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/2e0cd451-340d-42f2-a7b8-53aafe5542a4/call-vcf/inputs/268670742/upload.txt.gz --id ieu-b-103 --json /data/cromwell-executions/qc/2e0cd451-340d-42f2-a7b8-53aafe5542a4/call-vcf/inputs/268670742/ieu-b-103_data.json --ref /data/cromwell-executions/qc/2e0cd451-340d-42f2-a7b8-53aafe5542a4/call-vcf/inputs/1899004205/human_g1k_v37.fasta --dbsnp /data/cromwell-executions/qc/2e0cd451-340d-42f2-a7b8-53aafe5542a4/call-vcf/inputs/-307190728/dbsnp.v153.b37.vcf.gz --out /data/igd/ieu-b-103/ieu-b-103.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/2e0cd451-340d-42f2-a7b8-53aafe5542a4/call-ldsc/inputs/268670742/ieu-b-103.vcf.gz \
--ref-ld-chr /data/ref/eur_w_ld_chr/ \
--out /data/igd/ieu-b-103/ldsc.txt \
--w-ld-chr /data/ref/eur_w_ld_chr/
Beginning analysis at Fri Mar 19 13:11:57 2021
Reading summary statistics from /data/cromwell-executions/qc/2e0cd451-340d-42f2-a7b8-53aafe5542a4/call-ldsc/inputs/268670742/ieu-b-103.vcf.gz ...
Read summary statistics for 2529804 SNPs.
Dropped 864 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, 1150402 SNPs remain.
After merging with regression SNP LD, 1150402 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0612 (0.0109)
Lambda GC: 1.0336
Mean Chi^2: 1.0547
Intercept: 1.0001 (0.0069)
Ratio: 0.0012 (0.1254)
Analysis finished at Fri Mar 19 13:12:53 2021
Total time elapsed: 56.73sQC metrics
Metrics
Metrics
{
"af_correlation": "NA",
"inflation_factor": 1.0426,
"mean_EFFECT": 0.0001,
"n": 46368,
"n_snps": 2529804,
"n_clumped_hits": 11,
"n_p_sig": 188,
"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": 2529804,
"n_miss_AF_reference": 22414,
"n_est": "NA",
"ratio_se_n": "NA",
"mean_diff": "NaN",
"ratio_diff": "NaN",
"sd_y_est1": "NA",
"sd_y_est2": "NA",
"r2_sum1": 0,
"r2_sum2": 0,
"r2_sum3": 0,
"r2_sum4": 0,
"ldsc_nsnp_merge_refpanel_ld": 1150402,
"ldsc_nsnp_merge_regression_ld": 1150402,
"ldsc_observed_scale_h2_beta": 0.0612,
"ldsc_observed_scale_h2_se": 0.0109,
"ldsc_intercept_beta": 1.0001,
"ldsc_intercept_se": 0.0069,
"ldsc_lambda_gc": 1.0336,
"ldsc_mean_chisq": 1.0547,
"ldsc_ratio": 0.0018
}
Flags
| name | value |
|---|---|
| af_correlation | NA |
| inflation_factor | FALSE |
| n | FALSE |
| is_snpid_non_unique | FALSE |
| 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 | 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 | logical.mean | logical.count | numeric.mean | numeric.sd | numeric.p0 | numeric.p25 | numeric.p50 | numeric.p75 | numeric.p100 | numeric.hist |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| character | ID | 0 | 1.00000 | 3 | 42 | 0 | 2529799 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| character | REF | 0 | 1.00000 | 1 | 1 | 0 | 4 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| character | ALT | 0 | 1.00000 | 1 | 1 | 0 | 4 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| logical | AF | 2529804 | 0.00000 | NA | NA | NA | NA | NA | NaN | : | NA | NA | NA | NA | NA | NA | NA | NA |
| numeric | CHROM | 0 | 1.00000 | NA | NA | NA | NA | NA | NA | NA | 8.595755e+00 | 5.669368e+00 | 1.0000 | 4.000000e+00 | 8.000000e+00 | 1.200000e+01 | 2.30000e+01 | ▇▅▅▂▂ |
| numeric | POS | 0 | 1.00000 | NA | NA | NA | NA | NA | NA | NA | 7.886410e+07 | 5.579983e+07 | 6888.0000 | 3.255575e+07 | 7.020820e+07 | 1.144403e+08 | 2.49219e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.00000 | NA | NA | NA | NA | NA | NA | NA | 6.780000e-05 | 7.151900e-03 | -0.1892 | -3.300000e-03 | 0.000000e+00 | 3.400000e-03 | 1.96700e-01 | ▁▁▇▁▁ |
| numeric | SE | 0 | 1.00000 | NA | NA | NA | NA | NA | NA | NA | 5.795700e-03 | 4.078500e-03 | 0.0031 | 3.700000e-03 | 4.400000e-03 | 6.200000e-03 | 8.44000e-02 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.00000 | NA | NA | NA | NA | NA | NA | NA | 4.932469e-01 | 2.909962e-01 | 0.0000 | 2.398999e-01 | 4.909994e-01 | 7.456002e-01 | 1.00000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.00000 | NA | NA | NA | NA | NA | NA | NA | 4.932541e-01 | 2.910497e-01 | 0.0000 | 2.399947e-01 | 4.917677e-01 | 7.456925e-01 | 1.00000e+00 | ▇▇▇▇▇ |
| numeric | AF_reference | 22414 | 0.99114 | NA | NA | NA | NA | NA | NA | NA | 3.618150e-01 | 2.559484e-01 | 0.0000 | 1.465650e-01 | 3.003190e-01 | 5.465260e-01 | 1.00000e+00 | ▇▆▃▃▂ |
| numeric | N | 0 | 1.00000 | NA | NA | NA | NA | NA | NA | NA | 4.636800e+04 | 0.000000e+00 | 46368.0000 | 4.636800e+04 | 4.636800e+04 | 4.636800e+04 | 4.63680e+04 | ▁▁▇▁▁ |
Head and tail
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 721290 | rs12565286 | G | C | -0.0007 | 0.0111 | 0.9488001 | 0.9497163 | NA | 0.0371406 | 46368 |
| 1 | 723819 | rs11804171 | T | A | -0.0015 | 0.0111 | 0.8922000 | 0.8925050 | NA | 0.1345850 | 46368 |
| 1 | 723891 | rs2977670 | G | C | -0.0076 | 0.0208 | 0.7148008 | 0.7148243 | NA | 0.7799520 | 46368 |
| 1 | 750235 | rs12138618 | G | A | -0.0077 | 0.0204 | 0.7066006 | 0.7058385 | NA | NA | 46368 |
| 1 | 752566 | rs3094315 | G | A | 0.0037 | 0.0049 | 0.4495997 | 0.4501878 | NA | 0.7182510 | 46368 |
| 1 | 754192 | rs3131968 | A | G | 0.0121 | 0.0104 | 0.2451997 | 0.2446423 | NA | 0.6785140 | 46368 |
| 1 | 768448 | rs12562034 | G | A | 0.0032 | 0.0102 | 0.7565001 | 0.7537295 | NA | 0.1918930 | 46368 |
| 1 | 775659 | rs2905035 | A | G | 0.0106 | 0.0055 | 0.0533495 | 0.0539457 | NA | 0.7450080 | 46368 |
| 1 | 777122 | rs2980319 | A | T | 0.0109 | 0.0055 | 0.0473904 | 0.0474996 | NA | 0.7472040 | 46368 |
| 1 | 779322 | rs4040617 | A | G | -0.0106 | 0.0055 | 0.0548504 | 0.0539457 | NA | 0.2264380 | 46368 |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51216564 | rs9616970 | T | C | -0.0003 | 0.0075 | 0.9687000 | 0.9680931 | NA | 0.1563500 | 46368 |
| 22 | 51217134 | rs117417021 | A | G | -0.0034 | 0.0054 | 0.5274994 | 0.5289369 | NA | 0.2671730 | 46368 |
| 22 | 51222100 | rs114553188 | G | T | -0.0040 | 0.0096 | 0.6761997 | 0.6769222 | NA | 0.0880591 | 46368 |
| 22 | 51223637 | rs375798137 | G | A | -0.0028 | 0.0096 | 0.7737007 | 0.7705415 | NA | 0.0788738 | 46368 |
| 22 | 51229805 | rs9616985 | T | C | 0.0076 | 0.0103 | 0.4641999 | 0.4605971 | NA | 0.0730831 | 46368 |
| 23 | 35921591 | rs2204667 | C | G | -0.0029 | 0.0046 | 0.5290004 | 0.5284102 | NA | NA | 46368 |
| 23 | 51666786 | rs14115 | A | G | 0.0101 | 0.0075 | 0.1750000 | 0.1780876 | NA | NA | 46368 |
| 23 | 70163799 | rs1626496 | A | C | -0.0108 | 0.0067 | 0.1080001 | 0.1069749 | NA | NA | 46368 |
| 23 | 91415872 | rs6562597 | G | A | 0.0104 | 0.0125 | 0.4039001 | 0.4054089 | NA | 0.0021192 | 46368 |
| 23 | 118495837 | rs12882977 | G | A | -0.0003 | 0.0034 | 0.9243001 | 0.9296897 | NA | 0.2307280 | 46368 |
bcf preview
1 721290 rs12565286 G C . PASS . ES:SE:LP:SS:ID -0.0007:0.0111:0.0228253:46368:rs12565286
1 723819 rs11804171 T A . PASS . ES:SE:LP:SS:ID -0.0015:0.0111:0.0495378:46368:rs11804171
1 723891 rs2977670 G C . PASS . ES:SE:LP:SS:ID -0.0076:0.0208:0.145815:46368:rs2977670
1 750235 rs12138618 G A . PASS . ES:SE:LP:SS:ID -0.0077:0.0204:0.150826:46368:rs12138618
1 752566 rs3094315 G A . PASS . ES:SE:LP:SS:ID 0.0037:0.0049:0.347174:46368:rs3094315
1 754192 rs3131968 A G . PASS . ES:SE:LP:SS:ID 0.0121:0.0104:0.61048:46368:rs3131968
1 768448 rs12562034 G A . PASS . ES:SE:LP:SS:ID 0.0032:0.0102:0.121191:46368:rs12562034
1 775659 rs2905035 A G . PASS . ES:SE:LP:SS:ID 0.0106:0.0055:1.27287:46368:rs2905035
1 777122 rs2980319 A T . PASS . ES:SE:LP:SS:ID 0.0109:0.0055:1.32431:46368:rs2980319
1 779322 rs4040617 A G . PASS . ES:SE:LP:SS:ID -0.0106:0.0055:1.26082:46368:rs4040617
1 780785 rs2977612 T A . PASS . ES:SE:LP:SS:ID 0.0104:0.0055:1.2327:46368:rs2977612
1 785050 rs2905062 G A . PASS . ES:SE:LP:SS:ID 0.0096:0.0055:1.09637:46368:rs2905062
1 785989 rs2980300 T C . PASS . ES:SE:LP:SS:ID 0.0076:0.0054:0.797512:46368:rs2980300
1 798026 rs4951864 C T . PASS . ES:SE:LP:SS:ID -0.0043:0.0103:0.16877:46368:rs4951864
1 798801 rs12132517 G A . PASS . ES:SE:LP:SS:ID 0.0039:0.0103:0.153354:46368:rs12132517
1 798959 rs11240777 G A . PASS . ES:SE:LP:SS:ID -0.0071:0.0059:0.641494:46368:rs11240777
1 947034 rs2465126 G A . PASS . ES:SE:LP:SS:ID -0.0306:0.0241:0.69229:46368:rs2465126
1 949608 rs1921 G A . PASS . ES:SE:LP:SS:ID -0.0053:0.0091:0.250496:46368:rs1921
1 957898 rs2799064 G T . PASS . ES:SE:LP:SS:ID -0.0047:0.0093:0.211902:46368:rs2799064
1 962210 rs3128126 A G . PASS . ES:SE:LP:SS:ID -0.0019:0.0091:0.0799289:46368:rs3128126
1 990380 rs3121561 C T . PASS . ES:SE:LP:SS:ID -0.008:0.008:0.505289:46368:rs3121561
1 998501 rs3813193 G C . PASS . ES:SE:LP:SS:ID -0.0016:0.0097:0.0629337:46368:rs3813193
1 1003629 rs4075116 C T . PASS . ES:SE:LP:SS:ID 0.002:0.0046:0.175159:46368:rs4075116
1 1005806 rs3934834 C T . PASS . ES:SE:LP:SS:ID -0.0006:0.008:0.0247061:46368:rs3934834
1 1017170 rs3766193 C G . PASS . ES:SE:LP:SS:ID 0.0017:0.0041:0.168706:46368:rs3766193
1 1017197 rs3766192 C T . PASS . ES:SE:LP:SS:ID 0.0014:0.0042:0.128485:46368:rs3766192
1 1017587 rs3766191 C T . PASS . ES:SE:LP:SS:ID 0.0007:0.0084:0.032031:46368:rs3766191
1 1018562 rs9442371 C T . PASS . ES:SE:LP:SS:ID 0.0012:0.0041:0.119015:46368:rs9442371
1 1018704 rs9442372 A G . PASS . ES:SE:LP:SS:ID 0.0015:0.0039:0.157703:46368:rs9442372
1 1021346 rs10907177 A G . PASS . ES:SE:LP:SS:ID -0.0057:0.0097:0.252122:46368:rs10907177
1 1021415 rs386627436 A G . PASS . ES:SE:LP:SS:ID 0.0014:0.0048:0.111035:46368:rs386627436
1 1021583 rs10907178 A C . PASS . ES:SE:LP:SS:ID -0.0048:0.0098:0.202871:46368:rs10907178
1 1021695 rs9442398 A G . PASS . ES:SE:LP:SS:ID 0.002:0.0048:0.16877:46368:rs9442398
1 1022037 rs6701114 C T . PASS . ES:SE:LP:SS:ID 0.0029:0.0051:0.24382:46368:rs6701114
1 1025301 rs9442400 T C . PASS . ES:SE:LP:SS:ID 0.0043:0.0379:0.0414839:46368:rs9442400
1 1026707 rs4074137 C A . PASS . ES:SE:LP:SS:ID 0.0091:0.0075:0.637895:46368:rs4074137
1 1030565 rs6687776 C T . PASS . ES:SE:LP:SS:ID -0.0042:0.0081:0.221487:46368:rs6687776
1 1030633 rs6678318 G A . PASS . ES:SE:LP:SS:ID 0.017:0.012:0.801893:46368:rs6678318
1 1031540 rs776599533 A G . PASS . ES:SE:LP:SS:ID 0.0098:0.0078:0.669586:46368:rs776599533
1 1036959 rs1162868282 T C . PASS . ES:SE:LP:SS:ID 0.0037:0.0087:0.176852:46368:rs1162868282
1 1039098 rs11260595 C A . PASS . ES:SE:LP:SS:ID 0.0107:0.0273:0.158641:46368:rs11260595
1 1040026 rs6671356 T C . PASS . ES:SE:LP:SS:ID -0.0002:0.0086:0.00952795:46368:rs6671356
1 1041700 rs6604968 A G . PASS . ES:SE:LP:SS:ID 0.0086:0.012:0.323672:46368:rs6604968
1 1046164 rs386627439 C T . PASS . ES:SE:LP:SS:ID 0.0036:0.008:0.182831:46368:rs386627439
1 1048955 rs4970405 A G . PASS . ES:SE:LP:SS:ID 0.0062:0.0081:0.350568:46368:rs4970405
1 1049950 rs12726255 A G . PASS . ES:SE:LP:SS:ID -0.0002:0.0076:0.0105947:46368:rs12726255
1 1053452 rs4970409 G A . PASS . ES:SE:LP:SS:ID 0.0062:0.0087:0.326979:46368:rs4970409
1 1060235 rs7540009 G A . PASS . ES:SE:LP:SS:ID 0.0115:0.0293:0.157578:46368:rs7540009
1 1060608 rs17160824 G A . PASS . ES:SE:LP:SS:ID 0.0048:0.0086:0.242376:46368:rs17160824
1 1061115 rs17160826 T C . PASS . ES:SE:LP:SS:ID 0.0091:0.0091:0.502241:46368:rs17160826