LAVA: An integrated framework for local genetic correlation analysis

Werme, Josefin; Sluis, Sophie van der; Posthuma, Daniëlle; Leeuw, Christiaan de

doi:10.1101/2020.12.31.424652

Cited by 26 publications

(43 citation statements)

References 55 publications

(67 reference statements)

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“…LDSC 174 Assessment of genetic correlation between phenotypes using summary statistics as input; has various other functions, including partitioned SNP-based heritability and assessment of selection bias GCTA 173 Assessment of genetic correlation between phenotypes using raw genotypic data as input SumHer 264 Assessment of genetic correlation between phenotypes using summary statistics as input; has various other functions, including partitioned SNP-based heritability and assessment of selection bias superGNOVA 183 Assessment of local genetic correlations using GWAS summary statistics ρ-HESS 184 Assessment of local SNP-based heritability and genetic correlations using GWAS summary statistics LAVA 185 Assessment of local multivariate genetic correlations using GWAS summary statistics GenomicSEM 265 Assessment of multivariate genetic correlations based on GWAS summary statistics…”

Section: Genetic Correlationsmentioning

confidence: 99%

“…Both linkage disequilibrium score regression and genome-wide complex trait analysis allow the estimation of genetic correlations, or the extent to which genetic variants that account for a trait are also important for another trait, provided that the effects are in the same direction. Tools such as superGNOVA 183 , ρ-HESS 184 and LAVA 185 from a recent preprint article allow the estimation of local correlations, determining which specific genomic regions exert genetic effects on the correlated phenotypes in the same or opposing directions. Genetic correlations should be interpreted in the context of SNP-based heritabilities; for example, if these are low for the respective phenotypes, genetic correlation is not expected to play a major part in explaining why two traits correlate at the phenotypic level.…”

Section: Understanding Trait Genetic Architecturementioning

confidence: 99%

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Genome-wide association studies

Uffelmann

Huang

Munung

et al. 2021

Nat Rev Methods Primers

1,009

514

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Section: Genetic Correlationsmentioning

confidence: 99%

Section: Understanding Trait Genetic Architecturementioning

confidence: 99%

Genome-wide association studies

Uffelmann

Huang

Munung

et al. 2021

Nat Rev Methods Primers

1,009

514

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show abstract

“…To evaluate the severity of the issues that arise when interpreting TWAS as testing genetically mediated relationships between gene expression and phenotype, we performed extensive simulations and applied TWAS to real data. To serve as a reference, we used the local genetic correlation analysis in LAVA 8 , which directly tests the true genetic covariance cov( , ). To simplify comparison, we implemented TWAS analysis inside the LAVA framework, using the same preprocessing and test statistic as for the local genetic correlation analysis, ensuring that the only difference between the two analyses is the null model being evaluated (see Methods -LAVA implementation of TWAS).…”

Section: Main Textmentioning

confidence: 99%

“…has a non-central Wishart sampling distribution, which is used to obtain p-values to test cov( , ) = 0 using a simulation procedure (see Werme et al (2021) for details).…”

Section: Local Genetic Correlationmentioning

confidence: 99%

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On the interpretation of transcriptome-wide association studies

Leeuw

Werme

Savage

et al. 2021

Preprint

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Transcriptome-wide association studies (TWAS), which aim to detect relationships between gene expression and a phenotype, are commonly used for secondary analysis of genome-wide association study (GWAS) results. Results of TWAS analyses are often interpreted as indicating a genetically mediated relationship between gene expression and the phenotype, but because the traditional TWAS framework does not model the uncertainty in the expression quantitative trait loci (eQTL) effect estimates, this interpretation is not justified. In this study we outline the implications of this issue. Using simulations, we show severely inflated type 1 error rates for TWAS when evaluating a null hypothesis of no genetic relationship between gene expression and the phenotype. Moreover, in our application to real data only 51% of the TWAS associations were confirmed with local genetic correlation analysis, an approach which correctly evaluates the same null. Our results thus demonstrate that TWAS is unsuitable for investigating genetic relationships between gene expression and a phenotype.

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Mapping gene by early life stress interactions on child subcortical brain structures: A genome‐wide prospective study

et al. 2022

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Background: Although it is well-established that both genetics and the environment influence brain development, they are typically examined separately. Here, we aimed to prospectively investigate the interactive effects of genetic variants-from a genome-wide approach-and early life stress (ELS) on child subcortical brain structures, and their association with subsequent mental health problems.Method: Primary analyses were conducted using data from the Generation R Study (N = 2257), including genotype and cumulative prenatal and postnatal ELS scores (encompassing life events, contextual risk, parental risk, interpersonal risk, direct victimisation). Neuroimaging data were collected at age 10 years, including intracranial and subcortical brain volumes (accumbens, amygdala, caudate, hippocampus, pallidum, putamen, thalamus). Genome-wide association and genome-wide-byenvironment interaction analyses (GWEIS, run separately for prenatal/postnatal ELS) were conducted for eight brain outcomes (i.e., 24 genome-wide analyses) in the Generation R Study (discovery). Polygenic scores (PGS) using the resulting weights were calculated in an independent (target) cohort (adolescent brain cognitive development Study; N = 10,751), to validate associations with corresponding subcortical volumes and examine links to later mother-reported internalising and externalising problems.Results: One GWEIS-prenatal stress locus was associated with caudate volume (rs139505895, mapping onto PRSS12 and NDST3) and two GWEIS-postnatal stress loci with the accumbens (rs2397823 and rs3130008, mapping onto CUTA, SYN-GAP1, and TABP). Functional annotation revealed that these genes play a role in neuronal plasticity and synaptic function, and have been implicated in neurodevelopmental phenotypes, for example, intellectual disability, autism, and schizophrenia. None of these associations survived a more stringent correction for multiple testing across all analysis sets. In the validation sample, all PGS genotype were associated with their respective brain volumes, but no PGS GxE associated with any Koen Bolhuis and Rosa H. Mulder shared as first author.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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LAVA: An integrated framework for local genetic correlation analysis

Cited by 26 publications

References 55 publications

Genome-wide association studies

Genome-wide association studies

On the interpretation of transcriptome-wide association studies

Mapping gene by early life stress interactions on child subcortical brain structures: A genome‐wide prospective study

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