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_20084_469.vcf.gz --id UKB-b:15326 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20084_469.txt.gz --cohort_cases 1628 --cohort_controls 63321 --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": "2019-09-13T13:35:17.920857",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-15326/ukb-b-15326.vcf.gz; Date=Sat May 9 14:44:02 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-15326/UKB-b-15326_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-15326/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:44:07 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-15326/UKB-b-15326_data.vcf.gz ...
Read summary statistics for 2706309 SNPs.
Dropped 326 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, 680307 SNPs remain.
After merging with regression SNP LD, 680307 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0115 (0.0072)
Lambda GC: 1.0149
Mean Chi^2: 1.0141
Intercept: 0.9972 (0.0086)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Thu Oct 17 14:44:41 2019
Total time elapsed: 34.42sQC metrics
Metrics
Metrics
{
"af_correlation": 0.7912,
"inflation_factor": 1,
"mean_EFFECT": 1.1856e-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": 21466,
"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": 680307,
"ldsc_nsnp_merge_regression_ld": 680307,
"ldsc_observed_scale_h2_beta": 0.0115,
"ldsc_observed_scale_h2_se": 0.0072,
"ldsc_intercept_beta": 0.9972,
"ldsc_intercept_se": 0.0086,
"ldsc_lambda_gc": 1.0149,
"ldsc_mean_chisq": 1.0141,
"ldsc_ratio": -0.1986
}
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 | 4 | 58 | 0 | 2705986 | 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 | 2706309 | 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 | 8.663374e+00 | 5.768960e+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 | 7.853682e+07 | 5.662984e+07 | 5687.0000000 | 3.170309e+07 | 6.894672e+07 | 1.147147e+08 | 2.492013e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.200000e-06 | 9.370000e-04 | -0.0050723 | -6.289000e-04 | 2.200000e-06 | 6.274000e-04 | 4.859100e-03 | ▁▂▇▂▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 9.314000e-04 | 5.840000e-05 | 0.0008374 | 8.813000e-04 | 9.143000e-04 | 9.725000e-04 | 1.912400e-03 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.985726e-01 | 2.885073e-01 | 0.0000005 | 2.500000e-01 | 5.000000e-01 | 7.499995e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.985728e-01 | 2.884815e-01 | 0.0000005 | 2.485718e-01 | 4.986436e-01 | 7.482366e-01 | 9.999997e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.458832e-01 | 1.616383e-01 | 0.2149880 | 3.037680e-01 | 4.199840e-01 | 5.744130e-01 | 7.850120e-01 | ▇▆▅▃▃ |
| numeric | AF_reference | 21466 | 0.9920682 | NA | NA | NA | NA | NA | NA | NA | 4.283387e-01 | 1.828423e-01 | 0.0001997 | 2.829470e-01 | 4.097440e-01 | 5.625000e-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 | -0.0021901 | 0.0015433 | 0.1600000 | 0.1558776 | 0.623812 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0017855 | 0.0015388 | 0.2500000 | 0.2459301 | 0.399144 | NA | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0014760 | 0.0015135 | 0.3300000 | 0.3294658 | 0.455916 | 0.4207270 | NA |
| 1 | 534192 | rs6680723 | C | T | 0.0028192 | 0.0017238 | 0.1000000 | 0.1019552 | 0.242057 | NA | NA |
| 1 | 706368 | rs55727773 | A | G | -0.0009421 | 0.0010669 | 0.3800004 | 0.3772424 | 0.513304 | 0.2751600 | NA |
| 1 | 763394 | rs369924889 | G | A | -0.0023515 | 0.0012500 | 0.0599998 | 0.0599322 | 0.705804 | 0.6176120 | NA |
| 1 | 768253 | rs2977608 | A | C | -0.0005200 | 0.0010131 | 0.6100002 | 0.6077451 | 0.758252 | 0.4894170 | NA |
| 1 | 776546 | rs12124819 | A | G | 0.0000939 | 0.0011448 | 0.9299999 | 0.9346044 | 0.263729 | 0.0756789 | NA |
| 1 | 808631 | rs11240779 | G | A | -0.0009427 | 0.0010271 | 0.3599996 | 0.3586848 | 0.768280 | 0.4534740 | NA |
| 1 | 808928 | rs11240780 | C | T | -0.0009799 | 0.0010284 | 0.3400001 | 0.3406643 | 0.768417 | 0.4522760 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51180501 | rs5770999 | T | C | 0.0001136 | 0.0009998 | 0.9100000 | 0.9095350 | 0.712571 | 0.6369810 | NA |
| 22 | 51181919 | rs9616825 | G | C | -0.0001542 | 0.0009957 | 0.8800001 | 0.8768886 | 0.695031 | 0.6194090 | NA |
| 22 | 51182485 | rs6009961 | A | G | 0.0001306 | 0.0010031 | 0.9000000 | 0.8964053 | 0.714237 | 0.6383790 | NA |
| 22 | 51186143 | rs2879914 | T | C | -0.0013874 | 0.0009342 | 0.1400000 | 0.1374960 | 0.380077 | 0.2733630 | NA |
| 22 | 51186228 | rs3865766 | C | T | -0.0002943 | 0.0009099 | 0.7499995 | 0.7463428 | 0.449547 | 0.4532750 | NA |
| 22 | 51197266 | rs61290853 | A | G | -0.0001519 | 0.0009377 | 0.8700001 | 0.8712731 | 0.386693 | 0.4229230 | NA |
| 22 | 51198027 | rs34939255 | A | G | 0.0005739 | 0.0010635 | 0.5900000 | 0.5894319 | 0.254586 | 0.0984425 | NA |
| 22 | 51211106 | rs9628250 | T | C | 0.0001627 | 0.0010549 | 0.8800001 | 0.8774631 | 0.271468 | 0.1671330 | NA |
| 22 | 51212875 | rs2238837 | A | C | -0.0008753 | 0.0010028 | 0.3800004 | 0.3827553 | 0.331351 | 0.3724040 | NA |
| 22 | 51237063 | rs3896457 | T | C | -0.0011618 | 0.0010245 | 0.2599998 | 0.2567941 | 0.298393 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623812 ES:SE:LP:AF:ID -0.00219007:0.00154331:0.79588:0.623812:rs10399793
1 54676 rs2462492 C T . PASS AF=0.399144 ES:SE:LP:AF:ID 0.00178548:0.00153882:0.60206:0.399144:rs2462492
1 91536 rs6702460 G T . PASS AF=0.455916 ES:SE:LP:AF:ID 0.00147598:0.00151353:0.481486:0.455916:rs6702460
1 534192 rs6680723 C T . PASS AF=0.242057 ES:SE:LP:AF:ID 0.00281925:0.00172384:1:0.242057:rs6680723
1 706368 rs12029736 A G . PASS AF=0.513304 ES:SE:LP:AF:ID -0.000942087:0.00106693:0.420216:0.513304:rs12029736
1 763394 rs3115847 G A . PASS AF=0.705804 ES:SE:LP:AF:ID -0.00235154:0.00124996:1.22185:0.705804:rs3115847
1 768253 rs2977608 A C . PASS AF=0.758252 ES:SE:LP:AF:ID -0.000520019:0.0010131:0.21467:0.758252:rs2977608
1 776546 rs12124819 A G . PASS AF=0.263729 ES:SE:LP:AF:ID 9.39321e-05:0.00114477:0.0315171:0.263729:rs12124819
1 808631 rs11240779 G A . PASS AF=0.76828 ES:SE:LP:AF:ID -0.00094273:0.00102708:0.443698:0.76828:rs11240779
1 808928 rs11240780 C T . PASS AF=0.768417 ES:SE:LP:AF:ID -0.000979905:0.00102839:0.468521:0.768417:rs11240780
1 814495 rs74461805 C A . PASS AF=0.340108 ES:SE:LP:AF:ID -0.000529647:0.00146105:0.142668:0.340108:rs74461805
1 830181 rs28444699 A G . PASS AF=0.696612 ES:SE:LP:AF:ID -0.000105553:0.000977478:0.0409586:0.696612:rs28444699
1 831489 rs4970385 C T . PASS AF=0.705031 ES:SE:LP:AF:ID -0.000898101:0.00095977:0.455932:0.705031:rs4970385
1 831909 rs9697642 C T . PASS AF=0.705083 ES:SE:LP:AF:ID -0.000902805:0.000959769:0.455932:0.705083:rs9697642
1 832066 rs9697380 G C . PASS AF=0.705261 ES:SE:LP:AF:ID -0.000911034:0.000959724:0.468521:0.705261:rs9697380
1 832318 rs4500250 C A . PASS AF=0.705286 ES:SE:LP:AF:ID -0.000917183:0.000959858:0.468521:0.705286:rs4500250
1 832398 rs4553118 T C . PASS AF=0.730154 ES:SE:LP:AF:ID -0.00060187:0.000986337:0.267606:0.730154:rs4553118
1 832918 rs28765502 T C . PASS AF=0.294729 ES:SE:LP:AF:ID 0.000911333:0.000959828:0.468521:0.294729:rs28765502
1 833223 rs13303211 C T . PASS AF=0.237157 ES:SE:LP:AF:ID 0.000156786:0.00102185:0.0555173:0.237157:rs13303211
1 833302 rs28752186 C T . PASS AF=0.237168 ES:SE:LP:AF:ID 0.000155547:0.00102187:0.0555173:0.237168:rs28752186
1 833641 rs28594623 T C . PASS AF=0.240204 ES:SE:LP:AF:ID -1.425e-05:0.00101883:0.00436481:0.240204:rs28594623
1 833824 rs28484835 T C . PASS AF=0.23716 ES:SE:LP:AF:ID 0.000157155:0.00102191:0.0555173:0.23716:rs28484835
1 834832 rs4411087 G C . PASS AF=0.237633 ES:SE:LP:AF:ID 0.000172198:0.00102115:0.0604807:0.237633:rs4411087
1 835499 rs4422948 A G . PASS AF=0.241744 ES:SE:LP:AF:ID 0.000334411:0.00101351:0.130768:0.241744:rs4422948
1 836896 rs28705752 T C . PASS AF=0.269687 ES:SE:LP:AF:ID 0.000771623:0.000979319:0.366532:0.269687:rs28705752
1 838555 rs4970383 C A . PASS AF=0.245985 ES:SE:LP:AF:ID 0.000438225:0.00100821:0.180456:0.245985:rs4970383
1 839103 rs28562941 A G . PASS AF=0.270067 ES:SE:LP:AF:ID 0.000835975:0.000980122:0.408935:0.270067:rs28562941
1 840753 rs4970382 T C . PASS AF=0.400406 ES:SE:LP:AF:ID 0.000582375:0.000886017:0.29243:0.400406:rs4970382
1 841085 rs1574243 C G . PASS AF=0.236925 ES:SE:LP:AF:ID 0.000325801:0.00103003:0.124939:0.236925:rs1574243
1 842013 rs7419119 T G . PASS AF=0.215763 ES:SE:LP:AF:ID -0.000105185:0.00105849:0.0362122:0.215763:rs7419119
1 842362 rs28540380 C T . PASS AF=0.234687 ES:SE:LP:AF:ID -0.000196656:0.00104616:0.0705811:0.234687:rs28540380
1 843405 rs11516185 A G . PASS AF=0.362367 ES:SE:LP:AF:ID -0.00105001:0.00110236:0.468521:0.362367:rs11516185
1 847491 rs28407778 G A . PASS AF=0.21552 ES:SE:LP:AF:ID -0.000402634:0.00105448:0.154902:0.21552:rs28407778
1 850062 rs28723578 A T . PASS AF=0.21581 ES:SE:LP:AF:ID -0.00046024:0.00105344:0.180456:0.21581:rs28723578
1 850218 rs6664536 T A . PASS AF=0.589315 ES:SE:LP:AF:ID -0.000663278:0.000881213:0.346787:0.589315:rs6664536
1 850371 rs6679046 G T . PASS AF=0.603035 ES:SE:LP:AF:ID -0.000328705:0.000885428:0.148742:0.603035:rs6679046
1 850780 rs6657440 C T . PASS AF=0.603381 ES:SE:LP:AF:ID -0.000375119:0.000885445:0.173925:0.603381:rs6657440
1 851190 rs28609852 G A . PASS AF=0.215944 ES:SE:LP:AF:ID -0.000474927:0.00105334:0.187087:0.215944:rs28609852
1 851204 rs28552953 G C . PASS AF=0.225856 ES:SE:LP:AF:ID -8.27486e-06:0.00104628:0.00436481:0.225856:rs28552953
1 852037 rs4970463 G A . PASS AF=0.588673 ES:SE:LP:AF:ID -0.000597702:0.000882471:0.30103:0.588673:rs4970463
1 852063 rs28436996 G A . PASS AF=0.588649 ES:SE:LP:AF:ID -0.000615486:0.000882073:0.309804:0.588649:rs28436996
1 852875 rs13303369 C T . PASS AF=0.606715 ES:SE:LP:AF:ID -0.000394981:0.000886902:0.180456:0.606715:rs13303369
1 853954 rs1806509 C A . PASS AF=0.60688 ES:SE:LP:AF:ID -0.00036331:0.000886993:0.167491:0.60688:rs1806509
1 854777 rs13303019 A G . PASS AF=0.609504 ES:SE:LP:AF:ID -0.0003157:0.00088788:0.142668:0.609504:rs13303019
1 854978 rs13303057 A C . PASS AF=0.602505 ES:SE:LP:AF:ID -0.000285189:0.000885583:0.124939:0.602505:rs13303057
1 855075 rs6673914 C G . PASS AF=0.609473 ES:SE:LP:AF:ID -0.000306736:0.000887763:0.136677:0.609473:rs6673914
1 856099 rs28534711 T G . PASS AF=0.390835 ES:SE:LP:AF:ID 0.000265189:0.000888124:0.113509:0.390835:rs28534711
1 856108 rs28742275 A G . PASS AF=0.390777 ES:SE:LP:AF:ID 0.000266628:0.000888215:0.119186:0.390777:rs28742275
1 856476 rs4040605 A G . PASS AF=0.352504 ES:SE:LP:AF:ID 9.86293e-05:0.000911727:0.0409586:0.352504:rs4040605
1 858040 rs4970460 C A . PASS AF=0.218997 ES:SE:LP:AF:ID -0.000846618:0.00104983:0.376751:0.218997:rs4970460