Daniel Howrigan, Data group leader at Neale Lab (MGH, Broad Institute), for a workshop on data QC and cleaning in GWAS. Learn about goals of GWAS, genetic data visualization, QC methods, and more.

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Explore the essentials of Genome-Wide Association Studies (GWAS) and genetic data quality control as presented by Daniel Howrigan in the 2023 workshop. Delve into the goals of GWAS, genetic data characteristics, SNP variations, and genotyping techniques. Gain insights into moving from trait heritabi

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Delve into the Genetics and Genome Evolution (GGE) Bioinformatics for Genomics Lecture Series 2022 presented by Sven Bergmann. Explore topics like RNA-seq, differential expression analysis, clustering, gene expression data analysis, epigenetic data analysis, integrative analysis, CHIP-seq, HiC data,

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Innovative research on developing high stearic acid soybean germplasm is being conducted at the University of Missouri Delta Center. Challenges in breeding high stearic soybeans, such as sodium azide-induced deletions and mutant alleles in SACPDs, are being addressed. Promising lines identified from

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Meta-analysis in GWAS involves combining data across studies to estimate overall effects, explore cohort differences, improve power, and replicate findings. It includes joint vs. meta-analysis, methods, and types such as fixed effect and random effect meta-analyses.

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This document outlines a lab exercise on polymorphism and variant analysis, covering tasks such as running Quality Control analysis, Genome Wide Association Test (GWAS), and variant calling. Participants will gain familiarity with PLINK toolkit and explore genotype data of two ethnic groups. Instruc

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This lecture explores the use of summary statistics to assess population stratification and the impact of LD on association studies in statistical and quantitative genetics. It delves into LD scores, genomic inflation, polygenic inheritance, and separating SNP heritability from population stratifica

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Knowledge-based approaches are crucial in genetic data analysis to enhance the accuracy and scope of genome-wide association studies (GWAS). Stochastic Relational AI (Star-AI) offers a solution by leveraging probability theory and First Order Logic to capture complex genetic interactions. By integra

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This document outlines a genomic imputation pipeline for multiple GWAS studies using reference panels such as 1000 Genomes Phase I data. It covers steps like data matching, phasing, and imputation using tools like Beagle and Minimac. The expected output includes imputed dosages and quality measures.

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Exploring the utility of non-recombining paternal ancestry information in Genome-Wide Association Studies (GWAS) through the analysis of Y chromosomal haplogroups. This review delves into the implications of using Y chromosome and mitochondrial DNA data in tracing human migrations, ancestry, bottlen

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This guide provides step-by-step instructions on how to effectively use the BLINK C version for genetic analysis. It covers tasks such as preparing input files, handling phenotype and covariates data, implementing GWAS using BLINK, transforming genotype data, compressing to BLINK binary format, conv

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Exploring the evolution of statistical genomics techniques, this lecture delves into the history of FarmCPU and BLINK, addressing challenges in GWAS and the development of models like PC+SNP+e and PC+Kinship+e. It also covers popular software packages in the field and the importance of moving beyond

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Delve into the world of genomics and bioinformatics through the Genetics and Genome Evolution (GGE) lecture series by Sven Bergmann. Explore topics such as RNA-seq analysis, differential expression, gene expression measurement techniques, and integrative analysis with epigenetic data. Gain insights

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Explore concepts of statistical genomics in Lecture 11 focusing on power, type I error, and false discovery rate (FDR) in GWAS analysis. The content covers simulations of phenotype from genotype data, GWAS by correlation, resolution and bin approach, and implications on QTN bins for power and error

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GWAS, or Genome-Wide Association Studies, are a method used to map genes associated with traits or diseases by analyzing genetic markers throughout the genome. This process involves statistically testing the association between SNPs and traits using regression or chi-squared tests in a hypothesis-fr

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