Efficient and accurate mixed model association tool for single-cell eQTL analysis.

Understanding the genetic basis of gene expression can help us understand the molecular underpinnings of human traits and disease. Expression quantitative trait locus (eQTL) mapping can help in studying this relationship but have been shown to be very cell-type specific, motivating the use of single-cell RNA sequencing and single-cell eQTLs to obtain a more granular view of genetic regulation. Current methods for single-cell eQTL mapping either rely on the "pseudobulk" approach and traditional pipelines for bulk transcriptomics or do not scale well to large datasets. Here, we propose SAIGE-QTL, a robust and scalable tool that can directly map eQTLs using single-cell profiles without needing aggregation at the pseudobulk level. Additionally, SAIGE-QTL allows for testing the effects of less frequent/rare genetic variation through set-based tests, which is traditionally excluded from eQTL mapping studies. We evaluate the performance of SAIGE-QTL on both real and simulated data and demonstrate the improved power for eQTL mapping over existing pipelines.

GUIDE deconstructs genetic architectures using association studies.

Genome-wide association studies have revealed that the genetic architecture of most complex traits is characterized by a large number of distinct effects scattered across the genome. Functional enrichment analyses of these results suggest that the associations for any given complex trait are not purely random. Thus, we set out to leverage the genetic association results from many traits with a view to identifying the set of modules, or latent factors, that mediate these associations. The identification of such modules may aid in disease classification as well as the elucidation of complex disease mechanisms. We propose a method, Genetic Unmixing by Independent Decomposition (GUIDE), to estimate a set of statistically independent latent factors that best express the patterns of association across many traits. The resulting latent factors not only have desirable mathematical properties, such as sparsity and a higher variance explained (for both traits and variants), but are also able to single out and prioritize key biological features or pathophysiological mechanisms underlying a given trait or disease. Moreover, we show that these latent factors can index biological pathways as well as epidemiological and environmental influences that compose the genetic architecture of complex traits.