An integrated framework for local genetic correlation analysis

Abstract

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Specificity and overlap in the genetic architectures of functional and structural connectivity within cerebral resting-state networks

“…Negative r g between Alzheimer’s disease and FC within DMN was observed in the locus on chromosome 19 (BP 45,040,933-45,893,307, ρ = −0.56, 95% CI = −0.82 – −0.38, p = 9.23×10 -9 ), indicating that lower default mode network-FC was associated with higher genetic risk of Alzheimer’s disease. Note that this larger defined locus showed weak heritability ( p = 0.014) for visual network-FC despite the significance of APOC1 in the gene-based GWAS, which would make genetic correlation estimates with Alzheimer’s disease unreliable and uninterpretable 23 . Therefore, Alzheimer’s disease seems to show genetic overlap specifically with default mode network-FC.…”

“…Strongly localized or opposing local r g ’s possibly may go undetected, since genome-wide r g ’s are an average of the shared genetic association signal across the genome. We examined whether such relationships between SC and FC within any given RSN exist by performing local r g analysis using LAVA 23 , though we did not identify any significant r g on a locus level either (Supplementary Table 9).…”

“…For this purpose, we adopted a three-step local r g analysis pipeline similar to the genome-wide r g analysis approach described above. All three steps were performed in LAVA 23 , a local r g analysis tool, using SNP-based GWAS summary statistics as input. We followed the suggested sample overlap procedure (as described on https://github.com/josefin-werme/LAVA) to enable LAVA to model shared variance due to sample overlap as residual covariance and consequently remove upward bias in local r g estimates 23 .…”

“…All three steps were performed in LAVA 23 , a local r g analysis tool, using SNP-based GWAS summary statistics as input. We followed the suggested sample overlap procedure (as described on https://github.com/josefin-werme/LAVA) to enable LAVA to model shared variance due to sample overlap as residual covariance and consequently remove upward bias in local r g estimates 23 . Since our GWASs included European samples, the 1,000 Genomes Phase 3 European data served as genotype reference and formed the basis of the locus definition file.…”

“…We examine genetic correlations both between different RSNs, as well as across structural and functional domains. These genetic correlation analyses are extended to the locus level to facilitate the prioritisation of possible pleiotropic loci for future studies 23 . Altogether, we focus on the translation of RSN-associated genetic loci into biological interpretation and provide insights into the genetic specificity and overlap of RSN-FC and RSN-SC.…”