TY - JOUR
T1 - In search of causal variants
T2 - Refining disease association signals using cross-population contrasts
AU - Saccone, Nancy L.
AU - Saccone, Scott F.
AU - Goate, Alison M.
AU - Grucza, Richard A.
AU - Hinrichs, Anthony L.
AU - Rice, John P.
AU - Bierut, Laura J.
N1 - Funding Information:
We thank Weimin Duan and Louis Fox for data management and support. We also thank the anonymous reviewers, whose comments helped us improve the content and presentation of this paper. This work was funded by grants K01DA015129 (N.L.S.), K01DA16618 (R.A.G.), K01AA015572 (A.H.), and K02DA021237 (L.J.B.) from the National Institutes of Health, and by IRG-58-010-50 from the American Cancer Society (S.F.S.). The Family Study on Cocaine Dependence (FSCD) has been supported by R01DA013423 (L.J.B.) and R01DA019963 (L.J.B.) from the NIH. Genotyp-ing services were provided by the Center for Inherited Disease Research (CIDR). CIDR is fully funded through a federal contract from the National Institutes of Health to The Johns Hopkins University, contract number HHSN268200782096C. Conflict of Interest Statement: Drs. S. Saccone, A. Goate, A. Hinrichs, J. Rice and L. Bierut are listed as inventors on a patent "Markers of Addiction" (US 20070258898) held by Perlegen Sciences, Inc., covering the use of certain SNPs in determining the diagnosis, prognosis, and treatment of addiction. Dr. N. Saccone is the spouse of Dr. S. Saccone, who is listed on the above-named patent. Dr. Bierut has acted as a consultant for Pfizer, Inc. in 2008.
PY - 2008/8/29
Y1 - 2008/8/29
N2 - 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 ≥ 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.
AB - 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 ≥ 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.
UR - http://www.scopus.com/inward/record.url?scp=53249124836&partnerID=8YFLogxK
U2 - 10.1186/1471-2156-9-58
DO - 10.1186/1471-2156-9-58
M3 - Article
C2 - 18759969
AN - SCOPUS:53249124836
SN - 1471-2156
VL - 9
JO - BMC genetics
JF - BMC genetics
M1 - 58
ER -