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_41229_5HY.vcf.gz --id UKB-b:12508 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_41229_5HY.txt.gz --cohort_cases 1608 --cohort_controls 461402 --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": "2019-09-13T06:58:25.569065",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-12508/ukb-b-12508.vcf.gz; Date=Thu May 7 21:25:21 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-12508/UKB-b-12508_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-12508/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:42:03 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-12508/UKB-b-12508_data.vcf.gz ...
Read summary statistics for 2675841 SNPs.
Dropped 320 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, 673243 SNPs remain.
After merging with regression SNP LD, 673243 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0007 (0.0012)
Lambda GC: 1.0567
Mean Chi^2: 1.0451
Intercept: 1.0373 (0.0093)
Ratio: 0.8276 (0.2068)
Analysis finished at Thu Oct 17 14:42:43 2019
Total time elapsed: 39.93sQC metrics
Metrics
Metrics
{
"af_correlation": 0.7879,
"inflation_factor": 1.0475,
"mean_EFFECT": -7.0981e-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": 21198,
"n_est": "NA",
"ratio_se_n": "NA",
"mean_diff": "NaN",
"ratio_diff": "NaN",
"sd_y_est1": "NA",
"sd_y_est2": "NA",
"r2_sum1": 0,
"r2_sum2": 0,
"r2_sum3": 0,
"r2_sum4": 0,
"ldsc_nsnp_merge_refpanel_ld": 673243,
"ldsc_nsnp_merge_regression_ld": 673243,
"ldsc_observed_scale_h2_beta": 0.0007,
"ldsc_observed_scale_h2_se": 0.0012,
"ldsc_intercept_beta": 1.0373,
"ldsc_intercept_se": 0.0093,
"ldsc_lambda_gc": 1.0567,
"ldsc_mean_chisq": 1.0451,
"ldsc_ratio": 0.8271
}
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 | TRUE |
| 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 | 1.000000 | 4 | 58 | 0 | 2675524 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| character | REF | 0 | 1.000000 | 1 | 1 | 0 | 4 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| character | ALT | 0 | 1.000000 | 1 | 1 | 0 | 4 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| logical | N | 2675841 | 0.000000 | NA | NA | NA | NA | NA | NaN | : | NA | NA | NA | NA | NA | NA | NA | NA |
| numeric | CHROM | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 8.659919e+00 | 5.767228e+00 | 1.0000000 | 4.000000e+00 | 8.000000e+00 | 1.300000e+01 | 2.200000e+01 | <U+2587><U+2585><U+2583><U+2582><U+2582> |
| numeric | POS | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 7.855236e+07 | 5.662990e+07 | 5687.0000000 | 3.173364e+07 | 6.894924e+07 | 1.147174e+08 | 2.492013e+08 | <U+2587><U+2586><U+2585><U+2582><U+2581> |
| numeric | EFFECT | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | -7.000000e-07 | 1.343000e-04 | -0.0007176 | -9.160000e-05 | -8.000000e-07 | 9.000000e-05 | 8.214000e-04 | <U+2581><U+2582><U+2587><U+2581><U+2581> |
| numeric | SE | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 1.311000e-04 | 8.000000e-06 | 0.0001180 | 1.242000e-04 | 1.288000e-04 | 1.368000e-04 | 2.652000e-04 | <U+2587><U+2581><U+2581><U+2581><U+2581> |
| numeric | PVAL | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 4.919096e-01 | 2.906153e-01 | 0.0000002 | 2.399999e-01 | 4.899999e-01 | 7.400005e-01 | 1.000000e+00 | <U+2587><U+2587><U+2587><U+2587><U+2587> |
| numeric | PVAL_ztest | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 4.919109e-01 | 2.905875e-01 | 0.0000002 | 2.380164e-01 | 4.876401e-01 | 7.439186e-01 | 9.999988e-01 | <U+2587><U+2587><U+2587><U+2587><U+2587> |
| numeric | AF | 0 | 1.000000 | NA | NA | NA | NA | NA | NA | NA | 4.468805e-01 | 1.601606e-01 | 0.2176620 | 3.061020e-01 | 4.215640e-01 | 5.743490e-01 | 7.823380e-01 | <U+2587><U+2586><U+2585><U+2583><U+2583> |
| numeric | AF_reference | 21198 | 0.992078 | NA | NA | NA | NA | NA | NA | NA | 4.292194e-01 | 1.819160e-01 | 0.0001997 | 2.845450e-01 | 4.111420e-01 | 5.627000e-01 | 1.000000e+00 | <U+2582><U+2587><U+2587><U+2583><U+2581> |
Head and tail
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 49298 | rs200943160 | T | C | -0.0000298 | 0.0002171 | 0.8900000 | 0.8908801 | 0.623765 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0000018 | 0.0002151 | 0.9900000 | 0.9931992 | 0.400401 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0001623 | 0.0002118 | 0.4400003 | 0.4435378 | 0.456846 | 0.4207270 | NA |
| 1 | 534192 | rs6680723 | C | T | -0.0004108 | 0.0002419 | 0.0890000 | 0.0894647 | 0.240959 | NA | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0000475 | 0.0001502 | 0.7499995 | 0.7517688 | 0.515645 | 0.2751600 | NA |
| 1 | 763394 | rs369924889 | G | A | -0.0000057 | 0.0001761 | 0.9699999 | 0.9740610 | 0.706753 | 0.6176120 | NA |
| 1 | 768253 | rs2977608 | A | C | -0.0000861 | 0.0001437 | 0.5500004 | 0.5491205 | 0.761297 | 0.4894170 | NA |
| 1 | 776546 | rs12124819 | A | G | 0.0000072 | 0.0001605 | 0.9599999 | 0.9644400 | 0.265385 | 0.0756789 | NA |
| 1 | 808631 | rs11240779 | G | A | -0.0001363 | 0.0001459 | 0.3500000 | 0.3502096 | 0.772619 | 0.4534740 | NA |
| 1 | 808928 | rs11240780 | C | T | -0.0001165 | 0.0001461 | 0.4299995 | 0.4253501 | 0.772847 | 0.4522760 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51180501 | rs5770999 | T | C | 0.0001089 | 0.0001413 | 0.4400003 | 0.4409643 | 0.713656 | 0.6369810 | NA |
| 22 | 51181919 | rs9616825 | G | C | 0.0000895 | 0.0001406 | 0.5199996 | 0.5244465 | 0.695470 | 0.6194090 | NA |
| 22 | 51182485 | rs6009961 | A | G | 0.0001227 | 0.0001418 | 0.3900004 | 0.3866916 | 0.715502 | 0.6383790 | NA |
| 22 | 51186143 | rs2879914 | T | C | 0.0000629 | 0.0001315 | 0.6300007 | 0.6321116 | 0.381825 | 0.2733630 | NA |
| 22 | 51186228 | rs3865766 | C | T | -0.0000971 | 0.0001281 | 0.4500005 | 0.4488018 | 0.451061 | 0.4532750 | NA |
| 22 | 51197266 | rs61290853 | A | G | 0.0000104 | 0.0001323 | 0.9400001 | 0.9375041 | 0.386333 | 0.4229230 | NA |
| 22 | 51198027 | rs34939255 | A | G | 0.0002328 | 0.0001498 | 0.1199999 | 0.1201395 | 0.254562 | 0.0984425 | NA |
| 22 | 51211106 | rs9628250 | T | C | 0.0002517 | 0.0001485 | 0.0899995 | 0.0900469 | 0.271547 | 0.1671330 | NA |
| 22 | 51212875 | rs2238837 | A | C | 0.0000664 | 0.0001411 | 0.6400000 | 0.6379280 | 0.331457 | 0.3724040 | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0002162 | 0.0001444 | 0.1299999 | 0.1344991 | 0.297974 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623765 ES:SE:LP:AF:ID -2.97852e-05:0.000217108:0.05061:0.623765:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400401 ES:SE:LP:AF:ID -1.83332e-06:0.000215087:0.00436481:0.400401:rs2462492
1 91536 rs6702460 G T . PASS AF=0.456846 ES:SE:LP:AF:ID -0.000162272:0.000211779:0.356547:0.456846:rs6702460
1 534192 rs6680723 C T . PASS AF=0.240959 ES:SE:LP:AF:ID -0.000410815:0.000241908:1.05061:0.240959:rs6680723
1 706368 rs12029736 A G . PASS AF=0.515645 ES:SE:LP:AF:ID -4.75006e-05:0.000150172:0.124939:0.515645:rs12029736
1 763394 rs3115847 G A . PASS AF=0.706753 ES:SE:LP:AF:ID -5.72473e-06:0.000176062:0.0132283:0.706753:rs3115847
1 768253 rs2977608 A C . PASS AF=0.761297 ES:SE:LP:AF:ID -8.60858e-05:0.000143697:0.259637:0.761297:rs2977608
1 776546 rs12124819 A G . PASS AF=0.265385 ES:SE:LP:AF:ID 7.15617e-06:0.000160515:0.0177288:0.265385:rs12124819
1 808631 rs11240779 G A . PASS AF=0.772619 ES:SE:LP:AF:ID -0.000136287:0.000145889:0.455932:0.772619:rs11240779
1 808928 rs11240780 C T . PASS AF=0.772847 ES:SE:LP:AF:ID -0.000116495:0.000146135:0.366532:0.772847:rs11240780
1 814495 rs74461805 C A . PASS AF=0.340396 ES:SE:LP:AF:ID -0.000203032:0.000205911:0.49485:0.340396:rs74461805
1 830181 rs28444699 A G . PASS AF=0.697255 ES:SE:LP:AF:ID 0.00018056:0.000137765:0.721246:0.697255:rs28444699
1 831489 rs4970385 C T . PASS AF=0.705397 ES:SE:LP:AF:ID 0.000144148:0.000135272:0.537602:0.705397:rs4970385
1 831909 rs9697642 C T . PASS AF=0.705442 ES:SE:LP:AF:ID 0.00015159:0.000135268:0.585027:0.705442:rs9697642
1 832066 rs9697380 G C . PASS AF=0.705627 ES:SE:LP:AF:ID 0.000148738:0.000135274:0.568636:0.705627:rs9697380
1 832318 rs4500250 C A . PASS AF=0.705655 ES:SE:LP:AF:ID 0.000148659:0.000135288:0.568636:0.705655:rs4500250
1 832398 rs4553118 T C . PASS AF=0.730129 ES:SE:LP:AF:ID 0.000160349:0.000138974:0.60206:0.730129:rs4553118
1 832918 rs28765502 T C . PASS AF=0.294377 ES:SE:LP:AF:ID -0.000149953:0.000135282:0.568636:0.294377:rs28765502
1 833223 rs13303211 C T . PASS AF=0.236703 ES:SE:LP:AF:ID -0.000185468:0.000144028:0.69897:0.236703:rs13303211
1 833302 rs28752186 C T . PASS AF=0.236691 ES:SE:LP:AF:ID -0.000179331:0.00014403:0.677781:0.236691:rs28752186
1 833641 rs28594623 T C . PASS AF=0.23975 ES:SE:LP:AF:ID -0.000217585:0.000143568:0.886057:0.23975:rs28594623
1 833824 rs28484835 T C . PASS AF=0.236693 ES:SE:LP:AF:ID -0.000185334:0.000144029:0.69897:0.236693:rs28484835
1 834832 rs4411087 G C . PASS AF=0.237178 ES:SE:LP:AF:ID -0.000188115:0.000143918:0.721246:0.237178:rs4411087
1 835499 rs4422948 A G . PASS AF=0.241162 ES:SE:LP:AF:ID -0.000140571:0.000142914:0.481486:0.241162:rs4422948
1 836896 rs28705752 T C . PASS AF=0.269511 ES:SE:LP:AF:ID -0.000179204:0.000137901:0.721246:0.269511:rs28705752
1 838555 rs4970383 C A . PASS AF=0.246204 ES:SE:LP:AF:ID -0.000187332:0.000141931:0.721246:0.246204:rs4970383
1 839103 rs28562941 A G . PASS AF=0.270019 ES:SE:LP:AF:ID -0.000171735:0.000137998:0.677781:0.270019:rs28562941
1 840753 rs4970382 T C . PASS AF=0.400124 ES:SE:LP:AF:ID -0.000195357:0.000124777:0.920819:0.400124:rs4970382
1 841085 rs1574243 C G . PASS AF=0.237102 ES:SE:LP:AF:ID -0.000146973:0.000144933:0.508638:0.237102:rs1574243
1 842362 rs28540380 C T . PASS AF=0.235323 ES:SE:LP:AF:ID -0.000244366:0.000147104:1.01323:0.235323:rs28540380
1 843405 rs11516185 A G . PASS AF=0.362606 ES:SE:LP:AF:ID -0.000119034:0.000154893:0.356547:0.362606:rs11516185
1 850218 rs6664536 T A . PASS AF=0.590331 ES:SE:LP:AF:ID 0.000105695:0.000124413:0.39794:0.590331:rs6664536
1 850371 rs6679046 G T . PASS AF=0.603723 ES:SE:LP:AF:ID 0.000116789:0.000125111:0.455932:0.603723:rs6679046
1 850780 rs6657440 C T . PASS AF=0.603942 ES:SE:LP:AF:ID 0.000118301:0.000125094:0.468521:0.603942:rs6657440
1 851204 rs28552953 G C . PASS AF=0.224621 ES:SE:LP:AF:ID -0.000141563:0.000147997:0.468521:0.224621:rs28552953
1 852037 rs4970463 G A . PASS AF=0.589686 ES:SE:LP:AF:ID 9.85758e-05:0.000124615:0.366532:0.589686:rs4970463
1 852063 rs28436996 G A . PASS AF=0.589665 ES:SE:LP:AF:ID 0.000100928:0.000124559:0.376751:0.589665:rs28436996
1 852875 rs13303369 C T . PASS AF=0.607671 ES:SE:LP:AF:ID 0.000109734:0.000125373:0.420216:0.607671:rs13303369
1 853954 rs1806509 C A . PASS AF=0.607829 ES:SE:LP:AF:ID 0.000107447:0.00012539:0.408935:0.607829:rs1806509
1 854777 rs13303019 A G . PASS AF=0.610316 ES:SE:LP:AF:ID 0.00011596:0.000125514:0.443698:0.610316:rs13303019
1 854978 rs13303057 A C . PASS AF=0.603283 ES:SE:LP:AF:ID 0.000112251:0.000125142:0.431798:0.603283:rs13303057
1 855075 rs6673914 C G . PASS AF=0.610337 ES:SE:LP:AF:ID 0.000115052:0.000125516:0.443698:0.610337:rs6673914
1 856099 rs28534711 T G . PASS AF=0.389936 ES:SE:LP:AF:ID -0.000117164:0.00012554:0.455932:0.389936:rs28534711
1 856108 rs28742275 A G . PASS AF=0.38992 ES:SE:LP:AF:ID -0.000117178:0.000125546:0.455932:0.38992:rs28742275
1 856476 rs4040605 A G . PASS AF=0.350356 ES:SE:LP:AF:ID -0.000164635:0.000128971:0.69897:0.350356:rs4040605
1 858040 rs4970460 C A . PASS AF=0.217965 ES:SE:LP:AF:ID -0.000163995:0.000148407:0.568636:0.217965:rs4970460
1 858051 rs4970459 C T . PASS AF=0.219021 ES:SE:LP:AF:ID -0.000171092:0.000147889:0.60206:0.219021:rs4970459
1 858801 rs7418179 A G . PASS AF=0.765846 ES:SE:LP:AF:ID 0.000119808:0.000144775:0.387216:0.765846:rs7418179
1 860416 rs61464428 G A . PASS AF=0.766638 ES:SE:LP:AF:ID 0.000166924:0.000144847:0.60206:0.766638:rs61464428
1 860688 rs60837925 G A . PASS AF=0.766104 ES:SE:LP:AF:ID 0.000170815:0.000144747:0.619789:0.766104:rs60837925