Trends in Genetics
Research FocusGenome-wide association: a promising start to a long race
Section snippets
GWA analysis of gene expression levels in humans
Recently, after much anticipation, the first genome-wide association (GWA) studies in humans are beginning to appear in the literature 1, 2. Cheung and colleagues recently published the first GWA analysis of gene expression levels in a human population [3]. The idea behind their approach, which has been termed ‘expression genetics’ [4], is to subject levels of gene expression to the same genetic mapping techniques (i.e. linkage and association analysis) that one would use for more ‘complex’
LD coverage and gene identification
Although the Cheung et al. study employs ∼770 000 markers from the International HapMap project, a density that is likely to be matched by few association studies in the next few years, it is estimated that these data capture as little as 74% of the common variation in the human genome [6]. Therefore, there is a ∼25% chance that a common functional variant will not be tagged by a marker with a correlation ≧0.8 in these same HapMap samples. Moreover, the Cheung et al. analysis is based on an
Genetic heterogeneity
Another possibility concerns the structure of the QTLs underlying the gene expression traits. It is possible that many of the QTLs are not the result of a few common variants influencing the trait, but rather multiple rare variants each of small effect. In other words, a single QTL might consist of several genes in the same genomic region (locus heterogeneity) each comprising many variants (allelic heterogeneity). Because the power to detect association depends on the proportion of variance
Multiple testing
By its nature, GWA entails performing thousands of statistical tests. Evaluating each test against an uncorrected threshold would produce an excess of loci declared significant purely by chance. It is therefore necessary to control for multiple testing so that valuable resources are not wasted following up spurious associations. The approach employed by Cheung et al. was to use a Šidák correction (similar to the commonly used Bonferroni correction), which limits the probability of making at
Future analyses and directions
The study by Cheung et al. [3] offers the exciting prospect of combining the techniques of quantitative genetics and gene expression to gain important insights into biological networks and guide efforts in gene mapping 4, 22, 23. The expression genetics approach is already being used in model organisms including yeast 7, 9, 24, 25, 26, 27, maize [9] and mice 9, 28, 29, 30, 31. There is growing realization that merging classical genetics methods with those involving expression profiling (i.e.
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2008, Biochemical PharmacologyCitation Excerpt :A genetic architecture with modest individual contributions from variants at a number of loci that are likely to exert influences in each addicted individual supports use of association based approaches. Genome-wide association (see below) seems to be the best way to aid in defining which reported linkage results are more or less likely to be “real”, e.g. to represent reproducible observations (reviewed in [61–67]). There is now an increasing consensus that genome-wide association approaches are likely to yield positive results in complex disorders, such as addictions.