In search of causal variants: refining disease association signals using cross-population contrasts.
- Authors
- Saccone, Nancy L; Saccone, Scott F; Goate, Alison M; Grucza, Richard A; Hinrichs, Anthony L; Rice, John P; Bierut, Laura J
- Year
- 2008
- Journal
- BMC genetics
- PMID
- 18759969
- DOI
- 10.1186/1471-2156-9-58
- PMCID
- PMC2556340
BACKGROUND: Genome-wide association (GWA) using large numbers of single nucleotide polymorphisms (SNPs) is now a powerful, state-of-the-art approach to mapping human disease genes. When a GWA study detects association between a SNP and the disease, this signal usually represents association with a set of several highly correlated SNPs in strong linkage disequilibrium. The challenge we address is to distinguish among these correlated loci to highlight potential functional variants and prioritize them for follow-up. RESULTS: We implemented a systematic method for testing association across diverse population samples having differing histories and LD patterns, using a logistic regression framework. The hypothesis is that important underlying biological mechanisms are shared across human populations, and we can filter correlated variants by testing for heterogeneity of genetic effects in different population samples. This approach formalizes the descriptive comparison of p-values that has typified similar cross-population fine-mapping studies to date. We applied this method to correlated SNPs in the cholinergic nicotinic receptor gene cluster CHRNA5-CHRNA3-CHRNB4, in a case-control study of cocaine dependence composed of 504 European-American and 583 African-American samples. Of the 10 SNPs genotyped in the r2 > or = 0.8 bin for rs16969968, three demonstrated significant cross-population heterogeneity and are filtered from priority follow-up; the remaining SNPs include rs16969968 (heterogeneity p = 0.75). Though the power to filter out rs16969968 is reduced due to the difference in allele frequency in the two groups, the results nevertheless focus attention on a smaller group of SNPs that includes the non-synonymous SNP rs16969968, which retains a similar effect size (odds ratio) across both population samples. CONCLUSION: Filtering out SNPs that demonstrate cross-population heterogeneity enriches for variants more likely to be important and causative. Our approach provides an important and effective tool to help interpret results from the many GWA studies now underway.
The r2 ≥ 0.8 bin for rs16969968 in HapMap CEU. The red triangle denotes rs16969968, and is connected to all other SNPs (triangles) in the r2 bin by the dotted line. The vertical position of the triangles has no meaning, but allows all the SNPs to be shown without overlapping the triangles. The rs numbers for all SNPs in this bin are given in map order in Table 1.
LD (r2) in the HapMap YRI sample, for the r2 ≥ 0.8 bin for rs16969968 defined by HapMap CEU LD. Of the 21 bin members pictured in Figure 1, 20 were genotyped in HapMap YRI of which 4 were monomorphic. The numerals in the cells denote the r2 between the two SNPs corresponding to the cell. Cells with no numeral indicate r2 = 1. Cell shading indicates strength of r2 as shown by the numeral.
LD plot (r2) in our cocaine-dependence case-control samples. The quadrants display AA cases (upper right), EA cases (upper left) EA controls (lower left), AA controls (lower right).
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