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-1161/ieu-a-1161.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-1161/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 11:36:47 2020
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ieu-a-import/processed/ieu-a-1161/ieu-a-1161.vcf.gz ...
Read summary statistics for 191058 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, 136822 SNPs remain.
After merging with regression SNP LD, 136822 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: 0.4836 (0.0933)
Lambda GC: 1.2668
Mean Chi^2: 1.6928
Intercept: 1.1031 (0.0209)
Ratio: 0.1488 (0.0302)
Analysis finished at Wed Feb 5 11:36:56 2020
Total time elapsed: 9.58sQC metrics
Metrics
Metrics
{
"af_correlation": 0.403,
"inflation_factor": 1.3107,
"mean_EFFECT": 0.0003,
"n": 69970,
"n_snps": 191058,
"n_clumped_hits": 46,
"n_p_sig": 1703,
"n_mono": 0,
"n_ns": 1,
"n_mac": 0,
"is_snpid_unique": true,
"n_miss_EFFECT": 0,
"n_miss_SE": 0,
"n_miss_PVAL": 0,
"n_miss_AF": 1,
"n_miss_AF_reference": 1152,
"n_est": 70378.4281,
"ratio_se_n": 1.0029,
"mean_diff": -0.0001,
"ratio_diff": 0.8599,
"sd_y_est1": 2.1254,
"sd_y_est2": 2.1316,
"r2_sum1": 0.2419,
"r2_sum2": 0.0535,
"r2_sum3": 0.0532,
"r2_sum4": 0.0496,
"ldsc_nsnp_merge_refpanel_ld": 136822,
"ldsc_nsnp_merge_regression_ld": 136822,
"ldsc_observed_scale_h2_beta": 0.4836,
"ldsc_observed_scale_h2_se": 0.0933,
"ldsc_intercept_beta": 1.1031,
"ldsc_intercept_se": 0.0209,
"ldsc_lambda_gc": 1.2668,
"ldsc_mean_chisq": 1.6928,
"ldsc_ratio": 0.1488
}
Flags
| name | value |
|---|---|
| af_correlation | TRUE |
| 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 | 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 | 22 | 0 | 191058 | 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 | 2 | 0 | 5 | 0 | NA | NA | NA | NA | NA | NA | NA | NA |
| numeric | CHROM | 0 | 1.0000000 | NA | NA | NA | NA | NA | 8.863780e+00 | 5.775955e+00 | 1.0000e+00 | 4.000000e+00 | 8.000000e+00 | 1.300000e+01 | 2.200000e+01 | ▇▆▅▃▂ |
| numeric | POS | 0 | 1.0000000 | NA | NA | NA | NA | NA | 7.855541e+07 | 5.659370e+07 | 5.0814e+04 | 3.160572e+07 | 6.887496e+07 | 1.160603e+08 | 2.491747e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | 2.894000e-04 | 3.390980e-02 | -1.6213e+00 | -1.170000e-02 | -1.000000e-04 | 1.180000e-02 | 9.342900e+00 | ▇▁▁▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | 1.752190e-02 | 1.107346e-01 | 1.1300e-02 | 1.190000e-02 | 1.370000e-02 | 1.840000e-02 | 4.819230e+01 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | 4.503383e-01 | 3.019048e-01 | 0.0000e+00 | 1.800002e-01 | 4.400003e-01 | 7.099994e-01 | 1.000000e+00 | ▇▆▆▅▅ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | 4.550837e-01 | 3.022418e-01 | 0.0000e+00 | 1.811970e-01 | 4.414999e-01 | 7.174652e-01 | 1.000000e+00 | ▇▆▆▅▅ |
| numeric | AF | 1 | 0.9999948 | NA | NA | NA | NA | NA | 2.330795e-01 | 1.425261e-01 | 1.0000e-04 | 1.073000e-01 | 2.207000e-01 | 3.533000e-01 | 5.473000e-01 | ▇▇▆▆▃ |
| numeric | AF_reference | 1152 | 0.9939704 | NA | NA | NA | NA | NA | 3.488433e-01 | 2.521507e-01 | 1.9970e-04 | 1.393770e-01 | 2.885380e-01 | 5.265580e-01 | 9.996010e-01 | ▇▆▃▃▂ |
| numeric | N | 0 | 1.0000000 | NA | NA | NA | NA | NA | 6.997000e+04 | 0.000000e+00 | 6.9970e+04 | 6.997000e+04 | 6.997000e+04 | 6.997000e+04 | 6.997000e+04 | ▁▁▇▁▁ |
Head and tail
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 1018704 | rs9442372 | A | G | -0.0017 | 0.0115 | 0.8700001 | 0.8824800 | 0.4424 | 0.611022 | 69970 |
| 1 | 1062638 | rs9442373 | C | A | 0.0002 | 0.0114 | 0.9800000 | 0.9860027 | 0.4584 | 0.574281 | 69970 |
| 1 | 1094738 | rs4970362 | A | G | -0.0055 | 0.0120 | 0.6400000 | 0.6467130 | 0.3705 | 0.604034 | 69970 |
| 1 | 1099342 | rs9660710 | A | C | -0.0128 | 0.0220 | 0.5600000 | 0.5606892 | 0.0725 | 0.829872 | 69970 |
| 1 | 1106473 | rs4970420 | G | A | 0.0017 | 0.0145 | 0.9000000 | 0.9066688 | 0.1909 | 0.550319 | 69970 |
| 1 | 1110019 | rs11260542 | A | G | -0.0209 | 0.0195 | 0.2800000 | 0.2838122 | 0.0942 | 0.144569 | 69970 |
| 1 | 1119858 | rs1320565 | C | T | -0.0208 | 0.0214 | 0.3200000 | 0.3310691 | 0.0773 | 0.318690 | 69970 |
| 1 | 1135242 | rs9729550 | A | C | 0.0050 | 0.0130 | 0.6999999 | 0.7005224 | 0.2561 | 0.551917 | 69970 |
| 1 | 1138913 | rs3819001 | T | C | -0.0334 | 0.0247 | 0.1700000 | 0.1763028 | 0.0575 | 0.198482 | 69970 |
| 1 | 1156131 | rs2887286 | T | C | 0.0064 | 0.0154 | 0.6700003 | 0.6777141 | 0.1615 | 0.511182 | 69970 |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 50960682 | rs140524 | C | T | -0.0074 | 0.0151 | 0.6200004 | 0.6240870 | 0.1734 | 0.2302320 | 69970 |
| 22 | 50966914 | rs470119 | T | C | -0.0113 | 0.0117 | 0.3300000 | 0.3341383 | 0.3935 | 0.5547120 | 69970 |
| 22 | 50971266 | rs140522 | T | C | -0.0104 | 0.0122 | 0.3900004 | 0.3939594 | 0.3274 | 0.6190100 | 69970 |
| 22 | 50988062 | rs140519 | G | T | 0.0021 | 0.0114 | 0.8499999 | 0.8538483 | 0.5023 | 0.4051520 | 69970 |
| 22 | 50989326 | rs131778 | A | G | -0.0001 | 0.0114 | 0.9900000 | 0.9930011 | 0.4776 | 0.4984030 | 69970 |
| 22 | 51080760 | rs134774 | A | G | 0.0331 | 0.0203 | 0.1000000 | 0.1029870 | 0.0861 | 0.9536740 | 69970 |
| 22 | 51123505 | rs9616816 | G | A | 0.0102 | 0.0136 | 0.4499995 | 0.4532547 | 0.2273 | 0.3783950 | 69970 |
| 22 | 51151350 | rs6009951 | C | T | -0.0053 | 0.0116 | 0.6400000 | 0.6477454 | 0.4165 | 0.5559110 | 69970 |
| 22 | 51151724 | rs6010061 | C | T | -0.0020 | 0.0116 | 0.8600001 | 0.8631122 | 0.4126 | 0.6098240 | 69970 |
| 22 | 51178090 | rs2285395 | G | A | 0.0083 | 0.0251 | 0.7400005 | 0.7408883 | 0.0534 | 0.0666933 | 69970 |
bcf preview
1 1018704 rs9442372 A G . PASS AF=0.4424 ES:SE:LP:AF:SS:ID -0.0017:0.0115:0.0604807:0.4424:69970:rs9442372
1 1062638 rs9442373 C A . PASS AF=0.4584 ES:SE:LP:AF:SS:ID 0.0002:0.0114:0.00877392:0.4584:69970:rs9442373
1 1094738 rs4970362 A G . PASS AF=0.3705 ES:SE:LP:AF:SS:ID -0.0055:0.012:0.19382:0.3705:69970:rs4970362
1 1099342 rs9660710 A C . PASS AF=0.0725 ES:SE:LP:AF:SS:ID -0.0128:0.022:0.251812:0.0725:69970:rs9660710
1 1106473 rs4970420 G A . PASS AF=0.1909 ES:SE:LP:AF:SS:ID 0.0017:0.0145:0.0457575:0.1909:69970:rs4970420
1 1110019 rs11260542 A G . PASS AF=0.0942 ES:SE:LP:AF:SS:ID -0.0209:0.0195:0.552842:0.0942:69970:rs11260542
1 1119858 rs1320565 C T . PASS AF=0.0773 ES:SE:LP:AF:SS:ID -0.0208:0.0214:0.49485:0.0773:69970:rs1320565
1 1135242 rs9729550 A C . PASS AF=0.2561 ES:SE:LP:AF:SS:ID 0.005:0.013:0.154902:0.2561:69970:rs9729550
1 1138913 rs3819001 T C . PASS AF=0.0575 ES:SE:LP:AF:SS:ID -0.0334:0.0247:0.769551:0.0575:69970:rs3819001
1 1156131 rs2887286 T C . PASS AF=0.1615 ES:SE:LP:AF:SS:ID 0.0064:0.0154:0.173925:0.1615:69970:rs2887286
1 1162435 rs3766186 C A . PASS AF=0.0921 ES:SE:LP:AF:SS:ID 0.0094:0.0195:0.200659:0.0921:69970:rs3766186
1 1211292 rs6685064 C T . PASS AF=0.0682 ES:SE:LP:AF:SS:ID -0.0151:0.0225:0.30103:0.0682:69970:rs6685064
1 1239953 rs10399878 A G . PASS AF=0.0476 ES:SE:LP:AF:SS:ID -0.0467:0.0269:1.09691:0.0476:69970:rs10399878
1 1775175 rs11260622 A G . PASS AF=0.0402 ES:SE:LP:AF:SS:ID 0.0215:0.0286:0.346788:0.0402:69970:rs11260622
1 1978857 rs3121825 A G . PASS AF=0.3503 ES:SE:LP:AF:SS:ID -0.0022:0.012:0.0705811:0.3503:69970:rs3121825
1 1980639 rs6683042 G A . PASS AF=0.2348 ES:SE:LP:AF:SS:ID 0.0015:0.0135:0.0409586:0.2348:69970:rs6683042
1 1981118 rs3795277 A C . PASS AF=0.0839 ES:SE:LP:AF:SS:ID 0.0066:0.0205:0.130768:0.0839:69970:rs3795277
1 1983160 rs28703783 C A . PASS AF=0.2581 ES:SE:LP:AF:SS:ID 0.03:0.0132:1.69897:0.2581:69970:rs28703783
1 1983421 rs4648795 T C . PASS AF=0.405 ES:SE:LP:AF:SS:ID -0.0168:0.0116:0.853872:0.405:69970:rs4648795
1 1983701 rs28671574 G A . PASS AF=0.0825 ES:SE:LP:AF:SS:ID -0.001:0.0207:0.0177288:0.0825:69970:rs28671574
1 1984227 rs3128319 C T . PASS AF=0.2385 ES:SE:LP:AF:SS:ID -0.0025:0.0134:0.0705811:0.2385:69970:rs3128319
1 1985170 rs35020387 G A . PASS AF=0.3816 ES:SE:LP:AF:SS:ID 0.0031:0.0117:0.107905:0.3816:69970:rs35020387
1 1985553 rs13302968 T C . PASS AF=0.1459 ES:SE:LP:AF:SS:ID 0.0006:0.0163:0.0132283:0.1459:69970:rs13302968
1 1992977 rs17777295 G T . PASS AF=0.2472 ES:SE:LP:AF:SS:ID -0.0055:0.0132:0.173925:0.2472:69970:rs17777295
1 1993722 rs13303346 C T . PASS AF=0.1339 ES:SE:LP:AF:SS:ID -0.0052:0.0168:0.124939:0.1339:69970:rs13303346
1 1997150 rs2803294 G A . PASS AF=0.1879 ES:SE:LP:AF:SS:ID 0.0154:0.0146:0.537602:0.1879:69970:rs2803294
1 1997187 rs75227206 A G . PASS AF=0.1855 ES:SE:LP:AF:SS:ID -0.0099:0.0151:0.29243:0.1855:69970:rs75227206
1 1999345 rs28523801 T C . PASS AF=0.1387 ES:SE:LP:AF:SS:ID 0.0018:0.0165:0.0409586:0.1387:69970:rs28523801
1 1999680 rs28479788 G A . PASS AF=0.2332 ES:SE:LP:AF:SS:ID 0.009:0.0135:0.30103:0.2332:69970:rs28479788
1 1999840 rs13303005 C T . PASS AF=0.1354 ES:SE:LP:AF:SS:ID -0.004:0.0167:0.091515:0.1354:69970:rs13303005
1 2000592 rs2678939 C A . PASS AF=0.1754 ES:SE:LP:AF:SS:ID 0.0079:0.0149:0.229148:0.1754:69970:rs2678939
1 2001568 rs13303084 T C . PASS AF=0.1358 ES:SE:LP:AF:SS:ID -0.0054:0.0167:0.130768:0.1358:69970:rs13303084
1 2002502 rs13302898 C T . PASS AF=0.3012 ES:SE:LP:AF:SS:ID 0.0037:0.0125:0.119186:0.3012:69970:rs13302898
1 2004947 rs2678942 T C . PASS AF=0.3136 ES:SE:LP:AF:SS:ID 0.0022:0.0123:0.0705811:0.3136:69970:rs2678942
1 2005740 rs2459986 T C . PASS AF=0.1524 ES:SE:LP:AF:SS:ID -0.0003:0.0158:0.00877392:0.1524:69970:rs2459986
1 2006108 rs2254669 A G . PASS AF=0.3133 ES:SE:LP:AF:SS:ID 0.0044:0.0123:0.148742:0.3133:69970:rs2254669
1 2006127 rs16824697 G A . PASS AF=0.1706 ES:SE:LP:AF:SS:ID 0.0063:0.0151:0.173925:0.1706:69970:rs16824697
1 2006728 rs2678943 T C . PASS AF=0.3814 ES:SE:LP:AF:SS:ID -0.0013:0.0117:0.0409586:0.3814:69970:rs2678943
1 2009551 rs72636371 C T . PASS AF=0.0496 ES:SE:LP:AF:SS:ID 0.0398:0.0263:0.886057:0.0496:69970:rs72636371
1 2011300 rs13303213 C T . PASS AF=0.1274 ES:SE:LP:AF:SS:ID -0.0011:0.0172:0.0268721:0.1274:69970:rs13303213
1 2014196 rs28436686 G A . PASS AF=0.2915 ES:SE:LP:AF:SS:ID -0.0004:0.0125:0.0132283:0.2915:69970:rs28436686
1 2024507 rs9729600 T C . PASS AF=0.0943 ES:SE:LP:AF:SS:ID -0.005:0.0195:0.102373:0.0943:69970:rs9729600
1 2025607 rs61775405 G A . PASS AF=0.118 ES:SE:LP:AF:SS:ID -0.0108:0.0177:0.267606:0.118:69970:rs61775405
1 2031342 rs2376802 T C . PASS AF=0.5123 ES:SE:LP:AF:SS:ID -0.0005:0.0114:0.0177288:0.5123:69970:rs2376802
1 2037383 rs4648619 T G . PASS AF=0.2879 ES:SE:LP:AF:SS:ID 0.0052:0.0126:0.173925:0.2879:69970:rs4648619
1 2038824 rs908743 G A . PASS AF=0.4922 ES:SE:LP:AF:SS:ID -0.001:0.0114:0.0362122:0.4922:69970:rs908743
1 2040096 rs6666117 T C . PASS AF=0.114 ES:SE:LP:AF:SS:ID -0.005:0.0182:0.107905:0.114:69970:rs6666117
1 2045380 rs3107144 C A . PASS AF=0.3966 ES:SE:LP:AF:SS:ID -0.0033:0.0116:0.113509:0.3966:69970:rs3107144
1 2047547 rs3107145 G A . PASS AF=0.399 ES:SE:LP:AF:SS:ID -0.0052:0.0116:0.187087:0.399:69970:rs3107145
1 2048729 rs3107147 G A . PASS AF=0.4019 ES:SE:LP:AF:SS:ID -0.0037:0.0116:0.124939:0.4019:69970:rs3107147