A fully joint Bayesian quantitative trait locus mapping of human protein abundance in plasma

Ruffieux, Hélène; Carayol, Jérôme; Popescu, Radu; Harper, Mary‐Ellen; Dent, Robert; Astrup, Arne; Hager, Jörg; Davison, A. C.; Valsesia, Armand

doi:10.1101/524405

Cited by 9 publications

(10 citation statements)

References 71 publications

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“…Our investigation adds to the literature by relating a broad array of over 1300 plasma proteins to dietary patterns using multiplexed high-throughput single-stranded DNA aptamer technology that allows for efficient and deep profiling of the plasma proteome [35]. Of the 103 dietary pattern-related proteins in our investigation, 16 were associated with various metabolic traits (BMI, visceral adiposity, triglycerides, insulin resistance, and fasting glucose) in the Diet, Obesity, and Genes interventional trial [36,37] and 45 proteins were associated with cardiometabolic risk and/or incidence CVD in the Framingham Offspring Study [11]. In addition, our over representation analysis indicated that diet related proteins enriched biological pathways involved in cellular proliferation/metabolism and immune response/inflammation.…”

Section: Discussionmentioning

confidence: 90%

Proteomic and Metabolomic Correlates of Healthy Dietary Patterns: The Framingham Heart Study

Walker

Song

et al. 2020

Nutrients

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Data on proteomic and metabolomic signatures of healthy dietary patterns are limited. We evaluated the cross-sectional association of serum proteomic and metabolomic markers with three dietary patterns: the Alternative Healthy Eating Index (AHEI), the Dietary Approaches to Stop Hypertension (DASH) diet; and a Mediterranean-style (MDS) diet. We examined participants from the Framingham Offspring Study (mean age; 55 years; 52% women) who had complete proteomic (n = 1713) and metabolomic (n = 2284) data; using food frequency questionnaires to derive dietary pattern indices. Proteins and metabolites were quantified using the SomaScan platform and liquid chromatography/tandem mass spectrometry; respectively. We used multivariable-adjusted linear regression models to relate each dietary pattern index (independent variables) to each proteomic and metabolomic marker (dependent variables). Of the 1373 proteins; 103 were associated with at least one dietary pattern (48 with AHEI; 83 with DASH; and 8 with MDS; all false discovery rate [FDR] ≤ 0.05). We identified unique associations between dietary patterns and proteins (17 with AHEI; 52 with DASH; and 3 with MDS; all FDR ≤ 0.05). Significant proteins enriched biological pathways involved in cellular metabolism/proliferation and immune response/inflammation. Of the 216 metabolites; 65 were associated with at least one dietary pattern (38 with AHEI; 43 with DASH; and 50 with MDS; all FDR ≤ 0.05). All three dietary patterns were associated with a common signature of 24 metabolites (63% lipids). Proteins and metabolites associated with dietary patterns may help characterize intermediate phenotypes that provide insights into the molecular mechanisms mediating diet-related disease. Our findings warrant replication in independent populations

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Section: Discussionmentioning

confidence: 90%

Proteomic and Metabolomic Correlates of Healthy Dietary Patterns: The Framingham Heart Study

Walker

Song

et al. 2020

Nutrients

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“…To manage this tension between scalable inference and comprehensive joint modeling, we recently proposed a variational inference approach, called ATLASQTL, 5 which explicitly borrows information across thousands of molecular traits controlled by shared pathways and offers a robust fully Bayesian parametrization of hotspots; its increased sensitivity and that of earlier related models have been demonstrated in different molecular QTL studies. [4][5][6][7] In complement to the actual mapping task, biologists increasingly try to capitalize on the wealth of available epigenetic annotation sources to infer the functional potential of genetic variants. The standard strategy uses epigenetic marks mostly for prioritization of hits derived from marginal screening: it consists in looping through all the loci with statistically significant associations and, for each locus, inspecting marks to decide on ''a most promising'' functional candidate genetic variant among all those in linkage disequilibrium (LD).…”

Section: Introductionmentioning

confidence: 99%

EPISPOT: An epigenome-driven approach for detecting and interpreting hotspots in molecular QTL studies

Ruffieux

Fairfax

Nassiri

et al. 2021

The American Journal of Human Genetics

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We present EPISPOT, a fully joint framework which exploits large panels of epigenetic annotations as variant-level information to enhance molecular quantitative trait locus (QTL) mapping. Thanks to a purpose-built Bayesian inferential algorithm, EPISPOT accommodates functional information for both cis and trans actions, including QTL hotspot effects. It effectively couples simultaneous QTL analysis of thousands of genetic variants and molecular traits with hypothesis-free selection of biologically interpretable annotations which directly contribute to the QTL effects. This unified, epigenome-aided learning boosts statistical power and sheds light on the regulatory basis of the uncovered hits; EPISPOT therefore marks an essential step toward improving the challenging detection and functional interpretation of trans-acting genetic variants and hotspots. We illustrate the advantages of EPISPOT in simulations emulating real-data conditions and in a monocyte expression QTL study, which confirms known hotspots and finds other signals, as well as plausible mechanisms of action. In particular, by highlighting the role of monocyte DNase-I sensitivity sites from >150 epigenetic annotations, we clarify the mediation effects and cell-type specificity of major hotspots close to the lysozyme gene. Our approach forgoes the daunting and underpowered task of one-annotation-at-a-time enrichment analyses for prioritizing cis and trans QTL hits and is tailored to any transcriptomic, proteomic, or metabolomic QTL problem. By enabling principled epigenome-driven QTL mapping transcriptome-wide, EPISPOT helps progress toward a better functional understanding of genetic regulation.

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“…Indeed, widely-used marginal screening approaches [1,2] suffer from a large multiplicity burden and tend to lack of statistical power as they do not exploit the regulation patterns shared by the molecular entities, whereas joint modelling approaches [3,4] are often limited by the computational burden implied by the exploration of high-dimensional spaces of candidate variants and traits. To manage this tension between scalable inference and comprehensive joint modelling, we recently proposed a variational inference approach, called ATLASQTL [5], which explicitly borrows information across thousands of molecular traits controlled by shared pathways, and offers a robust fully Bayesian parametrisation of hotspots; its increased sensitivity and that of earlier related models have been demonstrated in different molecular QTL studies [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

EPISPOT: an epigenome-driven approach for detecting and interpreting hotspots in molecular QTL studies

Ruffieux

Fairfax

Nassiri

et al. 2020

Preprint

Self Cite

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We present EPISPOT, a fully joint framework which exploits large panels of epigenetic marks as variant-level information to enhance molecular quantitative trait locus (QTL) mapping. Thanks to a purpose-built Bayesian inferential algorithm, our approach effectively couples simultaneous QTL analysis of thousands of genetic variants and molecular traits genome-wide, and hypothesis-free selection of biologically interpretable marks which directly contribute to the QTL effects. This unified learning approach boosts statistical power and sheds light on the regulatory basis of the uncovered associations. EPISPOT is also tailored to the modelling of trans-acting genetic variants, including QTL hotspots, whose detection and functional interpretation are challenging with standard approaches. We illustrate the advantages of EPISPOT in simulations emulating real-data conditions and in an epigenome-driven monocyte expression QTL study which confirms known hotspots and reveals new ones, as well as plausible mechanisms of action. In particular, based on monocyte DNase-I sensitivity site annotations selected by the method from > 150 epigenetic annotations, we clarify the mediation effects and cell-type specificity of well-known master hotspots in the vicinity of the lyzosyme gene. EPISPOT is radically new in that it makes it possible to forgo the daunting and underpowered task of one-mark-at-a-time enrichment analyses for the prioritisation of QTL hits. Our method can be used to enhance the discovery and functional understanding of signals in QTL problems with all types of outcomes, be they transcriptomic, proteomic, lipidomic, metabolic or clinical.

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A fully joint Bayesian quantitative trait locus mapping of human protein abundance in plasma

Cited by 9 publications

References 71 publications

Proteomic and Metabolomic Correlates of Healthy Dietary Patterns: The Framingham Heart Study

Proteomic and Metabolomic Correlates of Healthy Dietary Patterns: The Framingham Heart Study

EPISPOT: An epigenome-driven approach for detecting and interpreting hotspots in molecular QTL studies

EPISPOT: an epigenome-driven approach for detecting and interpreting hotspots in molecular QTL studies

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