Teo Yik Y
Wellcome Trust Centre for Human Genetics, University of Oxford, UK.
Curr Opin Lipidol. 2008 Apr;19(2):133-43. doi: 10.1097/MOL.0b013e3282f5dd77.
Genetic association studies which survey the entire genome have become a common design for uncovering the genetic basis of common diseases, including lipid-related traits. Such studies have identified several novel loci which influence blood lipids. The present review highlights the statistical challenges associated with such large-scale genetic studies and discusses the available methodological strategies for handling these issues.
The successful analysis of genome-wide data assayed on commercial genotyping arrays depends on careful exploration of the data. Unaccounted sample failures, genotyping errors and population structure can introduce misleading signals that mimic genuine association. Careful interpretation of useful summary statistics and graphical data displays can minimize the extent of false associations that need to be followed up in replication or fine-mapping experiments.
Recently published genome-wide studies are beginning to yield valuable insights into the importance of well designed methodological and statistical techniques for sensible interpretation of the plethora of genetic data generated.
对全基因组进行调查的基因关联研究已成为揭示常见疾病（包括血脂相关性状）遗传基础的常用设计。此类研究已确定了几个影响血脂的新基因座。本综述重点介绍了与此类大规模基因研究相关的统计挑战，并讨论了处理这些问题的可用方法策略。
对商业基因分型阵列上检测的全基因组数据进行成功分析取决于对数据的仔细探索。未被发现的样本失败、基因分型错误和群体结构可能会引入模拟真实关联的误导性信号。对有用的汇总统计数据和图形数据显示进行仔细解读，可以最大限度地减少在复制或精细定位实验中需要跟进的假关联程度。
最近发表的全基因组研究开始对精心设计的方法和统计技术对于明智解读所产生的大量遗传数据的重要性提供有价值的见解。
