THE PRIMER :: MOLECULAR DIAGNOSTICS
and a first statistical value we get is strength of asso- ciation (here, 97 percent) which also is a surrogate measure of what the actual distance is between the marker and the gene. If we’re particularly lucky we may even have two or more genetic markers which demonstrate this sort of linkage association and based on their relative frequencies of association, we can narrow down the general area of DNA the gene likely resides in based on relative closeness to these markers. Luck in this case is greatly increased as marker density increases, of course—high density marker maps make the entire QTL mapping process easier than lower density maps.
More statistics: effect sizes
There is an unrelated second statistic we should look for with our now-linked marker, and that is effect size. Imagine for instance when we looked for markers associated with fingernail growth rate, we found three widely separated markers with statisti- cal relevance. All have one allele which clearly shows accelerated fingernail growth rate, but compared to some baseline reference value, Marker A shows a three percent increase in growth rate, Marker B shows a 22 percent increase, and Marker C shows a seven per- cent increase. (Strictly speaking the statistics would be more complicated than that, such as having a 95 percent confidence interval range of effect, but we’re ignoring these nuances here because they can—and do—fill entire textbooks. There are even multiple completely different statistical approaches to identify- ing our linked markers. For the sake of brevity, we’re avoiding all of that as the basic concepts as presented here remain valid across all these approaches.) These values suggest the relative scale of impact each gene has on the final phenotype.
And the candidate gene is… Now armed with the knowledge of what regions have the biggest impact on fingernail growth rates, we can proceed (probably from the biggest impact markers to lowest) to look for likely candidate genes. If we find an ORF (open reading frame, a potential protein coding region) near one of our markers, and we find its translated amino acid sequence for example has a high degree of similarity to a known keratin syn- thase gene, that would make sense and be a good candidate. Our evidence for its involvement would get even stronger if we find this gene expressed as an RNA (or even just a fragment of it, in parlance an EST, Expressed Sequence Tag) telling us that it’s an active gene. If we could then find either amino acid varia- tions in this candidate gene, or variances in its RNA expression level, which correlate with our phenotypic observations of fingernail growth rate, we can become increasingly certain that we’ve found our actual gene contributing to the continuous phenotype. A full proof would then best be done by targeted gene modification in whatever our model organism is for fingernail growth (the genetic equivalent of fulfill- ing Koch’s Postulate, by making the genetic change in an otherwise controlled background and environ- ment and observing the expected effect). If there is no well established model system, then at least cloning
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of the variant gene forms, in-vitro protein expression, and enzyme kinetics studies can be nearly as helpful by demonstrating that yes, the protein version as coded for by gene found associated to the “C” SNP has faster enzymatic behavior than the version found associated with the “A” SNP. Follow these approaches through on the other identified linked loci and candidate genes to rule them in or out, and we have now fulfilled our lab’s lifelong academic ambition of understanding multiple genes influencing fingernail growth rate.
Resolving complexities We mentioned above that this approach would attempt to address the challenge of “nature versus nurture.” Part of this is through the effect size values discussed above; generally, if the genetic effect is big enough, “nature” is more important than “nurture” and an effect is vis- ible even with disparate environmental factors. Other approaches however can include things like linking in family pedigree information, where sibling studies can be done on the hopes that environments will be similar; things like dizygotic twins can be particularly useful here. Alternatively, if there are suspected particular environ- mental factors, these may be accounted for in collected and paired metadata for each genetic subject. The sec- ond complexity—polygenic traits—was directly dealt with above, as we were able to identify multiple loci. The fourth complexity—our fudge factor of “variable penetrance”—to some extent may be explained away once we have our data in hand, as we start to show that all of loci A, B, and C are involved but we don’t see all of the impact of a particular allele at A unless we also have a particular allele at C. This is no lon- ger variable penetrance, it’s a definable epistatic gene interaction. Finally, although we said we’d ignore the third complexity of epigenetics, it’s becoming increas- ingly possible from a laboratory technical perspective to capture data on things like DNA methylation dur- ing sequencing. Analogous statistical approaches to identify differential patterns of epigenetic labeling of particular gene regions correlating to phenotype are of course possible and will likely become more common- place as the underlying data becomes more commonly available.
Having complete genomes of organisms in orders of magnitude is cheaper and easier than it was only a few years ago. Making sense of all of that informa- tion, by understanding how these genes contribute not just to discrete Mendelian traits but to all the complex polygenic continuously variable metrics we can imag- ine, is the next step in making good use of the data. Hopefully, the forgoing has demystified the process for those of you not already familiar with it. Should it merely have wet your appetite for the subject, there are numerous good up to date texts on the subject available.
John Brunstein, PhD, serves as an Editorial Advisory Board member for MLO. John is also President and CEO for British Columbia-based PathoID, Inc., which provides consulting for development and validation of molecular assays.
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