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/e08ff32c-d0d0-4758-9420-2c1b55a5d136/call-vcf/inputs/268670774/upload.txt.gz --id ieu-b-114 --json /data/cromwell-executions/qc/e08ff32c-d0d0-4758-9420-2c1b55a5d136/call-vcf/inputs/268670774/ieu-b-114_data.json --ref /data/cromwell-executions/qc/e08ff32c-d0d0-4758-9420-2c1b55a5d136/call-vcf/inputs/1899004205/human_g1k_v37.fasta --dbsnp /data/cromwell-executions/qc/e08ff32c-d0d0-4758-9420-2c1b55a5d136/call-vcf/inputs/-307190728/dbsnp.v153.b37.vcf.gz --out /data/igd/ieu-b-114/ieu-b-114.vcf.gz --alias alias.txt; 1.3.0",
"file_date": "2021-04-01T15:14:29.919995",
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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/e08ff32c-d0d0-4758-9420-2c1b55a5d136/call-ldsc/inputs/268670774/ieu-b-114.vcf.gz \
--ref-ld-chr /data/ref/eur_w_ld_chr/ \
--out /data/igd/ieu-b-114/ldsc.txt \
--w-ld-chr /data/ref/eur_w_ld_chr/
Beginning analysis at Thu Apr 1 15:14:51 2021
Reading summary statistics from /data/cromwell-executions/qc/e08ff32c-d0d0-4758-9420-2c1b55a5d136/call-ldsc/inputs/268670774/ieu-b-114.vcf.gz ...
Read summary statistics for 64432 SNPs.
Reading reference panel LD Score from /data/ref/eur_w_ld_chr/[1-22] ... (ldscore_fromlist)
Read reference panel LD Scores for 1290028 SNPs.
Removing partitioned LD Scores with zero variance.
Reading regression weight LD Score from /data/ref/eur_w_ld_chr/[1-22] ... (ldscore_fromlist)
Read regression weight LD Scores for 1290028 SNPs.
After merging with reference panel LD, 41411 SNPs remain.
After merging with regression SNP LD, 41411 SNPs remain.
WARNING: number of SNPs less than 200k; this is almost always bad.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.37 (0.0795)
Lambda GC: 1.4241
Mean Chi^2: 1.9482
Intercept: 1.201 (0.0327)
Ratio: 0.212 (0.0345)
Analysis finished at Thu Apr 1 15:14:58 2021
Total time elapsed: 7.38sQC metrics
Metrics
Metrics
{
"af_correlation": "NA",
"inflation_factor": 1.3716,
"mean_EFFECT": 0,
"n": 133010,
"n_snps": 64432,
"n_clumped_hits": 33,
"n_p_sig": 234,
"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": 64432,
"n_miss_AF_reference": 515,
"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": 41411,
"ldsc_nsnp_merge_regression_ld": 41411,
"ldsc_observed_scale_h2_beta": 0.37,
"ldsc_observed_scale_h2_se": 0.0795,
"ldsc_intercept_beta": 1.201,
"ldsc_intercept_se": 0.0327,
"ldsc_lambda_gc": 1.4241,
"ldsc_mean_chisq": 1.9482,
"ldsc_ratio": 0.212
}
Flags
| name | value |
|---|---|
| af_correlation | NA |
| inflation_factor | TRUE |
| n | FALSE |
| is_snpid_non_unique | FALSE |
| mean_EFFECT_nonfinite | FALSE |
| mean_EFFECT_05 | FALSE |
| mean_EFFECT_01 | FALSE |
| mean_chisq | TRUE |
| 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 | 5 | 33 | 0 | 64432 | 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 | AF | 64432 | 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 | 8.821657e+00 | 5.794757e+00 | 1.0000e+00 | 4.000000e+00 | 8.000000e+00 | 1.300000e+01 | 2.200000e+01 | ▇▅▅▃▂ |
| numeric | POS | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 7.722054e+07 | 5.589603e+07 | 1.3034e+04 | 3.159975e+07 | 6.645411e+07 | 1.136787e+08 | 2.491546e+08 | ▇▆▃▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.310000e-05 | 5.354100e-03 | -8.1000e-02 | -2.400000e-03 | 1.000000e-05 | 2.400000e-03 | 7.900000e-02 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.598800e-03 | 2.675300e-03 | 2.0000e-03 | 2.300000e-03 | 2.700000e-03 | 3.900000e-03 | 5.200000e-02 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.470294e-01 | 3.042627e-01 | 0.0000e+00 | 1.672780e-01 | 4.295735e-01 | 7.110117e-01 | 9.999950e-01 | ▇▆▅▅▅ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.471120e-01 | 3.042923e-01 | 0.0000e+00 | 1.672936e-01 | 4.298352e-01 | 7.111065e-01 | 1.000000e+00 | ▇▅▅▅▅ |
| numeric | AF_reference | 515 | 0.9920071 | NA | NA | NA | NA | NA | NA | NA | 3.395271e-01 | 2.596533e-01 | 1.9970e-04 | 1.238020e-01 | 2.691690e-01 | 5.187700e-01 | 1.000000e+00 | ▇▅▃▂▂ |
| numeric | N | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.330100e+05 | 0.000000e+00 | 1.3301e+05 | 1.330100e+05 | 1.330100e+05 | 1.330100e+05 | 1.330100e+05 | ▁▁▇▁▁ |
Head and tail
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 785050 | rs2905062 | G | A | -0.00230 | 0.0044 | 0.6020617 | 0.6011641 | NA | 0.626997 | 133010 |
| 1 | 1140435 | rs1815606 | G | T | -0.00070 | 0.0027 | 0.7991040 | 0.7954352 | NA | 0.712061 | 133010 |
| 1 | 1706136 | rs6603811 | T | C | 0.00230 | 0.0052 | 0.6623080 | 0.6582666 | NA | 0.746605 | 133010 |
| 1 | 1706160 | rs7531583 | A | G | 0.00042 | 0.0025 | 0.8669150 | 0.8665833 | NA | 0.583267 | 133010 |
| 1 | 1708801 | rs12044597 | A | G | -0.00099 | 0.0021 | 0.6355811 | 0.6373347 | NA | 0.360224 | 133010 |
| 1 | 1723031 | rs9660180 | G | A | 0.00022 | 0.0024 | 0.9253621 | 0.9269629 | NA | 0.358027 | 133010 |
| 1 | 1812688 | rs6603803 | A | G | -0.00100 | 0.0021 | 0.6247899 | 0.6339387 | NA | 0.360623 | 133010 |
| 1 | 1886519 | rs2748975 | C | A | -0.01000 | 0.0046 | 0.0277882 | 0.0297117 | NA | 0.607428 | 133010 |
| 1 | 1892325 | rs2803291 | T | C | 0.00240 | 0.0030 | 0.4202096 | 0.4237108 | NA | 0.835863 | 133010 |
| 1 | 2035379 | rs10910029 | A | G | -0.00410 | 0.0022 | 0.0611223 | 0.0623728 | NA | 0.531550 | 133010 |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 50878927 | rs12171249 | G | A | 0.00210 | 0.0025 | 0.3940570 | 0.4009084 | NA | 0.2062700 | 133010 |
| 22 | 50960682 | rs140524 | C | T | 0.00350 | 0.0031 | 0.2612552 | 0.2588842 | NA | 0.2302320 | 133010 |
| 22 | 50971266 | rs140522 | T | C | -0.00320 | 0.0023 | 0.1693012 | 0.1641332 | NA | 0.6190100 | 133010 |
| 22 | 51015674 | rs3213446 | C | T | 0.00620 | 0.0046 | 0.1830269 | 0.1777143 | NA | 0.1859030 | 133010 |
| 22 | 51018911 | rs2269382 | C | T | 0.00970 | 0.0049 | 0.0490083 | 0.0477494 | NA | 0.1222040 | 133010 |
| 22 | 51057923 | rs131718 | C | T | -0.00068 | 0.0022 | 0.7518791 | 0.7572524 | NA | 0.5517170 | 133010 |
| 22 | 51062832 | rs8142033 | G | A | -0.00480 | 0.0041 | 0.2341769 | 0.2417066 | NA | 0.1773160 | 133010 |
| 22 | 51163138 | rs715586 | C | T | 0.00049 | 0.0032 | 0.8775620 | 0.8782997 | NA | 0.0902556 | 133010 |
| 22 | 51165664 | rs8137951 | G | A | 0.00290 | 0.0024 | 0.2221749 | 0.2269191 | NA | 0.4063500 | 133010 |
| 22 | 51178090 | rs2285395 | G | A | -0.00390 | 0.0045 | 0.3935165 | 0.3861247 | NA | 0.0666933 | 133010 |
bcf preview
1 785050 rs2905062 G A . PASS . ES:SE:LP:SS:ID -0.0023:0.0044:0.220359:133010:rs2905062
1 1140435 rs1815606 G T . PASS . ES:SE:LP:SS:ID -0.0007:0.0027:0.0973967:133010:rs1815606
1 1706136 rs6603811 T C . PASS . ES:SE:LP:SS:ID 0.0023:0.0052:0.17894:133010:rs6603811
1 1706160 rs7531583 A G . PASS . ES:SE:LP:SS:ID 0.00042:0.0025:0.0620235:133010:rs7531583
1 1708801 rs12044597 A G . PASS . ES:SE:LP:SS:ID -0.00099:0.0021:0.196829:133010:rs12044597
1 1723031 rs9660180 G A . PASS . ES:SE:LP:SS:ID 0.00022:0.0024:0.0336883:133010:rs9660180
1 1812688 rs6603803 A G . PASS . ES:SE:LP:SS:ID -0.001:0.0021:0.204266:133010:rs6603803
1 1886519 rs1558021383 C A . PASS . ES:SE:LP:SS:ID -0.01:0.0046:1.55614:133010:rs1558021383
1 1892325 rs2803291 T C . PASS . ES:SE:LP:SS:ID 0.0024:0.003:0.376534:133010:rs2803291
1 2035379 rs10910029 A G . PASS . ES:SE:LP:SS:ID -0.0041:0.0022:1.2138:133010:rs10910029
1 2069172 rs425277 C T . PASS . ES:SE:LP:SS:ID 0.00051:0.0023:0.0845616:133010:rs425277
1 2119833 rs2460002 A G . PASS . ES:SE:LP:SS:ID 0.00092:0.0023:0.160052:133010:rs2460002
1 2142518 rs380190 A C . PASS . ES:SE:LP:SS:ID -0.0014:0.0027:0.219142:133010:rs380190
1 2180524 rs260513 G A . PASS . ES:SE:LP:SS:ID 0.00066:0.0021:0.122513:133010:rs260513
1 2182342 rs10797416 T C . PASS . ES:SE:LP:SS:ID -8.7e-05:0.0021:0.0143903:133010:rs10797416
1 2184855 rs12385717 G C . PASS . ES:SE:LP:SS:ID 0.003:0.0044:0.305913:133010:rs12385717
1 2204755 rs7553178 G A . PASS . ES:SE:LP:SS:ID 3.4e-05:0.0023:0.00522459:133010:rs7553178
1 2224645 rs796896153 A G . PASS . ES:SE:LP:SS:ID -0.0017:0.003:0.245638:133010:rs796896153
1 2231595 rs1809822 A C . PASS . ES:SE:LP:SS:ID -0.0041:0.0058:0.32108:133010:rs1809822
1 2251357 rs12080256 C A . PASS . ES:SE:LP:SS:ID -0.00096:0.0028:0.134302:133010:rs12080256
1 2280661 rs2055204 G A . PASS . ES:SE:LP:SS:ID -9.8e-05:0.0021:0.016875:133010:rs2055204
1 2291680 rs2840542 T C . PASS . ES:SE:LP:SS:ID -0.0006:0.0026:0.0870041:133010:rs2840542
1 2390331 rs10910077 G A . PASS . ES:SE:LP:SS:ID 0.004:0.0026:0.935344:133010:rs10910077
1 2409383 rs12742193 G A . PASS . ES:SE:LP:SS:ID -0.0004:0.0038:0.0374091:133010:rs12742193
1 2420913 rs6668720 A G . PASS . ES:SE:LP:SS:ID -0.0036:0.0023:0.921069:133010:rs6668720
1 2490898 rs1553129604 C A . PASS . ES:SE:LP:SS:ID -8.1e-05:0.0022:0.0132081:133010:rs1553129604
1 2490942 rs2281852 C A . PASS . ES:SE:LP:SS:ID 0.00064:0.0021:0.12146:133010:rs2281852
1 2541727 rs11589185 C T . PASS . ES:SE:LP:SS:ID -0.011:0.0053:1.47431:133010:rs11589185
1 2723345 rs4648360 C T . PASS . ES:SE:LP:SS:ID -0.0027:0.0021:0.710043:133010:rs4648360
1 2725952 rs1192117797 C T . PASS . ES:SE:LP:SS:ID 0.0005:0.0029:0.0637814:133010:rs1192117797
1 2727804 rs10909880 C T . PASS . ES:SE:LP:SS:ID -0.0033:0.0023:0.846575:133010:rs10909880
1 2829551 rs1869970 A G . PASS . ES:SE:LP:SS:ID 0.0015:0.0026:0.24956:133010:rs1869970
1 2839757 rs1557731784 A C . PASS . ES:SE:LP:SS:ID 0.0036:0.0023:0.939431:133010:rs1557731784
1 2842270 rs2045331 G A . PASS . ES:SE:LP:SS:ID 0.0038:0.0023:1.0118:133010:rs2045331
1 2890345 rs4648445 G A . PASS . ES:SE:LP:SS:ID -0.0001:0.0025:0.0144644:133010:rs4648445
1 2904056 rs10797373 G A . PASS . ES:SE:LP:SS:ID 8.3e-05:0.0025:0.0115021:133010:rs10797373
1 2976816 rs1138513 T C . PASS . ES:SE:LP:SS:ID 0.00011:0.0036:0.011129:133010:rs1138513
1 2996196 rs7525173 C G . PASS . ES:SE:LP:SS:ID -0.0045:0.0022:1.35011:133010:rs7525173
1 3037613 rs941540736 C T . PASS . ES:SE:LP:SS:ID -0.0035:0.0022:0.943309:133010:rs941540736
1 3074306 rs2651902 C T . PASS . ES:SE:LP:SS:ID -0.0013:0.0028:0.1899:133010:rs2651902
1 3089849 rs17390062 T C . PASS . ES:SE:LP:SS:ID -0.0064:0.0037:1.06878:133010:rs17390062
1 3103826 rs4233024 C T . PASS . ES:SE:LP:SS:ID 0.0037:0.0034:0.5551:133010:rs4233024
1 3105276 rs2817138 T G . PASS . ES:SE:LP:SS:ID 0.0024:0.0032:0.350787:133010:rs2817138
1 3110735 rs2651912 T C . PASS . ES:SE:LP:SS:ID 5.4e-05:0.0029:0.00650513:133010:rs2651912
1 3117678 rs12095716 G C . PASS . ES:SE:LP:SS:ID -0.0045:0.003:0.864403:133010:rs12095716
1 3138136 rs2817148 A G . PASS . ES:SE:LP:SS:ID -0.0027:0.0081:0.13195:133010:rs2817148
1 3144068 rs10158583 G A . PASS . ES:SE:LP:SS:ID -0.0073:0.0046:0.945889:133010:rs10158583
1 3144745 rs1553288 C T . PASS . ES:SE:LP:SS:ID -0.017:0.0049:3.21824:133010:rs1553288
1 3197747 rs10492940 C A . PASS . ES:SE:LP:SS:ID 0.0031:0.0032:0.496777:133010:rs10492940
1 3209631 rs4648376 G A . PASS . ES:SE:LP:SS:ID -0.0053:0.0021:1.972:133010:rs4648376