https://github.com/weinstockj/prs
Polygenic Risk Scores with Functional Neutral Network (PRSFNN)
https://github.com/weinstockj/prs
genetics genomics julia-language machine-learning statistical-genetics
Last synced: 9 months ago
JSON representation
Polygenic Risk Scores with Functional Neutral Network (PRSFNN)
- Host: GitHub
- URL: https://github.com/weinstockj/prs
- Owner: weinstockj
- Created: 2023-06-09T16:46:31.000Z (about 3 years ago)
- Default Branch: master
- Last Pushed: 2025-07-10T16:33:41.000Z (11 months ago)
- Last Synced: 2025-07-10T22:57:10.031Z (11 months ago)
- Topics: genetics, genomics, julia-language, machine-learning, statistical-genetics
- Language: Julia
- Homepage: https://weinstockj.github.io/PRS/dev/
- Size: 40.7 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# PRSFNN.jl
[](https://weinstockj.github.io/PRS/dev)
[](https://github.com/weinstockj/PRS/actions/workflows/ci.yml)
PRSFNN (Polygenic Risk Score with Functional Neural Networks) is a Julia module for calculating polygenic risk scores by integrating GWAS summary statistics with functional annotations using neural networks.
## Features
- Integration of GWAS summary statistics with functional annotations
- Linkage disequilibrium (LD) calculation and correction
- Coordinate Ascent Variational Inference (CAVI) for posterior effect size estimation
- Neural network models to learn the relationship between functional annotations and genetic effect sizes
## PRSFNN runner
Note: this Julia module is used for running PRSFNN in individual LD blocks and training the neural network. To run PRSFNN genome-wide is an HPC environment, see our workflow [here](https://github.com/weinstockj/PRSFNN-runner).
## Installation
```julia
# From the Julia REPL
using Pkg
Pkg.add(url="https://github.com/weinstockj/PRS.jl")
```
## Getting Started
Steps to load this module from the root directory:
1. Run `julia --color=yes --project=.` (requires Julia 1.9.0 or later)
2. Run `using Revise` # helpful while developing
3. Run `using PRSFNN`
Now the functions have been loaded.
To call an internal function, use `PRSFNN.function_name`
To run Julia in debugger mode: `JULIA_DEBUG=PRSFNN julia --color=yes --project=.`
## Usage
### Basic Example
```julia
using PRSFNN
# Run PRSFNN on a genomic region
result = main(
output_prefix = "chr3_block1",
annot_data_path = "path/to/annotations.parquet",
gwas_data_path = "path/to/gwas_stats.tsv",
ld_panel_path = "path/to/ld_panel"
)
```
### Simulating GWAS Data
For testing and development, you can simulate GWAS summary statistic data:
```julia
# Generate simulated data
raw = simulate_raw(;N = 10_000, P = 1_000, K = 100, h2 = 0.10)
# Extract sufficient statistics needed for PRS
summary_stats = estimate_sufficient_statistics(raw.X, raw.Y)
```
### Training a PRS Model
```julia
# Train the PRS model
N = size(raw.X, 1) # Number of samples
P = size(raw.X, 2) # Number of SNPs
XtX = construct_XtX(summary_stats.R, ones(P), N)
D = construct_D(XtX)
Xty = construct_Xty(summary_stats.coef, D)
σ2, R2, yty = infer_σ2(
summary_stats.coef,
summary_stats.SE,
XtX,
Xty,
N,
P;
estimate = true,
λ = 0.50 * N
)
K = size(raw.G, 2)
H = 5 # Number of hidden units
layer_1 = Dense(K => H, Flux.softplus; init = Flux.glorot_normal(gain = 0.005))
layer_output = Dense(H => 2)
layer_output.bias .= [StatsFuns.log(0.001), StatsFuns.logit(0.1)]
model = Chain(layer_1, layer_output)
initial_lr = 0.00005
optim_type = AdamW(initial_lr)
opt = Flux.setup(optim_type, model)
PRS = train_until_convergence(
summary_stats.coef,
summary_stats.SE,
summary_stats.R,
XtX,
Xty,
raw.G,
model = model,
opt = opt,
σ2 = σ2,
R2 = R2,
yty = yty,
N = fill(N, P), # Vector of sample sizes
train_nn = false,
max_iter = 5
)
```
## Documentation
For more detailed documentation, visit the [official documentation site](https://weinstockj.github.io/PRS/dev).
## Running the Tests
Run unit tests with:
```julia
julia --project=. test/runtests.jl
```
or interactively with:
```julia
includet("test/runtests.jl")
```
## Contact
Please address correspondence to:
- Josh Weinstock
- April Kim
- Alexis Battle
## Functions
```@docs
PRSFNN.main
PRSFNN.rss
PRSFNN.elbo
PRSFNN.joint_log_prob
PRSFNN.train_until_convergence
PRSFNN.fit_heritability_nn
PRSFNN.log_prior
PRSFNN.estimate_sufficient_statistics
PRSFNN.compute_LD
PRSFNN.fit_genome_wide_nn
PRSFNN.train_cavi
PRSFNN.simulate_raw
PRSFNN.infer_σ2
PRSFNN.poet_cov
```