Statistical Approaches to Gene x Environment Interactions for Complex Phenotypes (MIT Press)

Michael Windle (Editor)

  • 出版商: MIT
  • 出版日期: 2016-07-08
  • 售價: $1,750
  • 貴賓價: 9.8$1,715
  • 語言: 英文
  • 頁數: 304
  • 裝訂: Hardcover
  • ISBN: 0262034689
  • ISBN-13: 9780262034685
  • 相關分類: 生物資訊 Bioinformatics
  • 立即出貨 (庫存=1)

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商品描述

Findings from the Human Genome Project and from Genome-Wide Association (GWA) studies indicate that many diseases and traits manifest a more complex genomic pattern than previously assumed. These findings, and advances in high-throughput sequencing, suggest that there are many sources of influence -- genetic, epigenetic, and environmental. This volume investigates the role of the interactions of genes and environment (G × E) in diseases and traits (referred to by the contributors as complex phenotypes) including depression, diabetes, obesity, and substance use.

The contributors first present different statistical approaches or strategies to address G × E and G × G interactions with high-throughput sequenced data, including two-stage procedures to identify G × E and G × G interactions, marker-set approaches to assessing interactions at the gene level, and the use of a partial-least square (PLS) approach. The contributors then turn to specific complex phenotypes, research designs, or combined methods that may advance the study of G × E interactions, considering such topics as randomized clinical trials in obesity research, longitudinal research designs and statistical models, and the development of polygenic scores to investigate G × E interactions.

ContributorsFatima Umber Ahmed, Yin-Hsiu Chen, James Y. Dai, Caroline Y. Doyle, Zihuai He, Li Hsu, Shuo Jiao, Erin Loraine Kinnally, Yi-An Ko, Charles Kooperberg, Seunggeun Lee, Arnab Maity, Jeanne M. McCaffery, Bhramar Mukherjee, Sung Kyun Park, Duncan C. Thomas, Alexandre Todorov, Jung-Ying Tzeng, Tao Wang, Michael Windle, Min Zhang

商品描述(中文翻譯)

人類基因組計畫和全基因組關聯研究(GWA)的研究結果顯示,許多疾病和特徵呈現比先前假設更複雜的基因組模式。這些發現以及高通量測序技術的進步表明,基因、表觀遺傳和環境等多種因素對疾病和特徵產生影響。本書探討了基因與環境(G × E)在包括抑鬱症、糖尿病、肥胖和物質濫用在內的疾病和特徵(由貢獻者稱為複雜表型)中的相互作用。

貢獻者首先介紹了不同的統計方法或策略,以應對高通量測序數據中的G × E和G × G相互作用,包括兩階段程序以識別G × E和G × G相互作用,基因水平上評估相互作用的標記集方法,以及使用偏最小二乘(PLS)方法。然後,貢獻者轉向特定的複雜表型、研究設計或結合方法,以推進G × E相互作用的研究,包括肥胖研究中的隨機臨床試驗、長期研究設計和統計模型,以及開發多基因分數來研究G × E相互作用。

貢獻者包括Fatima Umber Ahmed、Yin-Hsiu Chen、James Y. Dai、Caroline Y. Doyle、Zihuai He、Li Hsu、Shuo Jiao、Erin Loraine Kinnally、Yi-An Ko、Charles Kooperberg、Seunggeun Lee、Arnab Maity、Jeanne M. McCaffery、Bhramar Mukherjee、Sung Kyun Park、Duncan C. Thomas、Alexandre Todorov、Jung-Ying Tzeng、Tao Wang、Michael Windle和Min Zhang。