An atlas of genetic scores to predict multi-omic traits

Xu, Yu; Ritchie, Scott C.; Liang, Yujian; Timmers, Paul R. H. J.; Pietzner, Maik; Lannelongue, Loïc; Tahir, Usman A.; May-Wilson, Sebastian; Foguet, Carles; Johansson, Åsa; Surendran, Praveen; Nath, Artika P.; Persyn, Elodie; Peters, James E.; Oliver‐Williams, Clare; Deng, Shuliang; Prins, Bram P.; Luan, Jian’an; Bomba, Lorenzo; Soranzo, Nicole; Angelantonio, Emanuele Di; Pirastu, Nicola; Tai, E. Shyong; Dam, Rob M van; Parkinson, Helen; Davenport, Emma E; Paul, Dirk S.; Yau, Christopher; Gerszten, Robert E.; Mälarstig, Anders; Danesh, John; Sim, Xueling; Langenberg, Claudia; Wilson, James F.; Butterworth, Adam S.; Inouye, Michael

doi:10.1038/s41586-023-05844-9

Cited by 59 publications

(32 citation statements)

References 85 publications

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“…To corroborate these observations, we used validated pQTS from the OMICSPRED study as a genetic proxy for circulating protein concentrations in all UKB participants without plasma proteomic data. (12) This gave an independent cohort of 363,953 UKB participants with white British ancestry, in whom in whom 984 infection deaths occurred. The OMICSPRED study was able to generate validated pQTS for 46 of the 259 protein hits described above.…”

Section: Resultsmentioning

confidence: 99%

“…We utilised validated Olink plasma protein quantitative trait scores (pQTS) developed by Xu et al (www.omicspred.org). (12) These genetic scores were derived from a subset of the INTERVAL cohort using Bayesian Ridge Regression with validation on NSPHS and ORCADE cohorts. Genetic scores were available for 46 of the 259 proteins that we identified as being most specifically associated with infection mortality.…”

Section: Methodsmentioning

confidence: 99%

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Plasma proteomic associates of infection mortality in UK Biobank

Drozd,

Hamilton,

Cheng

et al. 2024

Preprint

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Background: Infectious diseases are a major cause of mortality in spite of existing public health, anti-microbial and vaccine interventions. We aimed to define plasma proteomic associates of infection mortality and then apply Mendelian randomisation (MR) to yield biomarkers that may be causally associated. Methods: We used UK Biobank plasma proteomic data to associate 2,923 plasma proteins with infection mortality before 31st December 2019 (240 events in 52,520 participants). Since many plasma proteins also predict non-infection mortality, we focussed on those associated with >1.5-fold risk of infection mortality in an analysis excluding survivors. Protein quantitative trait scores (pQTS) were then used to identify whether genetically predicted protein levels also associated with infection mortality. To conduct Two Sample MR, we performed a genome-wide association study (GWAS) of infection mortality using UK Biobank participants without plasma proteomic data (n=363,953 including 984 infection deaths). Findings: After adjusting for clinical risk factors, 1,142 plasma proteins were associated with risk of infection mortality (false discovery rate <0.05). 259 proteins were associated with >1.5-fold increased risk of infection versus non-infection mortality. Of these, we identified genetically predicted increasing MERTK concentration was associated with increased risk of infection mortality. GWAS for infection mortality revealed no SNPs achieving genome-wide statistical significance (p<5x10-8). However, MR supported a causal association between increasing plasma MERTK protein and infection mortality (odds ratio 1.46 per unit; 95% CI 1.15-1.85; p=0.002). Interpretation: Plasma proteomics demonstrates many proteins are associated with infection mortality. MERTK warrants exploration as a potential therapeutic target.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Plasma proteomic associates of infection mortality in UK Biobank

Drozd,

Hamilton,

Cheng

et al. 2024

Preprint

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“…Eighth, another important future direction is to extend TGFM to incorporate gene sets that are enriched for disease heritability explained by cis-predicted expression 26 , which may also further increase fine-mapping power. Finally, we have focused here on cis-genetic components of gene expression, but TGFM could be extended to genetic components of other molecular traits [102][103][104][105][106][107] . Despite these limitations, TGFM is a robust and powerful method for fine-mapping causal tissues and genes at disease-associated loci.…”

Section: Discussionmentioning

confidence: 99%

Fine-mapping causal tissues and genes at disease-associated loci

Strober,

Zhang,

Amariuta

et al. 2023

Preprint

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Heritable diseases often manifest in a highly tissue-specific manner, with different disease loci mediated by genes in distinct tissues or cell types. We propose Tissue-Gene Fine-Mapping (TGFM), a fine-mapping method that infers the posterior probability (PIP) for each gene-tissue pair to mediate a disease locus by analyzing GWAS summary statistics (and in-sample LD) and leveraging eQTL data from diverse tissues to build cis-predicted expression models; TGFM also assigns PIPs to causal variants that are not mediated by gene expression in assayed genes and tissues. TGFM accounts for both co-regulation across genes and tissues and LD between SNPs (generalizing existing fine-mapping methods), and incorporates genome-wide estimates of each tissue’s contribution to disease as tissue-level priors. TGFM was well-calibrated and moderately well-powered in simulations; unlike previous methods, TGFM was able to attain correct calibration by modeling uncertainty in cis-predicted expression models. We applied TGFM to 45 UK Biobank diseases/traits (averageN= 316K) using eQTL data from 38 GTEx tissues. TGFM identified an average of 147 PIP > 0.5 causal genetic elements per disease/trait, of which 11% were gene-tissue pairs. Implicated gene-tissue pairs were concentrated in known disease-critical tissues, and causal genes were strongly enriched in disease-relevant gene sets. Causal gene-tissue pairs identified by TGFM recapitulated known biology (e.g.,TPO-thyroid for Hypothyroidism), but also included biologically plausible novel findings (e.g.,SLC20A2-artery aorta for Diastolic blood pressure). Further application of TGFM to single-cell eQTL data from 9 cell types in peripheral blood mononuclear cells (PBMC), analyzed jointly with GTEx tissues, identified 30 additional causal gene-PBMC cell type pairs at PIP > 0.5—primarily for autoimmune disease and blood cell traits, including the well-established role ofCTLA4in CD8+T cells for All autoimmune disease. In conclusion, TGFM is a robust and powerful method for fine-mapping causal tissues and genes at disease-associated loci.

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“…But in the long run, we would need to develop a useable catalog of these new insights regarding single cell expression, as well as a description of the proteins present in the extracellular fluids of each tissue. There may be differences between different ethnic groups (Liao et al, 2023; Xu et al, 2023; Zhang et al, 2023). Furthermore, there will be differences because of the transgenerational programing we know exists (Loyfer et al, 2023; Szyf, 2015), potentially creating important functional differences.…”

Section: Proposed Physiologic Stages Erikson's Psychologic Stages Ill...mentioning

confidence: 99%

The importance of age‐specific gene expression

Hall

2023

American J of Med Genetics Pt A

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An atlas of genetic scores to predict multi-omic traits

Cited by 59 publications

References 85 publications

Plasma proteomic associates of infection mortality in UK Biobank

Plasma proteomic associates of infection mortality in UK Biobank

Fine-mapping causal tissues and genes at disease-associated loci

The importance of age‐specific gene expression

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