Open AccessPRSice-2: Polygenic Risk Score software for biobank-scale data
Author(s) -
Shing Wan Choi,
Paul F. O’Reilly
Publication year - 2019
Publication title -
gigascience
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 2.947
H-Index - 54
ISSN - 2047-217X
DOI - 10.1093/gigascience/giz082
Subject(s) - biobank , computer science , overfitting , software , scalability , data mining , data science , inference , bioinformatics , machine learning , database , artificial intelligence , biology , programming language , artificial neural network
Polygenic risk score (PRS) analyses have become an integral part of biomedical research, exploited to gain insights into shared aetiology among traits, to control for genomic profile in experimental studies, and to strengthen causal inference, among a range of applications. Substantial efforts are now devoted to biobank projects to collect large genetic and phenotypic data, providing unprecedented opportunity for genetic discovery and applications. To process the large-scale data provided by such biobank resources, highly efficient and scalable methods and software are required.