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/fcec30b1-8009-4e33-9f2d-fc531d89200e/call-vcf/inputs/-261044477/upload.txt.gz --id ieu-b-4834 --json /data/cromwell-executions/qc/fcec30b1-8009-4e33-9f2d-fc531d89200e/call-vcf/inputs/-261044477/ieu-b-4834_data.json --ref /data/cromwell-executions/qc/fcec30b1-8009-4e33-9f2d-fc531d89200e/call-vcf/inputs/1899004205/human_g1k_v37.fasta --dbsnp /data/cromwell-executions/qc/fcec30b1-8009-4e33-9f2d-fc531d89200e/call-vcf/inputs/-307190728/dbsnp.v153.b37.vcf.gz --out /data/igd/ieu-b-4834/ieu-b-4834.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/fcec30b1-8009-4e33-9f2d-fc531d89200e/call-ldsc/inputs/-261044477/ieu-b-4834.vcf.gz \
--ref-ld-chr /data/ref/eur_w_ld_chr/ \
--out /data/igd/ieu-b-4834/ldsc.txt \
--w-ld-chr /data/ref/eur_w_ld_chr/
Beginning analysis at Wed Jan 5 10:10:41 2022
Reading summary statistics from /data/cromwell-executions/qc/fcec30b1-8009-4e33-9f2d-fc531d89200e/call-ldsc/inputs/-261044477/ieu-b-4834.vcf.gz ...
Read summary statistics for 7914265 SNPs.
Dropped 38713 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, 1196943 SNPs remain.
After merging with regression SNP LD, 1196943 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0238 (0.0076)
Lambda GC: 1.1458
Mean Chi^2: 1.153
Intercept: 1.1145 (0.0075)
Ratio: 0.7483 (0.0493)
Analysis finished at Wed Jan 5 10:12:27 2022
Total time elapsed: 1.0m:45.83sQC metrics
Metrics
Metrics
{
"af_correlation": "NA",
"inflation_factor": 1.1432,
"mean_EFFECT": -0.0012,
"n": 83626,
"n_snps": 7914324,
"n_clumped_hits": 8,
"n_p_sig": 15,
"n_mono": 0,
"n_ns": 285645,
"n_mac": 0,
"is_snpid_unique": false,
"n_miss_EFFECT": 0,
"n_miss_SE": 0,
"n_miss_PVAL": 0,
"n_miss_AF": 7914324,
"n_miss_AF_reference": 161320,
"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": 1196943,
"ldsc_nsnp_merge_regression_ld": 1196943,
"ldsc_observed_scale_h2_beta": 0.0238,
"ldsc_observed_scale_h2_se": 0.0076,
"ldsc_intercept_beta": 1.1145,
"ldsc_intercept_se": 0.0075,
"ldsc_lambda_gc": 1.1458,
"ldsc_mean_chisq": 1.153,
"ldsc_ratio": 0.7484
}
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 | TRUE |
| 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 | 14 | 0.9999982 | 3 | 58 | 0 | 7914198 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| character | REF | 0 | 1.0000000 | 1 | 43 | 0 | 16655 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| character | ALT | 0 | 1.0000000 | 1 | 44 | 0 | 6028 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| logical | AF | 7914324 | 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 | 8.637464e+00 | 5.748731e+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 | 7.894977e+07 | 5.633334e+07 | 3.0200e+02 | 3.260335e+07 | 6.961088e+07 | 1.147044e+08 | 2.492223e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -1.207500e-03 | 4.009950e-02 | -4.4490e-01 | -2.100000e-02 | -1.000000e-03 | 1.870000e-02 | 4.262000e-01 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.307670e-02 | 1.825370e-02 | 1.6000e-02 | 1.910000e-02 | 2.510000e-02 | 4.210000e-02 | 1.716000e-01 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.786502e-01 | 2.943797e-01 | 0.0000e+00 | 2.184998e-01 | 4.707995e-01 | 7.339002e-01 | 1.000000e+00 | ▇▇▆▆▆ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.786505e-01 | 2.943816e-01 | 0.0000e+00 | 2.184610e-01 | 4.708025e-01 | 7.338565e-01 | 1.000000e+00 | ▇▇▆▆▆ |
| numeric | AF_reference | 161320 | 0.9796167 | NA | NA | NA | NA | NA | NA | NA | 2.650630e-01 | 2.521589e-01 | 1.9970e-04 | 5.770770e-02 | 1.797120e-01 | 4.139380e-01 | 1.000000e+00 | ▇▃▂▁▁ |
| numeric | N | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 7.706800e+04 | 6.754715e+03 | 5.0192e+04 | 6.975000e+04 | 8.021900e+04 | 8.290600e+04 | 8.362600e+04 | ▁▁▃▁▇ |
Head and tail
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 797440 | rs58013264 | T | C | -0.0111 | 0.0251 | 0.6584003 | 0.6583220 | NA | 0.189497 | 72483 |
| 1 | 798400 | rs10900604 | A | G | -0.0276 | 0.0214 | 0.1971002 | 0.1971480 | NA | 0.410543 | 73881 |
| 1 | 798959 | rs11240777 | G | A | -0.0281 | 0.0213 | 0.1866999 | 0.1870859 | NA | 0.409944 | 75818 |
| 1 | 801467 | rs61768212 | G | C | -0.0103 | 0.0251 | 0.6812994 | 0.6815429 | NA | 0.193091 | 71763 |
| 1 | 804759 | rs7526310 | C | T | -0.0072 | 0.0252 | 0.7762006 | 0.7750970 | NA | 0.193890 | 71763 |
| 1 | 808223 | rs4951933 | G | C | 0.0108 | 0.0213 | 0.6116993 | 0.6121252 | NA | 0.452077 | 65259 |
| 1 | 808631 | rs11240779 | G | A | 0.0146 | 0.0209 | 0.4829999 | 0.4848242 | NA | 0.453474 | 72402 |
| 1 | 808928 | rs11240780 | C | T | 0.0138 | 0.0209 | 0.5085001 | 0.5090696 | NA | 0.452276 | 72402 |
| 1 | 845274 | rs112856858 | G | T | -0.0077 | 0.0223 | 0.7298002 | 0.7298753 | NA | 0.374002 | 65259 |
| 1 | 845635 | rs117086422 | C | T | -0.0135 | 0.0216 | 0.5313002 | 0.5319711 | NA | 0.158546 | 75023 |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51181685 | rs34756634 | G | A | 0.0171 | 0.0280 | 0.5406995 | 0.5413887 | NA | 0.7118610 | 65259 |
| 22 | 51181687 | rs9616955 | A | T | -0.0016 | 0.0382 | 0.9659999 | 0.9665905 | NA | 0.0485224 | 65259 |
| 22 | 51181759 | rs13056621 | A | G | 0.0031 | 0.0283 | 0.9136999 | 0.9127737 | NA | 0.6761180 | 78425 |
| 22 | 51182090 | rs28516879 | G | A | -0.0084 | 0.0358 | 0.8145000 | 0.8144906 | NA | 0.0467252 | 76767 |
| 22 | 51185848 | rs3865764 | G | A | -0.0069 | 0.0509 | 0.8917001 | 0.8921692 | NA | 0.9776360 | 71079 |
| 22 | 51186276 | rs8135434 | T | C | -0.0075 | 0.1366 | 0.9560000 | 0.9562143 | NA | 0.1076280 | 51306 |
| 22 | 51188319 | rs139620215 | T | C | 0.0829 | 0.0989 | 0.4019000 | 0.4019069 | NA | 0.0037939 | 69750 |
| 22 | 51197602 | rs187225588 | T | A | -0.0075 | 0.1467 | 0.9594000 | 0.9592261 | NA | 0.0175719 | 51306 |
| 22 | 51208568 | rs148425445 | G | T | 0.0252 | 0.1477 | 0.8645000 | 0.8645256 | NA | 0.1160140 | 51306 |
| 22 | 51216564 | rs9616970 | T | C | 0.0685 | 0.0922 | 0.4577001 | 0.4575119 | NA | 0.1563500 | 50912 |
bcf preview
1 797440 rs58013264 T C . PASS . ES:SE:LP:SS:ID -0.0111:0.0251:0.18151:72483:rs58013264
1 798400 rs10900604 A G . PASS . ES:SE:LP:SS:ID -0.0276:0.0214:0.705313:73881:rs10900604
1 798959 rs11240777 G A . PASS . ES:SE:LP:SS:ID -0.0281:0.0213:0.728856:75818:rs11240777
1 801467 rs61768212 G C . PASS . ES:SE:LP:SS:ID -0.0103:0.0251:0.166662:71763:rs61768212
1 804759 rs7526310 C T . PASS . ES:SE:LP:SS:ID -0.0072:0.0252:0.110026:71763:rs7526310
1 808223 rs1557576983 G C . PASS . ES:SE:LP:SS:ID 0.0108:0.0213:0.213462:65259:rs1557576983
1 808631 rs11240779 G A . PASS . ES:SE:LP:SS:ID 0.0146:0.0209:0.316053:72402:rs11240779
1 808928 rs1247187939 C T . PASS . ES:SE:LP:SS:ID 0.0138:0.0209:0.293709:72402:rs1247187939
1 845274 rs1324283754 G T . PASS . ES:SE:LP:SS:ID -0.0077:0.0223:0.136796:65259:rs1324283754
1 845635 rs117086422 C T . PASS . ES:SE:LP:SS:ID -0.0135:0.0216:0.27466:75023:rs117086422
1 845938 rs57760052 G A . PASS . ES:SE:LP:SS:ID -0.008:0.0216:0.149048:75023:rs57760052
1 846078 rs778265812 C T . PASS . ES:SE:LP:SS:ID -0.0188:0.0223:0.399354:75023:rs778265812
1 846398 rs58781670 G A . PASS . ES:SE:LP:SS:ID -0.0074:0.0218:0.132886:75023:rs58781670
1 846465 rs60454217 C T . PASS . ES:SE:LP:SS:ID 0.0627:0.0634:0.49147:69750:rs60454217
1 846543 rs79396034 G T . PASS . ES:SE:LP:SS:ID 0.0581:0.0662:0.420216:69750:rs79396034
1 846808 rs4475691 C T . PASS . ES:SE:LP:SS:ID -0.0184:0.0222:0.389872:75023:rs4475691
1 846864 rs1269142199 G C . PASS . ES:SE:LP:SS:ID -0.0197:0.0223:0.422738:75023:rs1269142199
1 847228 rs3905286 C T . PASS . ES:SE:LP:SS:ID -0.0186:0.0222:0.396206:75023:rs3905286
1 847491 rs1158719307 G A . PASS . ES:SE:LP:SS:ID -0.0174:0.022:0.367644:75023:rs1158719307
1 848090 rs4246505 G A . PASS . ES:SE:LP:SS:ID -0.0187:0.0221:0.400117:75023:rs4246505
1 848445 rs1156895099 G A . PASS . ES:SE:LP:SS:ID -0.0161:0.0221:0.331707:75023:rs1156895099
1 848456 rs11507767 A G . PASS . ES:SE:LP:SS:ID -0.0161:0.0221:0.331801:75023:rs11507767
1 848738 rs3829741 C T . PASS . ES:SE:LP:SS:ID -0.0185:0.0221:0.396206:75023:rs3829741
1 850062 rs28723578 A T . PASS . ES:SE:LP:SS:ID -0.0182:0.022:0.389659:75023:rs28723578
1 850123 rs28622257 C T . PASS . ES:SE:LP:SS:ID -0.0193:0.0221:0.417709:75023:rs28622257
1 851190 rs28609852 G A . PASS . ES:SE:LP:SS:ID -0.0179:0.022:0.382266:75023:rs28609852
1 851204 rs28552953 G C . PASS . ES:SE:LP:SS:ID -0.0178:0.0218:0.383524:75023:rs28552953
1 852664 rs28605311 C T . PASS . ES:SE:LP:SS:ID -0.0207:0.0224:0.450506:75023:rs28605311
1 852758 rs4970462 G C . PASS . ES:SE:LP:SS:ID -0.0238:0.0224:0.539553:73694:rs4970462
1 853239 rs4970380 A G . PASS . ES:SE:LP:SS:ID -0.0206:0.0224:0.44855:75023:rs4970380
1 853805 rs3748591 C T . PASS . ES:SE:LP:SS:ID 0.0404:0.0563:0.325139:69750:rs3748591
1 854250 rs7537756 A G . PASS . ES:SE:LP:SS:ID -0.0196:0.0223:0.422049:75023:rs7537756
1 854429 rs72902552 G A . PASS . ES:SE:LP:SS:ID 0.0404:0.0563:0.324863:69750:rs72902552
1 856436 rs34105146 TG T . PASS . ES:SE:LP:SS:ID -0.0179:0.023:0.360713:65259:rs34105146
1 856476 rs4040605 A G . PASS . ES:SE:LP:SS:ID -0.0136:0.0186:0.333295:73086:rs4040605
1 857177 rs386627408 T C . PASS . ES:SE:LP:SS:ID 0.0493:0.0552:0.429807:69750:rs386627408
1 858040 rs4970460 C A . PASS . ES:SE:LP:SS:ID -0.0263:0.0221:0.63097:73086:rs4970460
1 858051 rs4970459 C T . PASS . ES:SE:LP:SS:ID -0.0261:0.0221:0.625435:73086:rs4970459
1 858801 rs7418179 A G . PASS . ES:SE:LP:SS:ID 0.0192:0.0214:0.431916:71757:rs7418179
1 859404 rs71509444 C G . PASS . ES:SE:LP:SS:ID -0.0587:0.0546:0.549751:65259:rs71509444
1 859685 rs111572704 G A . PASS . ES:SE:LP:SS:ID -0.0338:0.0226:0.870955:65259:rs111572704
1 859690 rs71509445 C G . PASS . ES:SE:LP:SS:ID -0.0697:0.0609:0.598083:65259:rs71509445
1 859701 rs71509446 C G . PASS . ES:SE:LP:SS:ID -0.0697:0.0609:0.598083:65259:rs71509446
1 859913 rs1187056171 A G . PASS . ES:SE:LP:SS:ID -0.0498:0.06:0.391367:69750:rs1187056171
1 860416 rs61464428 G A . PASS . ES:SE:LP:SS:ID 0.0239:0.0212:0.588044:73086:rs61464428
1 860461 rs57465118 G A . PASS . ES:SE:LP:SS:ID -0.0415:0.0538:0.356449:69750:rs57465118
1 860521 rs57924093 C A . PASS . ES:SE:LP:SS:ID -0.0415:0.0538:0.356449:69750:rs57924093
1 860688 rs60837925 G A . PASS . ES:SE:LP:SS:ID 0.0241:0.0211:0.594142:73086:rs60837925
1 860854 rs57816555 T C . PASS . ES:SE:LP:SS:ID -0.0851:0.0672:0.686977:65259:rs57816555
1 861008 rs28521172 G C . PASS . ES:SE:LP:SS:ID -0.0417:0.0538:0.357931:69750:rs28521172