Update README.md

README.md

@@ -28,7 +28,6 @@ The following steps are for the widely used operating system (Ubuntu) on a virtu
 ##### Script: <a href="FAVORannotator_csv/Varinfo_gds.R">**Varinfo_gds.R**</a>
 ##### Input: GDS files of each chromosome and the FAVOR database information <a href="FAVORannotator_csv/FAVORdatabase_chrsplit.csv">**FAVORdatabase_chrsplit.csv**</a>. For more details, please see the R script.
 ##### Output: CSV files of the variants list. For each chromosome, the number of CSV files is listed in <a href="FAVORannotator_csv/FAVORdatabase_chrsplit.csv">**FAVORdatabase_chrsplit.csv**</a>.
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 Note: The physical positions of variants in the GDS file (of each chromosome) should be sorted in ascending order.
 
 #### Step 2: Annotate the variants using the FAVOR database through xsv software
@@ -43,8 +42,7 @@ The annotations in this file is a subset of `Anno_chrXX.csv`. <br>
 #### Step 3: Generate the annotated GDS (aGDS) file
 ##### Script: <a href="FAVORannotator_csv/gds2agds.R">**gds2agds.R**</a>
 ##### Input: GDS files and the CSV files of annotated variants list (`Anno_chrXX.csv` or `Anno_chrXX_STAARpipeline.csv`). For more details, please see the R script.
-##### Output: aGDS files including both the genotype and annotation information. 
-
+##### Output: aGDS files including both the genotype and annotation information.
 Note: FAVORannotator also supports the database in SQL format. Please see the <a href="https://github.com/zhouhufeng/FAVORannotator">**FAVORannotator** tutorial</a> for detailed usage of **FAVORannotator** (SQL version).
 
 ### Generate sparse Genetic Relatedness Matrix (GRM)
@@ -125,7 +123,6 @@ Perform LD pruning (stepwise selection) to select the subset of independent vari
 Summarize single-variant analysis results and perform conditional analysis of unconditionally significant variants by adjusting a list of known variants.
 #### Input: aGDS files, individual analysis results generated by STAARpipeline, STAAR null model and a list of known variants. For more details, please see the R script.
 #### Output: The summary includes the Manhattan plot, Q-Q plot, and conditional p-values of unconditionally significant variants.
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 Note: <a href="STAARpipelineSummary_Known_Loci_Individual_Analysis_Pruning.r">**STAARpipelineSummary_Known_Loci_Individual_Analysis_Pruning.r**</a> and <a href="STAARpipelineSummary_Known_Loci_Individual_Analysis_Pruning_Combination.r">**STAARpipelineSummary_Known_Loci_Individual_Analysis_Pruning_Combination.r**</a> show an example to select independent variants from both the known variants in literature and significant single variants detected in individual analysis, which can be used for variant-set conditional analysis.
 
 ### Step 2.1: Summarize gene-centric coding analysis results