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-10/ieu-a-10.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-10/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 11:44:26 2020
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ieu-a-import/processed/ieu-a-10/ieu-a-10.vcf.gz ...
Read summary statistics for 13871 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, 13612 SNPs remain.
After merging with regression SNP LD, 13612 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: 10.0915 (1.6126)
Lambda GC: 7.6358
Mean Chi^2: 11.971
Intercept: 3.2089 (0.3645)
Ratio: 0.2013 (0.0332)
Analysis finished at Wed Feb 5 11:44:34 2020
Total time elapsed: 7.65sQC metrics
Metrics
Metrics
{
"af_correlation": 0.8771,
"inflation_factor": 23.837,
"mean_EFFECT": 0.0037,
"n": 33000,
"n_snps": 13871,
"n_clumped_hits": 103,
"n_p_sig": 2860,
"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": 90,
"n_est": 33209.6952,
"ratio_se_n": 1.0032,
"mean_diff": -0.0018,
"ratio_diff": 11.7477,
"sd_y_est1": 2.1178,
"sd_y_est2": 2.1245,
"r2_sum1": 0.7904,
"r2_sum2": 0.1762,
"r2_sum3": 0.1751,
"r2_sum4": 0.1675,
"ldsc_nsnp_merge_refpanel_ld": 13612,
"ldsc_nsnp_merge_regression_ld": 13612,
"ldsc_observed_scale_h2_beta": 10.0915,
"ldsc_observed_scale_h2_se": 1.6126,
"ldsc_intercept_beta": 3.2089,
"ldsc_intercept_se": 0.3645,
"ldsc_lambda_gc": 7.6358,
"ldsc_mean_chisq": 11.971,
"ldsc_ratio": 0.2013
}
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 | 21 | 0 | 13871 | 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 | 8.739601e+00 | 6.132504e+00 | 1.00000e+00 | 3.000000e+00 | 7.000000e+00 | 1.300000e+01 | 2.200000e+01 | ▇▃▃▂▂ |
| numeric | POS | 0 | 1.0000000 | NA | NA | NA | NA | NA | 7.907289e+07 | 5.562832e+07 | 1.91843e+05 | 3.796476e+07 | 6.383187e+07 | 1.186147e+08 | 2.477826e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | 3.693100e-03 | 6.983370e-02 | -8.63698e-01 | -3.536710e-02 | 1.000500e-03 | 4.114190e-02 | 5.658820e-01 | ▁▁▆▇▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | 2.109830e-02 | 1.222560e-02 | 1.62898e-02 | 1.698060e-02 | 1.840000e-02 | 2.165480e-02 | 6.547000e-01 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | 5.605430e-02 | 1.449197e-01 | 0.00000e+00 | 4.000000e-07 | 9.910000e-04 | 3.291020e-02 | 9.998000e-01 | ▇▁▁▁▁ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | 2.345702e-01 | 3.014165e-01 | 0.00000e+00 | 2.527000e-04 | 6.494150e-02 | 4.227683e-01 | 1.000000e+00 | ▇▂▁▁▁ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | 4.001287e-01 | 2.352702e-01 | 2.62000e-04 | 2.070000e-01 | 3.590000e-01 | 5.805000e-01 | 9.910000e-01 | ▆▇▆▅▂ |
| numeric | AF_reference | 90 | 0.9935116 | NA | NA | NA | NA | NA | 3.924793e-01 | 2.304843e-01 | 4.59260e-03 | 1.976840e-01 | 3.590260e-01 | 5.634980e-01 | 9.938100e-01 | ▇▇▆▅▂ |
| numeric | N | 0 | 1.0000000 | NA | NA | NA | NA | NA | 3.300000e+04 | 0.000000e+00 | 3.30000e+04 | 3.300000e+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 | 1135242 | rs9729550 | A | C | 0.0411419 | 0.0188099 | 0.0003160 | 0.0287245 | 0.2620 | 0.5519170 | 33000 |
| 1 | 1140435 | rs1815606 | G | T | 0.0353671 | 0.0177280 | 0.0002186 | 0.0460445 | 0.3100 | 0.7120610 | 33000 |
| 1 | 1152631 | rs11721 | C | A | 0.0852598 | 0.0237003 | 0.0000052 | 0.0003214 | 0.0781 | 0.1585460 | 33000 |
| 1 | 1163804 | rs7515488 | C | T | 0.0843411 | 0.0229304 | 0.0000004 | 0.0002349 | 0.1470 | 0.1869010 | 33000 |
| 1 | 1165310 | rs11260562 | G | A | 0.0610951 | 0.0352196 | 0.0004346 | 0.0827957 | 0.0546 | 0.1018370 | 33000 |
| 1 | 1173611 | rs6697886 | G | A | 0.0852598 | 0.0237003 | 0.0000016 | 0.0003214 | 0.1310 | 0.2206470 | 33000 |
| 1 | 1174282 | rs4970364 | T | C | 0.0741794 | 0.0212300 | 0.0000003 | 0.0004757 | 0.2400 | 0.6581470 | 33000 |
| 1 | 1192515 | rs7524470 | A | G | 0.0324672 | 0.0310406 | 0.0254900 | 0.2955799 | 0.0390 | 0.0834665 | 33000 |
| 1 | 1193723 | rs12072310 | A | C | 0.0324672 | 0.0310406 | 0.0099850 | 0.2955799 | 0.0510 | 0.1743210 | 33000 |
| 1 | 1194804 | rs11804831 | T | C | 0.0741794 | 0.0212300 | 0.0000006 | 0.0004757 | 0.1830 | 0.6859030 | 33000 |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 50312225 | rs9616380 | T | C | 0.0324672 | 0.0210098 | 0.0015270 | 0.1222650 | 0.197 | 0.2472040 | 33000 |
| 22 | 50313438 | rs9616388 | T | C | 0.0324672 | 0.0210098 | 0.0017920 | 0.1222650 | 0.196 | 0.2498000 | 33000 |
| 22 | 50314809 | rs9616393 | C | T | 0.0685928 | 0.0267604 | 0.0001076 | 0.0103706 | 0.117 | 0.1126200 | 33000 |
| 22 | 50317049 | rs7410318 | T | C | 0.0324672 | 0.0210098 | 0.0022690 | 0.1222650 | 0.199 | 0.2961260 | 33000 |
| 22 | 50317330 | rs7410537 | G | A | 0.0324672 | 0.0210109 | 0.0023810 | 0.1222846 | 0.195 | 0.2959270 | 33000 |
| 22 | 50318946 | rs9616217 | C | T | 0.0324672 | 0.0210109 | 0.0027780 | 0.1222846 | 0.202 | 0.2959270 | 33000 |
| 22 | 50319170 | rs11545763 | A | G | 0.0685928 | 0.0267600 | 0.0000798 | 0.0103695 | 0.108 | 0.0674920 | 33000 |
| 22 | 50321623 | rs7287579 | C | T | 0.0324672 | 0.0210109 | 0.0043390 | 0.1222846 | 0.219 | 0.3438500 | 33000 |
| 22 | 50333415 | rs4073387 | G | A | 0.0685928 | 0.0267604 | 0.0000842 | 0.0103706 | 0.141 | 0.0898562 | 33000 |
| 22 | 51171693 | rs756638 | G | A | 0.0049875 | 0.0202795 | 0.1626002 | 0.8057287 | 0.277 | 0.3049120 | 33000 |
bcf preview
1 1135242 rs9729550 A C . PASS AF=0.262 ES:SE:LP:AF:SS:ID 0.0411419:0.0188099:3.50031:0.262:33000:rs9729550
1 1140435 rs1815606 G T . PASS AF=0.31 ES:SE:LP:AF:SS:ID 0.0353671:0.017728:3.66035:0.31:33000:rs1815606
1 1152631 rs11721 C A . PASS AF=0.0781 ES:SE:LP:AF:SS:ID 0.0852598:0.0237003:5.28693:0.0781:33000:rs11721
1 1163804 rs7515488 C T . PASS AF=0.147 ES:SE:LP:AF:SS:ID 0.0843411:0.0229304:6.44129:0.147:33000:rs7515488
1 1165310 rs11260562 G A . PASS AF=0.0546 ES:SE:LP:AF:SS:ID 0.0610951:0.0352196:3.36191:0.0546:33000:rs11260562
1 1173611 rs6697886 G A . PASS AF=0.131 ES:SE:LP:AF:SS:ID 0.0852598:0.0237003:5.8041:0.131:33000:rs6697886
1 1174282 rs4970364 T C . PASS AF=0.24 ES:SE:LP:AF:SS:ID 0.0741794:0.02123:6.55752:0.24:33000:rs4970364
1 1192515 rs7524470 A G . PASS AF=0.039 ES:SE:LP:AF:SS:ID 0.0324672:0.0310406:1.59363:0.039:33000:rs7524470
1 1193723 rs12072310 A C . PASS AF=0.051 ES:SE:LP:AF:SS:ID 0.0324672:0.0310406:2.00065:0.051:33000:rs12072310
1 1194804 rs11804831 T C . PASS AF=0.183 ES:SE:LP:AF:SS:ID 0.0741794:0.02123:6.23121:0.183:33000:rs11804831
1 1201640 rs11260570 T C . PASS AF=0.039 ES:SE:LP:AF:SS:ID 0.0324672:0.0310406:1.61852:0.039:33000:rs11260570
1 1206343 rs6667923 C A . PASS AF=0.0354 ES:SE:LP:AF:SS:ID 0.0324672:0.0310408:1.58137:0.0354:33000:rs6667923
1 1210471 rs11260577 T C . PASS AF=0.078 ES:SE:LP:AF:SS:ID 0.0324672:0.0310406:1.88107:0.078:33000:rs11260577
1 1218086 rs6603788 C T . PASS AF=0.0732 ES:SE:LP:AF:SS:ID 0.0324672:0.0310408:1.89722:0.0732:33000:rs6603788
1 1232319 rs604618 G A . PASS AF=0.0711 ES:SE:LP:AF:SS:ID 0.0440169:0.0316288:2.46168:0.0711:33000:rs604618
1 1233941 rs1739855 T C . PASS AF=0.072 ES:SE:LP:AF:SS:ID 0.0440169:0.0316299:2.55253:0.072:33000:rs1739855
1 1237036 rs2477782 T C . PASS AF=0.074 ES:SE:LP:AF:SS:ID 0.0440169:0.0316299:2.59125:0.074:33000:rs2477782
1 1239339 rs11260584 T G . PASS AF=0.9342 ES:SE:LP:AF:SS:ID -0.0440169:0.0316288:2.55862:0.9342:33000:rs11260584
1 1247494 rs12103 T C . PASS AF=0.818 ES:SE:LP:AF:SS:ID -0.0843411:0.0211594:7.15727:0.818:33000:rs12103
1 1249187 rs12142199 G A . PASS AF=0.811 ES:SE:LP:AF:SS:ID -0.0620354:0.0206799:5.19132:0.811:33000:rs12142199
1 1254136 rs4970432 G A . PASS AF=0.931 ES:SE:LP:AF:SS:ID -0.0440169:0.0316299:2.46878:0.931:33000:rs4970432
1 1257593 rs1810745 A G . PASS AF=0.9469 ES:SE:LP:AF:SS:ID -0.0440169:0.0316288:1.6751:0.9469:33000:rs1810745
1 1258246 rs3845293 A C . PASS AF=0.9345 ES:SE:LP:AF:SS:ID -0.0440169:0.0316288:2.44117:0.9345:33000:rs3845293
1 1287127 rs307380 G A . PASS AF=0.933 ES:SE:LP:AF:SS:ID -0.0440169:0.0316299:2.38796:0.933:33000:rs307380
1 1295323 rs34389364 G A . PASS AF=0.929 ES:SE:LP:AF:SS:ID -0.0440169:0.0316299:2.37049:0.929:33000:rs34389364
1 1295403 rs34876581 T C . PASS AF=0.9326 ES:SE:LP:AF:SS:ID -0.0440169:0.0316288:2.37161:0.9326:33000:rs34876581
1 1297422 rs2765021 C T . PASS AF=0.932 ES:SE:LP:AF:SS:ID -0.0440169:0.0316299:2.36151:0.932:33000:rs2765021
1 1314015 rs2649588 C T . PASS AF=0.927 ES:SE:LP:AF:SS:ID -0.0440169:0.0316299:2.28904:0.927:33000:rs2649588
1 1335790 rs1240708 A G . PASS AF=0.168 ES:SE:LP:AF:SS:ID 0.0723207:0.0216802:5.79697:0.168:33000:rs1240708
1 1365570 rs1695824 A C . PASS AF=0.218 ES:SE:LP:AF:SS:ID 0.0723207:0.0216802:5.32157:0.218:33000:rs1695824
1 1366830 rs873927 A G . PASS AF=0.218 ES:SE:LP:AF:SS:ID 0.0723207:0.0216802:5.32157:0.218:33000:rs873927
1 1665613 rs4648764 A G . PASS AF=0.457 ES:SE:LP:AF:SS:ID 0.0236781:0.01648:2.31336:0.457:33000:rs4648764
1 1684472 rs7407 C T . PASS AF=0.507 ES:SE:LP:AF:SS:ID -0.0236781:0.01648:2.39287:0.507:33000:rs7407
1 1687625 rs34306661 T C . PASS AF=0.436 ES:SE:LP:AF:SS:ID 0.0236781:0.01648:2.52014:0.436:33000:rs34306661
1 1689164 rs2235536 C T . PASS AF=0.424 ES:SE:LP:AF:SS:ID 0.0236781:0.0164812:2.49812:0.424:33000:rs2235536
1 1689762 rs2235537 T C . PASS AF=0.439 ES:SE:LP:AF:SS:ID 0.0236781:0.01648:2.54046:0.439:33000:rs2235537
1 1691050 rs35672141 C T . PASS AF=0.487 ES:SE:LP:AF:SS:ID -0.0236781:0.01648:2.4593:0.487:33000:rs35672141
1 1692321 rs1107910 T C . PASS AF=0.448 ES:SE:LP:AF:SS:ID 0.0236781:0.01648:2.58788:0.448:33000:rs1107910
1 1707740 rs12041925 T G . PASS AF=0.262 ES:SE:LP:AF:SS:ID 0.0188218:0.0184796:1.82102:0.262:33000:rs12041925
1 1708801 rs12044597 A G . PASS AF=0.497 ES:SE:LP:AF:SS:ID -0.0236781:0.0164812:2.74642:0.497:33000:rs12044597
1 1712230 rs10907194 C T . PASS AF=0.261 ES:SE:LP:AF:SS:ID 0.0188218:0.0184812:1.77806:0.261:33000:rs10907194
1 1713869 rs7534617 T G . PASS AF=0.263 ES:SE:LP:AF:SS:ID 0.0188218:0.0184796:1.78861:0.263:33000:rs7534617
1 1715011 rs742359 C A . PASS AF=0.259 ES:SE:LP:AF:SS:ID 0.0188218:0.0184812:1.77806:0.259:33000:rs742359
1 1721479 rs2272908 C T . PASS AF=0.498 ES:SE:LP:AF:SS:ID -0.0236781:0.01648:2.7948:0.498:33000:rs2272908
1 1722932 rs3737628 C T . PASS AF=0.496 ES:SE:LP:AF:SS:ID -0.0258308:0.0164797:2.94424:0.496:33000:rs3737628
1 1723031 rs9660180 G A . PASS AF=0.494 ES:SE:LP:AF:SS:ID -0.0258308:0.0164797:2.90728:0.494:33000:rs9660180
1 1748734 rs2180311 T C . PASS AF=0.498 ES:SE:LP:AF:SS:ID -0.0236781:0.0164812:2.77006:0.498:33000:rs2180311
1 1759054 rs10907187 G A . PASS AF=0.261 ES:SE:LP:AF:SS:ID 0.0188218:0.0184812:1.79263:0.261:33000:rs10907187
1 1759213 rs9786942 A G . PASS AF=0.498 ES:SE:LP:AF:SS:ID -0.0236781:0.0164812:2.70224:0.498:33000:rs9786942
1 1776269 rs4648727 C A . PASS AF=0.452 ES:SE:LP:AF:SS:ID 0.0236781:0.0164812:2.42713:0.452:33000:rs4648727