Genome and epigenome wide studies of neurological protein biomarkers in the Lothian Birth Cohort 1936

Hillary, Robert F.; McCartney, Daniel L.; Harris, Sarah E.; Stevenson, Anna J.; Seeboth, Anne; Zhang, Qian; Liewald, David C.; Evans, Kathryn; Ritchie, Craig W.; Tucker‐Drob, Elliot M.; Wray, Naomi R.; McRae, Allan F.; Visscher, Peter M.; Deary, Ian J.; Marioni, Riccardo E.

doi:10.1038/s41467-019-11177-x

Cited by 45 publications

(60 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This effect was found in all three tissues, suggesting that this is a common biological event. There was an inverse relationship between absolute value of the effect size (beta) and MAF (Extended Fig.8b), consistent with previous protein level genome-wide association studies (p-GWAS; 10,28 ). However, both cis and trans pQTL were strongly enriched for exonic variants (Odds Ratio = 3.71, 5.25, 4.19 for CSF, plasma, and brain respectively; Extended Fig.8c; Supplementary Figure 2).…”

Section: Functional Annotation and Potential Biological Mechanism Drisupporting

confidence: 89%

“…However, there are some extreme cases in which the top pQTL explains more than half of the variability in protein levels, such as in the case of rs2075803 (p.K100E) which explains 81% of the CSF Siglec-9, rs5498 (p.K469E) which explains 74.4% of plasma sICAM-1, and rs5498 (p.K469E) which explains 67.4% of brain PPAC (Extended Fig.8d, Table S3, S4, S5). The CSF Siglec-9, plasma sICAM-1, and brain PPAC have been replicated in other studies 16,[28][29][30] using a different proteomic approach indicating that these findings are not a platform driven finding.…”

Section: Functional Annotation and Potential Biological Mechanism Drimentioning

confidence: 63%

See 1 more Smart Citation

Genomic and multi-tissue proteomic integration for understanding the biology of disease and other complex traits

Cruchaga¹,

Yang²,

Farias³

et al. 2020

Preprint

View full text Add to dashboard Cite

Understanding the tissue-specific genetic architecture of protein levels is instrumental to understand the biology of health and disease. We generated a genomic atlas of protein levels in multiple neurologically relevant tissues (380 brain, 835 cerebrospinal fluid (CSF) and 529 plasma), by profiling thousands of proteins (713 CSF, 931 plasma and 1079 brain) in a large and well-characterized cohort. We identified 274, 127 and 32 protein quantitative loci (pQTL) for CSF, plasma and brain respectively. cis-pQTL were more likely to be shared across tissues but trans-pQTL tend to be tissue-specific. Between 44% to 68.2% of the pQTL do not colocalize with expression, splicing, methylation or histone QTLs, indicating that protein levels have a different genetic architecture to those that regulate gene expression. By combining our pQTL with Mendelian Randomization approaches we identified potential novel biomarkers and drug targets for neurodegenerative diseases including Alzheimer disease and frontotemporal dementia. Here we present the first multi-tissue study yielding hundred of novel pQTLs. This data will be instrumental to identify the functional gene from GWAS signals, identify novel biological protein-protein interactions, identify novel potential biomarkers and drug targets for complex traits.

show abstract

Section: Functional Annotation and Potential Biological Mechanism Drisupporting

confidence: 89%

Section: Functional Annotation and Potential Biological Mechanism Drimentioning

confidence: 63%

Genomic and multi-tissue proteomic integration for understanding the biology of disease and other complex traits

Cruchaga¹,

Yang²,

Farias³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…However, there are some extreme cases in which the top pQTL explains more than half of the variability in protein levels, such as in the case of rs2075803 (p.K100E) which explains 81% of the CSF Siglec-9, rs5498 (p.K469E) which explains 74.4% of plasma sICAM-1, and rs5498 (p.K469E) which explains 67.4% of brain PPAC (Extended Fig.3d, Table S3, S4, S5). The CSF Siglec-9, plasma sICAM-1, and brain PPAC have been replicated in other studies 13,[15][16][17] using a different proteomic approach indicating that these findings are not a platform driven finding.…”

Section: Multi-tissue Pqtlmentioning

confidence: 63%

Genomic and multi-tissue proteomic integration for understanding the biology of disease and other complex traits

Yang

Farias

Ibáñez

et al. 2020

Preprint

View full text Add to dashboard Cite

Expression quantitative trait loci (eQTL) mapping has successfully resolved some genome-wide association study (GWAS) loci for complex traits. However, there is a need for implementing additional "omic" approaches to untangle additional loci and provide a biological context for GWAS signals. We generated a detailed landscape of the genomic architecture of protein levels in multiple neurologically relevant tissues (brain, cerebrospinal fluid (CSF) and plasma), by profiling thousands of proteins in a large and well-characterized cohort. We identified 274, 127 and 32 protein quantitative loci (pQTL) for CSF, plasma and brain respectively. We demonstrated that cis-pQTL are more likely to be shared across tissues but trans-pQTL are tissue-specific. Between 78% to 87% of pQTL are not eQTL, indicating that protein levels have a different genetic architecture than gene expression. By combining our pQTL with Mendelian Randomization approaches we identified potential novel biomarkers and drug targets for neurodegenerative diseases including Alzheimer disease and frontotemporal dementia. In the context of personalized medicine, these results highlight the need for implementing additional functional genomic approaches beyond gene expression in order to understand the biology of complex traits, and to identify novel biomarkers and potential drug targets for those traits.

show abstract

“…An important issue linked to blood analysis is the underlying effect of genetics to determine stable differences in protein levels between individuals. The levels of blood proteins have previously been determined to be influenced both by genetic and environmental factors, as studied by mass spectrometry-based proteomics [1][2][3][4], nucleic-acid based assays [5][6][7][8], and immuno-based assays [9][10][11][12][13][14]. Effects based on sex [15], specific diets [15], age [16], and infections [17] have also been reported suggesting an important role for quantitative blood protein assays for individualized diagnosis of health and disease.…”

Section: Introductionmentioning

confidence: 99%

Whole-genome sequence association analysis of blood proteins in a longitudinal wellness cohort

et al. 2020

View full text Add to dashboard Cite

Background: The human plasma proteome is important for many biological processes and targets for diagnostics and therapy. It is therefore of great interest to understand the interplay of genetic and environmental factors to determine the specific protein levels in individuals and to gain a deeper insight of the importance of genetic architecture related to the individual variability of plasma levels of proteins during adult life. Methods: We have combined whole-genome sequencing, multiplex plasma protein profiling, and extensive clinical phenotyping in a longitudinal 2-year wellness study of 101 healthy individuals with repeated sampling. Analyses of genetic and non-genetic associations related to the variability of blood levels of proteins in these individuals were performed. Results: The analyses showed that each individual has a unique protein profile, and we report on the intraindividual as well as inter-individual variation for 794 plasma proteins. A genome-wide association study (GWAS) using 7.3 million genetic variants identified by whole-genome sequencing revealed 144 independent variants across 107 proteins that showed strong association (P < 6 × 10 −11) between genetics and the inter-individual variability on protein levels. Many proteins not reported before were identified (67 out of 107) with individual plasma level affected by genetics. Our longitudinal analysis further demonstrates that these levels are stable during the 2-year study period. The variability of protein profiles as a consequence of environmental factors was also analyzed with focus on the effects of weight loss and infections. Conclusions: We show that the adult blood levels of many proteins are determined at birth by genetics, which is important for efforts aimed to understand the relationship between plasma proteome profiles and human biology and disease.

show abstract

Genome and epigenome wide studies of neurological protein biomarkers in the Lothian Birth Cohort 1936

Cited by 45 publications

References 48 publications

Genomic and multi-tissue proteomic integration for understanding the biology of disease and other complex traits

Genomic and multi-tissue proteomic integration for understanding the biology of disease and other complex traits

Genomic and multi-tissue proteomic integration for understanding the biology of disease and other complex traits

Whole-genome sequence association analysis of blood proteins in a longitudinal wellness cohort

Contact Info

Product

Resources

About