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": "--json /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-C3_MELANOMA_SKIN/ukb-d-C3_MELANOMA_SKIN_data.json --ref /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/QC/genomes/b37/human_g1k_v37.fasta; 1.1.1",
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}
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/dev/ukb-d-import/processed/ukb-d-C3_MELANOMA_SKIN/ukb-d-C3_MELANOMA_SKIN.vcf.gz \
--ref-ld-chr /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-C3_MELANOMA_SKIN/ldsc.txt \
--w-ld-chr /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/
Beginning analysis at Mon Nov 25 14:49:15 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-C3_MELANOMA_SKIN/ukb-d-C3_MELANOMA_SKIN.vcf.gz ...
Read summary statistics for 10855954 SNPs.
Dropped 8869 SNPs with duplicated rs numbers.
Reading reference panel LD Score from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/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 /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/[1-22] ...
Read regression weight LD Scores for 1290028 SNPs.
After merging with reference panel LD, 1281635 SNPs remain.
After merging with regression SNP LD, 1281635 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0032 (0.0017)
Lambda GC: 1.0208
Mean Chi^2: 1.039
Intercept: 1.016 (0.0067)
Ratio: 0.4105 (0.1708)
Analysis finished at Mon Nov 25 14:51:05 2019
Total time elapsed: 1.0m:49.37sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9454,
"inflation_factor": 1.0122,
"mean_EFFECT": -2.6222e-06,
"n": 361194,
"n_snps": 10855954,
"n_clumped_hits": 8,
"n_p_sig": 1081,
"n_mono": 0,
"n_ns": 1130813,
"n_mac": 0,
"is_snpid_unique": false,
"n_miss_EFFECT": 0,
"n_miss_SE": 0,
"n_miss_PVAL": 0,
"n_miss_AF": 0,
"n_miss_AF_reference": 215460,
"n_est": 361683.765,
"ratio_se_n": 1.0007,
"mean_diff": -0,
"ratio_diff": 37.7039,
"sd_y_est1": 0.0837,
"sd_y_est2": 0.0838,
"r2_sum1": 8.8004e-06,
"r2_sum2": 0.0013,
"r2_sum3": 0.0013,
"r2_sum4": 0.0013,
"ldsc_nsnp_merge_refpanel_ld": 1281635,
"ldsc_nsnp_merge_regression_ld": 1281635,
"ldsc_observed_scale_h2_beta": 0.0032,
"ldsc_observed_scale_h2_se": 0.0017,
"ldsc_intercept_beta": 1.016,
"ldsc_intercept_se": 0.0067,
"ldsc_lambda_gc": 1.0208,
"ldsc_mean_chisq": 1.039,
"ldsc_ratio": 0.4103
}
Flags
| name | value |
|---|---|
| af_correlation | FALSE |
| inflation_factor | FALSE |
| n | FALSE |
| is_snpid_non_unique | TRUE |
| mean_EFFECT_nonfinite | FALSE |
| mean_EFFECT_05 | FALSE |
| mean_EFFECT_01 | FALSE |
| mean_chisq | FALSE |
| 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 | numeric.mean | numeric.sd | numeric.p0 | numeric.p25 | numeric.p50 | numeric.p75 | numeric.p100 | numeric.hist |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| character | ID | 0 | 1.0000000 | 3 | 94 | 0 | 10847668 | 0 | NA | NA | NA | NA | NA | NA | NA | NA |
| character | REF | 0 | 1.0000000 | 1 | 100 | 0 | 50693 | 0 | NA | NA | NA | NA | NA | NA | NA | NA |
| character | ALT | 0 | 1.0000000 | 1 | 342 | 0 | 31153 | 0 | NA | NA | NA | NA | NA | NA | NA | NA |
| numeric | CHROM | 0 | 1.0000000 | NA | NA | NA | NA | NA | 9.074856e+00 | 6.180021e+00 | 1.00000e+00 | 4.000000e+00 | 8.000000e+00 | 1.300000e+01 | 2.300000e+01 | ▇▅▅▂▂ |
| numeric | POS | 0 | 1.0000000 | NA | NA | NA | NA | NA | 7.880774e+07 | 5.603539e+07 | 3.02000e+02 | 3.258219e+07 | 6.977207e+07 | 1.147442e+08 | 2.492309e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | -2.600000e-06 | 5.860000e-04 | -7.64650e-03 | -2.422000e-04 | -3.900000e-06 | 2.326000e-04 | 7.543300e-03 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | 4.757000e-04 | 3.365000e-04 | 1.62100e-04 | 2.208000e-04 | 3.191000e-04 | 6.332000e-04 | 1.948000e-03 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | 4.974862e-01 | 2.896834e-01 | 0.00000e+00 | 2.454240e-01 | 4.973888e-01 | 7.482505e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | 4.974858e-01 | 2.896836e-01 | 0.00000e+00 | 2.454227e-01 | 4.973884e-01 | 7.482497e-01 | 9.999999e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | 2.359768e-01 | 2.620779e-01 | 4.93290e-03 | 2.604620e-02 | 1.227640e-01 | 3.776768e-01 | 9.950670e-01 | ▇▂▂▁▁ |
| numeric | AF_reference | 215460 | 0.9801528 | NA | NA | NA | NA | NA | 2.360700e-01 | 2.531289e-01 | 0.00000e+00 | 2.535940e-02 | 1.399760e-01 | 3.755990e-01 | 1.000000e+00 | ▇▂▂▁▁ |
| numeric | N | 0 | 1.0000000 | NA | NA | NA | NA | NA | 3.611940e+05 | 0.000000e+00 | 3.61194e+05 | 3.611940e+05 | 3.611940e+05 | 3.611940e+05 | 3.611940e+05 | ▁▁▇▁▁ |
Head and tail
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 692794 | rs530212009 | CA | C | 0.0000497 | 0.0003453 | 0.8856420 | 0.8856419 | 0.1106400 | 0.1894970 | 361194 |
| 1 | 693731 | rs12238997 | A | G | 0.0000668 | 0.0003262 | 0.8378441 | 0.8378435 | 0.1158300 | 0.1417730 | 361194 |
| 1 | 707522 | rs371890604 | G | C | 0.0000474 | 0.0003667 | 0.8971561 | 0.8971564 | 0.0973034 | 0.1293930 | 361194 |
| 1 | 717587 | rs144155419 | G | A | -0.0007803 | 0.0008751 | 0.3725701 | 0.3725693 | 0.0156880 | 0.0045926 | 361194 |
| 1 | 730087 | rs148120343 | T | C | 0.0005374 | 0.0004545 | 0.2370528 | 0.2370529 | 0.0564602 | 0.0127796 | 361194 |
| 1 | 731718 | rs142557973 | T | C | 0.0001514 | 0.0003094 | 0.6245080 | 0.6245070 | 0.1217380 | 0.1543530 | 361194 |
| 1 | 732032 | rs61770163 | A | C | -0.0000479 | 0.0003300 | 0.8845490 | 0.8845484 | 0.1211710 | 0.1555510 | 361194 |
| 1 | 734349 | rs141242758 | T | C | 0.0001697 | 0.0003096 | 0.5835889 | 0.5835890 | 0.1209650 | 0.1525560 | 361194 |
| 1 | 740284 | rs61770167 | C | T | 0.0003377 | 0.0014162 | 0.8115310 | 0.8115314 | 0.0057851 | 0.0023962 | 361194 |
| 1 | 749963 | rs529266287 | T | TAA | 0.0000347 | 0.0003052 | 0.9094499 | 0.9094498 | 0.8697420 | 0.7641770 | 361194 |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 23 | 154923311 | rs141127553 | C | T | 0.0000118 | 0.0003574 | 0.9736900 | 0.9736903 | 0.0561667 | 0.0309934 | 361194 |
| 23 | 154923374 | rs111332691 | T | A | 0.0003467 | 0.0003928 | 0.3774687 | 0.3774678 | 0.0447862 | 0.0116556 | 361194 |
| 23 | 154925045 | rs509981 | C | T | -0.0003139 | 0.0001890 | 0.0967966 | 0.0967963 | 0.2456060 | 0.3634440 | 361194 |
| 23 | 154925895 | rs538470 | C | T | -0.0003553 | 0.0001933 | 0.0660861 | 0.0660857 | 0.2419150 | 0.3634440 | 361194 |
| 23 | 154927581 | rs644138 | G | A | -0.0002702 | 0.0001777 | 0.1285210 | 0.1285200 | 0.3021620 | 0.4635760 | 361194 |
| 23 | 154929412 | rs557132 | C | T | -0.0003110 | 0.0001891 | 0.0999816 | 0.0999811 | 0.2454590 | 0.3568210 | 361194 |
| 23 | 154929637 | rs35185538 | CT | C | -0.0001963 | 0.0001973 | 0.3197238 | 0.3197250 | 0.2296970 | 0.3011920 | 361194 |
| 23 | 154929952 | rs4012982 | CAA | C | -0.0003842 | 0.0001988 | 0.0533495 | 0.0533498 | 0.2394250 | 0.3165560 | 361194 |
| 23 | 154930230 | rs781880 | A | G | -0.0003230 | 0.0001890 | 0.0874903 | 0.0874894 | 0.2458690 | 0.3618540 | 361194 |
| 23 | 154930487 | rs781879 | T | A | -0.0009179 | 0.0006485 | 0.1569362 | 0.1569359 | 0.0195623 | 0.1263580 | 361194 |
bcf preview
1 692794 rs530212009 CA C . PASS AF=0.11064 ES:SE:LP:AF:SS:ID 4.96543e-05:0.000345251:0.0527418:0.11064:361194:1_692794_CA_C
1 693731 rs12238997 A G . PASS AF=0.11583 ES:SE:LP:AF:SS:ID 6.67618e-05:0.00032622:0.0768368:0.11583:361194:rs12238997
1 707522 rs371890604 G C . PASS AF=0.0973034 ES:SE:LP:AF:SS:ID 4.74026e-05:0.000366739:0.047132:0.0973034:361194:rs371890604
1 717587 rs144155419 G A . PASS AF=0.015688 ES:SE:LP:AF:SS:ID -0.000780329:0.000875131:0.428792:0.015688:361194:rs144155419
1 730087 rs148120343 T C . PASS AF=0.0564602 ES:SE:LP:AF:SS:ID 0.000537355:0.000454467:0.625155:0.0564602:361194:rs148120343
1 731718 rs58276399 T C . PASS AF=0.121738 ES:SE:LP:AF:SS:ID 0.000151437:0.000309388:0.204462:0.121738:361194:rs58276399
1 732032 rs61770163 A C . PASS AF=0.121171 ES:SE:LP:AF:SS:ID -4.79211e-05:0.000330022:0.0532781:0.121171:361194:rs61770163
1 734349 rs141242758 T C . PASS AF=0.120965 ES:SE:LP:AF:SS:ID 0.000169682:0.000309554:0.233893:0.120965:361194:rs141242758
1 740284 rs61770167 C T . PASS AF=0.00578512 ES:SE:LP:AF:SS:ID 0.000337699:0.00141622:0.0906949:0.00578512:361194:rs61770167
1 749963 rs529266287 T TAA . PASS AF=0.869742 ES:SE:LP:AF:SS:ID 3.4708e-05:0.000305172:0.0412212:0.869742:361194:rs529266287
1 751343 rs28544273 T A . PASS AF=0.122916 ES:SE:LP:AF:SS:ID 4.26723e-05:0.000302232:0.051724:0.122916:361194:rs28544273
1 751488 rs200141114 G GA . PASS AF=0.142712 ES:SE:LP:AF:SS:ID -9.3745e-05:0.00029874:0.122818:0.142712:361194:rs200141114
1 751756 rs28527770 T C . PASS AF=0.123031 ES:SE:LP:AF:SS:ID 4.26474e-05:0.000301812:0.0517685:0.123031:361194:rs28527770
1 753405 rs3115860 C A . PASS AF=0.87089 ES:SE:LP:AF:SS:ID -0.000106329:0.000294105:0.144057:0.87089:361194:rs3115860
1 753425 rs3131970 T C . PASS AF=0.875472 ES:SE:LP:AF:SS:ID -8.10554e-06:0.000298979:0.00949643:0.875472:361194:rs3131970
1 753541 rs2073813 G A . PASS AF=0.128631 ES:SE:LP:AF:SS:ID 0.00010473:0.000294843:0.141201:0.128631:361194:rs2073813
1 754105 rs12184325 C T . PASS AF=0.0363613 ES:SE:LP:AF:SS:ID -0.000599668:0.000534912:0.581261:0.0363613:361194:rs12184325
1 754182 rs3131969 A G . PASS AF=0.870484 ES:SE:LP:AF:SS:ID -9.42382e-05:0.0002938:0.125869:0.870484:361194:rs3131969
1 754192 rs3131968 A G . PASS AF=0.870589 ES:SE:LP:AF:SS:ID -7.5037e-05:0.000293934:0.0977234:0.870589:361194:rs3131968
1 754334 rs3131967 T C . PASS AF=0.870481 ES:SE:LP:AF:SS:ID -9.33163e-05:0.000293789:0.124496:0.870481:361194:rs3131967
1 754433 rs150578204 G A . PASS AF=0.00504367 ES:SE:LP:AF:SS:ID -0.00152122:0.00150826:0.504221:0.00504367:361194:rs150578204
1 754458 rs142682604 G T . PASS AF=0.00501064 ES:SE:LP:AF:SS:ID -0.00153042:0.0015123:0.506479:0.00501064:361194:rs142682604
1 755435 rs184270342 T G . PASS AF=0.00558665 ES:SE:LP:AF:SS:ID 0.0012745:0.00146078:0.416859:0.00558665:361194:rs184270342
1 755890 rs3115858 A T . PASS AF=0.870563 ES:SE:LP:AF:SS:ID -8.64473e-05:0.000293272:0.114542:0.870563:361194:rs3115858
1 756434 rs61768170 G C . PASS AF=0.125985 ES:SE:LP:AF:SS:ID 6.21097e-05:0.000299245:0.0780135:0.125985:361194:rs61768170
1 756604 rs3131962 A G . PASS AF=0.870127 ES:SE:LP:AF:SS:ID -8.95684e-05:0.000292566:0.119477:0.870127:361194:rs3131962
1 757640 rs3115853 G A . PASS AF=0.869414 ES:SE:LP:AF:SS:ID -8.97566e-05:0.000292299:0.119878:0.869414:361194:rs3115853
1 757734 rs4951929 C T . PASS AF=0.870281 ES:SE:LP:AF:SS:ID -9.59965e-05:0.000292818:0.128991:0.870281:361194:rs4951929
1 757936 rs4951862 C A . PASS AF=0.870287 ES:SE:LP:AF:SS:ID -9.59331e-05:0.000292839:0.128885:0.870287:361194:rs4951862
1 758144 rs3131956 A G . PASS AF=0.870295 ES:SE:LP:AF:SS:ID -9.61833e-05:0.000292848:0.129258:0.870295:361194:rs3131956
1 758626 rs3131954 C T . PASS AF=0.870739 ES:SE:LP:AF:SS:ID -8.29082e-05:0.000293593:0.109219:0.870739:361194:rs3131954
1 759293 rs10157329 T A . PASS AF=0.0988442 ES:SE:LP:AF:SS:ID 0.000152611:0.000341173:0.183991:0.0988442:361194:rs10157329
1 759600 rs545998451 AGT A . PASS AF=0.00647588 ES:SE:LP:AF:SS:ID 0.001433:0.00131967:0.556679:0.00647588:361194:1_759600_AGT_A
1 759837 rs3115851 T A . PASS AF=0.874635 ES:SE:LP:AF:SS:ID -3.78772e-07:0.000297969:0.000440598:0.874635:361194:rs3115851
1 761732 rs2286139 C T . PASS AF=0.864027 ES:SE:LP:AF:SS:ID -9.23646e-05:0.000292203:0.123823:0.864027:361194:rs2286139
1 761752 rs1057213 C T . PASS AF=0.869425 ES:SE:LP:AF:SS:ID -8.20213e-05:0.00029504:0.107342:0.869425:361194:rs1057213
1 762273 rs3115849 G A . PASS AF=0.866371 ES:SE:LP:AF:SS:ID -0.00013324:0.000295099:0.186003:0.866371:361194:rs3115849
1 762485 rs12095200 C A . PASS AF=0.0987831 ES:SE:LP:AF:SS:ID -0.000147789:0.000353289:0.170239:0.0987831:361194:rs12095200
1 762589 rs3115848 G C . PASS AF=0.871556 ES:SE:LP:AF:SS:ID -5.0865e-05:0.000298461:0.0631461:0.871556:361194:rs3115848
1 762592 rs3131950 C G . PASS AF=0.871556 ES:SE:LP:AF:SS:ID -5.08615e-05:0.000298461:0.0631416:0.871556:361194:rs3131950
1 762601 rs3131949 T C . PASS AF=0.871555 ES:SE:LP:AF:SS:ID -5.09692e-05:0.000298467:0.0632827:0.871555:361194:rs3131949
1 762632 rs3131948 T A . PASS AF=0.871929 ES:SE:LP:AF:SS:ID -6.10406e-05:0.000298637:0.0767337:0.871929:361194:rs3131948
1 764191 rs7515915 T G . PASS AF=0.125562 ES:SE:LP:AF:SS:ID 1.00995e-05:0.000298873:0.011868:0.125562:361194:rs7515915
1 766007 rs61768174 A C . PASS AF=0.105428 ES:SE:LP:AF:SS:ID 0.000324748:0.000325364:0.497262:0.105428:361194:rs61768174
1 766105 rs2519015 T A . PASS AF=0.855376 ES:SE:LP:AF:SS:ID -3.72123e-05:0.000292093:0.0464215:0.855376:361194:rs2519015
1 768116 rs376645387 A AGTTTT . PASS AF=0.838358 ES:SE:LP:AF:SS:ID -0.000154719:0.000287764:0.228551:0.838358:361194:rs376645387
1 768253 rs2977608 A C . PASS AF=0.763351 ES:SE:LP:AF:SS:ID -0.000174552:0.000231596:0.345791:0.763351:361194:rs2977608
1 768448 rs12562034 G A . PASS AF=0.105921 ES:SE:LP:AF:SS:ID 0.000206312:0.000319083:0.285749:0.105921:361194:rs12562034
1 768819 rs12562811 C T . PASS AF=0.00768395 ES:SE:LP:AF:SS:ID 0.00166841:0.00118554:0.797683:0.00768395:361194:rs12562811
1 769138 rs59306077 CAT C . PASS AF=0.128938 ES:SE:LP:AF:SS:ID 9.90754e-05:0.000294692:0.132698:0.128938:361194:rs762168062