Interaction molecular QTL mapping discovers cellular and environmental modifiers of genetic regulatory effects

Kasela, Silva; Aguet, François; Kim-Hellmuth, Sarah; Brown, Brielin C.; Nachun, Daniel C.; Tracy, Russell P.; Durda, Peter; Liu, Yongmei; Taylor, Kent D.; Johnson, W. Craig; Van Den Berg, David; Gabriel, Stacey; Gupta, Namrata; Smith, Joshua D.; Blackwell, Thomas W.; Rotter, Jerome I.; Ardlie, Kristin G.; Manichaikul, Ani; Rich, Stephen S.; Barr, R. Graham; Lappalainen, Tuuli

doi:10.1016/j.ajhg.2023.11.013

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2024

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Cited by 4 publications

(1 citation statement)

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“…Identification of a set of eQTLs that are modulated by the same environmental variable can therefore reveal important upstream regulators and pathways through which the perturbation impacts biological function. 28 , 32 …”

Section: Introductionmentioning

confidence: 99%

eQTLs identify regulatory networks and drivers of variation in the individual response to sepsis

Burnham,

Milind,

Lee

et al. 2024

Cell Genomics

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Section: Introductionmentioning

confidence: 99%

eQTLs identify regulatory networks and drivers of variation in the individual response to sepsis

Burnham,

Milind,

Lee

et al. 2024

Cell Genomics

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Many roads to a gene-environment interaction

Westerman,

Sofer

2024

The American Journal of Human Genetics

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Improved multi-ancestry fine-mapping identifiescis-regulatory variants underlying molecular traits and disease risk

Lu,

Wang,

Carr

et al. 2024

Preprint

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Multi-ancestry statistical fine-mapping of cis-molecular quantitative trait loci (cis-molQTL) aims to improve the precision of distinguishing causal cis-molQTLs from tagging variants. However, existing approaches fail to reflect shared genetic architectures. To solve this limitation, we present the Sum of Shared Single Effects (SuShiE) model, which leverages LD heterogeneity to improve fine-mapping precision, infer cross-ancestry effect size correlations, and estimate ancestry-specific expression prediction weights. We apply SuShiE to mRNA expression measured in PBMCs (n=956) and LCLs (n=814) together with plasma protein levels (n=854) from individuals of diverse ancestries in the TOPMed MESA and GENOA studies. We find SuShiE fine-maps cis-molQTLs for 16% more genes compared with baselines while prioritizing fewer variants with greater functional enrichment. SuShiE infers highly consistent cis-molQTL architectures across ancestries on average; however, we also find evidence of heterogeneity at genes with predicted loss-of-function intolerance, suggesting that environmental interactions may partially explain differences in cis-molQTL effect sizes across ancestries. Lastly, we leverage estimated cis-molQTL effect-sizes to perform individual-level TWAS and PWAS on six white blood cell-related traits in AOU Biobank individuals (n=86k), and identify 44 more genes compared with baselines, further highlighting its benefits in identifying genes relevant for complex disease risk. Overall, SuShiE provides new insights into the cis-genetic architecture of molecular traits.

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Interaction molecular QTL mapping discovers cellular and environmental modifiers of genetic regulatory effects

Cited by 4 publications

References 80 publications

eQTLs identify regulatory networks and drivers of variation in the individual response to sepsis

eQTLs identify regulatory networks and drivers of variation in the individual response to sepsis

Many roads to a gene-environment interaction

Improved multi-ancestry fine-mapping identifiescis-regulatory variants underlying molecular traits and disease risk

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