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/sun/processed/PROT-a-706/PROT-a-706.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/sun/processed/PROT-a-706/ldsc.txt \
--w-ld-chr /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/
Beginning analysis at Mon Oct 28 20:34:12 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/sun/processed/PROT-a-706/PROT-a-706.vcf.gz ...
Read summary statistics for 10265284 SNPs.
Dropped 20 SNPs with duplicated rs numbers.
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, 1280408 SNPs remain.
After merging with regression SNP LD, 1280408 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.1581 (0.1241)
Lambda GC: 1.0081
Mean Chi^2: 1.0102
Intercept: 0.9999 (0.0058)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Mon Oct 28 20:35:42 2019
Total time elapsed: 1.0m:30.01sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9467,
"inflation_factor": 0.9945,
"mean_EFFECT": 0,
"n": "-Inf",
"n_snps": 10265284,
"n_clumped_hits": 2,
"n_p_sig": 284,
"n_mono": 0,
"n_ns": 884453,
"n_mac": 0,
"is_snpid_unique": false,
"n_miss_EFFECT": 0,
"n_miss_SE": 0,
"n_miss_PVAL": 0,
"n_miss_AF": 277,
"n_miss_AF_reference": 477442,
"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": 1280408,
"ldsc_nsnp_merge_regression_ld": 1280408,
"ldsc_observed_scale_h2_beta": 0.1581,
"ldsc_observed_scale_h2_se": 0.1241,
"ldsc_intercept_beta": 0.9999,
"ldsc_intercept_se": 0.0058,
"ldsc_lambda_gc": 1.0081,
"ldsc_mean_chisq": 1.0102,
"ldsc_ratio": -0.0098
}
Flags
| name | value |
|---|---|
| af_correlation | FALSE |
| inflation_factor | FALSE |
| n | TRUE |
| is_snpid_non_unique | TRUE |
| 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 | 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 | 10265275 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| character | REF | 0 | 1.0000000 | 1 | 97 | 0 | 38413 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| character | ALT | 0 | 1.0000000 | 1 | 412 | 0 | 24018 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| logical | N | 10265284 | 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 | 8.643747e+00 | 5.756573e+00 | 1.00000 | 4.000000e+00 | 8.000000e+00 | 1.300000e+01 | 2.200000e+01 | ▇▅▅▂▂ |
| numeric | POS | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 7.877894e+07 | 5.630825e+07 | 302.00000 | 3.256329e+07 | 6.917355e+07 | 1.143686e+08 | 2.492405e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.800000e-05 | 7.321440e-02 | -1.11410 | -3.000000e-02 | -1.000000e-04 | 3.000000e-02 | 1.058800e+00 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 5.959370e-02 | 4.190000e-02 | 0.02390 | 2.810000e-02 | 4.050000e-02 | 7.890000e-02 | 2.524000e-01 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.976935e-01 | 2.888757e-01 | 0.00000 | 2.454709e-01 | 5.011872e-01 | 7.413102e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.976610e-01 | 2.888075e-01 | 0.00000 | 2.468221e-01 | 4.972596e-01 | 7.472148e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | AF | 277 | 0.9999730 | NA | NA | NA | NA | NA | NA | NA | 2.512646e-01 | 2.751608e-01 | 0.00242 | 2.907000e-02 | 1.321000e-01 | 4.053500e-01 | 9.988300e-01 | ▇▂▂▁▁ |
| numeric | AF_reference | 477442 | 0.9534896 | NA | NA | NA | NA | NA | NA | NA | 2.332106e-01 | 2.519924e-01 | 0.00000 | 2.535940e-02 | 1.361820e-01 | 3.694090e-01 | 1.000000e+00 | ▇▂▂▁▁ |
Head and tail
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 10177 | rs367896724 | AC | A | 0.0388 | 0.0299 | 0.1949840 | 0.1944046 | 0.62806 | NA | NA |
| 1 | 10616 | rs376342519 | C | CCGCCGTTGCAAAGGCGCGCCG | -0.2453 | 0.1646 | 0.1348960 | 0.1361508 | 0.00751 | NA | NA |
| 1 | 11008 | rs575272151 | C | G | -0.1033 | 0.0518 | 0.0457088 | 0.0461293 | 0.08306 | 0.0880591 | NA |
| 1 | 13273 | rs531730856 | G | C | -0.0241 | 0.0434 | 0.5754399 | 0.5786898 | 0.13251 | 0.0950479 | NA |
| 1 | 13483 | rs554760071 | G | C | -0.3956 | 0.1957 | 0.0436516 | 0.0432320 | 0.00504 | 0.0019968 | NA |
| 1 | 14604 | rs541940975 | A | G | -0.0028 | 0.0370 | 0.9332541 | 0.9396771 | 0.18665 | 0.1475640 | NA |
| 1 | 14933 | rs199856693 | G | A | 0.0856 | 0.0677 | 0.2041738 | 0.2060859 | 0.04746 | 0.0283546 | NA |
| 1 | 18849 | rs533090414 | C | G | -0.0253 | 0.0797 | 0.7585776 | 0.7509095 | 0.96874 | 0.9518770 | NA |
| 1 | 30923 | rs806731 | G | T | -0.0051 | 0.0464 | 0.9120110 | 0.9124777 | 0.89387 | 0.8724040 | NA |
| 1 | 47159 | rs540662756 | T | C | -0.0147 | 0.0594 | 0.8128311 | 0.8045408 | 0.06190 | 0.0273562 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51237063 | rs3896457 | T | C | -0.0128 | 0.0268 | 0.6309573 | 0.6329264 | 0.28870 | 0.2050720 | NA |
| 22 | 51237364 | rs200607599 | A | G | 0.0639 | 0.1161 | 0.5754399 | 0.5820536 | 0.01151 | 0.0187700 | NA |
| 22 | 51237712 | rs370652263 | G | A | 0.0105 | 0.0513 | 0.8317640 | 0.8378234 | 0.06180 | 0.0690895 | NA |
| 22 | 51238394 | rs149712012 | C | T | -0.0269 | 0.1834 | 0.8912509 | 0.8833894 | 0.00454 | 0.0033946 | NA |
| 22 | 51239281 | rs8138215 | G | C | -0.3097 | 0.2010 | 0.1230269 | 0.1233664 | 0.00379 | 0.0111821 | NA |
| 22 | 51239296 | rs8137179 | T | C | -0.3097 | 0.2010 | 0.1230269 | 0.1233664 | 0.00379 | 0.0111821 | NA |
| 22 | 51239304 | rs8142977 | C | T | -0.3097 | 0.2010 | 0.1230269 | 0.1233664 | 0.00379 | 0.0111821 | NA |
| 22 | 51239794 | rs561893765 | C | A | -0.2149 | 0.2141 | 0.3162278 | 0.3155056 | 0.00333 | 0.0299521 | NA |
| 22 | 51241386 | rs568168135 | C | G | -0.1883 | 0.1653 | 0.2570402 | 0.2546444 | 0.00560 | 0.0139776 | NA |
| 22 | 51244237 | rs575160859 | C | T | -0.0164 | 0.1169 | 0.8912509 | 0.8884302 | 0.01136 | 0.0037939 | NA |
bcf preview
1 10177 rs367896724 AC A . PASS AF=0.62806 ES:SE:LP:AF:ID 0.0388:0.0299:0.710001:0.62806:rs367896724
1 10616 rs376342519 C CCGCCGTTGCAAAGGCGCGCCG . PASS AF=0.00751 ES:SE:LP:AF:ID -0.2453:0.1646:0.870001:0.00751:rs376342519
1 11008 rs575272151 C G . PASS AF=0.08306 ES:SE:LP:AF:ID -0.1033:0.0518:1.34:0.08306:rs575272151
1 13273 rs531730856 G C . PASS AF=0.13251 ES:SE:LP:AF:ID -0.0241:0.0434:0.24:0.13251:rs531730856
1 13483 rs554760071 G C . PASS AF=0.00504 ES:SE:LP:AF:ID -0.3956:0.1957:1.36:0.00504:rs554760071
1 14604 rs541940975 A G . PASS AF=0.18665 ES:SE:LP:AF:ID -0.0028:0.037:0.0300001:0.18665:rs541940975
1 14933 rs199856693 G A . PASS AF=0.04746 ES:SE:LP:AF:ID 0.0856:0.0677:0.69:0.04746:rs199856693
1 18849 rs533090414 C G . PASS AF=0.96874 ES:SE:LP:AF:ID -0.0253:0.0797:0.12:0.96874:rs533090414
1 30923 rs806731 G T . PASS AF=0.89387 ES:SE:LP:AF:ID -0.0051:0.0464:0.0399999:0.89387:rs806731
1 47159 rs540662756 T C . PASS AF=0.0619 ES:SE:LP:AF:ID -0.0147:0.0594:0.0899997:0.0619:rs540662756
1 49554 rs539322794 A G . PASS AF=0.08749 ES:SE:LP:AF:ID -0.04:0.0524:0.35:0.08749:rs539322794
1 54712 rs568927205 TTTTC T . PASS AF=0.43775 ES:SE:LP:AF:ID -0.037:0.0283:0.72:0.43775:rs568927205
1 54945 rs569799965 C A . PASS AF=0.00751 ES:SE:LP:AF:ID 0.1571:0.1699:0.45:0.00751:rs569799965
1 55249 rs200769871 C CTATGG . PASS AF=0.01031 ES:SE:LP:AF:ID -0.2933:0.1419:1.41:0.01031:rs200769871
1 59040 rs62637815 T C . PASS AF=0.08548 ES:SE:LP:AF:ID -0.0149:0.0528:0.11:0.08548:rs62637815
1 60351 rs62637817 A G . PASS AF=0.08009 ES:SE:LP:AF:ID -0.0197:0.0542:0.15:0.08009:rs62637817
1 61208 rs527362206 T C . PASS AF=0.00728 ES:SE:LP:AF:ID 0.1583:0.1709:0.45:0.00728:rs527362206
1 61743 rs184286948 G C . PASS AF=0.00759 ES:SE:LP:AF:ID 0.0541:0.1685:0.13:0.00759:rs184286948
1 63093 rs200092917 G A . PASS AF=0.02888 ES:SE:LP:AF:ID 0.0279:0.0875:0.12:0.02888:rs200092917
1 63671 rs80011619 G A . PASS AF=0.15883 ES:SE:LP:AF:ID -0.0657:0.0399:1:0.15883:rs80011619
1 64649 rs181431124 A C . PASS AF=0.02531 ES:SE:LP:AF:ID -0.0638:0.0931:0.31:0.02531:rs181431124
1 64931 rs62639104 G A . PASS AF=0.07866 ES:SE:LP:AF:ID -0.0283:0.0543:0.22:0.07866:rs62639104
1 66219 rs181028663 A T . PASS AF=0.01917 ES:SE:LP:AF:ID 0.0883:0.1018:0.41:0.01917:rs181028663
1 67107 rs545950684 A G . PASS AF=0.00703 ES:SE:LP:AF:ID 0.1053:0.1733:0.26:0.00703:rs545950684
1 68082 rs367789441 T C . PASS AF=0.07119 ES:SE:LP:AF:ID 0.0008:0.0573:-0:0.07119:rs367789441
1 69428 rs140739101 T G . PASS AF=0.03289 ES:SE:LP:AF:ID -0.0286:0.081:0.14:0.03289:rs140739101
1 69761 rs200505207 A T . PASS AF=0.07372 ES:SE:LP:AF:ID 0.0537:0.0566:0.46:0.07372:rs200505207
1 69897 rs200676709 T C . PASS AF=0.71804 ES:SE:LP:AF:ID -0.0044:0.0316:0.05:0.71804:rs200676709
1 77866 rs563593912 C T . PASS AF=0.0757 ES:SE:LP:AF:ID 0.003:0.0548:0.0199998:0.0757:rs563593912
1 81587 rs536406113 C T . PASS AF=0.06385 ES:SE:LP:AF:ID -0.0341:0.0597:0.25:0.06385:rs536406113
1 82163 rs139113303 G A . PASS AF=0.07738 ES:SE:LP:AF:ID -0.0385:0.0529:0.33:0.07738:rs139113303
1 82609 rs149189449 C G . PASS AF=0.07539 ES:SE:LP:AF:ID -0.0317:0.0537:0.26:0.07539:rs149189449
1 86065 rs116504101 G C . PASS AF=0.0764 ES:SE:LP:AF:ID -0.031:0.0536:0.25:0.0764:rs116504101
1 87409 rs139490478 C T . PASS AF=0.07661 ES:SE:LP:AF:ID -0.0358:0.0534:0.3:0.07661:rs139490478
1 88169 rs940550 C T . PASS AF=0.20467 ES:SE:LP:AF:ID -0.0085:0.0361:0.0899997:0.20467:rs940550
1 88172 rs940551 G A . PASS AF=0.07528 ES:SE:LP:AF:ID 0.0129:0.0553:0.0899997:0.07528:rs940551
1 88177 rs143215837 G C . PASS AF=0.07235 ES:SE:LP:AF:ID 0.0079:0.0564:0.05:0.07235:rs143215837
1 88316 rs113759966 G A . PASS AF=0.07124 ES:SE:LP:AF:ID 0.0142:0.0571:0.1:0.07124:rs113759966
1 88338 rs55700207 G A . PASS AF=0.07793 ES:SE:LP:AF:ID -0.0311:0.0534:0.25:0.07793:rs55700207
1 88710 rs186575039 C G . PASS AF=0.07789 ES:SE:LP:AF:ID -0.0302:0.053:0.25:0.07789:rs186575039
1 89599 rs375955515 A T . PASS AF=0.07642 ES:SE:LP:AF:ID -0.0229:0.0532:0.18:0.07642:rs375955515
1 91190 rs143856811 G A . PASS AF=0.08117 ES:SE:LP:AF:ID -0.0324:0.0517:0.28:0.08117:rs143856811
1 91340 rs376250562 C G . PASS AF=0.01521 ES:SE:LP:AF:ID -0.0264:0.1199:0.0799999:0.01521:rs376250562
1 91358 rs370712517 G T . PASS AF=0.01478 ES:SE:LP:AF:ID -0.0047:0.1208:0.0100001:0.01478:rs370712517
1 91421 rs28619159 T C . PASS AF=0.01861 ES:SE:LP:AF:ID -0.0438:0.1034:0.17:0.01861:rs28619159
1 91515 rs376723915 A C . PASS AF=0.54508 ES:SE:LP:AF:ID 0.0059:0.029:0.0799999:0.54508:rs376723915
1 91536 rs6702460 G T . PASS AF=0.56854 ES:SE:LP:AF:ID 0.002:0.0291:0.0300001:0.56854:rs6702460
1 92858 rs147061536 G T . PASS AF=0.22951 ES:SE:LP:AF:ID 0.0112:0.0348:0.13:0.22951:rs147061536
1 99687 rs139153227 C T . PASS AF=0.06852 ES:SE:LP:AF:ID 0.0076:0.0578:0.05:0.06852:rs139153227
1 104281 rs576842010 T C . PASS AF=0.04001 ES:SE:LP:AF:ID -0.0079:0.0748:0.0399999:0.04001:rs576842010