50 whys to look for genes: 10. Identify risk factors (using GWA studies)

If presence of a section of DNA (SNP) increases the odds of a disease, then look for genes close to the SNP, investigate the enzymes associated with the gene and use that as an entry point to investigation of the etiology of the disease, then try to design drug therapies to counteract any undesired function of those enzymes or their subsequent effects.

The search for SNPs associated with disease can be done by Genome-Wide Association (GWA) studies, which

have two parts: a) compare a group of individuals—the case—who have a disease or other trait with a group of individuals—the control—who do not have the trait but match the case in their distribution of other traits; and b) identify places (loci) on the genome that are significantly more frequent in the case compared with the control… The loci in GWA studies are SNPs (single-nucleotide polymorphisms), which are not the causal genetic factors for the trait, but are simply somewhere close to those factors on the genome. The case and control groups are large (up to around 200,000) and a huge number of SNPs (up to around 1 million) are assayed.

The first successful GWA study was published in 2005. Soon variants were identified that were associated, at least in the defined populations from which the case and control groups were drawn, with increased incidence in diseases such as diabetes, heart disease, and cancers (Khoury et al. 2007).


As it has turned out, however, for loci where variants have a statistically significant association with some medically significant trait, that association corresponds to a small increase in incidence of the trait (McCarthy et al. 2008). Expressed in terms of odds, the odds of an individual with the variant having the trait is generally no more than 1.5 times the odds of having it when the variant is not present. Moreover, even when many such associations are considered jointly, most of the variation in the trait remains unaccounted for (Ku et al. 2010…).

The hope had been to expose variants corresponding to a major increase in incidence of the trait, and from that to gain insight into the mechanisms of the disease. Some researchers claim new biomedical insights based on variants corresponding to a small increase (Wheeler and Barroso 2012) or expect the yield from GWA studies to improve once the causally relevant stretches of DNA near the SNPs are identified.

Notwithstanding these claims or hopes of new insights, there is active debate on the implications of the difficulty in identifying causally relevant genetic variants through GWA studies (Couzin-Frankel 2010). Some researchers go from the observation that many variants have a small effect (that is, an association with a small increase in incidence of the trait) to a conjecture that future advances in the understanding of a disease will come from finding rare variants (alleles) that have a strong effect (McClellan and King 2010).

Genetic heterogeneity is built into this conjecture in the sense that, even if insight about mechanisms emerged from an examination of the strong effect of the rare variant, most of the cases would not be associated with the rare variant. Moreover, the detection and identification of variants is obviously complicated by genetic heterogeneity in its various forms (e.g., mutations in a gene can occur at a variety of points in the gene, the clinical expression of such mutations can vary significantly, and different genetic variants may be expressed as the same clinical entity…).


The quotes above are from pages 141-2 in Taylor, Peter J. (2014) Nature-Nurture? No: Moving the Sciences of Variation and Heredity Beyond the Gaps.

Couzin-Frankel, J. (2010). “Major heart disease genes prove elusive.” Science 328(5983): 1220-1221.

Khoury, M. J., J. Little, et al. (2007). “On the synthesis and interpretation of consistent but weak gene-disease associations in the era of genome-wide association studies.” International Journal of Epidemiology 36: 439-445.

Ku, C. S., E. Y. Loy, et al. (2010). “The pursuit of genome-wide association studies: Where are we now?” Journal of Human Genetics 55(April): 195-206.

McCarthy, M. I., G. R. Abecasis, et al. (2008). “Genome-wide association studies for complex traits: consensus, uncertainty and challenges.” Nature Reviews Genetics 9(May): 356-369.

McClellan, J. and M.-C. King (2010). “Genetic heterogeneity in human disease.” Cell 141: 210-217.

Wheeler, E. and I. Barroso (2011). “Genome-wide association studies and type 2 diabetes.” Briefings in Functional Genomics 10 (2): 52-60.


(Introduction to this series of posts)


2 thoughts on “50 whys to look for genes: 10. Identify risk factors (using GWA studies)

  1. Pingback: 50 whys to look for genes: 11. Eliminate the distinction between familial and hereditary cancers | Intersecting Processes

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s