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/42f76e4e-5763-4154-a8ef-ac467a885e25/call-vcf/inputs/1292253946/upload.txt.gz --id ubm-b-1 --json /data/cromwell-executions/qc/42f76e4e-5763-4154-a8ef-ac467a885e25/call-vcf/inputs/1292253946/ubm-b-1_data.json --ref /data/cromwell-executions/qc/42f76e4e-5763-4154-a8ef-ac467a885e25/call-vcf/inputs/1899004205/human_g1k_v37.fasta --dbsnp /data/cromwell-executions/qc/42f76e4e-5763-4154-a8ef-ac467a885e25/call-vcf/inputs/-307190728/dbsnp.v153.b37.vcf.gz --out /data/igd/ubm-b-1/ubm-b-1.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/42f76e4e-5763-4154-a8ef-ac467a885e25/call-ldsc/inputs/1292253946/ubm-b-1.vcf.gz \
--ref-ld-chr /data/ref/eur_w_ld_chr/ \
--out /data/igd/ubm-b-1/ldsc.txt \
--w-ld-chr /data/ref/eur_w_ld_chr/
Beginning analysis at Fri Oct 13 18:49:25 2023
Reading summary statistics from /data/cromwell-executions/qc/42f76e4e-5763-4154-a8ef-ac467a885e25/call-ldsc/inputs/1292253946/ubm-b-1.vcf.gz ...
Read summary statistics for 0 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.
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 252, in _read_ld_sumstats
sumstats = _merge_and_log(ref_ld, sumstats, 'reference panel LD', log)
File "/ldsc/ldscore/sumstats.py", line 238, in _merge_and_log
raise ValueError(msg.format(N=len(sumstats), F=noun))
ValueError: After merging with reference panel LD, 0 SNPs remain.
Analysis finished at Fri Oct 13 18:51:15 2023
Total time elapsed: 1.0m:50.25sQC metrics
Metrics
Metrics
{
"af_correlation": "NA",
"inflation_factor": "NA",
"mean_EFFECT": -2.2857e-06,
"n": "-Inf",
"n_snps": 17103079,
"n_clumped_hits": 0,
"n_p_sig": 1,
"n_mono": 0,
"n_ns": 3663649,
"n_mac": 0,
"is_snpid_unique": false,
"n_miss_EFFECT": 0,
"n_miss_SE": 0,
"n_miss_PVAL": 0,
"n_miss_AF": 17103079,
"n_miss_AF_reference": 2380407,
"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": 0,
"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 | NA |
| inflation_factor | NA |
| n | TRUE |
| is_snpid_non_unique | TRUE |
| 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 | logical.mean | logical.count | numeric.mean | numeric.sd | numeric.p0 | numeric.p25 | numeric.p50 | numeric.p75 | numeric.p100 | numeric.hist |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| character | ID | 2235 | 0.9998693 | 3 | 64 | 0 | 17024571 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| character | REF | 0 | 1.0000000 | 1 | 88 | 0 | 22100 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| character | ALT | 0 | 1.0000000 | 1 | 662 | 0 | 96537 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| logical | AF | 17103079 | 0.0000000 | NA | NA | NA | NA | NA | NaN | : | NA | NA | NA | NA | NA | NA | NA | NA |
| logical | N | 17103079 | 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 | 9.213571e+00 | 6.308570e+00 | 1.0000000 | 4.000000e+00 | 8.000000e+00 | 1.400000e+01 | 2.300000e+01 | ▇▅▅▂▂ |
| numeric | POS | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 7.863644e+07 | 5.592124e+07 | 173.0000000 | 3.260191e+07 | 6.958721e+07 | 1.145443e+08 | 2.492405e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -2.300000e-06 | 6.494760e-02 | -0.8805100 | -1.628900e-02 | -3.290000e-05 | 1.624500e-02 | 8.298900e-01 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.694320e-02 | 4.427710e-02 | 0.0063576 | 1.026500e-02 | 2.590000e-02 | 7.798100e-02 | 2.593200e-01 | ▇▂▂▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.618854e-01 | 4.802231e-01 | 0.0000000 | 1.302801e-01 | 3.147103e-01 | 6.340405e-01 | 2.659194e+01 | ▇▁▁▁▁ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.879547e-01 | 2.920305e-01 | 0.0000000 | 2.322447e-01 | 4.844922e-01 | 7.408338e-01 | 9.999999e-01 | ▇▇▇▇▇ |
| numeric | AF_reference | 2380407 | 0.8608200 | NA | NA | NA | NA | NA | NA | NA | 1.605877e-01 | 2.359276e-01 | 0.0000000 | 1.397800e-03 | 3.454470e-02 | 2.352240e-01 | 1.000000e+00 | ▇▂▁▁▁ |
Head and tail
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 10177 | rs1264289758 | AC | A | -0.0155440 | 0.011609 | 0.7432998 | 0.1805833 | NA | NA | NA |
| 1 | 10352 | rs1557426776 | TA | T | -0.0023541 | 0.011962 | 0.0736665 | 0.8439855 | NA | NA | NA |
| 1 | 10511 | rs534229142 | G | A | 0.1903300 | 0.163710 | 0.6108604 | 0.2449900 | NA | 0.0001997 | NA |
| 1 | 10616 | rs1557426951 | C | CCGCCGTTGCAAAGGCGCGCCG | 0.0172900 | 0.075695 | 0.0865466 | 0.8193223 | NA | NA | NA |
| 1 | 11008 | rs575272151 | C | G | 0.0343850 | 0.019939 | 1.0725000 | 0.0846159 | NA | 0.0880591 | NA |
| 1 | 11012 | rs544419019 | C | G | 0.0343850 | 0.019939 | 1.0725000 | 0.0846159 | NA | 0.0880591 | NA |
| 1 | 13110 | rs540538026 | G | A | -0.0575420 | 0.026563 | 1.5185986 | 0.0302924 | NA | 0.0267572 | NA |
| 1 | 13116 | rs62635286 | T | G | -0.0160000 | 0.015687 | 0.5118103 | 0.3077508 | NA | 0.0970447 | NA |
| 1 | 13118 | rs200579949 | A | G | -0.0160000 | 0.015687 | 0.5118103 | 0.3077508 | NA | 0.0970447 | NA |
| 1 | 13273 | rs531730856 | G | C | 0.0228600 | 0.018366 | 0.6710996 | 0.2132453 | NA | 0.0950479 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 23 | 155236918 | rs3093527 | G | A | -0.021057 | 0.055569 | 0.1519700 | 0.7047367 | NA | 0.0515176 | NA |
| 23 | 155237811 | rs764129350 | G | A | -0.394370 | 0.154240 | 1.9761004 | 0.0105622 | NA | 0.0143770 | NA |
| 23 | 155237885 | rs776508517 | A | G | 0.137130 | 0.105400 | 0.7138303 | 0.1932435 | NA | 0.0309505 | NA |
| 23 | 155247345 | rs751554334 | T | C | 0.028634 | 0.071490 | 0.1619299 | 0.6887651 | NA | 0.1485620 | NA |
| 23 | 155254802 | rs4568757 | C | A | -0.026227 | 0.063213 | 0.1686301 | 0.6782160 | NA | 0.0315495 | NA |
| 23 | 155254881 | rs28415761 | C | A | -0.121330 | 0.105850 | 0.5991506 | 0.2516939 | NA | NA | NA |
| 23 | 155255153 | rs28742024 | G | A | 0.032927 | 0.091633 | 0.1430599 | 0.7193440 | NA | NA | NA |
| 23 | 155255277 | rs1045930 | C | G | -0.026426 | 0.129950 | 0.0763133 | 0.8388575 | NA | NA | NA |
| 23 | 155260480 | NA | T | A | -0.027088 | 0.012399 | 1.5388986 | 0.0289114 | NA | NA | NA |
| 23 | 155260480 | NA | T | G | -0.013158 | 0.013251 | 0.4938397 | 0.3207189 | NA | NA | NA |
bcf preview
1 10177 rs1264289758 AC A . PASS . ES:SE:LP:ID -0.015544:0.011609:0.128836:rs1264289758
1 10352 rs1557426776 TA T . PASS . ES:SE:LP:ID -0.0023541:0.011962:1.13273:rs1557426776
1 10511 rs534229142 G A . PASS . ES:SE:LP:ID 0.19033:0.16371:0.214058:rs534229142
1 10616 rs1557426951 C CCGCCGTTGCAAAGGCGCGCCG . PASS . ES:SE:LP:ID 0.01729:0.075695:1.06275:rs1557426951
1 11008 rs575272151 C G . PASS . ES:SE:LP:ID 0.034385:0.019939:-0.0303973:rs575272151
1 11012 rs544419019 C G . PASS . ES:SE:LP:ID 0.034385:0.019939:-0.0303973:rs544419019
1 13110 rs540538026 G A . PASS . ES:SE:LP:ID -0.057542:0.026563:-0.181443:rs540538026
1 13116 rs62635286 T G . PASS . ES:SE:LP:ID -0.016:0.015687:0.290891:rs62635286
1 13118 rs62028691 A G . PASS . ES:SE:LP:ID -0.016:0.015687:0.290891:rs62028691
1 13273 rs531730856 G C . PASS . ES:SE:LP:ID 0.02286:0.018366:0.173213:rs531730856
1 13453 rs568927457 T C . PASS . ES:SE:LP:ID -0.013219:0.070195:1.15329:rs568927457
1 13483 rs554760071 G C . PASS . ES:SE:LP:ID 0.017637:0.078887:1.07287:rs554760071
1 13494 rs1272445563 A G . PASS . ES:SE:LP:ID -0.098665:0.11681:0.39819:rs1272445563
1 13550 rs554008981 G A . PASS . ES:SE:LP:ID -0.10721:0.094801:0.230497:rs554008981
1 14464 rs546169444 A T . PASS . ES:SE:LP:ID 0.0054753:0.016534:0.884523:rs546169444
1 14599 rs707680 T A . PASS . ES:SE:LP:ID -0.0042671:0.015197:0.96445:rs707680
1 14604 rs1418508701 A G . PASS . ES:SE:LP:ID -0.0042671:0.015197:0.96445:rs1418508701
1 14930 rs6682385 A G . PASS . ES:SE:LP:ID -0.0067493:0.011778:0.607813:rs6682385
1 14933 rs199856693 G A . PASS . ES:SE:LP:ID -0.0062147:0.029223:1.09641:rs199856693
1 15211 rs3982632 T G . PASS . ES:SE:LP:ID -0.00019979:0.013595:2.29056:rs3982632
1 15245 rs576044687 C T . PASS . ES:SE:LP:ID 0.01087:0.17448:1.65508:rs576044687
1 15585 rs533630043 G A . PASS . ES:SE:LP:ID -0.026129:0.064662:0.78627:rs533630043
1 15644 rs564003018 G A . PASS . ES:SE:LP:ID 0.19981:0.1057:-0.0903286:rs564003018
1 15777 rs2691317 A G . PASS . ES:SE:LP:ID 0.00026695:0.050077:2.73257:rs2691317
1 15820 rs1316988498 G T . PASS . ES:SE:LP:ID -0.0038574:0.013893:0.969846:rs1316988498
1 15903 rs557514207 GC G . PASS . ES:SE:LP:ID -0.020636:0.011531:-0.0544598:rs557514207
1 16142 rs548165136 G A . PASS . ES:SE:LP:ID 0.10116:0.1021:0.307647:rs548165136
1 16949 rs199745162 A C . PASS . ES:SE:LP:ID -0.025696:0.041276:0.564156:rs199745162
1 18643 rs564023708 G A . PASS . ES:SE:LP:ID -0.046578:0.074967:0.565208:rs564023708
1 18849 rs533090414 C G . PASS . ES:SE:LP:ID -0.050469:0.035445:0.0909256:rs533090414
1 28590 rs1344649620 T TTGG . PASS . ES:SE:LP:ID 0.020558:0.032568:0.5568:rs1344649620
1 30923 rs1165072081 G T . PASS . ES:SE:LP:ID 0.028594:0.021837:0.142468:rs1165072081
1 46285 rs545414834 A ATAT . PASS . ES:SE:LP:ID 0.074786:0.13252:0.615808:rs545414834
1 47159 rs540662756 T C . PASS . ES:SE:LP:ID 0.023066:0.024618:0.339675:rs540662756
1 49298 rs10399793 T C . PASS . ES:SE:LP:ID -0.022605:0.013834:0.00425515:rs10399793
1 49315 rs1231347385 T A . PASS . ES:SE:LP:ID 0.10942:0.19532:0.619644:rs1231347385
1 49318 rs1231347385 A G . PASS . ES:SE:LP:ID -0.22353:0.14161:0.0262581:rs1231347385
1 49343 rs553572247 T C . PASS . ES:SE:LP:ID -0.0077935:0.14223:1.71209:rs553572247
1 49554 rs539322794 A G . PASS . ES:SE:LP:ID -0.024443:0.021119:0.21674:rs539322794
1 50891 rs542415070 T C . PASS . ES:SE:LP:ID 0.10021:0.098033:0.289654:rs542415070
1 51047 rs559500163 A T . PASS . ES:SE:LP:ID -0.0010563:0.139:2.57822:rs559500163
1 51049 rs528344458 A C . PASS . ES:SE:LP:ID -0.0010563:0.139:2.57822:rs528344458
1 51050 rs551668143 A T . PASS . ES:SE:LP:ID -0.0010563:0.139:2.57822:rs551668143
1 51053 rs565211799 G T . PASS . ES:SE:LP:ID -0.0010563:0.139:2.57822:rs565211799
1 51479 rs116400033 T A . PASS . ES:SE:LP:ID 0.026206:0.014706:-0.0516926:rs116400033
1 51762 rs1359003408 A G . PASS . ES:SE:LP:ID 0.12209:0.062567:-0.111363:rs1359003408
1 51765 rs1359003408 C G . PASS . ES:SE:LP:ID 0.12127:0.062646:-0.106055:rs1359003408
1 52152 rs1272293782 A ATAAT . PASS . ES:SE:LP:ID 0.22412:0.15683:0.0886985:rs1272293782
1 52238 rs2691277 T G . PASS . ES:SE:LP:ID 0.042497:0.045147:0.337035:rs2691277
1 52253 rs530867301 C G . PASS . ES:SE:LP:ID -0.072698:0.088744:0.415239:rs530867301