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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"Gwas2VCF_command": "--data /data/cromwell-executions/qc/a11061ae-78f0-4fcd-99aa-578156004707/call-vcf/inputs/562856221/upload.txt.gz --id ieu-b-43 --json /data/cromwell-executions/qc/a11061ae-78f0-4fcd-99aa-578156004707/call-vcf/inputs/562856221/ieu-b-43_data.json --ref /data/cromwell-executions/qc/a11061ae-78f0-4fcd-99aa-578156004707/call-vcf/inputs/1899004205/human_g1k_v37.fasta --dbsnp /data/cromwell-executions/qc/a11061ae-78f0-4fcd-99aa-578156004707/call-vcf/inputs/-307190728/dbsnp.v153.b37.vcf.gz --out /data/igd/ieu-b-43/ieu-b-43.vcf.gz --rm_chr_prefix --cohort_cases 515 --cohort_controls 2509; 1.2.1",
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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 /data/cromwell-executions/qc/a11061ae-78f0-4fcd-99aa-578156004707/call-ldsc/inputs/562856221/ieu-b-43.vcf.gz \
--ref-ld-chr /data/ref/eur_w_ld_chr/ \
--out /data/igd/ieu-b-43/ldsc.txt \
--w-ld-chr /data/ref/eur_w_ld_chr/
Beginning analysis at Fri Aug 21 17:39:07 2020
Reading summary statistics from /data/cromwell-executions/qc/a11061ae-78f0-4fcd-99aa-578156004707/call-ldsc/inputs/562856221/ieu-b-43.vcf.gz ...
Traceback (most recent call last):
File "./ldsc/ldsc.py", line 647, in <module>
sumstats.estimate_h2(args, log)
File "/ldsc/ldscore/sumstats.py", line 330, in estimate_h2
args, log, args.h2)
File "/ldsc/ldscore/sumstats.py", line 246, in _read_ld_sumstats
sumstats = _read_sumstats(args, log, fh, alleles=alleles, dropna=dropna)
File "/ldsc/ldscore/sumstats.py", line 167, in _read_sumstats
sumstats = ps.sumstats(fh, alleles=alleles, dropna=dropna)
File "/ldsc/ldscore/parse.py", line 85, in sumstats
x = read_vcf(fh, alleles, slh)
File "/ldsc/ldscore/parse.py", line 137, in read_vcf
o = [[rec.id, rec.samples[sample]['ES'][0]/rec.samples[sample]['SE'][0], rec.samples[sample]['SS'][0]] for rec in vcf_in.fetch()]
ZeroDivisionError: float division by zero
Analysis finished at Fri Aug 21 17:39:07 2020
Total time elapsed: 0.05sQC metrics
Metrics
Metrics
{
"af_correlation": 0.913,
"inflation_factor": 1.0581,
"mean_EFFECT": 0.0014,
"n": 3024,
"n_snps": 399503,
"n_clumped_hits": 1,
"n_p_sig": 1,
"n_mono": 0,
"n_ns": 964,
"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": 2283,
"n_est": 3030.2449,
"ratio_se_n": 1.001,
"mean_diff": "NaN",
"ratio_diff": "NaN",
"sd_y_est1": 2.6617,
"sd_y_est2": 2.6645,
"r2_sum1": 0.115,
"r2_sum2": 0.0162,
"r2_sum3": 0.0162,
"r2_sum4": 0.0148,
"ldsc_nsnp_merge_refpanel_ld": "NA",
"ldsc_nsnp_merge_regression_ld": "NA",
"ldsc_observed_scale_h2_beta": "NA",
"ldsc_observed_scale_h2_se": "NA",
"ldsc_intercept_beta": "NA",
"ldsc_intercept_se": "NA",
"ldsc_lambda_gc": "NA",
"ldsc_mean_chisq": "NA",
"ldsc_ratio": "NA"
}
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 | NA |
| n_p_sig | FALSE |
| miss_EFFECT | FALSE |
| miss_SE | FALSE |
| miss_PVAL | FALSE |
| ldsc_ratio | NA |
| ldsc_intercept_beta | NA |
| 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 | 25 | 0 | 399503 | 0 | 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 |
| character | ALT | 0 | 1.0000000 | 1 | 1 | 0 | 4 | 0 | NA | NA | NA | NA | NA | NA | NA | NA |
| numeric | CHROM | 0 | 1.0000000 | NA | NA | NA | NA | NA | 8.935680e+00 | 5.844948e+00 | 1.00000e+00 | 4.000000e+00 | 8.000000e+00 | 1.300000e+01 | 2.200000e+01 | ▇▆▅▃▂ |
| numeric | POS | 0 | 1.0000000 | NA | NA | NA | NA | NA | 7.756593e+07 | 5.666760e+07 | 3.30120e+04 | 3.048904e+07 | 6.804957e+07 | 1.142195e+08 | 2.492107e+08 | ▇▆▃▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | 1.445100e-03 | 1.045390e-01 | -1.64818e+00 | -5.921190e-02 | 1.801600e-03 | 6.208820e-02 | 1.095270e+00 | ▁▁▇▇▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | 9.442260e-02 | 3.654460e-02 | 0.00000e+00 | 7.132100e-02 | 8.119480e-02 | 1.040560e-01 | 5.483830e-01 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | 4.912069e-01 | 2.908254e-01 | 0.00000e+00 | 2.378001e-01 | 4.878002e-01 | 7.422001e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 964 | 0.9975870 | NA | NA | NA | NA | NA | 4.899817e-01 | 2.901067e-01 | 0.00000e+00 | 2.372260e-01 | 4.866606e-01 | 7.404859e-01 | 9.996910e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | 3.592723e-01 | 2.545821e-01 | 1.00057e-02 | 1.423720e-01 | 2.981750e-01 | 5.405390e-01 | 9.899170e-01 | ▇▆▃▃▂ |
| numeric | AF_reference | 2283 | 0.9942854 | NA | NA | NA | NA | NA | 3.611443e-01 | 2.407581e-01 | 1.99700e-04 | 1.615420e-01 | 3.043130e-01 | 5.307510e-01 | 9.974040e-01 | ▇▇▅▃▂ |
| numeric | N | 0 | 1.0000000 | NA | NA | NA | NA | NA | 3.024000e+03 | 0.000000e+00 | 3.02400e+03 | 3.024000e+03 | 3.024000e+03 | 3.024000e+03 | 3.024000e+03 | ▁▁▇▁▁ |
Head and tail
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 768448 | rs12562034 | G | A | 0.0256677 | 0.1101710 | 0.8158000 | 0.8157765 | 0.105726 | 0.191893 | 3024 |
| 1 | 1005806 | rs3934834 | C | T | 0.0459289 | 0.0955402 | 0.6307002 | 0.6307095 | 0.148488 | 0.223442 | 3024 |
| 1 | 1018704 | rs9442372 | A | G | 0.1103730 | 0.0694047 | 0.1118001 | 0.1117714 | 0.565981 | 0.611022 | 3024 |
| 1 | 1030565 | rs6687776 | C | T | 0.1972100 | 0.0899950 | 0.0284302 | 0.0284269 | 0.157866 | 0.306709 | 3024 |
| 1 | 1031540 | rs9651273 | A | G | 0.0573114 | 0.0776181 | 0.4603000 | 0.4602855 | 0.726524 | 0.881989 | 3024 |
| 1 | 1048955 | rs4970405 | A | G | 0.1371500 | 0.1089730 | 0.2082001 | 0.2081862 | 0.102731 | 0.110623 | 3024 |
| 1 | 1049950 | rs12726255 | A | G | 0.1527210 | 0.0951169 | 0.1083999 | 0.1083588 | 0.139536 | 0.289736 | 3024 |
| 1 | 1061166 | rs11807848 | T | C | 0.0487902 | 0.0693473 | 0.4817004 | 0.4817049 | 0.413427 | 0.329673 | 3024 |
| 1 | 1062638 | rs9442373 | C | A | -0.0109399 | 0.0665660 | 0.8695000 | 0.8694582 | 0.546023 | 0.574281 | 3024 |
| 1 | 1064979 | rs2298217 | C | T | 0.0582689 | 0.0946618 | 0.5382004 | 0.5381928 | 0.150994 | 0.164736 | 3024 |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51123505 | rs9616816 | G | A | 0.0554347 | 0.0814161 | 0.4958999 | 0.4959466 | 0.2263520 | 0.3783950 | 3024 |
| 22 | 51146139 | rs5770992 | A | G | 0.0713900 | 0.1050080 | 0.4966003 | 0.4965976 | 0.1161600 | 0.1136180 | 3024 |
| 22 | 51147015 | rs2040487 | A | G | 0.0401818 | 0.0699475 | 0.5656998 | 0.5656589 | 0.4133520 | 0.4556710 | 3024 |
| 22 | 51150473 | rs5770820 | G | A | 0.0079682 | 0.0833507 | 0.9238001 | 0.9238398 | 0.2318380 | 0.2462060 | 3024 |
| 22 | 51151724 | rs6010061 | C | T | 0.0459289 | 0.0697014 | 0.5099000 | 0.5099356 | 0.3985900 | 0.6098240 | 3024 |
| 22 | 51163138 | rs715586 | C | T | 0.0198026 | 0.0929076 | 0.8311999 | 0.8312155 | 0.1541430 | 0.0902556 | 3024 |
| 22 | 51165664 | rs8137951 | G | A | -0.0366640 | 0.0753759 | 0.6267004 | 0.6266727 | 0.2946060 | 0.4063500 | 3024 |
| 22 | 51171693 | rs756638 | G | A | -0.0314907 | 0.0769210 | 0.6822994 | 0.6822534 | 0.2760440 | 0.3049120 | 3024 |
| 22 | 51175626 | rs3810648 | A | G | -0.0441609 | 0.1502820 | 0.7689002 | 0.7688698 | 0.0564032 | 0.1084270 | 3024 |
| 22 | 51178090 | rs2285395 | G | A | -0.0350056 | 0.1620390 | 0.8289999 | 0.8289628 | 0.0479109 | 0.0666933 | 3024 |
bcf preview
1 768448 rs12562034 G A . PASS AF=0.105726 ES:SE:LP:AF:SS:NC:ID 0.0256677:0.110171:0.0884163:0.105726:3024:515:rs12562034
1 1005806 rs3934834 C T . PASS AF=0.148488 ES:SE:LP:AF:SS:NC:ID 0.0459289:0.0955402:0.200177:0.148488:3024:515:rs3934834
1 1018704 rs9442372 A G . PASS AF=0.565981 ES:SE:LP:AF:SS:NC:ID 0.110373:0.0694047:0.951558:0.565981:3024:515:rs9442372
1 1030565 rs6687776 C T . PASS AF=0.157866 ES:SE:LP:AF:SS:NC:ID 0.19721:0.089995:1.54622:0.157866:3024:515:rs6687776
1 1031540 rs776599533 A G . PASS AF=0.726524 ES:SE:LP:AF:SS:NC:ID 0.0573114:0.0776181:0.336959:0.726524:3024:515:rs776599533
1 1048955 rs4970405 A G . PASS AF=0.102731 ES:SE:LP:AF:SS:NC:ID 0.13715:0.108973:0.681519:0.102731:3024:515:rs4970405
1 1049950 rs12726255 A G . PASS AF=0.139536 ES:SE:LP:AF:SS:NC:ID 0.152721:0.0951169:0.964971:0.139536:3024:515:rs12726255
1 1061166 rs11807848 T C . PASS AF=0.413427 ES:SE:LP:AF:SS:NC:ID 0.0487902:0.0693473:0.317223:0.413427:3024:515:rs11807848
1 1062638 rs9442373 C A . PASS AF=0.546023 ES:SE:LP:AF:SS:NC:ID -0.0109399:0.066566:0.0607304:0.546023:3024:515:rs9442373
1 1064979 rs2298217 C T . PASS AF=0.150994 ES:SE:LP:AF:SS:NC:ID 0.0582689:0.0946618:0.269056:0.150994:3024:515:rs2298217
1 1066029 rs12145826 G A . PASS AF=0.0720194 ES:SE:LP:AF:SS:NC:ID -0.130564:0.137398:0.465974:0.0720194:3024:515:rs12145826
1 1087683 rs9442380 T C . PASS AF=0.929896 ES:SE:LP:AF:SS:NC:ID 0.115186:0.138468:0.392009:0.929896:3024:515:rs9442380
1 1090557 rs7553429 A C . PASS AF=0.0272798 ES:SE:LP:AF:SS:NC:ID 0.08158:0.204526:0.161214:0.0272798:3024:515:rs7553429
1 1094738 rs4970362 A G . PASS AF=0.644355 ES:SE:LP:AF:SS:NC:ID 0.00682323:0.071378:0.034422:0.644355:3024:515:rs4970362
1 1099342 rs9660710 A C . PASS AF=0.931713 ES:SE:LP:AF:SS:NC:ID 0.081644:0.138859:0.254535:0.931713:3024:515:rs9660710
1 1106473 rs4970420 G A . PASS AF=0.188064 ES:SE:LP:AF:SS:NC:ID -0.00521357:0.0874041:0.0211806:0.188064:3024:515:rs4970420
1 1119858 rs1320565 C T . PASS AF=0.0790578 ES:SE:LP:AF:SS:NC:ID -0.0726782:0.12933:0.240937:0.0790578:3024:515:rs1320565
1 1121794 rs11260549 G A . PASS AF=0.120641 ES:SE:LP:AF:SS:NC:ID 0.0620354:0.103178:0.261537:0.120641:3024:515:rs11260549
1 1135242 rs9729550 A C . PASS AF=0.265005 ES:SE:LP:AF:SS:NC:ID 0.0305292:0.0779476:0.157828:0.265005:3024:515:rs9729550
1 1163804 rs7515488 C T . PASS AF=0.149761 ES:SE:LP:AF:SS:NC:ID -0.0486652:0.097059:0.210349:0.149761:3024:515:rs7515488
1 1172907 rs715643 C T . PASS AF=0.0531373 ES:SE:LP:AF:SS:NC:ID 0.0573251:0.151486:0.151749:0.0531373:3024:515:rs715643
1 1176597 rs6675798 T C . PASS AF=0.0951217 ES:SE:LP:AF:SS:NC:ID -0.0871932:0.119826:0.330869:0.0951217:3024:515:rs6675798
1 1192515 rs7524470 A G . PASS AF=0.0376963 ES:SE:LP:AF:SS:NC:ID -0.0939811:0.184953:0.213746:0.0376963:3024:515:rs7524470
1 1194804 rs11804831 T C . PASS AF=0.183342 ES:SE:LP:AF:SS:NC:ID -0.0462534:0.0893796:0.218388:0.183342:3024:515:rs11804831
1 1211292 rs6685064 C T . PASS AF=0.0661819 ES:SE:LP:AF:SS:NC:ID -0.162166:0.144297:0.583193:0.0661819:3024:515:rs6685064
1 1425700 rs819980 T C . PASS AF=0.0782545 ES:SE:LP:AF:SS:NC:ID -0.0933222:0.130895:0.322484:0.0782545:3024:515:rs819980
1 1462766 rs9439462 C T . PASS AF=0.0378735 ES:SE:LP:AF:SS:NC:ID -0.242836:0.193251:0.680062:0.0378735:3024:515:rs9439462
1 1505255 rs6603793 C T . PASS AF=0.320414 ES:SE:LP:AF:SS:NC:ID -0.13102:0.0744987:1.10436:0.320414:3024:515:rs6603793
1 1706136 rs6603811 T C . PASS AF=0.949834 ES:SE:LP:AF:SS:NC:ID -0.0158733:0.160353:0.0356932:0.949834:3024:515:rs6603811
1 1706160 rs7531583 A G . PASS AF=0.77173 ES:SE:LP:AF:SS:NC:ID -0.00598207:0.0816328:0.0261336:0.77173:3024:515:rs7531583
1 1745726 rs16825336 G A . PASS AF=0.0980834 ES:SE:LP:AF:SS:NC:ID 0.0256677:0.11293:0.0860802:0.0980834:3024:515:rs16825336
1 1781220 rs6681938 T C . PASS AF=0.296768 ES:SE:LP:AF:SS:NC:ID -0.0208151:0.0752131:0.106793:0.296768:3024:515:rs6681938
1 1793111 rs10907192 A G . PASS AF=0.95454 ES:SE:LP:AF:SS:NC:ID 0.165111:0.172742:0.469544:0.95454:3024:515:rs10907192
1 1801034 rs1306328781 G A . PASS AF=0.267092 ES:SE:LP:AF:SS:NC:ID -0.0825124:0.0783526:0.534171:0.267092:3024:515:rs1306328781
1 1810090 rs7525092 C T . PASS AF=0.267745 ES:SE:LP:AF:SS:NC:ID -0.0774211:0.0782072:0.491874:0.267745:3024:515:rs7525092
1 1873625 rs12758705 G A . PASS AF=0.259702 ES:SE:LP:AF:SS:NC:ID -0.0213258:0.078242:0.10502:0.259702:3024:515:rs12758705
1 1874581 rs2803329 A G . PASS AF=0.801328 ES:SE:LP:AF:SS:NC:ID 0.0193867:0.0859554:0.0853396:0.801328:3024:515:rs2803329
1 2024064 rs1455744253 C T . PASS AF=0.169139 ES:SE:LP:AF:SS:NC:ID 0.228728:0.0871005:2.06359:0.169139:3024:515:rs1455744253
1 2026749 rs884080 A G . PASS AF=0.45587 ES:SE:LP:AF:SS:NC:ID 0.0778865:0.0687889:0.589223:0.45587:3024:515:rs884080
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1 2040936 rs4648808 T C . PASS AF=0.874966 ES:SE:LP:AF:SS:NC:ID -0.001998:0.0931574:0.00749066:0.874966:3024:515:rs4648808
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1 2051513 rs1190080979 T G . PASS AF=0.595941 ES:SE:LP:AF:SS:NC:ID -0.0667236:0.0691149:0.475864:0.595941:3024:515:rs1190080979
1 2058023 rs3128291 A G . PASS AF=0.877285 ES:SE:LP:AF:SS:NC:ID 0.00440971:0.104579:0.0148431:0.877285:3024:515:rs3128291
1 2068906 rs3128296 G T . PASS AF=0.869567 ES:SE:LP:AF:SS:NC:ID -0.0778865:0.0995767:0.36241:0.869567:3024:515:rs3128296
1 2069681 rs3753242 C T . PASS AF=0.0644843 ES:SE:LP:AF:SS:NC:ID 0.0497421:0.138333:0.14315:0.0644843:3024:515:rs3753242
1 2071340 rs424079 C A . PASS AF=0.605173 ES:SE:LP:AF:SS:NC:ID -0.0695261:0.0701104:0.492954:0.605173:3024:515:rs424079
1 2096638 rs3052 C T . PASS AF=0.110286 ES:SE:LP:AF:SS:NC:ID 0.0525924:0.108652:0.201764:0.110286:3024:515:rs3052
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