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LocNet: an intermediate step to prioritize genetic variants between GWAS and fine-mapping

This method incorporates dense linkage disequilibrium block structure of SNPs for prioritizing a set of genetic variants using GWAS summary statisticis before performing fine-mapping.

Prerequisites

MATLAB is required for the analysis. "denseLD.m" is a MATLAB function for dense block detection that originally from the "NICE.m" in a previous package https://github.com/shuochenstats/Network_program/tree/master/NICE_folder/NICE_detection. "denseLD.m" is a faster function than "NICE.m".

"denseLD.m" can be downloaded at https://github.com/cmomo/DenseLD_Data/blob/main.

Dense LD block detection can be performed either in MATLAB or called by R using function "dense_block" in the R package.

DenseLD also requires R (>= 3.5.0) and R packages genlasso, graphics, R.matlab and matlabr for the analysis. Install these packages before the analysis.

"denseLD_block.R" and "denseLD_order.R" are R functions can be used for DenseLD.

Implementation

Import functions in R:

source("denseLD_block.R")

source("denseLD_order.R")

Steps

The analysis consists of three may steps.

1. Detection of dense LD blocks

Note: an example conducted in MATLAB using function "denseLD.m" can be found in "SampleAnalysis/demo_mat.html".

2. Convert dense LD block structure to graph object

3. Prioritize causal variants by incorporting the graph object of LD matrix into fused-LASSO

Data

Raw genetic and phenotypic data are available in the UK Biobank (https://www.ukbiobank.ac.uk/). Qualified reseachers can access the data through UKB online database upon permission approval from the UKB.

"GWAS summary statistics_CPD.csv" is the GWAS summary statistics generated from raw data in this study.

"LDmatrix_chr15_78700Kto79230K_r2.zip" is the matrix of linkage disequilibrium scores estimated for the SNPs used in this study.

"simu_geno.csv" is the simulated genotypes for the simulation studies. "simu_ldmx.csv" is the LD matrix for simulated genotypes.

Datasets for analysis demonstration using a small sample data can be downloaded together from "data/data.zip".

Sample analysis display

"SampleAnalysis/DenseLD_demo.html" shows an example of applying LocNet method to prioritize a set of genetic variants with dense LD structure based on a simulated sample data.

The sample analysis using LocNet can be previewed with link: http://htmlpreview.github.io/?https://github.com/cmomo/DenseLD/blob/main/SampleAnalysis/DenseLD_demo.html

The sample analysis for dense LD block structure detection using MATLAB can be previewed with link: http://htmlpreview.github.io/?https://github.com/cmomo/DenseLD/blob/main/SampleAnalysis/demo_mat.html

Plots generated during analysis are uploaded in folder "png".

"heatmap_BPorder" and "heatmap_DENSEorder" are two heatmaps generated by MATLAB. These plots show the same LD matrix of the sample data in different orders (i.e., the natural physical positions and the order rearranged by DenseLD).

"Manhattan plot by BPorder-1" and "Manhattan plot by DENSEorder-1" are the two Manhattan plots generated by R. These plots show the p-values of the same sample data in different orders (i.e., the natural physical positions and the order rearranged by DenseLD).

"Trend plot of beta by lambda-1" shows the trend of beta shrinkage using DenseLD fine-mapping approach (i.e., fused-LASSO incorporating dense LD block structure).

Since one of the SNPs produced extreme values when fitting by the regression model, we generated a plot, "Trend plot of beta by lambda (remove extreme beta)-1", to remove the extreme SNPs in order to better display the trend of the other SNPs.

About

This method incorporates dense linkage disequilibrium block structure of SNPs for prioritizing a set of genetic variants using GWAS summary statisticis before performing fine-mapping.

Topics

causal-variantsdense-graphfine-mappinggwashaplotype-blockslinkage-disequilibrium

Resources

Readme
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LocNet: an intermediate step to prioritize genetic variants between GWAS and fine-mapping
This method incorporates dense linkage disequilibrium block structure of SNPs for prioritizing a set of genetic variants using GWAS summary statisticis before performing fine-mapping.
Prerequisites
MATLAB is required for the analysis.
"denseLD.m" is a MATLAB function for dense block detection that originally from the "NICE.m" in a previous package https://github.com/shuochenstats/Network_program/tree/master/NICE_folder/NICE_detection. "denseLD.m" is a faster function than "NICE.m".
"denseLD.m" can be downloaded at https://github.com/cmomo/DenseLD_Data/blob/main.
Dense LD block detection can be performed either in MATLAB or called by R using function "dense_block" in the R package.
DenseLD also requires R (>= 3.5.0) and R packages genlasso, graphics, R.matlab and matlabr for the analysis. Install these packages before the analysis.
"denseLD_block.R" and "denseLD_order.R" are R functions can be used for DenseLD.
Implementation
Import functions in R:
source("denseLD_block.R")
source("denseLD_order.R")
Steps
The analysis consists of three may steps.
1. Detection of dense LD blocks
Note: an example conducted in MATLAB using function "denseLD.m" can be found in "SampleAnalysis/demo_mat.html".
2. Convert dense LD block structure to graph object
3. Prioritize causal variants by incorporting the graph object of LD matrix into fused-LASSO
Data
Raw genetic and phenotypic data are available in the UK Biobank (https://www.ukbiobank.ac.uk/). Qualified reseachers can access the data through UKB online database upon permission approval from the UKB.
"GWAS summary statistics_CPD.csv" is the GWAS summary statistics generated from raw data in this study.
"LDmatrix_chr15_78700Kto79230K_r2.zip" is the matrix of linkage disequilibrium scores estimated for the SNPs used in this study.
"simu_geno.csv" is the simulated genotypes for the simulation studies. "simu_ldmx.csv" is the LD matrix for simulated genotypes.
Datasets for analysis demonstration using a small sample data can be downloaded together from "data/data.zip".
Sample analysis display
"SampleAnalysis/DenseLD_demo.html" shows an example of applying LocNet method to prioritize a set of genetic variants with dense LD structure based on a simulated sample data.
The sample analysis using LocNet can be previewed with link: http://htmlpreview.github.io/?https://github.com/cmomo/DenseLD/blob/main/SampleAnalysis/DenseLD_demo.html
The sample analysis for dense LD block structure detection using MATLAB can be previewed with link: http://htmlpreview.github.io/?https://github.com/cmomo/DenseLD/blob/main/SampleAnalysis/demo_mat.html
Plots generated during analysis are uploaded in folder "png".
"heatmap_BPorder" and "heatmap_DENSEorder" are two heatmaps generated by MATLAB. These plots show the same LD matrix of the sample data in different orders (i.e., the natural physical positions and the order rearranged by DenseLD).
"Manhattan plot by BPorder-1" and "Manhattan plot by DENSEorder-1" are the two Manhattan plots generated by R. These plots show the p-values of the same sample data in different orders (i.e., the natural physical positions and the order rearranged by DenseLD).
"Trend plot of beta by lambda-1" shows the trend of beta shrinkage using DenseLD fine-mapping approach (i.e., fused-LASSO incorporating dense LD block structure).
Since one of the SNPs produced extreme values when fitting by the regression model, we generated a plot, "Trend plot of beta by lambda (remove extreme beta)-1", to remove the extreme SNPs in order to better display the trend of the other SNPs.
About
This method incorporates dense linkage disequilibrium block structure of SNPs for prioritizing a set of genetic variants using GWAS summary statisticis before performing fine-mapping. 
Topics
causal-variantsdense-graphfine-mappinggwashaplotype-blockslinkage-disequilibrium
Resources
Readme
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LocNet: an intermediate step to prioritize genetic variants between GWAS and fine-mapping
This method incorporates dense linkage disequilibrium block structure of SNPs for prioritizing a set of genetic variants using GWAS summary statisticis before performing fine-mapping.
Prerequisites
MATLAB is required for the analysis.
"denseLD.m" is a MATLAB function for dense block detection that originally from the "NICE.m" in a previous package https://github.com/shuochenstats/Network_program/tree/master/NICE_folder/NICE_detection. "denseLD.m" is a faster function than "NICE.m".
"denseLD.m" can be downloaded at https://github.com/cmomo/DenseLD_Data/blob/main.
Dense LD block detection can be performed either in MATLAB or called by R using function "dense_block" in the R package.
DenseLD also requires R (>= 3.5.0) and R packages genlasso, graphics, R.matlab and matlabr for the analysis. Install these packages before the analysis.
"denseLD_block.R" and "denseLD_order.R" are R functions can be used for DenseLD.
Implementation
Import functions in R:
source("denseLD_block.R")
source("denseLD_order.R")
Steps
The analysis consists of three may steps.
1. Detection of dense LD blocks
Note: an example conducted in MATLAB using function "denseLD.m" can be found in "SampleAnalysis/demo_mat.html".
2. Convert dense LD block structure to graph object
3. Prioritize causal variants by incorporting the graph object of LD matrix into fused-LASSO
Data
Raw genetic and phenotypic data are available in the UK Biobank (https://www.ukbiobank.ac.uk/). Qualified reseachers can access the data through UKB online database upon permission approval from the UKB.
"GWAS summary statistics_CPD.csv" is the GWAS summary statistics generated from raw data in this study.
"LDmatrix_chr15_78700Kto79230K_r2.zip" is the matrix of linkage disequilibrium scores estimated for the SNPs used in this study.
"simu_geno.csv" is the simulated genotypes for the simulation studies. "simu_ldmx.csv" is the LD matrix for simulated genotypes.
Datasets for analysis demonstration using a small sample data can be downloaded together from "data/data.zip".
Sample analysis display
"SampleAnalysis/DenseLD_demo.html" shows an example of applying LocNet method to prioritize a set of genetic variants with dense LD structure based on a simulated sample data.
The sample analysis using LocNet can be previewed with link: http://htmlpreview.github.io/?https://github.com/cmomo/DenseLD/blob/main/SampleAnalysis/DenseLD_demo.html
The sample analysis for dense LD block structure detection using MATLAB can be previewed with link: http://htmlpreview.github.io/?https://github.com/cmomo/DenseLD/blob/main/SampleAnalysis/demo_mat.html
Plots generated during analysis are uploaded in folder "png".
"heatmap_BPorder" and "heatmap_DENSEorder" are two heatmaps generated by MATLAB. These plots show the same LD matrix of the sample data in different orders (i.e., the natural physical positions and the order rearranged by DenseLD).
"Manhattan plot by BPorder-1" and "Manhattan plot by DENSEorder-1" are the two Manhattan plots generated by R. These plots show the p-values of the same sample data in different orders (i.e., the natural physical positions and the order rearranged by DenseLD).
"Trend plot of beta by lambda-1" shows the trend of beta shrinkage using DenseLD fine-mapping approach (i.e., fused-LASSO incorporating dense LD block structure).
Since one of the SNPs produced extreme values when fitting by the regression model, we generated a plot, "Trend plot of beta by lambda (remove extreme beta)-1", to remove the extreme SNPs in order to better display the trend of the other SNPs.
About
This method incorporates dense linkage disequilibrium block structure of SNPs for prioritizing a set of genetic variants using GWAS summary statisticis before performing fine-mapping. 
Topics
causal-variantsdense-graphfine-mappinggwashaplotype-blockslinkage-disequilibrium
Resources
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LocNet: an intermediate step to prioritize genetic variants between GWAS and fine-mapping
This method incorporates dense linkage disequilibrium block structure of SNPs for prioritizing a set of genetic variants using GWAS summary statisticis before performing fine-mapping.
Prerequisites
MATLAB is required for the analysis.
"denseLD.m" is a MATLAB function for dense block detection that originally from the "NICE.m" in a previous package https://github.com/shuochenstats/Network_program/tree/master/NICE_folder/NICE_detection. "denseLD.m" is a faster function than "NICE.m".
"denseLD.m" can be downloaded at https://github.com/cmomo/DenseLD_Data/blob/main.
Dense LD block detection can be performed either in MATLAB or called by R using function "dense_block" in the R package.
DenseLD also requires R (>= 3.5.0) and R packages genlasso, graphics, R.matlab and matlabr for the analysis. Install these packages before the analysis.
"denseLD_block.R" and "denseLD_order.R" are R functions can be used for DenseLD.
Implementation
Import functions in R:
source("denseLD_block.R")
source("denseLD_order.R")
Steps
The analysis consists of three may steps.
1. Detection of dense LD blocks
Note: an example conducted in MATLAB using function "denseLD.m" can be found in "SampleAnalysis/demo_mat.html".
2. Convert dense LD block structure to graph object
3. Prioritize causal variants by incorporting the graph object of LD matrix into fused-LASSO
Data
Raw genetic and phenotypic data are available in the UK Biobank (https://www.ukbiobank.ac.uk/). Qualified reseachers can access the data through UKB online database upon permission approval from the UKB.
"GWAS summary statistics_CPD.csv" is the GWAS summary statistics generated from raw data in this study.
"LDmatrix_chr15_78700Kto79230K_r2.zip" is the matrix of linkage disequilibrium scores estimated for the SNPs used in this study.
"simu_geno.csv" is the simulated genotypes for the simulation studies. "simu_ldmx.csv" is the LD matrix for simulated genotypes.
Datasets for analysis demonstration using a small sample data can be downloaded together from "data/data.zip".
Sample analysis display
"SampleAnalysis/DenseLD_demo.html" shows an example of applying LocNet method to prioritize a set of genetic variants with dense LD structure based on a simulated sample data.
The sample analysis using LocNet can be previewed with link: http://htmlpreview.github.io/?https://github.com/cmomo/DenseLD/blob/main/SampleAnalysis/DenseLD_demo.html
The sample analysis for dense LD block structure detection using MATLAB can be previewed with link: http://htmlpreview.github.io/?https://github.com/cmomo/DenseLD/blob/main/SampleAnalysis/demo_mat.html
Plots generated during analysis are uploaded in folder "png".
"heatmap_BPorder" and "heatmap_DENSEorder" are two heatmaps generated by MATLAB. These plots show the same LD matrix of the sample data in different orders (i.e., the natural physical positions and the order rearranged by DenseLD).
"Manhattan plot by BPorder-1" and "Manhattan plot by DENSEorder-1" are the two Manhattan plots generated by R. These plots show the p-values of the same sample data in different orders (i.e., the natural physical positions and the order rearranged by DenseLD).
"Trend plot of beta by lambda-1" shows the trend of beta shrinkage using DenseLD fine-mapping approach (i.e., fused-LASSO incorporating dense LD block structure).
Since one of the SNPs produced extreme values when fitting by the regression model, we generated a plot, "Trend plot of beta by lambda (remove extreme beta)-1", to remove the extreme SNPs in order to better display the trend of the other SNPs.
About
This method incorporates dense linkage disequilibrium block structure of SNPs for prioritizing a set of genetic variants using GWAS summary statisticis before performing fine-mapping. 
Topics
causal-variantsdense-graphfine-mappinggwashaplotype-blockslinkage-disequilibrium
Resources
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LocNet: an intermediate step to prioritize genetic variants between GWAS and fine-mapping
This method incorporates dense linkage disequilibrium block structure of SNPs for prioritizing a set of genetic variants using GWAS summary statisticis before performing fine-mapping.
Prerequisites
MATLAB is required for the analysis.
"denseLD.m" is a MATLAB function for dense block detection that originally from the "NICE.m" in a previous package https://github.com/shuochenstats/Network_program/tree/master/NICE_folder/NICE_detection. "denseLD.m" is a faster function than "NICE.m".
"denseLD.m" can be downloaded at https://github.com/cmomo/DenseLD_Data/blob/main.
Dense LD block detection can be performed either in MATLAB or called by R using function "dense_block" in the R package.
DenseLD also requires R (>= 3.5.0) and R packages genlasso, graphics, R.matlab and matlabr for the analysis. Install these packages before the analysis.
"denseLD_block.R" and "denseLD_order.R" are R functions can be used for DenseLD.
Implementation
Import functions in R:
source("denseLD_block.R")
source("denseLD_order.R")
Steps
The analysis consists of three may steps.
1. Detection of dense LD blocks
Note: an example conducted in MATLAB using function "denseLD.m" can be found in "SampleAnalysis/demo_mat.html".
2. Convert dense LD block structure to graph object
3. Prioritize causal variants by incorporting the graph object of LD matrix into fused-LASSO
Data
Raw genetic and phenotypic data are available in the UK Biobank (https://www.ukbiobank.ac.uk/). Qualified reseachers can access the data through UKB online database upon permission approval from the UKB.
"GWAS summary statistics_CPD.csv" is the GWAS summary statistics generated from raw data in this study.
"LDmatrix_chr15_78700Kto79230K_r2.zip" is the matrix of linkage disequilibrium scores estimated for the SNPs used in this study.
"simu_geno.csv" is the simulated genotypes for the simulation studies. "simu_ldmx.csv" is the LD matrix for simulated genotypes.
Datasets for analysis demonstration using a small sample data can be downloaded together from "data/data.zip".
Sample analysis display
"SampleAnalysis/DenseLD_demo.html" shows an example of applying LocNet method to prioritize a set of genetic variants with dense LD structure based on a simulated sample data.
The sample analysis using LocNet can be previewed with link: http://htmlpreview.github.io/?https://github.com/cmomo/DenseLD/blob/main/SampleAnalysis/DenseLD_demo.html
The sample analysis for dense LD block structure detection using MATLAB can be previewed with link: http://htmlpreview.github.io/?https://github.com/cmomo/DenseLD/blob/main/SampleAnalysis/demo_mat.html
Plots generated during analysis are uploaded in folder "png".
"heatmap_BPorder" and "heatmap_DENSEorder" are two heatmaps generated by MATLAB. These plots show the same LD matrix of the sample data in different orders (i.e., the natural physical positions and the order rearranged by DenseLD).
"Manhattan plot by BPorder-1" and "Manhattan plot by DENSEorder-1" are the two Manhattan plots generated by R. These plots show the p-values of the same sample data in different orders (i.e., the natural physical positions and the order rearranged by DenseLD).
"Trend plot of beta by lambda-1" shows the trend of beta shrinkage using DenseLD fine-mapping approach (i.e., fused-LASSO incorporating dense LD block structure).
Since one of the SNPs produced extreme values when fitting by the regression model, we generated a plot, "Trend plot of beta by lambda (remove extreme beta)-1", to remove the extreme SNPs in order to better display the trend of the other SNPs.
About
This method incorporates dense linkage disequilibrium block structure of SNPs for prioritizing a set of genetic variants using GWAS summary statisticis before performing fine-mapping. 
Topics
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GitHub - cmomo/LocNet: This method incorporates dense linkage disequilibrium block structure of SNPs for prioritizing a set of genetic variants using GWAS summary statisticis before performing fine-mapping. · GitHub
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LocNet: an intermediate step to prioritize genetic variants between GWAS and fine-mapping
This method incorporates dense linkage disequilibrium block structure of SNPs for prioritizing a set of genetic variants using GWAS summary statisticis before performing fine-mapping.
Prerequisites
MATLAB is required for the analysis.
"denseLD.m" is a MATLAB function for dense block detection that originally from the "NICE.m" in a previous package https://github.com/shuochenstats/Network_program/tree/master/NICE_folder/NICE_detection. "denseLD.m" is a faster function than "NICE.m".
"denseLD.m" can be downloaded at https://github.com/cmomo/DenseLD_Data/blob/main.
Dense LD block detection can be performed either in MATLAB or called by R using function "dense_block" in the R package.
DenseLD also requires R (>= 3.5.0) and R packages genlasso, graphics, R.matlab and matlabr for the analysis. Install these packages before the analysis.
"denseLD_block.R" and "denseLD_order.R" are R functions can be used for DenseLD.
Implementation
Import functions in R:
source("denseLD_block.R")
source("denseLD_order.R")
Steps
The analysis consists of three may steps.
1. Detection of dense LD blocks
Note: an example conducted in MATLAB using function "denseLD.m" can be found in "SampleAnalysis/demo_mat.html".
2. Convert dense LD block structure to graph object
3. Prioritize causal variants by incorporting the graph object of LD matrix into fused-LASSO
Data
Raw genetic and phenotypic data are available in the UK Biobank (https://www.ukbiobank.ac.uk/). Qualified reseachers can access the data through UKB online database upon permission approval from the UKB.
"GWAS summary statistics_CPD.csv" is the GWAS summary statistics generated from raw data in this study.
"LDmatrix_chr15_78700Kto79230K_r2.zip" is the matrix of linkage disequilibrium scores estimated for the SNPs used in this study.
"simu_geno.csv" is the simulated genotypes for the simulation studies. "simu_ldmx.csv" is the LD matrix for simulated genotypes.
Datasets for analysis demonstration using a small sample data can be downloaded together from "data/data.zip".
Sample analysis display
"SampleAnalysis/DenseLD_demo.html" shows an example of applying LocNet method to prioritize a set of genetic variants with dense LD structure based on a simulated sample data.
The sample analysis using LocNet can be previewed with link: http://htmlpreview.github.io/?https://github.com/cmomo/DenseLD/blob/main/SampleAnalysis/DenseLD_demo.html
The sample analysis for dense LD block structure detection using MATLAB can be previewed with link: http://htmlpreview.github.io/?https://github.com/cmomo/DenseLD/blob/main/SampleAnalysis/demo_mat.html
Plots generated during analysis are uploaded in folder "png".
"heatmap_BPorder" and "heatmap_DENSEorder" are two heatmaps generated by MATLAB. These plots show the same LD matrix of the sample data in different orders (i.e., the natural physical positions and the order rearranged by DenseLD).
"Manhattan plot by BPorder-1" and "Manhattan plot by DENSEorder-1" are the two Manhattan plots generated by R. These plots show the p-values of the same sample data in different orders (i.e., the natural physical positions and the order rearranged by DenseLD).
"Trend plot of beta by lambda-1" shows the trend of beta shrinkage using DenseLD fine-mapping approach (i.e., fused-LASSO incorporating dense LD block structure).
Since one of the SNPs produced extreme values when fitting by the regression model, we generated a plot, "Trend plot of beta by lambda (remove extreme beta)-1", to remove the extreme SNPs in order to better display the trend of the other SNPs.
About
This method incorporates dense linkage disequilibrium block structure of SNPs for prioritizing a set of genetic variants using GWAS summary statisticis before performing fine-mapping. 
Topics
causal-variantsdense-graphfine-mappinggwashaplotype-blockslinkage-disequilibrium
Resources
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GitHub - cmomo/LocNet: This method incorporates dense linkage disequilibrium block structure of SNPs for prioritizing a set of genetic variants using GWAS summary statisticis before performing fine-mapping. · GitHub
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LocNet: an intermediate step to prioritize genetic variants between GWAS and fine-mapping
This method incorporates dense linkage disequilibrium block structure of SNPs for prioritizing a set of genetic variants using GWAS summary statisticis before performing fine-mapping.
Prerequisites
MATLAB is required for the analysis.
"denseLD.m" is a MATLAB function for dense block detection that originally from the "NICE.m" in a previous package https://github.com/shuochenstats/Network_program/tree/master/NICE_folder/NICE_detection. "denseLD.m" is a faster function than "NICE.m".
"denseLD.m" can be downloaded at https://github.com/cmomo/DenseLD_Data/blob/main.
Dense LD block detection can be performed either in MATLAB or called by R using function "dense_block" in the R package.
DenseLD also requires R (>= 3.5.0) and R packages genlasso, graphics, R.matlab and matlabr for the analysis. Install these packages before the analysis.
"denseLD_block.R" and "denseLD_order.R" are R functions can be used for DenseLD.
Implementation
Import functions in R:
source("denseLD_block.R")
source("denseLD_order.R")
Steps
The analysis consists of three may steps.
1. Detection of dense LD blocks
Note: an example conducted in MATLAB using function "denseLD.m" can be found in "SampleAnalysis/demo_mat.html".
2. Convert dense LD block structure to graph object
3. Prioritize causal variants by incorporting the graph object of LD matrix into fused-LASSO
Data
Raw genetic and phenotypic data are available in the UK Biobank (https://www.ukbiobank.ac.uk/). Qualified reseachers can access the data through UKB online database upon permission approval from the UKB.
"GWAS summary statistics_CPD.csv" is the GWAS summary statistics generated from raw data in this study.
"LDmatrix_chr15_78700Kto79230K_r2.zip" is the matrix of linkage disequilibrium scores estimated for the SNPs used in this study.
"simu_geno.csv" is the simulated genotypes for the simulation studies. "simu_ldmx.csv" is the LD matrix for simulated genotypes.
Datasets for analysis demonstration using a small sample data can be downloaded together from "data/data.zip".
Sample analysis display
"SampleAnalysis/DenseLD_demo.html" shows an example of applying LocNet method to prioritize a set of genetic variants with dense LD structure based on a simulated sample data.
The sample analysis using LocNet can be previewed with link: http://htmlpreview.github.io/?https://github.com/cmomo/DenseLD/blob/main/SampleAnalysis/DenseLD_demo.html
The sample analysis for dense LD block structure detection using MATLAB can be previewed with link: http://htmlpreview.github.io/?https://github.com/cmomo/DenseLD/blob/main/SampleAnalysis/demo_mat.html
Plots generated during analysis are uploaded in folder "png".
"heatmap_BPorder" and "heatmap_DENSEorder" are two heatmaps generated by MATLAB. These plots show the same LD matrix of the sample data in different orders (i.e., the natural physical positions and the order rearranged by DenseLD).
"Manhattan plot by BPorder-1" and "Manhattan plot by DENSEorder-1" are the two Manhattan plots generated by R. These plots show the p-values of the same sample data in different orders (i.e., the natural physical positions and the order rearranged by DenseLD).
"Trend plot of beta by lambda-1" shows the trend of beta shrinkage using DenseLD fine-mapping approach (i.e., fused-LASSO incorporating dense LD block structure).
Since one of the SNPs produced extreme values when fitting by the regression model, we generated a plot, "Trend plot of beta by lambda (remove extreme beta)-1", to remove the extreme SNPs in order to better display the trend of the other SNPs.
About
This method incorporates dense linkage disequilibrium block structure of SNPs for prioritizing a set of genetic variants using GWAS summary statisticis before performing fine-mapping. 
Topics
causal-variantsdense-graphfine-mappinggwashaplotype-blockslinkage-disequilibrium
Resources
Readme
Activity
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GitHub - cmomo/LocNet: This method incorporates dense linkage disequilibrium block structure of SNPs for prioritizing a set of genetic variants using GWAS summary statisticis before performing fine-mapping. · GitHub
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SampleAnalysis
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README.md
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Repository files navigation
LocNet: an intermediate step to prioritize genetic variants between GWAS and fine-mapping
This method incorporates dense linkage disequilibrium block structure of SNPs for prioritizing a set of genetic variants using GWAS summary statisticis before performing fine-mapping.
Prerequisites
MATLAB is required for the analysis.
"denseLD.m" is a MATLAB function for dense block detection that originally from the "NICE.m" in a previous package https://github.com/shuochenstats/Network_program/tree/master/NICE_folder/NICE_detection. "denseLD.m" is a faster function than "NICE.m".
"denseLD.m" can be downloaded at https://github.com/cmomo/DenseLD_Data/blob/main.
Dense LD block detection can be performed either in MATLAB or called by R using function "dense_block" in the R package.
DenseLD also requires R (>= 3.5.0) and R packages genlasso, graphics, R.matlab and matlabr for the analysis. Install these packages before the analysis.
"denseLD_block.R" and "denseLD_order.R" are R functions can be used for DenseLD.
Implementation
Import functions in R:
source("denseLD_block.R")
source("denseLD_order.R")
Steps
The analysis consists of three may steps.
1. Detection of dense LD blocks
Note: an example conducted in MATLAB using function "denseLD.m" can be found in "SampleAnalysis/demo_mat.html".
2. Convert dense LD block structure to graph object
3. Prioritize causal variants by incorporting the graph object of LD matrix into fused-LASSO
Data
Raw genetic and phenotypic data are available in the UK Biobank (https://www.ukbiobank.ac.uk/). Qualified reseachers can access the data through UKB online database upon permission approval from the UKB.
"GWAS summary statistics_CPD.csv" is the GWAS summary statistics generated from raw data in this study.
"LDmatrix_chr15_78700Kto79230K_r2.zip" is the matrix of linkage disequilibrium scores estimated for the SNPs used in this study.
"simu_geno.csv" is the simulated genotypes for the simulation studies. "simu_ldmx.csv" is the LD matrix for simulated genotypes.
Datasets for analysis demonstration using a small sample data can be downloaded together from "data/data.zip".
Sample analysis display
"SampleAnalysis/DenseLD_demo.html" shows an example of applying LocNet method to prioritize a set of genetic variants with dense LD structure based on a simulated sample data.
The sample analysis using LocNet can be previewed with link: http://htmlpreview.github.io/?https://github.com/cmomo/DenseLD/blob/main/SampleAnalysis/DenseLD_demo.html
The sample analysis for dense LD block structure detection using MATLAB can be previewed with link: http://htmlpreview.github.io/?https://github.com/cmomo/DenseLD/blob/main/SampleAnalysis/demo_mat.html
Plots generated during analysis are uploaded in folder "png".
"heatmap_BPorder" and "heatmap_DENSEorder" are two heatmaps generated by MATLAB. These plots show the same LD matrix of the sample data in different orders (i.e., the natural physical positions and the order rearranged by DenseLD).
"Manhattan plot by BPorder-1" and "Manhattan plot by DENSEorder-1" are the two Manhattan plots generated by R. These plots show the p-values of the same sample data in different orders (i.e., the natural physical positions and the order rearranged by DenseLD).
"Trend plot of beta by lambda-1" shows the trend of beta shrinkage using DenseLD fine-mapping approach (i.e., fused-LASSO incorporating dense LD block structure).
Since one of the SNPs produced extreme values when fitting by the regression model, we generated a plot, "Trend plot of beta by lambda (remove extreme beta)-1", to remove the extreme SNPs in order to better display the trend of the other SNPs.
About
This method incorporates dense linkage disequilibrium block structure of SNPs for prioritizing a set of genetic variants using GWAS summary statisticis before performing fine-mapping. 
Topics
causal-variantsdense-graphfine-mappinggwashaplotype-blockslinkage-disequilibrium
Resources
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