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Sep 8, 2020 · Here, we present a new high-performance statistical R package (SAIGEgds) for large-scale PheWAS using generalized linear mixed models. The package implements the SAIGE method in optimized C++ codes, taking advantage of sparse genotype dosages and integrating the efficient genomic data structure file format.
Scalable and accurate implementation of generalized mixed mode with the support of Genomic Data Structure (GDS) files and highly optimized C++ implementation.
Here we develop a new high-performance statistical package (SAIGEgds) for large-scale PheWAS using mixed models [2]. In this package, the SAIGE method is implemented with optimized C++ codes taking advantage of sparse structure of genotype dosages.
SAIGE uses state-of-art optimization strategies to reduce computational costs; hence, it is applicable to GWAS for thousands of phenotypes by large biobanks. We would like to benchmark SAIGE on the summit node for potentially scaling up the analysis.
In this session we talk about using SAIGE to run GWAS with generalized linear mixed models. slides.
It is designed for single variant tests in large-scale phenome-wide association studies (PheWAS) with millions of variants and hundreds of thousands of samples (e.g., UK Biobank genotype data), controlling for case-control imbalance and sample structure in single variant association studies.
SAIGE is an R package developed with Rcpp for genome-wide association tests in large-scale data sets and biobanks. The method accounts for sample relatedness based on the generalized mixed models allows for model fitting with either full or sparse genetic relationship matrix (GRM) works for quantitative and binary traits
SAIGE is an R package with Scalable and Accurate Implementation of Generalized mixed model (Chen, H. et al. 2016). It accounts for sample relatedness and is feasible for genetic association tests in large cohorts and biobanks (N > 400,000).
Step 1: fitting the null logistic/linear mixed model For binary traits, a null logistic mixed model will be fitted (–traitType=binary). For quantitative traits, a null linear mixed model will be fitted (–traitType=quantitative) and needs to be inverse normalized (–invNormalize=TRUE)
We introduce secure federated genome-wide association studies (SF-GWAS), a combination of secure computation frameworks and distributed algorithms that empowers eficient and accurate GWAS...
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