Genome-wide strategies for discovering genetic influences on cognition and cognitive disorders: methodological considerations

Abstract

Introduction: Genes play a well-documented role in determining normal cognitive function. This paper focuses on reviewing strategies for the identification of common genetic variation in genes that modulate normal and abnormal cognition with a genome-wide association scan (GWAS). GWASs make it possible to survey the entire genome to discover important but unanticipated genetic influences.

Methods: The use of a quantitative phenotype in combination with a GWAS provides many advantages over a case-control design, both in power and in physiological understanding of the underlying cognitive processes. We review the major features of this approach, and show how, using a General Linear Model method, the contribution of each Single Nucleotide Polymorphism (SNP) to the phenotype is determined, and adjustments then made for multiple tests. An example of the strategy is presented, in which fMRI measures of cortical inefficiency while performing a working memory task are used as the quantitative phenotype. We estimate power under different effect sizes (10-30%) and variations in allelic frequency for a Quantitative Trait (QT) (10-20%), and compare them to a case-control design with an Odds Ratio (OR) of 1.5, showing how a QT approach is superior to a traditional case-control. In the presented example, this method identifies putative susceptibility genes for schizophrenia which affect prefrontal efficiency and have functions related to cell migration, forebrain development and stress response.

Conclusion: The use of QT as phenotypes provide increased statistical power over categorical association approaches and when combined with a GWAS creates a strategy for identification of unanticipated genes that modulate cognitive processes and cognitive disorders.

Figure 1

The graph shows the power distribution curves for QT analysis contrasted with a case-control design at p < .01 and 10⁻⁷ (OR = 1.5). The x axis portrays the sample sizes and on the y axis the power at each value of the sample size for a 10% percentage variance explained for the QT, a QT MAF of 10% and a marker SNP MAF at 20%. The results for QT and case-control are displayed in black and grey respectively. Compared to QT the case-control curves are shifted to the right indicating that much larger sample sizes are required to reach the same power.

Figure 2

The graph shows the power distribution curves for QT analysis, contrasted with a case control design using the same parameters as in Fig 1 but with the marker SNP MAF at 10%. Comparing Fig 1 and Fig 2 highlight the effects of the match between tagging and causal SNP MAFs shown in more detail in Fig 3.

Figure 3

The graph depicts the sample size required for a power = 80 % for a QT phenotype with tagging SNP frequencies from .05 to 0.5 given a 10%, 20%, and 30% MAF for the QT and when the total variance explained by the QT is 10, 20 and 30%. The curves show the sample size required for any combination of the QT values: the lowest sample size is N=132 and is reached when there is agreement between the allelic frequencies of the tagging SNPs with that of the actual QT allele, which is unknown. This is true independent of the total amount of variance explained by the QT.