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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"gwas_harmonisation_command": "--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/vcf_09_19b/bgzip_vcf/data.batch_41204_R634.vcf.gz --id UKB-b:13845 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_41204_R634.txt.gz --cohort_cases 2751 --cohort_controls 460259 --ref /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/human_g1k_v37.fasta --json /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/ukb_gwas.json; 1.1.1",
"file_date": "2019-09-13T09:47:18.225389",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-13845/ukb-b-13845.vcf.gz; Date=Sat May 9 23:09:08 2020"
}
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/public/UKB-b-13845/UKB-b-13845_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-13845/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:43:00 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-13845/UKB-b-13845_data.vcf.gz ...
Read summary statistics for 3855303 SNPs.
Dropped 683 SNPs with duplicated rs numbers.
Reading reference panel LD Score from ../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 ../reference/eur_w_ld_chr/[1-22] ...
Read regression weight LD Scores for 1290028 SNPs.
After merging with reference panel LD, 925241 SNPs remain.
After merging with regression SNP LD, 925241 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0036 (0.0011)
Lambda GC: 1.0438
Mean Chi^2: 1.0422
Intercept: 1.0055 (0.0086)
Ratio: 0.1314 (0.2044)
Analysis finished at Thu Oct 17 14:43:49 2019
Total time elapsed: 48.96sQC metrics
Metrics
Metrics
{
"af_correlation": 0.8692,
"inflation_factor": 1.0475,
"mean_EFFECT": -5.2135e-07,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 0,
"n_p_sig": 0,
"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": 31303,
"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": 925241,
"ldsc_nsnp_merge_regression_ld": 925241,
"ldsc_observed_scale_h2_beta": 0.0036,
"ldsc_observed_scale_h2_se": 0.0011,
"ldsc_intercept_beta": 1.0055,
"ldsc_intercept_se": 0.0086,
"ldsc_lambda_gc": 1.0438,
"ldsc_mean_chisq": 1.0422,
"ldsc_ratio": 0.1303
}
Flags
| name | value |
|---|---|
| af_correlation | FALSE |
| inflation_factor | FALSE |
| n | TRUE |
| is_snpid_non_unique | FALSE |
| 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 | 3854623 | 0 | NA | NA | 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 | NA | NA |
| character | ALT | 0 | 1.0000000 | 1 | 1 | 0 | 4 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| logical | N | 3855303 | 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 | 8.661047e+00 | 5.767784e+00 | 1.0000000 | 4.000000e+00 | 8.000000e+00 | 1.300000e+01 | 2.200000e+01 | ▇▅▃▂▂ |
| numeric | POS | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 7.857193e+07 | 5.675786e+07 | 828.0000000 | 3.161991e+07 | 6.891253e+07 | 1.147226e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -5.000000e-07 | 1.906000e-04 | -0.0013372 | -1.272000e-04 | -9.000000e-07 | 1.252000e-04 | 1.364600e-03 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.848000e-04 | 2.350000e-05 | 0.0001541 | 1.644000e-04 | 1.772000e-04 | 2.010000e-04 | 5.637000e-04 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.926095e-01 | 2.906861e-01 | 0.0000003 | 2.399999e-01 | 4.899999e-01 | 7.400005e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.926099e-01 | 2.906583e-01 | 0.0000003 | 2.388183e-01 | 4.905770e-01 | 7.444082e-01 | 9.999993e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.002132e-01 | 2.080711e-01 | 0.1272270 | 2.187380e-01 | 3.550460e-01 | 5.555470e-01 | 8.727720e-01 | ▇▅▃▃▂ |
| numeric | AF_reference | 31303 | 0.9918805 | NA | NA | NA | NA | NA | NA | NA | 3.884618e-01 | 2.136092e-01 | 0.0000000 | 2.128590e-01 | 3.506390e-01 | 5.433310e-01 | 1.000000e+00 | ▅▇▅▃▁ |
Head and tail
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 49298 | rs200943160 | T | C | -0.0000372 | 0.0002836 | 0.9000000 | 0.8955392 | 0.623765 | 0.782149 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0000196 | 0.0002810 | 0.9400001 | 0.9444535 | 0.400401 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0001068 | 0.0002767 | 0.6999999 | 0.6993736 | 0.456846 | 0.420727 | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0001372 | 0.0003160 | 0.6600001 | 0.6641239 | 0.240959 | NA | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0002833 | 0.0001962 | 0.1499999 | 0.1487929 | 0.515645 | 0.275160 | NA |
| 1 | 729679 | rs4951859 | C | G | -0.0001994 | 0.0002295 | 0.3900004 | 0.3850311 | 0.843204 | 0.639976 | NA |
| 1 | 736289 | rs79010578 | T | A | 0.0002080 | 0.0002477 | 0.4000000 | 0.4010382 | 0.132335 | 0.139577 | NA |
| 1 | 752566 | rs3094315 | G | A | -0.0001462 | 0.0002223 | 0.5099998 | 0.5107690 | 0.838945 | 0.718251 | NA |
| 1 | 752721 | rs3131972 | A | G | -0.0001460 | 0.0002220 | 0.5099998 | 0.5109943 | 0.838573 | 0.653355 | NA |
| 1 | 753405 | rs3115860 | C | A | -0.0002085 | 0.0002383 | 0.3800004 | 0.3814457 | 0.869776 | 0.751797 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51197266 | rs61290853 | A | G | 0.0000530 | 0.0001728 | 0.7600007 | 0.7589353 | 0.386333 | 0.4229230 | NA |
| 22 | 51198027 | rs34939255 | A | G | -0.0001040 | 0.0001957 | 0.5999997 | 0.5951508 | 0.254562 | 0.0984425 | NA |
| 22 | 51210289 | rs112565862 | C | T | -0.0002812 | 0.0002605 | 0.2800000 | 0.2803913 | 0.129958 | 0.1018370 | NA |
| 22 | 51211106 | rs9628250 | T | C | -0.0000923 | 0.0001940 | 0.6300007 | 0.6341281 | 0.271547 | 0.1671330 | NA |
| 22 | 51211392 | rs3888396 | T | C | -0.0003040 | 0.0002581 | 0.2399999 | 0.2389447 | 0.132638 | 0.1641370 | NA |
| 22 | 51212875 | rs2238837 | A | C | 0.0003587 | 0.0001843 | 0.0519996 | 0.0516663 | 0.331457 | 0.3724040 | NA |
| 22 | 51213613 | rs34726907 | C | T | 0.0000746 | 0.0002428 | 0.7600007 | 0.7587285 | 0.127814 | 0.1727240 | NA |
| 22 | 51216564 | rs9616970 | T | C | 0.0000791 | 0.0002418 | 0.7400005 | 0.7436401 | 0.128328 | 0.1563500 | NA |
| 22 | 51219006 | rs28729663 | G | A | 0.0000725 | 0.0002367 | 0.7600007 | 0.7594715 | 0.137950 | 0.2052720 | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0002579 | 0.0001887 | 0.1700000 | 0.1716023 | 0.297974 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623765 ES:SE:LP:AF:ID -3.72423e-05:0.000283646:0.0457575:0.623765:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400401 ES:SE:LP:AF:ID 1.95787e-05:0.000281006:0.0268721:0.400401:rs2462492
1 91536 rs6702460 G T . PASS AF=0.456846 ES:SE:LP:AF:ID -0.000106846:0.000276684:0.154902:0.456846:rs6702460
1 534192 rs6680723 C T . PASS AF=0.240959 ES:SE:LP:AF:ID -0.000137236:0.000316047:0.180456:0.240959:rs6680723
1 706368 rs12029736 A G . PASS AF=0.515645 ES:SE:LP:AF:ID -0.000283268:0.000196195:0.823909:0.515645:rs12029736
1 729679 rs4951859 C G . PASS AF=0.843204 ES:SE:LP:AF:ID -0.000199387:0.000229533:0.408935:0.843204:rs4951859
1 736289 rs79010578 T A . PASS AF=0.132335 ES:SE:LP:AF:ID 0.000208035:0.000247729:0.39794:0.132335:rs79010578
1 752566 rs3094315 G A . PASS AF=0.838945 ES:SE:LP:AF:ID -0.000146185:0.000222287:0.29243:0.838945:rs3094315
1 752721 rs3131972 A G . PASS AF=0.838573 ES:SE:LP:AF:ID -0.00014595:0.000222048:0.29243:0.838573:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869776 ES:SE:LP:AF:ID -0.000208538:0.000238265:0.420216:0.869776:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129876 ES:SE:LP:AF:ID 0.000165073:0.000238753:0.309804:0.129876:rs2073813
1 754182 rs3131969 A G . PASS AF=0.869117 ES:SE:LP:AF:ID -0.000202486:0.000237798:0.408935:0.869117:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869215 ES:SE:LP:AF:ID -0.000197113:0.000237893:0.387216:0.869215:rs3131968
1 754334 rs3131967 T C . PASS AF=0.869121 ES:SE:LP:AF:ID -0.000212447:0.000237794:0.431798:0.869121:rs3131967
1 754503 rs3115859 G A . PASS AF=0.838026 ES:SE:LP:AF:ID -0.000130273:0.000221432:0.251812:0.838026:rs3115859
1 754964 rs3131966 C T . PASS AF=0.838657 ES:SE:LP:AF:ID -0.000137439:0.000222054:0.267606:0.838657:rs3131966
1 755775 rs3131965 A G . PASS AF=0.83977 ES:SE:LP:AF:ID -0.000125797:0.000225057:0.236572:0.83977:rs3131965
1 755890 rs3115858 A T . PASS AF=0.8694 ES:SE:LP:AF:ID -0.000219303:0.000237519:0.443698:0.8694:rs3115858
1 756604 rs3131962 A G . PASS AF=0.868947 ES:SE:LP:AF:ID -0.000227962:0.000236921:0.468521:0.868947:rs3131962
1 757640 rs3115853 G A . PASS AF=0.867898 ES:SE:LP:AF:ID -0.000214127:0.000236467:0.431798:0.867898:rs3115853
1 757734 rs4951929 C T . PASS AF=0.86909 ES:SE:LP:AF:ID -0.000228354:0.000237115:0.468521:0.86909:rs4951929
1 757936 rs4951862 C A . PASS AF=0.869098 ES:SE:LP:AF:ID -0.000228234:0.000237133:0.468521:0.869098:rs4951862
1 758144 rs3131956 A G . PASS AF=0.869106 ES:SE:LP:AF:ID -0.000228565:0.000237139:0.468521:0.869106:rs3131956
1 758626 rs3131954 C T . PASS AF=0.869584 ES:SE:LP:AF:ID -0.000216458:0.00023779:0.443698:0.869584:rs3131954
1 760912 rs1048488 C T . PASS AF=0.838306 ES:SE:LP:AF:ID -0.000113614:0.000221012:0.21467:0.838306:rs1048488
1 761147 rs3115850 T C . PASS AF=0.838427 ES:SE:LP:AF:ID -0.000111493:0.000221168:0.21467:0.838427:rs3115850
1 761732 rs2286139 C T . PASS AF=0.862254 ES:SE:LP:AF:ID -0.0001982:0.000236281:0.39794:0.862254:rs2286139
1 763394 rs3115847 G A . PASS AF=0.706753 ES:SE:LP:AF:ID -0.000129932:0.000230021:0.244125:0.706753:rs3115847
1 768253 rs2977608 A C . PASS AF=0.761297 ES:SE:LP:AF:ID -0.000269285:0.000187737:0.823909:0.761297:rs2977608
1 769223 rs60320384 C G . PASS AF=0.129581 ES:SE:LP:AF:ID 0.000210608:0.00023861:0.420216:0.129581:rs60320384
1 771823 rs2977605 T C . PASS AF=0.868906 ES:SE:LP:AF:ID -0.00024174:0.000237336:0.508638:0.868906:rs2977605
1 771967 rs59066358 G A . PASS AF=0.129682 ES:SE:LP:AF:ID 0.000185314:0.000238456:0.356547:0.129682:rs59066358
1 772755 rs2905039 A C . PASS AF=0.868916 ES:SE:LP:AF:ID -0.000232483:0.00023734:0.481486:0.868916:rs2905039
1 776546 rs12124819 A G . PASS AF=0.265385 ES:SE:LP:AF:ID -0.000102663:0.000209709:0.207608:0.265385:rs12124819
1 777122 rs2980319 A T . PASS AF=0.870039 ES:SE:LP:AF:ID -0.000230924:0.000237824:0.481486:0.870039:rs2980319
1 778745 rs1055606 A G . PASS AF=0.12858 ES:SE:LP:AF:ID 0.000172028:0.000238764:0.327902:0.12858:rs1055606
1 779322 rs4040617 A G . PASS AF=0.128877 ES:SE:LP:AF:ID 0.000174188:0.000238358:0.337242:0.128877:rs4040617
1 780785 rs2977612 T A . PASS AF=0.86878 ES:SE:LP:AF:ID -0.000221871:0.00023719:0.455932:0.86878:rs2977612
1 782981 rs6594026 C T . PASS AF=0.129518 ES:SE:LP:AF:ID 0.00016124:0.000238282:0.30103:0.129518:rs6594026
1 785050 rs2905062 G A . PASS AF=0.868533 ES:SE:LP:AF:ID -0.000218149:0.000237135:0.443698:0.868533:rs2905062
1 785989 rs2980300 T C . PASS AF=0.868474 ES:SE:LP:AF:ID -0.00021624:0.000237282:0.443698:0.868474:rs2980300
1 787399 rs2905055 G T . PASS AF=0.860776 ES:SE:LP:AF:ID -0.000222201:0.00023712:0.455932:0.860776:rs2905055
1 787606 rs3863622 G T . PASS AF=0.128466 ES:SE:LP:AF:ID 0.000175763:0.000239566:0.337242:0.128466:rs3863622
1 787685 rs2905054 G T . PASS AF=0.861487 ES:SE:LP:AF:ID -0.000222475:0.000237275:0.455932:0.861487:rs2905054
1 787844 rs2905053 C T . PASS AF=0.869224 ES:SE:LP:AF:ID -0.00022986:0.00023815:0.481486:0.869224:rs2905053
1 791191 rs111818025 G A . PASS AF=0.127884 ES:SE:LP:AF:ID 0.000155426:0.000241101:0.283997:0.127884:rs111818025
1 795988 rs59380221 C T . PASS AF=0.143053 ES:SE:LP:AF:ID 0.000152998:0.000245946:0.275724:0.143053:rs59380221
1 798400 rs10900604 A G . PASS AF=0.206591 ES:SE:LP:AF:ID 0.000221629:0.000200277:0.568636:0.206591:rs10900604
1 798959 rs11240777 G A . PASS AF=0.20642 ES:SE:LP:AF:ID 0.000220869:0.000200363:0.568636:0.20642:rs11240777
1 808631 rs11240779 G A . PASS AF=0.772619 ES:SE:LP:AF:ID -0.00023025:0.000190601:0.638272:0.772619:rs11240779