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/ukb-a-import/processed/ukb-a-66/ukb-a-66.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/ukb-a-import/processed/ukb-a-66/ldsc.txt \
--w-ld-chr /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/
Beginning analysis at Sun Feb 16 03:00:05 2020
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-a-import/processed/ukb-a-66/ukb-a-66.vcf.gz ...
Read summary statistics for 10877936 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, 1281483 SNPs remain.
After merging with regression SNP LD, 1281483 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0531 (0.0044)
Lambda GC: 1.2426
Mean Chi^2: 1.406
Intercept: 1.0447 (0.0105)
Ratio: 0.1101 (0.0259)
Analysis finished at Sun Feb 16 03:01:54 2020
Total time elapsed: 1.0m:49.25sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9533,
"inflation_factor": 1.1515,
"mean_EFFECT": 4.0182e-06,
"n": 337159,
"n_snps": 10877936,
"n_clumped_hits": 73,
"n_p_sig": 13530,
"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": 356865,
"n_est": 335660.7007,
"ratio_se_n": 0.9978,
"mean_diff": 7.9242e-06,
"ratio_diff": 14.0704,
"sd_y_est1": 0.322,
"sd_y_est2": 0.3213,
"r2_sum1": 0.0017,
"r2_sum2": 0.0159,
"r2_sum3": 0.016,
"r2_sum4": 0.0158,
"ldsc_nsnp_merge_refpanel_ld": 1281483,
"ldsc_nsnp_merge_regression_ld": 1281483,
"ldsc_observed_scale_h2_beta": 0.0531,
"ldsc_observed_scale_h2_se": 0.0044,
"ldsc_intercept_beta": 1.0447,
"ldsc_intercept_se": 0.0105,
"ldsc_lambda_gc": 1.2426,
"ldsc_mean_chisq": 1.406,
"ldsc_ratio": 0.1101
}
Flags
| name | value |
|---|---|
| af_correlation | FALSE |
| inflation_factor | FALSE |
| 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 | 58 | 0 | 10877936 | 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 | 8.598638e+00 | 5.745666e+00 | 1.00000e+00 | 4.000000e+00 | 8.000000e+00 | 1.300000e+01 | 2.200000e+01 | ▇▅▃▂▂ |
| numeric | POS | 0 | 1.0000000 | NA | NA | NA | NA | NA | 7.911383e+07 | 5.624931e+07 | 8.28000e+02 | 3.287294e+07 | 6.988889e+07 | 1.148258e+08 | 2.492251e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | 4.000000e-06 | 4.743800e-03 | -5.47030e-02 | -1.378300e-03 | -5.500000e-06 | 1.365800e-03 | 7.651230e-02 | ▁▂▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | 3.377700e-03 | 3.199100e-03 | 7.74900e-04 | 9.571000e-04 | 1.838200e-03 | 4.912000e-03 | 1.760730e-02 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | 4.764562e-01 | 2.955466e-01 | 0.00000e+00 | 2.153401e-01 | 4.691957e-01 | 7.324585e-01 | 1.000000e+00 | ▇▇▆▆▆ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | 4.764557e-01 | 2.955469e-01 | 0.00000e+00 | 2.153395e-01 | 4.691955e-01 | 7.324578e-01 | 9.999999e-01 | ▇▇▆▆▆ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | 1.820865e-01 | 2.503832e-01 | 1.00090e-03 | 6.878700e-03 | 5.191440e-02 | 2.739880e-01 | 9.989990e-01 | ▇▂▁▁▁ |
| numeric | AF_reference | 356865 | 0.9671937 | NA | NA | NA | NA | NA | 1.877709e-01 | 2.439191e-01 | 0.00000e+00 | 5.191700e-03 | 7.468050e-02 | 2.873400e-01 | 1.000000e+00 | ▇▂▁▁▁ |
| numeric | N | 0 | 1.0000000 | NA | NA | NA | NA | NA | 3.371590e+05 | 0.000000e+00 | 3.37159e+05 | 3.371590e+05 | 3.371590e+05 | 3.371590e+05 | 3.371590e+05 | ▁▁▇▁▁ |
Head and tail
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 693731 | rs12238997 | A | G | -0.0013917 | 0.0012935 | 0.2819668 | 0.2819671 | 0.1169260 | 0.1417730 | 337159 |
| 1 | 717587 | rs144155419 | G | A | -0.0011614 | 0.0034744 | 0.7381676 | 0.7381659 | 0.0143862 | 0.0045926 | 337159 |
| 1 | 730087 | rs148120343 | T | C | -0.0023596 | 0.0018024 | 0.1905031 | 0.1905007 | 0.0554733 | 0.0127796 | 337159 |
| 1 | 731718 | rs142557973 | T | C | -0.0021501 | 0.0012271 | 0.0797554 | 0.0797555 | 0.1216580 | 0.1543530 | 337159 |
| 1 | 734349 | rs141242758 | T | C | -0.0020600 | 0.0012278 | 0.0934028 | 0.0934020 | 0.1215130 | 0.1525560 | 337159 |
| 1 | 740284 | rs61770167 | C | T | -0.0077371 | 0.0055893 | 0.1662769 | 0.1662767 | 0.0058556 | 0.0023962 | 337159 |
| 1 | 742813 | rs112573343 | C | T | -0.0151134 | 0.0105477 | 0.1518969 | 0.1518972 | 0.0015525 | 0.1030350 | 337159 |
| 1 | 753405 | rs3115860 | C | A | 0.0020456 | 0.0011666 | 0.0795170 | 0.0795163 | 0.8706760 | 0.7517970 | 337159 |
| 1 | 753541 | rs2073813 | G | A | -0.0018990 | 0.0011695 | 0.1044201 | 0.1044181 | 0.1288330 | 0.3019170 | 337159 |
| 1 | 754182 | rs3131969 | A | G | 0.0020338 | 0.0011653 | 0.0809506 | 0.0809503 | 0.8703180 | 0.6785140 | 337159 |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51219387 | rs9616832 | T | C | 0.0002868 | 0.0015025 | 0.8486070 | 0.8486063 | 0.0729806 | 0.0654952 | 337159 |
| 22 | 51219704 | rs147475742 | G | A | 0.0010015 | 0.0020140 | 0.6190133 | 0.6190150 | 0.0418225 | 0.0473243 | 337159 |
| 22 | 51219766 | rs182321900 | C | T | -0.0063908 | 0.0103547 | 0.5371109 | 0.5371118 | 0.0014546 | NA | 337159 |
| 22 | 51220088 | rs566371895 | G | A | 0.0005614 | 0.0067728 | 0.9339401 | 0.9339404 | 0.0039843 | 0.0003994 | 337159 |
| 22 | 51220146 | rs868950473 | C | T | -0.0100033 | 0.0101996 | 0.3267142 | 0.3267140 | 0.0015139 | NA | 337159 |
| 22 | 51221731 | rs115055839 | T | C | 0.0004803 | 0.0015034 | 0.7493729 | 0.7493731 | 0.0728101 | 0.0625000 | 337159 |
| 22 | 51223637 | rs375798137 | G | A | 0.0007423 | 0.0017711 | 0.6751387 | 0.6751386 | 0.0540289 | 0.0788738 | 337159 |
| 22 | 51226692 | rs150189434 | G | A | -0.0078030 | 0.0105900 | 0.4612273 | 0.4612257 | 0.0013983 | 0.0155751 | 337159 |
| 22 | 51229805 | rs9616985 | T | C | 0.0005481 | 0.0015091 | 0.7164370 | 0.7164361 | 0.0727671 | 0.0730831 | 337159 |
| 22 | 51237063 | rs3896457 | T | C | -0.0006850 | 0.0009184 | 0.4557535 | 0.4557522 | 0.2968410 | 0.2050720 | 337159 |
bcf preview
1 693731 rs12238997 A G . PASS AF=0.116926 ES:SE:LP:AF:SS:ID -0.00139168:0.00129349:0.549802:0.116926:337159:rs12238997
1 717587 rs144155419 G A . PASS AF=0.0143862 ES:SE:LP:AF:SS:ID -0.00116145:0.00347445:0.131845:0.0143862:337159:rs144155419
1 730087 rs148120343 T C . PASS AF=0.0554733 ES:SE:LP:AF:SS:ID -0.00235956:0.00180243:0.720098:0.0554733:337159:rs148120343
1 731718 rs58276399 T C . PASS AF=0.121658 ES:SE:LP:AF:SS:ID -0.00215008:0.00122714:1.09824:0.121658:337159:rs58276399
1 734349 rs141242758 T C . PASS AF=0.121513 ES:SE:LP:AF:SS:ID -0.00205997:0.00122784:1.02964:0.121513:337159:rs141242758
1 740284 rs61770167 C T . PASS AF=0.00585559 ES:SE:LP:AF:SS:ID -0.00773707:0.00558929:0.779168:0.00585559:337159:rs61770167
1 742813 rs112573343 C T . PASS AF=0.0015525 ES:SE:LP:AF:SS:ID -0.0151134:0.0105477:0.818451:0.0015525:337159:rs112573343
1 753405 rs3115860 C A . PASS AF=0.870676 ES:SE:LP:AF:SS:ID 0.00204558:0.00116657:1.09954:0.870676:337159:rs3115860
1 753541 rs2073813 G A . PASS AF=0.128833 ES:SE:LP:AF:SS:ID -0.00189902:0.00116949:0.981216:0.128833:337159:rs2073813
1 754182 rs3131969 A G . PASS AF=0.870318 ES:SE:LP:AF:SS:ID 0.00203375:0.00116534:1.09178:0.870318:337159:rs3131969
1 754192 rs3131968 A G . PASS AF=0.870409 ES:SE:LP:AF:SS:ID 0.00201359:0.00116589:1.07492:0.870409:337159:rs3131968
1 754334 rs3131967 T C . PASS AF=0.870037 ES:SE:LP:AF:SS:ID 0.00203162:0.00116529:1.09014:0.870037:337159:rs3131967
1 754433 rs150578204 G A . PASS AF=0.00465304 ES:SE:LP:AF:SS:ID -0.00450972:0.00594552:0.348579:0.00465304:337159:rs150578204
1 754458 rs142682604 G T . PASS AF=0.00464711 ES:SE:LP:AF:SS:ID -0.00443086:0.00596172:0.339751:0.00464711:337159:rs142682604
1 755435 rs184270342 T G . PASS AF=0.00590897 ES:SE:LP:AF:SS:ID -0.00673562:0.00580567:0.609107:0.00590897:337159:rs184270342
1 755890 rs3115858 A T . PASS AF=0.870329 ES:SE:LP:AF:SS:ID 0.00213895:0.00116327:1.18075:0.870329:337159:rs3115858
1 756604 rs3131962 A G . PASS AF=0.87002 ES:SE:LP:AF:SS:ID 0.00210711:0.00116048:1.15855:0.87002:337159:rs3131962
1 757640 rs3115853 G A . PASS AF=0.869421 ES:SE:LP:AF:SS:ID 0.00213997:0.00115937:1.18761:0.869421:337159:rs3115853
1 757734 rs4951929 C T . PASS AF=0.870219 ES:SE:LP:AF:SS:ID 0.0020984:0.00116144:1.14993:0.870219:337159:rs4951929
1 757936 rs4951862 C A . PASS AF=0.870219 ES:SE:LP:AF:SS:ID 0.00209789:0.00116152:1.14938:0.870219:337159:rs4951862
1 758144 rs3131956 A G . PASS AF=0.870222 ES:SE:LP:AF:SS:ID 0.00209166:0.00116156:1.1442:0.870222:337159:rs3131956
1 758626 rs3131954 C T . PASS AF=0.870396 ES:SE:LP:AF:SS:ID 0.00213325:0.00116454:1.17408:0.870396:337159:rs3131954
1 761732 rs2286139 C T . PASS AF=0.867743 ES:SE:LP:AF:SS:ID 0.00211833:0.00115899:1.17012:0.867743:337159:rs2286139
1 766007 rs61768174 A C . PASS AF=0.106958 ES:SE:LP:AF:SS:ID -0.00203716:0.00129049:0.94146:0.106958:337159:rs61768174
1 768253 rs2977608 A C . PASS AF=0.763932 ES:SE:LP:AF:SS:ID 0.000984979:0.00091963:0.546463:0.763932:337159:rs2977608
1 768448 rs12562034 G A . PASS AF=0.105257 ES:SE:LP:AF:SS:ID 0.000692348:0.00126952:0.232468:0.105257:337159:rs12562034
1 768819 rs12562811 C T . PASS AF=0.00692766 ES:SE:LP:AF:SS:ID 0.00167629:0.00471638:0.141297:0.00692766:337159:rs12562811
1 769223 rs60320384 C G . PASS AF=0.128651 ES:SE:LP:AF:SS:ID -0.0019453:0.00116775:1.0189:0.128651:337159:rs60320384
1 769224 rs141644775 G A . PASS AF=0.00143239 ES:SE:LP:AF:SS:ID 0.0245737:0.0109723:1.60004:0.00143239:337159:rs141644775
1 770181 rs146076599 A G . PASS AF=0.00907328 ES:SE:LP:AF:SS:ID -0.00374418:0.00447502:0.394942:0.00907328:337159:rs146076599
1 770377 rs112563271 A T . PASS AF=0.00692321 ES:SE:LP:AF:SS:ID 0.001692:0.00473687:0.142098:0.00692321:337159:rs112563271
1 771823 rs2977605 T C . PASS AF=0.869934 ES:SE:LP:AF:SS:ID 0.00199321:0.00116257:1.06328:0.869934:337159:rs2977605
1 771967 rs59066358 G A . PASS AF=0.128709 ES:SE:LP:AF:SS:ID -0.00186875:0.00116722:0.961094:0.128709:337159:rs59066358
1 772755 rs2905039 A C . PASS AF=0.870112 ES:SE:LP:AF:SS:ID 0.00196282:0.00116262:1.03924:0.870112:337159:rs2905039
1 774736 rs28830877 A C . PASS AF=0.998797 ES:SE:LP:AF:SS:ID 0.0168018:0.012078:0.784645:0.998797:337159:rs28830877
1 776556 rs151160018 C T . PASS AF=0.00851871 ES:SE:LP:AF:SS:ID -0.00375802:0.00436303:0.409989:0.00851871:337159:rs151160018
1 777122 rs2980319 A T . PASS AF=0.871122 ES:SE:LP:AF:SS:ID 0.00201763:0.0011646:1.07992:0.871122:337159:rs2980319
1 777232 rs112618790 C T . PASS AF=0.0961319 ES:SE:LP:AF:SS:ID 0.000489639:0.0013446:0.145242:0.0961319:337159:rs112618790
1 778745 rs1055606 A G . PASS AF=0.127871 ES:SE:LP:AF:SS:ID -0.00178769:0.00116843:0.899567:0.127871:337159:rs1055606
1 779322 rs4040617 A G . PASS AF=0.127984 ES:SE:LP:AF:SS:ID -0.00172687:0.00116685:0.857332:0.127984:337159:rs4040617
1 780785 rs2977612 T A . PASS AF=0.870455 ES:SE:LP:AF:SS:ID 0.00215293:0.00116269:1.19332:0.870455:337159:rs2977612
1 781367 rs149821290 A C . PASS AF=0.00989772 ES:SE:LP:AF:SS:ID -0.00146625:0.00413809:0.140807:0.00989772:337159:rs149821290
1 781845 rs61768199 A G . PASS AF=0.1044 ES:SE:LP:AF:SS:ID -0.00216122:0.00130747:1.00729:0.1044:337159:rs61768199
1 782721 rs185280546 G A . PASS AF=0.0143713 ES:SE:LP:AF:SS:ID -0.003836:0.00353896:0.555339:0.0143713:337159:rs185280546
1 782981 rs6594026 C T . PASS AF=0.128338 ES:SE:LP:AF:SS:ID -0.00196153:0.00116749:1.03183:0.128338:337159:rs6594026
1 783193 rs145767270 G C . PASS AF=0.0143639 ES:SE:LP:AF:SS:ID -0.00400262:0.00355397:0.584918:0.0143639:337159:rs145767270
1 783194 rs138555831 G T . PASS AF=0.0143713 ES:SE:LP:AF:SS:ID -0.00397804:0.00355354:0.580135:0.0143713:337159:rs138555831
1 783711 rs184266993 G A . PASS AF=0.00541372 ES:SE:LP:AF:SS:ID -0.00275276:0.00568154:0.202024:0.00541372:337159:rs184266993
1 785050 rs2905062 G A . PASS AF=0.87014 ES:SE:LP:AF:SS:ID 0.00208355:0.00116287:1.13563:0.87014:337159:rs2905062
1 785989 rs2980300 T C . PASS AF=0.87009 ES:SE:LP:AF:SS:ID 0.00210278:0.00116361:1.15031:0.87009:337159:rs2980300