Testing for association based on excess allele sharing in a sample of related cases and controls
Testing for association based on excess allele sharing in a sample of related cases and controls
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DOI：
10.1007/s00439-007-0345-z
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发表时间：
2007-06-01
期刊：
影响因子：
5.3
通讯作者：
Roeder, Kathyrn
中科院分区：
文献类型：
--
作者：
Klei, Lambertus;Roeder, Kathyrn
样本由不相关的病例和对照、小谱系和大得多的谱系组成，这对关联研究提出了独特的挑战。很少有方法可以有效地分析这样一个广泛的数据结构。在本文中，我们介绍了一种新的匹配统计，非常适合于复杂的数据结构，并比较它与文献中的基于频率的方法。为了调查和比较这些方法的能力，我们模拟了基于复杂谱系的数据集。我们研究了疾病等位基因与标记等位基因（或等同的单倍型）的各种水平的连锁不平衡（LD）的影响。对于低频率标记等位基因/单倍型，基于频率的统计在检测关联方面更强大。相反，对于高频标记等位基因，匹配统计量具有更大的功效。当疾病等位基因频率与连锁标记等位基因的频率紧密匹配时，基于频率的统计的最高功效发生。相反，匹配统计量的最大功效总是出现在中间标记等位基因频率，而不管疾病等位基因频率如何。此外，匹配和基于频率的统计表现出很小的相关性。我们的结论是，这两种方法可以被看作是互补的，在寻找疾病和标记物之间的可能关联，为许多不同的情况。
Samples consisting of a mix of unrelated cases and controls, small pedigrees, and much larger pedigrees present a unique challenge for association studies. Few methods are available for efficient analysis of such a broad spectrum of data structures. In this paper we introduce a new matching statistic that is well suited to complex data structures and compare it with frequency-based methods available in the literature. To investigate and compare the power of these methods we simulate datasets based on complex pedigrees. We examine the influence of various levels of linkage disequilibrium (LD) of the disease allele with a marker allele (or equivalently a haplotype). For low frequency marker alleles/haplotypes, frequency-based statistics are more powerful in detecting association. In contrast, for high frequency marker alleles, the matching statistic has greater power. The highest power for frequency-based statistics occurs when the disease allele frequency closely matches the frequency of the linked marker allele. In contrast maximum power of the matching statistic always occurs for intermediate marker allele frequency regardless of the disease allele frequency. Moreover, the matching and frequency-based statistics exhibit little correlation. We conclude that these two approaches can be viewed as complementary in finding possible association between a disease and a marker for many different situations.