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関連する概念動画

Pedigree Analysis01:35

Pedigree Analysis

Overview
Pedigree Analysis01:35

Pedigree Analysis

Overview
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
Coefficient of Correlation01:12

Coefficient of Correlation

The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the strength of the linear...
Calculating and Interpreting the Linear Correlation Coefficient01:11

Calculating and Interpreting the Linear Correlation Coefficient

The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable, x, and the dependent variable, y. Hence, it is also known as the Pearson product-moment correlation coefficient. It can be calculated using the following equation:
Genetic Variation01:25

Genetic Variation

Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
Genes exist in different versions called alleles, which...

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関連する実験動画

Updated: May 12, 2026

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
10:17

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

Published on: November 3, 2010

プロトコル:Logicaを用いた異種集団間局所的遺伝相関の推定

Boran Gao1, Zheng Li2, Xiang Zhou3

  • 1Department of Statistics, Purdue University, West Lafayette, IN 47907, USA; Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA.

STAR protocols
|December 25, 2025
PubMed
まとめ
この要約は機械生成です。

本研究では、ゲノムワイド関連解析(GWAS)要約統計量を用いて集団間遺伝相関を推定する新しい手法であるLogicaを紹介します。このプロトコルは、共有遺伝子構造のスケーラブルな推論を可能にします。

キーワード:
バイオインフォマティクス計算科学遺伝学ゲノミクス健康科学

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Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
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Last Updated: May 12, 2026

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
10:17

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

Published on: November 3, 2010

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
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Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

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CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

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科学分野:

  • 遺伝学
  • バイオインフォマティクス
  • 集団遺伝学

背景:

  • 多様な集団間での遺伝相関を理解することは、共有遺伝子構造を解読するために重要です。
  • 既存の方法はスケーラビリティに欠けるか、個人レベルのデータが必要な場合があります。

研究 の 目的:

  • 異種集団間局所的遺伝相関を推定するための再現可能なプロトコルを提示すること。
  • 要約統計量を使用して共有遺伝子構造の正確かつスケーラブルな推論を可能にすること。

主な方法:

  • 尤度ベースのフレームワークであるLogicaを利用しました。
  • ゲノムワイド関連解析(GWAS)からの要約統計量を使用しました。
  • 集団ごとの連鎖不平衡(LD)情報を組み込みました。

主要な成果:

  • 遺伝率の局所レベル推定のためのプロトコルを開発しました。
  • 異種集団間遺伝相関の推定を可能にしました。
  • スケーラブルな推論に必要な入力と分析手順の概要を説明しました。

結論:

  • Logicaプロトコルは、異種集団間遺伝相関分析のための再現可能な方法を提供します。
  • 共有遺伝子構造の正確かつスケーラブルな推論を容易にします。
  • 集団遺伝学およびGWAS研究に有用なツールです。