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Am J Hum Genet. Jan 2006; 78(1): 130–136.
Published online Nov 16, 2005. doi:  10.1086/499287
PMCID: PMC1380210

Variants Associated with Common Disease Are Not Unusually Differentiated in Frequency across Populations

Abstract

Genetic variants that contribute to risk of common disease may differ in frequency across populations more than random variants in the genome do, perhaps because they have been exposed to population-specific natural selection. To assess this hypothesis empirically, we analyzed data from two groups of single-nucleotide polymorphisms (SNPs) that have shown reproducible (n=9) or reported (n=39) associations with common diseases. We compared the frequency differentiation (between Europeans and Africans) of the disease-associated SNPs with that of random SNPs in the genome. These common-disease–associated SNPs are not significantly more differentiated across populations than random SNPs. Thus, for the data examined here, ethnicity will not be a good predictor of genotype at many common-disease–associated SNPs, just as it is rarely a good predictor of genotype at random SNPs in the genome.

An open question in medical and population genetics is how much information a person’s self-identified ancestry (ethnicity) conveys about his or her risk of common disease (Risch et al. 2002; Burchard et al. 2003; Cooper et al. 2003). One way in which ethnicity could be informative about common-disease risk is if risk alleles vary in frequency among populations, which would allow ethnicity to be a predictor of whether a person has a risk allele. This correlation between ethnicity and genotype would be strongest if the disease-associated variants were differentiated in frequency. Although it is known that random variants in the genome are not particularly differentiated across populations on average (Lewontin 1972; Bowcock et al. 1991; Rosenberg et al. 2002), it has been hypothesized that, because of population-specific natural selection, functional SNPs associated with common disease may be more differentiated (Akey et al. 2002; Bamshad et al. 2004). There has been no empirical attempt to address this question, largely because so few disease-associated SNPs have been identified to date.

We set out to test the hypothesis that common-disease–associated SNPs are more differentiated than random SNPs by conducting an empirical evaluation of population differentiation in 48 SNPs associated with common disease. We wanted to study SNPs that were associated with common, complex traits, so we explicitly excluded variants associated with Mendelian diseases. The SNPs were all identified in a way that would not create a bias toward unusually high or low levels of frequency differentiation across populations, since each of them was initially identified in studies of single populations.

We first studied nine SNPs reproducibly associated with common disease (table 1). These SNPs satisfied two criteria: (1) >75% of replication studies showed a statistically significant association (Hirschhorn et al. 2002) or the association was significant after meta-analysis of replication studies (Lohmueller et al. 2003) and (2) allele-frequency information was publicly available for the SNPs in both West African and European-derived populations.

Table 1
Allele-Frequency Data for Nine Reproducible Associations

Second, we studied 39 SNPs that have been reported to be associated with common disease (table 2) but for which association has not necessarily been replicated. These were identified by checking the genes sequenced by the Seattle SNPs project (Seattle SNPs Web site) for overlap with the SNPs reported to be associated with common disease in the OMIM and PubMed databases or in table 1 of Hirschhorn et al. (2002).

Table 2
Allele-Frequency Data for 39 Reported Associations

To assess whether the disease-associated SNPs are more differentiated across populations than random SNPs in the genome, we compared the two groups of disease-associated SNPs with SNPs from two public databases (table 3). The first database (“WICGR”) was generated by the Whitehead/MIT Center for Genome Research and includes frequency data for SNPs genotyped in European American and Nigerian populations (see The SNP Consortium Allele Frequency/Genotype Project Web site). Since this data set includes a West African population, it was compared to the reproducible-disease-association group. The second database (“Perlegen”) consists of SNPs for which frequency information is available in both European and African American populations (Hinds et al. 2005); it also has the virtue of including genotypes of the same samples that were studied for the Seattle SNPs project. For both databases, the physical map position, gene name, and SNP type were downloaded from dbSNP by a batch query of “rs” numbers (National Center for Biotechnology Information, dbSNP build 120, March 2004). SNPs were excluded from analysis if they were (a) not polymorphic, (b) mapped to more than one chromosomal location, or (c) within 20 kb of each other. The final WICGR data set consisted of 2,377 SNPs, and the final Perlegen data set consisted of 103,536 SNPs. To measure differentiation between European- and African-derived populations for the SNPs in all four data sets, we calculated FST (Weir and Cockerham 1984; Weir 1996), a classic measure of the frequency differentiation of a polymorphism.

Table 3
Summary of FST Values for Comparison of Disease-Association and Genomewide Data Sets

To determine whether the average FST of 0.159 in the group of nine SNPs that were reproducibly associated with common disease was significantly larger than the average for random SNPs in the WICGR data set, we subsampled the WICGR data 10,000 times, counting the proportion of times that nine SNPs randomly chosen from WICGR had an average FST[gt-or-equal, slanted]0.159. We did not find a significant increase in average FST in the reproducible-association set relative to the random group (P=.12). The same subsampling method also did not detect an excess in the percentage of SNPs with FST>0.3 (P=.26). To obtain an upper bound on the level of differentiation at common-disease–associated SNPs, we performed bootstrap resamplings of the data from the nine reproducibly associated SNPs. Of 10,000 bootstrap replicates, 95% had average FST values in the range 0.074–0.274, which, as expected, is consistent with the average FST of the random SNPs.

A potential concern with this analysis is that different numbers of samples were used to calculate FST for the disease-associated and WICGR data sets. We therefore repeated our analysis after randomly dropping samples from the WICGR data set and the reproducible-disease-association data set until only 72 African and 72 European alleles for each SNP remained. More specifically, for the reproducible-disease-association group, we performed the random sample-dropping procedure for the nine SNPs 1,000 times and recalculated FST for each replicate. The average FST and the percentage of FST values >0.3 in both data sets were extremely similar and were nearly identical to those observed in our original data sets, and we again could not reject the null hypothesis of no difference between the disease-associated and random SNPs (table 3).

We followed an identical protocol to compare FST in the group of SNPs with reported disease association to FST of SNPs in the Perlegen database. Here, there was no problem of sample size or sample mismatch, since the Perlegen set was genotyped in the same European American and African American individuals who were assayed for the disease-associated SNPs by the Seattle SNPs project. We did not find a significant increase in average FST (P=.13) or in the percentage of SNPs with FST>0.3 (P=.29) in the reported-disease-association group relative to the Perlegen data set. To obtain a 95% CI for the differentiation, we performed 10,000 bootstrap resamplings of the 39 SNPs. The 95% CI is 0.074–0.171 and includes the genomewide average FST of the Perlegen data set (0.083) (table 3), which explains why the null hypothesis of no excess differentiation in the disease-associated SNPs relative to the random SNPs cannot be rejected. Because more SNPs are available for the reported-association group than for the reproducible-association group, we were able to put a more stringent upper bound on FST for the reported-association group.

Another question in medical genetics is whether disease-associated SNPs in the genome are more differentiated than random nonsynonymous SNPs (Freedman et al. 2004). To test this, we performed an analysis of the reported-association group in comparison with 6,763 nonsynonymous SNPs from the Perlegen data set (which may, of course, include some disease-associated SNPs). We did not find a significant increase in average FST (P=.06) or in the percentage of SNPs with FST>0.3 (P=.13) in the reported-disease-association group relative to the nonsynonymous SNPs from the Perlegen data set.

The SNPs associated with common disease that we investigated do not show much higher levels of differentiation than those of random SNPs. Thus, in these cases, ethnicity is a poor predictor of an individual’s genotype, which is also the pattern for random variants in the genome. This lends support to the hypothesis that many population differences in disease risk are environmental, rather than genetic, in origin. However, some exceptional SNPs associated with common disease are highly differentiated in frequency across populations, because of either a history of random drift or natural selection. The exceptional SNPs given in tables tables11 and and22 are located in AGT, DRD3, ALOX5AP, ICAM1, IL1B, IL4, IL6, IL8, and PON1. Of note, evidence of selection has been observed for AGT (Nakajima et al. 2004), IL4 (Rockman et al. 2003), IL8 (Hull et al. 2001), and PON1 (Allebrandt et al. 2002). Yet, for the vast majority of the common-disease–associated polymorphisms we examined, ethnicity is likely to be a poor predictor of an individual’s genotype.

Acknowledgments

We thank Joel Hirschhorn for helpful discussions and for providing formatted versions of The SNP Consortium data. We also thank Jason Lohmueller and two anonymous reviewers for critical readings of the manuscript. K.E.L. is supported by a Barry Goldwater Scholarship and is a Georgetown-Hughes Undergraduate Research Scholar. D.R. is the recipient of a Burroughs Wellcome Career Development Award in the Biomedical Sciences.

Web Resources

The URLs for data presented herein are as follows:

HapMap Project, http://www.hapmap.org/
Online Mendelian Inheritance in Man (OMIM), http://www.ncbi.nlm.nih.gov/Omim/
Seattle SNPs, http://pga.gs.washington.edu/ (for National Heart, Lung, and Blood Institute Program for Genomic Application, Seattle SNPs, Seattle, WA [July 2004])
The SNP Consortium Allele Frequency/Genotype Project, http://snp.cshl.org/allele_frequency_project/ (for the WICGR data set)

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