Prospects and pitfalls in whole genome association studies

Abstract

Recent large-scale studies of common genetic variation throughout the human genome are making it feasible to conduct whole genome studies of genotype-phenotype associations. Such studies have the potential to uncover novel contributors to common complex traits and thus lead to insights into the aetiology of multifactorial phenotypes. Despite this promise, it is important to recognize that the availability of genetic markers and the ability to assay them at realistic cost does not guarantee success of this approach. There are a number of practical issues that require close attention, some forms of allelic architecture are not readily amenable to the association approach with even the most rigorous design, and doubtless new hurdles will emerge as the studies begin. Here we discuss the promise and current challenges of the whole genome approach, and raise some issues to consider in interpreting the results of the first whole genome studies.

Figure 1

Variability and decay in linkage disequilibrium (LD) by physical spacing on chromosome 22. Pairwise D′ coefficients are plotted against the physical separation of the markers, revealing a general trend of decay with distance, but also extensive variability. The curve overlaid on the scatterplot is from a fitted model of expected decay $E(D_{ij})=D_{\text{low}}+(D_{\text{high}}-D_{\text{low}}){(1-r)}^t$, where D_low, D_high and t (the number of generations of decay) are estimated from the data. The raw data and this model were described by Dawson et al. (2002).

Figure 2

Comparison of population recombination rates and pairwise LD on chromosome 20. The left column shows estimates of recombination rates between adjacent markers and the right column shows D′ coefficients for the same markers. Panel (a) plots values for Asian samples (y-axis) against Caucasians (x-axis). Panel (b) plots values for African Americans against Caucasians. Panel (c) plots values for African Americans against Asians. The samples are described in (Ke et al. 2004). The population recombination rates are described in detail in (Evans & Cardon 2005).

Figure 3

Increase in availability of genetic markers. The number of markers available in dbSNP is shown as a function of the dbSNP release date (all data from www.ncbi.nlm.nih.gov/SNP). The full distribution (dark grey; greater than 10 million in 2005) reflects all non-redundant SNPs in dbSNP, while the distribution in light grey shows the number of validated SNPs.

Figure 4

Initial profile of whole genome association results: Effects of giSNPs. (a) An eQTL (gene expression level as a quantitative trait locus) association scan using only non-redundant markers (i.e. those for which r²<1.0). (b) The same results as (a) but including the redundant markers. The peaks of identical amplitude in (b) reflect the genotypic identity between the markers. From the data at hand, it is not clear which of the peaks, if any, reflect the aetiological alleles, thus emphasizing the difficulties with location inference in association studies. The expression data used in these analyses are described in Morley et al. (2004); the HapMap genotype data are from the December 2004 release (www.hapmap.org).