NodeGWAS

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# NodeGWAS

Leveraging Graph Pangenomes for Sensitive and Accurate Association Analysis in Diverse Diploid and Polyploid Species

## Overview
![Workflow Diagram](https://raw.githubusercontent.com/zhangyixing3/NodeGWAS/main/workflow.png)

## <span id="installation">Installation</span>

```bash
git clone git@github.com:zhangyixing3/NodeGWAS.git
cd NodeGWAS && cargo build --release
```
The binary file `nodegwas` will be located in the `NodeGWAS/target/release` directory.

## Running NodeGWAS

### 1. Prepare Graph Pangenome
Use [minigraph-cactus](https://github.com/ComparativeGenomicsToolkit/cactus/blob/master/doc/pangenome.md) or [pggb](https://github.com/pangenome/pggb) pipeline to construct the graph pangenome

Chop the graph to reduce node length:
```bash
vg mod -X 32 97samples.full.vg > 97samples.full.chop.32.vg
vg convert -f 97samples.full.chop.32.vg > 97samples.full.chop.32.gfa
```
Create d10 graph and build giraffe index:
```bash
vg clip 97samples.full.chop.32.vg -d 10 -P 035_Col_CEN_v12 -m 1000 | \
    vg clip -d 1 - -P 035_Col_CEN_v12 | \
    vg clip -sS - -P 035_Col_CEN_v12 > 97samples.full.chop.32.d10.vg
vg convert -f 97samples.full.chop.32.d10.vg > 97samples.full.chop.32.d10.gfa
vg autoindex -p mapping.d5 -g 97samples.full.chop.32.d10.gfa -w giraffe -t 40 -M 600G
```
Mapping resequencing data using `vg giraffe`:
```bash
vg giraffe -t 10 -p -Z 97samples.full.chop.32.d10.giraffe.gbz -m 97samples.full.chop.32.d10.min -d 97samples.full.chop.32.d10.dist \
    -f 1803_1.fq.gz -f 1803_2.fq.gz \
    1>1803.gam 2>1803.gam.log
```
- **1803.gam**: Graph pangenome alignment format.

### 2. Convert GAM to node counts

```bash
(vg filter 1803.gam -r 0.90 -fu -m 1 -q 15 -D 999 -t 2 -x \
    97samples.full.chop.32.d10.xg | \
    vg view -aM - | nodegwas count -n 1803.filter.node) 1>AH1803.filter.node.log 2>&1
```
### 3. Create Nodetable

Combine node counts from each sample:
```bash
nodegwas rmerge -i sample.list -o node_table -n 2 -t
```

sample.list format:
```
../nodes/10020.gam.filter.node.gz	10020
../nodes/10022.gam.filter.node.gz	10022
../nodes/10023.gam.filter.node.gz	10023
../nodes/1002.gam.filter.node.gz	1002
../nodes/1061.gam.filter.node.gz	1061
../nodes/1062.gam.filter.node.gz	1062
```
 - **Column 1**: File path to node counts   
 - **Column 2**: Sample ID

### 4. Perform GWAS
```bash
#  Convert Nodetable to VCF
nodegwas tovcf -k nodegwas2.gz
# vcf to tped
plink --vcf nodegwas2.vcf.gz --recode 12 transpose --out emmax_in --maf 0.05 --geno 0.1 --allow-extra-chr --threads 10
# tped to bed
plink --tfile emmax_in --make-bed --out emmax_in --allow-extra-chr --threads 10
# kinship matrix
gemma -bfile emmax_in -gk 2 -p FT10.order.1
# GWAS analysis using gemma
gemma -bfile emmax_in -k output/result.sXX.txt -lmm 2 -p FT10.order.1
```

### 5. Assign back to Linear Genome Coordinates
```bash
nodegwas rliftover -g 97samples.full.chop.gfa -f 035 -o 97samples.full.chop
```
 - **97samples.full.chop.bubble.positions**: Bubble structures in linear coordinates  
 - **97samples.full.chop.non_ref.node.positions**: Non-reference node positions   
 - **035.node.positions**: Cumulative offsets of nodes in the reference

```bash
awk '$10<0.01{print $2,$1,$3,$10}' result.assoc.txt > result.assoc.txt.finally.filter
python ./node_position.py result.assoc.txt.finally.filter full_97.non_ref.node.positions 035.node.positions > result.assoc.txt.finally.filter.position
```

### Example Output
```bash
$ head result.assoc.txt.finally.filter.position
chrsome	start	end	node_id	pvalue
1	3956	4460	5126	4.131444e-03
1	4465	4466	5410	7.229633e-03
1	4465	4466	5411	4.894490e-03
1	4465	4466	5415	3.666478e-03
1	4465	4466	5416	3.060831e-03
```

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