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-968/ieu-a-968.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-968/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 11:38:45 2020
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ieu-a-import/processed/ieu-a-968/ieu-a-968.vcf.gz ...
Read summary statistics for 10647 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, 10548 SNPs remain.
After merging with regression SNP LD, 10548 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: 6.0408 (0.8069)
Lambda GC: 6.2088
Mean Chi^2: 8.0483
Intercept: 2.6781 (0.3057)
Ratio: 0.2381 (0.0434)
Analysis finished at Wed Feb 5 11:38:53 2020
Total time elapsed: 8.34sQC metrics
Metrics
Metrics
{
"af_correlation": 0.8901,
"inflation_factor": 22.6659,
"mean_EFFECT": 0.0023,
"n": 33000,
"n_snps": 10647,
"n_clumped_hits": 73,
"n_p_sig": 1466,
"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": 61,
"n_est": 32978.492,
"ratio_se_n": 0.9997,
"mean_diff": -0.0012,
"ratio_diff": 9.4415,
"sd_y_est1": 2.2836,
"sd_y_est2": 2.2829,
"r2_sum1": 0.5466,
"r2_sum2": 0.1048,
"r2_sum3": 0.1049,
"r2_sum4": 0.0989,
"ldsc_nsnp_merge_refpanel_ld": 10548,
"ldsc_nsnp_merge_regression_ld": 10548,
"ldsc_observed_scale_h2_beta": 6.0408,
"ldsc_observed_scale_h2_se": 0.8069,
"ldsc_intercept_beta": 2.6781,
"ldsc_intercept_se": 0.3057,
"ldsc_lambda_gc": 6.2088,
"ldsc_mean_chisq": 8.0483,
"ldsc_ratio": 0.2381
}
Flags
| name | value |
|---|---|
| af_correlation | FALSE |
| inflation_factor | TRUE |
| n | FALSE |
| 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 | TRUE |
| n_clumped_hits | FALSE |
| r2_sum1 | TRUE |
| 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 | 10647 | 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 | 8.207946e+00 | 6.059583e+00 | 1.00000e+00 | 3.00000e+00 | 7.000000e+00 | 1.200000e+01 | 2.200000e+01 | ▇▃▃▂▂ |
| numeric | POS | 0 | 1.0000000 | NA | NA | NA | NA | NA | 7.797658e+07 | 5.740263e+07 | 6.53510e+04 | 3.43452e+07 | 6.230621e+07 | 1.115241e+08 | 2.476071e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | 2.343100e-03 | 6.267550e-02 | -1.09064e+00 | -3.44879e-02 | 2.995500e-03 | 4.018180e-02 | 5.504310e-01 | ▁▁▁▇▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | 2.261410e-02 | 1.137840e-02 | 1.74200e-02 | 1.81301e-02 | 1.956980e-02 | 2.324020e-02 | 4.910010e-01 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | 4.990520e-02 | 1.283649e-01 | 0.00000e+00 | 5.70000e-06 | 1.322000e-03 | 2.986000e-02 | 9.984000e-01 | ▇▁▁▁▁ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | 2.508297e-01 | 3.050132e-01 | 0.00000e+00 | 1.84300e-03 | 9.329590e-02 | 4.473162e-01 | 1.000000e+00 | ▇▂▁▁▁ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | 3.986139e-01 | 2.302886e-01 | 9.59000e-04 | 2.09500e-01 | 3.720000e-01 | 5.610000e-01 | 9.928700e-01 | ▆▇▆▅▂ |
| numeric | AF_reference | 61 | 0.9942707 | NA | NA | NA | NA | NA | 3.923903e-01 | 2.258780e-01 | 3.99360e-03 | 2.08067e-01 | 3.562300e-01 | 5.671922e-01 | 9.916130e-01 | ▆▇▅▃▂ |
| numeric | N | 0 | 1.0000000 | NA | NA | NA | NA | NA | 3.300000e+04 | 0.000000e+00 | 3.30000e+04 | 3.30000e+04 | 3.300000e+04 | 3.300000e+04 | 3.300000e+04 | ▁▁▇▁▁ |
Head and tail
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 1163804 | rs7515488 | C | T | 0.1043600 | 0.0243701 | 0.0000005 | 0.0000185 | 0.1470 | 0.186901 | 33000 |
| 1 | 1165310 | rs11260562 | G | A | 0.0741794 | 0.0374192 | 0.0010470 | 0.0474358 | 0.0546 | 0.101837 | 33000 |
| 1 | 1174282 | rs4970364 | T | C | 0.1133290 | 0.0225601 | 0.0000000 | 0.0000005 | 0.2400 | 0.658147 | 33000 |
| 1 | 1194804 | rs11804831 | T | C | 0.1133290 | 0.0225601 | 0.0000000 | 0.0000005 | 0.1830 | 0.685903 | 33000 |
| 1 | 1210471 | rs11260577 | T | C | 0.0620354 | 0.0329803 | 0.0004989 | 0.0599742 | 0.0780 | 0.482628 | 33000 |
| 1 | 1218086 | rs6603788 | C | T | 0.0620354 | 0.0329801 | 0.0004286 | 0.0599726 | 0.0732 | 0.469449 | 33000 |
| 1 | 1239935 | rs12410087 | A | C | 0.1052610 | 0.0226000 | 0.0000000 | 0.0000032 | 0.1080 | 0.160144 | 33000 |
| 1 | 1247494 | rs12103 | T | C | -0.1052610 | 0.0226005 | 0.0000000 | 0.0000032 | 0.8180 | 0.319289 | 33000 |
| 1 | 1249187 | rs12142199 | G | A | -0.0989399 | 0.0220000 | 0.0000000 | 0.0000069 | 0.8110 | 0.310104 | 33000 |
| 1 | 1254136 | rs4970432 | G | A | -0.0778865 | 0.0336199 | 0.0005642 | 0.0205213 | 0.9310 | 0.719249 | 33000 |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 50446988 | rs137864 | C | T | 0.0430595 | 0.0179390 | 0.0000012 | 0.0163803 | 0.508 | 0.581869 | 33000 |
| 22 | 50470516 | rs11101958 | C | T | -0.0077298 | 0.0241006 | 0.0828495 | 0.7484146 | 0.168 | 0.223642 | 33000 |
| 22 | 50472712 | rs12484423 | C | T | -0.0163327 | 0.0240813 | 0.1804002 | 0.4976248 | 0.164 | 0.217452 | 33000 |
| 22 | 50474943 | rs1010282 | G | A | -0.0163327 | 0.0240813 | 0.1647999 | 0.4976248 | 0.162 | 0.223842 | 33000 |
| 22 | 50971266 | rs140522 | T | C | -0.0620354 | 0.0180499 | 0.0000078 | 0.0005884 | 0.669 | 0.619010 | 33000 |
| 22 | 50978024 | rs140521 | C | A | -0.0852598 | 0.0210201 | 0.0000011 | 0.0000499 | 0.699 | 0.840455 | 33000 |
| 22 | 51091914 | rs8142140 | C | T | 0.0363319 | 0.0213802 | 0.0014100 | 0.0892580 | 0.209 | 0.233427 | 33000 |
| 22 | 51094926 | rs9616810 | C | T | 0.0363319 | 0.0213802 | 0.0006047 | 0.0892580 | 0.210 | 0.222444 | 33000 |
| 22 | 51101938 | rs6010044 | A | C | 0.0363319 | 0.0213805 | 0.0008547 | 0.0892625 | 0.202 | 0.239018 | 33000 |
| 22 | 51109992 | rs9628185 | T | C | -0.0039076 | 0.0177608 | 0.0647799 | 0.8258605 | 0.472 | 0.405351 | 33000 |
bcf preview
1 1163804 rs7515488 C T . PASS AF=0.147 ES:SE:LP:AF:SS:ID 0.10436:0.0243701:6.30803:0.147:33000:rs7515488
1 1165310 rs11260562 G A . PASS AF=0.0546 ES:SE:LP:AF:SS:ID 0.0741794:0.0374192:2.98005:0.0546:33000:rs11260562
1 1174282 rs4970364 T C . PASS AF=0.24 ES:SE:LP:AF:SS:ID 0.113329:0.0225601:7.90553:0.24:33000:rs4970364
1 1194804 rs11804831 T C . PASS AF=0.183 ES:SE:LP:AF:SS:ID 0.113329:0.0225601:9.43652:0.183:33000:rs11804831
1 1210471 rs11260577 T C . PASS AF=0.078 ES:SE:LP:AF:SS:ID 0.0620354:0.0329803:3.30199:0.078:33000:rs11260577
1 1218086 rs6603788 C T . PASS AF=0.0732 ES:SE:LP:AF:SS:ID 0.0620354:0.0329801:3.36795:0.0732:33000:rs6603788
1 1239935 rs12410087 A C . PASS AF=0.108 ES:SE:LP:AF:SS:ID 0.105261:0.0226:7.90309:0.108:33000:rs12410087
1 1247494 rs12103 T C . PASS AF=0.818 ES:SE:LP:AF:SS:ID -0.105261:0.0226005:9.5632:0.818:33000:rs12103
1 1249187 rs12142199 G A . PASS AF=0.811 ES:SE:LP:AF:SS:ID -0.0989399:0.022:9.12177:0.811:33000:rs12142199
1 1254136 rs4970432 G A . PASS AF=0.931 ES:SE:LP:AF:SS:ID -0.0778865:0.0336199:3.24857:0.931:33000:rs4970432
1 1258246 rs3845293 A C . PASS AF=0.9345 ES:SE:LP:AF:SS:ID -0.0778865:0.0336204:3.2636:0.9345:33000:rs3845293
1 1287127 rs307380 G A . PASS AF=0.933 ES:SE:LP:AF:SS:ID -0.0778865:0.0336199:3.24588:0.933:33000:rs307380
1 1295323 rs34389364 G A . PASS AF=0.929 ES:SE:LP:AF:SS:ID -0.0778865:0.0336199:3.23181:0.929:33000:rs34389364
1 1295403 rs34876581 T C . PASS AF=0.9326 ES:SE:LP:AF:SS:ID -0.0778865:0.0336204:3.22446:0.9326:33000:rs34876581
1 1297065 rs2765023 C T . PASS AF=0.106 ES:SE:LP:AF:SS:ID 0.105261:0.0226005:7.49349:0.106:33000:rs2765023
1 1297422 rs2765021 C T . PASS AF=0.932 ES:SE:LP:AF:SS:ID -0.0778865:0.0336199:3.28108:0.932:33000:rs2765021
1 1305561 rs17160669 C T . PASS AF=0.105 ES:SE:LP:AF:SS:ID 0.105261:0.0226005:7.0364:0.105:33000:rs17160669
1 1333598 rs35242196 C A . PASS AF=0.0964 ES:SE:LP:AF:SS:ID 0.10436:0.0231213:6.98801:0.0964:33000:rs35242196
1 1335790 rs1240708 A G . PASS AF=0.168 ES:SE:LP:AF:SS:ID 0.10436:0.02312:8.84043:0.168:33000:rs1240708
1 1365570 rs1695824 A C . PASS AF=0.218 ES:SE:LP:AF:SS:ID 0.10436:0.02312:6.81502:0.218:33000:rs1695824
1 1366830 rs873927 A G . PASS AF=0.218 ES:SE:LP:AF:SS:ID 0.10436:0.02312:6.80911:0.218:33000:rs873927
1 1665613 rs4648764 A G . PASS AF=0.457 ES:SE:LP:AF:SS:ID 0.0561472:0.0177599:4.30671:0.457:33000:rs4648764
1 1684472 rs7407 C T . PASS AF=0.507 ES:SE:LP:AF:SS:ID -0.0561472:0.0177599:4.50349:0.507:33000:rs7407
1 1687625 rs34306661 T C . PASS AF=0.436 ES:SE:LP:AF:SS:ID 0.0561472:0.0177599:4.44021:0.436:33000:rs34306661
1 1689164 rs2235536 C T . PASS AF=0.424 ES:SE:LP:AF:SS:ID 0.0561472:0.0177588:4.451:0.424:33000:rs2235536
1 1689762 rs2235537 T C . PASS AF=0.439 ES:SE:LP:AF:SS:ID 0.0561472:0.0177599:4.37335:0.439:33000:rs2235537
1 1691050 rs35672141 C T . PASS AF=0.487 ES:SE:LP:AF:SS:ID -0.0561472:0.0177599:4.99654:0.487:33000:rs35672141
1 1707740 rs12041925 T G . PASS AF=0.262 ES:SE:LP:AF:SS:ID 0.0344014:0.019791:2.52886:0.262:33000:rs12041925
1 1708801 rs12044597 A G . PASS AF=0.497 ES:SE:LP:AF:SS:ID -0.0561472:0.0177588:5.80355:0.497:33000:rs12044597
1 1712230 rs10907194 C T . PASS AF=0.261 ES:SE:LP:AF:SS:ID 0.0344014:0.0197888:2.5812:0.261:33000:rs10907194
1 1713869 rs7534617 T G . PASS AF=0.263 ES:SE:LP:AF:SS:ID 0.0344014:0.019791:2.60854:0.263:33000:rs7534617
1 1715011 rs742359 C A . PASS AF=0.259 ES:SE:LP:AF:SS:ID 0.0344014:0.0197888:2.61618:0.259:33000:rs742359
1 1721479 rs2272908 C T . PASS AF=0.498 ES:SE:LP:AF:SS:ID -0.0561472:0.0177599:5.87746:0.498:33000:rs2272908
1 1722932 rs3737628 C T . PASS AF=0.496 ES:SE:LP:AF:SS:ID -0.0575232:0.0177701:5.86107:0.496:33000:rs3737628
1 1723031 rs9660180 G A . PASS AF=0.494 ES:SE:LP:AF:SS:ID -0.0575232:0.0177701:5.90904:0.494:33000:rs9660180
1 1748734 rs2180311 T C . PASS AF=0.498 ES:SE:LP:AF:SS:ID -0.0561472:0.0177588:5.65326:0.498:33000:rs2180311
1 1759213 rs9786942 A G . PASS AF=0.498 ES:SE:LP:AF:SS:ID -0.0561472:0.0177588:5.65326:0.498:33000:rs9786942
1 1776269 rs4648727 C A . PASS AF=0.452 ES:SE:LP:AF:SS:ID 0.0561472:0.0177588:4.94885:0.452:33000:rs4648727
1 1781220 rs6681938 T C . PASS AF=0.286 ES:SE:LP:AF:SS:ID -0.0563587:0.0194202:5.13996:0.286:33000:rs6681938
1 1797947 rs9660106 A G . PASS AF=0.495 ES:SE:LP:AF:SS:ID -0.0561472:0.0177588:5.56944:0.495:33000:rs9660106
1 1801034 rs4648592 G A . PASS AF=0.262 ES:SE:LP:AF:SS:ID 0.0344014:0.0197888:2.62233:0.262:33000:rs4648592
1 1809509 rs11260624 C T . PASS AF=0.261 ES:SE:LP:AF:SS:ID 0.0344014:0.0197888:2.60924:0.261:33000:rs11260624
1 1810090 rs7525092 C T . PASS AF=0.262 ES:SE:LP:AF:SS:ID 0.0344014:0.0197888:2.58586:0.262:33000:rs7525092
1 1812688 rs6603803 A G . PASS AF=0.488 ES:SE:LP:AF:SS:ID -0.0561472:0.0177588:5.26297:0.488:33000:rs6603803
1 1821625 rs6603805 G A . PASS AF=0.263 ES:SE:LP:AF:SS:ID 0.0344014:0.0197888:2.56051:0.263:33000:rs6603805
1 1844046 rs2474460 C T . PASS AF=0.486 ES:SE:LP:AF:SS:ID -0.0561472:0.0177599:5.41218:0.486:33000:rs2474460
1 1846357 rs2748988 G A . PASS AF=0.49 ES:SE:LP:AF:SS:ID -0.0561472:0.0177599:5.46067:0.49:33000:rs2748988
1 1846765 rs2247560 G A . PASS AF=0.488 ES:SE:LP:AF:SS:ID -0.0561472:0.0177599:5.44285:0.488:33000:rs2247560
1 1847856 rs2748986 G T . PASS AF=0.522 ES:SE:LP:AF:SS:ID -0.0561472:0.0177599:4.69529:0.522:33000:rs2748986
1 1852134 rs2490548 A G . PASS AF=0.487 ES:SE:LP:AF:SS:ID -0.0561472:0.0177588:5.42852:0.487:33000:rs2490548