Revealing polygenic pleiotropy using genetic risk scores for asthma

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Summary

In this study we examined how genetic risk for asthma associates with different features of the disease and with other medical conditions and traits. Using summary statistics from two multi-ancestry genome-wide association studies of asthma, we modeled polygenic risk scores (PRSs) and validated their predictive performance in the UK Biobank. We then performed phenome-wide association studies of the asthma PRSs with 371 heritable traits in the UK Biobank. We identified 228 total significant associations across a variety of organ systems, including associations that varied by PRS model, sex, age of asthma onset, ancestry, and human leukocyte antigen region alleles. Our results highlight pervasive pleiotropy between asthma and numerous other traits and conditions and elucidate pathways that contribute to asthma and its comorbidities.

Abstract

We performed phenome-wide association studies of asthma polygenic risk scores in the UK Biobank, identifying significant associations across a variety of organ systems, including associations that varied by genetic risk model, sex, age of asthma onset, ancestry, and human leukocyte antigen region alleles.

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A global reference for human genetic variation

Lachlan Coin, Robert Garry, Oleksyk Taras (2018)

The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.

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Second-generation PLINK: rising to the challenge of larger and richer datasets

Christopher Chang, Carson Chow, Laurent Tellier … (2015)

PLINK 1 is a widely used open-source C/C++ toolset for genome-wide association studies (GWAS) and research in population genetics. However, the steady accumulation of data from imputation and whole-genome sequencing studies has exposed a strong need for even faster and more scalable implementations of key functions. In addition, GWAS and population-genetic data now frequently contain probabilistic calls, phase information, and/or multiallelic variants, none of which can be represented by PLINK 1's primary data format. To address these issues, we are developing a second-generation codebase for PLINK. The first major release from this codebase, PLINK 1.9, introduces extensive use of bit-level parallelism, O(sqrt(n))-time/constant-space Hardy-Weinberg equilibrium and Fisher's exact tests, and many other algorithmic improvements. In combination, these changes accelerate most operations by 1-4 orders of magnitude, and allow the program to handle datasets too large to fit in RAM. This will be followed by PLINK 2.0, which will introduce (a) a new data format capable of efficiently representing probabilities, phase, and multiallelic variants, and (b) extensions of many functions to account for the new types of information. The second-generation versions of PLINK will offer dramatic improvements in performance and compatibility. For the first time, users without access to high-end computing resources can perform several essential analyses of the feature-rich and very large genetic datasets coming into use.

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pROC: an open-source package for R and S+ to analyze and compare ROC curves

Xavier Robin, Natacha Turck, Alexandre Hainard … (2013)

Background Receiver operating characteristic (ROC) curves are useful tools to evaluate classifiers in biomedical and bioinformatics applications. However, conclusions are often reached through inconsistent use or insufficient statistical analysis. To support researchers in their ROC curves analysis we developed pROC, a package for R and S+ that contains a set of tools displaying, analyzing, smoothing and comparing ROC curves in a user-friendly, object-oriented and flexible interface. Results With data previously imported into the R or S+ environment, the pROC package builds ROC curves and includes functions for computing confidence intervals, statistical tests for comparing total or partial area under the curve or the operating points of different classifiers, and methods for smoothing ROC curves. Intermediary and final results are visualised in user-friendly interfaces. A case study based on published clinical and biomarker data shows how to perform a typical ROC analysis with pROC. Conclusions pROC is a package for R and S+ specifically dedicated to ROC analysis. It proposes multiple statistical tests to compare ROC curves, and in particular partial areas under the curve, allowing proper ROC interpretation. pROC is available in two versions: in the R programming language or with a graphical user interface in the S+ statistical software. It is accessible at http://expasy.org/tools/pROC/ under the GNU General Public License. It is also distributed through the CRAN and CSAN public repositories, facilitating its installation.

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Author and article information

Contributors

Carole Ober

Nathan Schoettler

Journal

Journal ID (nlm-ta): HGG Adv

Journal ID (iso-abbrev): HGG Adv

Title: Human Genetics and Genomics Advances

Publisher: Elsevier

ISSN (Electronic): 2666-2477

Publication date PMC-release: 17 August 2023

Publication date Collection: 12 October 2023

Publication date (Electronic): 17 August 2023

Volume: 4

Issue: 4

Electronic Location Identifier: 100233

Affiliations

[1 ]Department of Human Genetics, University of Chicago, Chicago, IL, USA

[2 ]Biological Sciences Collegiate Division, University of Chicago, Chicago, IL, USA

[3 ]Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA

[4 ]Section of Pulmonary and Critical Care Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA

Author notes

[∗ ]Corresponding author cober@ 123456uchicago.edu

[∗∗ ]Corresponding author nschoettle@ 123456bsd.uchicago.edu

[5]

Lead contact

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Publisher Item ID: S2666-2477(23)00065-9 Publisher ID: 100233

DOI: 10.1016/j.xhgg.2023.100233

PMC ID: 10474095

PubMed ID: 37663543

SO-VID: 20c99da0-6daf-4d10-825f-01ebc2f3b917

License:

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Revealing polygenic pleiotropy using genetic risk scores for asthma

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Most cited references 89

A global reference for human genetic variation

Second-generation PLINK: rising to the challenge of larger and richer datasets

pROC: an open-source package for R and S+ to analyze and compare ROC curves

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