Genome and epigenome wide studies of plasma protein biomarkers for Alzheimer’s disease implicate TBCA and TREM2 in disease risk

Hillary, Robert F.; Gadd, Danni A; McCartney, Daniel L.; Shi, Liu; Campbell, Archie; Martin, Richard M; Ritchie, Craig W.; Deary, Ian J.; Evans, Kathryn; Nevado‐Holgado, Alejo J.; Hayward, Caroline; Porteous, David J.; McIntosh, Andrew M.; Lovestone, Simon; Robinson, Matthew R.; Marioni, Riccardo E.

doi:10.1101/2021.06.07.21258457

Cited by 4 publications

(6 citation statements)

References 90 publications

(99 reference statements)

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“…3). When accounting for 26 pQTMs that were previously reported by Zaghlool et al and 10 pQTMs that were previously reported by Hillary et al 14,19 , 2892 of the 2928 fully-adjusted pQTMs were previously unreported. Of these 2892 pQTMs, 1109 involved the levels of 41 proteins that were measured by Zaghlool et al (973 pQTMs for PAPPA and 136 additional pQTMs for the levels of 40 proteins), whereas 1783 pQTMs involved the levels of proteins that were previously unmeasured (1116 pQTMs for PRG3 and 667 further pQTMs for 148 proteins).…”

Section: Pleiotropic Pqtm Associations In the Fully-adjusted Mwasmentioning

confidence: 82%

“…This suggested that DNAm not only reflects variability in the proteome but is closely related to chronic systemic inflammation. Hillary et al have also assessed epigenetic signatures for 281 SOMAmer protein measurements that were previously associated with Alzheimer’s disease, in the Generation Scotland cohort that we utilised in this study 19 . However, proteome-wide assessment of pQTMs has not been tested against a comprehensive spectrum of brain health traits.…”

Section: Introductionmentioning

confidence: 99%

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Integrated methylome and phenome study of the circulating proteome reveals markers pertinent to brain health

et al. 2022

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Characterising associations between the methylome, proteome and phenome may provide insight into biological pathways governing brain health. Here, we report an integrated DNA methylation and phenotypic study of the circulating proteome in relation to brain health. Methylome-wide association studies of 4058 plasma proteins are performed (N = 774), identifying 2928 CpG-protein associations after adjustment for multiple testing. These are independent of known genetic protein quantitative trait loci (pQTLs) and common lifestyle effects. Phenome-wide association studies of each protein are then performed in relation to 15 neurological traits (N = 1,065), identifying 405 associations between the levels of 191 proteins and cognitive scores, brain imaging measures or APOE e4 status. We uncover 35 previously unreported DNA methylation signatures for 17 protein markers of brain health. The epigenetic and proteomic markers we identify are pertinent to understanding and stratifying brain health.

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Section: Pleiotropic Pqtm Associations In the Fully-adjusted Mwasmentioning

confidence: 82%

Section: Introductionmentioning

confidence: 99%

Integrated methylome and phenome study of the circulating proteome reveals markers pertinent to brain health

et al. 2022

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“…3). When accounting for 26 pQTMs that were previously reported by Zaghlool et al and 10 pQTMs that were previously reported by Hillary et al 14,19 , 2,892 of the 2,928 fully-adjusted pQTMs were novel. Of these 2,892 novel pQTMs, 1,109 involved the levels of 41 proteins that were measured by Zaghlool et al (973 pQTMs for PAPPA and 136 additional pQTMs for the levels of 40 proteins), whereas 1,783 pQTMs involved the levels of proteins that were previously unmeasured (1,116 pQTMs for PRG3 and 667 further pQTMs for 148 proteins).…”

Section: Pleiotropic Pqtm Associations In the Fully-adjusted Mwasmentioning

confidence: 83%

“…Three associations did not replicate at nominal P < 0.05; however, the direction of effect was consistent in all cases. There were 2,854 novel SOMAmer pQTM associations in the protein EWAS, when accounting for 41 associations that were previously reported by EWAS from Zaghlool et al (31 associations) and Hillary et al (10 associations) 1,18 . In addition to the 1,141 pQTMs involving PAPPA, there were 1,157 pQTMs that involved the Proteoglycan 3 Precursor (PGR3) protein.…”

Section: Epigenome-wide Studies Of 4235 Plasma Proteinsmentioning

confidence: 84%

Integrated methylome and phenome study of the circulating proteome reveals markers pertinent to brain health

Gadd

Hillary

McCartney

et al. 2021

Preprint

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Characterising associations between the epigenome, proteome and phenome may provide insight into molecular regulation of biological pathways governing health. However, epigenetic signatures for many neurologically-associated plasma protein markers remain uncharacterised. Here, we report an epigenome and phenome-wide association study of the circulating proteome in relation to brain health. We perform epigenome-wide studies of 4,235 plasma proteins (n=778), identifying 2,895 CpG-protein associations (protein quantitative trait methylation loci; pQTMs) after stringent correction for multiple testing. These were independent of known genetic protein quantitative trait loci (pQTL) and common lifestyle effects, extending current knowledge by analysing a further 3,286 protein measurements with 2,854 novel pQTMs. We then perform a phenome-wide study of each protein in relation to neurological traits in 1,065 individuals, identifying 644 proteins related to cognitive, brain imaging phenotypes or APOE status. By integrating our pQTM dataset with our phenome association study, we uncovered 88 epigenetic associations for protein markers of neurological traits, 83 of which were previously unreported. These data are pertinent to understanding heterogeneity in brain health.

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“…An additional level of information that can inform causal relationships between genotype and phenotype is the proteome [98], which in addition to interpretation of GWA and TWA results, can provide targets for future functional studies and drug targets [99]. Proteome-wide association studies aggregate the signal of all variants that jointly affect a protein-coding gene and assess their overall impact on the protein's function and the phenotype of interest [100].…”

Section: Combining Multi-omics Data For Prediction and Fine-mapping U...mentioning

confidence: 99%

Application of Bayesian genomic prediction methods to genome-wide association analyses

Wolc

Dekkers

2022

Genet Sel Evol

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Background Bayesian genomic prediction methods were developed to simultaneously fit all genotyped markers to a set of available phenotypes for prediction of breeding values for quantitative traits, allowing for differences in the genetic architecture (distribution of marker effects) of traits. These methods also provide a flexible and reliable framework for genome-wide association (GWA) studies. The objective here was to review developments in Bayesian hierarchical and variable selection models for GWA analyses. Results By fitting all genotyped markers simultaneously, Bayesian GWA methods implicitly account for population structure and the multiple-testing problem of classical single-marker GWA. Implemented using Markov chain Monte Carlo methods, Bayesian GWA methods allow for control of error rates using probabilities obtained from posterior distributions. Power of GWA studies using Bayesian methods can be enhanced by using informative priors based on previous association studies, gene expression analyses, or functional annotation information. Applied to multiple traits, Bayesian GWA analyses can give insight into pleiotropic effects by multi-trait, structural equation, or graphical models. Bayesian methods can also be used to combine genomic, transcriptomic, proteomic, and other -omics data to infer causal genotype to phenotype relationships and to suggest external interventions that can improve performance. Conclusions Bayesian hierarchical and variable selection methods provide a unified and powerful framework for genomic prediction, GWA, integration of prior information, and integration of information from other -omics platforms to identify causal mutations for complex quantitative traits.

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Genome and epigenome wide studies of plasma protein biomarkers for Alzheimer’s disease implicate TBCA and TREM2 in disease risk

Cited by 4 publications

References 90 publications

Integrated methylome and phenome study of the circulating proteome reveals markers pertinent to brain health

Integrated methylome and phenome study of the circulating proteome reveals markers pertinent to brain health

Integrated methylome and phenome study of the circulating proteome reveals markers pertinent to brain health

Application of Bayesian genomic prediction methods to genome-wide association analyses

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