weighted GWAS (heteroskedastic GWAS)
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Updated
Jun 7, 2018 - HTML
weighted GWAS (heteroskedastic GWAS)
R package 📦 with ggplot2 extensions for GWAS summary statistics (still in 🚧)
Reviewing the (preliminary) summary statistics from the COVID19 Host Genetics Initiative meta-analysis of genome-wide association studies (GWAS).
From QC to summary statistics
Comparison between VCF and plain text storage formats for GWAS summary statistics
Code and simulations using biologically annotated neural networks
Colocalization analysis of Meconium Ileus Genome-Wide Association Study (GWAS) (Gong et al., 2019) signal around SLC26A9 with spirometry association analysis in the UK Biobank and Spirometa Consortium (Shrine et., 2019) around the same region to elucidate whether there is a shared genetic contribution to disease
GWAS on rice RNA seq data, features use of statgenGWAS, and experimentation with different methods of pop. structure control
BioMM: Biological-informed Multi-stage Machine learning framework for phenotype prediction using omics data
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.
the hierfstat package
Publicly shared SNP-to-gene mappings of GWAS generated by MAGMA.
CHARGE Consortium: collaboration for mapping targets from 1000G GWAS on coronary artery calcification (CAC) with Maryam Kavousi, Maxime Bos, Clint L. Miller, and Pat Peyser.
Genome-Wide Association Study (GWAS) and Polygenic Risk Score (PRS) Shiny App!
Evaluating the breeding potential of cultivated lentils for protein and amino acid content and quality
Full analysis code and interactive Manhattan plots for "Gene-vegetarianism interactions in calcium, estimated glomerular filtration rate, and testosterone identified in genome-wide analysis across 30 biomarkers"
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