Lin Wan-Yu, Schaid Daniel J
Institute of Epidemiology, National Taiwan University, Taipei, Taiwan.
Hum Hered. 2007;63(1):35-46. doi: 10.1159/000098460. Epub 2007 Jan 11.
A challenging issue in genetic mapping of complex human diseases is localizing disease susceptibility genes when the genetic effects are small to moderate. There are greater complexities when multiple loci are linked to a chromosomal region. Liang et al. [Hum Hered 2001;51:64-78] proposed a robust multipoint method that can simultaneously estimate both the position of a trait locus and its effect on disease status by using affected sib pairs (ASPs). Based on the framework of generalized estimating equations (GEEs), the estimate and standard error of the position of a trait locus are robust to different genetic models. To utilize other relative pairs collected in pedigree data, Schaid et al. [Am J Hum Genet 2005;76:128-138] extended Liang's method to various types of affected relative pairs (ARPs) by two approaches: unconstrained and constrained methods. However, the above methods are limited to situations in which only one trait locus exists on the chromosome of interest. The mean functions are no longer correctly specified when there are multiple causative loci linked to a chromosomal region. To overcome this, Biernacka et al. [Genet Epidemiol 2005;28:33-47] considered the multipoint methods for ASPs to allow for two linked disease genes. We further generalize the approach to cover other types of ARPs. To reflect realistic situations for complex human diseases, we set modest sizes of genetic effects in our simulation. Our results suggest that several hundred independent pedigrees are needed, and markers with high information, to provide reliable estimates of trait locus positions and their confidence intervals. Bootstrap resampling can correct the downward bias of the robust variance for location estimates. These methods are applied to a prostate cancer linkage study on chromosome 20 and compared with the results for the one-locus model [Am J Hum Genet 2005;76:128-138]. We have implemented the multipoint IBD mapping for one and two linked loci in our software GEEARP, which allows analyses for five general types of ARPs.
在复杂人类疾病的基因定位中,一个具有挑战性的问题是,当基因效应为小到中等时,定位疾病易感基因。当多个基因座与一个染色体区域连锁时,情况会更加复杂。梁等人[《人类遗传学》2001年;51:64 - 78]提出了一种稳健的多点方法,该方法可以通过使用患病同胞对(ASP)同时估计性状基因座的位置及其对疾病状态的影响。基于广义估计方程(GEE)的框架,性状基因座位置的估计值和标准误差对不同的遗传模型具有稳健性。为了利用家系数据中收集的其他亲属对,沙伊德等人[《美国人类遗传学杂志》2005年;76:128 - 138]通过两种方法将梁的方法扩展到各种类型的患病亲属对(ARP):无约束方法和约束方法。然而,上述方法仅限于感兴趣的染色体上仅存在一个性状基因座的情况。当有多个致病基因座与一个染色体区域连锁时,均值函数就不再正确设定。为了克服这一问题,比尔纳茨卡等人[《遗传流行病学》2005年;28:33 - 47]考虑了针对ASP的多点方法,以允许存在两个连锁的疾病基因。我们进一步推广该方法以涵盖其他类型的ARP。为了反映复杂人类疾病的实际情况,我们在模拟中设定了适度大小的基因效应。我们的结果表明,需要几百个独立家系以及具有高信息含量的标记,才能提供性状基因座位置及其置信区间的可靠估计。自助重采样可以校正位置估计的稳健方差的向下偏差。这些方法应用于20号染色体上的前列腺癌连锁研究,并与单基因座模型的结果[《美国人类遗传学杂志》2005年;76:128 - 138]进行比较。我们已在我们的软件GEEARP中实现了针对一个和两个连锁基因座的多点IBD定位,该软件允许对五种一般类型的ARP进行分析。