Protein prediction for trait mapping in diverse populations

Schubert, Ryan; Geoffroy, Elyse; Gregga, Isabelle; Mulford, Ashley; Aguet, François; Ardlie, Kristin; Gerszten, Robert E.; Clish, Clary B.; Berg, David Van Den; Taylor, Kent D.; Durda, Peter; Johnson, W. Craig; Cornell, Elaine; Guo, Xiuqing; Liu, Yongmei; Tracy, Russell P.; Conomos, Matthew P.; Blackwell, Tom; Papanicolaou, George; Lappalainen, Tuuli; Mikhaylova, Anna V.; Thornton, Timothy A.; Cho, Michael H.; Gignoux, Christopher R.; Lange, Leslie A.; Lange, Ethan M.; Rich, Stephen S.; Rotter, Jerome I.; Manichaikul, Ani; Im, Hae Kyung; Wheeler, Heather E.

doi:10.1101/2021.08.11.455912

Cited by 5 publications

(10 citation statements)

References 84 publications

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“…For example, JHS proteomics data was generated in 3 batches [21], and separately from the methylomics data. In contrast, MESA proteomics and methylomics data were all generated through the MESA TOPMed pilot over a short time period [22,23]. Results shown in Fig 5 supported our expectations: overall we observe that a lower number of JHS inferred CVs are needed to explain the outcomes with higher r2 compared to JHS inferred PCs, indicating that top CVs inferred from JHS data tend to capture biological variations while top PCs tend to reflect more assay-specific technical variations.…”

Section: Resultssupporting

confidence: 69%

“…Longitudinal multi-omics data was generated in MESA through a pilot program from NHLBI’s Trans-Omics for Precision Medicine Initiative (TOPMed) at exam 1 (2000-2002) and exam 5 (2010-2011), including ~ 1,000 participants for each exam with methylomics data (Illumina MethylationEPIC BeadChip array) [41] and proteomics (SOMAscan 1.3k array) [21,22]. WGS data are described below.…”

Section: Methodsmentioning

confidence: 99%

Section: Proteomics Cvs Explain Considerable Amounts Of Variation In ...mentioning

confidence: 99%

“…For each CCA-prioritized feature of each assay, we first mapped them to genes, and then performed pathway enrichment analysis on these genes utilizing three databases -DisGeNET [22][23][24] (enrichDGN function in DOSE R package, with default settings), Gene Ontology (GO) [25,26] (enrichGO function in clusterProfiler R package, with default settings) and Kyoto Encyclopedia of Genes and Genomes (KEGG) [27][28][29] (enrichKEGG function in clusterProfiler R package, with default settings). For methylomics, we mapped CpG sites to the nearest genes using annotations released by Illumina.…”

Section: Modified Gram-schmidt Strategymentioning

confidence: 99%

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Uncovering Cross-Cohort Molecular Features with Multi-Omics Integration Analysis

Jiang

Aguet

Ardlie

et al. 2022

Preprint

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Integrative approaches that simultaneously model multi-omics data have gained increasing popularity because they provide holistic system biology views of multiple or all components in a biological system of interest. Canonical correlation analysis (CCA) is a correlation-based integrative method. It was initially designed to extract latent features shared between two assays by finding the linear combinations of features – referred to as canonical vectors (CVs) – within each assay that achieve maximal across-assay correlation. Sparse multiple CCA (SMCCA), a widely-used derivative of CCA, allows more than two assays but can result in non-orthogonal CVs when applied to high-dimensional data. Here, we incorporated a variation of the Gram-Schmidt (GS) algorithm with SMCCA to improve orthogonality among CVs. Applying our SMCCA-GS method to proteomics and methylomics data from the Multi-Ethnic Study of Atherosclerosis (MESA) and Jackson Heart Study (JHS), we identified strong associations between blood cell counts and protein abundance. This finding suggests that adjustment of blood cell composition should be considered in protein-based association studies. Importantly, CVs obtained from two independent cohorts demonstrate transferability across the cohorts. For example, proteomic CVs learned from JHS explain similar amounts of blood cell count phenotypic variance in MESA, explaining 39.0% ~ 50.0% variation in JHS and 38.9% ~ 49.1% in MESA, similar transferability was observed for other omics-CV-trait pairs. This suggests that biologically meaningful and cohort-agnostic variation is captured by CVs. We further developed Sparse Supervised Multiple CCA (SSMCCA) to allow supervised integration analysis for more than two assays. We anticipate that applying our SMCCA-GS and SSMCCA on various cohorts would help identify cohort-agnostic biologically meaningful relationships between multi-omics data and phenotypic traits.

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Section: Resultssupporting

confidence: 69%

Section: Methodsmentioning

confidence: 99%

Section: Proteomics Cvs Explain Considerable Amounts Of Variation In ...mentioning

confidence: 99%

Section: Modified Gram-schmidt Strategymentioning

confidence: 99%

See 2 more Smart Citations

Uncovering Cross-Cohort Molecular Features with Multi-Omics Integration Analysis

Jiang

Aguet

Ardlie

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The European and African ancestry summary statistics for MD were tested against multi-ancestry brain eQTLs from European and African American samples 27 . For Hispanic/Latinx ancestry, we tested gene and protein expression of blood tissue from Multi-Ethnic Study of Atherosclerosis and Trans-omics for Precision Medicine 69 . For African ancestry, we tested gene expression of blood from GENOA study 70 and proteome expression of blood 71 .…”

Section: Colocalisation Analysismentioning

confidence: 99%

Multi-ancestry GWAS of major depression aids locus discovery, fine-mapping, gene prioritisation, and causal inference

Meng

Navoly

Giannakopoulou

et al. 2022

Preprint

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Most genome-wide association studies (GWAS) of major depression (MD) have been conducted in samples of European ancestry. Here we report a multi-ancestry GWAS of MD, adding data from 21 studies with 88,316 MD cases and 902,757 controls to previously reported data from individuals of European ancestry. This includes samples of African (36% of effective sample size), East Asian (26%) and South Asian (6%) ancestry and Hispanic/Latinx participants (32%). The multi-ancestry GWAS identified 190 significantly associated loci, 53 of them novel. For previously reported loci from GWAS in European ancestry the power-adjusted transferability ratio was 0.6 in the Hispanic/Latinx group and 0.3 in each of the other groups. Fine-mapping benefited from additional sample diversity: the number of credible sets with ≤5 variants increased from 3 to 12. A transcriptome-wide association study identified 354 significantly associated genes, 205 of them novel. Mendelian Randomisation showed a bidirectional relationship with BMI exclusively in samples of European ancestry. This first multi-ancestry GWAS of MD demonstrates the importance of large diverse samples for the identification of target genes and putative mechanisms.

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

Yang

Mishra

Perera

2023

Clin Pharma and Therapeutics

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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.

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Protein prediction for trait mapping in diverse populations

Cited by 5 publications

References 84 publications

Uncovering Cross-Cohort Molecular Features with Multi-Omics Integration Analysis

Uncovering Cross-Cohort Molecular Features with Multi-Omics Integration Analysis

Multi-ancestry GWAS of major depression aids locus discovery, fine-mapping, gene prioritisation, and causal inference

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

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