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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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/dev/ieu-a-import/processed/ieu-a-6/ieu-a-6.vcf.gz \
--ref-ld-chr /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ieu-a-import/processed/ieu-a-6/ldsc.txt \
--w-ld-chr /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/
Beginning analysis at Wed Feb 5 11:33:50 2020
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ieu-a-import/processed/ieu-a-6/ieu-a-6.vcf.gz ...
Read summary statistics for 539613 SNPs.
Reading reference panel LD Score from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/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 /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/[1-22] ...
Read regression weight LD Scores for 1290028 SNPs.
After merging with reference panel LD, 514439 SNPs remain.
After merging with regression SNP LD, 514439 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0647 (0.0196)
Lambda GC: 1.0926
Mean Chi^2: 1.0947
Intercept: 1.0614 (0.0072)
Ratio: 0.6485 (0.076)
Analysis finished at Wed Feb 5 11:34:03 2020
Total time elapsed: 12.21sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9399,
"inflation_factor": 1.0931,
"mean_EFFECT": -0.0001,
"n": 30453,
"n_snps": 539613,
"n_clumped_hits": 3,
"n_p_sig": 29,
"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": 3202,
"n_est": 28460.8722,
"ratio_se_n": 0.9667,
"mean_diff": 0,
"ratio_diff": 2.5026,
"sd_y_est1": 2.1502,
"sd_y_est2": 2.0787,
"r2_sum1": 0.0358,
"r2_sum2": 0.0077,
"r2_sum3": 0.0083,
"r2_sum4": 0.0082,
"ldsc_nsnp_merge_refpanel_ld": 514439,
"ldsc_nsnp_merge_regression_ld": 514439,
"ldsc_observed_scale_h2_beta": 0.0647,
"ldsc_observed_scale_h2_se": 0.0196,
"ldsc_intercept_beta": 1.0614,
"ldsc_intercept_se": 0.0072,
"ldsc_lambda_gc": 1.0926,
"ldsc_mean_chisq": 1.0947,
"ldsc_ratio": 0.6484
}
Flags
| name | value |
|---|---|
| af_correlation | FALSE |
| inflation_factor | FALSE |
| n | FALSE |
| is_snpid_non_unique | FALSE |
| mean_EFFECT_nonfinite | FALSE |
| mean_EFFECT_05 | FALSE |
| mean_EFFECT_01 | FALSE |
| mean_chisq | FALSE |
| n_p_sig | FALSE |
| miss_EFFECT | FALSE |
| miss_SE | FALSE |
| miss_PVAL | FALSE |
| ldsc_ratio | TRUE |
| 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 | numeric.mean | numeric.sd | numeric.p0 | numeric.p25 | numeric.p50 | numeric.p75 | numeric.p100 | numeric.hist |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| character | ID | 0 | 1.0000000 | 3 | 30 | 0 | 539613 | 0 | 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 |
| character | ALT | 0 | 1.0000000 | 1 | 1 | 0 | 4 | 0 | NA | NA | NA | NA | NA | NA | NA | NA |
| numeric | CHROM | 0 | 1.0000000 | NA | NA | NA | NA | NA | 8.789751e+00 | 5.783061e+00 | 1.0000e+00 | 4.000000e+00 | 8.000000e+00 | 1.300000e+01 | 2.300000e+01 | ▇▅▅▂▂ |
| numeric | POS | 0 | 1.0000000 | NA | NA | NA | NA | NA | 7.794116e+07 | 5.665020e+07 | 1.8856e+04 | 3.102050e+07 | 6.862144e+07 | 1.143486e+08 | 2.492107e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | -5.580000e-05 | 2.718170e-02 | -3.3790e-01 | -1.550000e-02 | 0.000000e+00 | 1.550000e-02 | 3.455000e-01 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | 2.395080e-02 | 1.029080e-02 | 1.6300e-02 | 1.770000e-02 | 2.040000e-02 | 2.600000e-02 | 1.409000e-01 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | 4.863210e-01 | 2.921061e-01 | 0.0000e+00 | 2.301998e-01 | 4.806999e-01 | 7.396002e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | 4.863195e-01 | 2.921049e-01 | 0.0000e+00 | 2.301393e-01 | 4.806465e-01 | 7.398216e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | 3.717760e-01 | 2.544763e-01 | 1.0000e-02 | 1.550000e-01 | 3.150000e-01 | 5.730000e-01 | 9.900000e-01 | ▇▆▃▃▂ |
| numeric | AF_reference | 3202 | 0.9940661 | NA | NA | NA | NA | NA | 3.722214e-01 | 2.445588e-01 | 1.9970e-04 | 1.671330e-01 | 3.180910e-01 | 5.503190e-01 | 9.962060e-01 | ▇▇▅▃▂ |
| numeric | N | 0 | 1.0000000 | NA | NA | NA | NA | NA | 2.883952e+04 | 4.305464e+03 | 8.8580e+03 | 3.038200e+04 | 3.041800e+04 | 3.043200e+04 | 3.045300e+04 | ▁▁▁▁▇ |
Head and tail
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 752566 | rs3094315 | G | A | 0.0156 | 0.0315 | 0.6204003 | 0.6204321 | 0.810 | 0.718251 | 13897 |
| 1 | 768448 | rs12562034 | G | A | -0.0507 | 0.0215 | 0.0184502 | 0.0183668 | 0.191 | 0.191893 | 30342 |
| 1 | 846808 | rs4475691 | C | T | 0.0173 | 0.0344 | 0.6146997 | 0.6150297 | 0.217 | 0.254792 | 13823 |
| 1 | 900505 | rs28705211 | G | C | -0.0150 | 0.0319 | 0.6378007 | 0.6381982 | 0.282 | 0.168730 | 13767 |
| 1 | 918384 | rs13303118 | G | T | -0.0559 | 0.0298 | 0.0604798 | 0.0606774 | 0.605 | 0.492612 | 13777 |
| 1 | 928836 | rs9777703 | C | T | -0.0005 | 0.0929 | 0.9956000 | 0.9957057 | 0.971 | 0.890176 | 13811 |
| 1 | 943468 | rs3121567 | T | C | 0.0927 | 0.0800 | 0.2467999 | 0.2465581 | 0.962 | 0.893770 | 13815 |
| 1 | 1005806 | rs3934834 | C | T | -0.0175 | 0.0220 | 0.4255004 | 0.4263491 | 0.179 | 0.223442 | 30310 |
| 1 | 1018704 | rs9442372 | A | G | 0.0288 | 0.0169 | 0.0886605 | 0.0883546 | 0.556 | 0.611022 | 30351 |
| 1 | 1021415 | rs3737728 | A | G | 0.0167 | 0.0193 | 0.3877002 | 0.3868824 | 0.745 | 0.812700 | 30430 |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51147015 | rs2040487 | A | G | 0.0264 | 0.0169 | 0.1170000 | 0.1182573 | 0.433 | 0.4556710 | 30414 |
| 22 | 51150473 | rs5770820 | G | A | 0.0246 | 0.0192 | 0.2013001 | 0.2001059 | 0.254 | 0.2462060 | 30395 |
| 22 | 51151350 | rs6009951 | C | T | -0.0008 | 0.0300 | 0.9784000 | 0.9787256 | 0.396 | 0.5559110 | 13830 |
| 22 | 51151724 | rs6010061 | C | T | 0.0121 | 0.0173 | 0.4829999 | 0.4842884 | 0.384 | 0.6098240 | 30265 |
| 22 | 51163138 | rs715586 | C | T | 0.0074 | 0.0241 | 0.7592993 | 0.7588023 | 0.140 | 0.0902556 | 30405 |
| 22 | 51165664 | rs8137951 | G | A | 0.0057 | 0.0180 | 0.7492004 | 0.7514965 | 0.317 | 0.4063500 | 30436 |
| 22 | 51171693 | rs756638 | G | A | -0.0172 | 0.0199 | 0.3863003 | 0.3874112 | 0.236 | 0.3049120 | 30403 |
| 22 | 51175626 | rs3810648 | A | G | 0.0015 | 0.0366 | 0.9670001 | 0.9673090 | 0.055 | 0.1084270 | 30436 |
| 22 | 51178090 | rs2285395 | G | A | 0.0462 | 0.0390 | 0.2360000 | 0.2361696 | 0.048 | 0.0666933 | 30443 |
| 23 | 51666786 | rs14115 | A | G | 0.0203 | 0.0323 | 0.5304995 | 0.5296876 | 0.072 | NA | 30406 |
bcf preview
1 752566 rs3094315 G A . PASS AF=0.81 ES:SE:LP:AF:SS:ID 0.0156:0.0315:0.207328:0.81:13897:rs3094315
1 768448 rs12562034 G A . PASS AF=0.191 ES:SE:LP:AF:SS:ID -0.0507:0.0215:1.734:0.191:30342:rs12562034
1 846808 rs4475691 C T . PASS AF=0.217 ES:SE:LP:AF:SS:ID 0.0173:0.0344:0.211337:0.217:13823:rs4475691
1 900505 rs28705211 G C . PASS AF=0.282 ES:SE:LP:AF:SS:ID -0.015:0.0319:0.195315:0.282:13767:rs28705211
1 918384 rs13303118 G T . PASS AF=0.605 ES:SE:LP:AF:SS:ID -0.0559:0.0298:1.21839:0.605:13777:rs13303118
1 928836 rs9777703 C T . PASS AF=0.971 ES:SE:LP:AF:SS:ID -0.0005:0.0929:0.00191511:0.971:13811:rs9777703
1 943468 rs3121567 T C . PASS AF=0.962 ES:SE:LP:AF:SS:ID 0.0927:0.08:0.607655:0.962:13815:rs3121567
1 1005806 rs3934834 C T . PASS AF=0.179 ES:SE:LP:AF:SS:ID -0.0175:0.022:0.3711:0.179:30310:rs3934834
1 1018704 rs9442372 A G . PASS AF=0.556 ES:SE:LP:AF:SS:ID 0.0288:0.0169:1.05227:0.556:30351:rs9442372
1 1021415 rs3737728 A G . PASS AF=0.745 ES:SE:LP:AF:SS:ID 0.0167:0.0193:0.411504:0.745:30430:rs3737728
1 1030565 rs6687776 C T . PASS AF=0.199 ES:SE:LP:AF:SS:ID -0.0422:0.0212:1.33479:0.199:30435:rs6687776
1 1031540 rs9651273 A G . PASS AF=0.779 ES:SE:LP:AF:SS:ID 0.041:0.0206:1.32985:0.779:30371:rs9651273
1 1048955 rs4970405 A G . PASS AF=0.139 ES:SE:LP:AF:SS:ID -0.0591:0.0243:1.81844:0.139:30418:rs4970405
1 1049950 rs12726255 A G . PASS AF=0.178 ES:SE:LP:AF:SS:ID -0.0208:0.0219:0.465974:0.178:30436:rs12726255
1 1061166 rs11807848 T C . PASS AF=0.39 ES:SE:LP:AF:SS:ID -0.0403:0.0172:1.71332:0.39:30409:rs11807848
1 1062638 rs9442373 C A . PASS AF=0.594 ES:SE:LP:AF:SS:ID 0.0197:0.0171:0.599462:0.594:30349:rs9442373
1 1064979 rs2298217 C T . PASS AF=0.164 ES:SE:LP:AF:SS:ID -0.0056:0.0226:0.0940962:0.164:30408:rs2298217
1 1066029 rs12145826 G A . PASS AF=0.052 ES:SE:LP:AF:SS:ID -0.0068:0.0381:0.0663103:0.052:30419:rs12145826
1 1087683 rs9442380 T C . PASS AF=0.892 ES:SE:LP:AF:SS:ID 0.0316:0.0272:0.611544:0.892:30436:rs9442380
1 1090557 rs7553429 A C . PASS AF=0.061 ES:SE:LP:AF:SS:ID 0.0155:0.0351:0.181444:0.061:30398:rs7553429
1 1094738 rs4970362 A G . PASS AF=0.665 ES:SE:LP:AF:SS:ID 0.0336:0.0247:0.762205:0.665:14745:rs4970362
1 1099342 rs9660710 A C . PASS AF=0.913 ES:SE:LP:AF:SS:ID 0.0085:0.0297:0.110194:0.913:30428:rs9660710
1 1106473 rs4970420 G A . PASS AF=0.296 ES:SE:LP:AF:SS:ID 0.0131:0.0188:0.31399:0.296:30413:rs4970420
1 1119858 rs1320565 C T . PASS AF=0.179 ES:SE:LP:AF:SS:ID -0.0315:0.0226:0.786748:0.179:30437:rs1320565
1 1121794 rs11260549 G A . PASS AF=0.173 ES:SE:LP:AF:SS:ID -0.0059:0.0223:0.10133:0.173:30422:rs11260549
1 1135242 rs9729550 A C . PASS AF=0.338 ES:SE:LP:AF:SS:ID 0.0053:0.0179:0.115885:0.338:30401:rs9729550
1 1152631 rs11721 C A . PASS AF=0.086 ES:SE:LP:AF:SS:ID -0.0024:0.03:0.0284458:0.086:30419:rs11721
1 1156131 rs2887286 T C . PASS AF=0.325 ES:SE:LP:AF:SS:ID -0.0184:0.0191:0.473919:0.325:30431:rs2887286
1 1158277 rs3813199 G A . PASS AF=0.094 ES:SE:LP:AF:SS:ID -0.0032:0.0288:0.0401004:0.094:30438:rs3813199
1 1162435 rs3766186 C A . PASS AF=0.094 ES:SE:LP:AF:SS:ID -0.0046:0.0288:0.0595333:0.094:30410:rs3766186
1 1163804 rs7515488 C T . PASS AF=0.159 ES:SE:LP:AF:SS:ID 0.003:0.023:0.0479344:0.159:30407:rs7515488
1 1172907 rs715643 C T . PASS AF=0.039 ES:SE:LP:AF:SS:ID -0.0983:0.0617:0.953895:0.039:19951:rs715643
1 1176597 rs6675798 T C . PASS AF=0.116 ES:SE:LP:AF:SS:ID -0.0015:0.0264:0.0207249:0.116:30389:rs6675798
1 1192515 rs7524470 A G . PASS AF=0.044 ES:SE:LP:AF:SS:ID 0.0586:0.0408:0.822464:0.044:30440:rs7524470
1 1194804 rs11804831 T C . PASS AF=0.302 ES:SE:LP:AF:SS:ID 0.0179:0.0189:0.4642:0.302:30365:rs11804831
1 1211292 rs6685064 C T . PASS AF=0.198 ES:SE:LP:AF:SS:ID 0.0111:0.022:0.211902:0.198:30395:rs6685064
1 1242084 rs3737717 A G . PASS AF=0.175 ES:SE:LP:AF:SS:ID -0.0007:0.0312:0.00784429:0.175:14694:rs3737717
1 1288583 rs3845295 C G . PASS AF=0.889 ES:SE:LP:AF:SS:ID -0.0041:0.0441:0.0330608:0.889:13809:rs3845295
1 1305561 rs17160669 C T . PASS AF=0.263 ES:SE:LP:AF:SS:ID -0.0267:0.0327:0.383735:0.263:13820:rs17160669
1 1310924 rs2765033 T C . PASS AF=0.952 ES:SE:LP:AF:SS:ID -0.0129:0.0392:0.129596:0.952:30328:rs2765033
1 1314015 rs2649588 C T . PASS AF=0.891 ES:SE:LP:AF:SS:ID -0.0385:0.0268:0.822464:0.891:30410:rs2649588
1 1334475 rs17851391 G C . PASS AF=0.024 ES:SE:LP:AF:SS:ID -0.0926:0.0998:0.451488:0.024:13820:rs17851391
1 1425700 rs819980 T C . PASS AF=0.2 ES:SE:LP:AF:SS:ID 0.0325:0.0216:0.875496:0.2:30383:rs819980
1 1447325 rs6690515 G A . PASS AF=0.214 ES:SE:LP:AF:SS:ID 0.0101:0.0347:0.112495:0.214:13815:rs6690515
1 1462766 rs9439462 C T . PASS AF=0.127 ES:SE:LP:AF:SS:ID 0.0243:0.0259:0.457299:0.127:30405:rs9439462
1 1478180 rs3766178 T C . PASS AF=0.318 ES:SE:LP:AF:SS:ID 0.0127:0.0181:0.315424:0.318:30429:rs3766178
1 1485984 rs2031709 C A . PASS AF=0.09 ES:SE:LP:AF:SS:ID -0.0234:0.0302:0.357832:0.09:30360:rs2031709
1 1486834 rs3128342 C A . PASS AF=0.326 ES:SE:LP:AF:SS:ID 0.0213:0.0185:0.602929:0.326:30426:rs3128342
1 1493727 rs880051 G A . PASS AF=0.337 ES:SE:LP:AF:SS:ID -0.0089:0.021:0.173407:0.337:20317:rs880051
1 1497824 rs2296716 C T . PASS AF=0.189 ES:SE:LP:AF:SS:ID 0.0045:0.0216:0.0774302:0.189:30432:rs2296716