Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Ratzeburger Allee 160, Haus 4, 23538 Lübeck, Germany.
BMC Genet. 2005 Dec 30;6 Suppl 1(Suppl 1):S50. doi: 10.1186/1471-2156-6-S1-S50.
In the Haseman-Elston approach the squared phenotypic difference is regressed on the proportion of alleles shared identical by descent (IBD) to map a quantitative trait to a genetic marker. In applications the IBD distribution is estimated and usually cannot be determined uniquely owing to incomplete marker information. At Genetic Analysis Workshop (GAW) 13, Jacobs et al. [BMC Genet 2003, 4(Suppl 1):S82] proposed to improve the power of the Haseman-Elston algorithm by weighting for information available from marker genotypes. The authors did not show, however, the validity of the employed asymptotic distribution. In this paper, we use the simulated data provided for GAW 14 and show that weighting Haseman-Elston by marker information results in increased type I error rates. Specifically, we demonstrate that the number of significant findings throughout the chromosome is significantly increased with weighting schemes. Furthermore, we show that the classical Haseman-Elston method keeps its nominal significance level when applied to the same data. We therefore recommend to use Haseman-Elston with marker informativity weights only in conjunction with empirical p-values. Whether this approach in fact yields an increase in power needs to be investigated further.
在 Haseman-Elston 方法中,平方表型差异被回归到共享相同由血统决定的等位基因(IBD)的比例上,以将数量性状映射到遗传标记上。在应用中,IBD 分布是通过估计得到的,由于标记信息不完整,通常无法唯一确定。在遗传分析研讨会(GAW)13 上,Jacobs 等人 [BMC Genet 2003, 4(Suppl 1):S82] 提出通过对来自标记基因型的可用信息进行加权来提高 Haseman-Elston 算法的功效。然而,作者并没有展示所采用的渐近分布的有效性。在本文中,我们使用为 GAW 14 提供的模拟数据,表明通过标记信息加权 Haseman-Elston 会导致 I 型错误率增加。具体来说,我们证明了加权方案会显著增加整个染色体上的显著发现数量。此外,我们还表明,当应用于相同数据时,经典的 Haseman-Elston 方法保持其名义显著性水平。因此,我们建议仅在结合经验 p 值的情况下使用具有标记信息量权重的 Haseman-Elston。这种方法是否实际上能提高功效,需要进一步研究。