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Posts
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conferences
Explaining potential epistasis in genomic data using symbolic representations of complex black box models
Presented at Bruins in Genomics, 2022
Epistasis, known as the interaction among genetic variants, has long been hypothesized to play a major role in explaining missing heritability. Though recent studies have found many candidate variants demonstrating epistasis signals in the UKBiobank, it remains a controversial question how to interpret the findings. Nonlinear models have shown potential in capturing these signals, but we require additional explanation methods to understand the projected relationships. Here, we utilize symbolic pursuit, a form of symbolic regression that provides a closed-form, interpretable model which generalizes first order explanations. Furthermore, we extend this study by applying Taylor expansions to the model, balancing interpretability with performance while improving its generalizability. We found the method performed reliably and was consistent with other methods across a variety of simulated data. This work contains strong implications for its use on large genomic datasets and its ability to capture nonlinear interactions without prior knowledge of the genetic architecture.
Authors: Anand, A., Anand, P.
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A scalable adaptive quadratic kernel method for interpretable epistasis analysis in complex traits
Presented at Research in Computational Molecular Biology (RECOMB Main Conference), 2024
Our knowledge of the contribution of genetic interactions (epistasis) to variation in human complex traits remains limited, partly due to the lack of efficient, powerful, and interpretable algorithms to detect interactions. Recently proposed approaches for set-based association tests show promise in improving power to detect epistasis by examining the aggregated effects of multiple variants. Nevertheless, these methods either do not scale to large numbers of individuals available in Biobank datasets or do not provide interpretable results. We, therefore, propose QuadKAST, a scalable algorithm focused on testing pairwise interaction effects (also termed as quadratic effects) of a set of genetic variants on a trait and quantifying the proportion of phenotypic variance explained by these effects.
Authors: Fu, B.*, Anand, P.*, Anand, A.*, Mefford, J., & Sankararaman, S.
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hobbies
Cottontail Fizz
One of my favorite characters from League of Legends!
publications
A scalable adaptive quadratic kernel method for interpretable epistasis analysis in complex traits
Published in Genome Research, 2024
Our knowledge of the contribution of genetic interactions (epistasis) to variation in human complex traits remains limited, partly due to the lack of efficient, powerful, and interpretable algorithms to detect interactions. Recently proposed approaches for set-based association tests show promise in improving power to detect epistasis by examining the aggregated effects of multiple variants. Nevertheless, these methods either do not scale to large numbers of individuals available in Biobank datasets or do not provide interpretable results. We, therefore, propose QuadKAST, a scalable algorithm focused on testing pairwise interaction effects (also termed as quadratic effects) of a set of genetic variants on a trait and quantifying the proportion of phenotypic variance explained by these effects.
Authors: Fu, B.*, Anand, P.*, Anand, A.*, Mefford, J., & Sankararaman, S.
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A biobank-scale test of marginal epistasis reveals genome-wide signals of polygenic interaction effects
Published in Nature Genetics, 2025
The contribution of genetic interactions (epistasis) to human complex trait variation remains poorly understood due, in part, to the statistical and computational challenges involved in testing for interaction effects. Here we introduce FAME (FAst Marginal Epistasis test), a method that can test for marginal epistasis of a single-nucleotide polymorphism (SNP) on a quantitative trait (whether the effect of an SNP on the trait is modulated by genetic background). FAME is computationally efficient, enabling tests of marginal epistasis on biobank-scale data. Applying FAME to genome-wide association study (GWAS)-significant trait-SNP associations across 53 quantitative traits and ≈300 000 unrelated White British individuals in the UK Biobank (UKBB), we identified 16 significant marginal epistasis signals across 12 traits. Leveraging the scalability of FAME, we further localized marginal epistasis signals across chromosomes and estimated the proportion of variance explained by marginal epistasis effects. Our study provides evidence for interactions between individual genetic variants and polygenic background influencing complex traits.
Authors: Fu, B., Pazokitoroudi, A., Xue, A., Anand, A., Anand, P., Zaitlen, N., & Sankararaman, S.
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A biobank-scale method for learning modulators of gene-environment interaction underlying human complex traits from multiple environmental exposures
Published in bioRxiv, 2026
It is increasingly recognized that genetic effects on complex traits and diseases are shaped by environmental context. Biobanks that measure diverse environmental exposures alongside genotypes and phenotypes at scale enable systematic study of gene-environment (G×E) interactions. Existing approaches, however, are limited in their ability to accurately model polygenic G×E involving many exposures across genome-wide genetic variants. It is unclear which exposure combinations are relevant for a given trait while distinguishing true interactions from environment-dependent heteroskedastic noise. To address these challenges, we develop Efficient multi-eNvironmental Gene-environment Interaction iNference Estimator (ENGINE), a supervised variance-component framework that learns an embedding that combines multiple environmental exposures while jointly estimating additive, G×E, and heteroskedastic noise components. To enable biobank-scale inference, ENGINE makes a single pass over the genotype matrix to cache genotype-dependent summaries, then assembles normal-equation components and gradients at each iteration. In simulations, ENGINE controls type I error rates, achieves high power, and accurately recovers the environmental embedding while remaining efficient at biobank-scale. Applied to five complex traits paired with lifestyle exposures in N = 291,273 unrelated white British individuals and M = 454,207 common SNPs (MAF> 0.01) from the UK Biobank, ENGINE recovered G×E variance that was on average 1.4-fold larger than that captured by a single exposure and 5.5-fold larger than that captured by the first principal component of the exposures.
Authors: Liu, Z., Ramteke, A., Anand, A., Gorla, A., Jeong, M., & Sankararaman, S.
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Metapipeline-DNA: A comprehensive germline and somatic genomics Nextflow pipeline
Published in Cell Reports Methods, 2026
Rapid improvements in DNA sequencing technologies have expanded the breadth of genomic features, ranging from nuclear, mitochondrial, and evolutionary variation in germline and somatic contexts, which can be elucidated from sequencing data. In parallel, analytical workflows required to process and identify these features have become increasingly complex, relying on specialized tools and algorithms with varying assumptions and computational requirements. Comprehensive analysis, therefore, requires significant integration effort, limiting scalability, reproducibility, and consistent quality controls. To address this need for a flexible, robust framework that accommodates diverse sequencing methods and feature classes while being highly scalable and adaptable across computational environments, we created metapipeline-DNA to automate genomic analyses.
Authors: Patel, Y., Zhu, C., Yamaguchi, T. N., Wang, N. K., Anand, A., et al.
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Comprehensive gene heritability estimation reveals the genetic architecture of rare coding variants underlying complex traits
Published in bioRxiv, 2026
Whole-exome sequencing (WES) enables high-resolution interrogation of the contribution of rare coding variants to complex trait variation. However, existing methods for heritability estimation attributed to rare-coding variants are often limited by the effects of linkage disequilibrium (LD) and by the sparse nature of rare variant data. We introduce FLEX (Fast, LD-aware Estimation of eXome-wide and gene-level heritability), a scalable and flexible framework for estimating and partitioning heritability across genes or sets of genes using WES data. FLEX integrates all coding variants– from common to ultra-rare – within a unifled model and corrects for LD-induced effects to improve the accuracy of heritability estimates. In addition, FLEX supports both individual-level and summary statistic data and is computationally efflcient for biobank-scale datasets. FLEX provides an adaptable and accurate approach for quantifying gene-level heritability, advancing our understanding of the genetic architecture of complex traits, and facilitating the discovery of trait-relevant genes.
Authors: Liu, Z., Fu, B., Jeong, M., Anand, A., et al.
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talks
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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