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/ukb-a-import/processed/ukb-a-283/ukb-a-283.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/ukb-a-import/processed/ukb-a-283/ldsc.txt \
--w-ld-chr /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/
Beginning analysis at Sun Feb 16 02:49:25 2020
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-a-import/processed/ukb-a-283/ukb-a-283.vcf.gz ...
Read summary statistics for 10877936 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, 1281483 SNPs remain.
After merging with regression SNP LD, 1281483 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.2157 (0.007)
Lambda GC: 2.0243
Mean Chi^2: 2.5411
Intercept: 1.1187 (0.0143)
Ratio: 0.077 (0.0093)
Analysis finished at Sun Feb 16 02:51:10 2020
Total time elapsed: 1.0m:44.99sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9533,
"inflation_factor": 1.582,
"mean_EFFECT": 0,
"n": 331226,
"n_snps": 10877936,
"n_clumped_hits": 277,
"n_p_sig": 29298,
"n_mono": 0,
"n_ns": 0,
"n_mac": 0,
"is_snpid_unique": true,
"n_miss_EFFECT": 0,
"n_miss_SE": 0,
"n_miss_PVAL": 0,
"n_miss_AF": 0,
"n_miss_AF_reference": 356865,
"n_est": 329750.825,
"ratio_se_n": 0.9978,
"mean_diff": -1.0141e-06,
"ratio_diff": 0.4975,
"sd_y_est1": 0.9744,
"sd_y_est2": 0.9722,
"r2_sum1": 0.0444,
"r2_sum2": 0.0467,
"r2_sum3": 0.0469,
"r2_sum4": 0.047,
"ldsc_nsnp_merge_refpanel_ld": 1281483,
"ldsc_nsnp_merge_regression_ld": 1281483,
"ldsc_observed_scale_h2_beta": 0.2157,
"ldsc_observed_scale_h2_se": 0.007,
"ldsc_intercept_beta": 1.1187,
"ldsc_intercept_se": 0.0143,
"ldsc_lambda_gc": 2.0243,
"ldsc_mean_chisq": 2.5411,
"ldsc_ratio": 0.077
}
Flags
| name | value |
|---|---|
| af_correlation | FALSE |
| inflation_factor | TRUE |
| n | FALSE |
| is_snpid_non_unique | FALSE |
| 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 | 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 | 58 | 0 | 10877936 | 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 | 8.598638e+00 | 5.745666e+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 | 7.911383e+07 | 5.624931e+07 | 8.28000e+02 | 3.287294e+07 | 6.988889e+07 | 1.148258e+08 | 2.492251e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | 3.580000e-05 | 1.524870e-02 | -1.74028e-01 | -5.115200e-03 | 1.690000e-05 | 5.158300e-03 | 1.838770e-01 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | 1.031100e-02 | 9.765900e-03 | 2.36560e-03 | 2.921600e-03 | 5.611600e-03 | 1.499450e-02 | 5.358860e-02 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | 4.252528e-01 | 3.073154e-01 | 0.00000e+00 | 1.403440e-01 | 3.962397e-01 | 6.904623e-01 | 1.000000e+00 | ▇▅▅▅▅ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | 4.252523e-01 | 3.073156e-01 | 0.00000e+00 | 1.403431e-01 | 3.962393e-01 | 6.904629e-01 | 1.000000e+00 | ▇▅▅▅▅ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | 1.820865e-01 | 2.503832e-01 | 1.00090e-03 | 6.878700e-03 | 5.191440e-02 | 2.739880e-01 | 9.989990e-01 | ▇▂▁▁▁ |
| numeric | AF_reference | 356865 | 0.9671937 | NA | NA | NA | NA | NA | 1.877709e-01 | 2.439191e-01 | 0.00000e+00 | 5.191700e-03 | 7.468050e-02 | 2.873400e-01 | 1.000000e+00 | ▇▂▁▁▁ |
| numeric | N | 0 | 1.0000000 | NA | NA | NA | NA | NA | 3.312260e+05 | 0.000000e+00 | 3.31226e+05 | 3.312260e+05 | 3.312260e+05 | 3.312260e+05 | 3.312260e+05 | ▁▁▇▁▁ |
Head and tail
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 693731 | rs12238997 | A | G | 0.0008269 | 0.0039515 | 0.8342390 | 0.8342390 | 0.1169260 | 0.1417730 | 331226 |
| 1 | 717587 | rs144155419 | G | A | -0.0049356 | 0.0106144 | 0.6419379 | 0.6419373 | 0.0143862 | 0.0045926 | 331226 |
| 1 | 730087 | rs148120343 | T | C | -0.0075918 | 0.0055058 | 0.1679380 | 0.1679375 | 0.0554733 | 0.0127796 | 331226 |
| 1 | 731718 | rs142557973 | T | C | -0.0002497 | 0.0037486 | 0.9468960 | 0.9468958 | 0.1216580 | 0.1543530 | 331226 |
| 1 | 734349 | rs141242758 | T | C | 0.0001752 | 0.0037508 | 0.9627440 | 0.9627437 | 0.1215130 | 0.1525560 | 331226 |
| 1 | 740284 | rs61770167 | C | T | 0.0242297 | 0.0170556 | 0.1554240 | 0.1554243 | 0.0058556 | 0.0023962 | 331226 |
| 1 | 742813 | rs112573343 | C | T | 0.0047042 | 0.0321292 | 0.8835941 | 0.8835942 | 0.0015525 | 0.1030350 | 331226 |
| 1 | 753405 | rs3115860 | C | A | -0.0008420 | 0.0035631 | 0.8131881 | 0.8131877 | 0.8706760 | 0.7517970 | 331226 |
| 1 | 753541 | rs2073813 | G | A | 0.0000150 | 0.0035722 | 0.9966590 | 0.9966594 | 0.1288330 | 0.3019170 | 331226 |
| 1 | 754182 | rs3131969 | A | G | -0.0003520 | 0.0035594 | 0.9212169 | 0.9212172 | 0.8703180 | 0.6785140 | 331226 |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51219387 | rs9616832 | T | C | 0.0044478 | 0.0045857 | 0.3320802 | 0.3320800 | 0.0729806 | 0.0654952 | 331226 |
| 22 | 51219704 | rs147475742 | G | A | 0.0059022 | 0.0061467 | 0.3369388 | 0.3369384 | 0.0418225 | 0.0473243 | 331226 |
| 22 | 51219766 | rs182321900 | C | T | -0.0207496 | 0.0315146 | 0.5102735 | 0.5102732 | 0.0014546 | NA | 331226 |
| 22 | 51220088 | rs566371895 | G | A | 0.0108655 | 0.0207393 | 0.6003438 | 0.6003420 | 0.0039843 | 0.0003994 | 331226 |
| 22 | 51220146 | rs868950473 | C | T | -0.0234337 | 0.0310392 | 0.4502658 | 0.4502663 | 0.0015139 | NA | 331226 |
| 22 | 51221731 | rs115055839 | T | C | 0.0040492 | 0.0045886 | 0.3775383 | 0.3775377 | 0.0728101 | 0.0625000 | 331226 |
| 22 | 51223637 | rs375798137 | G | A | -0.0005504 | 0.0054045 | 0.9188851 | 0.9188852 | 0.0540289 | 0.0788738 | 331226 |
| 22 | 51226692 | rs150189434 | G | A | -0.0267025 | 0.0322060 | 0.4070399 | 0.4070389 | 0.0013983 | 0.0155751 | 331226 |
| 22 | 51229805 | rs9616985 | T | C | 0.0042062 | 0.0046059 | 0.3611353 | 0.3611345 | 0.0727671 | 0.0730831 | 331226 |
| 22 | 51237063 | rs3896457 | T | C | 0.0045490 | 0.0028044 | 0.1047840 | 0.1047835 | 0.2968410 | 0.2050720 | 331226 |
bcf preview
1 693731 rs12238997 A G . PASS AF=0.116926 ES:SE:LP:AF:SS:ID 0.000826927:0.00395152:0.0787095:0.116926:331226:rs12238997
1 717587 rs144155419 G A . PASS AF=0.0143862 ES:SE:LP:AF:SS:ID -0.00493561:0.0106144:0.192507:0.0143862:331226:rs144155419
1 730087 rs148120343 T C . PASS AF=0.0554733 ES:SE:LP:AF:SS:ID -0.00759179:0.00550584:0.774851:0.0554733:331226:rs148120343
1 731718 rs58276399 T C . PASS AF=0.121658 ES:SE:LP:AF:SS:ID -0.000249675:0.00374857:0.0236977:0.121658:331226:rs58276399
1 734349 rs141242758 T C . PASS AF=0.121513 ES:SE:LP:AF:SS:ID 0.000175202:0.00375078:0.0164892:0.121513:331226:rs141242758
1 740284 rs61770167 C T . PASS AF=0.00585559 ES:SE:LP:AF:SS:ID 0.0242297:0.0170556:0.808482:0.00585559:331226:rs61770167
1 742813 rs112573343 C T . PASS AF=0.0015525 ES:SE:LP:AF:SS:ID 0.00470418:0.0321292:0.0537472:0.0015525:331226:rs112573343
1 753405 rs3115860 C A . PASS AF=0.870676 ES:SE:LP:AF:SS:ID -0.000842019:0.00356311:0.089809:0.870676:331226:rs3115860
1 753541 rs2073813 G A . PASS AF=0.128833 ES:SE:LP:AF:SS:ID 1.49562e-05:0.00357219:0.00145341:0.128833:331226:rs2073813
1 754182 rs3131969 A G . PASS AF=0.870318 ES:SE:LP:AF:SS:ID -0.000352022:0.00355935:0.0356381:0.870318:331226:rs3131969
1 754192 rs3131968 A G . PASS AF=0.870409 ES:SE:LP:AF:SS:ID -0.00030991:0.00356104:0.0312141:0.870409:331226:rs3131968
1 754334 rs3131967 T C . PASS AF=0.870037 ES:SE:LP:AF:SS:ID -0.000359298:0.00355922:0.0364053:0.870037:331226:rs3131967
1 754433 rs150578204 G A . PASS AF=0.00465304 ES:SE:LP:AF:SS:ID 0.00180934:0.0181387:0.0359559:0.00465304:331226:rs150578204
1 754458 rs142682604 G T . PASS AF=0.00464711 ES:SE:LP:AF:SS:ID 0.0014524:0.0181872:0.0285618:0.00464711:331226:rs142682604
1 755435 rs184270342 T G . PASS AF=0.00590897 ES:SE:LP:AF:SS:ID 0.00798828:0.017727:0.185581:0.00590897:331226:rs184270342
1 755890 rs3115858 A T . PASS AF=0.870329 ES:SE:LP:AF:SS:ID -0.000923956:0.00355302:0.0997274:0.870329:331226:rs3115858
1 756604 rs3131962 A G . PASS AF=0.87002 ES:SE:LP:AF:SS:ID -0.00113868:0.00354459:0.126084:0.87002:331226:rs3131962
1 757640 rs3115853 G A . PASS AF=0.869421 ES:SE:LP:AF:SS:ID -0.000825632:0.00354114:0.0885004:0.869421:331226:rs3115853
1 757734 rs4951929 C T . PASS AF=0.870219 ES:SE:LP:AF:SS:ID -0.00104136:0.00354745:0.114017:0.870219:331226:rs4951929
1 757936 rs4951862 C A . PASS AF=0.870219 ES:SE:LP:AF:SS:ID -0.00103703:0.0035477:0.113481:0.870219:331226:rs4951862
1 758144 rs3131956 A G . PASS AF=0.870222 ES:SE:LP:AF:SS:ID -0.00100622:0.00354781:0.109744:0.870222:331226:rs3131956
1 758626 rs3131954 C T . PASS AF=0.870396 ES:SE:LP:AF:SS:ID -0.000802759:0.00355692:0.0854226:0.870396:331226:rs3131954
1 761732 rs2286139 C T . PASS AF=0.867743 ES:SE:LP:AF:SS:ID 0.000225339:0.00353996:0.0226221:0.867743:331226:rs2286139
1 766007 rs61768174 A C . PASS AF=0.106958 ES:SE:LP:AF:SS:ID -0.000105981:0.00394211:0.00941609:0.106958:331226:rs61768174
1 768253 rs2977608 A C . PASS AF=0.763932 ES:SE:LP:AF:SS:ID -0.00423626:0.0028073:0.881752:0.763932:331226:rs2977608
1 768448 rs12562034 G A . PASS AF=0.105257 ES:SE:LP:AF:SS:ID 0.00719404:0.00387452:1.19827:0.105257:331226:rs12562034
1 768819 rs12562811 C T . PASS AF=0.00692766 ES:SE:LP:AF:SS:ID -0.0154058:0.0143865:0.546322:0.00692766:331226:rs12562811
1 769223 rs60320384 C G . PASS AF=0.128651 ES:SE:LP:AF:SS:ID 0.00054155:0.00356696:0.0558501:0.128651:331226:rs60320384
1 769224 rs141644775 G A . PASS AF=0.00143239 ES:SE:LP:AF:SS:ID 0.0448967:0.0335242:0.743532:0.00143239:331226:rs141644775
1 770181 rs146076599 A G . PASS AF=0.00907328 ES:SE:LP:AF:SS:ID -0.0113423:0.0136538:0.391322:0.00907328:331226:rs146076599
1 770377 rs112563271 A T . PASS AF=0.00692321 ES:SE:LP:AF:SS:ID -0.0156921:0.0144478:0.556854:0.00692321:331226:rs112563271
1 771823 rs2977605 T C . PASS AF=0.869934 ES:SE:LP:AF:SS:ID -0.000760576:0.00355095:0.0807127:0.869934:331226:rs2977605
1 771967 rs59066358 G A . PASS AF=0.128709 ES:SE:LP:AF:SS:ID 0.000413335:0.00356531:0.0420548:0.128709:331226:rs59066358
1 772755 rs2905039 A C . PASS AF=0.870112 ES:SE:LP:AF:SS:ID -0.000643989:0.00355112:0.067478:0.870112:331226:rs2905039
1 774736 rs28830877 A C . PASS AF=0.998797 ES:SE:LP:AF:SS:ID -0.0197619:0.0368866:0.227581:0.998797:331226:rs28830877
1 776556 rs151160018 C T . PASS AF=0.00851871 ES:SE:LP:AF:SS:ID 0.0179829:0.0133212:0.751943:0.00851871:331226:rs151160018
1 777122 rs2980319 A T . PASS AF=0.871122 ES:SE:LP:AF:SS:ID -0.000240465:0.00355712:0.0240616:0.871122:331226:rs2980319
1 777232 rs112618790 C T . PASS AF=0.0961319 ES:SE:LP:AF:SS:ID 0.00854149:0.0041038:1.42711:0.0961319:331226:rs112618790
1 778745 rs1055606 A G . PASS AF=0.127871 ES:SE:LP:AF:SS:ID -0.000127718:0.0035689:0.0125782:0.127871:331226:rs1055606
1 779322 rs4040617 A G . PASS AF=0.127984 ES:SE:LP:AF:SS:ID -0.000251808:0.00356407:0.0251775:0.127984:331226:rs4040617
1 780785 rs2977612 T A . PASS AF=0.870455 ES:SE:LP:AF:SS:ID 0.00025598:0.00355129:0.0257011:0.870455:331226:rs2977612
1 781367 rs149821290 A C . PASS AF=0.00989772 ES:SE:LP:AF:SS:ID 0.00366553:0.0126168:0.112714:0.00989772:331226:rs149821290
1 781845 rs61768199 A G . PASS AF=0.1044 ES:SE:LP:AF:SS:ID -0.000418188:0.003994:0.0378149:0.1044:331226:rs61768199
1 782721 rs185280546 G A . PASS AF=0.0143713 ES:SE:LP:AF:SS:ID 0.00442176:0.0108039:0.166:0.0143713:331226:rs185280546
1 782981 rs6594026 C T . PASS AF=0.128338 ES:SE:LP:AF:SS:ID -0.000854488:0.00356608:0.0911784:0.128338:331226:rs6594026
1 783193 rs145767270 G C . PASS AF=0.0143639 ES:SE:LP:AF:SS:ID 0.00321383:0.0108497:0.115167:0.0143639:331226:rs145767270
1 783194 rs138555831 G T . PASS AF=0.0143713 ES:SE:LP:AF:SS:ID 0.00318564:0.0108485:0.114058:0.0143713:331226:rs138555831
1 783711 rs184266993 G A . PASS AF=0.00541372 ES:SE:LP:AF:SS:ID -0.00671285:0.0173357:0.155778:0.00541372:331226:rs184266993
1 785050 rs2905062 G A . PASS AF=0.87014 ES:SE:LP:AF:SS:ID 0.00035605:0.00355189:0.0361404:0.87014:331226:rs2905062
1 785989 rs2980300 T C . PASS AF=0.87009 ES:SE:LP:AF:SS:ID 0.000425263:0.00355415:0.0434676:0.87009:331226:rs2980300