Ex-Vivo Modeling for Heritability Assessment and Genetic Mapping in Pharmacogenomics.

Alison Motsinger-Reif, Chad Brown, Tammy Havener, Nicholas Hardison, Eric Peters, Andrew Beam, Lorri Everrit, Howard McLeod
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Abstract

The investigation of genetic factors that determine differential drug response is a key goal of pharmacogenomics (PGX), and relies on the often-untested assumption that differential response is heritable. While limitations in traditional study design often prohibit heritability (h2) estimates in PGX, new approaches may allow such estimates. We demonstrate an ex vivo model system to determine the h2 of drug-induced cell killing and performed genome-wide analysis for gene mapping. The cytotoxic effect of 29 diverse chemotherapeutic agents on lymphoblastoid cell lines (LCLs) derived from family- and population-based cohorts was investigated. We used a high throughput format to determine cytotoxicity of the drugs on LCLs and developed a new evolutionary computation approach to fit response curves for each individual. Variance components analysis determined the h2 for each drug response and a wide range of values was observed across drugs. Genome-wide analysis was performed using new analytical approaches. These results lay the groundwork for future studies to uncover genes influencing chemotherapeutic response and demonstrate a new computational framework for performing such analysis.

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药物基因组学中遗传力评估和基因定位的离体建模。
研究决定差异药物反应的遗传因素是药物基因组学(PGX)的一个关键目标,它依赖于通常未经验证的假设,即差异反应是可遗传的。虽然传统研究设计的局限性经常禁止对PGX进行遗传力(h2)估计,但新方法可能允许这样的估计。我们展示了一个离体模型系统来确定药物诱导细胞杀伤的h2,并进行了全基因组分析以进行基因定位。研究了29种不同化疗药物对淋巴母细胞样细胞系(LCLs)的细胞毒性作用,这些细胞系来自基于家庭和人群的队列。我们使用高通量格式来确定药物对LCLs的细胞毒性,并开发了一种新的进化计算方法来拟合每个个体的反应曲线。方差成分分析确定了每种药物反应的h2,并且在不同药物之间观察到广泛的值。采用新的分析方法进行全基因组分析。这些结果为未来的研究奠定了基础,以揭示影响化疗反应的基因,并展示了执行此类分析的新计算框架。
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