Biology subjects

Musharoff, S. A.

Publications and source records attributed to Musharoff, S. A..

3 recordsLinked to original sources

bioRxiv2026

Sparse Linear Algebra Accelerates Genotype Representation Graph Computation at Biobank Scale

Biobank-scale genomic analyses are increasingly constrained by computational costs, as hundreds of thousands to millions of samples and variants must be analyzed together. The genotype representation graph (GRG) compactly encodes population genetic variation to accelerate computation, but the current approach does not exploit modern accelerator architectures. This work introduces Mikado, a new methodology for expressing GRG-based computation using sparse linear algebra primitives. Under a reverse topological ordering of the graph nodes, the GRG adjacency matrix is strictly block-lower-triangular, and the genotype matrix-vector product becomes a sparse triangular solve that can be further decomposed into a pipelined sequence of blocked sparse matrix-vector multiplies. By decoupling computation from graph representation, our approach exposes fine-grained parallelism and enables hardware-optimized sparse primitives on GPUs. Mikado achieves an order-of-magnitude speedup and cost savings for PCA and BOLT-LMM compared with the original GRG traversal approach, including on All of Us cohorts. It provides a scalable, hardware-portable, researcher-friendly tool for population genetics at biobank scale.

genomics↗

bioRxiv2025

Examining the Effect of Social Determinants of Health on Human Trait Heritability

Social factors of health (SFOH) are critical determinants of human traits but are rarely incorporated into genetic models. Here, we assess how participant-completed SFOH survey data influence heritability estimates and trait variance in 85,963 diverse All of Us participants spanning multiple ancestries and racial identities. We summarized SFOH survey data using multiple correspondence analysis (MCA) into axes representing distinct social dimensions (e.g., social support, perceived stress) and developed a Social Similarity Index (SSI) to capture social environment similarity among individuals. To analyze trait heritability, we used Haseman-Elston (HE) regression for its sensitivity to residual structure. Including SFOH measures as covariates in HE regression models significantly reduced heritability estimates for four of 18 traits, three of which are anthropometric: body mass index, hip circumference, waist circumference, and HDL cholesterol. This suggests that unmodeled social structure can be misattributed to genetic effects. Though several SFOH measures have non-zero heritability when adjusting for three genetic principal components (h2{approx} 0.01-0.09), these are reduced to similar estimates when adjusting for seven genetic principal components (h2{approx} 0.01-0.02). This convergence across SFOH measures, which capture different social dimensions, indicates that SFOH measures capture population stratification, emphasizing their utility for reducing confounding in heritability estimates. SFOH measures were also associated with trait variance, with anthropometric traits--those with reduced heritability--exhibiting the most extensive dispersion effects. This implies that social environment influences trait variability. Our findings highlight the necessity of including social and environmental factors in genetic studies to reduce potential confounding.

genetics↗

bioRxiv2021

Effect sizes of causal variants for gene expression and complex traits differ between populations

Despite the growing number of genome-wide association studies (GWAS), it remains unclear to what extent gene-by-gene and gene-by-environment interactions influence complex traits in humans. The magnitude of genetic interactions in complex traits has been difficult to quantify because GWAS are generally underpowered to detect individual interactions of small effect. Here, we develop a method to test for genetic interactions that aggregates information across all trait-associated loci. Specifically, we test whether SNPs in regions of European ancestry shared between European American and admixed African American individuals have the same causal effect sizes. We hypothesize that in African Americans, the presence of genetic interactions will drive the causal effect sizes of SNPs in regions of European ancestry to be more similar to those of SNPs in regions of African ancestry. We apply our method to two traits: gene expression in 296 African Americans and 482 European Americans in the Multi-Ethnic Study of Atherosclerosis (MESA) and low-density lipoprotein cholesterol (LDL-C) in 74K African Americans and 296K European Americans in the Million Veteran Program (MVP). We find significant evidence for genetic interactions in our analysis of gene expression; for LDL-C, we observe a similar point estimate although this is not significant, likely due to lower statistical power. These results suggest that gene-by-gene or gene-by-environment interactions modify the effect sizes of causal variants in human complex traits.

genetics↗