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-9/ieu-a-9.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-9/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 11:33:08 2020
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ieu-a-import/processed/ieu-a-9/ieu-a-9.vcf.gz ...
Read summary statistics for 76933 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, 51681 SNPs remain.
After merging with regression SNP LD, 51681 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: 0.2129 (0.0496)
Lambda GC: 1.3162
Mean Chi^2: 1.5048
Intercept: 1.077 (0.0338)
Ratio: 0.1525 (0.0669)
Analysis finished at Wed Feb 5 11:33:17 2020
Total time elapsed: 9.15sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9306,
"inflation_factor": 1.3123,
"mean_EFFECT": 0.0005,
"n": 107418,
"n_snps": 76933,
"n_clumped_hits": 10,
"n_p_sig": 109,
"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": 501,
"n_est": 102952.1511,
"ratio_se_n": 0.979,
"mean_diff": -0.0003,
"ratio_diff": 237.1447,
"sd_y_est1": 2.6477,
"sd_y_est2": 2.592,
"r2_sum1": 0.0447,
"r2_sum2": 0.0064,
"r2_sum3": 0.0067,
"r2_sum4": 0.0063,
"ldsc_nsnp_merge_refpanel_ld": 51681,
"ldsc_nsnp_merge_regression_ld": 51681,
"ldsc_observed_scale_h2_beta": 0.2129,
"ldsc_observed_scale_h2_se": 0.0496,
"ldsc_intercept_beta": 1.077,
"ldsc_intercept_se": 0.0338,
"ldsc_lambda_gc": 1.3162,
"ldsc_mean_chisq": 1.5048,
"ldsc_ratio": 0.1525
}
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 | 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 | numeric.mean | numeric.sd | numeric.p0 | numeric.p25 | numeric.p50 | numeric.p75 | numeric.p100 | numeric.hist |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| character | ID | 0 | 1.0000000 | 5 | 22 | 0 | 76933 | 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 | 8.553664e+00 | 5.571715e+00 | 1.00000e+00 | 4.000000e+00 | 8.000000e+00 | 1.200000e+01 | 2.200000e+01 | ▇▆▅▃▂ |
| numeric | POS | 0 | 1.0000000 | NA | NA | NA | NA | NA | 7.599609e+07 | 5.523722e+07 | 4.56530e+04 | 2.894011e+07 | 6.453400e+07 | 1.120635e+08 | 2.491546e+08 | ▇▆▃▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | 5.488000e-04 | 2.106180e-02 | -1.48845e-01 | -1.114900e-02 | 4.460000e-04 | 1.222400e-02 | 1.994100e-01 | ▁▃▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | 1.656470e-02 | 6.963700e-03 | 1.08120e-02 | 1.183300e-02 | 1.390800e-02 | 1.858600e-02 | 1.038830e-01 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | 4.550781e-01 | 3.001037e-01 | 0.00000e+00 | 1.862332e-01 | 4.397279e-01 | 7.145934e-01 | 9.999820e-01 | ▇▆▆▆▅ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | 4.550649e-01 | 3.001045e-01 | 0.00000e+00 | 1.862373e-01 | 4.396935e-01 | 7.145217e-01 | 1.000000e+00 | ▇▆▆▆▅ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | 3.465507e-01 | 2.596724e-01 | 1.03020e-02 | 1.242660e-01 | 2.775850e-01 | 5.257970e-01 | 9.886510e-01 | ▇▅▃▂▂ |
| numeric | AF_reference | 501 | 0.9934878 | NA | NA | NA | NA | NA | 3.468647e-01 | 2.527169e-01 | 9.98400e-04 | 1.367810e-01 | 2.807510e-01 | 5.181710e-01 | 9.964060e-01 | ▇▆▃▂▂ |
| numeric | N | 0 | 1.0000000 | NA | NA | NA | NA | NA | 1.017710e+05 | 8.534607e+03 | 3.97180e+04 | 1.005170e+05 | 1.052130e+05 | 1.070290e+05 | 1.074180e+05 | ▁▁▁▁▇ |
Head and tail
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 1706136 | rs6603811 | T | C | -0.005703 | 0.018393 | 0.7565158 | 0.7565126 | 0.916669 | 0.7466050 | 102472 |
| 1 | 1706160 | rs7531583 | A | G | -0.010088 | 0.012280 | 0.4113287 | 0.4113625 | 0.732961 | 0.5832670 | 106922 |
| 1 | 1708801 | rs12044597 | A | G | -0.023707 | 0.011330 | 0.0364259 | 0.0364019 | 0.481177 | 0.3602240 | 102702 |
| 1 | 1723031 | rs9660180 | G | A | -0.017866 | 0.012300 | 0.1463420 | 0.1463570 | 0.480706 | 0.3580270 | 92359 |
| 1 | 1812688 | rs6603803 | A | G | -0.019260 | 0.011335 | 0.0892997 | 0.0892887 | 0.480130 | 0.3606230 | 103550 |
| 1 | 1823922 | rs6688000 | A | G | -0.005784 | 0.018682 | 0.7568887 | 0.7568630 | 0.902267 | 0.7651760 | 103707 |
| 1 | 2069172 | rs425277 | C | T | -0.020675 | 0.012189 | 0.0898442 | 0.0898477 | 0.279653 | 0.2252400 | 105073 |
| 1 | 2180524 | rs260513 | G | A | -0.022537 | 0.013042 | 0.0839847 | 0.0839824 | 0.454835 | 0.4191290 | 69960 |
| 1 | 2182342 | rs10797416 | T | C | -0.036504 | 0.011511 | 0.0015260 | 0.0015180 | 0.383879 | 0.2486020 | 105190 |
| 1 | 2184855 | rs12385717 | G | C | 0.026027 | 0.021742 | 0.2312352 | 0.2312738 | 0.067124 | 0.0613019 | 101895 |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 50588313 | rs12484907 | G | A | -0.019081 | 0.021606 | 0.3771551 | 0.3771637 | 0.074848 | 0.0814696 | 102817 |
| 22 | 50661869 | rs5771242 | T | C | 0.004795 | 0.013569 | 0.7237874 | 0.7238043 | 0.763453 | 0.8512380 | 100234 |
| 22 | 50858813 | rs7410608 | G | C | 0.010613 | 0.011649 | 0.3622547 | 0.3622610 | 0.357772 | 0.4916130 | 100085 |
| 22 | 50873694 | rs2273259 | T | C | 0.014193 | 0.013765 | 0.3024851 | 0.3024970 | 0.347396 | 0.4802320 | 76193 |
| 22 | 51015674 | rs3213446 | C | T | 0.004544 | 0.021834 | 0.8351250 | 0.8351386 | 0.057046 | 0.1859030 | 98772 |
| 22 | 51018911 | rs2269382 | C | T | 0.000636 | 0.023589 | 0.9784850 | 0.9784903 | 0.051347 | 0.1222040 | 91326 |
| 22 | 51062832 | rs8142033 | G | A | 0.008647 | 0.019318 | 0.6544538 | 0.6544321 | 0.095726 | 0.1773160 | 100607 |
| 22 | 51163138 | rs715586 | C | T | 0.014420 | 0.016630 | 0.3858886 | 0.3858830 | 0.148083 | 0.0902556 | 95129 |
| 22 | 51165664 | rs8137951 | G | A | 0.018693 | 0.012073 | 0.1215189 | 0.1215426 | 0.313417 | 0.4063500 | 101063 |
| 22 | 51178090 | rs2285395 | G | A | 0.021418 | 0.025456 | 0.4001410 | 0.4001388 | 0.064712 | 0.0666933 | 97693 |
bcf preview
1 1706136 rs6603811 T C . PASS AF=0.916669 ES:SE:LP:AF:SS:ID -0.005703:0.018393:0.121182:0.916669:102472:rs6603811
1 1706160 rs7531583 A G . PASS AF=0.732961 ES:SE:LP:AF:SS:ID -0.010088:0.01228:0.385811:0.732961:106922:rs7531583
1 1708801 rs12044597 A G . PASS AF=0.481177 ES:SE:LP:AF:SS:ID -0.023707:0.01133:1.43859:0.481177:102702:rs12044597
1 1723031 rs9660180 G A . PASS AF=0.480706 ES:SE:LP:AF:SS:ID -0.017866:0.0123:0.834631:0.480706:92359:rs9660180
1 1812688 rs6603803 A G . PASS AF=0.48013 ES:SE:LP:AF:SS:ID -0.01926:0.011335:1.04915:0.48013:103550:rs6603803
1 1823922 rs6688000 A G . PASS AF=0.902267 ES:SE:LP:AF:SS:ID -0.005784:0.018682:0.120968:0.902267:103707:rs6688000
1 2069172 rs425277 C T . PASS AF=0.279653 ES:SE:LP:AF:SS:ID -0.020675:0.012189:1.04651:0.279653:105073:rs425277
1 2180524 rs260513 G A . PASS AF=0.454835 ES:SE:LP:AF:SS:ID -0.022537:0.013042:1.0758:0.454835:69960:rs260513
1 2182342 rs10797416 T C . PASS AF=0.383879 ES:SE:LP:AF:SS:ID -0.036504:0.011511:2.81645:0.383879:105190:rs10797416
1 2184855 rs12385717 G C . PASS AF=0.067124 ES:SE:LP:AF:SS:ID 0.026027:0.021742:0.635946:0.067124:101895:rs12385717
1 2224645 rs2645091 A G . PASS AF=0.844979 ES:SE:LP:AF:SS:ID -0.008355:0.015216:0.234341:0.844979:104081:rs2645091
1 2280661 rs2055204 G A . PASS AF=0.554667 ES:SE:LP:AF:SS:ID -0.003397:0.011345:0.116553:0.554667:100866:rs2055204
1 2291680 rs2840542 T C . PASS AF=0.804127 ES:SE:LP:AF:SS:ID -0.021078:0.013963:0.882275:0.804127:105093:rs2840542
1 2490942 rs2281852 C A . PASS AF=0.493853 ES:SE:LP:AF:SS:ID 0.013811:0.011251:0.658314:0.493853:103593:rs2281852
1 2723345 rs4648360 C T . PASS AF=0.429605 ES:SE:LP:AF:SS:ID -0.010552:0.011598:0.440177:0.429605:102005:rs4648360
1 2725952 rs7538063 C T . PASS AF=0.167641 ES:SE:LP:AF:SS:ID -0.003199:0.015248:0.0789339:0.167641:93055:rs7538063
1 2839757 rs4648432 A C . PASS AF=0.275602 ES:SE:LP:AF:SS:ID -0.004086:0.012139:0.132882:0.275602:105430:rs4648432
1 2842270 rs2045331 G A . PASS AF=0.281395 ES:SE:LP:AF:SS:ID -0.006242:0.012142:0.21664:0.281395:105443:rs2045331
1 2904056 rs10797373 G A . PASS AF=0.676313 ES:SE:LP:AF:SS:ID 0.024131:0.014042:1.06689:0.676313:65904:rs10797373
1 2996196 rs7525173 C G . PASS AF=0.528517 ES:SE:LP:AF:SS:ID -0.03125:0.012825:1.82854:0.528517:86667:rs7525173
1 3089849 rs17390062 T C . PASS AF=0.099318 ES:SE:LP:AF:SS:ID -0.062093:0.023127:2.13829:0.099318:90616:rs17390062
1 3103826 rs4233024 C T . PASS AF=0.860065 ES:SE:LP:AF:SS:ID 0.015461:0.018121:0.405017:0.860065:68587:rs4233024
1 3105276 rs2817138 T G . PASS AF=0.863697 ES:SE:LP:AF:SS:ID 0.010506:0.015309:0.307528:0.863697:105041:rs2817138
1 3144068 rs10158583 G A . PASS AF=0.080834 ES:SE:LP:AF:SS:ID -0.006947:0.01907:0.145305:0.080834:92775:rs10158583
1 3144745 rs1553288 C T . PASS AF=0.07014 ES:SE:LP:AF:SS:ID -0.01345:0.019976:0.30036:0.07014:101908:rs1553288
1 3197747 rs10492940 C A . PASS AF=0.1364 ES:SE:LP:AF:SS:ID -0.001132:0.016648:0.024208:0.1364:103741:rs10492940
1 3209631 rs4648376 G A . PASS AF=0.552998 ES:SE:LP:AF:SS:ID 0.006799:0.011091:0.26769:0.552998:104734:rs4648376
1 3209767 rs2500294 C T . PASS AF=0.564631 ES:SE:LP:AF:SS:ID 0.001176:0.011121:0.0382131:0.564631:104718:rs2500294
1 3214170 rs7553849 T C . PASS AF=0.768712 ES:SE:LP:AF:SS:ID -0.002031:0.013439:0.0555854:0.768712:98586:rs7553849
1 3229185 rs12743017 G A . PASS AF=0.092478 ES:SE:LP:AF:SS:ID 0.001227:0.020394:0.0213644:0.092478:102072:rs12743017
1 3308034 rs2493275 G A . PASS AF=0.096886 ES:SE:LP:AF:SS:ID 0.002855:0.019977:0.0523918:0.096886:105187:rs2493275
1 3328659 rs2493292 C T . PASS AF=0.131784 ES:SE:LP:AF:SS:ID 0.033189:0.016625:1.33813:0.131784:104050:rs2493292
1 3684321 rs1181871 A G . PASS AF=0.568872 ES:SE:LP:AF:SS:ID 0.020171:0.011517:1.09763:0.568872:103671:rs1181871
1 3691727 rs1175549 A C . PASS AF=0.234088 ES:SE:LP:AF:SS:ID 0.008186:0.013144:0.27291:0.234088:105217:rs1175549
1 3765424 rs10492943 G T . PASS AF=0.15856 ES:SE:LP:AF:SS:ID -0.029387:0.015333:1.25731:0.15856:106799:rs10492943
1 4039896 rs11587331 C T . PASS AF=0.565714 ES:SE:LP:AF:SS:ID -0.001518:0.010958:0.0507135:0.565714:102719:rs11587331
1 4080885 rs4378174 C T . PASS AF=0.143027 ES:SE:LP:AF:SS:ID -0.002217:0.015742:0.0516086:0.143027:102544:rs4378174
1 4116604 rs3856168 T C . PASS AF=0.917108 ES:SE:LP:AF:SS:ID 0.0227:0.018916:0.638108:0.917108:105557:rs3856168
1 4122464 rs10799180 T C . PASS AF=0.85731 ES:SE:LP:AF:SS:ID 0.017747:0.016131:0.566659:0.85731:95214:rs10799180
1 4272139 rs12402543 T A . PASS AF=0.157244 ES:SE:LP:AF:SS:ID -0.005922:0.016107:0.14684:0.157244:105526:rs12402543
1 4367810 rs17448695 C T . PASS AF=0.084644 ES:SE:LP:AF:SS:ID 0.005679:0.022592:0.0960873:0.084644:97967:rs17448695
1 4407985 rs523634 A G . PASS AF=0.228002 ES:SE:LP:AF:SS:ID 0.019702:0.012849:0.902462:0.228002:107245:rs523634
1 4444706 rs349413 C A . PASS AF=0.737873 ES:SE:LP:AF:SS:ID -0.010374:0.01238:0.395716:0.737873:103647:rs349413
1 4546183 rs350173 C T . PASS AF=0.082671 ES:SE:LP:AF:SS:ID -0.01227:0.019933:0.269065:0.082671:100341:rs350173
1 4551679 rs780614 T C . PASS AF=0.234786 ES:SE:LP:AF:SS:ID 0.011729:0.013074:0.43224:0.234786:106289:rs780614
1 4573099 rs4480324 A G . PASS AF=0.704196 ES:SE:LP:AF:SS:ID 0.004576:0.012251:0.14949:0.704196:106973:rs4480324
1 4640902 rs1024139 T C . PASS AF=0.188085 ES:SE:LP:AF:SS:ID 0.015822:0.014097:0.582191:0.188085:98543:rs1024139
1 4641799 rs241224 G T . PASS AF=0.925445 ES:SE:LP:AF:SS:ID -0.015434:0.022093:0.314433:0.925445:106954:rs241224
1 4666494 rs164780 C G . PASS AF=0.689663 ES:SE:LP:AF:SS:ID -0.012391:0.012941:0.470703:0.689663:96882:rs164780
1 4672755 rs6703429 T C . PASS AF=0.269433 ES:SE:LP:AF:SS:ID -0.030312:0.015394:1.31012:0.269433:81144:rs6703429