Proteome-wide Mendelian randomization in global biobank meta-analysis reveals multi-ancestry drug targets for common diseases

Zhao, Huiling; Rasheed, Humaria; Nøst, Therese Haugdahl; Cho, Yoonsu; Liu, Yi; Bhatta, Laxmi; Bhattacharya, Arjun; Hemani, Gibran; Smith, George Davey; Brumpton, Ben; Zhou, Wei; Neale, Benjamin M.; Gaunt, Tom R.; Zheng, Jie

doi:10.1016/j.xgen.2022.100195

Cited by 42 publications

(35 citation statements)

References 88 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using the combined, cross-ancestry VTE GWAS meta-analysis results, we performed a proteome Mendelian randomization (MR) analysis with high-confidence genomic instruments corresponding to protein QTL (pQTL) for 1216 circulating plasma proteins that passed consistency and pleiotropy filters, as previously described. 29 Additional information is available in the Supplemental Material. To account for multiple testing, associations passing the Bonferroni-corrected threshold (N=1256, P <3.98×10 -5 ) were considered statistically significant.…”

Section: Methodsmentioning

confidence: 99%

Cross-Ancestry Investigation of Venous Thromboembolism Genomic Predictors

et al. 2022

Self Cite

View full text Add to dashboard Cite

Background: Venous thromboembolism (VTE) is a life-threatening vascular event with environmental and genetic determinants. Recent VTE genome-wide association studies (GWAS) meta-analyses involved nearly 30 000 VTE cases and identified up to 40 genetic loci associated with VTE risk, including loci not previously suspected to play a role in hemostasis. The aim of our research was to expand discovery of new genetic loci associated with VTE by using cross-ancestry genomic resources. Methods: We present new cross-ancestry meta-analyzed GWAS results involving up to 81 669 VTE cases from 30 studies, with replication of novel loci in independent populations and loci characterization through in silico genomic interrogations. Results: In our genetic discovery effort that included 55 330 participants with VTE (47 822 European, 6320 African, and 1188 Hispanic ancestry), we identified 48 novel associations, of which 34 were replicated after correction for multiple testing. In our combined discovery-replication analysis (81 669 VTE participants) and ancestry-stratified meta-analyses (European, African, and Hispanic), we identified another 44 novel associations, which are new candidate VTE-associated loci requiring replication. In total, across all GWAS meta-analyses, we identified 135 independent genomic loci significantly associated with VTE risk. A genetic risk score of the significantly associated loci in Europeans identified a 6-fold increase in risk for those in the top 1% of scores compared with those with average scores. We also identified 31 novel transcript associations in transcriptome-wide association studies and 8 novel candidate genes with protein quantitative-trait locus Mendelian randomization analyses. In silico interrogations of hemostasis and hematology traits and a large phenome-wide association analysis of the 135 GWAS loci provided insights to biological pathways contributing to VTE, with some loci contributing to VTE through well-characterized coagulation pathways and others providing new data on the role of hematology traits, particularly platelet function. Many of the replicated loci are outside of known or currently hypothesized pathways to thrombosis. Conclusions: Our cross-ancestry GWAS meta-analyses identified new loci associated with VTE. These findings highlight new pathways to thrombosis and provide novel molecules that may be useful in the development of improved antithrombosis treatments.

show abstract

Section: Methodsmentioning

confidence: 99%

Cross-Ancestry Investigation of Venous Thromboembolism Genomic Predictors

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…As genomic and proteomic sampling efforts increase in Africa, we expect additional pQTLs will be found (both cis and trans acting variants), improving their reliability for downstream analyses such as Mendelian Randomization and proteome-wide association studies (PWAS) [5]. More importantly, as specifically highlighted for the novel cis-pQTL of F3 , it is an open question as to the value of leveraging targeted proteomic information when making predictions on pharmacogenomic intervention.…”

Section: Discussionmentioning

confidence: 99%

“…Recent advancements in targeted proteomics have allowed for richer characterizations of the proteome at larger scales. Insights into large-scale proteomic variation has led to improved understanding of causal mechanisms underlying disease, drug repurposing, and the impact of genetic variants on protein abundance [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

A survey of proteomic variation across two ethnic groups in Nigeria and its relationship to obesity risk

Biddanda¹,

Arce²,

Eze-Echesi³

et al. 2022

Preprint

View full text Add to dashboard Cite

Proteomic variation between individuals has immense potential for identifying novel drug targets and disease mechanisms. However, with high-throughput proteomic technologies still in their infancy, they have largely been applied in large majority European ancestry cohorts (e.g. the UK Biobank). An open question is the degree to which proteomic signatures seen in European and other groups mirror those seen in diverse populations, such as cohorts from Africa. Coupled with genetic information, we can also gain a better understanding of the role of genetic variants in the regulation of the proteome and subsequent disease mechanisms. To address the gap in our understanding of proteomic variation in individuals of African ancestry, we collected proteomic data from 176 individuals across two ethnic groups (Igbo and Yoruba) in Nigeria. These individuals were also stratified into high BMI (BMI > 30 kg/m2) and normal BMI (20 kg/m2< BMI < 30 kg/m2) categories. We characterized differences in plasma protein abundance using the Olink Explore 1536 panel between high and normal BMI individuals, finding strong associations consistent with previously known signals in individuals of European descent. We additionally found 73 sentinel cis-pQTL in this dataset, with 21 lead cis-pQTL not observed in catalogs of variation from European-ancestry individuals. In summary, our study highlights the value of leveraging proteomic data in cohorts of diverse ancestry for investigating trait-specific mechanisms and discovering novel genetic regulators of the plasma proteome.

show abstract

“…Zhao et al . conducted a multi‐ancestry proteome‐wide Mendelian randomization used the multi‐ancestry GWAS data from Global Biobank Meta‐Analysis Initiative (GBMI) in 1.22 million European Americans and 32,658 African Americans 120 . This study identified 14 protein‐disease pairs, which showed different disease risk effect between two ancestries.…”

Section: Proteomicsmentioning

confidence: 99%

“…Zhao et al conducted a multi-ancestry proteome-wide Mendelian randomization used the multi-ancestry GWAS data from Global Biobank Meta-Analysis Initiative (GBMI) in 1.22 million European Americans and 32,658 African Americans. 120 This study identified 14 protein-disease pairs, which showed different disease risk effect between two ancestries. For example, SERPINF1 showed a strong causal effect on stroke in African ancestry individuals (P = 3.76 × 10 −5 ), but not in European ancestry individuals (P = 0.83).…”

Section: Protein Quantitative Trait Locus Analysismentioning

confidence: 99%

Multi‐Omics Studies in Historically Excluded Populations: The Road to Equity

Yang

Mishra

Perera

2023

Clin Pharma and Therapeutics

View full text Add to dashboard Cite

Over the past few decades, genomewide association studies (GWASs) have identified the specific genetics variants contributing to many complex diseases by testing millions of genetic variations across the human genome against a variety of phenotypes. However, GWASs are limited in their ability to uncover mechanistic insight given that most significant associations are found in non‐coding region of the genome. Furthermore, the lack of diversity in studies has stymied the advance of precision medicine for many historically excluded populations. In this review, we summarize most popular multi‐omics approaches (genomics, transcriptomics, proteomics, and metabolomics) related to precision medicine and highlight if diverse populations have been included and how their findings have advance biological understanding of disease and drug response. New methods that incorporate local ancestry have been to improve the power of GWASs for admixed populations (such as African Americans and Latinx). Because most signals from GWAS are in the non‐coding region, other machine learning and omics approaches have been developed to identify the potential causative single‐nucleotide polymorphisms and genes that explain these phenotypes. These include polygenic risk scores, expression quantitative trait locus mapping, and transcriptome‐wide association studies. Analogous protein methods, such as proteins quantitative trait locus mapping, proteome‐wide association studies, and metabolomic approaches provide insight into the consequences of genetic variation on protein abundance. Whereas, integrated multi‐omics studies have improved our understanding of the mechanisms for genetic association, we still lack the datasets and cohorts for historically excluded populations to provide equity in precision medicine and pharmacogenomics.

show abstract

Proteome-wide Mendelian randomization in global biobank meta-analysis reveals multi-ancestry drug targets for common diseases

Cited by 42 publications

References 88 publications

Cross-Ancestry Investigation of Venous Thromboembolism Genomic Predictors

Cross-Ancestry Investigation of Venous Thromboembolism Genomic Predictors

A survey of proteomic variation across two ethnic groups in Nigeria and its relationship to obesity risk

Multi‐Omics Studies in Historically Excluded Populations: The Road to Equity

Contact Info

Product

Resources

About