda GC: 1.3948
Mean Chi^2: 1.882
Intercept: 1.0931 (0.013)
Ratio: 0.1056 (0.0147)
Analysis finished at Thu Oct 17 14:47:07 2019
Total time elapsed: 1.0m:25.69sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9487,
"inflation_factor": 1.3107,
"mean_EFFECT": 0,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 194,
"n_p_sig": 25767,
"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": 111613,
"n_est": "NA",
"ratio_se_n": "NA",
"mean_diff": "NaN",
"ratio_diff": "NaN",
"sd_y_est1": "NA",
"sd_y_est2": "NA",
"r2_sum1": 0,
"r2_sum2": 0,
"r2_sum3": 0,
"r2_sum4": 0,
"ldsc_nsnp_merge_refpanel_ld": 1287944,
"ldsc_nsnp_merge_regression_ld": 1287944,
"ldsc_observed_scale_h2_beta": 0.2576,
"ldsc_observed_scale_h2_se": 0.0232,
"ldsc_intercept_beta": 1.0931,
"ldsc_intercept_se": 0.013,
"ldsc_lambda_gc": 1.3948,
"ldsc_mean_chisq": 1.882,
"ldsc_ratio": 0.1056
}
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 | 9291345 | 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 | 9301580 | 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 | 8.635407e+00 | 5.754174e+00 | 1.0000000 | 4.000000e+00 | 8.000000e+00 | 1.300000e+01 | 2.200000e+01 | <U+2587><U+2585><U+2583><U+2582><U+2582> |
| numeric | POS | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 7.880929e+07 | 5.630792e+07 | 828.0000000 | 3.250016e+07 | 6.939145e+07 | 1.145405e+08 | 2.492385e+08 | <U+2587><U+2586><U+2585><U+2582><U+2581> |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.170000e-05 | 1.336290e-02 | -0.1973750 | -5.195200e-03 | 2.400000e-06 | 5.199200e-03 | 3.011530e-01 | <U+2581><U+2587><U+2585><U+2581><U+2581> |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 9.702400e-03 | 7.646900e-03 | 0.0032536 | 3.921200e-03 | 6.190600e-03 | 1.330200e-02 | 8.149260e-02 | <U+2587><U+2581><U+2581><U+2581><U+2581> |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.581644e-01 | 3.009446e-01 | 0.0000000 | 1.900002e-01 | 4.400003e-01 | 7.199992e-01 | 1.000000e+00 | <U+2587><U+2586><U+2586><U+2585><U+2586> |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.581658e-01 | 3.009211e-01 | 0.0000000 | 1.876041e-01 | 4.440385e-01 | 7.185972e-01 | 1.000000e+00 | <U+2587><U+2586><U+2586><U+2586><U+2586> |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.138889e-01 | 2.578306e-01 | 0.0023950 | 1.754800e-02 | 9.266800e-02 | 3.358620e-01 | 9.976050e-01 | <U+2587><U+2582><U+2581><U+2581><U+2581> |
| numeric | AF_reference | 111613 | 0.9880006 | NA | NA | NA | NA | NA | NA | NA | 2.148842e-01 | 2.496225e-01 | 0.0000000 | 1.477640e-02 | 1.110220e-01 | 3.356630e-01 | 1.000000e+00 | <U+2587><U+2582><U+2581><U+2581><U+2581> |
Head and tail
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 49298 | rs200943160 | T | C | -0.0021033 | 0.0060054 | 0.7300002 | 0.7261612 | 0.623876 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0005548 | 0.0059613 | 0.9299999 | 0.9258490 | 0.399277 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | 0.0010330 | 0.0094999 | 0.9100000 | 0.9134097 | 0.103776 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0002242 | 0.0058731 | 0.9699999 | 0.9695499 | 0.456144 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | 0.0092740 | 0.0116071 | 0.4199997 | 0.4242931 | 0.074421 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | 0.0059511 | 0.0067090 | 0.3800004 | 0.3750624 | 0.241223 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | 0.0212121 | 0.0083272 | 0.0109999 | 0.0108555 | 0.913002 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | 0.0028592 | 0.0056014 | 0.6100002 | 0.6097449 | 0.116804 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | -0.0106974 | 0.0081764 | 0.1900002 | 0.1907615 | 0.067758 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0053841 | 0.0041441 | 0.1900002 | 0.1938729 | 0.515064 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51219006 | rs28729663 | G | A | 0.0026281 | 0.0050343 | 0.5999997 | 0.6016489 | 0.137276 | 0.2052720 | NA |
| 22 | 51219387 | rs9616832 | T | C | 0.0029161 | 0.0065423 | 0.6600001 | 0.6557966 | 0.072949 | 0.0654952 | NA |
| 22 | 51219704 | rs147475742 | G | A | -0.0018767 | 0.0087322 | 0.8300000 | 0.8298292 | 0.041766 | 0.0473243 | NA |
| 22 | 51221190 | rs369304721 | G | A | -0.0026741 | 0.0087535 | 0.7600007 | 0.7599952 | 0.049246 | NA | NA |
| 22 | 51221731 | rs115055839 | T | C | 0.0033271 | 0.0065472 | 0.6100002 | 0.6113320 | 0.072429 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | -0.0031741 | 0.0076785 | 0.6800001 | 0.6793346 | 0.054392 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | -0.0031226 | 0.0077184 | 0.6899999 | 0.6857928 | 0.053998 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | 0.0033195 | 0.0065710 | 0.6100002 | 0.6134327 | 0.072296 | 0.0730831 | NA |
| 22 | 51232488 | rs376461333 | A | G | 0.0034887 | 0.0153914 | 0.8200001 | 0.8206843 | 0.020176 | NA | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0005209 | 0.0040003 | 0.9000000 | 0.8963901 | 0.297604 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623876 ES:SE:LP:AF:ID -0.0021033:0.00600537:0.136677:0.623876:rs10399793
1 54676 rs2462492 C T . PASS AF=0.399277 ES:SE:LP:AF:ID -0.000554814:0.00596134:0.0315171:0.399277:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103776 ES:SE:LP:AF:ID 0.00103301:0.00949992:0.0409586:0.103776:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456144 ES:SE:LP:AF:ID -0.000224194:0.00587313:0.0132283:0.456144:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074421 ES:SE:LP:AF:ID 0.00927402:0.0116071:0.376751:0.074421:rs8179466
1 534192 rs6680723 C T . PASS AF=0.241223 ES:SE:LP:AF:ID 0.00595108:0.00670899:0.420216:0.241223:rs6680723
1 546697 rs12025928 A G . PASS AF=0.913002 ES:SE:LP:AF:ID 0.0212121:0.00832723:1.95861:0.913002:rs12025928
1 693731 rs12238997 A G . PASS AF=0.116804 ES:SE:LP:AF:ID 0.00285915:0.00560137:0.21467:0.116804:rs12238997
1 705882 rs72631875 G A . PASS AF=0.067758 ES:SE:LP:AF:ID -0.0106974:0.00817638:0.721246:0.067758:rs72631875
1 706368 rs12029736 A G . PASS AF=0.515064 ES:SE:LP:AF:ID -0.00538407:0.00414412:0.721246:0.515064:rs12029736
1 714596 rs149887893 T C . PASS AF=0.033495 ES:SE:LP:AF:ID -0.0110162:0.0103664:0.537602:0.033495:rs149887893
1 715265 rs12184267 C T . PASS AF=0.0372 ES:SE:LP:AF:ID -0.0124096:0.00941195:0.721246:0.0372:rs12184267
1 715367 rs12184277 A G . PASS AF=0.037327 ES:SE:LP:AF:ID -0.013201:0.00937489:0.79588:0.037327:rs12184277
1 717485 rs12184279 C A . PASS AF=0.03698 ES:SE:LP:AF:ID -0.0139149:0.00944667:0.853872:0.03698:rs12184279
1 717587 rs144155419 G A . PASS AF=0.016372 ES:SE:LP:AF:ID -0.00564863:0.0147328:0.154902:0.016372:rs144155419
1 720381 rs116801199 G T . PASS AF=0.037559 ES:SE:LP:AF:ID -0.0126845:0.00933839:0.769551:0.037559:rs116801199
1 721290 rs12565286 G C . PASS AF=0.03765 ES:SE:LP:AF:ID -0.0125863:0.00930907:0.744727:0.03765:rs12565286
1 722670 rs116030099 T C . PASS AF=0.101703 ES:SE:LP:AF:ID 0.0116288:0.00682114:1.05552:0.101703:rs116030099
1 723891 rs2977670 G C . PASS AF=0.958404 ES:SE:LP:AF:ID 0.00981006:0.00898215:0.568636:0.958404:rs2977670
1 724849 rs12126395 C A . PASS AF=0.031736 ES:SE:LP:AF:ID -0.018955:0.0163866:0.60206:0.031736:rs12126395
1 725060 rs865924913 A T . PASS AF=0.052664 ES:SE:LP:AF:ID -0.0159824:0.0132459:0.638272:0.052664:rs865924913
1 726794 rs28454925 C G . PASS AF=0.037136 ES:SE:LP:AF:ID -0.0120825:0.00937244:0.69897:0.037136:rs28454925
1 729632 rs116720794 C T . PASS AF=0.03745 ES:SE:LP:AF:ID -0.0128332:0.00929052:0.769551:0.03745:rs116720794
1 729679 rs4951859 C G . PASS AF=0.842154 ES:SE:LP:AF:ID 0.000360218:0.00484721:0.0268721:0.842154:rs4951859
1 730087 rs148120343 T C . PASS AF=0.056052 ES:SE:LP:AF:ID 0.00276463:0.00787133:0.136677:0.056052:rs148120343
1 731718 rs58276399 T C . PASS AF=0.122759 ES:SE:LP:AF:ID 0.00367556:0.00531542:0.309804:0.122759:rs58276399
1 732989 rs369030935 C T . PASS AF=0.025716 ES:SE:LP:AF:ID -0.0080544:0.0130739:0.267606:0.025716:rs369030935
1 734349 rs141242758 T C . PASS AF=0.121976 ES:SE:LP:AF:ID 0.00345075:0.0053181:0.283997:0.121976:rs141242758
1 736289 rs79010578 T A . PASS AF=0.133203 ES:SE:LP:AF:ID -0.00089886:0.00523095:0.0655015:0.133203:rs79010578
1 736689 rs181876450 T C . PASS AF=0.011254 ES:SE:LP:AF:ID -0.00417266:0.0189357:0.0809219:0.011254:rs181876450
1 740284 rs61770167 C T . PASS AF=0.005812 ES:SE:LP:AF:ID 0.0440561:0.0242848:1.1549:0.005812:rs61770167
1 752478 rs146277091 G A . PASS AF=0.037391 ES:SE:LP:AF:ID -0.0112976:0.00919578:0.657577:0.037391:rs146277091
1 752566 rs3094315 G A . PASS AF=0.837805 ES:SE:LP:AF:ID -0.000788285:0.00469182:0.0604807:0.837805:rs3094315
1 752721 rs3131972 A G . PASS AF=0.837416 ES:SE:LP:AF:ID -0.00119501:0.00468619:0.09691:0.837416:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869074 ES:SE:LP:AF:ID -0.00561848:0.00503118:0.585027:0.869074:rs3115860
1 753541 rs2073813 G A . PASS AF=0.130624 ES:SE:LP:AF:ID 0.00628749:0.00504097:0.677781:0.130624:rs2073813
1 754063 rs12184312 G T . PASS AF=0.037885 ES:SE:LP:AF:ID -0.0102638:0.00904496:0.585027:0.037885:rs12184312
1 754105 rs12184325 C T . PASS AF=0.038144 ES:SE:LP:AF:ID -0.00981157:0.0089869:0.568636:0.038144:rs12184325
1 754182 rs3131969 A G . PASS AF=0.868404 ES:SE:LP:AF:ID -0.00607819:0.00502093:0.638272:0.868404:rs3131969
1 754192 rs3131968 A G . PASS AF=0.868506 ES:SE:LP:AF:ID -0.006307:0.00502288:0.677781:0.868506:rs3131968
1 754211 rs12184313 G A . PASS AF=0.038068 ES:SE:LP:AF:ID -0.00972507:0.00902689:0.552842:0.038068:rs12184313
1 754334 rs3131967 T C . PASS AF=0.868397 ES:SE:LP:AF:ID -0.00592125:0.00502059:0.619789:0.868397:rs3131967
1 754433 rs150578204 G A . PASS AF=0.005179 ES:SE:LP:AF:ID 0.0226831:0.0257081:0.420216:0.005179:rs150578204
1 754458 rs142682604 G T . PASS AF=0.00515 ES:SE:LP:AF:ID 0.0223459:0.0257628:0.408935:0.00515:rs142682604
1 754503 rs3115859 G A . PASS AF=0.836913 ES:SE:LP:AF:ID -0.00137191:0.00467443:0.113509:0.836913:rs3115859
1 754629 rs10454459 A G . PASS AF=0.038077 ES:SE:LP:AF:ID -0.00949649:0.0090394:0.537602:0.038077:rs10454459
1 754964 rs3131966 C T . PASS AF=0.83755 ES:SE:LP:AF:ID -0.00128457:0.00468755:0.107905:0.83755:rs3131966
1 755240 rs181660517 T G . PASS AF=0.013278 ES:SE:LP:AF:ID 0.0165683:0.016745:0.49485:0.013278:rs181660517
1 755435 rs184270342 T G . PASS AF=0.005468 ES:SE:LP:AF:ID 0.00159239:0.0254317:0.0222764:0.005468:rs184270342
1 755775 rs3131965 A G . PASS AF=0.838787 ES:SE:LP:AF:ID -0.0014994:0.00475213:0.124939:0.838787:rs3131965