Molecular quantitative trait loci

Aguet, François; Alasoo, Kaur; Li, Yang; Battle, Alexis; Im, Hae Kyung; Montgomery, Stephen B.; Lappalainen, Tuuli

doi:10.1038/s43586-022-00188-6

Cited by 35 publications

(30 citation statements)

References 188 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Genome wide association studies (GWAS) provide increasingly detailed associations between genomic loci and phenotypes but are missing a mechanistic link to the proteins mediating the effect 38,41,42 . With a view to close this gap, we next investigated whether any of our novel pQTLs were known to be associated with phenotypic traits.…”

Section: Novel Pqtls Link Proteins To Gwas Resultsmentioning

confidence: 99%

Plasma Proteome Variation and its Genetic Determinants in Children and Adolescents

Niu

Stinson

Holm

et al. 2023

Preprint

View full text Add to dashboard Cite

The levels of specific proteins in human blood are the most commonly used indicators of potential health-related problems. Understanding the genetic and other determinants of the human plasma proteome can aid in biomarker research and drug development. Diverse factors including genetics, age, sex, body mass index (BMI), growth and development including puberty can affect the circulating levels of proteins. Affinity-based proteomics can infer the relationship between blood protein levels and these factors at a large scale. Compared to these methods, mass spectrometry (MS)-based proteomics provides much higher specificity of identification and quantification, but existing studies are limited by small sample sizes or low numbers of quantified proteins. Here we aim to elucidate to which extent genomic variation affects plasma protein levels across diverse age ranges and cohort characteristics. Employing a streamlined and highly quantitative MS-based plasma proteomics workflow, we measured the plasma proteome of 2,147 children and adolescents. Levels of 90% of these proteins were significantly associated with age, sex, BMI or genetics. More than 1,000 protein quantitative trait loci (pQTLs) - a third of which were novel - regulated protein levels between a few percent and up to 30-fold. These replicated excellently in an independent cohort of 558 adults, with highly concordant effect sizes (Pearson r > 0.97). We developed a framework to eliminate artefactual pQTLs due to protein-altering variants, paving the way for large-scale interrogation of pQTLs using MS-based proteomics. Our data reveal unexpectedly extensive genetic impacts on plasma protein levels, consistent from childhood into adulthood. These findings have implications for biomarker research and drug development.

show abstract

Section: Novel Pqtls Link Proteins To Gwas Resultsmentioning

confidence: 99%

Plasma Proteome Variation and its Genetic Determinants in Children and Adolescents

Niu

Stinson

Holm

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…In current approaches, frequentist association testing involves approximations of Henderson's mixedlinear model equation [4], represented as follows: y = j∈S x j β j + ĝ/ ∈S + ϵ, 2 where the set S contains either DNA variants on a focal chromosome (leave-one-chromosome-out approach, see [5]) or at a single loci (leave-one-out approach). The regression coefficient at each position is re-estimated using least squares and p-values are obtained for the test that an effect is equal to zero.…”

Section: Association Testing: False Positive Rate and Statistical Powermentioning

confidence: 99%

“…These massive datasets have the potential to generate great biological insight, but they are currently being generated more abundantly than they can efficiently be analyzed. Generally, classical statistical theory is relied upon [2,3] for two main learning problems: (i) estimation and statistical testing of the relationship between genome and health outcome, and (ii) prediction of health outcomes from the genome. For both tasks, restricted maximum likelihood [4], ridge regression [5], variational inference [6], or Gibbs sampling [7] approaches are commonly used.…”

Section: Introductionmentioning

confidence: 99%

Joint modelling of whole genome sequence data for human height via approximate message passing

Depope,

Bajzik,

Mondelli

et al. 2023

Preprint

View full text Add to dashboard Cite

Efficient utilization of large-scale biobank data is crucial for inferring the genetic basis of disease and predicting health outcomes from the DNA. Yet we lack accurate methods that scale to data where electronic health records are linked to whole genome sequence information. To address this issue, we develop a new algorithmic paradigm based on Approximate Message Passing (AMP), which is specifically tailored for genomic prediction and association testing. Our method yields comparable out-of-sample prediction accuracy to the state of the art on UK Biobank traits, whilst dramatically improving computational complexity, with a 10x-speed up in the run time. In addition, AMP theory provides a joint association testing framework, which outperforms the commonly used REGENIE method, in a third of the compute time. This first, truly large-scale application of the AMP framework lays the foundations for a far wider range of statistical analyses for hundreds of millions of variables measured on millions of people.

show abstract

“…genotype-phenotype mapping (GPM), has been a long-lasting problem with important applications (1,2). Indeed, making sense of genetic variation at the phenotypic level enables the understanding of trait variation between and within species as well as the emergence and evolution of phenotypes (3). For instance, reverse genetics approaches, e.g.…”

Section: Introductionmentioning

confidence: 99%

Refining the resolution of the yeast genotype-phenotype map using single-cell RNA-sequencing

N’Guessan,

Tong,

Heydari

et al. 2023

Preprint

View full text Add to dashboard Cite

Genotype-phenotype mapping (GPM) or the association of trait variation to genetic variation has been a long-lasting problem in biology. The existing approaches to this problem allowed researchers to partially understand within- and between-species variation as well as the emergence or evolution of phenotypes. However, traditional GPM methods typically ignore the transcriptome or have low statistical power due to challenges related to dataset scale. Thus, it is not clear to what extent selection modulates transcriptomes and whether cis- or trans-regulatory elements are more important. To overcome these challenges, we leveraged the cost efficiency and scalability of single-cell RNA sequencing (scRNA-seq) by collecting data from 18,233 yeast cells from 4,489 segregants of a cross between the laboratory strain BY4741 and the vineyard strain RM11-1a. More precisely, we performed eQTL mapping with the scRNA-seq data to identify single-cell eQTL (sc-eQTL) and transcriptome variation patterns associated to fitness variation inferred from the segregants' bulk fitness assay. Due to the larger scale of our dataset, we were able to recapitulate results from decades of work in GPM from yeast bulk assays while revealing new associations between phenotypic and transcriptomic variations. The multidimensionality of this dataset also allowed us to measure phenotype and expression heritability and partition the variance of cell fitness into genotype and expression components to highlight selective pressure at both levels. Altogether these results suggest that integrating large-scale scRNA-seq data into GPM improves our understanding of trait variation in the context of transcriptomic regulation.

show abstract

Molecular quantitative trait loci

Cited by 35 publications

References 188 publications

Plasma Proteome Variation and its Genetic Determinants in Children and Adolescents

Plasma Proteome Variation and its Genetic Determinants in Children and Adolescents

Joint modelling of whole genome sequence data for human height via approximate message passing

Refining the resolution of the yeast genotype-phenotype map using single-cell RNA-sequencing

Contact Info

Product

Resources

About