High resolution T2 association tests of complex diseases based on family data
High resolution T2 association tests of complex diseases based on family data
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DOI：
10.1046/j.1529-8817.2004.00151.x
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发表时间：
2005-03-01
影响因子：
1.9
通讯作者：
Xiong, MM
中科院分区：
文献类型：
--
作者：
Fan, RZ;Knapp, M;Xiong, MM
本文提出基于家族的 Hoteffing's T-2 检验，用于复杂疾病的高分辨率连锁不平衡 (LD) 作图或关联研究。假设多个标记或单倍型块的基因型数据可用于核心家族样本，其中一些后代受到影响。配对 Hotelling 的 T-2 检验统计量建议用于高分辨率关联研究，使用父母作为受影响后代的对照，基于两种编码方法：单倍型/等位基因编码和基因型编码。成对的 Hotelling 的 T-2 检验不仅考虑了单倍型块或标记之间的相关性，而且还考虑了每个亲代-后代对内的相关性。该方法扩展了用于人口病例对照关联研究的两个样本 Hotelling 的 T-2 检验统计量，由于家庭成员之间遗传数据的相关性，该统计量对于家庭数据无效。大样本理论下严格的数学和统计证明证明了该方法的有效性。计算检验统计量的非中心参数近似值以用于功效和样本量计算。从功效比较和I型误差计算结果表明，基于单倍型/等位基因编码的检验统计量优于基因型编码的检验统计量。使用多个标记的分析可以提供比单个标记分析更高的功效。如果仅使用一个标记，则基于单倍型/等位基因编码的检验统计量几乎与 1-TDT 的检验统计量相同。此外，还提供了用于数据分析的排列程序。该方法适用于德国哮喘家庭研究的数据。基于配对 Hotelling 的 T-2 统计检验的结果证实了之前的发现。然而，配对的霍特林 T-2 检验产生的 P 值比先前研究的 P 值小得多。排列测试产生与先前研究相似的结果；此外，通过排列检验，额外的标记组合被证明是显着的。拟议的配对 Hotelling 的 T 2 统计检验在绘制复杂疾病图谱方面具有潜在的强大作用。已编写 SAS 宏 Hotel_fam.sas 来实现数据分析方法。
This paper proposes family based Hoteffing's T-2 tests for high resolution linkage disequilibrium (LD) mapping or association studies of complex diseases. Assume that genotype data of multiple markers or haplotype blocks are available for a sample of nuclear families, in which some offspring are affected. Paired Hotelling's T-2 test statistics are proposed for a high resolution association study using parents as controls for affected offspring, based on two coding methods: haplotype/allele coding and genotype coding. The paired Hotelling's T-2 tests take not only the correlation between the haplotype blocks or markers into account, but also take the correlation within each parent-offspring pair into account. The method extends two sample Hotelling's T-2 test statistics for population case control association studies, which are not valid for family data due to correlation of genetic data among family members. The validity of the proposed method is justified by rigorous mathematical and statistical proof under the large sample theory. The non-centrality parameter approximations of the test statistics are calculated for power and sample size calculations. From power comparison and type I error calculations, it is shown that the test statistic based on haplotype/allele coding is advantageous over the test statistic of genotype coding. Analysis using multiple markers may provide higher power than single marker analysis. If only one marker is utilized the power of the test statistic based on haplotype/allele coding is nearly identical to that of 1-TDT Moreover, a permutation procedure is provided for data analysis. The method is applied to data from a German asthma family study. The results based on the paired Hotelling's T-2 statistic tests confirm the previous findings. However, the paired Hotelling's T-2 tests produce much smaller P-values than those of the previous study. The permutation tests produce similar results to those of the previous study; moreover, additional marker combinations are shown to be significant by permutation tests. The proposed paired Hotelling's T 2 statistic tests are potentially powerful in mapping complex diseases. A SAS Macro, Hotel_fam.sas, has been written to implement the method for data analysis.