IEU GWAS QC Report

name	value
subcategory	Psychiatric / neurological
id	ieu-b-18
trait	multiple sclerosis
consortium	International Multiple Sclerosis Genetics Consortium
author	Patsopoulos NA
group_name	public
coverage	whole genome
build	HG19/GRCh37
ncase	47429
study_design	Meta-analysis of case-control studies
ncontrol	68374
qc_prior_to_upload	removed sites without rsid, converted OR to beta and SE and removed Inf results
imputation_panel	HRC
sample_size	115803
pmid	31604244
population	European
doi	10.1126/science.aav7188
mr	1
sex	Males and Females
nsnp	6304359
year	2019
unit	log odds ratio
category	Binary

name	value
gwas_id	ieu-b-18
bcf_file	ieu-b-18.vcf.gz
qc_metrics_file	qc_metrics.json
metadata	metadata.json
refdata	1kg_v3_nomult.bcf
compile_time	2021-02-24 16:50 UTC
build_version	master-2dbfcc4-2020-10-23

manhattan_plot

qq_plot

af_plot

pz_plot

beta_std_plot

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}

*********************************************************************
* 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/efdf2918-41f7-4e74-ac72-1943734e8393/call-ldsc/inputs/562856133/ieu-b-18.vcf.gz \
--ref-ld-chr /data/ref/eur_w_ld_chr/ \
--out /data/igd/ieu-b-18/ldsc.txt \
--w-ld-chr /data/ref/eur_w_ld_chr/ 

Beginning analysis at Thu Jul  2 11:31:39 2020
Reading summary statistics from /data/cromwell-executions/qc/efdf2918-41f7-4e74-ac72-1943734e8393/call-ldsc/inputs/562856133/ieu-b-18.vcf.gz ...
Read summary statistics for 6304141 SNPs.
Dropped 28044 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, 1208721 SNPs remain.
After merging with regression SNP LD, 1208721 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.1001 (0.0086)
Lambda GC: 1.1459
Mean Chi^2: 1.2449
Intercept: 1.0228 (0.0089)
Ratio: 0.093 (0.0363)
Analysis finished at Thu Jul  2 11:33:21 2020
Total time elapsed: 1.0m:42.64s

{
    "af_correlation": "NA",
    "inflation_factor": 1.1541,
    "mean_EFFECT": -0.0007,
    "n": "-Inf",
    "n_snps": 6304358,
    "n_clumped_hits": 72,
    "n_p_sig": 22711,
    "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": 6304358,
    "n_miss_AF_reference": 133998,
    "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": 1208721,
    "ldsc_nsnp_merge_regression_ld": 1208721,
    "ldsc_observed_scale_h2_beta": 0.1001,
    "ldsc_observed_scale_h2_se": 0.0086,
    "ldsc_intercept_beta": 1.0228,
    "ldsc_intercept_se": 0.0089,
    "ldsc_lambda_gc": 1.1459,
    "ldsc_mean_chisq": 1.2449,
    "ldsc_ratio": 0.0931
}

name	value
name	value
af_correlation	NA
inflation_factor	FALSE
n	TRUE
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

General metrics

af_correlation: Correlation coefficient between AF and AF_reference.
inflation_factor (lambda): Genomic inflation factor.
mean_EFFECT: Mean of EFFECT size.
n: Maximum value of reported sample size across all SNPs, $n$ .
n_clumped_hits: Number of clumped hits.
n_snps: Number of SNPs
n_p_sig: Number of SNPs with pvalue below 5e-8.
n_mono: Number of monomorphic (MAF == 1 or MAF == 0) SNPs.
n_ns: Number of SNPs with nonsense values:
- alleles other than A, C, G or T.
- P-values < 0 or > 1.
- negative or infinite standard errors (<= 0 or = Infinity).
- infinite beta estimates or allele frequencies < 0 or > 1.
n_mac: Number of cases where MAC ( $2 \times N \times MAF$ ) is less than 6.
is_snpid_unique: true if the combination of ID REF ALT is unique and therefore no duplication in snpid.
n_miss_<*>: Number of NA observations 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 expect ratio_se_n to 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 of diff and the mean of diff2 (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<*>: r2 statistics under various assumptions
- 1: $r^2 = \sum_j{\frac{2 \times \beta_j^2 \times {MAF}_j \times (1 - {MAF}_j)}{\texttt{var1}}}$ , $\texttt{var1} = 1$ .
- 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: NOT is_snpid_unique.
mean_EFFECT_nonfinite: mean(EFFECT) is NA, NaN, or Inf.
mean_EFFECT_05: abs(mean(EFFECT)) > 0.5.
mean_EFFECT_01: abs(mean(EFFECT)) > 0.1.
mean_chisq: ldsc_mean_chisq > 1.3 or ldsc_mean_chisq < 0.7.
n_p_sig: n_p_sig > 1000.
miss_<*>: n_miss_<*> / n_snps > 0.01.
ldsc_ratio: ldsc_ratio > 0.5
ldsc_intercept_beta: ldsc_intercept_beta > 1.5
n_clumped_hits: n_clumped_hits > 1000
r2_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 $\beta_j$

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
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	190	0.9999699	3	58	0	6298247	0	NA	NA	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	NA	NA
character	ALT	0	1.0000000	1	1	0	4	0	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
logical	AF	6304358	0.0000000	NA	NA	NA	NA	NA	NaN	:	NA	NA	NA	NA	NA	NA	NA	NA
logical	N	6304358	0.0000000	NA	NA	NA	NA	NA	NaN	:	NA	NA	NA	NA	NA	NA	NA	NA
numeric	CHROM	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	8.665710e+00	5.763473e+00	1.0000000	4.000000e+00	8.000000e+00	1.300000e+01	2.200000e+01	▇▅▅▂▂
numeric	POS	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	7.845000e+07	5.644676e+07	828.0000000	3.184148e+07	6.887496e+07	1.143450e+08	2.492223e+08	▇▆▅▂▁
numeric	EFFECT	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	-6.620000e-04	6.378320e-02	-4.1783800	-1.734960e-02	4.001000e-04	1.816400e-02	4.906280e+00	▁▁▇▁▁
numeric	SE	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	3.318160e-02	4.144470e-02	0.0107826	1.772500e-02	2.170220e-02	3.229540e-02	1.569400e+00	▇▁▁▁▁
numeric	PVAL	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	4.755653e-01	2.957547e-01	0.0000000	2.146001e-01	4.687001e-01	7.319999e-01	9.999000e-01	▇▇▆▆▆
numeric	PVAL_ztest	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	4.755653e-01	2.957547e-01	0.0000000	2.146008e-01	4.686991e-01	7.319995e-01	9.999000e-01	▇▇▆▆▆
numeric	AF_reference	133998	0.9787452	NA	NA	NA	NA	NA	NA	NA	3.205810e-01	2.522943e-01	0.0000000	1.130190e-01	2.490020e-01	4.862220e-01	1.000000e+00	▇▅▃▂▁

CHROM	POS	ID	REF	ALT	EFFECT	SE	PVAL	PVAL_ztest	AF	AF_reference	N
CHROM	POS	ID	REF	ALT	EFFECT	SE	PVAL	PVAL_ztest	AF	AF_reference	N
1	51803	rs62637812	T	C	-0.0182658	0.0722086	0.8003000	0.8003001	NA	NA	NA
1	54676	rs2462492	C	T	-0.0150121	0.0586260	0.7979001	0.7979002	NA	NA	NA
1	62271	rs28599927	A	G	-0.0117690	0.0514308	0.8190000	0.8189997	NA	NA	NA
1	66162	rs201684885	A	T	-0.0177567	0.0754741	0.8140000	0.8140001	NA	NA	NA
1	74681	rs13328683	G	T	0.0116678	0.0506186	0.8177000	0.8177000	NA	NA	NA
1	91536	rs6702460	G	T	-0.0025031	0.0864472	0.9769000	0.9769000	NA	0.420727	NA
1	91581	rs151118460	G	A	-0.0015011	0.0843424	0.9858000	0.9858000	NA	0.423722	NA
1	234481	rs8179403	T	A	0.0019980	0.0817444	0.9805000	0.9805000	NA	NA	NA
1	662622	rs61769339	G	A	0.0046890	0.0422833	0.9117001	0.9116999	NA	0.147564	NA
1	729679	rs4951859	C	G	-0.0074720	0.0438186	0.8645999	0.8646001	NA	0.639976	NA

CHROM	POS	ID	REF	ALT	EFFECT	SE	PVAL	PVAL_ztest	AF	AF_reference	N
CHROM	POS	ID	REF	ALT	EFFECT	SE	PVAL	PVAL_ztest	AF	AF_reference	N
22	51190878	rs143051537	G	A	0.0090588	0.0390819	0.8167001	0.8167002	NA	0.4626600	NA
22	51191334	rs541575022	C	G	-0.1213320	0.0841412	0.1493001	0.1493011	NA	0.1042330	NA
22	51192518	rs5771005	C	G	0.0070749	0.0431962	0.8698999	0.8699000	NA	0.2398160	NA
22	51192586	rs5771006	G	A	0.0325640	0.0413431	0.4308995	0.4308999	NA	0.0848642	NA
22	51194639	rs6009964	T	C	0.0089597	0.0438781	0.8382001	0.8382002	NA	0.2418130	NA
22	51194679	rs6009965	T	C	-0.0112632	0.0437421	0.7968000	0.7967997	NA	0.4758390	NA
22	51196164	rs8136603	A	T	-0.1219520	0.0858594	0.1554999	0.1555004	NA	0.1427720	NA
22	51197266	rs61290853	A	G	-0.0214279	0.0448745	0.6330004	0.6330009	NA	0.4229230	NA
22	51198232	rs3859893	C	G	0.0170538	0.0444454	0.7012001	0.7011991	NA	0.2623800	NA
22	51199281	rs4040021	T	C	-0.0070246	0.0427896	0.8695999	0.8696001	NA	0.4612620	NA

1   51803   rs62637812  T   C   .   PASS    .   ES:SE:LP:ID -0.0182658:0.0722086:0.0967472:rs62637812
1   54676   rs2462492   C   T   .   PASS    .   ES:SE:LP:ID -0.0150121:0.058626:0.0980515:rs2462492
1   62271   rs28599927  A   G   .   PASS    .   ES:SE:LP:ID -0.011769:0.0514308:0.0867161:rs28599927
1   66162   rs201684885 A   T   .   PASS    .   ES:SE:LP:ID -0.0177567:0.0754741:0.0893756:rs201684885
1   74681   rs13328683  G   T   .   PASS    .   ES:SE:LP:ID 0.0116678:0.0506186:0.087406:rs13328683
1   91536   rs1251109649    G   T   .   PASS    .   ES:SE:LP:ID -0.00250313:0.0864472:0.0101499:rs1251109649
1   91581   rs1524604   G   A   .   PASS    .   ES:SE:LP:ID -0.00150113:0.0843424:0.00621119:rs1524604
1   234481  rs8179403   T   A   .   PASS    .   ES:SE:LP:ID 0.001998:0.0817444:0.0085524:rs8179403
1   662622  rs61769339  G   A   .   PASS    .   ES:SE:LP:ID 0.00468899:0.0422833:0.040148:rs61769339
1   729679  rs4951859   C   G   .   PASS    .   ES:SE:LP:ID -0.00747201:0.0438186:0.0631848:rs4951859
1   736523  rs71490526  T   C   .   PASS    .   ES:SE:LP:ID -0.00608147:0.0496431:0.0445528:rs71490526
1   750055  rs11240771  T   C   .   PASS    .   ES:SE:LP:ID 0.0988264:0.2019:0.204468:rs11240771
1   750138  rs61770171  G   A   .   PASS    .   ES:SE:LP:ID 0.0174469:0.0367861:0.197021:rs61770171
1   752566  rs3094315   G   A   .   PASS    .   ES:SE:LP:ID -0.0192142:0.0364006:0.223589:rs3094315
1   752721  rs3131972   A   G   .   PASS    .   ES:SE:LP:ID -0.0196065:0.0365637:0.227825:rs3131972
1   752894  rs3131971   T   C   .   PASS    .   ES:SE:LP:ID -0.0292675:0.0379433:0.356054:rs3131971
1   753474  rs2073814   C   G   .   PASS    .   ES:SE:LP:ID -0.0242047:0.0364108:0.295678:rs2073814
1   753541  rs1388595942    G   A   .   PASS    .   ES:SE:LP:ID 0.0179381:0.0366154:0.204676:rs1388595942
1   754182  rs3131969   A   G   .   PASS    .   ES:SE:LP:ID -0.0180364:0.0365102:0.206699:rs3131969
1   754192  rs3131968   A   G   .   PASS    .   ES:SE:LP:ID -0.0181346:0.036646:0.207118:rs3131968
1   754334  rs3131967   T   C   .   PASS    .   ES:SE:LP:ID -0.0255703:0.0370162:0.31007:rs3131967
1   754503  rs3115859   G   A   .   PASS    .   ES:SE:LP:ID -0.0247902:0.0364135:0.304518:rs3115859
1   754964  rs3131966   C   T   .   PASS    .   ES:SE:LP:ID -0.0278097:0.0372977:0.34113:rs3131966
1   755775  rs3131965   A   G   .   PASS    .   ES:SE:LP:ID -0.0199987:0.0373959:0.227092:rs3131965
1   755890  rs1280367067    A   T   .   PASS    .   ES:SE:LP:ID -0.0224462:0.0361187:0.272215:rs1280367067
1   756380  rs3131963   T   A   .   PASS    .   ES:SE:LP:ID -0.0160702:0.0442045:0.144966:rs3131963
1   756434  rs61768170  G   C   .   PASS    .   ES:SE:LP:ID 0.0223484:0.0362002:0.270026:rs61768170
1   756479  rs61768171  C   A   .   PASS    .   ES:SE:LP:ID 0.0179381:0.0369133:0.202732:rs61768171
1   756604  rs3131962   A   G   .   PASS    .   ES:SE:LP:ID -0.0200967:0.0365958:0.234406:rs3131962
1   756912  rs6699990   A   G   .   PASS    .   ES:SE:LP:ID -0.254797:0.306241:0.392116:rs6699990
1   757640  rs3115853   G   A   .   PASS    .   ES:SE:LP:ID -0.0234235:0.0362958:0.285084:rs3115853
1   757734  rs1557551770    C   T   .   PASS    .   ES:SE:LP:ID -0.0232281:0.0361572:0.283496:rs1557551770
1   757936  rs1360886751    C   A   .   PASS    .   ES:SE:LP:ID -0.0233258:0.0362658:0.283913:rs1360886751
1   758144  rs3131956   A   G   .   PASS    .   ES:SE:LP:ID -0.0116321:0.0347329:0.13212:rs3131956
1   758626  rs3131954   C   T   .   PASS    .   ES:SE:LP:ID -0.010841:0.0343783:0.123493:rs3131954
1   760912  rs1048488   C   T   .   PASS    .   ES:SE:LP:ID -0.0164637:0.0356593:0.190912:rs1048488
1   761147  rs3115850   T   C   .   PASS    .   ES:SE:LP:ID -0.0160702:0.0354697:0.186753:rs3115850
1   761752  rs1057213   C   T   .   PASS    .   ES:SE:LP:ID -0.0113355:0.0342676:0.130299:rs1057213
1   762273  rs3115849   G   A   .   PASS    .   ES:SE:LP:ID -0.0116321:0.0345279:0.133004:rs3115849
1   764191  rs7515915   T   G   .   PASS    .   ES:SE:LP:ID 0.0117309:0.0341218:0.136083:rs7515915
1   766007  rs61768174  A   C   .   PASS    .   ES:SE:LP:ID 0.0113355:0.0344052:0.129713:rs61768174
1   768253  rs2977608   A   C   .   PASS    .   ES:SE:LP:ID 0.0455205:0.0343495:0.732594:rs2977608
1   768448  rs12562034  G   A   .   PASS    .   ES:SE:LP:ID -0.0442083:0.0313504:0.799971:rs12562034
1   769223  rs60320384  C   G   .   PASS    .   ES:SE:LP:ID 0.0118298:0.0340796:0.13757:rs60320384
1   769963  rs7518545   G   A   .   PASS    .   ES:SE:LP:ID -0.0457379:0.0326299:0.793174:rs7518545
1   771823  rs2977605   T   C   .   PASS    .   ES:SE:LP:ID -0.0115332:0.0347818:0.130651:rs2977605
1   771967  rs59066358  G   A   .   PASS    .   ES:SE:LP:ID 0.0114344:0.0346635:0.129889:rs59066358
1   772755  rs2905039   A   C   .   PASS    .   ES:SE:LP:ID -0.0115332:0.0347125:0.130944:rs2905039
1   774837  rs1470678699    T   A   .   PASS    .   ES:SE:LP:ID 0.0148886:0.0381843:0.157017:rs1470678699
1   774874  rs1327209068    A   C   .   PASS    .   ES:SE:LP:ID -0.0168571:0.0397617:0.172889:rs1327209068

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