IEU GWAS information

Trait: Height.

name	value
author	Howe LJ
consortium	Within family GWAS consortium
subcategory	NA
category	NA
build	HG19/GRCh37
population	European
trait	Height
sex	Males and Females
group_name	public
year	2022
unit	cm
id	ieu-b-4813
ontology	EFO:0004339
note	Sibling estimate
sample_size	75030

Report information

name	value
gwas_id	ieu-b-4813
bcf_file	ieu-b-4813.vcf.gz
qc_metrics_file	qc_metrics.json
metadata	metadata.json
refdata	1kg_v3_nomult.bcf
compile_time	2022-01-05 05:03 UTC
build_version	master-74213fc-2021-03-26

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/3f53b219-7748-424e-9dd7-03143dcbf53b/call-ldsc/inputs/-261044540/ieu-b-4813.vcf.gz \
--ref-ld-chr /data/ref/eur_w_ld_chr/ \
--out /data/igd/ieu-b-4813/ldsc.txt \
--w-ld-chr /data/ref/eur_w_ld_chr/ 

Beginning analysis at Wed Jan  5 04:50:39 2022
Reading summary statistics from /data/cromwell-executions/qc/3f53b219-7748-424e-9dd7-03143dcbf53b/call-ldsc/inputs/-261044540/ieu-b-4813.vcf.gz ...
Read summary statistics for 7190027 SNPs.
Dropped 19706 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, 1198394 SNPs remain.
After merging with regression SNP LD, 1198394 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.3382 (0.0186)
Lambda GC: 1.3423
Mean Chi^2: 1.5578
Intercept: 1.0557 (0.0107)
Ratio: 0.0999 (0.0191)
Analysis finished at Wed Jan  5 04:52:13 2022
Total time elapsed: 1.0m:34.23s

QC metrics

Metrics

{
    "af_correlation": "NA",
    "inflation_factor": 1.3021,
    "mean_EFFECT": 0.0002,
    "n": 75030,
    "n_snps": 7190052,
    "n_clumped_hits": 124,
    "n_p_sig": 10583,
    "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": 7190052,
    "n_miss_AF_reference": 46751,
    "n_est": "NA",
    "ratio_se_n": "NA",
    "mean_diff": "NaN",
    "ratio_diff": "NaN",
    "sd_y_est1": "NaN",
    "sd_y_est2": "NA",
    "r2_sum1": 0,
    "r2_sum2": 0,
    "r2_sum3": 0,
    "r2_sum4": 0,
    "ldsc_nsnp_merge_refpanel_ld": 1198394,
    "ldsc_nsnp_merge_regression_ld": 1198394,
    "ldsc_observed_scale_h2_beta": 0.3382,
    "ldsc_observed_scale_h2_se": 0.0186,
    "ldsc_intercept_beta": 1.0557,
    "ldsc_intercept_se": 0.0107,
    "ldsc_lambda_gc": 1.3423,
    "ldsc_mean_chisq": 1.5578,
    "ldsc_ratio": 0.0999
}

Flags

name	value
af_correlation	NA
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	FALSE
r2_sum2	FALSE
r2_sum3	FALSE
r2_sum4	FALSE

Definitions

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 \(\beta_j\)

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	8	0.9999989	3	58	0	7190027	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	7190052	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	8.630855e+00	5.744864e+00	1.0000e+00	4.000000e+00	8.000000e+00	1.300000e+01	2.200000e+01	▇▅▅▂▂
numeric	POS	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	7.894628e+07	5.636140e+07	8.2800e+02	3.251470e+07	6.958857e+07	1.147814e+08	2.492223e+08	▇▆▅▂▁
numeric	EFFECT	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	1.670000e-04	7.975170e-02	-1.0049e+00	-3.930000e-02	1.000000e-04	3.960000e-02	9.867000e-01	▁▁▇▁▁
numeric	SE	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	6.147380e-02	3.496350e-02	2.7300e-02	3.520000e-02	4.600000e-02	7.690000e-02	2.112000e-01	▇▂▁▁▁
numeric	PVAL	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	4.568136e-01	3.004711e-01	0.0000e+00	1.865001e-01	4.415003e-01	7.166993e-01	1.000000e+00	▇▆▆▆▆
numeric	PVAL_ztest	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	4.568137e-01	3.004714e-01	0.0000e+00	1.865009e-01	4.415272e-01	7.167305e-01	1.000000e+00	▇▆▆▆▆
numeric	AF_reference	46751	0.9934978	NA	NA	NA	NA	NA	NA	NA	2.660777e-01	2.523029e-01	1.9970e-04	5.910540e-02	1.805110e-01	4.143370e-01	1.000000e+00	▇▃▂▁▁
numeric	N	0	1.0000000	NA	NA	NA	NA	NA	NA	NA	6.957251e+04	6.092366e+03	4.2447e+04	6.720600e+04	7.180700e+04	7.431100e+04	7.503000e+04	▁▁▁▂▇

Head and tail

CHROM	POS	ID	REF	ALT	EFFECT	SE	PVAL	PVAL_ztest	AF	AF_reference	N
1	797440	rs58013264	T	C	0.0061	0.0523	0.9076000	0.9071495	NA	0.189497	58335
1	798400	rs10900604	A	G	-0.0111	0.0428	0.7963000	0.7953684	NA	0.410543	59725
1	798959	rs11240777	G	A	0.0029	0.0419	0.9454999	0.9448205	NA	0.409944	61885
1	801467	rs61768212	G	C	0.0124	0.0525	0.8137000	0.8132849	NA	0.193091	57957
1	804759	rs7526310	C	T	0.0177	0.0526	0.7368006	0.7364924	NA	0.193890	57957
1	808631	rs11240779	G	A	-0.0199	0.0417	0.6328999	0.6332067	NA	0.453474	58725
1	808928	rs11240780	C	T	-0.0200	0.0417	0.6315998	0.6315002	NA	0.452276	58725
1	833927	rs28593608	T	C	0.0954	0.0523	0.0681193	0.0681382	NA	0.187500	42632
1	834198	rs28385272	T	C	0.0979	0.0523	0.0611505	0.0612214	NA	0.168730	42632
1	834928	rs4422949	A	G	0.0964	0.0523	0.0653296	0.0652980	NA	0.203674	42632

CHROM	POS	ID	REF	ALT	EFFECT	SE	PVAL	PVAL_ztest	AF	AF_reference	N
22	51195550	rs148968329	C	A	-0.0221	0.0784	0.7776998	0.7780296	NA	0.0391374	44125
22	51196164	rs8136603	A	T	-0.0461	0.0875	0.5986004	0.5982928	NA	0.1427720	47696
22	51196296	rs9616961	G	C	-0.0275	0.0786	0.7266997	0.7264342	NA	0.0395367	44125
22	51197576	rs147713773	G	C	-0.0268	0.0787	0.7329005	0.7334547	NA	0.0471246	44125
22	51197602	rs187225588	T	A	-0.0716	0.0873	0.4119002	0.4121247	NA	0.0175719	48415
22	51198569	rs142671391	G	C	-0.0808	0.0940	0.3901998	0.3900237	NA	0.1110220	47696
22	51198906	rs6010079	G	A	-0.0206	0.0792	0.7948001	0.7947859	NA	0.0421326	44125
22	51202748	rs9616963	A	G	-0.0225	0.0789	0.7760005	0.7755131	NA	0.0391374	44125
22	51208568	rs148425445	G	T	-0.0835	0.0868	0.3356997	0.3360589	NA	0.1160140	48415
22	51222100	rs114553188	G	T	-0.0732	0.0922	0.4273001	0.4272384	NA	0.0880591	47696

bcf preview

1   797440  rs58013264  T   C   .   PASS    .   ES:SE:LP:SS:ID  0.0061:0.0523:0.0421055:58335:rs58013264
1   798400  rs10900604  A   G   .   PASS    .   ES:SE:LP:SS:ID  -0.0111:0.0428:0.0989233:59725:rs10900604
1   798959  rs11240777  G   A   .   PASS    .   ES:SE:LP:SS:ID  0.0029:0.0419:0.0243385:61885:rs11240777
1   801467  rs61768212  G   C   .   PASS    .   ES:SE:LP:SS:ID  0.0124:0.0525:0.0895357:57957:rs61768212
1   804759  rs7526310   C   T   .   PASS    .   ES:SE:LP:SS:ID  0.0177:0.0526:0.13265:57957:rs7526310
1   808631  rs11240779  G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.0199:0.0417:0.198665:58725:rs11240779
1   808928  rs1247187939    C   T   .   PASS    .   ES:SE:LP:SS:ID  -0.02:0.0417:0.199558:58725:rs1247187939
1   833927  rs28593608  T   C   .   PASS    .   ES:SE:LP:SS:ID  0.0954:0.0523:1.16673:42632:rs28593608
1   834198  rs28385272  T   C   .   PASS    .   ES:SE:LP:SS:ID  0.0979:0.0523:1.2136:42632:rs28385272
1   834928  rs4422949   A   G   .   PASS    .   ES:SE:LP:SS:ID  0.0964:0.0523:1.18489:42632:rs4422949
1   834999  rs28570054  G   A   .   PASS    .   ES:SE:LP:SS:ID  0.0958:0.0523:1.17613:42632:rs28570054
1   836896  rs28705752  T   C   .   PASS    .   ES:SE:LP:SS:ID  0.1181:0.048:1.86044:42632:rs28705752
1   836924  rs72890788  G   A   .   PASS    .   ES:SE:LP:SS:ID  0.1018:0.0521:1.29286:42632:rs72890788
1   838387  rs4970384   T   C   .   PASS    .   ES:SE:LP:SS:ID  0.0995:0.052:1.25477:42632:rs4970384
1   838555  rs4970383   C   A   .   PASS    .   ES:SE:LP:SS:ID  0.0924:0.0495:1.2088:42632:rs4970383
1   839103  rs28562941  A   G   .   PASS    .   ES:SE:LP:SS:ID  0.1137:0.048:1.75007:42632:rs28562941
1   842013  rs1191510089    T   G   .   PASS    .   ES:SE:LP:SS:ID  0.1128:0.0524:1.50307:42632:rs1191510089
1   845635  rs117086422 C   T   .   PASS    .   ES:SE:LP:SS:ID  0.0491:0.0423:0.608007:66564:rs117086422
1   845938  rs57760052  G   A   .   PASS    .   ES:SE:LP:SS:ID  0.0462:0.0419:0.56928:66564:rs57760052
1   846078  rs778265812 C   T   .   PASS    .   ES:SE:LP:SS:ID  0.0322:0.0433:0.340084:66564:rs778265812
1   846398  rs58781670  G   A   .   PASS    .   ES:SE:LP:SS:ID  0.0503:0.0425:0.626353:66564:rs58781670
1   846465  rs60454217  C   T   .   PASS    .   ES:SE:LP:SS:ID  0.177:0.13:0.76045:61888:rs60454217
1   846543  rs79396034  G   T   .   PASS    .   ES:SE:LP:SS:ID  0.1519:0.14:0.556268:55259:rs79396034
1   846808  rs4475691   C   T   .   PASS    .   ES:SE:LP:SS:ID  0.0355:0.0431:0.387428:66564:rs4475691
1   846864  rs1269142199    G   C   .   PASS    .   ES:SE:LP:SS:ID  0.0277:0.0433:0.282579:66275:rs1269142199
1   847228  rs3905286   C   T   .   PASS    .   ES:SE:LP:SS:ID  0.0372:0.0426:0.416915:66564:rs3905286
1   847491  rs1158719307    G   A   .   PASS    .   ES:SE:LP:SS:ID  0.0439:0.042:0.529149:66564:rs1158719307
1   848090  rs4246505   G   A   .   PASS    .   ES:SE:LP:SS:ID  0.0444:0.0421:0.535212:66564:rs4246505
1   848445  rs1156895099    G   A   .   PASS    .   ES:SE:LP:SS:ID  0.0469:0.0422:0.575282:66564:rs1156895099
1   848456  rs11507767  A   G   .   PASS    .   ES:SE:LP:SS:ID  0.0468:0.0422:0.573977:66564:rs11507767
1   848738  rs3829741   C   T   .   PASS    .   ES:SE:LP:SS:ID  0.0449:0.0421:0.542118:66564:rs3829741
1   850062  rs28723578  A   T   .   PASS    .   ES:SE:LP:SS:ID  0.0458:0.042:0.560194:66275:rs28723578
1   850123  rs28622257  C   T   .   PASS    .   ES:SE:LP:SS:ID  0.0435:0.0421:0.521145:66564:rs28622257
1   851190  rs28609852  G   A   .   PASS    .   ES:SE:LP:SS:ID  0.0471:0.0419:0.582362:66564:rs28609852
1   851204  rs28552953  G   C   .   PASS    .   ES:SE:LP:SS:ID  0.0463:0.0416:0.575118:66275:rs28552953
1   852664  rs28605311  C   T   .   PASS    .   ES:SE:LP:SS:ID  0.0463:0.0425:0.558776:66564:rs28605311
1   852758  rs4970462   G   C   .   PASS    .   ES:SE:LP:SS:ID  0.046:0.043:0.545765:65556:rs4970462
1   853239  rs4970380   A   G   .   PASS    .   ES:SE:LP:SS:ID  0.0418:0.0425:0.488518:66564:rs4970380
1   853805  rs3748591   C   T   .   PASS    .   ES:SE:LP:SS:ID  0.0757:0.1161:0.288446:61888:rs3748591
1   854250  rs7537756   A   G   .   PASS    .   ES:SE:LP:SS:ID  0.0432:0.0423:0.511731:66564:rs7537756
1   854429  rs72902552  G   A   .   PASS    .   ES:SE:LP:SS:ID  0.0805:0.1161:0.311313:61888:rs72902552
1   856476  rs4040605   A   G   .   PASS    .   ES:SE:LP:SS:ID  -0.0206:0.0372:0.237022:58725:rs4040605
1   857177  rs386627408 T   C   .   PASS    .   ES:SE:LP:SS:ID  0.07:0.1157:0.263285:61888:rs386627408
1   858040  rs4970460   C   A   .   PASS    .   ES:SE:LP:SS:ID  0.0619:0.0426:0.83535:64115:rs4970460
1   858051  rs4970459   C   T   .   PASS    .   ES:SE:LP:SS:ID  0.0658:0.0426:0.913996:64115:rs4970459
1   858801  rs7418179   A   G   .   PASS    .   ES:SE:LP:SS:ID  -0.0696:0.0417:1.02269:63396:rs7418179
1   859913  rs1187056171    A   G   .   PASS    .   ES:SE:LP:SS:ID  -0.1231:0.1251:0.487716:61888:rs1187056171
1   860416  rs61464428  G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.0778:0.0413:1.22709:64115:rs61464428
1   860461  rs57465118  G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.0879:0.1125:0.36191:61888:rs57465118
1   860521  rs57924093  C   A   .   PASS    .   ES:SE:LP:SS:ID  -0.091:0.1125:0.378305:61888:rs57924093

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bcf preview