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_23123.vcf.gz --id UKB-b:20188 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_23123.txt.gz --cohort_controls 454724 --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": "2019-09-13T04:02:42.340627",
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"bcftools_viewCommand": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-20188/ukb-b-20188.vcf.gz; Date=Sun May 10 11:00:44 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-20188/UKB-b-20188_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-20188/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:42:07 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-20188/UKB-b-20188_data.vcf.gz ...
Read summary statistics for 9851866 SNPs.
Dropped 14738 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, 1289166 SNPs remain.
After merging with regression SNP LD, 1289166 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.2071 (0.0058)
Lambda GC: 2.3386
Mean Chi^2: 3.0693
Intercept: 1.1756 (0.0167)
Ratio: 0.0849 (0.0081)
Analysis finished at Thu Oct 17 14:43:48 2019
Total time elapsed: 1.0m:40.97sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9499,
"inflation_factor": 1.8418,
"mean_EFFECT": 0,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 394,
"n_p_sig": 49712,
"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": 184849,
"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": 1289166,
"ldsc_nsnp_merge_regression_ld": 1289166,
"ldsc_observed_scale_h2_beta": 0.2071,
"ldsc_observed_scale_h2_se": 0.0058,
"ldsc_intercept_beta": 1.1756,
"ldsc_intercept_se": 0.0167,
"ldsc_lambda_gc": 2.3386,
"ldsc_mean_chisq": 3.0693,
"ldsc_ratio": 0.0849
}
Flags
| name | value |
|---|---|
| af_correlation | FALSE |
| inflation_factor | TRUE |
| n | TRUE |
| 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 | 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 | 9837196 | 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 | 9851866 | 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 | 8.622825e+00 | 5.748290e+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 | 7.886027e+07 | 5.628334e+07 | 828.0000000 | 3.259061e+07 | 6.948835e+07 | 1.145912e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.420000e-05 | 8.198200e-03 | -0.2216550 | -3.117900e-03 | 1.580000e-05 | 3.158700e-03 | 1.280710e-01 | ▁▁▁▇▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 5.244800e-03 | 4.964100e-03 | 0.0014655 | 1.796800e-03 | 3.012900e-03 | 6.951600e-03 | 7.710040e-02 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.015017e-01 | 3.106746e-01 | 0.0000000 | 1.100001e-01 | 3.599996e-01 | 6.700003e-01 | 1.000000e+00 | ▇▅▃▃▃ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.015030e-01 | 3.106527e-01 | 0.0000000 | 1.078758e-01 | 3.600487e-01 | 6.674019e-01 | 1.000000e+00 | ▇▅▃▃▃ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.035077e-01 | 2.568614e-01 | 0.0009850 | 1.316900e-02 | 7.791400e-02 | 3.164560e-01 | 9.990060e-01 | ▇▂▁▁▁ |
| numeric | AF_reference | 184849 | 0.9812372 | NA | NA | NA | NA | NA | NA | NA | 2.068392e-01 | 2.482924e-01 | 0.0000000 | 1.198080e-02 | 9.984030e-02 | 3.202880e-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 | -0.0006043 | 0.0026942 | 0.8200001 | 0.8225337 | 0.623738 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | 0.0016093 | 0.0026696 | 0.5500004 | 0.5466285 | 0.400346 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | 0.0079145 | 0.0042675 | 0.0640000 | 0.0636510 | 0.103559 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | 0.0006408 | 0.0026280 | 0.8100000 | 0.8073530 | 0.456846 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | -0.0084751 | 0.0051840 | 0.1000000 | 0.1020779 | 0.074495 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | 0.0022176 | 0.0030022 | 0.4600002 | 0.4601321 | 0.240935 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | -0.0008971 | 0.0037452 | 0.8100000 | 0.8106868 | 0.913459 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | 0.0023394 | 0.0025173 | 0.3500000 | 0.3527164 | 0.116211 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | 0.0035110 | 0.0036858 | 0.3400001 | 0.3408001 | 0.067328 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0002644 | 0.0018640 | 0.8900000 | 0.8872125 | 0.515777 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51219704 | rs147475742 | G | A | 0.0049327 | 0.0039311 | 0.2099999 | 0.2095529 | 0.041968 | 0.0473243 | NA |
| 22 | 51219766 | rs182321900 | C | T | 0.0018124 | 0.0182948 | 0.9199999 | 0.9210860 | 0.001943 | NA | NA |
| 22 | 51220146 | rs868950473 | C | T | -0.0011963 | 0.0181205 | 0.9500000 | 0.9473639 | 0.001991 | NA | NA |
| 22 | 51221190 | rs369304721 | G | A | 0.0042808 | 0.0039240 | 0.2800000 | 0.2753048 | 0.049763 | NA | NA |
| 22 | 51221731 | rs115055839 | T | C | 0.0047086 | 0.0029352 | 0.1100001 | 0.1086769 | 0.073282 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | -0.0005156 | 0.0034559 | 0.8800001 | 0.8814042 | 0.054488 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | -0.0004260 | 0.0034726 | 0.9000000 | 0.9023730 | 0.054117 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | 0.0044873 | 0.0029459 | 0.1299999 | 0.1277010 | 0.073116 | 0.0730831 | NA |
| 22 | 51232488 | rs376461333 | A | G | -0.0007321 | 0.0069390 | 0.9199999 | 0.9159784 | 0.020055 | NA | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0040461 | 0.0018027 | 0.0250000 | 0.0248025 | 0.297903 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623738 ES:SE:LP:AF:ID -0.000604264:0.00269415:0.0861861:0.623738:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400346 ES:SE:LP:AF:ID 0.00160927:0.00266957:0.259637:0.400346:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103559 ES:SE:LP:AF:ID 0.00791454:0.00426748:1.19382:0.103559:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456846 ES:SE:LP:AF:ID 0.000640815:0.00262799:0.091515:0.456846:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074495 ES:SE:LP:AF:ID -0.00847511:0.00518399:1:0.074495:rs8179466
1 534192 rs6680723 C T . PASS AF=0.240935 ES:SE:LP:AF:ID 0.00221755:0.00300225:0.337242:0.240935:rs6680723
1 546697 rs12025928 A G . PASS AF=0.913459 ES:SE:LP:AF:ID -0.000897123:0.00374519:0.091515:0.913459:rs12025928
1 693731 rs12238997 A G . PASS AF=0.116211 ES:SE:LP:AF:ID 0.00233942:0.00251731:0.455932:0.116211:rs12238997
1 705882 rs72631875 G A . PASS AF=0.067328 ES:SE:LP:AF:ID 0.00351104:0.0036858:0.468521:0.067328:rs72631875
1 706368 rs12029736 A G . PASS AF=0.515777 ES:SE:LP:AF:ID 0.000264371:0.00186397:0.05061:0.515777:rs12029736
1 714596 rs149887893 T C . PASS AF=0.032989 ES:SE:LP:AF:ID -0.00141396:0.00469948:0.119186:0.032989:rs149887893
1 715265 rs12184267 C T . PASS AF=0.0366 ES:SE:LP:AF:ID -0.0021137:0.00426912:0.207608:0.0366:rs12184267
1 715367 rs12184277 A G . PASS AF=0.036719 ES:SE:LP:AF:ID -0.00223278:0.00425265:0.221849:0.036719:rs12184277
1 717485 rs12184279 C A . PASS AF=0.036416 ES:SE:LP:AF:ID -0.00299201:0.00428355:0.318759:0.036416:rs12184279
1 717587 rs144155419 G A . PASS AF=0.01639 ES:SE:LP:AF:ID -0.00029841:0.00659755:0.0177288:0.01639:rs144155419
1 720381 rs116801199 G T . PASS AF=0.036956 ES:SE:LP:AF:ID -0.00257191:0.00423607:0.267606:0.036956:rs116801199
1 721290 rs12565286 G C . PASS AF=0.037054 ES:SE:LP:AF:ID -0.00274491:0.00422144:0.283997:0.037054:rs12565286
1 722670 rs116030099 T C . PASS AF=0.101245 ES:SE:LP:AF:ID 0.0019555:0.00307464:0.283997:0.101245:rs116030099
1 723891 rs2977670 G C . PASS AF=0.959094 ES:SE:LP:AF:ID 0.00295704:0.00407045:0.327902:0.959094:rs2977670
1 724849 rs12126395 C A . PASS AF=0.031462 ES:SE:LP:AF:ID 0.00972567:0.00738455:0.721246:0.031462:rs12126395
1 725060 rs865924913 A T . PASS AF=0.053252 ES:SE:LP:AF:ID -0.0026755:0.00587508:0.187087:0.053252:rs865924913
1 726794 rs28454925 C G . PASS AF=0.036575 ES:SE:LP:AF:ID -0.0032217:0.00424855:0.346787:0.036575:rs28454925
1 729632 rs116720794 C T . PASS AF=0.036893 ES:SE:LP:AF:ID -0.00378131:0.00420987:0.431798:0.036893:rs116720794
1 729679 rs4951859 C G . PASS AF=0.843323 ES:SE:LP:AF:ID -0.000485758:0.00218105:0.0861861:0.843323:rs4951859
1 730087 rs148120343 T C . PASS AF=0.055859 ES:SE:LP:AF:ID -0.00221961:0.00353214:0.275724:0.055859:rs148120343
1 731718 rs58276399 T C . PASS AF=0.122188 ES:SE:LP:AF:ID 0.00174216:0.00238789:0.327902:0.122188:rs58276399
1 732989 rs369030935 C T . PASS AF=0.025733 ES:SE:LP:AF:ID -0.00958377:0.00586721:1:0.025733:rs369030935
1 734349 rs141242758 T C . PASS AF=0.121428 ES:SE:LP:AF:ID 0.00197926:0.00238891:0.387216:0.121428:rs141242758
1 736289 rs79010578 T A . PASS AF=0.132216 ES:SE:LP:AF:ID 0.000211931:0.00235416:0.0315171:0.132216:rs79010578
1 736689 rs181876450 T C . PASS AF=0.011123 ES:SE:LP:AF:ID -0.00210686:0.00856442:0.091515:0.011123:rs181876450
1 740284 rs61770167 C T . PASS AF=0.005693 ES:SE:LP:AF:ID 0.0135108:0.0110492:0.657577:0.005693:rs61770167
1 742813 rs112573343 C T . PASS AF=0.002288 ES:SE:LP:AF:ID 0.0121271:0.0184952:0.29243:0.002288:rs112573343
1 746189 rs139221807 A G . PASS AF=0.001037 ES:SE:LP:AF:ID 0.00900139:0.030254:0.113509:0.001037:rs139221807
1 752478 rs146277091 G A . PASS AF=0.036807 ES:SE:LP:AF:ID -0.00303507:0.00416734:0.327902:0.036807:rs146277091
1 752566 rs3094315 G A . PASS AF=0.839052 ES:SE:LP:AF:ID -0.000287709:0.00211207:0.05061:0.839052:rs3094315
1 752721 rs3131972 A G . PASS AF=0.838679 ES:SE:LP:AF:ID 0.000118561:0.00210975:0.0177288:0.838679:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869875 ES:SE:LP:AF:ID -0.00165896:0.00226413:0.337242:0.869875:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129765 ES:SE:LP:AF:ID 0.00090927:0.00226886:0.161151:0.129765:rs2073813
1 754063 rs12184312 G T . PASS AF=0.037319 ES:SE:LP:AF:ID -0.00362871:0.0040967:0.420216:0.037319:rs12184312
1 754105 rs12184325 C T . PASS AF=0.037564 ES:SE:LP:AF:ID -0.0035755:0.00407076:0.420216:0.037564:rs12184325
1 754182 rs3131969 A G . PASS AF=0.869219 ES:SE:LP:AF:ID -0.00125585:0.00225968:0.236572:0.869219:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869316 ES:SE:LP:AF:ID -0.00124829:0.00226058:0.236572:0.869316:rs3131968
1 754211 rs12184313 G A . PASS AF=0.037517 ES:SE:LP:AF:ID -0.00370632:0.00408862:0.443698:0.037517:rs12184313
1 754334 rs3131967 T C . PASS AF=0.869222 ES:SE:LP:AF:ID -0.00122035:0.00225963:0.229148:0.869222:rs3131967
1 754433 rs150578204 G A . PASS AF=0.005133 ES:SE:LP:AF:ID -0.00527144:0.0115825:0.187087:0.005133:rs150578204
1 754458 rs142682604 G T . PASS AF=0.005099 ES:SE:LP:AF:ID -0.00533015:0.0116125:0.187087:0.005099:rs142682604
1 754503 rs3115859 G A . PASS AF=0.838135 ES:SE:LP:AF:ID 0.000180466:0.00210394:0.0315171:0.838135:rs3115859
1 754629 rs10454459 A G . PASS AF=0.037529 ES:SE:LP:AF:ID -0.00405202:0.0040944:0.49485:0.037529:rs10454459
1 754964 rs3131966 C T . PASS AF=0.838767 ES:SE:LP:AF:ID 5.02844e-05:0.00210987:0.00877392:0.838767:rs3131966
1 755240 rs181660517 T G . PASS AF=0.013776 ES:SE:LP:AF:ID 0.00123243:0.00736146:0.0604807:0.013776:rs181660517