Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis

Autor: Kristi Läll, Etienne J. Orliac, Marion Patxot, Zoltán Kutalik, Athanasios Kousathanas, Reedik Mägi, Sven Erik Ojavee, Daniel Trejo Banos, Matthew R. Robinson, Krista Fischer
Jazyk: angličtina
Rok vydání: 2021
Předmět:
0301 basic medicine
Multifactorial Inheritance
Statistical methods
Computer science
General Physics and Astronomy
Disease
030105 genetics & heredity
Genome-wide association studies
Genome
Databases
Genetic
Age of Onset
Event (probability theory)
Multidisciplinary
Statistics
Age Factors
Contrast (statistics)
Genomics
Variance (accounting)
Phenotype
Biobank
Cardiovascular Diseases
Hypertension
Female
Menopause
Algorithms
Estonia
Science
Bayesian probability
Computational biology
Biology
Polymorphism
Single Nucleotide
Article
General Biochemistry
Genetics and Molecular Biology
03 medical and health sciences
Genetic variation
Humans
Computer Simulation
Genetic Association Studies
Menarche
Models
Genetic
Genome
Human
Bayes Theorem
General Chemistry
United Kingdom
Genetic architecture
030104 developmental biology
Diabetes Mellitus
Type 2
Genomic architecture
Genome-Wide Association Study
Zdroj: Nature Communications, Vol 12, Iss 1, Pp 1-17 (2021)
Nature communications, vol. 12, no. 1, pp. 2337
Nature Communications
ISSN: 2041-1723
Popis: While recent advancements in computation and modelling have improved the analysis of complex traits, our understanding of the genetic basis of the time at symptom onset remains limited. Here, we develop a Bayesian approach (BayesW) that provides probabilistic inference of the genetic architecture of age-at-onset phenotypes in a sampling scheme that facilitates biobank-scale time-to-event analyses. We show in extensive simulation work the benefits BayesW provides in terms of number of discoveries, model performance and genomic prediction. In the UK Biobank, we find many thousands of common genomic regions underlying the age-at-onset of high blood pressure (HBP), cardiac disease (CAD), and type-2 diabetes (T2D), and for the genetic basis of onset reflecting the underlying genetic liability to disease. Age-at-menopause and age-at-menarche are also highly polygenic, but with higher variance contributed by low frequency variants. Genomic prediction into the Estonian Biobank data shows that BayesW gives higher prediction accuracy than other approaches.
Few genome-wide association studies have explored the genetic architecture of age-of-onset for traits and diseases. Here, the authors develop a Bayesian approach to improve prediction in timing-related phenotypes and perform age-of-onset analyses across complex traits in the UK Biobank.
Databáze: OpenAIRE
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