### Install POP-TOOLS

Source: https://github.com/qlu-lab/pop-tools/blob/main/README.md

Installs the POP-TOOLS toolkit and its required modules using Git and pip. This involves cloning the repository and installing dependencies from a requirements file.

```Shell
git clone https://github.com/qlu-lab/POP-TOOLS
cd POP-TOOLS
pip install -r requirements.txt
```

--------------------------------

### Install POP-TOOLS Dependencies with Pip

Source: https://github.com/qlu-lab/pop-tools/wiki/0.-Preparation-for-POP‐TOOLS

Installs the Python dependencies for POP-TOOLS using pip by referencing the requirements.txt file. Ensure Python 3.8+ is installed.

```shell
pip install -r requirements.txt
```

--------------------------------

### Clone POP-TOOLS Repository

Source: https://github.com/qlu-lab/pop-tools/wiki/0.-Preparation-for-POP‐TOOLS

Clones the POP-TOOLS repository from GitHub using git and navigates into the project directory. This is the initial step for installation.

```shell
git clone https://github.com/qlu-lab/POP-TOOLS
cd POP-TOOLS
```

--------------------------------

### Test POP-GWAS Installation

Source: https://github.com/qlu-lab/pop-tools/wiki/0.-Preparation-for-POP‐TOOLS

Runs the POP-GWAS script with the -h flag to display command-line options, verifying a successful installation.

```python
python ./POP-GWAS.py -h
```

--------------------------------

### Install POP-TOOLS Dependencies with Mamba

Source: https://github.com/qlu-lab/pop-tools/wiki/0.-Preparation-for-POP‐TOOLS

Creates a Conda environment with POP-TOOLS dependencies using an environment.yml file. Requires Mamba or Conda to be installed.

```shell
mamba env create --file environment.yml
```

--------------------------------

### Run POP-GWAS for Quantitative Phenotype

Source: https://github.com/qlu-lab/pop-tools/wiki/1.-POP‐GWAS

This snippet demonstrates how to execute POP-GWAS for a quantitative phenotype, using Head Bone Mineral Density (Head_BMD) as an example. It specifies the required GWAS summary statistics files and the output directory.

```Shell
cd POP-TOOLS

trait=Head_BMD

python3 ./POP-GWAS.py \
--gwas-yhat-unlab ./test/data/${trait}_yhat_unlab.txt.gz \
--gwas-y-lab ./test/data/${trait}_y_lab.txt.gz \
--gwas-yhat-lab ./test/data/${trait}_yhat_lab.txt.gz \
--out ./test/result/${trait}
```

--------------------------------

### Run POP-GWAS for Binary Phenotype

Source: https://github.com/qlu-lab/pop-tools/wiki/1.-POP‐GWAS

This snippet illustrates how to run POP-GWAS for a binary phenotype, using Type-2 Diabetes (T2D) as an example. It includes the `--bt` flag to indicate a binary trait and specifies the necessary input files and output path.

```Shell
cd POP-TOOLS

trait=T2D

python3 ./POP-GWAS.py \
--gwas-yhat-unlab ./test/data/${trait}_yhat_unlab.txt.gz \
--gwas-y-lab ./test/data/${trait}_y_lab.txt.gz \
--gwas-yhat-lab ./test/data/${trait}_yhat_lab.txt.gz \
--bt \
--out ./test/result/${trait}
```

--------------------------------

### Update POP-TOOLS Python Dependencies

Source: https://github.com/qlu-lab/pop-tools/wiki/0.-Preparation-for-POP‐TOOLS

Updates the Python dependencies for POP-TOOLS using pip, ensuring all requirements are up-to-date.

```shell
pip install --upgrade -r requirements.txt
```

--------------------------------

### Activate POP-TOOLS Conda Environment

Source: https://github.com/qlu-lab/pop-tools/wiki/0.-Preparation-for-POP‐TOOLS

Activates the Conda environment created for POP-TOOLS. Replace '<path_to_envir>' with the actual path obtained from 'conda info --envs'.

```shell
conda activate <path_to_envir>
```

--------------------------------

### Update POP-TOOLS with Git

Source: https://github.com/qlu-lab/pop-tools/wiki/0.-Preparation-for-POP‐TOOLS

Updates the local POP-TOOLS repository to the latest version from the remote repository using git pull.

```shell
git pull
```

--------------------------------

### Run POP-GWAS with ML-assisted GWAS

Source: https://github.com/qlu-lab/pop-tools/wiki/1.-POP‐GWAS

This snippet shows how to execute the POP-GWAS script with essential and optional flags for conducting ML-assisted GWAS. It requires paths to GWAS summary statistics for imputed and observed phenotypes in both labeled and unlabeled datasets, along with an output prefix. An optional flag indicates if the phenotype is binary.

```python
python3 ./POP-GWAS.py \
--gwas-yhat-unlab <Path to GWAS summary statistics file of imputed phenotype in unlabeled data> \
--gwas-y-lab <Path to GWAS summary statistics file of observed phenotype in labeled data> \
--gwas-yhat-lab <Path to GWAS summary statistics file of imputed phenotype in labeled data> \
--out <The prefix of path to output summary statistics> \
--bt <Whether the phenotype is binary or not>
```

--------------------------------

### View POP-GWAS Output for Quantitative Phenotype

Source: https://github.com/qlu-lab/pop-tools/wiki/1.-POP‐GWAS

This command shows how to view the initial lines of the output file generated by POP-GWAS for a quantitative phenotype analysis. The output includes standard GWAS columns like CHR, BP, SNP, BETA, SE, P, and N_eff.

```Shell
head ./test/result/Head_BMD_POP-GWAS.txt
```

--------------------------------

### View POP-GWAS Output for Binary Phenotype

Source: https://github.com/qlu-lab/pop-tools/wiki/1.-POP‐GWAS

This command displays the beginning of the output file from a POP-GWAS analysis on a binary phenotype. The output columns are similar to the quantitative case but include additional metrics relevant to binary traits, such as N_eff_case and N_eff_control.

```Shell
head ./test/result/T2D_POP-GWAS.txt
```

--------------------------------

### Update POP-TOOLS Conda Environment

Source: https://github.com/qlu-lab/pop-tools/wiki/0.-Preparation-for-POP‐TOOLS

Updates the POP-TOOLS Conda environment using the environment.yml file, applying any changes to dependencies.

```shell
mamba env update --file environment.yml
```

--------------------------------

### GWAS Binary Phenotype Summary Statistics (with BETA and SE)

Source: https://github.com/qlu-lab/pop-tools/wiki/0.-Preparation-for-POP‐TOOLS

This snippet illustrates GWAS summary statistics for a binary phenotype, including BETA (effect size) and SE (standard error) in addition to the basic required columns. This format is useful for more detailed downstream analyses.

```text
CHR	BP	SNP	A1	A2	EAF	BETA	SE	Z	P	N_case	N_control
22	16051249	rs62224609	C	T	0.0985074	0.0813246	0.0366686	2.21782669641055	0.0279080826194481	4482	96886
22	16057417	rs62224618	T	C	0.100879	0.0739664	0.0362431	2.04084087729802	0.0428913780659107	4513	97555
22	16495833	rs79847867	A	C	0.0769438	0.0335083	0.0417085	0.803392593835789	0.421746998039787	4513	97555
...
```

--------------------------------

### GWAS Binary Phenotype Summary Statistics (Basic)

Source: https://github.com/qlu-lab/pop-tools/wiki/0.-Preparation-for-POP‐TOOLS

This snippet shows the basic required columns for GWAS summary statistics when analyzing a binary phenotype. It includes chromosome, base pair position, SNP identifier, alleles, effect allele frequency, z-score, p-value, and sample sizes for cases and controls.

```text
CHR	BP	SNP	A1	A2	EAF	Z	P	N_case	N_control
22	16051249	rs62224609	C	T	0.0985074	2.21782669641055	0.0279080826194481	4482	96886
22	16057417	rs62224618	T	C	0.100879	2.04084087729802	0.0428913780659107	4513	97555
22	16495833	rs79847867	A	C	0.0769438	0.803392593835789	0.421746998039787	4513	97555
...
```

--------------------------------

### Gene-Level Burden Test RVAS with POP-RARE

Source: https://github.com/qlu-lab/pop-tools/wiki/2.-POP‐GWAS-for-Rare‐Variant-Association-Studies

Conducts ML-assisted gene-level burden test RVAS for quantitative or binary phenotypes. It takes three burden test summary statistics files as input and requires the --burden flag. Results are saved to a specified output file.

```bash
cd POP-TOOLS

trait=Head_BMD

python3 ./POP-RARE.py \
--gwas-yhat-unlab ./test/data/${trait}_yhat_unlab.Burden.txt.gz \
--gwas-y-lab ./test/data/${trait}_y_lab.Burden.txt.gz \
--gwas-yhat-lab ./test/data/${trait}_yhat_lab.Burden.txt.gz \
--burden \
--out ./test/result/${trait}
```

--------------------------------

### Single-Variant RVAS with POP-RARE

Source: https://github.com/qlu-lab/pop-tools/wiki/2.-POP‐GWAS-for-Rare‐Variant-Association-Studies

Performs ML-assisted RVAS on a quantitative or binary phenotype using single-variant summary statistics. Requires three RVAS summary statistics files (unlabeled imputed, labeled observed, labeled imputed) and outputs results to a specified file.

```bash
cd POP-TOOLS

trait=Head_BMD

python3 ./POP-RARE.py \
--gwas-yhat-unlab ./test/data/${trait}_yhat_unlab.RVAS.txt.gz \
--gwas-y-lab ./test/data/${trait}_y_lab.RVAS.txt.gz \
--gwas-yhat-lab ./test/data/${trait}_yhat_lab.RVAS.txt.gz \
--out ./test/result/${trait}
```

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