Sriram Sankararaman

The paper uses Neanderthal ancestry patterns to identify genomic regions enriched with recessive deleterious mutations using a machine learning classifier.

SUMMIT improves heritability and genetic correlation estimation across diverse ancestries using LD scores and GWAS summary statistics, showing better accuracy in non-European populations.

This paper introduces REGENA, a method to disentangle gene-environment interaction mechanisms, finding that half of significant GxE signals are scale-dependent and effect heterogeneity drives most remaining GxE heritability.

The paper introduces ENGINE, a method to infer gene-environment interactions from large-scale biobank data, finding that combining multiple exposures increases detected GxE variance.

keju improves inference in MPRA data by accounting for batch and modality-specific uncertainty, achieving higher sensitivity and lower false positive rates.

This paper introduces COXMM, a model for estimating heritability of time-to-event traits, showing it outperforms non-TTE methods and reveals heritability patterns in longitudinal data.

The paper shows that phenotype scale choice affects GxE signals in biobank data, with log scaling revealing meaningful effects and explaining a significant portion of PGSxSex interactions.

The paper introduces ARG-RHE, a method to estimate heritability and perform association testing using ancestral recombination graphs, showing improved results over imputation alone.

This paper introduces FLEX, a method for estimating gene-level heritability from whole-exome sequencing data, showing rare coding variants contribute significantly to complex trait heritability.

The paper characterizes the epigenomic and 3D genomic landscape of human subcutaneous adipose tissue at the cell-type level and links epigenomic variation to abdominal obesity risk.

The paper investigates why carriers of pathogenic variants in monogenic metabolic conditions show variable disease severity, finding support for variant effect sizes, polygenic backgrounds, and epistasis.

This study quantifies the heritability of drug response to statins, metformin, warfarin, and methotrexate in UK Biobank participants and identifies genetic modifiers of drug effects and side effects.

The paper uses unmapped reads from whole-exome sequencing data in the UK BioBank to construct features and identify 21 novel genetic loci associated with immune-related traits.

The paper evaluates the impact of non-random mating and environmental transmission on genetic methods and introduces a simulation tool to improve robustness.

SUM-RHE estimates SNP heritability using summary statistics with accuracy comparable to individual-level methods.

The paper introduces QuadKAST, a scalable algorithm for detecting epistasis in complex traits, and identifies 32 trait-gene pairs with significant quadratic effects in UK Biobank data.

VISTA improves structural variant detection by integrating multiple callers with a novel filtering algorithm, achieving high accuracy across various SV types and genomic contexts.

The paper introduces a variance components method to estimate gene-environment interaction heritability in complex traits using UK Biobank data, finding significant GxE effects for several trait-environment pairs.

Admix-kit provides tools for genetic analysis of admixed populations, enabling simulation, association testing, and polygenic scoring.

AutoComplete improves phenotype imputation in biobank data, enhancing GWAS power and genetic discovery.

The paper introduces MANOCCA, a method to test the effect of predictors on the covariance matrix of multivariate outcomes, showing it outperforms existing methods in simulations and identifies significant associations in real data.

The paper tests for polygenic epistasis in biobank-scale data and finds 16 trait-loci pairs with strong evidence of interaction effects.

FastKAST detects non-linear genetic effects on traits in large datasets by providing efficient kernel-based tests with calibrated hypothesis testing.

This paper introduces MR-Twin, a Mendelian Randomization method robust to population stratification, and shows it reduces false positives compared to standard methods in UK Biobank data.

This paper introduces GPC, a deep generative model for genetic variation data that improves imputation accuracy and privacy preservation compared to existing methods.

This study assesses the contribution of Neanderthal introgression to human complex traits using UK Biobank data, finding significant but depleted effects compared to modern human variants.

This paper introduces PACA, a contrastive learning method for subphenotype subtyping, which outperforms existing methods by capturing weak subphenotypic variation in complex diseases.

This paper shows that cross-trait assortative mating explains genetic correlations between traits, suggesting many reported genetic similarities may be overestimated.

The paper develops a statistical framework to model replication rates in GWAS by accounting for winner's curse and study-specific heterogeneity, showing significant variation in confounding factors across studies.

The paper explores the relationship between genomic ancestry and health outcomes using a biobank linked to electronic health records, finding disease stratification across ancestry clusters.