Single-nucleotide polymorphisms (SNPs) are a class of attractive genetic markers for population genetic studies and for identifying genetic variations underlying complex traits. However, the usefulness and efficiency of SNPs in comparison to microsatellites in different scientific contexts, e.g., population structure inference or association analysis, still must be systematically evaluated through large empirical studies. In this article, we use the Collaborative Studies on Genetics of Alcoholism (COGA) data from Genetic Analysis Workshop 14 (GAW14) to compare the performance of microsatellites and SNPs in the whole human genome in the context of population structure inference. A total of 328 microsatellites and 15,840 SNPs are used to infer population structure in 236 unrelated individuals. We find that, on average, the informativeness of random microsatellites is four to twelve times that of random SNPs for various population comparisons, which is consistent with previous studies. Our results also indicate that for the combined set of microsatellites and SNPs, SNPs constitute the majority among the most informative markers and the use of these SNPs leads to better inference of population structure than the use of microsatellites. We also find that the inclusion of less informative markers may add noise and worsen the results

Chen, Liang

Liu, Nianjun

Oh, Cheongeun

Wang, Shuang

Zhao, Hongyu

BMC Genetics

PubMed

Columbia University Academic Commons

BioMed CentralBMC GeneticsssOpen AcceProceedingsComparison of single-nucleotide polymorphisms and microsatellites in inference of population structureNianjun Liu1,2, Liang Chen3, Shuang Wang4, Cheongeun Oh1,5 and Hongyu Zhao*1,6Address: 1Department of Epidemiology and Public Health, Yale University, New Haven, CT 06520, USA, 2Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294,USA, 3Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, CT 06520, USA, 4Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY 10032, USA, 5Department of Biostatistics, Department of Preventive Medicine, University of Medicine and Dentistry of New Jersey, Newark, NJ 07101, USA and 6Department of Genetics, Yale University, New Haven, CT 06520, USAEmail: Nianjun Liu - nliu@uab.edu; Liang Chen - liang.chen@yale.edu; Shuang Wang - shuang.wang@columbia.edu; Cheongeun Oh - cheongeun.oh@yale.edu; Hongyu Zhao* - hongyu.zhao@yale.edu* Corresponding author    AbstractSingle-nucleotide polymorphisms (SNPs) are a class of attractive genetic markers for populationgenetic studies and for identifying genetic variations underlying complex traits. However, theusefulness and efficiency of SNPs in comparison to microsatellites in different scientific contexts,e.g., population structure inference or association analysis, still must be systematically evaluatedthrough large empirical studies. In this article, we use the Collaborative Studies on Genetics ofAlcoholism (COGA) data from Genetic Analysis Workshop 14 (GAW14) to compare theperformance of microsatellites and SNPs in the whole human genome in the context of populationstructure inference. A total of 328 microsatellites and 15,840 SNPs are used to infer populationstructure in 236 unrelated individuals. We find that, on average, the informativeness of randommicrosatellites is four to twelve times that of random SNPs for various population comparisons,which is consistent with previous studies. Our results also indicate that for the combined set ofmicrosatellites and SNPs, SNPs constitute the majority among the most informative markers andthe use of these SNPs leads to better inference of population structure than the use ofmicrosatellites. We also find that the inclusion of less informative markers may add noise andworsen the results.BackgroundPopulation structure inference from genetic markers isvery important in a variety of contexts, such as in admix-ture and association mapping, evolutionary studies,forensics, medical risk prediction, and wildlife manage-ment [1-5]. Statistical methods have been proposed forpopulation structure inference using multilocus geno-types [1,3,5,6] and have been widely used in practice[2,3,5,7].Single-nucleotide polymorphisms (SNPs) are a class ofattractive genetic markers for population genetic studiesand for identifying genetic variations underlying complextraits. This is because SNPs are highly abundant, function-ally relevant, have relatively low mutation rates, and offermore rapid and highly automated genotyping. Withrecent efforts to identify SNPs in the human genome, link-age disequilibirum studies in different populations [8,9],and advancements in the efficiency of high-throughputfrom Genetic Analysis Workshop 14: Microsatellite and single-nucleotide polymorphismNoordwijkerhout, The Netherlands, 7-10 September 2004Published: 30 December 2005BMC Genetics 2005, 6(Suppl 1):S26 doi:10.1186/1471-2156-6-S1-S26<supplement> <title> <p>Genetic Analysis Workshop 14: Microsatellite and single-nucleotide polymorphism</p> </title> <editor>Joan E Bailey-Wilson, Laura Almasy, Mariza de Andrade, Julia Bailey, Heike Bickeböller, Heather J Cordell, E Warwick Daw, Lynn Goldin, Ellen L Goode, Courtney Gray-McGuire, Wayne H ning, ail Jarvik, Brion S Maher, Nancy Mendell, Andrew D Paterson, John Rice, Glen Satten, Brian Suar z, Veronica Vieland, Marsha Wilcox, Heping Zhang, Andre s Ziegler and Jean W MacCluer</editor> <note>Proceedings</note> </suppleme t>Page 1 of 5(page number not for citation purposes)BMC Genetics 2005, 6:S26genotyping technology, genome-wide screens of high-density SNPs are becoming increasingly feasible for stud-ies involving a large sample of individuals. However, theusefulness and efficiency of SNPs still need to be demon-strated through large empirical studies, especially in thecontext of population structure inference, where few stud-ies have been done [4]. To fill in this gap, in this article wecompare the information content (informativeness) ofSNPs and microsatellites throughout the whole humangenome, and compare the performance of SNPs and mic-rosatellites in the context of population structure infer-ence.MethodsInformativeness of markersThere are many measures of marker information contentfor various purposes [4,10,11]. There is a brief review ofmeasures of marker informativeness for population struc-ture inference in Rosenberg et al. [4]. In this study, weemploy the measure of informativeness for assignment(In) proposed in that article [4].Consider that there are i = 1, 2,..., K populations and m =1, 2, ..., L loci, with K ≥ 2 and L ≥ 1. Locus m has alleles j= 1, 2, ..., N(m). The average frequency of allele j at locus macross the K populations is defined as [4]:where  is the relative frequency for allele j of locus min population i. The informativeness is defined as [4]:Bayesian approach for population structure inferencePritchard et al. [1,6] introduced a model-based clusteringmethod (STRUCTURE) using multilocus genotype data toinfer population structure and assign individuals to pop-ulations. They used Bayesian formulation and generatedthe posterior distribution using a Markov chain MonteCarlo method based on Gibbs sampling. This is the dom-inant method currently used. We use STRUCTURE 2.0 inour analysis.DataThe data we use is the Collaborative Study on the Geneticsof Alcoholism (COGA) dataset provided by the GeneticAnalysis Workshop 14 (GAW14). There are 328 microsat-ellites and 16,312 SNPs (4,752 SNPs are provided by Illu-mina and 11,560 SNPs are provided by Affymetrix)genotyped over all 23 pairs of human chromosomes,including the sex chromosome. A total of 1,614 individu-als belonged to eight self-reported ethnic groups: 12American Indians, 4 Pacific Islanders, 191 Black non-His-panic, 14 Black Hispanic, 1,074 White non-Hispanic, 78White Hispanic, 12 others, and 229 without ethnic infor-mation. We used all 328 microsatellites and 15,840"clean" SNPs which are provided by GAW14 with Mende-lian errors removed (4,720 SNPs from Illumina and11,120 SNPs from Affymetrix). From the 1,614 familymembers, we chose unrelated (founders whose parentsare not in the dataset) individuals with both SNPs andmicrosatellites genotype data. There were 248 individualsleft. Among these individuals, there were 4 American Indi-ans, 2 Pacific Islanders, 18 Black non-Hispanic, 3 BlackHispanic, 206 White non-Hispanic, 12 White Hispanic, 2others, and 1 without ethnic information. In order foreach ethnic group to have enough sample size, we finallyincluded 236 unrelated individuals in our study fromthree ethnic groups: 18 Black non-Hispanic, 206 Whitenon-Hispanic, and 12 White Hispanic.ResultsA first level of sub-structure (e.g., Black vs. White) wasdetectable using these data. However, higher orders ofsub-structure (e.g., Black non-Hispanic vs. White His-panic) were not detectable. Nonetheless, we present someresults on informativeness comparisons between thesehigher order sub-structures for illustrative purposes.ppKjm ijmiK( )( ),==∑1pijm( )I Q J p ppKpn j jjNijijiK( ; ) ( log log ).= − += =∑ ∑1 1The distributions of informativeness (In) of microsatellites and SNPs for Black non-Hispanic vs. WhiteFigure 1The distributions of informativeness (In) of microsatellites and SNPs for Black non-Hispanic vs. White. The dotted curves are  the estimated kernel densities of the data.Page 2 of 5(page number not for citation purposes)BMC Genetics 2005, 6:S26Comparison of information content of microsatellites and SNPsFigure 1 shows the distribution of marker informativenessof microsatellites and SNPs for Black non-Hispanic vs.White. The patterns are similar for the distributionsbetween other sets of source populations (i.e., Black non-Hispanic vs. White non-Hispanic, Black non-Hispanic vs.White Hispanic, and White non-Hispanic vs. White His-panic, figures not shown). On average, microsatellitescontain more information than SNPs. This difference issignificant (data not shown). For Black non-Hispanic vs.White, random microsatellites have greater informative-ness than random SNPs (Figure 2). The same conclusionholds for other sets of source populations (figures notshown). The ratios of the median microsatellite informa-tiveness to median SNP informativeness were 4.17 forBlack non-Hispanic vs. White non-Hispanic, 6.84 forBlack non-Hispanic vs. White Hispanic, 11.79 for Whitenon-Hispanic vs. White Hispanic, and 4.19 for Black non-Hispanic vs. White (both Hispanic and non-Hispanic).The ratios of the means were 2.47, 3.797, 6.27, and 2.49,respectively, and the 50th percentile of microsatelliteinformativeness corresponds to the 86th, 94th, 98th, and86th percentiles of SNP informativeness in the four com-parisons. These results are consistent with those observedby Rosenberg et al. [4].Figure 3 shows the percentage of SNPs among differentnumbers of most informative markers (markers with thehighest information content values) for Black non-His-panic vs. White. The patterns were similar for other sourcepopulations (figures not shown). Most of the time, SNPsrepresent the majority among the most informative mark-ers. This is contrary to Rosenberg's observation [4] that"highly informative loci constitute a greater fraction ofmicrosatellites than of SNPs".Comparison of performance of microsatellites and snps in inferring population structureWe used STRUCTURE 2.0 [1,6] with all 236 individuals(assuming 2 subpopulations) using the markers with thehighest informativeness. For various choices of thenumber of markers, M, five STRUCTURE runs were per-formed with M microsatellites of highest In(among allmicrosatellites), and M SNPs of highest In(among allSNPs), respectively. Two individuals with SNP data com-pletely missing were excluded when SNPs were used in theanalysis. All STRUCTURE runs employed the admixturemodel for individual ancestry [1], the F model for allelefrequency correlations [6], and a burn-in period of length10,000 followed by 10,000 iterations.An individual was considered to be assigned accuratelywhen the greatest proportion of the ancestry identifies thesame ethnicity as the pre-defined population group of theindividual (by self-identification). Assignment accuracywas defined as the proportion of correctly assigned ethnic-ities. For each value of M, the assignment accuracies of the5 STRUCTURE runs are shown in Figure 4.Figure 4 indicates that SNPs of the highest informative-ness perform uniformly better than the same number ofmicrosatellites of the highest informativeness, especiallywhen a small number of markers are used. Another find-ing is that as the number of most informative microsatel-lites used increases, the result improves. But after a certainvalue, increasing the number of microsatellites worsensthe result. For example, when the top 160 most informa-tive microsatellites are used, all the individuals areassigned correctly to the correct subpopulations. Butwhen all 328 microsatellites are used, one individual wasmisclassified.DiscussionIn this article we use COGA data to compare empiricallymicrosatellites and SNPs in the context of populationstructure inference. Consistent with the findings in Rosen-berg et al. [4], we find that, on average, microsatellites aremuch more informative than SNPs for population struc-ture inference (Figure 1). So a randomly chosen set of mic-rosatellites should have greater informativeness (4 to 12times) than a random chosen set of SNPs (Figure 2). Ourresults are based on only two subpopulations, and weexpect the difference to be greater when more subpopula-tions are involved. A surprising finding in our study is thatInformativeness quantiles for microsatellites and SNPs for Black non-Hispanic vs. WhiteFigure 2Informativeness quantiles for microsatellites and SNPs for Black non-Hispanic vs. WhitePage 3 of 5(page number not for citation purposes)BMC Genetics 2005, 6:S26although SNPs are less informative than microsatelliteson average, among the most informative markers, SNPsusually constitute the majority (Figure 3). This is incon-sistent with the findings in Rosenberg et al. [4]. The mainreason may be that there are many more SNPs in our studythan in theirs, with 328 microsatellites and 15,840 SNPs,compared with fewer than 400 markers studied by Rosen-berg et al. [4]. When we analyze the 4,720 SNPs from Illu-mina and 11,120 SNPs from Affymetrix separately, we cansee that the percentage of SNPs in the most informativemarkers increases as the total number of SNPs increases(data not shown). Another reason may be that in Rosen-berg et al. [4], the individuals and populations in the mic-rosatellite and SNP datasets were different. Thus, webelieve that our data may better represent the relative use-fulness of SNPs versus microsatellites. Figures 3 and 4confirm the conclusion of Rosenberg et al. [4] that In doesindeed measure the ability to infer population structure.These two figures indicate that at the right quantity, SNPscan be more informative for population structure infer-ence. Because markers with high informativeness areadded first, markers added later have less and less inform-ativeness. Figure 4 indicates that the inclusion of lessinformative markers may add noise and worsen theresults.The major limitations of our study are that we have onlytwo subpopulations, and the numbers of individuals inthe two subpopulations are not balanced. Therefore, stud-ies with more subpopulations and larger and balancedsamples are needed to make more thorough empiricalcomparisons.Our results used the subjects' self-identification for initialpopulation group classification. Many researchers pro-mote self-identified race/ethnicity as being the most validmeasure for most epidemiological studies [12-14]. TheNational Institutes of Health now requires documenta-tion of minority inclusion on all new grant submissionsand considers self-reported race/ethnicity status to be thepreferred method of categorization [15]. Gomez et al. [16]showed that accuracy of self-reported ethnicity was highamong Blacks and Whites. In our analysis, only Blacks andWhites are used. We conjecture that the self-reported eth-nicity should be very accurate.In conclusion, we have compared microsatellites andSNPs in the context of population structure inference.Although microsatellites are more informative than SNPsin general, our findings show great promise for usingSNPs when a large number of SNPs are available.AbbreviationsCOGA: Collaborative Study on the Genetics of Alcohol-ismGAW14: Genetic Analysis Workshop 14SNP: Single-nucleotide polymorphismThe assignment accuracies for the 5 runs of microsatellites and SNPs each for variou numb rs of lociFigure 4The assignment accuracies for the 5 runs of microsatellites and SNPs each for various numbers of loci.The percentage of SNPs among the most informative mark-ers wher  microsatellites and SNPs are pooled together (for Black non-Hispanic vs. WhiteFigur  3The percentage of SNPs among the most informative mark-ers where microsatellites and SNPs are pooled together (for Black non-Hispanic vs. White).Page 4 of 5(page number not for citation purposes)BMC Genetics 2005, 6:S26Publish with BioMed Central   and  every scientist can read your work free of charge"BioMed Central will be the most significant development for disseminating the results of biomedical research in our lifetime."Sir Paul Nurse, Cancer Research UKYour research papers will be:available free of charge to the entire biomedical communitypeer reviewed and published immediately upon acceptancecited in PubMed and archived on PubMed Central yours — you keep the copyrightSubmit your manuscript here:http://www.biomedcentral.com/info/publishing_adv.aspBioMedcentralAuthors' contributionsNL participated in the design of the study, performed theanalysis, and drafted the manuscript. LC, SW, and CO par-ticipated in the design of the study and reviewed the man-uscript. HZ conceived the study, participated in its designand helped to draft the manuscript. All authors read andapproved the final manuscript.AcknowledgementsWe thank Dr. Noah A. Rosenberg for sending us the program for calculat-ing genetic marker informativeness proposed in [4]. We also thank Dr. Naixin Li for helping with data format. This work was supported in part by NIH grant R01 GM59507 and NSF grant DMS 0241160.References1. Pritchard JK, Stephens M, Donnelly P: Inference of populationstructure using multilocus genotype data.  Genetics 2000,155:945-959.2. Rosenberg NA, Burke T, Elo K, Feldman MW, Freidlin PJ, GroenenMA, Hillel J, Maki-Tanila A, Tixier-Boichard M, Vignal A, Wimmers K,Weigend S: Empirical evaluation of genetic clustering meth-ods using multilocus genotypes from 20 chicken breeds.Genetics 2001, 159:699-713.3. Hoggart CJ, Esteban JP, Shriver MD, Bonilla C, Kittles RA, ClaytonDG, McKeigue PM: Control of confounding of genetic associa-tion in stratified populations.  Am J Human Genet 2003,72:1492-1504.4. Rosenberg NA, Li LM, Ward R, Pritchard JK: Informativeness ofgenetic markers for inference of ancestry.  Am J Hum Genet2003, 73:1402-1422.5. Patterson N, Hattangadi N, Lane B, Lohmueller KE, Hafler DA,Oksenberg JR, Hauser SL, Smith MW, O'Brien SJ, Altshuler D, DalyMJ, Reich D: Methods for high-density admixture mapping ofdisease genes.  Am J Hum Genet 2004, 74:979-1000.6. Falush D, Stephens M, Pritchard JK: Inference of population struc-ture using multilocus genotype data: linked loci and corre-lated allele frequencies.  Genetics 2003, 164:1567-1587.7. Paetkau D, Calvert W, Stirling I, Strobeck C: Microsatellite analy-sis of population structure in Canadian polar bears.  Mol Ecol1995, 4:347-354.8. International SNP Map Working Group: A map of the humangenome sequence variation contains 1.42 million singlenucleotide polymorphisms.  Nature 2001, 409:928-933.9. International HapMap Consortium: The international HapMapproject.  Nature 2003, 426:789-795.10. Kruglyak L, Daly MJ, Reeve-Daly MP, Lander ES: Parametric andnonparametric linkage analysis: a unified multipointapproach.  Am J Hum Genet 1996, 58:1347-1363.11. Anderson E, Thompson E: A model-based method for identify-ing species hybrids using multilocus genetic data.  Genetics2002, 160:1217-1229.12. Risch N, Burchard E, Ziv E, Tang H: Categorization of humans inbiomedical research: genes, race and disease.  Genome Biol2002, 3:1-2007 [http://genomebiology.com/2002/3/7/comment/2007].13. Rosenberg NA, Pritchard JK, Weber JL, Cann HM, Kidd KK, Zhivot-ovsky LA, Feldman MW: Genetic structure of human popula-tions.  Science 2002, 298:2381-2385.14. 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Comparison of single-nucleotide polymorphisms and microsatellites in inference of population structure

Nianjun Liu

Liang Chen

Shuang Wang

Cheongeun Oh

Hongyu Zhao

Springer - Publisher Connector

Department of Health and Human Services: Public Health Service Grant application PHS 398 [http:// grants1.nih.gov/grants/funding/phs398/phs398.html]. revised May,

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Feldman MW: Genetic structure of human populations. Science

Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics

Inference of population structure using multilocus genotype data. Genetics

International HapMap Consortium: The international HapMap project. Nature

JK: Informativeness of genetic markers for inference of ancestry.

Map Working Group: A map of the human genome sequence variation contains 1.42 million single nucleotide polymorphisms. Nature

McKeigue PM: Control of confounding of genetic association in stratified populations.

Methods for high-density admixture mapping of disease genes.

Microsatellite analysis of population structure in Canadian polar bears. Mol Ecol

Practical population group assignment with selected informative markers: characteristics and properties of bayesian clustering via STRUCTURE. Genet Epidemiol

S: Inconsistencies between self-reported ethnicity and ethnicity recorded in a health maintenance organization. Ann Epidemiol

Tang H: Categorization of humans in biomedical research: genes, race and disease. Genome Biol

Weigend S: Empirical evaluation of genetic clustering methods using multilocus genotypes from 20 chicken breeds. Genetics

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Comparison of single-nucleotide polymorphisms and microsatellites in inference of population structure

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