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 /data/cromwell-executions/qc/0439bf79-c5d6-44c1-8183-ef9974a26b6f/call-ldsc/inputs/562856195/ieu-b-38.vcf.gz \
--ref-ld-chr /data/ref/eur_w_ld_chr/ \
--out /data/igd/ieu-b-38/ldsc.txt \
--w-ld-chr /data/ref/eur_w_ld_chr/
Beginning analysis at Thu Aug 20 17:45:12 2020
Reading summary statistics from /data/cromwell-executions/qc/0439bf79-c5d6-44c1-8183-ef9974a26b6f/call-ldsc/inputs/562856195/ieu-b-38.vcf.gz ...
Read summary statistics for 7088067 SNPs.
Dropped 18970 SNPs with duplicated rs numbers.
Reading reference panel LD Score from /data/ref/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 /data/ref/eur_w_ld_chr/[1-22] ...
Read regression weight LD Scores for 1290028 SNPs.
After merging with reference panel LD, 1203548 SNPs remain.
After merging with regression SNP LD, 1203548 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.1316 (0.0046)
Lambda GC: 2.0747
Mean Chi^2: 3.115
Intercept: 1.1505 (0.0196)
Ratio: 0.0712 (0.0093)
Analysis finished at Thu Aug 20 17:47:41 2020
Total time elapsed: 2.0m:28.94sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9402,
"inflation_factor": 1.916,
"mean_EFFECT": -0.0009,
"n": 745787,
"n_snps": 7088083,
"n_clumped_hits": 461,
"n_p_sig": 74356,
"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": 0,
"n_miss_AF_reference": 44712,
"n_est": 741766.9694,
"ratio_se_n": 0.9973,
"mean_diff": 0.0008,
"ratio_diff": 5537.3609,
"sd_y_est1": 18.6199,
"sd_y_est2": 18.5696,
"r2_sum1": 15.8295,
"r2_sum2": 0.0457,
"r2_sum3": 0.0459,
"r2_sum4": 0.0458,
"ldsc_nsnp_merge_refpanel_ld": 1203548,
"ldsc_nsnp_merge_regression_ld": 1203548,
"ldsc_observed_scale_h2_beta": 0.1316,
"ldsc_observed_scale_h2_se": 0.0046,
"ldsc_intercept_beta": 1.1505,
"ldsc_intercept_se": 0.0196,
"ldsc_lambda_gc": 2.0747,
"ldsc_mean_chisq": 3.115,
"ldsc_ratio": 0.0712
}
Flags
| name | value |
|---|---|
| af_correlation | FALSE |
| inflation_factor | TRUE |
| n | FALSE |
| is_snpid_non_unique | TRUE |
| mean_EFFECT_nonfinite | FALSE |
| mean_EFFECT_05 | FALSE |
| mean_EFFECT_01 | FALSE |
| mean_chisq | TRUE |
| n_p_sig | TRUE |
| miss_EFFECT | FALSE |
| miss_SE | FALSE |
| miss_PVAL | FALSE |
| ldsc_ratio | FALSE |
| ldsc_intercept_beta | FALSE |
| n_clumped_hits | FALSE |
| r2_sum1 | TRUE |
| 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 | 58 | 0 | 7088074 | 0 | 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 |
| character | ALT | 0 | 1.0000000 | 1 | 1 | 0 | 4 | 0 | NA | NA | NA | NA | NA | NA | NA | NA |
| numeric | CHROM | 0 | 1.0000000 | NA | NA | NA | NA | NA | 8.618154e+00 | 5.733636e+00 | 1.00000e+00 | 4.000000e+00 | 8.000000e+00 | 1.300000e+01 | 2.200000e+01 | ▇▅▅▂▂ |
| numeric | POS | 0 | 1.0000000 | NA | NA | NA | NA | NA | 7.894880e+07 | 5.615450e+07 | 6.68900e+03 | 3.267243e+07 | 6.967551e+07 | 1.145593e+08 | 2.492183e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | -8.712000e-04 | 9.071510e-02 | -1.38380e+00 | -4.650000e-02 | -5.000000e-04 | 4.540000e-02 | 1.228700e+00 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | 5.701510e-02 | 3.150200e-02 | 1.70000e-03 | 3.310000e-02 | 4.330000e-02 | 7.110000e-02 | 2.045000e-01 | ▇▆▂▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | 3.951111e-01 | 3.121712e-01 | 0.00000e+00 | 9.858930e-02 | 3.505000e-01 | 6.621997e-01 | 1.000000e+00 | ▇▃▃▃▃ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | 3.951113e-01 | 3.121717e-01 | 0.00000e+00 | 9.858650e-02 | 3.505209e-01 | 6.621761e-01 | 1.000000e+00 | ▇▃▃▃▃ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | 2.688679e-01 | 2.606549e-01 | 1.00000e-02 | 5.330000e-02 | 1.707000e-01 | 4.236000e-01 | 9.900000e-01 | ▇▃▂▁▁ |
| numeric | AF_reference | 44712 | 0.9936919 | NA | NA | NA | NA | NA | 2.681288e-01 | 2.522186e-01 | 1.99700e-04 | 6.170130e-02 | 1.827080e-01 | 4.167330e-01 | 1.000000e+00 | ▇▃▂▂▁ |
| numeric | N | 0 | 1.0000000 | NA | NA | NA | NA | NA | 7.133146e+05 | 3.094613e+04 | 2.53159e+05 | 7.058510e+05 | 7.253300e+05 | 7.331190e+05 | 7.457870e+05 | ▁▁▁▁▇ |
Head and tail
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 752566 | rs3094315 | G | A | 0.0687 | 0.0447 | 0.1240001 | 0.1243147 | 0.8363 | 0.7182510 | 639410 |
| 1 | 885689 | rs4970452 | G | A | 0.2350 | 0.0727 | 0.0012340 | 0.0012273 | 0.9463 | 0.9384980 | 629875 |
| 1 | 885699 | rs4970376 | A | G | 0.2403 | 0.0724 | 0.0009015 | 0.0009032 | 0.9456 | 0.9219250 | 630477 |
| 1 | 886006 | rs4970375 | T | C | 0.2408 | 0.0723 | 0.0008683 | 0.0008667 | 0.9457 | 0.9221250 | 632092 |
| 1 | 887801 | rs3828047 | A | G | 0.2382 | 0.0720 | 0.0009413 | 0.0009385 | 0.9456 | 0.9223240 | 635922 |
| 1 | 888639 | rs3748596 | T | C | 0.2472 | 0.0725 | 0.0006506 | 0.0006505 | 0.9458 | 0.9227240 | 637170 |
| 1 | 888659 | rs3748597 | T | C | 0.2323 | 0.0716 | 0.0011830 | 0.0011769 | 0.9453 | 0.9227240 | 639044 |
| 1 | 889238 | rs3828049 | G | A | -0.2282 | 0.0726 | 0.0016700 | 0.0016708 | 0.0530 | 0.0565096 | 640081 |
| 1 | 892745 | rs13303227 | G | A | 0.2401 | 0.0721 | 0.0008727 | 0.0008682 | 0.9454 | 0.9207270 | 632219 |
| 1 | 893280 | rs4970371 | G | A | 0.2378 | 0.0726 | 0.0010530 | 0.0010548 | 0.9465 | 0.9390970 | 634520 |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51171667 | rs41281537 | G | A | -0.1177 | 0.1127 | 0.2963999 | 0.2963163 | 0.0215 | 0.0577077 | 646222 |
| 22 | 51171693 | rs756638 | G | A | 0.0064 | 0.0353 | 0.8555000 | 0.8561297 | 0.2776 | 0.3049120 | 677882 |
| 22 | 51171783 | rs115712102 | C | T | 0.0114 | 0.1471 | 0.9380999 | 0.9382271 | 0.0137 | 0.0067891 | 594466 |
| 22 | 51172460 | rs5770824 | T | C | -0.0858 | 0.1112 | 0.4405995 | 0.4403616 | 0.0225 | 0.0684904 | 628868 |
| 22 | 51174331 | rs5770825 | C | G | 0.0051 | 0.0403 | 0.8993000 | 0.8992959 | 0.7636 | 0.7917330 | 580912 |
| 22 | 51174662 | rs1858748 | T | C | 0.0127 | 0.0383 | 0.7393005 | 0.7401968 | 0.7312 | 0.7328270 | 591495 |
| 22 | 51175626 | rs3810648 | A | G | 0.0293 | 0.0635 | 0.6447005 | 0.6444992 | 0.0613 | 0.1084270 | 731429 |
| 22 | 51177257 | rs73174437 | C | T | -0.0150 | 0.0908 | 0.8685000 | 0.8687880 | 0.0348 | 0.0091853 | 640640 |
| 22 | 51178090 | rs2285395 | G | A | 0.0247 | 0.0680 | 0.7166003 | 0.7164291 | 0.0549 | 0.0666933 | 710879 |
| 22 | 51182399 | rs56238942 | A | G | 0.0108 | 0.0628 | 0.8637000 | 0.8634576 | 0.0770 | 0.0992412 | 611720 |
bcf preview
1 752566 rs3094315 G A . PASS AF=0.8363 ES:SE:LP:AF:SS:ID 0.0687:0.0447:0.906578:0.8363:639410:rs3094315
1 885689 rs533420572 G A . PASS AF=0.9463 ES:SE:LP:AF:SS:ID 0.235:0.0727:2.90868:0.9463:629875:rs533420572
1 885699 rs1387393173 A G . PASS AF=0.9456 ES:SE:LP:AF:SS:ID 0.2403:0.0724:3.04503:0.9456:630477:rs1387393173
1 886006 rs1430555697 T C . PASS AF=0.9457 ES:SE:LP:AF:SS:ID 0.2408:0.0723:3.06133:0.9457:632092:rs1430555697
1 887801 rs3828047 A G . PASS AF=0.9456 ES:SE:LP:AF:SS:ID 0.2382:0.072:3.02627:0.9456:635922:rs3828047
1 888639 rs3748596 T C . PASS AF=0.9458 ES:SE:LP:AF:SS:ID 0.2472:0.0725:3.18669:0.9458:637170:rs3748596
1 888659 rs3748597 T C . PASS AF=0.9453 ES:SE:LP:AF:SS:ID 0.2323:0.0716:2.92702:0.9453:639044:rs3748597
1 889238 rs3828049 G A . PASS AF=0.053 ES:SE:LP:AF:SS:ID -0.2282:0.0726:2.77728:0.053:640081:rs3828049
1 892745 rs13303227 G A . PASS AF=0.9454 ES:SE:LP:AF:SS:ID 0.2401:0.0721:3.05913:0.9454:632219:rs13303227
1 893280 rs4970371 G A . PASS AF=0.9465 ES:SE:LP:AF:SS:ID 0.2378:0.0726:2.97757:0.9465:634520:rs4970371
1 918384 rs13303118 G T . PASS AF=0.5818 ES:SE:LP:AF:SS:ID 0.0533:0.0332:0.961777:0.5818:633345:rs13303118
1 918573 rs2341354 A G . PASS AF=0.5896 ES:SE:LP:AF:SS:ID 0.0518:0.0333:0.919374:0.5896:634595:rs2341354
1 924898 rs6665000 C A . PASS AF=0.9642 ES:SE:LP:AF:SS:ID 0.3172:0.0895:3.40594:0.9642:627215:rs6665000
1 926351 rs6671243 C T . PASS AF=0.9641 ES:SE:LP:AF:SS:ID 0.3092:0.0894:3.26289:0.9641:627230:rs6671243
1 926431 rs4970403 A T . PASS AF=0.9641 ES:SE:LP:AF:SS:ID 0.3046:0.0894:3.18535:0.9641:627575:rs4970403
1 926621 rs4970351 A C . PASS AF=0.9641 ES:SE:LP:AF:SS:ID 0.3099:0.0894:3.27671:0.9641:626806:rs4970351
1 928578 rs28394749 G A . PASS AF=0.9641 ES:SE:LP:AF:SS:ID 0.3071:0.0894:3.22952:0.9641:629535:rs28394749
1 928836 rs9777703 C T . PASS AF=0.964 ES:SE:LP:AF:SS:ID 0.305:0.0892:3.20183:0.964:630400:rs9777703
1 929327 rs386627424 A G . PASS AF=0.964 ES:SE:LP:AF:SS:ID 0.3015:0.0894:3.12656:0.964:626510:rs386627424
1 940005 rs2799056 A G . PASS AF=0.3985 ES:SE:LP:AF:SS:ID -0.0344:0.0335:0.516984:0.3985:628273:rs2799056
1 940096 rs4503294 C T . PASS AF=0.5651 ES:SE:LP:AF:SS:ID 0.0786:0.0331:1.75696:0.5651:628055:rs4503294
1 941284 rs3128116 C T . PASS AF=0.3952 ES:SE:LP:AF:SS:ID -0.0367:0.0333:0.567031:0.3952:634220:rs3128116
1 941334 rs57683598 G A . PASS AF=0.395 ES:SE:LP:AF:SS:ID -0.0377:0.0333:0.588044:0.395:634796:rs57683598
1 941539 rs9778087 C T . PASS AF=0.3993 ES:SE:LP:AF:SS:ID -0.0385:0.0334:0.603626:0.3993:630132:rs9778087
1 943687 rs2465140 G C . PASS AF=0.3948 ES:SE:LP:AF:SS:ID -0.0359:0.033:0.556737:0.3948:644433:rs2465140
1 943968 rs1378202313 C T . PASS AF=0.5646 ES:SE:LP:AF:SS:ID 0.0822:0.0327:1.92263:0.5646:640164:rs1378202313
1 944564 rs3128117 T C . PASS AF=0.3961 ES:SE:LP:AF:SS:ID -0.035:0.0329:0.540457:0.3961:647670:rs3128117
1 949608 rs1921 G A . PASS AF=0.3968 ES:SE:LP:AF:SS:ID -0.0243:0.0326:0.341321:0.3968:659404:rs1921
1 950243 rs1891906 A C . PASS AF=0.3935 ES:SE:LP:AF:SS:ID -0.0346:0.0328:0.533577:0.3935:651118:rs1891906
1 950677 rs9331223 T C . PASS AF=0.5665 ES:SE:LP:AF:SS:ID 0.085:0.0326:2.03485:0.5665:643771:rs9331223
1 951564 rs2465141 A G . PASS AF=0.3901 ES:SE:LP:AF:SS:ID -0.0399:0.0333:0.635074:0.3901:636420:rs2465141
1 952003 rs3128118 G A . PASS AF=0.3893 ES:SE:LP:AF:SS:ID -0.0383:0.0331:0.605548:0.3893:644616:rs3128118
1 952428 rs9442611 G A . PASS AF=0.5535 ES:SE:LP:AF:SS:ID 0.0963:0.0329:2.46648:0.5535:631745:rs9442611
1 953952 rs9442612 G A . PASS AF=0.5544 ES:SE:LP:AF:SS:ID 0.1057:0.033:2.87031:0.5544:629628:rs9442612
1 954032 rs75267490 C G . PASS AF=0.3423 ES:SE:LP:AF:SS:ID -0.0482:0.0345:0.788879:0.3423:629830:rs75267490
1 954777 rs61766299 C A . PASS AF=0.5539 ES:SE:LP:AF:SS:ID 0.1013:0.033:2.66999:0.5539:628854:rs61766299
1 956852 rs9777931 C T . PASS AF=0.5499 ES:SE:LP:AF:SS:ID 0.1054:0.0328:2.8755:0.5499:632762:rs9777931
1 957120 rs3121553 G C . PASS AF=0.3432 ES:SE:LP:AF:SS:ID -0.0507:0.0345:0.84649:0.3432:629011:rs3121553
1 957898 rs2799064 G T . PASS AF=0.3506 ES:SE:LP:AF:SS:ID -0.053:0.0344:0.909037:0.3506:627456:rs2799064
1 958248 rs2001744 A G . PASS AF=0.3689 ES:SE:LP:AF:SS:ID -0.0812:0.034:1.77083:0.3689:628689:rs2001744
1 958731 rs2341363 A G . PASS AF=0.3707 ES:SE:LP:AF:SS:ID -0.0772:0.0339:1.64035:0.3707:630644:rs2341363
1 958905 rs2710890 A G . PASS AF=0.3713 ES:SE:LP:AF:SS:ID -0.0777:0.0339:1.65857:0.3713:631280:rs2710890
1 959169 rs3845292 G C . PASS AF=0.545 ES:SE:LP:AF:SS:ID 0.0957:0.033:2.42643:0.545:626810:rs3845292
1 960409 rs1284216646 G C . PASS AF=0.5597 ES:SE:LP:AF:SS:ID 0.1078:0.0329:2.97675:0.5597:633296:rs1284216646
1 962606 rs4970393 G A . PASS AF=0.5734 ES:SE:LP:AF:SS:ID 0.1039:0.0331:2.76751:0.5734:631044:rs4970393
1 962891 rs4970394 C T . PASS AF=0.5781 ES:SE:LP:AF:SS:ID 0.0973:0.0332:2.46993:0.5781:629754:rs4970394
1 967658 rs4970349 C T . PASS AF=0.5683 ES:SE:LP:AF:SS:ID 0.1082:0.0329:2.99183:0.5683:634988:rs4970349
1 984302 rs9442391 T C . PASS AF=0.5751 ES:SE:LP:AF:SS:ID 0.0743:0.0332:1.60171:0.5751:628719:rs9442391
1 990380 rs3121561 C T . PASS AF=0.2894 ES:SE:LP:AF:SS:ID -0.0303:0.0365:0.390726:0.2894:618981:rs3121561
1 990417 rs2465136 T C . PASS AF=0.295 ES:SE:LP:AF:SS:ID -0.0328:0.0362:0.43866:0.295:621761:rs2465136