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. 2005 Nov 16;78(1):130–136. doi: 10.1086/499287

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

Kirk E Lohmueller 1,3, Matthew M Mauney 2, David Reich 4,5, John M Braverman 1
PMCID: PMC1380210  PMID: 16385456

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

Frequency
Gene Diseasea SNP Associated Alleleb Europeand Africane δf FST Reference(s)c
CTLA4 T1DM Thr17Ala Ala .38 (1,670) .209 (402) .171 .06 Osei-Hyiaman et al. 2001; Lohmueller et al. 2003
DRD3 Schizophrenia Ser9Gly Ser/Ser .67 (202) .116 (112) .554 .458 Crocq et al. 1996; Lohmueller et al. 2003
AGT Hypertension Thr235Met Thr .42 (3,034) .91 (658) .49 .358 Rotimi et al. 1996; Nakajima et al. 2002
PRNP CJD Met129Val Met .72 (138) .556 (72) .164 .049 Hirschhorn et al. 2002; Soldevila et al. 2003
F5 DVT Arg506Gln Gln .044 (1,236) .00 (251) .044 .03 Rees et al. 1995; Hirschhorn et al. 2002
HFE HFE Cys382Tyr Tyr .038 (2,900) .00 (806) .038 .024 Feder et al. 1996; Merryweather-Clarke et al. 1997
MTHFR DVT C677T T .3 (188) .066 (468) .234 .205 Schneider et al. 1998; Ray et al. 2002
PPARG T2DM Pro12Ala Pro .925 (120) 1.0 (120) .075 .067 Altshuler et al. 2000; HapMap Project
KCNJ11 T2DM Asp23Lys Lys .36 (96) .09 (98) .27 .182 Florez et al. 2004
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a

CJD = Creutzfeldt-Jacob disease; DVT = deep venous thrombosis; HFE = hemochromatosis; T1DM = type I diabetes; T2DM = type II diabetes.

b

The associated allele is the SNP associated with disease, regardless of whether it is the derived or the ancestral allele. The frequencies for this allele are given.

c

The reference that claims this to be a reproducible association, as well as the reference from which the allele frequencies were taken. For allele frequencies obtained from a meta-analysis, only the reference claiming reproducible association is given.

d

Allele frequency obtained from the literature involving a European population. Either the general population frequency or the frequency in control groups in an association study was used. To reduce bias, when a control frequency was used for Europeans, a control frequency was also used for Africans. The total number of chromosomes surveyed is given in parentheses after each frequency.

e

Allele frequency obtained from the literature involving a West African population. The total number of chromosomes surveyed is given in parentheses after each frequency.

f

δ = The difference in the allele frequency between Europeans and Africans.

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

Frequency
Gene Disease/Phenotypea SNP Associated Alleleb Europeand Africane δf FST Referencec
ADRB1 MI Arg389Gly Arg .717 (46) .467 (30) .251 .1 Iwai et al. 2003
ALOX5AP MI, stroke rs10507391 T .682 (44) .159 (44) .523 .425 Helgadottir et al. 2004
CAT Hypertension −844 (C/T) Tg .714 (42) .659 (44) .055 0 Jiang et al. 2001
CCR2 AIDS susceptibility Ile64Val Val .87 (46) .813 (48) .057 0 Smith et al. 1997
CD36 Malaria Y to stop Stop 0 (46) .083 (48) .083 .062 Aitman et al. 2000
F13 MI Val34Leu Val .762 (42) .795 (44) .033 0 Kohler et al. 1999
FGA Pulmonary embolism Thr312Ala Ala .2 (40) .5 (42) .3 .159 Carter et al. 2000
GP1BA CAD Thr145Met Met .022 (46) .167 (48) .145 .095 Gonzalez-Conejero et al. 1998
ICAM1 MS Lys469Glu Lys .643 (42) .875 (48) .232 .12 Nejentsev et al. 2003
ICAM1 Malaria Lys29Met Met 0 (46) .354 (48) .354 .335 Fernandez-Reyes et al. 1997
IFNGR1 Hp infection −56 (C/T) T .455 (44) .604 (48) .15 .023 Thye et al. 2003
IL13 Asthma −1055 (C/T) T .196 (46) .25 (44) .054 0 van der Pouw Kraan et al. 1999
IL13 Bronchial asthma Arg110Gln Gln .273 (44) .119 (42) .154 .05 Heinzmann et al. 2003
IL1A AD −889 (C/T) T .295 (44) .391 (46) .096 0 Nicoll et al. 2000
IL1B Gastric cancer −31 (C/T) T .826 (46) .375 (48) .451 .335 El-Omar et al. 2000
IL3 RA −16 (C/T) C .739 (46) .875 (48) .136 .037 Yamada et al. 2001
IL4 Asthma −590 (T/C) T .174 (46) .708 (48) .534 .436 Noguchi et al. 1998
IL4R Asthma Gln576Arg Arg .295 (44) .565 (46) .27 .118 Hershey et al. 1997
IL6 Juvenile arthritis −174 (C/G) G .5 (44) 1 (46) .5 .494 Fishman et al. 1998
IL8 RSV bronchiolitis −251 (T/A) Th .659 (44) .229 (48) .43 .301 Hull et al. 2000
ITGA2 MI 807 (C/T) T .316 (38) .25 (48) .066 0 Moshfegh et al. 1999
LTA MI Thr26Asn Asn .357 (42) .5 (44) .143 .018 Ozaki et al. 2002
MC1R Fair skin Val92Met Met .068 (44) 0 (44) .068 .047 Valverde et al. 1995
NOS3 MI Glu298Asp Asp .5 (44) .136 (44) .364 .247 Shimasaki et al. 1998
PLAU AD Pro141Leu Pro .659 (44) .979 (48) .32 .287 Finckh et al. 2003
PON1 CAD Arg192Gln Arg .174 (46) .727 (44) .553 .461 Serrato and Marian 1995
PON2 CAD Cys311Ser Ser .826 (46) .762 (42) .064 0 Sanghera et al. 1998
PTGS2 Colon cancer −765 (G/C) C .238 (42) .292 (48) .054 0 Koh et al. 2004
PTPN22i RA Arg620Trp Trp .084 (1,120) .024 (818) .059 .03 Begovich et al. 2004
SELE CAD Ser128Arg Arg .091 (44) .021 (48) .07 .025 Wenzel et al. 1994
SELL IgA nephropathy Pro238Ser Ser .065 (46) .333 (48) .268 .183 Takei et al. 2002
SELP MI Thr715Pro Thr .864 (44) .977 (44) .114 .063 Herrmann et al. 1998
SFTPB ARDS Ile131Thr Thr .5 (44) .348 (46) .152 .025 Lin et al. 2000
SPD RSV infection Met11Thr Met .568 (44) .478 (46) .09 0 Lahti et al. 2002
TF AD Pro570Ser Pro .957 (46) .935 (46) .022 0 Zhang et al. 2003
THBD MI Ala455Val Ala .87 (46) .848 (46) .022 0 Norlund et al. 1997
THBS4 MI Ala387Pro Pro .341 (44) .083 (48) .258 .166 Topol et al. 2001
TNFA Infectious disease −308 (A/G) A .182 (44) .205 (44) .023 0 Bayley et al. 2004
VCAM1 Stroke in SCD Gly413Ala Gly 1 (46) .938 (48) .063 .041 Taylor et al. 2002
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a

AD = Alzheimer disease; AIDS = acquired immunodeficiency syndrome; ARDS = acute respiratory distress syndrome; CAD = coronary artery disease; Hp = Helicobacter pylori; MI = myocardial infarction; MS = multiple sclerosis; RA = rheumatoid arthritis; RSV = respiratory syncytial virus; SCD = sickle cell disease.

b

The associated allele is the SNP associated with disease, regardless of whether it is the derived or the ancestral allele. The frequencies for this allele are given.

c

The reference that reported association with the listed disease/phenotype.

d

Frequency obtained from the Seattle SNPs database for the European sample. The total number of chromosomes surveyed is given in parentheses after each frequency.

e

Frequency obtained from the Seattle SNPs database for the African American sample. The total number of chromosomes surveyed is given in parentheses after each frequency.

f

δ = The difference in the allele frequency between African Americans and Europeans.

g

Associated allele in database is A.

h

Associated allele in reference is A.

i

This SNP was not from the Seattle SNPs database; instead, allele frequencies from Begovich et al. (2004) were used.

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

Data Set Populations Studied No. of SNPs Average FSTa No. (%) with FST>.3
WICGR:
 All SNPs European and Nigerian 2,377 .119 237 (9.97)
 72 allelesb European and Nigerian 2,348 .113 233 (9.92)
Perlegen:
 All SNPs European and African American 1,465,325 .083 88,138 (6.01)
 ⩾20 kb apartc European and African American 103,536 .085 6,717 (6.49)
Reproducible associations European and West African 9 .159 (.074–.274)d 2 (22.22)
Reported associations European and African American 39 .120 (.077–.171)d 7 (17.95)
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a

Average FST values for African-derived and European-derived populations.

b

WICGR data set after the sample size was decreased to 72 chromosomes at each SNP for both populations (see main text).

c

Only SNPs that are at least 20 kb apart were used; this should decrease correlations among SNPs that are due to linkage disequilibrium.

d

Average FST and, in parentheses, 95% CIs obtained by bootstrapping.

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⩾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 1 and 2 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:

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

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