The Alzheimer's Disease Functional Genomics Consortium,

bioRxiv2025

mfSuSiE enables multi-cell-type fine-mapping and multi-omic integration of chromatin accessibility QTLs in aging brain.

Molecular quantitative trait locus (QTL) studies increasingly profile chromatin accessibility, histone modifications, DNA methylation, RNA modifications such as N6-methyladenosine (m6A), and transcription across multiple cell types using high-throughput sequencing, generating dense base-pair-resolved measurements. The conventional approach of testing each variant against each molecular feature independently suffers from severe multiple testing burden and ignores linkage disequilibrium and spatial correlation. Existing fine-mapping methods only partially address these challenges and are sub-optimal for analyzing such datasets: multivariate approaches such as mvSuSiE jointly analyze multiple molecular contexts but are designed for a single trait value per context and cannot accommodate thousands of base-resolution measurements per context, while functional approaches such as fSuSiE model spatial structure across thousands of measurements but analyze each context separately. Here, we introduce mfSuSiE, which integrates multivariate analysis with wavelet-based functional regression to jointly fine-map thousands of base-resolution traits across multiple cell types. In simulations, mfSuSiE identified causal variants and affected molecular features more accurately than fSuSiE, while mvSuSiE cannot be applied to this type of data. Applied to single-nucleus chromatin accessibility data from six brain cell types from postmortem aging human brains, mfSuSiE substantially increased discovery and resolution, with substantial power gains for cell types with limited samples. Multi-cell-type analysis revealed extensive sharing of regulatory effects on chromatin accessibility (caQTL). Importantly, mfSuSiE produces Bayesian inference compatible with the SuSiE framework, enabling systematic multi-omic integration. Applied to Alzheimers disease loci, we integrated caQTL with expression QTLs, epigenomic QTLs, and GWAS, observing regulatory patterns suggesting complex mechanisms at loci including EARS2, CHRNE, SCIMP, and RABEP1.

genetics↗

bioRxiv2025

fSuSiE enables fine-mapping of QTLs from genome-scale molecularprofiles

Molecular quantitative trait locus (QTL) studies seek to identify the causal variants affecting molecular traits like DNA methylation and histone modifications. However, existing fine-mapping tools are not well suited to high-dimensional molecular traits, and so analyses of these traits typically proceed by considering each variant and each molecular measurement independently, ignoring the LD among variants and the spatial correlation in effects between nearby sites. This severely limits accuracy in identifying causal variants and quantifying their molecular trait effects. Here, we introduce fSuSiE ("functional Sum of Single Effects"), a fine-mapping method that addresses these challenges by explicitly modeling the spatial structure of genetic effects on molecular traits. fSuSiE integrates wavelet-based functional regression with the computationally efficient "Sum of Single Effects" framework to simultaneously finemap causal variants and identify the molecular traits they affect. In simulations, fSuSiE identified causal variants and affected CpGs more accurately than methods that ignore spatial structure. In applications to DNA methylation and histone acetylation (H3K9ac) data from the ROSMAP study of the dorsolateral prefrontal cortex, fSuSiE achieved dramatically higher resolution than existing methods (e.g., identifying 6,355 single-variant methylation credible sets compared to only 328 from an existing approach). Applied to Alzheimers disease (AD) risk loci, fSuSiE identified potential causal variants colocalizing with AD GWAS signals for established genes, including CASS4 and CR1/CR2, suggesting specific potential regulatory mechanisms underlying these AD risk loci.

genetics↗