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-13/ieu-a-13.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-13/ldsc.txt \
--w-ld-chr /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/
Beginning analysis at Tue Feb 4 11:50:08 2020
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ieu-a-import/processed/ieu-a-13/ieu-a-13.vcf.gz ...
Read summary statistics for 121777 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, 50448 SNPs remain.
After merging with regression SNP LD, 50448 SNPs remain.
WARNING: number of SNPs less than 200k; this is almost always bad.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 1.8473 (1.243)
Lambda GC: 1.1072
Mean Chi^2: 1.0955
Intercept: 1.0487 (0.0213)
Ratio: 0.5104 (0.2228)
Analysis finished at Tue Feb 4 11:50:18 2020
Total time elapsed: 9.75sQC metrics
Metrics
Metrics
{
"af_correlation": 0.7936,
"inflation_factor": 1.1506,
"mean_EFFECT": -0.0492,
"n": 1174,
"n_snps": 121777,
"n_clumped_hits": 1,
"n_p_sig": 1,
"n_mono": 0,
"n_ns": 0,
"n_mac": 3282,
"is_snpid_unique": true,
"n_miss_EFFECT": 0,
"n_miss_SE": 0,
"n_miss_PVAL": 0,
"n_miss_AF": 288,
"n_miss_AF_reference": 811,
"n_est": 1175.4105,
"ratio_se_n": 1.0006,
"mean_diff": 0.057,
"ratio_diff": 2.6531,
"sd_y_est1": 2.7479,
"sd_y_est2": 2.7495,
"r2_sum1": 0.1126,
"r2_sum2": 0.0149,
"r2_sum3": 0.0149,
"r2_sum4": 0.032,
"ldsc_nsnp_merge_refpanel_ld": 50448,
"ldsc_nsnp_merge_regression_ld": 50448,
"ldsc_observed_scale_h2_beta": 1.8473,
"ldsc_observed_scale_h2_se": 1.243,
"ldsc_intercept_beta": 1.0487,
"ldsc_intercept_se": 0.0213,
"ldsc_lambda_gc": 1.1072,
"ldsc_mean_chisq": 1.0955,
"ldsc_ratio": 0.5099
}
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 | 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 | 5 | 25 | 0 | 121777 | 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 | 8.097966e+00 | 5.774464e+00 | 1.00000e+00 | 3.000000e+00 | 6.000000e+00 | 1.200000e+01 | 2.200000e+01 | ▇▅▃▂▂ |
| numeric | POS | 0 | 1.0000000 | NA | NA | NA | NA | NA | 7.895711e+07 | 5.781914e+07 | 6.34110e+04 | 3.286898e+07 | 6.219831e+07 | 1.176130e+08 | 2.492107e+08 | ▇▅▃▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | -4.915560e-02 | 1.045244e+01 | -8.48770e+02 | -1.289380e-01 | 3.027000e-04 | 1.230180e-01 | 8.487700e+02 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | 4.234232e-01 | 4.820506e+00 | 1.06893e-01 | 1.220790e-01 | 1.514490e-01 | 2.396430e-01 | 3.856660e+02 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | 4.789084e-01 | 2.924754e-01 | 0.00000e+00 | 2.242962e-01 | 4.693826e-01 | 7.316746e-01 | 9.999800e-01 | ▇▇▇▆▆ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | 4.791804e-01 | 2.922354e-01 | 0.00000e+00 | 2.248497e-01 | 4.696120e-01 | 7.316953e-01 | 9.999804e-01 | ▇▇▇▆▆ |
| numeric | AF | 288 | 0.9976350 | NA | NA | NA | NA | NA | 3.188701e-01 | 2.862978e-01 | 5.05000e-04 | 7.533000e-02 | 2.278000e-01 | 5.121000e-01 | 9.994950e-01 | ▇▃▂▂▂ |
| numeric | AF_reference | 811 | 0.9933403 | NA | NA | NA | NA | NA | 2.907768e-01 | 2.567245e-01 | 1.99700e-04 | 7.907350e-02 | 2.122600e-01 | 4.512780e-01 | 1.000000e+00 | ▇▃▂▂▁ |
| numeric | N | 0 | 1.0000000 | NA | NA | NA | NA | NA | 1.174000e+03 | 0.000000e+00 | 1.17400e+03 | 1.174000e+03 | 1.174000e+03 | 1.174000e+03 | 1.174000e+03 | ▁▁▇▁▁ |
Head and tail
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 1118275 | rs61733845 | C | T | 0.0313315 | 0.139612 | 0.8224341 | 0.8224318 | 0.2242 | 0.185703 | 1174 |
| 1 | 1120431 | rs1320571 | G | A | -0.1410810 | 0.145387 | 0.3314691 | 0.3318559 | 0.2091 | 0.185304 | 1174 |
| 1 | 1135242 | rs9729550 | A | C | 0.0007017 | 0.115197 | 0.9951400 | 0.9951402 | 0.4141 | 0.551917 | 1174 |
| 1 | 1140435 | rs1815606 | G | T | -0.0467251 | 0.114448 | 0.6830288 | 0.6830792 | 0.6354 | 0.712061 | 1174 |
| 1 | 1163804 | rs7515488 | C | T | 0.0793085 | 0.151160 | 0.5997137 | 0.5998155 | 0.1591 | 0.186901 | 1174 |
| 1 | 1165310 | rs11260562 | G | A | -0.1304980 | 0.226966 | 0.5652089 | 0.5653135 | 0.0697 | 0.101837 | 1174 |
| 1 | 1173611 | rs6697886 | G | A | 0.0357525 | 0.156017 | 0.8187309 | 0.8187463 | 0.1510 | 0.220647 | 1174 |
| 1 | 1186502 | rs6603785 | A | T | -0.0636855 | 0.158657 | 0.6880928 | 0.6881230 | 0.1542 | 0.246206 | 1174 |
| 1 | 1194804 | rs11804831 | T | C | -0.1232210 | 0.114300 | 0.2804581 | 0.2810119 | 0.6162 | 0.685903 | 1174 |
| 1 | 1218086 | rs6603788 | C | T | -0.0618022 | 0.120508 | 0.6079656 | 0.6080582 | 0.3520 | 0.469449 | 1174 |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 50960682 | rs140524 | C | T | -0.0641224 | 0.138687 | 0.6437763 | 0.6438279 | 0.20860 | 0.230232 | 1174 |
| 22 | 50966914 | rs470119 | T | C | 0.1396130 | 0.120833 | 0.2473882 | 0.2479180 | 0.65000 | 0.554712 | 1174 |
| 22 | 50971752 | rs131794 | A | C | 0.3268860 | 0.191481 | 0.0869801 | 0.0877949 | 0.86920 | 0.832668 | 1174 |
| 22 | 50988193 | rs131779 | A | G | -0.0649348 | 0.113493 | 0.5671462 | 0.5672217 | 0.56460 | 0.580272 | 1174 |
| 22 | 50999182 | rs140518 | C | T | 0.2150090 | 0.137409 | 0.1169160 | 0.1176445 | 0.74700 | 0.765575 | 1174 |
| 22 | 51078251 | rs4040041 | C | T | -0.0700269 | 0.116928 | 0.5490881 | 0.5492469 | 0.52020 | 0.466653 | 1174 |
| 22 | 51094926 | rs9616810 | C | T | -0.0456348 | 0.129471 | 0.7244560 | 0.7244849 | 0.27270 | 0.222444 | 1174 |
| 22 | 51105556 | rs9616812 | C | T | -0.0288849 | 0.116676 | 0.8044590 | 0.8044710 | 0.35300 | 0.362819 | 1174 |
| 22 | 51109992 | rs9628185 | T | C | -0.0397972 | 0.116840 | 0.7333664 | 0.7333951 | 0.36250 | 0.405351 | 1174 |
| 22 | 51156666 | rs9628187 | C | T | 0.0035748 | 0.206057 | 0.9861590 | 0.9861587 | 0.07879 | 0.129992 | 1174 |
bcf preview
1 1118275 rs61733845 C T . PASS AF=0.2242 ES:SE:LP:AF:SS:ID 0.0313315:0.139612:0.0848989:0.2242:1174:rs61733845
1 1120431 rs1320571 G A . PASS AF=0.2091 ES:SE:LP:AF:SS:ID -0.141081:0.145387:0.479557:0.2091:1174:rs1320571
1 1135242 rs9729550 A C . PASS AF=0.4141 ES:SE:LP:AF:SS:ID 0.000701652:0.115197:0.00211582:0.4141:1174:rs9729550
1 1140435 rs1815606 G T . PASS AF=0.6354 ES:SE:LP:AF:SS:ID -0.0467251:0.114448:0.165561:0.6354:1174:rs1815606
1 1163804 rs7515488 C T . PASS AF=0.1591 ES:SE:LP:AF:SS:ID 0.0793085:0.15116:0.222056:0.1591:1174:rs7515488
1 1165310 rs11260562 G A . PASS AF=0.0697 ES:SE:LP:AF:SS:ID -0.130498:0.226966:0.247791:0.0697:1174:rs11260562
1 1173611 rs6697886 G A . PASS AF=0.151 ES:SE:LP:AF:SS:ID 0.0357525:0.156017:0.0868588:0.151:1174:rs6697886
1 1186502 rs6603785 A T . PASS AF=0.1542 ES:SE:LP:AF:SS:ID -0.0636855:0.158657:0.162353:0.1542:1174:rs6603785
1 1194804 rs11804831 T C . PASS AF=0.6162 ES:SE:LP:AF:SS:ID -0.123221:0.1143:0.552132:0.6162:1174:rs11804831
1 1218086 rs6603788 C T . PASS AF=0.352 ES:SE:LP:AF:SS:ID -0.0618022:0.120508:0.216121:0.352:1174:rs6603788
1 1227897 rs3737721 A G . PASS AF=0.1899 ES:SE:LP:AF:SS:ID 0.063492:0.139505:0.187752:0.1899:1174:rs3737721
1 1231656 rs1749951 G A . PASS AF=0.09394 ES:SE:LP:AF:SS:ID -0.153868:0.205429:0.343348:0.09394:1174:rs1749951
1 1233941 rs1739855 T C . PASS AF=0.3414 ES:SE:LP:AF:SS:ID -0.0421974:0.118658:0.141411:0.3414:1174:rs1739855
1 1241529 rs1536168 A G . PASS AF=0.8862 ES:SE:LP:AF:SS:ID 0.126131:0.186866:0.301488:0.8862:1174:rs1536168
1 1242468 rs2274264 G A . PASS AF=0.1874 ES:SE:LP:AF:SS:ID 0.0770219:0.14016:0.234623:0.1874:1174:rs2274264
1 1247494 rs12103 T C . PASS AF=0.4056 ES:SE:LP:AF:SS:ID 0.0290177:0.11467:0.0967993:0.4056:1174:rs12103
1 1249187 rs12142199 G A . PASS AF=0.4045 ES:SE:LP:AF:SS:ID 0.0222403:0.114873:0.0723874:0.4045:1174:rs12142199
1 1254255 rs62623580 G A . PASS AF=0.03283 ES:SE:LP:AF:SS:ID -0.231482:0.339288:0.305483:0.03283:1174:rs62623580
1 1335790 rs1240708 A G . PASS AF=0.5919 ES:SE:LP:AF:SS:ID 0.00203915:0.115252:0.00617418:0.5919:1174:rs1240708
1 1493727 rs880051 G A . PASS AF=0.5875 ES:SE:LP:AF:SS:ID 0.170322:0.118841:0.820626:0.5875:1174:rs880051
1 1497824 rs2296716 C T . PASS AF=0.2692 ES:SE:LP:AF:SS:ID 0.131827:0.128806:0.514575:0.2692:1174:rs2296716
1 1611995 rs4074196 A G . PASS AF=0.4364 ES:SE:LP:AF:SS:ID -0.187926:0.117095:0.968139:0.4364:1174:rs4074196
1 1706160 rs7531583 A G . PASS AF=0.5141 ES:SE:LP:AF:SS:ID -0.0430598:0.114623:0.150497:0.5141:1174:rs7531583
1 1721479 rs2272908 C T . PASS AF=0.3551 ES:SE:LP:AF:SS:ID -0.0753667:0.119844:0.276305:0.3551:1174:rs2272908
1 1723031 rs9660180 G A . PASS AF=0.3551 ES:SE:LP:AF:SS:ID -0.0753667:0.119844:0.276305:0.3551:1174:rs9660180
1 1781220 rs6681938 T C . PASS AF=0.1914 ES:SE:LP:AF:SS:ID -0.126804:0.151011:0.397068:0.1914:1174:rs6681938
1 1838516 rs2377037 C A . PASS AF=0.1556 ES:SE:LP:AF:SS:ID 0.0136718:0.158765:0.0308754:0.1556:1174:rs2377037
1 1840038 rs2474461 T C . PASS AF=0.7796 ES:SE:LP:AF:SS:ID 0.0916481:0.141043:0.287657:0.7796:1174:rs2474461
1 1853288 rs1884454 G T . PASS AF=0.1545 ES:SE:LP:AF:SS:ID 0.118924:0.154128:0.356453:0.1545:1174:rs1884454
1 1855319 rs2295362 C T . PASS AF=0.2702 ES:SE:LP:AF:SS:ID -0.195612:0.134333:0.839772:0.2702:1174:rs2295362
1 1871337 rs16824543 G A . PASS AF=0.2712 ES:SE:LP:AF:SS:ID -0.207201:0.13481:0.908154:0.2712:1174:rs16824543
1 1873625 rs12758705 G A . PASS AF=0.1556 ES:SE:LP:AF:SS:ID 0.111469:0.153263:0.330864:0.1556:1174:rs12758705
1 1881070 rs4648596 G A . PASS AF=0.1934 ES:SE:LP:AF:SS:ID -0.295945:0.160328:1.19268:0.1934:1174:rs4648596
1 1888193 rs3820011 C A . PASS AF=0.1881 ES:SE:LP:AF:SS:ID -0.0601878:0.149905:0.162391:0.1881:1174:rs3820011
1 2024064 rs2459994 C T . PASS AF=0.4083 ES:SE:LP:AF:SS:ID 0.0837102:0.114978:0.331173:0.4083:1174:rs2459994
1 2146966 rs7512482 T C . PASS AF=0.2227 ES:SE:LP:AF:SS:ID 0.249984:0.130086:1.26801:0.2227:1174:rs7512482
1 2202774 rs6673129 C T . PASS AF=0.1581 ES:SE:LP:AF:SS:ID -0.193744:0.169554:0.597302:0.1581:1174:rs6673129
1 2229478 rs12562937 C T . PASS AF=0.2056 ES:SE:LP:AF:SS:ID -0.145496:0.146847:0.492969:0.2056:1174:rs12562937
1 2283896 rs2840528 A G . PASS AF=0.4707 ES:SE:LP:AF:SS:ID -0.0166789:0.114028:0.0536917:0.4707:1174:rs2840528
1 2290143 rs34587196 G A . PASS AF=0.00202 ES:SE:LP:AF:SS:ID 0.538631:1.0386:0.218988:0.00202:1174:rs34587196
1 2404256 rs2494626 C T . PASS AF=0.2465 ES:SE:LP:AF:SS:ID 0.0398802:0.132602:0.117143:0.2465:1174:rs2494626
1 2407781 rs78504402 C T . PASS AF=0.04444 ES:SE:LP:AF:SS:ID -0.170896:0.295473:0.24957:0.04444:1174:rs78504402
1 2408471 rs115996655 G A . PASS AF=0.00202 ES:SE:LP:AF:SS:ID 0.492824:1.05238:0.194149:0.00202:1174:rs115996655
1 2408834 rs11588930 G A . PASS AF=0.02172 ES:SE:LP:AF:SS:ID -0.310587:0.44224:0.316665:0.02172:1174:rs11588930
1 2409892 rs12727342 A G . PASS AF=0.652 ES:SE:LP:AF:SS:ID 0.107589:0.115981:0.451899:0.652:1174:rs12727342
1 2410789 rs11799501 C T . PASS AF=0.6081 ES:SE:LP:AF:SS:ID 0.122264:0.114411:0.545494:0.6081:1174:rs11799501
1 2412279 rs4648638 A T . PASS AF=0.3696 ES:SE:LP:AF:SS:ID -0.0844071:0.114082:0.338021:0.3696:1174:rs4648638
1 2412293 rs12731186 C T . PASS AF=0.09495 ES:SE:LP:AF:SS:ID 0.139155:0.181915:0.352541:0.09495:1174:rs12731186
1 2413166 rs115810747 A G . PASS AF=0.008081 ES:SE:LP:AF:SS:ID 0.750356:0.48945:0.904228:0.008081:1174:rs115810747
1 2413464 rs114424485 G C . PASS AF=0.001515 ES:SE:LP:AF:SS:ID 0.748963:1.10313:0.303628:0.001515:1174:rs114424485