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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"gwas_harmonisation_command": "--json /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-RHEUMA_NOS/ukb-d-RHEUMA_NOS_data.json --ref /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/QC/genomes/b37/human_g1k_v37.fasta; 1.1.1",
"file_date": "2019-11-25T13:34:45.418112",
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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-d-import/processed/ukb-d-RHEUMA_NOS/ukb-d-RHEUMA_NOS.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-d-import/processed/ukb-d-RHEUMA_NOS/ldsc.txt \
--w-ld-chr /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/
Beginning analysis at Mon Nov 25 15:00:13 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-RHEUMA_NOS/ukb-d-RHEUMA_NOS.vcf.gz ...
Read summary statistics for 9704048 SNPs.
Dropped 7333 SNPs with duplicated rs numbers.
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, 1275553 SNPs remain.
After merging with regression SNP LD, 1275553 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0024 (0.0012)
Lambda GC: 1.0128
Mean Chi^2: 1.0177
Intercept: 1.0006 (0.006)
Ratio: 0.0345 (0.339)
Analysis finished at Mon Nov 25 15:01:51 2019
Total time elapsed: 1.0m:37.59sQC metrics
Metrics
Metrics
{
"af_correlation": 0.94,
"inflation_factor": 1.0114,
"mean_EFFECT": 1.4207e-06,
"n": 361194,
"n_snps": 9704048,
"n_clumped_hits": 5,
"n_p_sig": 6912,
"n_mono": 0,
"n_ns": 1064251,
"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": 193231,
"n_est": 360971.3633,
"ratio_se_n": 0.9997,
"mean_diff": 0,
"ratio_diff": 1707.6447,
"sd_y_est1": 0.0596,
"sd_y_est2": 0.0596,
"r2_sum1": 9.4329e-06,
"r2_sum2": 0.0027,
"r2_sum3": 0.0027,
"r2_sum4": 0.0025,
"ldsc_nsnp_merge_refpanel_ld": 1275553,
"ldsc_nsnp_merge_regression_ld": 1275553,
"ldsc_observed_scale_h2_beta": 0.0024,
"ldsc_observed_scale_h2_se": 0.0012,
"ldsc_intercept_beta": 1.0006,
"ldsc_intercept_se": 0.006,
"ldsc_lambda_gc": 1.0128,
"ldsc_mean_chisq": 1.0177,
"ldsc_ratio": 0.0339
}
Flags
| name | value |
|---|---|
| af_correlation | FALSE |
| 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 | 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 | 94 | 0 | 9697261 | 0 | NA | NA | NA | NA | NA | NA | NA | NA |
| character | REF | 0 | 1.0000000 | 1 | 98 | 0 | 47151 | 0 | NA | NA | NA | NA | NA | NA | NA | NA |
| character | ALT | 0 | 1.0000000 | 1 | 342 | 0 | 29723 | 0 | NA | NA | NA | NA | NA | NA | NA | NA |
| numeric | CHROM | 0 | 1.0000000 | NA | NA | NA | NA | NA | 9.079157e+00 | 6.175440e+00 | 1.00000e+00 | 4.000000e+00 | 8.000000e+00 | 1.300000e+01 | 2.300000e+01 | ▇▅▅▂▂ |
| numeric | POS | 0 | 1.0000000 | NA | NA | NA | NA | NA | 7.874342e+07 | 5.611232e+07 | 3.02000e+02 | 3.243847e+07 | 6.962927e+07 | 1.147532e+08 | 2.492297e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | 1.400000e-06 | 3.216000e-04 | -2.66400e-03 | -1.531000e-04 | -1.100000e-06 | 1.513000e-04 | 5.389400e-03 | ▁▇▁▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | 2.749000e-04 | 1.572000e-04 | 1.15400e-04 | 1.536000e-04 | 2.052000e-04 | 3.520000e-04 | 8.995000e-04 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | 4.979368e-01 | 2.896999e-01 | 0.00000e+00 | 2.469127e-01 | 4.975755e-01 | 7.495351e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | 4.979363e-01 | 2.897002e-01 | 0.00000e+00 | 2.469117e-01 | 4.975757e-01 | 7.495348e-01 | 9.999999e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | 2.615211e-01 | 2.628767e-01 | 9.75800e-03 | 4.395000e-02 | 1.593820e-01 | 4.161310e-01 | 9.902420e-01 | ▇▂▂▁▁ |
| numeric | AF_reference | 193231 | 0.9800876 | NA | NA | NA | NA | NA | 2.610091e-01 | 2.536915e-01 | 0.00000e+00 | 4.992010e-02 | 1.745210e-01 | 4.117410e-01 | 1.000000e+00 | ▇▃▂▁▁ |
| numeric | N | 0 | 1.0000000 | NA | NA | NA | NA | NA | 3.611940e+05 | 0.000000e+00 | 3.61194e+05 | 3.611940e+05 | 3.611940e+05 | 3.611940e+05 | 3.611940e+05 | ▁▁▇▁▁ |
Head and tail
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 692794 | rs530212009 | CA | C | -0.0000152 | 0.0002458 | 0.9505770 | 0.9505769 | 0.1106400 | 0.1894970 | 361194 |
| 1 | 693731 | rs12238997 | A | G | -0.0000978 | 0.0002323 | 0.6735689 | 0.6735687 | 0.1158300 | 0.1417730 | 361194 |
| 1 | 707522 | rs371890604 | G | C | -0.0002655 | 0.0002611 | 0.3093342 | 0.3093318 | 0.0973034 | 0.1293930 | 361194 |
| 1 | 717587 | rs144155419 | G | A | 0.0000540 | 0.0006231 | 0.9309490 | 0.9309487 | 0.0156880 | 0.0045926 | 361194 |
| 1 | 730087 | rs148120343 | T | C | -0.0004730 | 0.0003236 | 0.1437749 | 0.1437741 | 0.0564602 | 0.0127796 | 361194 |
| 1 | 731718 | rs142557973 | T | C | -0.0000815 | 0.0002203 | 0.7112938 | 0.7112937 | 0.1217380 | 0.1543530 | 361194 |
| 1 | 732032 | rs61770163 | A | C | -0.0002139 | 0.0002350 | 0.3626461 | 0.3626448 | 0.1211710 | 0.1555510 | 361194 |
| 1 | 734349 | rs141242758 | T | C | -0.0000901 | 0.0002204 | 0.6827929 | 0.6827933 | 0.1209650 | 0.1525560 | 361194 |
| 1 | 749963 | rs529266287 | T | TAA | 0.0002102 | 0.0002173 | 0.3332737 | 0.3332724 | 0.8697420 | 0.7641770 | 361194 |
| 1 | 751343 | rs28544273 | T | A | -0.0002666 | 0.0002152 | 0.2152911 | 0.2152900 | 0.1229160 | 0.2426120 | 361194 |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 23 | 154923311 | rs141127553 | C | T | 0.0000184 | 0.0002545 | 0.9422660 | 0.9422666 | 0.0561667 | 0.0309934 | 361194 |
| 23 | 154923374 | rs111332691 | T | A | -0.0001028 | 0.0002797 | 0.7131485 | 0.7131475 | 0.0447862 | 0.0116556 | 361194 |
| 23 | 154925045 | rs509981 | C | T | 0.0001525 | 0.0001346 | 0.2569763 | 0.2569762 | 0.2456060 | 0.3634440 | 361194 |
| 23 | 154925895 | rs538470 | C | T | 0.0001547 | 0.0001377 | 0.2610550 | 0.2610542 | 0.2419150 | 0.3634440 | 361194 |
| 23 | 154927581 | rs644138 | G | A | 0.0001373 | 0.0001266 | 0.2781211 | 0.2781209 | 0.3021620 | 0.4635760 | 361194 |
| 23 | 154929412 | rs557132 | C | T | 0.0001544 | 0.0001346 | 0.2513559 | 0.2513551 | 0.2454590 | 0.3568210 | 361194 |
| 23 | 154929637 | rs35185538 | CT | C | 0.0002891 | 0.0001405 | 0.0395950 | 0.0395936 | 0.2296970 | 0.3011920 | 361194 |
| 23 | 154929952 | rs4012982 | CAA | C | 0.0001666 | 0.0001416 | 0.2393371 | 0.2393359 | 0.2394250 | 0.3165560 | 361194 |
| 23 | 154930230 | rs781880 | A | G | 0.0001458 | 0.0001346 | 0.2786012 | 0.2785995 | 0.2458690 | 0.3618540 | 361194 |
| 23 | 154930487 | rs781879 | T | A | -0.0007577 | 0.0004617 | 0.1007639 | 0.1007632 | 0.0195623 | 0.1263580 | 361194 |
bcf preview
1 692794 rs530212009 CA C . PASS AF=0.11064 ES:SE:LP:AF:SS:ID -1.5236e-05:0.000245812:0.0220127:0.11064:361194:1_692794_CA_C
1 693731 rs12238997 A G . PASS AF=0.11583 ES:SE:LP:AF:SS:ID -9.78416e-05:0.000232262:0.171618:0.11583:361194:rs12238997
1 707522 rs371890604 G C . PASS AF=0.0973034 ES:SE:LP:AF:SS:ID -0.000265451:0.00026111:0.509572:0.0973034:361194:rs371890604
1 717587 rs144155419 G A . PASS AF=0.015688 ES:SE:LP:AF:SS:ID 5.39904e-05:0.000623077:0.0310741:0.015688:361194:rs144155419
1 730087 rs148120343 T C . PASS AF=0.0564602 ES:SE:LP:AF:SS:ID -0.000473022:0.000323571:0.842317:0.0564602:361194:rs148120343
1 731718 rs58276399 T C . PASS AF=0.121738 ES:SE:LP:AF:SS:ID -8.15291e-05:0.000220278:0.147951:0.121738:361194:rs58276399
1 732032 rs61770163 A C . PASS AF=0.121171 ES:SE:LP:AF:SS:ID -0.000213901:0.000234969:0.440517:0.121171:361194:rs61770163
1 734349 rs141242758 T C . PASS AF=0.120965 ES:SE:LP:AF:SS:ID -9.00662e-05:0.000220397:0.165711:0.120965:361194:rs141242758
1 749963 rs529266287 T TAA . PASS AF=0.869742 ES:SE:LP:AF:SS:ID 0.000210224:0.000217276:0.477199:0.869742:361194:rs529266287
1 751343 rs28544273 T A . PASS AF=0.122916 ES:SE:LP:AF:SS:ID -0.000266644:0.000215183:0.666974:0.122916:361194:rs28544273
1 751488 rs200141114 G GA . PASS AF=0.142712 ES:SE:LP:AF:SS:ID -0.00025099:0.000212697:0.623447:0.142712:361194:rs200141114
1 751756 rs28527770 T C . PASS AF=0.123031 ES:SE:LP:AF:SS:ID -0.000267949:0.000214884:0.672811:0.123031:361194:rs28527770
1 753405 rs3115860 C A . PASS AF=0.87089 ES:SE:LP:AF:SS:ID 0.000182094:0.000209397:0.415088:0.87089:361194:rs3115860
1 753425 rs3131970 T C . PASS AF=0.875472 ES:SE:LP:AF:SS:ID 0.000248803:0.000212867:0.615329:0.875472:361194:rs3131970
1 753541 rs2073813 G A . PASS AF=0.128631 ES:SE:LP:AF:SS:ID -0.000187573:0.000209922:0.429958:0.128631:361194:rs2073813
1 754105 rs12184325 C T . PASS AF=0.0363613 ES:SE:LP:AF:SS:ID 0.000539563:0.000380847:0.805325:0.0363613:361194:rs12184325
1 754182 rs3131969 A G . PASS AF=0.870484 ES:SE:LP:AF:SS:ID 0.000174808:0.00020918:0.394335:0.870484:361194:rs3131969
1 754192 rs3131968 A G . PASS AF=0.870589 ES:SE:LP:AF:SS:ID 0.000172777:0.000209275:0.388243:0.870589:361194:rs3131968
1 754334 rs3131967 T C . PASS AF=0.870481 ES:SE:LP:AF:SS:ID 0.000173483:0.000209172:0.390523:0.870481:361194:rs3131967
1 755890 rs3115858 A T . PASS AF=0.870563 ES:SE:LP:AF:SS:ID 0.000163631:0.000208804:0.36327:0.870563:361194:rs3115858
1 756434 rs61768170 G C . PASS AF=0.125985 ES:SE:LP:AF:SS:ID -0.000177142:0.000213056:0.391764:0.125985:361194:rs61768170
1 756604 rs3131962 A G . PASS AF=0.870127 ES:SE:LP:AF:SS:ID 0.000156012:0.000208301:0.343064:0.870127:361194:rs3131962
1 757640 rs3115853 G A . PASS AF=0.869414 ES:SE:LP:AF:SS:ID 0.000160503:0.000208111:0.355988:0.869414:361194:rs3115853
1 757734 rs4951929 C T . PASS AF=0.870281 ES:SE:LP:AF:SS:ID 0.000163184:0.00020848:0.362723:0.870281:361194:rs4951929
1 757936 rs4951862 C A . PASS AF=0.870287 ES:SE:LP:AF:SS:ID 0.000163221:0.000208495:0.362798:0.870287:361194:rs4951862
1 758144 rs3131956 A G . PASS AF=0.870295 ES:SE:LP:AF:SS:ID 0.000162968:0.000208502:0.36207:0.870295:361194:rs3131956
1 758626 rs3131954 C T . PASS AF=0.870739 ES:SE:LP:AF:SS:ID 0.000157531:0.000209033:0.345747:0.870739:361194:rs3131954
1 759293 rs10157329 T A . PASS AF=0.0988442 ES:SE:LP:AF:SS:ID -0.000236415:0.000242909:0.480932:0.0988442:361194:rs10157329
1 759837 rs3115851 T A . PASS AF=0.874635 ES:SE:LP:AF:SS:ID 0.00018948:0.000212148:0.429717:0.874635:361194:rs3115851
1 761732 rs2286139 C T . PASS AF=0.864027 ES:SE:LP:AF:SS:ID 8.40367e-05:0.000208043:0.163513:0.864027:361194:rs2286139
1 761752 rs1057213 C T . PASS AF=0.869425 ES:SE:LP:AF:SS:ID 0.000170508:0.000210062:0.379902:0.869425:361194:rs1057213
1 762273 rs3115849 G A . PASS AF=0.866371 ES:SE:LP:AF:SS:ID 0.000131084:0.000210105:0.273521:0.866371:361194:rs3115849
1 762485 rs12095200 C A . PASS AF=0.0987831 ES:SE:LP:AF:SS:ID -0.000192297:0.000251535:0.352057:0.0987831:361194:rs12095200
1 762589 rs3115848 G C . PASS AF=0.871556 ES:SE:LP:AF:SS:ID 0.000140174:0.000212499:0.292872:0.871556:361194:rs3115848
1 762592 rs3131950 C G . PASS AF=0.871556 ES:SE:LP:AF:SS:ID 0.000140176:0.000212499:0.292876:0.871556:361194:rs3131950
1 762601 rs3131949 T C . PASS AF=0.871555 ES:SE:LP:AF:SS:ID 0.000140108:0.000212503:0.292696:0.871555:361194:rs3131949
1 762632 rs3131948 T A . PASS AF=0.871929 ES:SE:LP:AF:SS:ID 0.000147154:0.000212624:0.310793:0.871929:361194:rs3131948
1 764191 rs7515915 T G . PASS AF=0.125562 ES:SE:LP:AF:SS:ID -0.000197284:0.000212792:0.451166:0.125562:361194:rs7515915
1 766007 rs61768174 A C . PASS AF=0.105428 ES:SE:LP:AF:SS:ID -0.000163536:0.000231653:0.318562:0.105428:361194:rs61768174
1 766105 rs2519015 T A . PASS AF=0.855376 ES:SE:LP:AF:SS:ID 0.000125125:0.000207965:0.261698:0.855376:361194:rs2519015
1 768116 rs376645387 A AGTTTT . PASS AF=0.838358 ES:SE:LP:AF:SS:ID -3.35423e-05:0.000204883:0.0605027:0.838358:361194:rs376645387
1 768253 rs2977608 A C . PASS AF=0.763351 ES:SE:LP:AF:SS:ID 7.53773e-05:0.000164892:0.188709:0.763351:361194:rs2977608
1 768448 rs12562034 G A . PASS AF=0.105921 ES:SE:LP:AF:SS:ID 2.13058e-05:0.000227181:0.0337264:0.105921:361194:rs12562034
1 769138 rs59306077 CAT C . PASS AF=0.128938 ES:SE:LP:AF:SS:ID -0.000191162:0.000209815:0.440998:0.128938:361194:rs762168062
1 769223 rs60320384 C G . PASS AF=0.128608 ES:SE:LP:AF:SS:ID -0.000164723:0.000209621:0.36454:0.128608:361194:rs60320384
1 769963 rs7518545 G A . PASS AF=0.105213 ES:SE:LP:AF:SS:ID 3.72645e-05:0.000228897:0.0601434:0.105213:361194:rs7518545
1 770886 rs371458725 G A . PASS AF=0.104268 ES:SE:LP:AF:SS:ID 2.36206e-06:0.000230726:0.00356188:0.104268:361194:rs371458725
1 771410 rs2519006 C T . PASS AF=0.830106 ES:SE:LP:AF:SS:ID 0.000153503:0.000204251:0.344547:0.830106:361194:rs2519006
1 771823 rs2977605 T C . PASS AF=0.87012 ES:SE:LP:AF:SS:ID 0.000162573:0.000208683:0.36056:0.87012:361194:rs2977605
1 771967 rs59066358 G A . PASS AF=0.128668 ES:SE:LP:AF:SS:ID -0.000166016:0.000209523:0.368396:0.128668:361194:rs59066358