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Titlebook: Genetic Epidemiology; Methods and Protocol Evangelos Evangelou Book 2018 Springer Science+Business Media, LLC, part of Springer Nature 2018

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樓主: cerebral
41#
發(fā)表于 2025-3-28 16:57:51 | 只看該作者
Genome-Wide Association Studies,e, common variant” theory, and will review how we finally afforded to capture the common variance in genome to make GWAS possible. Finally, we will go over technical aspects of GWAS such as genotype imputation, epidemiologic designs, analysis methods, and considerations such as genomic inflation, multiple testing, and replication.
42#
發(fā)表于 2025-3-28 20:45:09 | 只看該作者
Das Methoden-Repertoire von Lehrerne applicable for family-based rare variant analysis. Here, I present three published statistical approaches for family-based rare variant analysis for: 1. continuous traits, 2. binary traits, and 3. multiple correlated traits.
43#
發(fā)表于 2025-3-29 01:47:27 | 只看該作者
44#
發(fā)表于 2025-3-29 05:16:11 | 只看該作者
Novel Methods for Family-Based Genetic Studies,e applicable for family-based rare variant analysis. Here, I present three published statistical approaches for family-based rare variant analysis for: 1. continuous traits, 2. binary traits, and 3. multiple correlated traits.
45#
發(fā)表于 2025-3-29 08:19:26 | 只看該作者
From Identification to Function: Current Strategies to Prioritise and Follow-Up GWAS Results,evelop better preventive and curative strategies? Current efforts are shifting to focus on these questions as we move from identifying variants to understanding their effects. Here I provide a broad overview of the main technical concerns and current bottlenecks as we approach this new phase.
46#
發(fā)表于 2025-3-29 15:09:49 | 只看該作者
47#
發(fā)表于 2025-3-29 16:43:43 | 只看該作者
48#
發(fā)表于 2025-3-29 20:58:34 | 只看該作者
49#
發(fā)表于 2025-3-30 00:47:52 | 只看該作者
Genome-Wide Association Studies,nome-wide association studies (GWAS). In this chapter, we review the key concepts that underlie the GWAS approach. We will describe the “common disease, common variant” theory, and will review how we finally afforded to capture the common variance in genome to make GWAS possible. Finally, we will go
50#
發(fā)表于 2025-3-30 05:39:28 | 只看該作者
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