Functional Genomics Section, Department of Clinical Genetics, Center for Neurogenomics and Cognitive Research, VU University and VU University Medical Center, Amsterdam, The Netherlands.
PLoS One. 2010 Nov 10;5(11):e13929. doi: 10.1371/journal.pone.0013929.
The variance explained by genetic variants as identified in (genome-wide) genetic association studies is typically small compared to family-based heritability estimates. Explanations of this 'missing heritability' have been mainly genetic, such as genetic heterogeneity and complex (epi-)genetic mechanisms.
We used comprehensive simulation studies to show that three phenotypic measurement issues also provide viable explanations of the missing heritability: phenotypic complexity, measurement bias, and phenotypic resolution. We identify the circumstances in which the use of phenotypic sum-scores and the presence of measurement bias lower the power to detect genetic variants. In addition, we show how the differential resolution of psychometric instruments (i.e., whether the instrument includes items that resolve individual differences in the normal range or in the clinical range of a phenotype) affects the power to detect genetic variants.
We conclude that careful phenotypic data modelling can improve the genetic signal, and thus the statistical power to identify genetic variants by 20-99%.
与基于家庭的遗传力估计相比，（全基因组）遗传关联研究中确定的遗传变异所解释的方差通常较小。对这种“遗传力缺失”的解释主要是遗传的，例如遗传异质性和复杂的（表观遗传）遗传机制。
我们使用全面的模拟研究表明，三种表型测量问题也为遗传力缺失提供了可行的解释：表型复杂性、测量偏差和表型分辨率。我们确定了使用表型总和评分和存在测量偏差会降低检测遗传变异的能力的情况。此外，我们展示了心理计量工具的分辨率差异（即工具是否包括解决表型正常范围或临床范围个体差异的项目）如何影响检测遗传变异的能力。
我们得出结论，仔细的表型数据建模可以提高遗传信号，从而提高通过 20-99%的遗传变异的统计能力。
