Battelle Center for Mathematical Medicine at The Research Institute, Nationwide Children's Hospital Columbus, OH, USA ; Department of Statistics and Pediatrics, Ohio State University Columbus, OH, USA.
Front Genet. 2013 Oct 24;4:200. doi: 10.3389/fgene.2013.00200. eCollection 2013.
When studying the genetics of inherited diseases, researchers often collect data on affected families, unrelated cases, and healthy controls. However, the joint analysis of such heterogeneous data is difficult, and the simpler analysis of homogeneous subsets is often suboptimal. For example, while case-control tests of association are sensitive to allele frequency differences, the preferential transmission of risk alleles from heterozygous parents to their affected offspring is typically ignored. Similarly, the transmission disequilibrium test (TDT) fails to incorporate the difference in allele frequencies when testing for association. To boost the power of modern genetic studies, we propose POPFAM - a fast and efficient test of association that can accommodate large affected families, unrelated cases, and controls. We use simulations to assess the type I error and power of POPFAM across different genetic models, and minor allele frequencies. For comparison, we examine the power of competing methods: the trend test, a Wald test (equivalent to the TDT), and SCOUT. Our results show that POPFAM maintains the correct type I error, and that it is more powerful than the trend test or the TDT. It performs as well as, or better than the likelihood ratio test SCOUT, which was developed specifically for case-parent/case-control data. Furthermore, when applied to the human leukocyte antigen genotypes of 401 type 1 diabetic families, POPFAM confirmed the previously reported association between DRB1(*)03:01 and microvascular complications (p = 0.04). In general, we expect our proposed test to facilitate the identification of clinically important genomic regions, and to better inform the design of follow-up sequencing efforts.
在研究遗传性疾病的遗传学时，研究人员通常会收集受影响的家族、无关病例和健康对照的数据。然而，对这种异质数据进行联合分析是困难的，对同质子集进行更简单的分析通常不是最优的。例如，虽然病例对照关联检验对等位基因频率差异很敏感，但风险等位基因从杂合父母优先传递给受影响的后代通常被忽略。同样，传递不平衡检验（TDT）在关联检验时未能纳入等位基因频率的差异。为了提高现代遗传研究的功效，我们提出了 POPFAM-一种能够容纳大型受影响的家族、无关病例和对照的关联检验的快速而有效的方法。我们使用模拟来评估不同遗传模型和次要等位基因频率下 POPFAM 的 I 型错误和功效。作为比较，我们研究了竞争方法的功效：趋势检验、Wald 检验（相当于 TDT）和 SCOUT。我们的结果表明，POPFAM 保持了正确的 I 型错误，并且比趋势检验或 TDT 更有效。它的功效与专门为病例-父母/病例-对照数据开发的似然比检验 SCOUT 相当，或者更好。此外，当应用于 401 个 1 型糖尿病家族的人类白细胞抗原基因型时，POPFAM 证实了先前报道的 DRB1(*)03:01 与微血管并发症之间的关联（p=0.04）。总的来说，我们期望我们提出的检验能够促进对临床重要基因组区域的识别，并更好地为后续测序工作的设计提供信息。
