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/0cddccd3-66b2-4eb5-bf41-8a330bdbbfab/call-vcf/inputs/-261044473/upload.txt.gz --id ieu-b-4838 --json /data/cromwell-executions/qc/0cddccd3-66b2-4eb5-bf41-8a330bdbbfab/call-vcf/inputs/-261044473/ieu-b-4838_data.json --ref /data/cromwell-executions/qc/0cddccd3-66b2-4eb5-bf41-8a330bdbbfab/call-vcf/inputs/1899004205/human_g1k_v37.fasta --dbsnp /data/cromwell-executions/qc/0cddccd3-66b2-4eb5-bf41-8a330bdbbfab/call-vcf/inputs/-307190728/dbsnp.v153.b37.vcf.gz --out /data/igd/ieu-b-4838/ieu-b-4838.vcf.gz --alias alias.txt; 1.3.0",
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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/0cddccd3-66b2-4eb5-bf41-8a330bdbbfab/call-ldsc/inputs/-261044473/ieu-b-4838.vcf.gz \
--ref-ld-chr /data/ref/eur_w_ld_chr/ \
--out /data/igd/ieu-b-4838/ldsc.txt \
--w-ld-chr /data/ref/eur_w_ld_chr/
Beginning analysis at Wed Jan 5 12:41:27 2022
Reading summary statistics from /data/cromwell-executions/qc/0cddccd3-66b2-4eb5-bf41-8a330bdbbfab/call-ldsc/inputs/-261044473/ieu-b-4838.vcf.gz ...
Read summary statistics for 6719230 SNPs.
Dropped 18738 SNPs with duplicated rs numbers.
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, 1186489 SNPs remain.
After merging with regression SNP LD, 1186489 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.2412 (0.0262)
Lambda GC: 1.1325
Mean Chi^2: 1.1468
Intercept: 1.0429 (0.0073)
Ratio: 0.2921 (0.0496)
Analysis finished at Wed Jan 5 12:43:07 2022
Total time elapsed: 1.0m:40.03sQC metrics
Metrics
Metrics
{
"af_correlation": "NA",
"inflation_factor": 1.1197,
"mean_EFFECT": -0,
"n": 22593,
"n_snps": 6719302,
"n_clumped_hits": 3,
"n_p_sig": 8,
"n_mono": 0,
"n_ns": 0,
"n_mac": 0,
"is_snpid_unique": false,
"n_miss_EFFECT": 0,
"n_miss_SE": 0,
"n_miss_PVAL": 0,
"n_miss_AF": 6719302,
"n_miss_AF_reference": 53316,
"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": 1186489,
"ldsc_nsnp_merge_regression_ld": 1186489,
"ldsc_observed_scale_h2_beta": 0.2412,
"ldsc_observed_scale_h2_se": 0.0262,
"ldsc_intercept_beta": 1.0429,
"ldsc_intercept_se": 0.0073,
"ldsc_lambda_gc": 1.1325,
"ldsc_mean_chisq": 1.1468,
"ldsc_ratio": 0.2922
}
Flags
| name | value |
|---|---|
| af_correlation | NA |
| 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 | 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 | 45 | 0.9999933 | 3 | 58 | 0 | 6719206 | 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 | 6719302 | 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 | 8.660922e+00 | 5.761272e+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 | 7.862500e+07 | 5.646681e+07 | 8.2800e+02 | 3.212334e+07 | 6.907324e+07 | 1.145122e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -1.890000e-05 | 1.887290e-02 | -1.7880e-01 | -9.900000e-03 | 0.000000e+00 | 9.800000e-03 | 1.679000e-01 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 1.609430e-02 | 8.095100e-03 | 8.8000e-03 | 9.900000e-03 | 1.240000e-02 | 2.030000e-02 | 5.020000e-02 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.812381e-01 | 2.937850e-01 | 0.0000e+00 | 2.224001e-01 | 4.753998e-01 | 7.352008e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.812402e-01 | 2.937904e-01 | 0.0000e+00 | 2.224649e-01 | 4.753789e-01 | 7.352262e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | AF_reference | 53316 | 0.9920652 | NA | NA | NA | NA | NA | NA | NA | 2.828646e-01 | 2.508425e-01 | 1.9970e-04 | 7.727640e-02 | 2.032750e-01 | 4.369010e-01 | 1.000000e+00 | ▇▃▂▂▁ |
| numeric | N | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.062050e+04 | 2.225197e+03 | 1.3564e+04 | 1.873700e+04 | 2.167800e+04 | 2.259300e+04 | 2.259300e+04 | ▁▂▂▃▇ |
Head and tail
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 49298 | rs200943160 | T | C | 0.0023 | 0.0116 | 0.8442001 | 0.8428292 | NA | 0.7821490 | 16660 |
| 1 | 54676 | rs2462492 | C | T | -0.0043 | 0.0123 | 0.7290998 | 0.7266438 | NA | NA | 16660 |
| 1 | 86028 | rs114608975 | T | C | -0.0089 | 0.0223 | 0.6902001 | 0.6898172 | NA | 0.0277556 | 16165 |
| 1 | 91536 | rs6702460 | G | T | -0.0126 | 0.0121 | 0.2971002 | 0.2977260 | NA | 0.4207270 | 16660 |
| 1 | 234313 | rs8179466 | C | T | -0.0273 | 0.0269 | 0.3109997 | 0.3101679 | NA | NA | 16165 |
| 1 | 534192 | rs6680723 | C | T | 0.0174 | 0.0141 | 0.2167999 | 0.2171870 | NA | NA | 16660 |
| 1 | 546697 | rs12025928 | A | G | 0.0140 | 0.0198 | 0.4812004 | 0.4795225 | NA | NA | 16165 |
| 1 | 693731 | rs12238997 | A | G | -0.0187 | 0.0166 | 0.2597000 | 0.2599514 | NA | 0.1417730 | 16806 |
| 1 | 705882 | rs72631875 | G | A | -0.0135 | 0.0230 | 0.5578995 | 0.5572329 | NA | 0.0315495 | 16165 |
| 1 | 706368 | rs55727773 | A | G | -0.0098 | 0.0109 | 0.3654997 | 0.3686087 | NA | 0.2751600 | 16660 |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51218377 | rs2519461 | G | C | 0.0174 | 0.0185 | 0.3483999 | 0.3469404 | NA | 0.0826677 | 19232 |
| 22 | 51219006 | rs28729663 | G | A | 0.0036 | 0.0143 | 0.8010000 | 0.8012357 | NA | 0.2052720 | 19106 |
| 22 | 51219387 | rs9616832 | T | C | 0.0117 | 0.0199 | 0.5554002 | 0.5565728 | NA | 0.0654952 | 16660 |
| 22 | 51219704 | rs147475742 | G | A | 0.0127 | 0.0247 | 0.6065003 | 0.6071331 | NA | 0.0473243 | 18737 |
| 22 | 51221190 | rs369304721 | G | A | 0.0232 | 0.0221 | 0.2941000 | 0.2938221 | NA | NA | 18737 |
| 22 | 51221731 | rs115055839 | T | C | 0.0114 | 0.0200 | 0.5675002 | 0.5686777 | NA | 0.0625000 | 16660 |
| 22 | 51222100 | rs114553188 | G | T | 0.0020 | 0.0221 | 0.9277001 | 0.9278917 | NA | 0.0880591 | 18737 |
| 22 | 51223637 | rs375798137 | G | A | 0.0024 | 0.0222 | 0.9132000 | 0.9139099 | NA | 0.0788738 | 18737 |
| 22 | 51229805 | rs9616985 | T | C | 0.0133 | 0.0200 | 0.5068005 | 0.5060505 | NA | 0.0730831 | 16660 |
| 22 | 51237063 | rs3896457 | T | C | -0.0080 | 0.0112 | 0.4772995 | 0.4750505 | NA | 0.2050720 | 17301 |
bcf preview
1 49298 rs10399793 T C . PASS . ES:SE:LP:SS:ID 0.0023:0.0116:0.0735546:16660:rs10399793
1 54676 rs2462492 C T . PASS . ES:SE:LP:SS:ID -0.0043:0.0123:0.137213:16660:rs2462492
1 86028 rs114608975 T C . PASS . ES:SE:LP:SS:ID -0.0089:0.0223:0.161025:16165:rs114608975
1 91536 rs1251109649 G T . PASS . ES:SE:LP:SS:ID -0.0126:0.0121:0.527097:16660:rs1251109649
1 234313 rs8179466 C T . PASS . ES:SE:LP:SS:ID -0.0273:0.0269:0.50724:16165:rs8179466
1 534192 rs6680723 C T . PASS . ES:SE:LP:SS:ID 0.0174:0.0141:0.663941:16660:rs6680723
1 546697 rs12025928 A G . PASS . ES:SE:LP:SS:ID 0.014:0.0198:0.317674:16165:rs12025928
1 693731 rs12238997 A G . PASS . ES:SE:LP:SS:ID -0.0187:0.0166:0.585528:16806:rs12238997
1 705882 rs72631875 G A . PASS . ES:SE:LP:SS:ID -0.0135:0.023:0.253444:16165:rs72631875
1 706368 rs963699400 A G . PASS . ES:SE:LP:SS:ID -0.0098:0.0109:0.437113:16660:rs963699400
1 714596 rs149887893 T C . PASS . ES:SE:LP:SS:ID 0.0098:0.0311:0.12332:16165:rs149887893
1 715265 rs12184267 C T . PASS . ES:SE:LP:SS:ID 0.0213:0.0299:0.322667:16165:rs12184267
1 715367 rs12184277 A G . PASS . ES:SE:LP:SS:ID 0.0187:0.0299:0.273925:16165:rs12184277
1 717485 rs12184279 C A . PASS . ES:SE:LP:SS:ID 0.0221:0.0301:0.334044:16165:rs12184279
1 720381 rs116801199 G T . PASS . ES:SE:LP:SS:ID 0.0207:0.0301:0.308388:16165:rs116801199
1 721290 rs12565286 G C . PASS . ES:SE:LP:SS:ID 0.0219:0.0299:0.334513:16165:rs12565286
1 722670 rs116030099 T C . PASS . ES:SE:LP:SS:ID -0.0061:0.0194:0.121766:16165:rs116030099
1 723891 rs2977670 G C . PASS . ES:SE:LP:SS:ID -0.0118:0.0273:0.176526:16165:rs2977670
1 726794 rs28454925 C G . PASS . ES:SE:LP:SS:ID 0.0285:0.0299:0.46916:16165:rs28454925
1 729632 rs116720794 C T . PASS . ES:SE:LP:SS:ID 0.0322:0.0298:0.553774:16165:rs116720794
1 729679 rs4951859 C G . PASS . ES:SE:LP:SS:ID 0.0072:0.0145:0.207328:17301:rs4951859
1 730087 rs148120343 T C . PASS . ES:SE:LP:SS:ID 0.0187:0.0237:0.366835:16165:rs148120343
1 731718 rs58276399 T C . PASS . ES:SE:LP:SS:ID -0.0133:0.0159:0.394695:17432:rs58276399
1 732989 rs369030935 C T . PASS . ES:SE:LP:SS:ID -0.0403:0.0385:0.529149:16165:rs369030935
1 734349 rs141242758 T C . PASS . ES:SE:LP:SS:ID -0.0148:0.0159:0.453334:17432:rs141242758
1 736289 rs1254887344 T A . PASS . ES:SE:LP:SS:ID -0.0082:0.0156:0.221632:17301:rs1254887344
1 752478 rs146277091 G A . PASS . ES:SE:LP:SS:ID 0.0209:0.0296:0.318036:16165:rs146277091
1 752566 rs3094315 G A . PASS . ES:SE:LP:SS:ID 0.0065:0.0139:0.195588:18496:rs3094315
1 752721 rs3131972 A G . PASS . ES:SE:LP:SS:ID 0.004:0.014:0.111147:17927:rs3131972
1 753405 rs3115860 C A . PASS . ES:SE:LP:SS:ID 0.0079:0.0151:0.22062:17432:rs3115860
1 753541 rs1388595942 G A . PASS . ES:SE:LP:SS:ID -0.0081:0.0155:0.219539:17432:rs1388595942
1 754063 rs12184312 G T . PASS . ES:SE:LP:SS:ID 0.0198:0.0297:0.297311:16165:rs12184312
1 754105 rs12184325 C T . PASS . ES:SE:LP:SS:ID 0.0192:0.0298:0.284164:16165:rs12184325
1 754182 rs3131969 A G . PASS . ES:SE:LP:SS:ID 0.0054:0.0151:0.141944:17432:rs3131969
1 754192 rs3131968 A G . PASS . ES:SE:LP:SS:ID 0.0054:0.0151:0.141944:17432:rs3131968
1 754211 rs12184313 G A . PASS . ES:SE:LP:SS:ID 0.0219:0.0296:0.337053:16165:rs12184313
1 754334 rs3131967 T C . PASS . ES:SE:LP:SS:ID 0.0052:0.0151:0.136558:17432:rs3131967
1 754503 rs3115859 G A . PASS . ES:SE:LP:SS:ID 0.0035:0.014:0.094366:17927:rs3115859
1 754629 rs10454459 A G . PASS . ES:SE:LP:SS:ID 0.0207:0.0296:0.31399:16165:rs10454459
1 754964 rs3131966 C T . PASS . ES:SE:LP:SS:ID 0.0052:0.014:0.148497:17927:rs3131966
1 755775 rs3131965 A G . PASS . ES:SE:LP:SS:ID 0.0074:0.014:0.223154:17927:rs3131965
1 755890 rs1280367067 A T . PASS . ES:SE:LP:SS:ID 0.0081:0.0151:0.228339:17432:rs1280367067
1 756604 rs3131962 A G . PASS . ES:SE:LP:SS:ID 0.0079:0.0151:0.223371:17432:rs3131962
1 757640 rs3115853 G A . PASS . ES:SE:LP:SS:ID 0.0054:0.015:0.143694:17432:rs3115853
1 757734 rs1557551770 C T . PASS . ES:SE:LP:SS:ID 0.0082:0.0151:0.233513:17432:rs1557551770
1 757936 rs1360886751 C A . PASS . ES:SE:LP:SS:ID 0.0082:0.0151:0.233513:17432:rs1360886751
1 758144 rs3131956 A G . PASS . ES:SE:LP:SS:ID 0.0082:0.0151:0.233513:17432:rs3131956
1 758626 rs3131954 C T . PASS . ES:SE:LP:SS:ID 0.008:0.0151:0.225994:17432:rs3131954
1 759036 rs114525117 G A . PASS . ES:SE:LP:SS:ID 0.0175:0.03:0.252045:16165:rs114525117
1 760912 rs1048488 C T . PASS . ES:SE:LP:SS:ID 0.0054:0.014:0.156331:17927:rs1048488