genomic-sem

Name: genomic-sem
Author: GPTomics

1.1kstars185forks

How to install genomic-sem?

gptomics/bioskills/genomic-sem

$npx -y skills add GPTomics/bioSkills --skill genomic-sem

Installs into the current project.

Summary

Fits structural equation models to GWAS summary statistics using GenomicSEM (Grotzinger 2019), including common-factor models, confirmatory factor models, ESEM, common-factor GWAS with Q_SNP heterogeneity, multivariate Wald tests, and stratified GenomicSEM partitioned heritabilit

Files · 1

View on GitHub

SKILL.md

1	## Version Compatibility
2
3	Reference examples tested with: GenomicSEM 0.0.5+ (GitHub `GenomicSEM/GenomicSEM`), lavaan 0.6-17+, LDSC v1.0.1+ (Python 3; prefer `abdenlab/ldsc-python3` v2.0.0 -- `belowlab/ldsc` v3.0.1 README states the CLI is broken; Docker `jtb114/ldsc:latest` is the belowlab fallback), baselineLD_v2.2 annotations (alkesgroup.broadinstitute.org/LDSCORE), MTAG 1.0.8+ (Python; `JonJala/mtag`), R 4.4+.
4
5	Before using code patterns, verify installed versions match. If versions differ:
6	- R: `packageVersion('GenomicSEM')` then `?ldsc`, `?commonfactor`, `?usermodel`, `?commonfactorGWAS`
7	- Python (LDSC, MTAG): `<tool>.py -h` and inspect the source under `ldsc/` or `mtag/`
8
9	GenomicSEM is GitHub-only (never on CRAN). If `ldsc()` or `usermodel()` throws an error about lavaan syntax or non-positive-definite covariance, introspect the installed API (`getMethod('ldsc')`) and adapt rather than retrying.
10
11	# Genomic SEM
12
13	"Model the latent genetic architecture across several correlated GWAS" -> Treat each GWAS as a measured indicator of one or more latent genetic factors and fit a structural equation model to the LDSC-derived genetic covariance matrix S and its sampling covariance V (Grotzinger 2019 Nat Hum Behav 3:513). The framework extends naturally to a multivariate GWAS in which a SNP is regressed on a latent factor (common-factor GWAS), with Q_SNP testing whether the SNP effect is homogeneous across factor loadings. Sample overlap between input GWAS is absorbed by the off-diagonals of V; ignoring V inflates Type-I.
14
15	- R: `GenomicSEM::ldsc()` produces the (S, V) covariance pair from munged sumstats
16	- R: `GenomicSEM::commonfactor()` fits a single-factor CFA across all traits in S
17	- R: `GenomicSEM::usermodel()` fits an arbitrary lavaan-syntax model
18	- R: `GenomicSEM::commonfactorGWAS()` runs SNP -> factor multivariate GWAS with Q_SNP
19	- R: `GenomicSEM::userGWAS()` runs arbitrary multivariate SNP regression with per-path Q_SNP
20	- Python (alternative): `mtag.py --sumstats t1,t2,t3 --out mtag_out` (multi-trait power boost on individual traits)
21
22	## Statistical Model Taxonomy
23
24	\| Method \| Latent structure \| Min traits \| SNP-level test \| Strength \| Fails when \|
25	\|--------\|------------------\|-----------\|----------------\|----------\|------------\|
26	\| Common-factor CFA (Grotzinger 2019) \| Single F loading all traits \| 3 \| None (model-fit only) \| Tests whether shared variance is unidimensional \| Heterogeneous architecture; CFI < 0.9; near-zero loadings \|
27	\| User-specified CFA (`usermodel`) \| Pre-specified lavaan syntax \| 3 \| None \| Confirmatory; arbitrary structure \| Misspecified model; identification under-determined \|
28	\| ESEM \| Exploratory rotation; cross-loadings allowed \| 6+ \| None \| When factor count and structure unknown \| Few traits; collinear traits; rotation arbitrary \|
29	\| Common-factor GWAS (`commonfactorGWAS`) \| SNP -> F -> trait1..k \| 3 \| Wald on F + Q_SNP heterogeneity \| Discovers SNPs acting via the common factor; flags Q_SNP outliers \| Q_SNP-significant SNPs not interpretable as factor SNPs \|
30	\| User GWAS (`userGWAS`) \| Arbitrary SNP-path lavaan \| 3 \| Wald per path + Q_SNP \| Tests SNP on any specified path \| Highly parameterized models lose power \|
31	\| Multivariate Wald test \| Joint test across SNP -> trait paths \| 2+ \| Joint chi-square \| Boost power when SNP affects multiple traits \| Heterogeneous SNP effects collapse joint test \|
32	\| Stratified GenomicSEM (Grotzinger AD et al 2022 Nat Genet 54:548) \| Factor model with sLDSC-partitioned annotations \| 3 \| Per-annotation factor tau \| Localizes heritability of the factor to functional categories \| Same sLDSC failure modes (small annotation, collinearity) \|
33	\| MTAG (Turley 2018 Nat Genet 50:229) \| Empirical-Bayes shrinkage across correlated traits \| 2 \| Per-trait shrunk z-score \| Boosts marginal power for any input trait \| MaxFDR > 5% indicates heterogeneity violates MTAG assumption \|
34
35	Methodology evolves; verify the current Grotzinger 2023+ tutorials at `github.com/GenomicSEM/GenomicSEM/wiki` before locking a method. ESEM rotation choice (geomin vs target rotation) is an active area; report sensitivity to rotation.

People who liked this also viewed

6 picks

Type

genomic-sem

1.1kstars185forks

View on GitHub

How to install genomic-sem?

gptomics/bioskills/genomic-sem

$npx -y skills add GPTomics/bioSkills --skill genomic-sem

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	## Version Compatibility
2
3	Reference examples tested with: GenomicSEM 0.0.5+ (GitHub `GenomicSEM/GenomicSEM`), lavaan 0.6-17+, LDSC v1.0.1+ (Python 3; prefer `abdenlab/ldsc-python3` v2.0.0 -- `belowlab/ldsc` v3.0.1 README states the CLI is broken; Docker `jtb114/ldsc:latest` is the belowlab fallback), baselineLD_v2.2 annotations (alkesgroup.broadinstitute.org/LDSCORE), MTAG 1.0.8+ (Python; `JonJala/mtag`), R 4.4+.
4
5	Before using code patterns, verify installed versions match. If versions differ:
6	- R: `packageVersion('GenomicSEM')` then `?ldsc`, `?commonfactor`, `?usermodel`, `?commonfactorGWAS`
7	- Python (LDSC, MTAG): `<tool>.py -h` and inspect the source under `ldsc/` or `mtag/`
8
9	GenomicSEM is GitHub-only (never on CRAN). If `ldsc()` or `usermodel()` throws an error about lavaan syntax or non-positive-definite covariance, introspect the installed API (`getMethod('ldsc')`) and adapt rather than retrying.
10
11	# Genomic SEM
12
13	"Model the latent genetic architecture across several correlated GWAS" -> Treat each GWAS as a measured indicator of one or more latent genetic factors and fit a structural equation model to the LDSC-derived genetic covariance matrix S and its sampling covariance V (Grotzinger 2019 Nat Hum Behav 3:513). The framework extends naturally to a multivariate GWAS in which a SNP is regressed on a latent factor (common-factor GWAS), with Q_SNP testing whether the SNP effect is homogeneous across factor loadings. Sample overlap between input GWAS is absorbed by the off-diagonals of V; ignoring V inflates Type-I.
14
15	- R: `GenomicSEM::ldsc()` produces the (S, V) covariance pair from munged sumstats
16	- R: `GenomicSEM::commonfactor()` fits a single-factor CFA across all traits in S
17	- R: `GenomicSEM::usermodel()` fits an arbitrary lavaan-syntax model
18	- R: `GenomicSEM::commonfactorGWAS()` runs SNP -> factor multivariate GWAS with Q_SNP
19	- R: `GenomicSEM::userGWAS()` runs arbitrary multivariate SNP regression with per-path Q_SNP
20	- Python (alternative): `mtag.py --sumstats t1,t2,t3 --out mtag_out` (multi-trait power boost on individual traits)
21
22	## Statistical Model Taxonomy
23
24	\| Method \| Latent structure \| Min traits \| SNP-level test \| Strength \| Fails when \|
25	\|--------\|------------------\|-----------\|----------------\|----------\|------------\|
26	\| Common-factor CFA (Grotzinger 2019) \| Single F loading all traits \| 3 \| None (model-fit only) \| Tests whether shared variance is unidimensional \| Heterogeneous architecture; CFI < 0.9; near-zero loadings \|
27	\| User-specified CFA (`usermodel`) \| Pre-specified lavaan syntax \| 3 \| None \| Confirmatory; arbitrary structure \| Misspecified model; identification under-determined \|
28	\| ESEM \| Exploratory rotation; cross-loadings allowed \| 6+ \| None \| When factor count and structure unknown \| Few traits; collinear traits; rotation arbitrary \|
29	\| Common-factor GWAS (`commonfactorGWAS`) \| SNP -> F -> trait1..k \| 3 \| Wald on F + Q_SNP heterogeneity \| Discovers SNPs acting via the common factor; flags Q_SNP outliers \| Q_SNP-significant SNPs not interpretable as factor SNPs \|
30	\| User GWAS (`userGWAS`) \| Arbitrary SNP-path lavaan \| 3 \| Wald per path + Q_SNP \| Tests SNP on any specified path \| Highly parameterized models lose power \|
31	\| Multivariate Wald test \| Joint test across SNP -> trait paths \| 2+ \| Joint chi-square \| Boost power when SNP affects multiple traits \| Heterogeneous SNP effects collapse joint test \|
32	\| Stratified GenomicSEM (Grotzinger AD et al 2022 Nat Genet 54:548) \| Factor model with sLDSC-partitioned annotations \| 3 \| Per-annotation factor tau \| Localizes heritability of the factor to functional categories \| Same sLDSC failure modes (small annotation, collinearity) \|
33	\| MTAG (Turley 2018 Nat Genet 50:229) \| Empirical-Bayes shrinkage across correlated traits \| 2 \| Per-trait shrunk z-score \| Boosts marginal power for any input trait \| MaxFDR > 5% indicates heterogeneity violates MTAG assumption \|
34
35	Methodology evolves; verify the current Grotzinger 2023+ tutorials at `github.com/GenomicSEM/GenomicSEM/wiki` before locking a method. ESEM rotation choice (geomin vs target rotation) is an active area; report sensitivity to rotation.

People who liked this also viewed

6 picks

Type