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_20088_343.vcf.gz --id UKB-b:16295 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20088_343.txt.gz --cohort_cases 3214 --cohort_controls 61735 --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": "2019-09-13T13:40:02.571029",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-16295/ukb-b-16295.vcf.gz; Date=Sat May 9 17:13:42 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-16295/UKB-b-16295_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-16295/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:44:59 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-16295/UKB-b-16295_data.vcf.gz ...
Read summary statistics for 4153394 SNPs.
Dropped 822 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, 980880 SNPs remain.
After merging with regression SNP LD, 980880 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.011 (0.0077)
Lambda GC: 1.0179
Mean Chi^2: 1.0115
Intercept: 0.996 (0.0084)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Thu Oct 17 14:45:50 2019
Total time elapsed: 51.71sQC metrics
Metrics
Metrics
{
"af_correlation": 0.8814,
"inflation_factor": 1.0475,
"mean_EFFECT": 3.0605e-06,
"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": 34015,
"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": 980880,
"ldsc_nsnp_merge_regression_ld": 980880,
"ldsc_observed_scale_h2_beta": 0.011,
"ldsc_observed_scale_h2_se": 0.0077,
"ldsc_intercept_beta": 0.996,
"ldsc_intercept_se": 0.0084,
"ldsc_lambda_gc": 1.0179,
"ldsc_mean_chisq": 1.0115,
"ldsc_ratio": -0.3478
}
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 | 4152577 | 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 | 4153394 | 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 | 8.654941e+00 | 5.765640e+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 | 7.858543e+07 | 5.674169e+07 | 828.0000000 | 3.165089e+07 | 6.894426e+07 | 1.146732e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.100000e-06 | 1.451900e-03 | -0.0083934 | -9.577000e-04 | 3.100000e-06 | 9.584000e-04 | 9.445700e-03 | ▁▂▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.426400e-03 | 2.120000e-04 | 0.0011617 | 1.243000e-03 | 1.356100e-03 | 1.570400e-03 | 4.269400e-03 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.964693e-01 | 2.886887e-01 | 0.0000007 | 2.500000e-01 | 4.899999e-01 | 7.499995e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.964697e-01 | 2.886604e-01 | 0.0000007 | 2.462589e-01 | 4.948867e-01 | 7.464186e-01 | 9.999999e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.876982e-01 | 2.174559e-01 | 0.1088990 | 1.984360e-01 | 3.370990e-01 | 5.473447e-01 | 8.911000e-01 | ▇▅▃▂▂ |
| numeric | AF_reference | 34015 | 0.9918103 | NA | NA | NA | NA | NA | NA | NA | 3.774533e-01 | 2.200793e-01 | 0.0000000 | 1.962860e-01 | 3.344650e-01 | 5.355430e-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 | 0.0004770 | 0.0021408 | 0.8200001 | 0.8236693 | 0.623812 | 0.782149 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0033798 | 0.0021346 | 0.1100001 | 0.1133448 | 0.399144 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0012352 | 0.0020995 | 0.5600000 | 0.5563097 | 0.455916 | 0.420727 | NA |
| 1 | 534192 | rs6680723 | C | T | 0.0005497 | 0.0023912 | 0.8200001 | 0.8181982 | 0.242057 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | -0.0000023 | 0.0019939 | 1.0000000 | 0.9990710 | 0.117313 | 0.141773 | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0012154 | 0.0014800 | 0.4100001 | 0.4115455 | 0.513304 | 0.275160 | NA |
| 1 | 729679 | rs4951859 | C | G | 0.0009286 | 0.0017244 | 0.5900000 | 0.5902054 | 0.841441 | 0.639976 | NA |
| 1 | 731718 | rs142557973 | T | C | -0.0004431 | 0.0018940 | 0.8200001 | 0.8150105 | 0.123078 | 0.154353 | NA |
| 1 | 734349 | rs141242758 | T | C | -0.0004767 | 0.0018946 | 0.8000000 | 0.8013216 | 0.122330 | 0.152556 | NA |
| 1 | 736289 | rs79010578 | T | A | -0.0008540 | 0.0018598 | 0.6499995 | 0.6461240 | 0.134139 | 0.139577 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51198027 | rs34939255 | A | G | 0.0010774 | 0.0014753 | 0.4700002 | 0.4651951 | 0.254586 | 0.0984425 | NA |
| 22 | 51208537 | rs72619593 | G | A | -0.0007715 | 0.0019715 | 0.6999999 | 0.6955674 | 0.120936 | 0.1142170 | NA |
| 22 | 51210289 | rs112565862 | C | T | 0.0018023 | 0.0019662 | 0.3599996 | 0.3593411 | 0.129762 | 0.1018370 | NA |
| 22 | 51211106 | rs9628250 | T | C | 0.0016410 | 0.0014634 | 0.2599998 | 0.2621592 | 0.271468 | 0.1671330 | NA |
| 22 | 51211392 | rs3888396 | T | C | 0.0020571 | 0.0019487 | 0.2900000 | 0.2911218 | 0.132383 | 0.1641370 | NA |
| 22 | 51212875 | rs2238837 | A | C | -0.0030655 | 0.0013911 | 0.0280001 | 0.0275495 | 0.331351 | 0.3724040 | NA |
| 22 | 51213613 | rs34726907 | C | T | -0.0022135 | 0.0018461 | 0.2300001 | 0.2305182 | 0.126422 | 0.1727240 | NA |
| 22 | 51216564 | rs9616970 | T | C | -0.0020307 | 0.0018388 | 0.2700001 | 0.2694373 | 0.126887 | 0.1563500 | NA |
| 22 | 51219006 | rs28729663 | G | A | -0.0021571 | 0.0018002 | 0.2300001 | 0.2308244 | 0.136315 | 0.2052720 | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0018898 | 0.0014212 | 0.1800002 | 0.1836140 | 0.298393 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623812 ES:SE:LP:AF:ID 0.000477037:0.00214083:0.0861861:0.623812:rs10399793
1 54676 rs2462492 C T . PASS AF=0.399144 ES:SE:LP:AF:ID -0.00337979:0.0021346:0.958607:0.399144:rs2462492
1 91536 rs6702460 G T . PASS AF=0.455916 ES:SE:LP:AF:ID -0.00123522:0.00209953:0.251812:0.455916:rs6702460
1 534192 rs6680723 C T . PASS AF=0.242057 ES:SE:LP:AF:ID 0.00054966:0.00239125:0.0861861:0.242057:rs6680723
1 693731 rs12238997 A G . PASS AF=0.117313 ES:SE:LP:AF:ID -2.32159e-06:0.0019939:-0:0.117313:rs12238997
1 706368 rs12029736 A G . PASS AF=0.513304 ES:SE:LP:AF:ID -0.00121535:0.00148001:0.387216:0.513304:rs12029736
1 729679 rs4951859 C G . PASS AF=0.841441 ES:SE:LP:AF:ID 0.000928634:0.00172436:0.229148:0.841441:rs4951859
1 731718 rs58276399 T C . PASS AF=0.123078 ES:SE:LP:AF:ID -0.000443129:0.00189398:0.0861861:0.123078:rs58276399
1 734349 rs141242758 T C . PASS AF=0.12233 ES:SE:LP:AF:ID -0.000476749:0.00189459:0.09691:0.12233:rs141242758
1 736289 rs79010578 T A . PASS AF=0.134139 ES:SE:LP:AF:ID -0.000853952:0.00185984:0.187087:0.134139:rs79010578
1 752566 rs3094315 G A . PASS AF=0.837029 ES:SE:LP:AF:ID 0.000422237:0.0016681:0.09691:0.837029:rs3094315
1 752721 rs3131972 A G . PASS AF=0.836733 ES:SE:LP:AF:ID 0.000485991:0.00166688:0.113509:0.836733:rs3131972
1 753405 rs3115860 C A . PASS AF=0.868562 ES:SE:LP:AF:ID 0.000152008:0.00179115:0.0315171:0.868562:rs3115860
1 753541 rs2073813 G A . PASS AF=0.131004 ES:SE:LP:AF:ID 1.69208e-05:0.00179575:0.00436481:0.131004:rs2073813
1 754182 rs3131969 A G . PASS AF=0.867976 ES:SE:LP:AF:ID 0.00018067:0.00178827:0.0362122:0.867976:rs3131969
1 754192 rs3131968 A G . PASS AF=0.86805 ES:SE:LP:AF:ID 0.000223769:0.001789:0.0457575:0.86805:rs3131968
1 754334 rs3131967 T C . PASS AF=0.867987 ES:SE:LP:AF:ID 0.000165441:0.00178823:0.0315171:0.867987:rs3131967
1 754503 rs3115859 G A . PASS AF=0.836159 ES:SE:LP:AF:ID 0.000360674:0.001662:0.0809219:0.836159:rs3115859
1 754964 rs3131966 C T . PASS AF=0.836793 ES:SE:LP:AF:ID 0.000413646:0.00166656:0.09691:0.836793:rs3131966
1 755775 rs3131965 A G . PASS AF=0.838109 ES:SE:LP:AF:ID 0.000270637:0.00169002:0.0604807:0.838109:rs3131965
1 755890 rs3115858 A T . PASS AF=0.868228 ES:SE:LP:AF:ID 0.000109433:0.00178592:0.0222764:0.868228:rs3115858
1 756604 rs3131962 A G . PASS AF=0.867742 ES:SE:LP:AF:ID 2.05607e-05:0.00178119:0.00436481:0.867742:rs3131962
1 757640 rs3115853 G A . PASS AF=0.866644 ES:SE:LP:AF:ID 8.73377e-05:0.00177793:0.0177288:0.866644:rs3115853
1 757734 rs4951929 C T . PASS AF=0.867911 ES:SE:LP:AF:ID -3.3746e-05:0.001783:0.00877392:0.867911:rs4951929
1 757936 rs4951862 C A . PASS AF=0.867923 ES:SE:LP:AF:ID -3.60796e-05:0.00178312:0.00877392:0.867923:rs4951862
1 758144 rs3131956 A G . PASS AF=0.867932 ES:SE:LP:AF:ID -4.08875e-05:0.00178319:0.00877392:0.867932:rs3131956
1 758626 rs3131954 C T . PASS AF=0.868398 ES:SE:LP:AF:ID 2.75785e-05:0.00178788:0.00436481:0.868398:rs3131954
1 760912 rs1048488 C T . PASS AF=0.836369 ES:SE:LP:AF:ID 0.000337924:0.00165786:0.0757207:0.836369:rs1048488
1 761147 rs3115850 T C . PASS AF=0.836495 ES:SE:LP:AF:ID 0.000384811:0.00165899:0.0861861:0.836495:rs3115850
1 761732 rs2286139 C T . PASS AF=0.860851 ES:SE:LP:AF:ID 0.000252479:0.00177686:0.05061:0.860851:rs2286139
1 763394 rs3115847 G A . PASS AF=0.705804 ES:SE:LP:AF:ID 0.000576318:0.00173391:0.130768:0.705804:rs3115847
1 768253 rs2977608 A C . PASS AF=0.758252 ES:SE:LP:AF:ID -0.000507334:0.00140535:0.142668:0.758252:rs2977608
1 769223 rs60320384 C G . PASS AF=0.13066 ES:SE:LP:AF:ID 0.000131342:0.00179506:0.0268721:0.13066:rs60320384
1 771823 rs2977605 T C . PASS AF=0.867852 ES:SE:LP:AF:ID 3.89992e-05:0.00178526:0.00877392:0.867852:rs2977605
1 771967 rs59066358 G A . PASS AF=0.130756 ES:SE:LP:AF:ID 0.000190801:0.00179386:0.0362122:0.130756:rs59066358
1 772755 rs2905039 A C . PASS AF=0.867878 ES:SE:LP:AF:ID 2.86882e-05:0.00178536:0.00436481:0.867878:rs2905039
1 776546 rs12124819 A G . PASS AF=0.263729 ES:SE:LP:AF:ID 0.000907659:0.00158799:0.244125:0.263729:rs12124819
1 777122 rs2980319 A T . PASS AF=0.869067 ES:SE:LP:AF:ID 0.000275933:0.00179006:0.0555173:0.869067:rs2980319
1 778745 rs1055606 A G . PASS AF=0.129594 ES:SE:LP:AF:ID -7.97848e-05:0.00179689:0.0177288:0.129594:rs1055606
1 779322 rs4040617 A G . PASS AF=0.129863 ES:SE:LP:AF:ID -0.000196435:0.00179383:0.0409586:0.129863:rs4040617
1 780785 rs2977612 T A . PASS AF=0.867843 ES:SE:LP:AF:ID 0.000176971:0.00178562:0.0362122:0.867843:rs2977612
1 782981 rs6594026 C T . PASS AF=0.130424 ES:SE:LP:AF:ID -0.000178108:0.00179417:0.0362122:0.130424:rs6594026
1 785050 rs2905062 G A . PASS AF=0.867493 ES:SE:LP:AF:ID 0.000348849:0.00178509:0.0705811:0.867493:rs2905062
1 785989 rs2980300 T C . PASS AF=0.867448 ES:SE:LP:AF:ID 0.000356121:0.00178633:0.0757207:0.867448:rs2980300
1 787399 rs2905055 G T . PASS AF=0.859749 ES:SE:LP:AF:ID 0.000430884:0.00178578:0.091515:0.859749:rs2905055
1 787606 rs3863622 G T . PASS AF=0.129403 ES:SE:LP:AF:ID -3.21374e-05:0.00180346:0.00436481:0.129403:rs3863622
1 787685 rs2905054 G T . PASS AF=0.860334 ES:SE:LP:AF:ID 0.000299186:0.00178678:0.0604807:0.860334:rs2905054
1 787844 rs2905053 C T . PASS AF=0.868172 ES:SE:LP:AF:ID 0.000383066:0.00179238:0.0809219:0.868172:rs2905053
1 791191 rs111818025 G A . PASS AF=0.128878 ES:SE:LP:AF:ID 8.62748e-05:0.00181443:0.0177288:0.128878:rs111818025
1 795988 rs59380221 C T . PASS AF=0.144405 ES:SE:LP:AF:ID -0.000527331:0.00184705:0.107905:0.144405:rs59380221