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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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 /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ieu-a-import/processed/ieu-a-1232/ieu-a-1232.vcf.gz \
--ref-ld-chr /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ieu-a-import/processed/ieu-a-1232/ldsc.txt \
--w-ld-chr /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/
Beginning analysis at Wed Feb 5 09:22:31 2020
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ieu-a-import/processed/ieu-a-1232/ieu-a-1232.vcf.gz ...
Read summary statistics for 15223150 SNPs.
Reading reference panel LD Score from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/[1-22] ...
Read reference panel LD Scores for 1290028 SNPs.
Removing partitioned LD Scores with zero variance.
Reading regression weight LD Score from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/[1-22] ...
Read regression weight LD Scores for 1290028 SNPs.
After merging with reference panel LD, 1218949 SNPs remain.
After merging with regression SNP LD, 1218949 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0048 (0.0104)
Lambda GC: 1.0076
Mean Chi^2: 1.0157
Intercept: 1.0117 (0.0063)
Ratio: 0.7481 (0.3999)
Analysis finished at Wed Feb 5 09:25:00 2020
Total time elapsed: 2.0m:28.66sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9601,
"inflation_factor": 1.0383,
"mean_EFFECT": -0.9077,
"n": 42090,
"n_snps": 15223150,
"n_clumped_hits": 4,
"n_p_sig": 55,
"n_mono": 0,
"n_ns": 774482,
"n_mac": 268172,
"is_snpid_unique": false,
"n_miss_EFFECT": 0,
"n_miss_SE": 0,
"n_miss_PVAL": 0,
"n_miss_AF": 1043,
"n_miss_AF_reference": 159448,
"n_est": 40702.5551,
"ratio_se_n": 0.9834,
"mean_diff": 0.9001,
"ratio_diff": 7.3094,
"sd_y_est1": 7.0623,
"sd_y_est2": 6.9449,
"r2_sum1": 0.1912,
"r2_sum2": 0.0038,
"r2_sum3": 0.004,
"r2_sum4": 0.004,
"ldsc_nsnp_merge_refpanel_ld": 1218949,
"ldsc_nsnp_merge_regression_ld": 1218949,
"ldsc_observed_scale_h2_beta": 0.0048,
"ldsc_observed_scale_h2_se": 0.0104,
"ldsc_intercept_beta": 1.0117,
"ldsc_intercept_se": 0.0063,
"ldsc_lambda_gc": 1.0076,
"ldsc_mean_chisq": 1.0157,
"ldsc_ratio": 0.7452
}
Flags
| name | value |
|---|---|
| af_correlation | FALSE |
| inflation_factor | FALSE |
| n | FALSE |
| is_snpid_non_unique | TRUE |
| mean_EFFECT_nonfinite | FALSE |
| mean_EFFECT_05 | TRUE |
| mean_EFFECT_01 | TRUE |
| 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 | numeric.mean | numeric.sd | numeric.p0 | numeric.p25 | numeric.p50 | numeric.p75 | numeric.p100 | numeric.hist |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| character | ID | 0 | 1.0000000 | 3 | 47 | 0 | 15223148 | 0 | NA | NA | NA | NA | NA | NA | NA | NA |
| character | REF | 0 | 1.0000000 | 1 | 101 | 0 | 37357 | 0 | NA | NA | NA | NA | NA | NA | NA | NA |
| character | ALT | 0 | 1.0000000 | 1 | 105 | 0 | 18029 | 0 | NA | NA | NA | NA | NA | NA | NA | NA |
| numeric | CHROM | 0 | 1.0000000 | NA | NA | NA | NA | NA | 8.899981e+00 | 6.257718e+00 | 1.000e+00 | 4.000000e+00 | 7.000000e+00 | 1.400000e+01 | 2.400000e+01 | ▇▆▂▃▂ |
| numeric | POS | 0 | 1.0000000 | NA | NA | NA | NA | NA | 7.931426e+07 | 5.683715e+07 | 8.280e+02 | 3.250388e+07 | 6.965838e+07 | 1.158435e+08 | 2.492402e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | -9.076725e-01 | 3.991129e+00 | -2.039e+01 | -1.307000e-01 | -5.568000e-03 | 8.843000e-02 | 2.039000e+01 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | 1.697493e+00 | 7.081598e+00 | 2.026e-03 | 6.125000e-02 | 1.709000e-01 | 6.607000e-01 | 1.000000e+03 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | 4.945573e-01 | 2.878809e-01 | 0.000e+00 | 2.454002e-01 | 4.919002e-01 | 7.422001e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | 4.946896e-01 | 2.878238e-01 | 0.000e+00 | 2.456373e-01 | 4.920215e-01 | 7.423019e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | AF | 1043 | 0.9999315 | NA | NA | NA | NA | NA | 1.572296e-01 | 2.465552e-01 | 0.000e+00 | 2.373000e-03 | 2.251000e-02 | 2.154000e-01 | 9.999880e-01 | ▇▁▁▁▁ |
| numeric | AF_reference | 159448 | 0.9895260 | NA | NA | NA | NA | NA | 1.569808e-01 | 2.339252e-01 | 0.000e+00 | 2.913900e-03 | 3.454470e-02 | 2.208470e-01 | 1.000000e+00 | ▇▁▁▁▁ |
| numeric | N | 0 | 1.0000000 | NA | NA | NA | NA | NA | 4.209000e+04 | 0.000000e+00 | 4.209e+04 | 4.209000e+04 | 4.209000e+04 | 4.209000e+04 | 4.209000e+04 | ▁▁▇▁▁ |
Head and tail
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 10616 | rs376342519 | CCGCCGTTGCAAAGGCGCGCCG | C | 0.12150 | 0.33710 | 0.7185996 | 0.7185277 | 0.991000 | 0.9930110 | 42090 |
| 1 | 14933 | rs199856693 | G | A | 0.07507 | 0.20690 | 0.7166993 | 0.7167302 | 0.047970 | 0.0283546 | 42090 |
| 1 | 15774 | rs374029747 | G | A | 0.69560 | 0.46220 | 0.1323000 | 0.1323302 | 0.006579 | 0.0119808 | 42090 |
| 1 | 16949 | rs199745162 | A | C | -0.03842 | 0.27780 | 0.8900000 | 0.8900026 | 0.016750 | 0.0139776 | 42090 |
| 1 | 51479 | rs116400033 | T | A | 0.15090 | 0.08650 | 0.0810606 | 0.0810704 | 0.232100 | 0.1281950 | 42090 |
| 1 | 52185 | rs201374420 | TTAA | T | -0.57160 | 1.14900 | 0.6188993 | 0.6188534 | 0.005882 | 0.0053914 | 42090 |
| 1 | 54353 | rs140052487 | C | A | -1.42000 | 1.59000 | 0.3718998 | 0.3718134 | 0.001339 | 0.0089856 | 42090 |
| 1 | 54490 | rs141149254 | G | A | 0.20980 | 0.09537 | 0.0277901 | 0.0278173 | 0.166100 | 0.0960463 | 42090 |
| 1 | 55164 | rs3091274 | C | A | 0.17960 | 0.30700 | 0.5584998 | 0.5585368 | 0.980700 | 0.9233230 | 42090 |
| 1 | 55326 | rs3107975 | T | C | -0.14060 | 0.52500 | 0.7888002 | 0.7888459 | 0.028260 | 0.0459265 | 42090 |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 23 | 154925045 | rs509981 | C | T | -0.08288 | 0.05850 | 0.1566001 | 0.1565554 | 0.250000 | 0.3634440 | 42090 |
| 23 | 154925895 | rs538470 | C | T | -0.07959 | 0.06051 | 0.1884000 | 0.1884025 | 0.252700 | 0.3634440 | 42090 |
| 23 | 154926376 | rs116490668 | C | T | -3.02000 | 1.55500 | 0.0521495 | 0.0521223 | 0.001859 | 0.0349669 | 42090 |
| 23 | 154927185 | rs185685661 | T | C | -0.07090 | 0.10190 | 0.4866996 | 0.4865665 | 0.139300 | 0.1796030 | 42090 |
| 23 | 154927199 | rs645904 | C | T | -0.08256 | 0.05856 | 0.1585999 | 0.1585881 | 0.248000 | 0.3674170 | 42090 |
| 23 | 154927581 | rs644138 | G | A | -0.10190 | 0.05718 | 0.0746002 | 0.0747343 | 0.299900 | 0.4635760 | 42090 |
| 23 | 154929412 | rs557132 | C | T | -0.08014 | 0.05863 | 0.1715999 | 0.1716639 | 0.250400 | 0.3568210 | 42090 |
| 23 | 154929952 | rs4012982 | CAA | C | -0.08249 | 0.05869 | 0.1598998 | 0.1598665 | 0.250900 | 0.3165560 | 42090 |
| 23 | 154930230 | rs781880 | A | G | -0.08152 | 0.05899 | 0.1669998 | 0.1669935 | 0.249000 | 0.3618540 | 42090 |
| 24 | 13537468 | rs7203107 | A | G | 0.01883 | 0.29690 | 0.9493999 | 0.9494304 | 0.985590 | NA | 42090 |
bcf preview
1 10616 rs376342519 CCGCCGTTGCAAAGGCGCGCCG C . PASS AF=0.991 ES:SE:LP:AF:SS:ID 0.1215:0.3371:0.143513:0.991:42090:rs376342519
1 14933 rs199856693 G A . PASS AF=0.04797 ES:SE:LP:AF:SS:ID 0.07507:0.2069:0.144663:0.04797:42090:rs199856693
1 15774 rs374029747 G A . PASS AF=0.006579 ES:SE:LP:AF:SS:ID 0.6956:0.4622:0.87844:0.006579:42090:rs374029747
1 16949 rs199745162 A C . PASS AF=0.01675 ES:SE:LP:AF:SS:ID -0.03842:0.2778:0.05061:0.01675:42090:rs199745162
1 51479 rs116400033 T A . PASS AF=0.2321 ES:SE:LP:AF:SS:ID 0.1509:0.0865:1.09119:0.2321:42090:rs116400033
1 52185 rs201374420 TTAA T . PASS AF=0.005882 ES:SE:LP:AF:SS:ID -0.5716:1.149:0.20838:0.005882:42090:rs201374420
1 54353 rs140052487 C A . PASS AF=0.001339 ES:SE:LP:AF:SS:ID -1.42:1.59:0.429574:0.001339:42090:rs140052487
1 54490 rs141149254 G A . PASS AF=0.1661 ES:SE:LP:AF:SS:ID 0.2098:0.09537:1.55611:0.1661:42090:rs141149254
1 55164 rs3091274 C A . PASS AF=0.9807 ES:SE:LP:AF:SS:ID 0.1796:0.307:0.252977:0.9807:42090:rs3091274
1 55326 rs3107975 T C . PASS AF=0.02826 ES:SE:LP:AF:SS:ID -0.1406:0.525:0.103033:0.02826:42090:rs3107975
1 55545 rs28396308 C T . PASS AF=0.2248 ES:SE:LP:AF:SS:ID -0.03978:0.09905:0.162412:0.2248:42090:rs28396308
1 57183 rs368339209 A G . PASS AF=0.0007041 ES:SE:LP:AF:SS:ID -1.123:1.956:0.24726:0.0007041:42090:rs368339209
1 57292 rs201418760 C T . PASS AF=0.02393 ES:SE:LP:AF:SS:ID 0.0703:0.4911:0.0524683:0.02393:42090:rs201418760
1 58814 rs114420996 G A . PASS AF=0.1003 ES:SE:LP:AF:SS:ID -0.1994:0.1459:0.764724:0.1003:42090:rs114420996
1 61543 rs201849102 T C . PASS AF=0.0003079 ES:SE:LP:AF:SS:ID -0.6791:3.127:0.0819172:0.0003079:42090:rs201849102
1 61743 rs184286948 G C . PASS AF=0.009155 ES:SE:LP:AF:SS:ID -0.4322:0.4422:0.483597:0.009155:42090:rs184286948
1 61920 rs62637820 G A . PASS AF=0.02945 ES:SE:LP:AF:SS:ID 0.1385:0.2366:0.253132:0.02945:42090:rs62637820
1 63093 rs200092917 G A . PASS AF=0.0207 ES:SE:LP:AF:SS:ID -0.3579:0.3329:0.549135:0.0207:42090:rs200092917
1 64649 rs181431124 A C . PASS AF=0.02715 ES:SE:LP:AF:SS:ID 0.1867:0.2564:0.331148:0.02715:42090:rs181431124
1 66219 rs181028663 A T . PASS AF=0.01624 ES:SE:LP:AF:SS:ID -0.3102:0.3504:0.424812:0.01624:42090:rs181028663
1 68082 rs367789441 T C . PASS AF=0.06526 ES:SE:LP:AF:SS:ID 0.175:0.1498:0.614751:0.06526:42090:rs367789441
1 68596 rs372212855 T G . PASS AF=0.003797 ES:SE:LP:AF:SS:ID -0.9026:0.6924:0.715795:0.003797:42090:rs372212855
1 69428 rs140739101 T G . PASS AF=0.04215 ES:SE:LP:AF:SS:ID -0.07963:0.1991:0.161718:0.04215:42090:rs140739101
1 69534 rs190717287 T C . PASS AF=0.0003164 ES:SE:LP:AF:SS:ID 1.818:1.278:0.81051:0.0003164:42090:rs190717287
1 69761 rs200505207 A T . PASS AF=0.06821 ES:SE:LP:AF:SS:ID 0.2347:0.1441:0.985479:0.06821:42090:rs200505207
1 69897 rs200676709 T C . PASS AF=0.7661 ES:SE:LP:AF:SS:ID 0.07263:0.0984:0.336865:0.7661:42090:rs200676709
1 72297 rs200651397 G GTAT . PASS AF=0.006341 ES:SE:LP:AF:SS:ID -1.13:0.704:0.96457:0.006341:42090:rs200651397
1 74790 rs13328700 C G . PASS AF=0.03421 ES:SE:LP:AF:SS:ID 0.04682:0.1998:0.0890023:0.03421:42090:rs13328700
1 74792 rs13328684 G A . PASS AF=0.03421 ES:SE:LP:AF:SS:ID 0.04682:0.1998:0.0890023:0.03421:42090:rs13328684
1 76854 rs367666799 A G . PASS AF=0.06856 ES:SE:LP:AF:SS:ID -0.05635:0.1678:0.132474:0.06856:42090:rs367666799
1 82163 rs139113303 G A . PASS AF=0.06791 ES:SE:LP:AF:SS:ID -0.04847:0.1688:0.111259:0.06791:42090:rs139113303
1 82609 rs149189449 C G . PASS AF=0.06818 ES:SE:LP:AF:SS:ID -0.05077:0.1683:0.117532:0.06818:42090:rs149189449
1 83514 rs201754587 C T . PASS AF=0.3501 ES:SE:LP:AF:SS:ID 0.06318:0.08967:0.317765:0.3501:42090:rs201754587
1 84139 rs183605470 A T . PASS AF=0.02132 ES:SE:LP:AF:SS:ID -0.4425:0.3618:0.655019:0.02132:42090:rs183605470
1 86028 rs114608975 T C . PASS AF=0.04742 ES:SE:LP:AF:SS:ID -0.0133:0.1832:0.025903:0.04742:42090:rs114608975
1 86065 rs116504101 G C . PASS AF=0.06995 ES:SE:LP:AF:SS:ID -0.01146:0.1666:0.0244763:0.06995:42090:rs116504101
1 86331 rs115209712 A G . PASS AF=0.1018 ES:SE:LP:AF:SS:ID -0.1958:0.1453:0.749824:0.1018:42090:rs115209712
1 87021 rs188486692 T C . PASS AF=0.00763 ES:SE:LP:AF:SS:ID 0.03011:0.501:0.0213174:0.00763:42090:rs188486692
1 87360 rs180907504 C T . PASS AF=0.01853 ES:SE:LP:AF:SS:ID -0.1961:0.2844:0.309272:0.01853:42090:rs180907504
1 87409 rs139490478 C T . PASS AF=0.07088 ES:SE:LP:AF:SS:ID -0.03319:0.1654:0.075204:0.07088:42090:rs139490478
1 88169 rs940550 C T . PASS AF=0.2022 ES:SE:LP:AF:SS:ID -0.03695:0.09474:0.157079:0.2022:42090:rs940550
1 88172 rs940551 G A . PASS AF=0.06423 ES:SE:LP:AF:SS:ID 0.1831:0.1513:0.645892:0.06423:42090:rs940551
1 88177 rs143215837 G C . PASS AF=0.06377 ES:SE:LP:AF:SS:ID 0.1926:0.1523:0.686555:0.06377:42090:rs143215837
1 88188 rs148331237 C A . PASS AF=0.007142 ES:SE:LP:AF:SS:ID 0.1929:0.4191:0.190238:0.007142:42090:rs148331237
1 88236 rs186918018 C T . PASS AF=0.01279 ES:SE:LP:AF:SS:ID 0.5378:0.5791:0.452102:0.01279:42090:rs186918018
1 88316 rs113759966 G A . PASS AF=0.06376 ES:SE:LP:AF:SS:ID 0.202:0.1526:0.73189:0.06376:42090:rs113759966
1 88338 rs55700207 G A . PASS AF=0.08209 ES:SE:LP:AF:SS:ID -0.08824:0.1583:0.238598:0.08209:42090:rs55700207
1 88710 rs186575039 C G . PASS AF=0.07086 ES:SE:LP:AF:SS:ID -0.03234:0.1654:0.0731433:0.07086:42090:rs186575039
1 89599 rs375955515 A T . PASS AF=0.07086 ES:SE:LP:AF:SS:ID -0.03234:0.1654:0.0731433:0.07086:42090:rs375955515
1 89946 rs138808727 A T . PASS AF=0.2272 ES:SE:LP:AF:SS:ID 0.1153:0.08712:0.731188:0.2272:42090:rs138808727