admixture mapping comes of age*proach to using ancestry to map genes by mckeigue in 1998 was...

GG11CH04-Winkler ARI 4 August 2010 16:2

Admixture MappingComes of Age∗

Cheryl A. Winkler,1 George W. Nelson,1

and Michael W. Smith2

1Basic Science Program, SAIC-Frederick, Inc., Laboratory of Genomic Diversity,NCI Frederick, Frederick, MD 217022Advanced Technology Program, SAIC-Frederick, Inc., National CancerInstitute-Frederick, Frederick, MD 21702; email: [email protected]

Annu. Rev. Genomics Hum. Genet. 2010. 11:65–89

First published online as a Review in Advance onJuly 1, 2010

The Annual Review of Genomics and Human Geneticsis online at genom.annualreviews.org

This article’s doi:10.1146/annurev-genom-082509-141523

Copyright c© 2010 by Annual Reviews.All rights reserved

1527-8204/10/0922-0065$20.00

∗This is a work of the U.S. Government and is notsubject to copyright protection in the UnitedStates.

Key Words

mapping by admixture linkage disequilibrium, risk loci, geneticmapping, hidden Markov chain, health disparities

Abstract

Admixture mapping is based on the hypothesis that differences in diseaserates between populations are due in part to frequency differences indisease-causing genetic variants. In admixed populations, these geneticvariants occur more often on chromosome segments inherited fromthe ancestral population with the higher disease variant frequency. Agenome scan for disease association requires only enough markers toidentify the ancestral chromosome segments; for recently admixed pop-ulations, such as African Americans, 1,500–2,500 ancestry-informativemarkers (AIMs) are sufficient. The method was proposed over 50 yearsago, but the AIM panels and statistical methods required have onlyrecently become available. Since the first admixture scan in 2005, thegenetic bases for a range of diseases/traits have been identified by admix-ture mapping. Here, we provide a historical perspective, review AIMpanels and software packages, and discuss recent successes and unex-pected insights into human diseases that exhibit disparate rates acrosshuman populations.

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FurtherANNUALREVIEWS

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INTRODUCTION

A major goal of human genetics is to identifygenetic variation predisposing to complex,common human diseases. Genome-wide-association scans (GWAS) have led to thediscovery of thousands of alleles associatedwith human diseases and traits (31), but GWASare costly and, because of the large numberof single nucleotide polymorphisms (SNPs)analyzed, incur a stiff statistical penalty (61).This is problematic because of the growingrealization that most common-risk alleles havesmall effect sizes while alleles with large effectsizes tend to be much less frequent (37). Tohave the power to identify small to moderateeffect requires thousands of cases and controls(Table 1). Admixture mapping provides anattractive and more powered alternative toGWAS for gene discovery in admixed popula-tions for a subset of diseases/traits that are dif-ferentially distributed in the ancestral (parental)populations (49). The idea is straightforward—the genetic variation causing the disease/trait ofinterest will be more frequent on chromosomesegments derived from the parental populationwith the higher disease/trait incidence.

In this review, we provide only a briefoverview of the theoretical basis and statisticmethods for admixture mapping as these havebeen expertly treated by others (41, 43, 69, 72,85). Instead, we provide a comprehensive re-view of admixture mapping software programs,the development of admixture panels, applica-tions, and successes in the 5-year span mark-ing the passage from an elegant theoretical ap-proach to a powerful gene mapping applicationwith several notable successes.

Table 1 Whole-genome-wide mapping methods for gene discovery

Linkage Admixture Association ReferenceStatistical power Low High Medium to high 49, 61Number of SNPs Low < 1,000 Low 1,500–3,000 Very high >500,000 50Resolution Low 20–30 cM Intermediate 1–10 cM High 1–10 cM 35, 36Sensitivity to genetic heterogeneity Low Low to moderate High 81

HISTORICAL PERSPECTIVE

The human diaspora that has occurred in thelast 400–600 years has resulted in gene flow be-tween previously separated human subpopula-tions (Figure 1). Meiotic crossover in admixedpopulations leads to a mosaic of chromosomalsegments derived from one or the otherancestral (parental) subpopulation (Figure2). The duration, direction, and rate of geneflow between two populations influence theproportion of admixture and the length of chro-mosomal segments derived from the ancestralpopulations, which will vary among individuals.In admixed populations where gene flow com-menced within the last several hundred years(e.g., African Americans or Hispanics), linkedalleles will show extended linkage disequilib-rium (LD) relative to the ancestral populations.

Using the admixture linkage disequilibrium(ALD) generated in recently admixed humanpopulations to assign the traits to linkagegroups was first proposed by Rife in 1954 (59),but it took nearly 4 decades for the approachto gain serious attention. In 1988, Chakroborty& Weiss (13) renewed interest in ALD to mapgenes by positing a classical linkage approachanalogous to family or hybrid animal studiesthat exploits long range LD to limit the numberof markers required for genome-wide coverage.This was followed by a flurry of theoretical pa-pers in the 1990s (11, 41, 60, 72); these earlypioneers advanced various statistical strategiesand methods, all of which were based on theassociation between a marker allele and traitto assign genes to a linkage group—a methodStephens et al. (72) termed mapping by admix-ture linkage disequilibrium (MALD).

66 Winkler · Nelson · Smith

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1526

1492

1450

1500s

1499

Figure 1Major migrations and diasporas, 1400–1800, that are sources of important admixed populations for admixture mapping.

In 1998, McKeigue (42) proposed an alter-native approach to disease gene localization thattested for the linkage of the disease or trait withparental ancestry at each locus, defined as 0, 1,or 2 allele copies inherited from the ancestralpopulations. McKeigue named this approachadmixture mapping because it is based on theassociation of local chromosomal ancestrywith the disease rather than on LD betweenthe marker and phenotype (Figure 3). It wasquickly appreciated that admixture mappingcould be applied to case-control studies by com-paring locus-specific ancestry at each ancestry-informative marker (AIM) between cases andcontrols. Hoggart et al. (32) and Montana &Pritchard (45) showed through simulations thatthe extent of ancestry at each locus could alsobe compared to genome-wide average ancestryfor a case-only study design (32, 45). Theydemonstrated that for rare diseases, an affected-case-only design is highly efficient and betterpowered than a case-control design. A statis-tical deviation in local ancestry away from thegenome-wide average could be used to identifya peak region of interest. In studies where both

cases and controls are available, most inves-tigators elect to calculate both a case-controlstatistic quantifying the difference in ancestrybetween cases and controls, and a case-onlystatistic comparing the extent of ancestryat each locus to the genome-wide average.Like hybrid or family linkage studies, amoderate number (≈1,500–2,500) of ancestry-informative markers is needed for the initialgenome-wide scan, followed by fine mappingwith additional markers to identify the causalallele (49). Further, Patterson has shown thatadmixture mapping has the power to detectassociation with relatively modest odds ratioswith 2,500 or fewer cases (Figure 4); this offersan advantage over standard GWAS resultsbecause it uses far fewer markers and thus hasa much more modest correction for multiplecomparisons.

The development of the theoretical ap-proach to using ancestry to map genes byMcKeigue in 1998 was followed rapidly bythe development of statistical methods, pan-els of AIMs for admixture typing, and softwareprograms, many of which became available by

www.annualreviews.org • Admixture Mapping Comes of Age 67

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Generationsof admixture

Today

Continentalisolation ofpopulations

Figure 2Schematic pattern of chromosomal ancestryresulting from a moderate number (∼8–20) ofgenerations since a two-way admixture event.Starting with the second generation, recombinationproduces chromosomal blocks of differentcontinental ancestries. The present day admixedpopulation has a varying extent of overall ancestryand has blocks of ancestry that vary in size bothbecause of the random nature of recombination andbecause the original chromosomes have been subjectto recombination for different numbers ofgenerations.

2004. The successful application of admixturemapping rapidly followed the publication ofwell-designed Latino and African genotypingpanels of AIMs (16, 50, 70). Admixture map-ping has come of age and is now being appliedto a wide range of traits and diseases for whichit is hypothesized that the differences in disease

rates across populations are due to population-specific frequency differences of the causalvariant(s).

THE PROCESS OF ADMIXTURE

Gene flow between reproductively isolatedpopulations results in chromosomal admixturewith contributions from each contributing an-cestral population. The gene flow can be a sin-gle event in time or continuous over many gen-erations. The gene flow results in the tem-porary generation of long haplotype blocks,which includes polymorphic variants, derivedfrom one or the other ancestral population(Figure 2). These blocks of alleles in ALD areextremely extended in the first few generationsfollowing introgression, but the LD segmentsbecome progressively shorter by recombinationwith increasing generations. The length of hap-lotype segments derived from each of the ances-tral parent populations is a function of both thenumber of generations since the initial admix-ture event and the duration of gene flow.

In recently admixed populations, the ex-tent of ALD is intermediate between ances-tral populations in which recombination hasoccurred over hundreds of thousands of gen-erations and families in which recombinationhas occurred in only a few generations. Thealternative fixation of the FY− allele of theDuffy antigen receptor for chemokines (DARCor FY) in African populations and the FY+allele in European populations were used toempirically track ALD between the FY lo-cus and neighboring markers in African Amer-icans. ALD was found to extend across a30-cM region, centered on the FY locus, butwas strongest for a flanking interval of 5–10 cM(Figure 5) (35). These results are in agreementwith other studies that indicate similar-sized re-gions of ALD (49). Using a set of 3,011 AIMsspanning the genomes of African Americans,Patterson and colleagues estimated that strongALD extends on average for approximately17 cM (49), indicating an average of six toseven generations of admixture (49, 70). Thesefindings of extended ALD in the recently


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admixed African American population providedempirical support for the practical applicationof admixture mapping.

ADMIXED POPULATIONS

Although gene flow and resulting admixturehave occurred throughout human history, itis the relatively recent gene flow betweencontinental populations that is currently mostamenable for admixture mapping. The forceddiaspora of Africans to the Americas in the six-teenth century has resulted in two-way admix-ture between Africans and Europeans in theUnited States (Figure 1). On average, AfricanAmericans have gametes that are approximately80% African derived and 20% European de-rived (49, 58). Of course, for any single indi-vidual, African ancestry may vary from 100%African derived to 1% (44, 47, 48, 51). Admix-ture has also occurred between the Spanish andAmerindians as a result of the Spanish conquestand colonization of the New World over threecenturies, beginning with the 1492 discoveryof the New World. In a survey of ancestry es-timates of Latino populations from California(Los Angeles), Mexico, Brazil, and Columbia,the Latino populations from Mexico and LosAngeles had approximately 50% European an-cestry and 40% North American Amerindian,whereas Latinos from Brazil and Columbia had71% European ancestry; average African an-cestry ranges from approximately 4% in Mex-icans to 10–11% in South American Latinos(Table 2) (53). Other surveys of Latino ances-try indicate considerable heterogeneity amongregions, with a range of 33–95% European an-cestry, 0–58% Native American, and 0–29%West African; the proportions show regionaland geographic variation (8, 10, 16, 36) as wellas differences in ancestry associated with so-cioeconomic status (24).

Unique admixed populations are the so-termed Cape Colored residing in the westernCape of South Africa (50) and the Uyghurs ofwest China (81, 82). The Cape Colored pop-ulation is genetically heterogeneous, with ad-mixture contributions from the isiXhosa, Eu-

a Cases b Population controls

Anc

estr

y (%

)

75

100

50

Anc

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y (%

)

75

100

50

Figure 3Schematic of the pattern of chromosomal admixture around a disease locus.We suppose the disease is inherited from the majority ancestry population(dark green), with the minority ancestry population shown in light green. Thegraphs show the percentage of ancestry derived from the dark green segment ofchromosome. (a) In the region of the disease locus (yellow bar), there is anexcess of majority ancestry blocks among cases, revealed as a spike in a graph ofaverage ancestry for cases along the chromosome. The orange bar indicates thelocation of the disease gene. (b) Among population controls, the distribution ofancestry blocks is random across the chromosome. The spike of ancestry can bequantified either by comparing case ancestry with control ancestry at the samelocation or by comparing peak case ancestry with average case ancestry acrossall chromosomes.

ropeans, South Asians, and Indonesians. Sincethe isiXhosa are themselves admixed, with ma-jor ancestry contribution from the Bantu andto a lesser degree from the Bushman (San),the South African Cape Colored are at leastfive-way admixed (50). Zinjiang territory infar west China straddles the Silk Trade route


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Increased risk (on average) due to one population’s ancestry

Num

ber o

f sam

ples

2,500

1,500

500

1.2 1.4 1.6 1.8 2.0

Locus where risk is higher in the populationcontributing more ancestry (e.g., multiplemyeloma in African Americans)

Locus where risk is higher in the populationcontributing less ancestry (e.g., testicularcancer in African Americans)

Figure 4Number of samples required to detect a disease or trait locus with perfectinformation on ancestry and the same proportions of two-way ancestry in eachparent. The sample number needed to detect an association in AfricanAmericans is estimated by averaging the power for a given risk model and thepercentage of ancestry over the percentages of ancestry seen in AfricanAmericans (European ancestry ∼20 ± 12%). In practice, the power is robustfor ancestry ranging from 10–90%. From Reference (49).

connecting East Asia with Central Asia andEurope. Studies by Xu & Jin (82) found thatthe Uyghurs, representing 50% of the pop-ulation of the Xinjiang Uyghur AutonomousRegion in northwest China (>9.4 million),

Distance from DARC (FY)

Corr

ecte

d PV

+/+ vs –/–

10–14

10–12

10–10

10–8

10–6

10–4

10–2

0–40 –30 –20 –10 0 10 20 30 40

Figure 5The extent of admixture linkage disequilibrium (ALD) around the DuffyAntigen Receptor for Chemokines (DARC or FY) gene. The alternative fixation ofthe FY− allele in sub-Saharan Africa and the FY+ allele in Europeanpopulations is an extreme example of differentiation between two continentalpopulations; however, it does allow the tracking of ALD between the FY allelesand 17 neighboring markers. The x-axis shows the position of the neighboringmarkers relative to the DARC locus and the y-axis shows the strength of theassociations with the DARC− allele. The gray dotted line represents acorrected probability of 0.05. Adapted from Reference (35).

have significant amounts of European ances-try, estimated at approximately 50%. Assum-ing a single pulse of admixture, STRUCTUREestimates the Asian–European admixture oc-curred 2,080–2,720 years ago (104–136 gener-ations), whereas ADMIXMAP dates the eventto 1,680–2,400 years ago (84–120 generations).The length of the chromosomal regions de-rived from East Asian and European popula-tions averages 2.4 cM and 4.1 cM, respectively;therefore, it will take approximately tenfold asmany AIMs for genome-wide coverage. Otherpopulations where recent admixture has beendocumented are the Australian Aboriginals (63)and the Pacific Island populations (e.g., Hawaii,Norfolk Island) (28, 29, 40). These populationsoffer unique opportunities to identify genes as-sociated with medical conditions or physiolog-ical traits that differ across populations.

With increasing knowledge gained throughresequencing and high-density genotyping ar-rays of diverse populations, we anticipate theidentification of more admixed populationsand finer grained discernment for both inter-and intracontinental ancestry contributions.To date, high density, genome-wide admix-ture mapping panels have been constructedonly for African Americans, Latino/Hispanics,and Uyghurs (Table 3)—populations withintercontinental ancestry mixtures. Within-continent admixture can also be exploited forgene mapping; the 1,000 Genomes Project andInternational HapMap Project will no doubtidentify a subset of SNPs that is differenti-ated among intracontinental populations (e.g.,northern and southern Europeans) to constructfine-grained admixture maps for these admixedpopulations.

Two-way admixture between continentalpopulations has been modeled and applied toadmixture studies, but three-way admixtureis not uncommon, particularly among Lati-nos/Hispanics, who may have ancestral con-tributions from northern, central, or southernNative Americans, Africans, and Europeans.Further, as observed in the Cape Colored popu-lation in the western Cape of South Africa, geneflow may occur between continental groups


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Table 2 Ancestry estimates of four Latino populations

Percent ancestry by population (%)Latino (Los Angeles) Mexican Brazilian Columbian

European (United States) 48 52 71 71North Amerindiana 40 43 10 7South Amerindianb 4 2 8 12African (Ghana) 8 4 11 10

aZapotec, Mixe, Mixtec, Maya, Mazahuas, Purepechas.bEmbera, Kogi, Quenchua, Ticuna, Waunana, Zenu. Zapotec ancestry was 15% and 17% in Latino (Los Angeles) and 2%each in Brazilian and Columbian Latinos. No single South American Amerindian population sampled contributed >3%ancestry to South American Latinos sampled.Table modified from Reference (53).

(e.g., African Americans), within continentalgroups (e.g., isiXhosa in South Africa), or incombination (e.g., Cape Colored ethnic groupin South Africa). Current statistical methodsand panels for ancestry-informative markers areoptimized for two-way admixture between con-tinental groups. However, as discussed below,tools for distinguishing recent and ancient ad-mixture, as well as fine-grained intracontinentaladmixture, are being developed.

METHODS AND PROGRAMS

Here, we give a brief conceptual review of thegeneral algorithmic approach to inferring chro-mosomal ancestry and of using this inference toestimate the likelihood that a genomic region isassociated with disease (Table 3). Zhu gives anextensive review of the mathematical issues ofadmixture mapping (85).

The proposal of McKeigue to infer theancestry of stretches of chromosome was thestarting point for practical use of admixturemapping (42). The accuracy of inference ofancestry for a single locus is strictly set by theallele frequency difference between the two an-cestral populations; for the extremely rare caseof an allele with a fixed difference [fixation index(Fst) = 1] between populations, the allele in anadmixed individual must be inherited from thepopulations carrying that allele. This is key toMcKeigue’s proposal; however, this inferencebecomes rapidly less certain as the frequencydifference [delta (δ)] becomes smaller. By con-

sidering the ancestry of chromosomal segmentsrather than that of individual loci, one takesadvantage of the fact that, as long as the averagedistance between tested loci is less than the av-erage distance of recombination fragments be-tween the admixed populations, adjacent mark-ers will have a significantly greater-than-chancelikelihood of being on the same segment.

This tendency is exploited by the hiddenMarkov model (HMM) algorithms that formthe basis of the ancestry calculations (42, 49).Considering a chromosome in an admixed in-dividual, by assumption, each locus is inheritedfrom one of the ancestral groups. However,we actually observe only the allele, not theancestry. Starting from a particular locus, ifwe proceed in one direction down the chro-mosome, we encounter successive tested loci.Between one locus and the next, there may ormay not have been a recombination event sincethe original population admixture events. Ifthere was a recombination event, the ancestryof the loci switches. We assume the chance ofcrossover between any two loci is independentof the presence or absence of recombinationbetween previous loci (this is not strictly truebiologically but is a reasonable approximation).With this assumption, the succession of ances-tries along the sequence of loci forms a Markovprocess; that is, the chance that locus n is ofAfrican ancestry depends on whether locus n−1is African or European, and on the likelihood ofrecombination between n−1 and n, but not onthe ancestry of any of the earlier loci. Because


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Observed allele1 111 00 0 0

0.7 0.6 0.5 0.7 0.6 0.8 0.6 0.6

0.4 0.2 0.3 0.5 0.3 0.4 0.2 0.3

Allele 1 frequency ancestry A

Allele 1 frequency ancestry B

A AAA B BBB Chromosome ancestry

Figure 6Example of the influence of underlying chromosomal ancestry on observed genotype. For simplicity, wesuppose we are viewing a single chromosome (X chromosome or autosomal chromosome with knownphase). Observation of the genotype at a locus allows a probabilistic inference of the ancestry of the locus;e.g., for locus n, the observed allele 1 is more likely to have come from an A chromosome than from a Bchromosome (here, for simplicity, allele 1 is always the more frequent allele in ancestral population A).Where recombination has occurred since the admixture event, the chromosomal ancestry switches, so thereis a succession of blocks of alternating ancestry. The observed alleles will probabilistically follow the allelefrequencies from the underlying ancestral population of that chromosomal block. The task is to useknowledge of the ancestral allele frequencies, proportion of A and B ancestry, and amount of recombination(a function of the genetic distance between the loci and the time since admixture) to infer the succeedingblocks of A and B ancestry from observation of the genotypes.

we cannot actually observe the ancestry of theloci, this is a hidden Markov model.

What we actually observe are the genotypesat the loci. Knowing the overall ancestry ofthe individual, and the likelihood of recombi-nation between successive loci, we may calcu-late probabilities of different sequences of an-cestries of the loci. If we know the ancestralallele frequencies at each locus, we know theprobabilities of observing a given allele at eachlocus, given a specific sequence of ancestries(Figure 6). It is now a computationally inten-sive, but tractable, problem to infer the prob-ability of a given sequence of ancestry, giventhe observed sequence of alleles. The programsdiverge in the mathematical approach to thisinference of ancestries from genotypes, withone group using a Markov chain Monte Carlo(MCMC) algorithm (22, 32, 45, 49) and a sec-ond group using maximum-likelihood infer-ence (Table 3) (73, 86). The output of theseinferences is an estimate of whether an individ-ual has zero, one, or two chromsomes inheritedfrom a particular ancestry, at a given point onthe chromsome (Figure 7).

The common assumption in these programsis that the loci are not in linkage disequilib-rium in any of the ancestral populations. Thisassumption is required if we assume that theprobability of a particular allele at locus n is

determined totally by the ancestry of that lo-cus. Conversely, if there is LD in an ancestralpopulation between locus n and locus n−1, andif the two loci both have that ancestry, then theallele frequency at locus n is also a function ofthe allele present at locus n−1, by the definitionof LD.

SCORING DISEASEASSOCIATION

If there is in fact a detectable ancestry-associated disease association due to geneticvariation, there will be a peak around the dis-ease locus in the calculated fraction of ances-try from the ancestral population at greaterrisk for the disease group. A measure of thestrength and statistical significance of this peakis needed; available software programs supplyseveral alternative but fairly equivalent mea-sures. For case-only data, all of these mea-sures involve a comparison of the proportionof ancestry from the ancestral population withhigher frequency of the risk allele, at a putativedisease associated marker, compared with thegenome average or average over unrelated sec-tions of the genome. A straightforward exampleis the Z score statistic used in both ADMIX-PROGRAM and MALDsoft (45, 86), whichfor a case-only design compares the difference


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Num

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Figure 7Ideal output from a chromosomal ancestry inference program: ancestry for an autosomal chromosome pairfrom an individual (a) and from a second individual (b). For each point along the chromosome, the programindicates whether 0, 1, or 2 chromosomes carry the specified ancestry (light green). Realistically, programsindicate the probability of carrying 0, 1, or 2 chromosomes from the specified ancestry; in favorable cases,the program predicts the ancestry with near certainty.

between the calculated proportion of ancestryat the marker m and the overall proportion ofancestry, with the variance in the proportion ofancestry:

ZC (m) = pd (m) − pd (unl)σ (pd (m))

,

where ZC (m) is the Z score for the marker;pd (m) and pd (unl) are, respectively, the calcu-lated proportions of ancestry from the risk pop-ulation at the marker and at an unlinked re-gion of the genome; and σ (pd (m)) is the vari-ance of the calculated proportion of ancestryat the marker. Z scores for case-control de-signs are calculated in an analogous manner.Alternative approaches (ANCESTRYMAP,ADMIXMAP) calculate a likelihood ratio forthe probabilities of the calculated proportionof risk population ancestry at the marker, com-paring the probability under the assumptionthat the marker is linked to a disease locus withthe probability under the assumption that themarker is unlinked (32, 49).

NEWER SOFTWAREANALYSIS PROGRAMS

Part of the original motivation for developingadmixture mapping was economizing on geno-typing; it was not anticipated how rapidly typing∼1M SNPs per individual would become stan-dard. Thus the methods described above rely onseveral thousand SNPs, which, to be optimallyinformative, are chosen to have large frequencydifferences between the ancestral populationsand, for simplicity in the HMM calculation,should not have significant LD between mark-ers in the ancestral populations. However, thereare fundamental reasons why typing a denseset of markers, e.g., from a standard GWASpanel, intrinsically provides more informationfor ancestry determination. Above all, such apanel makes information on short-range haplo-types from the ancestral populations available;these are powerful for ancestry estimation as,although it is extremely rare for an allele of asingle polymorphism to be unique to a conti-nental population, it is quite common for a spe-cific haplotype to be unique to a population.


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Allowing ancestral LD between markers im-plies relaxing the Markov model assumption,since with LD the genotypes within a sin-gle inherited block follow their own Markovmodel; thus the genotype probabilities are afunction of the previous genotype as well asof the hidden Markov model. Thus such ap-proaches have been referred to as Markov hid-den Markov Models (MHMMs) (85). These al-gorithms must deal with the substantially in-creased complexity of considering the multiplehaplotype states within each ancestral popula-tion in addition to the multiple ancestral pop-ulations. As of this writing, to our knowledge,none of these programs (listed in Table 3) in-clude a disease-association calculation; rather,the programs output estimated ancestral popu-lation frequencies across the genome, which canbe applied by the user to association analysis.

ANCESTRY-INFORMATIVEMARKERS FOR ADMIXTUREMAPPING

Ancestry informative markers are genetic poly-morphisms that differ in allele frequencies be-tween the ancestral populations. Although anymarker can be used [e.g., single tandem re-peats (STRs), single nucleotide polymorphisms(SNPs)], the most often employed are bial-lelic SNPs; SNPs are abundant, evenly spacedacross the genome, and readily genotyped.Table 4 summarizes genome-wide AIM panelsinformative for ancestry between continentalpopulations. Admixture panels comprise mark-ers with high information content for ances-try determination that are evenly spaced forgenome-wide coverage. The optimal density ofthe panel is determined by the length of theALD blocks, which is in part determined bythe number of generations since the admix-ture event—increasing generations lead to a de-cay of ALD and restoration of linkage equilib-rium that result in shorter ALD blocks. ShorterALD blocks or more complex ancestry (e.g.,three- or four-way admixture ancestry) will re-quire a higher density of markers to differenti-ate chromosome ancestry transition due to mei-

Table 4 Admixture mapping panels

Number of AIMs Ancestral populations ReferenceLatinos

1,649 SNPs Amerindian versus European and African 535,287 SNPs Amerindian versus European 762,010 SNPs Amerindian versus European 38

African Americans744 STRs European versus African/Asian 683,011 SNPs European versus African 704,222 SNPs European versus African 771,509 SNPs European versus African (Illumina) 26, 49, 70

East Asian Uyghurs8,150 SNPs East Asian versus European 82

otic crossover events. The key requirement foradmixture mapping panels is a set of geneticmarkers that provides information at each lo-cus of the ancestry origin of the allele. In addi-tion, the markers need to be distributed acrossthe genome, to be independent (i.e., not in LDwith each other), and dense enough to provideresolution of the ancestry transition from oneancestral chromosomal state to the other.

A number of methods can be used to deter-mine the information content of a marker or aset of markers. Generally, markers are selectedbased on their large differences, or delta (δ), inallele frequency between the ancestral popula-tions. Alleles with an Fst = 1 provide maxi-mal information, but such highly differentiatedmarkers are the exception. Usually, a fixationindex (Fst) > 0.5 is considered sufficient for an-cestry differentiation. The information contentof markers for distinguishing two ancestral pop-ulations can be quantified by any of six meth-ods: the absolute allele frequency difference(δ) between the two population samples; Fst,a measure of intra- and interpopulation; vari-ation the Shannon information content (SIC);the Fisher information content (FIC); pairwiseKullback–Leibler divergence; and the informa-tiveness for assignment (In) (62, 85). A com-parison of the six methods by Rosenberg et al.(62) indicates that the measurements are highlycorrelated, although In may perform slightlybetter.


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A major challenge in marker selection is ge-netic heterogeneity that may exist among pop-ulations contributing to the ancestral parentalpopulation—this can be reduced by testingAIMs in several subpopulations and select-ing SNPs that have low information con-tent (e.g., Fst < 0.05 in pairwise comparisonsamong North and South American Amerindianpopulations) (53). In contrast to the rela-tive homogeneity of Europeans and WestAfricans, Amerindian populations are more ge-netically diverse (53, 70). It should be notedthat admixture maps constructed to date arefor admixture mapping in populations wherethe founding ancestral populations are conti-nental populations—African and European orAmerindian and European. It should also bepossible to use admixture mapping for con-tinental subpopulations using the same prin-ciples; however, large SNP databases will berequired to select SNPs that differentiate be-tween two closely related founder populationssince the allele frequency differences are esti-mated to be on average much lower comparedto intercontinental [e.g., among African (17,77) or Amerindian populations (16, 53)] allele-frequency differences.

The first admixture panel comprised 744STRs for admixture mapping in African Amer-ican and Hispanic (Latino) populations (68).This admixture map was superseded in 2004by a high-density admixture map for AfricanAmericans (70). The development of the firsthigh-density SNP map illustrates the chal-lenges of SNP selection for ancestry informa-tiveness, prior to the publication of the firstphase of the International HapMap Project in2007 (2). A total of 450,000 SNPs from var-ious public and private sources were queriedto obtain a subset of nonredundant mark-ers with concordant physical and genetic (De-Code) positions and known frequencies in Eu-ropeans and Africans. The “best” SNPs wereselected using a computer program written tochoose SNPs that were evenly spaced across thegenome and that were maximally informativefor ancestry at each locus along the genome.Using an iterative greedy algorithm, SNPs were

selected to add the most information to SNPsthat had already been selected. That is, someinformation may already be provided by pre-viously selected SNPs in the 8-cM window ofthe candidate gene; new markers were addedonly if they provided additional information asdetermined by the SIC. Additional criteria foreach added SNP were that it had to be at least50 kb from its nearest neighbor; the estimatesof frequencies from databases were adjusted toaccount for sampling fluctuation that might in-flate differential frequencies; and the estimatesof frequencies were adjusted by transformingall SNPs so they were 7% closer to 0.5. Thisprompted the program to select markers for themap close to even the most informative SNPs—thereby insuring power even if genotypes weremissing.

Once the SNPs were selected, they weregenotyped on European Americans, AfricanAmericans, sub-Saharan Africans, and MexicanAmericans. The validated SNPs were includedon the final mapping panel if they (a) geno-typed successfully in West African and Euro-pean American parental populations; (b) con-formed to Hardy–Weinberg equilibrium in theparental populations; (c) had a minimum levelof informativeness (SIC > 0.035), out of a maxi-mal of 0.709 at the DARC null (FY−) locus; and(d) were similar in frequency for intracontinen-tal populations. In addition, SNPs were elim-inated if they were spaced <50 kb from eachother or if they were in LD in the parentalpopulations. The final map comprised 3,011AIMs with a 1.2-cM spacing and 70% infor-mativeness for distinguishing between Africanand European origins of chromosomal seg-ments. Illumina now has a commercially avail-able product for admixture mapping, with 1,509ancestry-informative SNPs for admixture stud-ies in African Americans, developed in collab-oration with the Reich and Patterson group(26, 49).

A second-generation, high-density map forAfrican Americans was published in 2006 (77)using extensive SNP (∼4 million) genotype-frequency data obtained for Asians fromBeijing (BEI) and Tokyo (CHB), West African


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Yoruba from Ibadan, Nigeria (YRI), andCEPH Europeans (CEU) by the InternationalHapMap Project first and second phases (2,25, 75) for initial SNP selection. Over 300,000SNPs showing a high degree of allele frequencydifferences between YRI and CEU, with Fst >

0.25 and Fisher’s information content (FIC) >

1.0, were selected for further evaluation. TheFIC identified SNPs that were particularlyinformative where one parental population(African) contributed substantially more thanthat of the other population (European) inthe admixed African American population.Using the FIC values, >5,000 SNPs from theoriginal 300,000 were selected by choosing amaximum of 4 SNPs in a 2-Mb window, witha minimal distance of 100 kb between SNPs,and by eliminating SNPs that either providedredundant information or were difficult togenotype. This set of selected HapMap SNPswas further tested on two populations ofWest Africans (Bini and Kanuri), as well asthe HapMap CEU and an independent setof Europeans, for differences in FIC, Fst,delta (δ), and allele frequencies to identifyand remove SNPs that showed heterogeneousallele frequencies within continental popula-tions or that were not in Hardy–Weinbergequilibrium in the parental populations. Thefinal SNP set comprised 4,222 SNP AIMs.This admixture map included only SNPsthat were separated by a minimum of 100kb, but the SNPs were not eliminated ifthey were in LD with each other. It hasbeen proposed that LD between SNPs mightresult in false-positive signals. The results ofsimulations using case-only and case-controlalgorithms for the effects of LD between SNPswith ADMIXMAP, STRUCTURE, andMALDSOFT (Table 3) indicated thatANCESTRYMAP was more sensitive tofalse-positive peaks of excess ancestry with acase-only design, but all programs were robustagainst false positive ancestry signals usingcase-control algorithms (77).

Both the Smith et al. (70) and the Tian et al.(77) African American admixture mapping pan-els were constructed using SNP rather than mi-

crosatellite markers and rigorous criteria forSNP selection. The Tian et al. panel had alarger SNP pool for AIM selection; this per-mitted the group to select SNPs that were closeto fixation in West Africans by first choosingSNPs with high FIC rather than high Fst val-ues, thereby increasing the informativeness inAfrican American subjects where the individualadmixture was 20:80 European: African (77). Itshould be noted that the Tian et al. panel pro-vides a very dense map with 4,222 SNP AIMsand also a less dense map of 2,000 SNP AIMs;the 4,222 AIMs are reported to extract >60%and >70% of the admixture information formore than 98% and 90%of the genome, respec-tively, whereas information extraction with the2,000 AIMs was decreased. At 80% ancestry in-formation, the 4,222 AIMs provided coveragefor more than 60% of the genome comparedwith only 35% for the 2,000 AIMs. The use ofdenser SNP panels may be more informativefor diseases with small ethnicity/ancestry riskratios (77).

Latinos/Hispanics in the United States andthroughout Latin America are largely a mixof European and Native American ancestry,resulting from European colonial rule fromthe fifteenth to nineteenth century, and insome regions include a variable degree ofAfrican ancestry (53, 64, 66). The ancestryof Latino/Hispanic populations also showsconsiderable regional differences due in part tohistorical differences in the extent of Europeanand African immigration, the density of NativeAmerican populations, and the duration ofgene flow (64, 66). Hence, the admixture ofLatinos/Hispanics may be two- or three-way,with varying degrees of ancestry contributionfrom Native Americans, Europeans, andAfricans. Added challenges to designing His-panic/Latino SNP panels are that the NativeAmerican populations are genetically hetero-geneous (12, 16, 18, 70) and have considerablygreater linkage disequilibrium than other pop-ulations (18). Attesting to the interest of genediscovery for high-frequency conditions (no-tably type 2 diabetes and metabolic syndrome)that occur more often in Native Americans and


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Hispanic/Latinos compared with theirEuropean counterparts (Table 4), three SNPadmixture maps were published in a singlevolume of the American Journal of HumanGenetics (38, 53, 76). Although each of thepanels differs in selection criteria and targetedHispanic/Latino populations, they make theapplication of admixture mapping to thisdiverse population practical.

The Mao et al. panel comprises 2,120 SNPs,with high-frequency differences between Na-tive American and European populations andan average intermarker genetic distance of1.7 cM. The SNPs were selected from theAffymetrix GeneChip Human Mapping 500-Karray used to obtain genotypes for populationsamples from Europeans, MesoAmericans(Mexico) comprising Maya and Nahua, andSouth Americans comprising Aymara/Quechua(Boliva) and Quechua (Peru). The primarycriteria for SNP selection were maximizingallele frequency differences between Amerindi-ans and Europeans and minimizing the allelefrequency differences, or delta (δ), among theNative American populations, making the panelappropriate for admixture mapping in two-wayadmixed populations throughout the Americas.

The Tian et al. SNP panel for Mexi-can American admixture mapping (76) is sim-ilar to the Mao et al. panel (38) in thatthe markers were selected to differeniate be-tween Amerindian and European ancestries.The AIMs were selected from over 400,000SNPs chosen from the gene-centric 100-K Il-lumina array, the 317-K Human Hap array(that utilized HapMap data to select haplotype-tagging SNPs), and a set of 20,000 selected ontheir high Fst > 0.25 values between East Asiansand Europeans in the International HapMapProject. Because of the shared ancestry betweenAsians and Amerindians, these SNPs were en-riched for ancestry information content. TwoAmerindian populations, Pima (Arizona) andMayan (Guatemala), were used to eliminatethe subset of SNPs that differentiated only oneAmerindian group from Europeans. The finalpanel of 8,144 SNPs had an Fst > 30 (mean =0.48), with all but 3 separated by a minimum

of 50 kb. A subset of this panel (5,287 SNPs)was shown to discriminate between Europeansand Amerindians from South America, indicat-ing that it, like the Mao et al. panel, would bebroadly useful for admixture studies of disparateHispanic/Latino populations.

It is not uncommon for Hispanics/Latinos tohave African ancestry. Price et al. (53) designedan SNP admixture panel that differentiatesAmerindian ancestry from both European andAfrican ancestry. SNPs were identified thathad similar allele frequencies between Africansand Europeans but substantially different allelefrequencies in Amerindians; a small amount ofAfrican ancestry was unlikely to be powered fordisease-gene detection but might be sufficientto inflate signals if African and Amerindianalleles had similar allele frequencies (53).By decreasing the complexity introduced bythree-way admixture, the panel can be usefullyanalyzed using currently available admixturesoftware programs developed for two-wayadmixture. The panel is also robust for bothNorth and South American admixed popula-tions in which Amerindian ancestry increasesor decreases risk of disease. The investigatorswere careful to select markers that reducedwithin-Amerindian differentiation by using sixpopulations from Central and North Americaand six from South America. A particularand unique strength of this panel is that thecomplexity of three-way ancestry is controlledby the selection of SNPs having similar allelefrequencies between Europeans and Africans(53).

GENE DISCOVERY BYADMIXTURE MAPPING

In the fall of 2005, the first two genome-wideadmixture studies were reported in Nature Ge-netics for hypertension and for multiple sclero-sis in European Americans (56, 84). Five yearslater, admixture scans have been conducted fora range of traits and diseases that have differ-ent rates in Europeans, Latinos, and AfricanAmericans (Table 5). Admixture mapping hasbeen successfully applied to discrete disease


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phenotypes [prostate cancer (9, 26) and non-diabetic kidney disease (33, 34)] and quantita-tive traits [e.g., interleukin 6 and IL6 solublereceptor levels (57), lipid levels (5, 6), obesity(14, 15), and white blood cell counts (46, 55)],as well as hypertension (83, 84), type 2 diabetes(21, 33), breast cancer (23), and peripheral arte-rial disease (67). Because of the extensive lengthof ALD, it was widely predicted that the ad-mixture scan, like family linkage studies, wouldcoarsely map a region of interest, and extensivefine mapping would be needed to identify a dis-ease gene and its causal variation. However, inpractice, admixture mapping has been remark-ably adaptable to fine mapping. Reich et al. (57)provided the proof of principle that admixturemapping could effectively be used to fine-mapa gene or causal variant. By adding SNPs to the95%-credible interval identified in the admix-ture scan, SNP associations with the disease ortrait that are above and beyond the admixtureancestry association can be identified. Highlysignificant, validated associations using this ap-proach have now been reported for nondiabetickidney disease in the region of MYH9 (34), lowwhite blood cell counts due to neutropenia withthe DARC FY− null promoter mutation (33),and IL-6 and IL-6-soluble receptor levels toa causal, functional allele of the IL-6 receptorgene (57). Without European–African admix-ture, the identification of the DARC promoterallele (−46 T > C) association with white bloodcell counts (WBCs) would have been difficultif not impossible as the alternative alleles areclose to fixation in each ancestral population.The FY− mutation abrogates DARC expres-sion on erythroid cells by disrupting the bind-ing site for the GATA1 erythroid transcriptionfactor—homozygotes for the mutation are pro-tected against Plasmodium vivax malaria. Thenull mutation (−46C) is nearly fixed in sub-Saharan Africa, whereas the alternative wild-type allele (−46T) is fixed outside of Africa(78). The association of increased Europeanancestry with high WBC and African ances-try with low WBC due to benign neutrope-nia was critical to finding the association (46,55).

Admixture mapping also provides informa-tion about the contribution of nongenetic, so-ciocultural factors and genetic factors in ma-jor U.S. health disparities, e.g., hypertension(71), kidney disease (19), prostate cancer (27),and early onset, invasive breast cancer (1, 23).Each of these common diseases, causes of con-siderable morbidity and mortality, is more fre-quent in African Americans compared to theirEuropean American counterparts. Although itis hypothesized that the increased burden ofthese diseases is multifactoral—that is, a combi-nation of genetic and environmental factors—ancestry mapping may provide clues to the rel-ative contribution and the effect size of geneticfactors contributing to the differential risk. Ifexcess global African ancestry is noted acrossthe entire genome in the affected group rela-tive to the control group but there is no signif-icant rise in local ancestry at a particular locus,this may point to a stronger role for sociocul-tural factors (e.g., access to health care, diet, orlifestyle) that may be tracking ancestry (20, 30).Deo et al. (20) conducted a well-powered ad-mixture scan using a robust set of AIMs for hy-pertension. This study, consistent with another(74), indicates that hypertensive cases tend tohave higher African ancestry compared to nor-motensive controls. However, there were nosignificant or suggestive increases in local chro-mosomal African ancestry using either a case-only or case-control statistic using the ANCES-TRYMAP program. The Deo group was ableto eliminate 98% of the genome for harbor-ing genetic variation with OR > 1.7 associ-ated with hypertension by exclusion mapping.While this does not exclude multiple small-effect genes contributing to the hypertension inpersons of African ancestry, it does suggest thatthere are no large-effect genes that explain thedisparate risk. This finding is consistent withthe research of Gavlee et al. (30) and others in-dicating that sociocultural factors such as racialand cultural identity may play an important rolein this health disparity. However, the propor-tion of this epidemiological difference that canbe ascribed to genetic or environmental factorsis unknown.


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Tab

le5

Surv

eyof

adm

ixtu

resc

ans

for

dise

ases

and

trai

ts

Phe

noty

pe/T

rait

1A

dmix

edpo

pula

tion

2A

IMs3

Cas

e/C

ontr

ols

Chr

.4G

ene/

SNP

Stat

isti

c5P

rogr

amR

efer

ence

Hyp

erte

nsio

nA

A26

9ST

Rs

737/

573

6q24

.1Z

=4.

14↑A

STR

UC

TU

RE

83

21q2

1Z

=4.

34↑A

Hyp

erte

nsio

nA

A2,

270

1,74

36q

21Z

c=

2.1↑

AZ

c/ct

l.=

2.8↑

AA

DM

IXP

RO

GR

AM

84

EA

1,00

06q

21rs

2272

996

NS

MA

581

6q21

rs22

7299

6P

=0.

021

AA

1,74

36q

21rs

2272

996

P=

0.00

05H

yper

tens

ion

AA

1,82

41,

670/

387

6q24

.3rs

2272

996

NS↑

AP

=0.

016

AN

CE

STR

YM

AP

20

Mul

tiple

scle

rosi

sA

A1,

082

605/

1,04

31

Cen

trom

eric

(no

gene

s)L

OD

LG

=3.

3↑E

LO

DC

C=

5.2↑

EA

NC

EST

RY

MA

P56

Il6

and

IL6s

Rle

vels

AA

1,34

32,

858

1IL

6RL

OD

LG

=2.

03↑A

LO

DC

C=

4.59

↑AA

NC

EST

RY

MA

P57

1,34

31

IL6R

OR

=1.

6,P

<10

−12

AN

CE

STR

YM

AP

EA

1,34

31

rs81

9228

4O

R=

1.5,

P<

10−1

2A

NC

EST

RY

MA

PT

NF-

α,T

NF-

α

sR1,

and

sR2;

IL−

2sR

;PA

I1

AA

1,34

32,

858

NS

AN

CE

STR

YM

AP

FSG

San

dH

IVA

NA

A1,

272

241/

222

22q1

3.1

LO

DL

G=

10.5

↑AL

OD

CC

=13

.7↑A

AN

CE

STR

YM

AP

34

Non

diab

etic

ESR

D1,

272

241/

192

22q1

3.1

rs48

2181

OR

=2.

2;P

<10

-7

HIV

AN

1,27

253

/241

22q1

3.1

rs48

2181

OR

=6.

6;P

<10

−7

FSG

S1,

272

181/

370

22q1

3.1

rs48

2181

OR

=4.

0;P

=10

−13

EA

1,27

212

5/22

122

q13.

1rs

4821

81O

R=

9.7;

P=

0.02

Non

diab

etic

ESR

DA

A1,

354

669/

806

22q1

2L

OD

LG

=5.

7↑A

LO

DC

Ctl

=9.

3↑A

AN

CE

STR

YM

AP

33

1,35

466

9/80

622

q12

rs48

2181

OR

=2.

3;P

<10

−9

Dia

betic

ESR

D1,

354

703/

806

22q1

2rs

4821

81N

S


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Pro

stat

e(a

llca

ses)

AA

1,36

51,

597/

873

8q24

3.8

Mb

LO

DL

G=

4.1↑

AA

NC

EST

RY

MA

P26

<ag

e72

1,36

51,

176

8q24

3.8

Mb

LO

DL

G=

7.1↑

A

Pro

stat

eca

ncer

(ent

ire

grou

p)A

A1,

321

482/

261

2p14

rs67

2439

5Z

=−2

.67↑

EP

<8

×10

−3A

DM

IXM

AP

9

>ag

e60

1,32

1Su

bset

of48

2/26

12p

14rs

6724

395

Z=

−4.2

↑EP

<3

×10

−5

Ent

ire

grou

p1,

321

482/

261

5q35

rs77

2908

4Z

=3.

2↑A

P<

2×

10−3

5q35

rs69

2843

Z=

3.15

↑AP

<2

×10

7q31

.31

rs21

4136

0Z

=−4

.6↑E

P=

3.6

×10

7q31

.33

rs68

3493

Z=

−4.5

↑EP

=6

×10

−6

10p1

3rs

4623

785

Z=

−3.1

↑AP

=2

×10

−3

Obe

sity

AA

1,35

03,

531

2q23

.3L

OD

LG

=4.

1↑E

AN

CE

STR

YM

AP

5

BM

IL

OD

LG

=4.

07↑E

BM

IA

A1,

411

15,2

80X

q25

LO

DL

G=

5.94

↑AZ

=−3

.94

AN

CE

STR

YM

AP

14

Xq1

3L

OD

LG

=2.

2↑A

Z=

−4.6

2

5q13

.3L

OD

LG

=6.

27↑A

BM

IA

A28

41,

344

3q29

D3S

1311

r l=

−2.9

5↑E

STR

UC

TU

RE

5ST

Rs

5q14

-5q2

5D

5S25

01r l

=−2

.73↑

E

15q2

6D

15S8

16r l

=−2

.73↑

AP

erip

hera

lart

eria

ldi

seas

e:an

kle-

arm

inde

x

AA

1,53

61,

040

11rs

9665

943

P<

0.00

02↑A

STR

UC

TU

RE

67

P=

0.00

8A

DM

IXM

AP

LO

DL

G=

3.9

AN

CE

STR

YM

AP

(Con

tinue

d)


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Tab

le5

(Con

tin

ued

)

Phe

noty

pe/T

rait

1A

dmix

edpo

pula

tion

2A

IMs3

Cas

e/C

ontr

ols

Chr

.4G

ene/

SNP

Stat

isti

c5P

rogr

amR

efer

ence

Per

iphe

rala

rter

ial

dise

ase:

ankl

e-ar

min

dex

AA

1,53

61,

040

753

1,79

3

rs96

6594

3O

R1.

53,P

<0.

009

OR

1.59

,P=

0.00

3O

R1.

56,P

<0.

001

LD

L-C

leve

lsA

A28

4ST

Rs

1,04

43q 4q

D3S

2427

D4S

2367

Z=

−2.3

5↓E

,P>

0.05

Z=

−2.3

3↓E

,P>

0.05

STR

UC

TU

RE

6

HD

L-C

leve

lsA

A28

4ST

Rs

1,04

48q

D8S

1136

Z=

−4.1

3↓E

,P=

0.00

114

qG

AT

A19

3A07

Z=

−3.0

0↓E

,P>

0.05

9qZ

=2.

69↑E

,P>

0.05

TG

leve

lsA

A28

4ST

Rs

1,04

48q 15

qD

8S11

79D

15S6

52Z

=2.

58↑E

,P>

0.05

Z=

−2.7

5↓E

,P>

0.05

Typ

e2

diab

etes

AA

4,48

61,

344

12rs

1565

728

P=

0.00

03;L

OD

G,N

SA

DM

IXM

AP

21B

reas

tcan

cer

AA

1,50

01,

484

LO

DG

,NS

AN

CE

STR

YM

AP

23

ER

+PR

+ve

rsus

ER

−PR

−O

R2.

84,P

=0.

026↑

E

Loc

aliz

edtu

mor

sA

AO

R=

2.65

,P=

0.02

9↑E

Whi

tebl

ood

cell

coun

tA

A1,

536

1,18

41q

DA

RC

FY−

LO

DL

G>

30A

NC

EST

RY

MA

P46

,55

Rs2

8147

78↑E

high

WB

CA

DM

IXM

AP

↑Alo

wW

BC

STR

UC

TU

RE

Abb

revi

atio

ns:N

S(n

otsi

gnifi

cant

);SN

P(s

ingl

enu

cleo

tide

poly

mor

phis

m);

1 IL

6(in

terl

euki

n6)

;IL

6sR

(IL

6so

lubl

ere

cept

or);

TN

F(t

umor

necr

osis

fact

or);

TN

FsR

1an

dsR

2(T

NF

solu

ble

rece

ptor

1an

d2)

;PA

I1(P

lasm

inog

enA

ctiv

ator

Inhi

bito

r1)

;FSG

S(f

ocal

segm

enta

lglo

mer

ulos

cler

osis

);H

IVA

N(H

IV-a

ssoc

iate

dne

phro

path

y);E

SRD

(end

stag

ere

nald

isea

se);

BM

I(b

ody

mas

sin

dex)

;LD

L-C

↑(e

leva

ted

low

dens

itylip

opro

tein

-cho

lest

erol

);H

DL

-C↑

(ele

vate

dhi

ghde

nsity

lipop

rote

in-c

hole

ster

ol);

TG

↑(e

leva

ted

trig

lyce

ride

s);E

R(e

stro

gen

rece

ptor

);P

R(p

roge

ster

one

rece

ptor

);2 A

A(A

fric

anA

mer

ican

);E

A(E

urop

ean

Am

eric

an);

MA

(Mex

ican

Am

eric

an);

3 AIM

s(a

nces

try

info

rmat

ive

mar

kers

);ST

R(s

hort

tand

emre

peat

);4 C

hr.

(chr

omos

ome)

;LO

DL

G(lo

cus

geno

me

stat

istic

);L

OD

CC

tl(c

ase

cont

rols

tatis

tic);

5 sta

tistic

alsi

gnifi

canc

e:Z

(Zsc

ore)

>4;

LO

D(lo

gari

thm

ofth

eod

ds) L

(locu

s)G

(gen

ome)

>2,

LO

DC

(cas

e)C

tl(co

ntro

l)>

5;(r

atio

of)r

1(Z

scor

e)>

1.96

;OR

(odd

sra

tio);

P(p

roba

bilit

y);E

(Eur

opea

nan

cest

ry);

A(A

fric

anan

cest

ry);

↑(el

evat

ed);

↓(de

crea

sed)

.


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On the other hand, there is a genetic basis forthe predilection of prostate cancer, nondiabetickidney disease, and lower white blood countsdue to benign neutropenia in African Amer-icans. African American men in the UnitedStates have the highest incidence and mortalityfrom prostate cancer in the world—the risk is1.6-fold higher in African Americans comparedwith European Americans. An admixture scanidentified a region of increased African ancestryin the 8q24 region in younger African Ameri-can men with prostate cancer, a region previ-ously implicated by a linkage study of Icelanderswith prostate cancer and replicated in AfricanAmericans (3). The Freedman et al. (26) studyestimated that African ancestry at the 8q24 re-gion explains as much as 49% of prostate can-cer incidence in the African American popula-tion. African American women are more likelyto present with aggressive breast cancer tumorsthat do not express estrogen or progesterone re-ceptors (ER−PR−) whereas European womengenerally present with ER+PR+ tumors thatare more responsive to treatment (4, 52). An ad-mixture scan of African American women withbreast cancer found an overall increase in Euro-pean ancestry in women with ER+PR+ tumorsand with localized tumors. Unlike prostate can-cer, there were no genome-wide-significant as-sociations with African or European ances-try at any specific locus and breast cancer,hormone receptor status, or grade. The in-crease in European global ancestry with local-ized tumors and ER+ status positively suggeststhat differences in breast cancer risk are unlikelyto be due to large-effect genetic variation (OR >

1.5) but may be due to population differencesin multiple small- to moderate-effect geneticvariants and/or population differences in non-genetic factors (e.g., parity, age of menarche on-set, physical activity) (7, 23). The resolution ofthe role of ancestry in breast cancer risk willrequire larger population sizes and inclusion ofmore patients with ER−/PR− breast cancer tu-mors to detect smaller effect-size associations.

It has long been recognized that AfricanAmericans are at increased risk for chronic

and end stage renal disease. HIV-associatednephropathy (HIVAN) is rarely observed in in-dividuals not of African descent, and focal seg-mental glomerulosclerosis (FSGS) is fourfoldhigher in African Americans compared withEuropean Americans. End stage renal diseasedue to any cause is also threefold higher inAfrican Americans. It has long been hypoth-esized that this disparity was due to geneticrisk factors. Two independent studies, one us-ing biopsy-proven HIVAN and FSGS as out-comes and the other nondiabetic end stage kid-ney disease, identified peak of excess Africanancestry on chromosome 22. Subsequent asso-ciation analysis identified risk alleles in MYH9on chromosome 22 as strongly associated withHIVAN, FSGS, and nondiabetic end stage re-nal disease (OR = 5, 4, 2.2, respectively) af-ter correcting for local ancestry. Although thecausal allele has not yet been identified, the as-sociated markers occur with high frequency inAfrican Americans (60%) and are infrequent orabsent in other populations. The attributablerisk for HIVAN and FSGS is 100% and 70%,respectively, thus the increased risk of majorforms of kidney disease in the African Ameri-can population has a strong genetic basis.

SUMMARY

These studies attest to the utility of admixturemapping to quantify the contributions of an-cestry to many traits and diseases that are dis-parate across populations. Although most ofthe applications to date have been in AfricanAmericans, admixture mapping holds promisefor identifying the role of genetics and ancestryfor conditions such as metabolic syndrome, type2 diabetes, obesity, and gallbladder disease withhigher incidence in persons of Amerindian an-cestry compared to Europeans (39, 79, 80). Pat-terson and colleagues (49) have shown that thepower for admixture mapping was robust forancestry ranging from 10–90% and can detectdisease associations for diseases on either themajority or minority ancestry with near equalpower (Figure 8) (49). As shown in Figure 9,


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Chromosome 22 position (Mb)

African ancestry (%

)

Locu

s–ge

nom

e od

ds (b

illio

ns) Locus–genome

statisticCasesControls

0

20 30 40 50

10

20

80

90

0

5

10Re

lati

ve to

gen

ome

aver

age

(%)

Cases

Chromosome (Mb)1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 X

–5

Genome-wide Peak

9.28.77.7

12.410.18.8

10.5 13.7

Initial screen (1290 markers)Even markersOdd markers

Dense markers at peak

Locus–genome LOD score

Controls

22

Figure 8Results of the admixture mapping genome scan for combined kidney diseases focal segmental glomerulosclerosis (FSGS) andHIV-associated nephropathy (HIVAN), which are, respectively, fourfold and 60-fold more frequent in African Americans than inEuropean Americans. The sharp peak of African ancestry among cases occurs in the region of MYH9 on chromosome 22. The insetshows the close up of the peak, and the localization of the association to a 95% credible interval of ∼3 Mb. Also shown are genome-wide and peak (LOD) scores for several calculations; genome-wide LOD scores greater than 2 are considered significant. Adapted fromReference (34).

human cancers also have disparate rates in di-verse populations. Since these diseases are com-plex, the relevant diseases and traits are thosefor which the disparate rates are not fully ex-plained by environmental factors. However, an-cestry may also be tracking environmental in-fluences such as socioeconomic status, accessto health care, and sociocultural factors thatinfluence complex diseases—the identificationof excess global parental ancestry in an af-fected group in the absence of a local spike in

parental ancestry in the affected group suggeststhe importance of nongenetic (i.e., cultural andenvironmental) factors correlated with ances-try. These insights are critical to developingpublic health policies and interventions to re-duce the disease burden of complex diseasesdue to environmental factors and to improveclinical outcomes for diseases with a biologi-cal basis through rational-based drug develop-ment, personalized drug therapies, and geneticscreening.


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Less common in AA (< 0.67)Lymphocytic leukemiaBrain and other nervous systemEndocrine systemBrainThyroidUrinary bladderAcute lymphocytic leukemiaUreterPleuraEye orbitTestisSkin, excluding basal squamousLipMelanoma of the skin

More common in AA (> 1.5)Uterus NOSOropharynxMyelomaHypopharynxSplenic flexureStomachLiverSmall intestineLiver intrahepatic bile ductDescending colonLarynxCervix uteriProstate

Afr

ican

Am

eric

an to

Eur

opea

n A

mer

ican

rati

o

African Americans (per 100,000 person years)

10

1

0.1

0.1 1 10 100

Figure 9Relative frequencies of cancers in African Americans and European Americans. Cancers with significant differences in frequency (red orgreen) are potential targets for admixture mapping. Data were extracted using SEER software using U.S. cancer incidence from2000–2005, age adjusted using 2000 census results as the standard. Incidence rates were calculated separately for European (EA) andAmerican Americans (AA) for the number of cases per 100,000 person years.

DISCLOSURE STATEMENT

The authors are listed as inventors on a patent application for the use of MYH9 genetic variation asa predictor of propensity for kidney disease. This project has been funded in whole or in part withFederal funds from the National Cancer Institute, National Institutes of Health, under contractHHSN261200800001E. The content of this publication does not necessarily reflect the views orpolicies of the Department of Health and Human Services, nor does mention of trade names,commercial products, or organizations imply endorsement by the U.S. Government.

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AR422-GG10-FM ARI 30 July 2010 11:26

Annual Review ofGenomics andHuman Genetics

Volume 11, 2010

Contents

Genomics of Long-Range Regulatory ElementsJames P. Noonan and Andrew S. McCallion � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 1

The Mitochondrial Proteome and Human DiseaseSarah E. Calvo and Vamsi K. Mootha � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �25

Contrasting Methods of Quantifying Fine Structureof Human RecombinationAndrew G. Clark, Xu Wang, and Tara Matise � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �45

Admixture Mapping Comes of AgeCheryl A. Winkler, George W. Nelson, and Michael W. Smith � � � � � � � � � � � � � � � � � � � � � � � � � � � �65

Genetics of Coronary Artery DiseaseKiran Musunuru and Sekar Kathiresan � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �91

Biology and Genetics of HairYutaka Shimomura and Angela M. Christiano � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 109

Profiling the Cancer GenomePrue A. Cowin, Michael Anglesio, Dariush Etemadmoghadam,and David D.L. Bowtell � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 133

Genetics of Early Onset Cognitive ImpairmentHans Hilger Ropers � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 161

Signaling Pathways in Human Skeletal DysplasiasDustin Baldridge, Oleg Shchelochkov, Brian Kelley, and Brendan Lee � � � � � � � � � � � � � � � � � � 189

Evolution of Lactation: Ancient Origin and Extreme Adaptations ofthe Lactation SystemChristophe M. Lefevre, Julie A. Sharp, and Kevin R. Nicholas � � � � � � � � � � � � � � � � � � � � � � � � � � 219

Genome Evolution in Reptilia, the Sister Group of MammalsDaniel E. Janes, Christopher L. Organ, Matthew K. Fujita, Andrew M. Shedlock,and Scott V. Edwards � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 239

The Neutral Theory of Molecular Evolution in the Genomic EraMasatoshi Nei, Yoshiyuki Suzuki, and Masafumi Nozawa � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 265

Chromosomes, Conflict, and Epigenetics: ChromosomalSpeciation RevisitedJudith D. Brown and Rachel J. O’Neill � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 291

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Dispatches from the Evolution Wars: Shifting Tacticsand Expanding BattlefieldsGlenn Branch, Eugenie C. Scott, and Joshua Rosenau � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 317

Public Attitudes and Beliefs About GeneticsCeleste M. Condit � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 339

Informed Consent in Genomics and Genetic ResearchAmy L. McGuire and Laura M. Beskow � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 361

Patents in Genomics and Human GeneticsRobert Cook-Deegan and Christopher Heaney � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 383

Consumers’ Views of Direct-to-Consumer Genetic InformationColleen M. McBride, Christopher H. Wade, and Kimberly A. Kaphingst � � � � � � � � � � � � � � � 427

Indexes

Cumulative Index of Contributing Authors, Volumes 2–11 � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 447

Cumulative Index of Chapter Titles, Volumes 2–11 � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 451

Errata

An online log of corrections to Annual Review of Genomics and Human Genetics articlesmay be found at http://genom.annualreviews.org

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admixture mapping comes of age*proach to using ancestry to map genes by mckeigue in 1998 was...

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